U.S. Department
of Commerce
Volume ttO
Number 2
April 2012
Fishery
Bulletin
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of Commerce
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and Atmospheric
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National! Marine
Fisheries Service
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for Fisheries
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U.S. Department
of Commerce
Seattle, Washington
Volume 110
Number 2
April 2012
Fishery
Bulletin
Contents
Articles
143-155 Matkin, Craig O., John W. Durban, Eva L. Saulitis,
Russel D. Andrews, Janice M. Straley, Dena R. Matkin,
and Graeme M. Ellis
Contrasting abundance and residency patterns of two sympatric
populations of transient killer whales ( Oranus orca)
in the northern Gulf of Alaska
156-175 Rudershausen, Paul J., Jeffrey A. Buckel, Greg E. Bolton,
Randy W. Gregory, Tyler W. Averett, and Paul B. Conn
A comparison between circle hook and J hook performance
in the dolphinfish, yellowfin tuna, and wahoo troll fishery
off the coast of North Carolina
The National Marine Fisheries
Service (NMFS) does not approve,
recommend, or endorse any proprie-
tary product or proprietary material
mentioned in this publication. No
reference shall be made to NMFS,
or to this publication furnished by
NMFS, in any advertising or sales
promotion which would indicate or
imply that NMFS approves, recom-
mends, or endorses any proprietary
product or proprietary material
mentioned herein, or which has
as its purpose an intent to cause
directly or indirectly the advertised
product to be used or purchased
because of this NMFS publication.
176-192 Able, Kenneth W., Thomas M. Grothues, Jason T. Turnure,
Donald M. Byrne, and Paul Clerkin
Distribution, movements, and habitat use of small striped bass
( Morone soxatilis ) across multiple spatial scales
193-204 Tinus, Craig A.
Prey preference of Imgcod ( Ophiodon elongatus), a top marine
predator: implications for ecosystem-based fisheries management
205-222 Keller, Aimee A., John R. Wallace, Beth H. Horness,
Owen S. Hamel, and Ian J. Stewart
Variations in eastern North Pacific demersal fish biomass based
on the U.S. west coast groundfish bottom trawl survey (2003-2010)
The NMFS Scientific Publications
Office is not responsible for the con-
tents of the articles or for the stan-
dard of English used in them.
223-229 Ohnishi, Shuhei, Takashi Yamakawa, Hiroshi Okamura,
and Tatsuro Akamine
A note on the von Bertalanffy growth function concerning
the allocation of surplus energy to reproduction
II
Fishery Bulletin 1 10(2)
230-241
Baremore, Ivy E., Dana M. Bethea, and Kate 1. Andrews
Gillnet selectivity for juvenile blacktip sharks ( Carcharhinus limbotus)
242-256
Beacham, Terry D., Brenda McIntosh, Cathy MacConnachie, Brian Spilsted, and Bruce A. White
Population structure of pink salmon (Oncorhynchus gorbuscha) in British Columbia and Washington,
determined with microsatellites
257-270
Caldarone, Elaine M., Sharon A. MacLean, and Beth Sharack
Evaluation of bioelectrical impedance analysis and Fulton's condition factor as noniethal techniques
for estimating short-term responses in postsmolt Atlantic salmon (Salmo salar) to food availability
271-279
Stachura, Megan M., Chris R. Lunsford, Cara J. Rodgveller, and Jonathan Heifetz
Estimation of discard mortality of sablefish (Anoplopoma fimbria) in Alaska longline fisheries
280-281
Guidelines for authors
143
Contrasting abundance and residency patterns
of two sympatric populations
of transient killer whales ( Orcinus orca )
in the northern Gulf of Alaska
Abstract — Two sympatric popula-
tions of “transient” (mammal-eating)
killer whales were photo-identified
over 27 years (1984-2010) in Prince
William Sound and Kenai Fjords,
coastal waters of the northern Gulf
of Alaska (GOA). A total of 88 indi-
viduals were identified during 203
encounters with “ATI” transients (22
individuals) and 91 encounters with
“GOA” transients (66 individuals).
The median number of individuals
identified annually was similar for
both populations (AT1=7; GOA = 8),
but mark-recapture estimates showed
the ATI whales to have much higher
fidelity to the study area, whereas the
GOA whales had a higher exchange of
individuals. Apparent survival esti-
mates were generally high for both
populations, but there was a signifi-
cant reduction in the survival of ATI
transients after the Exxon Valdez oil
spill in 1989, with an abrupt decline
in estimated abundance from a high
of 22 in 1989 to a low of seven whales
at the end of 2010. There was no
detectable decline in GOA population
abundance or survival over the same
period, but abundance ranged from
just 6 to 18 whales annually. Resight-
ing data from adjacent coastal waters
and movement tracks from satellite
tags further indicated that the GOA
whales are part of a larger popula-
tion with a more extensive range,
whereas ATI whales are resident to
the study area.
Manuscript submitted 1 June 2011.
Manuscript accepted 18 October 2011.
Fish. Bull. 110:143-155 (2012).
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
Craig O. Matkin (contact author)1
John W. Durban2 3
Eva L. Saulitis'
Russel D. Andrews4
Killer whales (Orcinus orca ) in the
eastern North Pacific can be geneti-
cally and acoustically separated
into three nonassociating lineages:
“resident,” “transient,” and “offshore”
(Ford and Ellis, 1999; Matkin et ah,
1999; Barrett-Lennard, 2000; Yurk
et al., 2002). Of these lineages, Morin
et al. (2010) found the transients to
be the most genetically divergent
and indicated that they should be
considered a separate species. Only
the transient form has been observed
consuming marine mammals in this
region and observations indicate
that they feed on marine mammals
exclusively (Ford et al., 1998; Sauli-
tis et al., 2000; Herman et al., 2005;
Matkin et al., 2007a, 2007b; Barrett-
Lennard et al., 2011). The potential
for these whales to affect trajectories
of prey populations has led to con-
siderable debate over the role of pre-
dation by transient killer whales in
the decline of coastal pinnipeds and
sea otters in western Alaska (e.g.,
Estes et al., 1998, 2009; Springer
et al., 2003, 2008; DeMaster et al.,
2006; Wade et al., 2007, 2009). In
Janice M. Straley5
Dena R. Matkin1
Graeme M. Ellis6
addition to data on feeding habits,
evaluation of their top-down impact
requires data on abundance and res-
idency patterns of these transient
killer whales within specific marine
systems, particularly with respect
to the abundance and trends of their
primary prey.
The coastal waters of Prince Wil-
liam Sound and the Kenai Fjords
in the northern Gulf of Alaska are
unique in being regularly used by
two sympatric populations of tran-
sient killer whales (Matkin et al.,
1999). Members of both the Gulf
of Alaska-Aleutian Islands-Bering
Sea transient stock and the ATI
transient stock (Allen and Angliss,
2010) have been photographically
identified over the past 27 summer
seasons (Matkin et al., 1999, 2008).
Individuals from both populations
regularly use the same region but
have never been recorded swimming
together and do not associate (Mat-
kin et al., 1999), and they can be
separated by behavior (Matkin et al.,
1999; Saulitis et al., 2000), by acous-
tics (Yurk et al., 2002, 2010; Saulitis
Email address for contact author cmatkm@acsalaska.net
1 North Gulf Oceanic Society
3430 Mam St., Suite B1
Homer, AK 99603
2 National Marine Mammal Laboratory
Alaska Fisheries Science Center
National Marine Fisheries Service, NOAA
7600 Sand Point Way NE
Seattle, WA 98115
3 Protected Resources Division
Southwest Fisheries Science Center
National Marine Fisheries Service, NOAA
3333 N. Torrey Pines Ct.
La Jolla, CA 92037
4 School of Fisheries and Ocean Sciences
University of Alaska Fairbanks
and Alaska SeaLife Center
301 Railway Ave.
Seward, AK 99664
5 University of Alaska Southeast
Sitka Campus
1332 Seward Ave
Sitka, AK 99835
6 Department of Fisheries and Oceans
Pacific Biological Station
3190 Hammond Bay Rd.
Nanaimo, British Columbia, V9R 5K6 Canada
144
Fishery Bulletin 1 10(2)
et al., 2005), and by genetics (Barrett-Lennard et al.,
2000).
Because of the lack of conclusive studies of genetic
divergence across their range, the Gulf of Alaska-
Aleutian Islands-Bering Sea transient stock includes
all transient killer whales found in Alaskan waters
west of southeastern Alaska other than the ATI stock
(Allen and Angliss, 2010). However, photographic
mark-recapture analyses indicate little apparent
overlap between the Gulf of Alaska whales and the
western segment of the stock (Matkin et al., 2007a;
Durban et al., 2010). In this article we will refer to
the non-ATI transients in the study area only as
the Gulf of Alaska (GOA) transient population and
consider their range to be the Gulf of Alaska and
north gulf coast, which stretches from southeast-
ern Alaska west through the Kodiak Island region.
Although the full range and offshore distribution
of the GOA transients is poorly defined, they have
been photographed irregularly to the southwest of
Prince William Sound-Kenai Fjords study area in
Kachemak Bay, lower Cook Inlet, and Kodiak Island
waters (Maniscalco et al., 2007; Matkin et al., 1999;
C. Matkin, unpubl. data).
The ATI transients are considered a separate stock,
are classified as depleted under the Marine Mammal
Protection Act, and currently number only seven in-
dividuals (Allen and Angliss, 2010). The home range
of the ATI transient population appears much more
restricted than that of the sympatric GOA transients
(Matkin et al., 1999; Scheel et al., 2001) or the para-
patric west coast transients of southeastern Alaska,
British Columbia, and Washington State coastal wa-
ters (Ford and Ellis, 1999). ATI individuals have not
been identified outside of the coastal waters of Prince
William Sound and the Kenai Fjords (Matkin et al.,
1999; Saulitis et al., 2005). Because of its limited
range, small population size, and the consistent re-
sightings of subgroups and individuals, the population
dynamics of the ATI population have been monitored
directly from annual photographic data (Matkin et al.,
2008). However, for the GOA transients, the infrequent
resightings of individuals, fluidity in group structure,
and larger population size have made it impossible to
directly track births and deaths and require a mark-
recapture sampling approach to estimate abundance
and assess population changes.
In this article we fit mark-recapture models to
long-term photographic identification data (1984 to
2010) to examine abundance trends, site fidelity, and
demography for the ATI and GOA transients in the
coastal waters of Prince William Sound and the Ke-
nai Fjords. We compare our results with previously
described changes in the ATI population (Matkin et
al., 2008) and contrast these results with our paral-
lel analysis of the GOA transient population. We use
photographic resighting data and satellite telemetry
data to further differentiate the range of the two
populations and provide a context for their differing
abundance trends.
Materials and methods
Photographic mark-recapture
Identification photographs of killer whales were
obtained from the waters of Prince William Sound,
Kenai Fjords National Park, and the adjacent coastal
waters of the northern Gulf of Alaska (Fig. 1A). The
entire region was not surveyed in any given year; how-
ever, survey effort was focused towards Prince William
Sound in the earlier years of the study (1980s) and was
more evenly balanced across the region in later years.
Photographic surveys were conducted between April
and September over the 27-year period between 1984
and 2010. In order to increase capture probabilities,
survey effort was focused in areas known to be used by
killer whales, or in response to sighting reports. Data
were collected from a variety of platforms; all were
small vessels less than 15 meters in length powered
by either gasoline-outboard or diesel-inboard engines.
During an encounter, whales were approached at a
distance of 15 to 45 m and photographs were taken of
the left side of each whale present, showing details of
the dorsal fin and saddle patch (Matkin et al., 1999).
Photographs were obtained with either 1) a Nikon
F-100 SLR camera1 with fixed 300-mm lens and Fuji
Neopan 1600 black and white film, or 2) Nikon D70
and D200 digital cameras with 80-200 mm zoom or
300-mm fixed lenses. Individual whales were distin-
guished by the shape and pattern of natural markings
on their dorsal fins and adjacent saddle patch (Matkin
et al., 1999) and were subsequently matched to cata-
logs of photographs from previous years. Individual
matches were corroborated by using co-occurrence
with consistent associates because transient killer
whales have been shown to travel in stable (and often
life-long) matrilineal groupings (Ford and Ellis 1999;
Matkin et al., 1999). Photographs were evaluated for
quality, and only photographs resulting in reliable
identifications were used. Typically, the entire group
was photographed. Membership in the ATI or GOA
transient population was determined either by genetic
sampling, acoustic analysis, or observation of repeated
association with other members of the population.
We treated these photographic identifications and
re-identifications as “captures” and “recaptures” to
which analytical mark-recapture techniques could be
applied for estimation of abundance and demographic
parameters (Hammond, 1986, 1987, 1990).
Individual whales were not seen in every year that
they were known to be alive, likely in part because of
the movement patterns of whales relative to the geo-
graphical boundaries of the study area. This factor
highlighted the need to allow for temporary emigration
in the capture-recapture modeling. The popular Cor-
mack Jolly Seber (CJS) model for estimating survival
1 Mention of trade names or commercial companies is for
identification purposes only and does not imply endorsement
by the National Marine Fisheries Service, NOAA.
Matkm et al Abundance and residency patterns of two sympatric populations of Orcmus orca in the northern Gulf of Alaska
145
1 50°W
1 40C,W
1 30°W
B
Prince William
Sound
'
* *
fjords 4 • "*
*rf r
Montague Strait
WSg "Wm f
r^r^i— 1 — | — I — r
0 37.5 75
150 Kilometers
! 000m contour
Figure 1
(A) Location of the coastal study area of Prince William Sound and
Kenai Fjords. (B) Locations of encounters with ATI (203, closed circles)
and Gulf of Alaska (GOA) (91, open circles) transient killer whales
(Orcinus orca) between 1984 and 2010, during which photo-identification
data were collected.
(Lebreton et al., 1992) does not account
for animals that emigrate from the
study area and return later. Instead,
we followed Whitehead (1990) in de-
veloping a mark-recapture model that
parameterized emigration and re-im-
migration probabilities in addition to
survival. Our model was based on an
individual-specific factorization (e.g.,
Schofield et al., 2009), allowing modu-
larization into conditional distributions
for capture probability, availability of
whales for capture (temporary emigra-
tion), and death. This formulation al-
lowed imputation of partially observed
data on availability in the study area
(available in the study area when ac-
tually identified) and survival status
(alive when identified and between
years of repeat identification), provid-
ing identifiability of parameters and
enabling time-varying formulations.
Specifically, the model had the param-
eters tpf, Kt, A,, and 03, where <\>t l is the
probability of survival from time t— 1 to
time t; A,_; is the probability of tempo-
rary emigration from the study area at
time t- 1; Kt is the annual probability of
re-immigration back into the study ar-
ea; and 03, is the probability of capture
at time t for whales alive and avail-
able to be captured in the study area.
Note that owing to the geographic re-
strictions of the surveys and the likely
wider ranging patterns of the whales,
survival in this case represented ap-
parent survival that could comprise
either death or permanent emigration
(at least for the duration of the study).
To fully quantify uncertainty about
the unknown parameters, we adopted a
Bayesian approach to model fitting and
inference, where estimates were pre-
sented as full probability distributions
(Gelman et al., 1995). The Bayesian
approach requires prior distributions to
be specified for all model parameters,
and we adopted similar hierarchical
priors for each set of probability terms
® )
g _ Bernoulli(0.5),
where logit(a) = log(a/(l-a).
The prior distribution for each parameter was thus
determined by two hyper-parameters: p represented
the mean value across each set of parameters and the
standard deviation term o represented the year-to-year
variability over the set, on the logit scale. Uniform(0,l)
prior distributions were placed on each of the five mean
probabilities pAA.h-.ra anc] a uniform(0,10) prior distri-
bution was adopted for ctAA kb auow annual differ-
ences from the logit-transformed means to emerge. The
probability (evidence) of temporal variability in each
parameter vector was assessed through indicator vari-
146
Fishery Bulletin 1 10(2)
ables g (e.g., Kuo and Mallick, 1998). Each of these
indicators was assigned a Bernoulli prior distribution,
such that the prior probability of including any annual
effect was 0.5.
We used the freely available WinBlJGS software
(Lunn et ah, 2000) to implement Markov Chain Monte
Carlo (MCMC) sampling to make repeated draws from
the “posterior distribution” of each parameter — the pri-
or distribution was updated conditionally on the data
and structural relationships of the model. We sampled
10,000 values from the posterior distribution of each
parameter, after discarding an initial burn-in deter-
mined by the method of Brooks and Gelman (1998).
The sampled values were then used to estimate sum-
mary statistics for the posterior distributions. MCMC
approaches can similarly be used to sample from the
posterior distribution of quantities that can be derived
as functions of parameters. Notably, we used the same
MCMC simulation approach to generate predictive ob-
servations from the model parameters and compared
the fit of our re-immigration model to a standard Cor-
mack Jolly Seber model based on the mean squared pre-
dicted error (MSPE; Gelfand and Ghosh, 1998; Durban
et al., 2010). As with other model selection methods,
this predictive approach achieves a compromise between
the goodness-of-fit and a penalty for model complexity
(Gelfand and Ghosh, 1998). As such, the model with the
smallest MSPE was estimated to provide the best fit.
Assessing trends
We used estimates of the capture probabilities (03,) to
derive estimates of the abundance of animals {Nt) using
the study area in any given annual survey period (t).
These parameters were linked to the observed data by
specifying the number of individuals actually observed
in the study area (nt) as a binomial sample from the
study area abundance (Nt) with the binomial proportion
given by the estimated G3r To assess trends across years,
we modeled each Nt as Poisson distributed and adopted a
model for the unknown Poisson means (m() that governed
the form of the variation between years. Specifically,
we therefore adopted a flexible change-point model to
describe temporal transitions (e.g., Carlin et ah, 1992):
log (mt) = /30 + gPfifiit - c) + e,N
etN ~ N(0, c A) (2)
gP ~ Bernoulli(0.5).
The parameter )30 described the general intercept of the
model (or level of abundance on the log scale before the
change-point), and the function 50 represented a step
function, defined as 1 if its argument was zero or posi-
tive and zero otherwise. The parameter j\ described the
magnitude of a step change (on a log scale), at time c
(known as a change-point). We assumed the timing of
the change-point was unknown and used the data to
assess the evidence for a change-point in each of the 27
years. This problem therefore involved estimating the
posterior distribution of the unknown temporal change-
point (c) to identify when a change-point may have
occurred, and with what probability. The model offers a
flexible approach for modelling changes in abundance,
because uncertainty about the year of the change-point
results in uncertainty over how the trend is apportioned
over the time series of between-year transitions. Because
the step function 50 was specified on a discrete time
period (t - c), we placed a discrete uniform prior for c
over T-21 years) (e.g., Carlin et ah, 1992):
c ~ U(1,T) (3)
with discrete prior probability of 1/T being placed on
each of the 27 years. We assumed that the direction and
magnitude of the change was unknown, and we there-
fore assigned diffuse prior distributions for the hyper-
parameters [i0 and /3,, each with mean 0 and standard
deviation of 10. We assessed the probability of a trend
in abundance by estimating the indicator probability^
of including the trend parameter /3j in the model for the
abundance estimates.
Rather than perform this trend analysis independent-
ly of the mark-recapture model, we combined these two
components into a single Bayesian hierarchical model to
propagate uncertainty in estimation of capture probabil-
ities (03,) into estimates of abundance (Nt) and trend pa-
rameters. We did not assume that the Nt fell exactly on
the trend line, or had a common variance, but instead
we included annual random-effects terms (etN) that al-
lowed over-dispersion in contrast to a fixed-effects Pois-
son trend model. A normal random effects distribution
was adopted for the etN ~ N(0, oN), with overdispersion
controlled by the standard deviation ( oN ), which was
assigned a uniform (0,10) prior distribution. As with
the mark-recapture parameters, we used WinBUGS to
sample 10,000 values from the marginal posterior dis-
tributions for the annual estimates of abundance, Nr
Additionally, interest was focused on making inference
about the posterior distributions of the parameters of
the trend model, specifically the change-point (c), the
rate of change (/3j), and the probability of a trend (gP).
Tracking whale movements
To examine movements of whales relative to our mark-
recapture modeling estimates (extent of temporary emigra-
tion away from the study area), we compared photographs
used in our analysis with those taken during parallel
research efforts in southeastern Alaska, British Columbia,
and Washington State (e.g., Ford and Ellis, 1999) to iden-
tify annual overlap of individuals. Previous analyses had
shown no overlap of ATI or GOA transients with those in
the Aleutian Islands (Durban et al., 2010). In addition, we
attached satellite transmitter tags to individual GOA and
ATI transient whales to provide fine-scale tracks of daily
movements. The tag design was a low impact minimally
percutaneous external-electronics transmitter (LIMPET)
satellite tag (Andrews et al., 2008). In this tag, the main
electronics package, an Argos-linked, location-only SPOT
Matkin et at: Abundance and residency patterns of two sympatric populations of Orcmus orca in the northern Gulf of Alaska
147
■ ATI
GOA
lllllllllllllllll
4^* of") 4^ oT'' 4^ *4? cO' O?- Cp Cp CS^ oP cO' Cv Q\^ Cv cC^ CV C\^
op cjo cp cjo .Op .dp c? J or cp . o? < cp cf J®* ^ ^ ^ ^ ^ ^
Year
Figure 2
The number of individuals photographically identified from the ATI and
Gulf of Alaska (GOA) transient killer whale ( Orcinus orca) populations in
each annual April-September period, 1984-2010.
5 transmitter (Wildlife Computers,
Redmond, WA), is housed in an epoxy
casing with dimensions of 65x30x22
mm. The tag is held externally on
the dorsal fin of the whale by two
4-mm-diameter medical-grade tita-
nium darts that were affixed to the
bottom of the tag, for a total mass
of 49 g. The darts were designed to
penetrate 6.5 cm into the connective
tissue in the dorsal fin and remain
embedded with a series of backward-
facing barbs which acted as anchors
for the darts. The LIMPET tags were
projected onto the whales by using a
crossbow with 150-lb draw weight,
and the tag was held on the end of an
arrow in a special rubber boot.
This type of satellite tag transmits
ultra-high frequency (UHF) radio
signals to Argos receivers onboard
weather satellites in sun-synchro-
nous polar orbits. To conserve power,
transmissions are limited by a sub-
mersion sensor to times when the
whale is at the surface. Locations
were calculated by the Argos sys-
tem by the method of least squares
( http://www.argos-svstem.org. accessed October 2007),
and we determined the plausibility of each location us-
ing the Douglas Argos filter, vers. 7.03 (Douglas2). We
retained locations with high location accuracies (LC2
and LC3), as well as consecutive points separated by
less than 3 km. All other locations were removed if the
rate of movement between consecutive locations exceeded
25 km/h or the angle formed by the previous and sub-
sequent locations indicated extreme return-movements.
The angle of each triad of points and the distance be-
tween the shortest leg of the triad was assessed by the
filter and compared with the dimensionless rate coeffi-
cient (Ratecoef) that was set to 25. Location data were
imported into Google Earth (Google, Mountain View,
CA) for basic visual inspection and into ArcMap 9.3.1
(ESRI, Redlands, CA) for further analysis. Distance
traveled was calculated for each tagged animal, as well
as a calculation of oceanic home range developed by
subtracting the land area from the total area in the
Minimum Convex Polygon, which was the polygon that
described the perimeter of all filtered satellite locations
received during the period of attachment.
Results
During the 27 years of this study we averaged 106
(range=29-249) vessel days per year with at least 59
2 Douglas, D. 2007. The Douglas Argos-Filter. [Available at
http://alaska.usgs.gov/science/biologv/spatial/manual.html.
accessed 1 October 2007.]
days logged in all years except for 1987 (29 days). During
these surveys we recorded a total of 203 encounters
with members of the ATI transient population and 91
encounters with members of the GOA transient popula-
tion (Fig. 1). Over 27 annual (May-September) periods,
a total of 88 individual whales were documented. There
were three times as many GOA individuals (66) as ATI
individuals (22), but the average number of individu-
als identified in each summer interval was similar for
both populations (GOA: median=8, range 0-18; ATI:
median=7, range 4-22; Fig. 2). This finding reflected a
higher resighting rate for individual ATI whales; indi-
viduals were seen in a median of nine different annual
intervals (range 3-25) compared with a median of just
two intervals (range 1-16) for GOA whales (Fig. 3). It
is notable that 7 of 22 total ATI whales were identified
in more than 20 annual intervals, whereas only 1 of 66
GOA whales was identified in more than 10 intervals
(Fig. 3). The number of ATI individuals seen each year
clearly declined across the study period from around 20
individuals in the 1980s to fewer than 10 individuals
in the 2000s, whereas the number of GOA individuals
remained at a more consistent but low number with
a median 8 individuals identified per year (Fig. 2).
However, to formally assess changes in abundance, we
adjusted our sighting data for capture probabilities using
mark-recapture models.
The mark-recapture model with emigration and re-
immigration provided a better fit to the photo-identifi-
cation data than the standard CJS model, for both ATI
and GOA individuals. For GOA whales, there were 49
discrepancies between 1079 observed and predicted data
148
Fishery Bulletin 1 10(2)
points under the re-immigration model, compared with
190 of 1079 data points for the CJS model, translating
to a mean squared predicted error of 0.04 and 0.18 re-
spectively. For ATI whales, there were 25 discrepancies
from 569 data points under the re-immigration model,
compared with 91 in 569 for the CJS model, correspond-
ing to MSPEs of 0.04 and 0.16, respectively. Inference
was therefore based on parameter estimates from the
flexible emigration-re-immigration model, which in-
dicated notable differences in the fidelity of the two
populations to the study area (Table 1).
GOA transients showed a much higher rate of ex-
change of individuals in the study area, with a rela-
tively high probability of emigration (posterior median
pA=0.55) and low rate of re-immigration (pK'=0.17),
compared to a low rate of emigration and high rate
of re-immigration for the ATI population (pA=0.08,
pK'=0.77), implying high study area fidelity for the
ATI whales. Similarly, the average
probability of capture was higher
for AT1(/jb = 0.98) compared with
GOA (jU® = 0.83) individuals, imply-
ing that almost all of the ATI in-
dividuals in the study area were
photographed in each year, likely
because of a higher fidelity to the
study area and smaller range. Al-
though the average apparent sur-
vival was high for both populations
(GOA ^=0.98; ATI /i*=0.99), there
were noticeable annual deviations
from the average (Fig. 4). Although
there was a substantial dip in the
GOA transients’ apparent survival
in one year, 1986, there was a con-
sistent trend in the ATI population,
with survival from 1989 to 1990
showing a marked decrease (poste-
rior median = 0.68, 95% probability
interval = 0.48 to 0.86) compared
with the overall average, with no
overlap in 95% probability intervals
between this estimate and those for
most other years.
The trends in abundance of the
two populations, based on estimates
of abundance and parameters of the
20
Number of years seen
Figure 3
Frequency plot of the number of individual whales photographed in different
numbers of annual sampling periods, for both the ATI and Gulf of Alaska
(GOA) transient killer whale (Orcinus orca) populations.
Table 1
Fit of photographic identification data to the mark-recapture model with emigration and re-immigration, for both ATI and Gulf of
Alaska (GOA) transient killer whale ( Orcinus orca) populations. l=the probability that an individual in the study area migrates
out of it each year; v=the probability that an individual not in the study area population migrates back into it each year; 0=the
annual probability of survival, G5=the annual probability of capture (identification) in the study area. Estimates are presented
as the 0.025,0.50, and 0.975 probability intervals of the posterior probability distribution (i.e., median surrounded by 95% prob-
ability intervals) for the average (p) value across May-September periods, plus the probability of between-year differences in
parameters over the 27 time periods, given by the posterior probability p(g= 1) of each respective time-varying indicator variable
g. Additionally, the parameter /3j is included to indicate the magnitude and direction of abundance trend (on the log scale).
Posterior estimates
Population
Emigration
ft [p(gA=D]
Re-immigration
gK lp(gK= D)
Survival
ft [p(ft=l)]
Capture
ft [p(gw= D]
Trend
ft [p(ft=l>l
GOA
0.21,0.55,0.80
[1.00]
0.02,0.17,0.67
[1.00]
0.94,0.98,0.99
[1.00]
0.55,0.83,0.99
[1.00]
-1.2,04,1.3
[0.02]
ATI
0.02,0.08,0.23
[1.00]
0.18,0.77,0.97
[0.71]
0.96,0.99,1.00
[1.00]
0.92,0.98,1.00
[0.64]
-1.1, -0.8, -0.5
[1.00]
Matkin et at: Abundance and residency patterns of two sympatric populations of Orcmus orca in the northern Gulf of Alaska
149
T T T T T
0.8
> 0.6
O
.S'
I n I t n i
ATI
6^ 6^ of?’ cf*® bP>' 6piV cA
cv v't <=r
V V* V V V V V \’ V V S’ V V V V \
Year
9-
"co
>
£
3
00
O
£
j5
s
o
cl
HHH
GOA
0.2
Year
Figure 4
Annual estimates of the probability of survival t, for both ATI and Gulf of
Alaska (GOA) transient killer whales (Orcinus orca). Solid squares represent
the posterior median, and bars represent the 95% probability intervals.
trend model, revealed contrast-
ing patterns (Figs. 5 and 6). The
estimated number of GOA whales
using the study area in each an-
nual period, Nt, showed relatively
little variation from a low pos-
terior median of six whales in
1996 and 2005 to a high of 18
in 1990. The ATI whales showed
evidence of greater abundance
changes, from an estimated high
of around 22 in 1989 to a low of
seven at the end of the series. As
a result, there was strong evi-
dence that the abundance of ATI
whales declined over the study pe-
riod and unequivocal support for
inclusion of the trend model for
abundance with p(gP= 1) = 1. The
entire posterior distribution for
the trend parameter, /3lt fell be-
low zero, indicating a probability
of 1.00 of a downward trend. In
contrast, the posterior distribu-
tion for the trend parameter was
evenly spread above and below
zero for GOA whales, with 51% of
the posterior density in favor of a
negative trend. As a result, there
was little support for including
a model for trends in abundance
with p(gP= 1) = 0.02. Correspond-
ingly, the posterior density for
the change-point was distributed
evenly across all years for GOA
whales and reflected no obvious
changes in abundance. For ATI
transients, in contrast, there
was a distinct peak in the poste-
rior probability distribution for a
change-point, and 97% of the pos-
terior density for an abundance
change occurred in the five years
after 1989.
Emigration of GOA transients
away from the study area was
also supported by photographic
resighting data from southeast-
ern Alaska and British Columbia
(Table 2). For 1995-2007 there
were 16 encounters with GOA transient whales in
these adjacent regions, including one (5 June 2001)
with GOA transients in association with known mem-
bers of the west coast transient population (Matkin
et al., 2007b). There were no resighting data outside
of Prince William Sound-Kenai Fjords for ATI tran-
sients despite substantial survey effort in southeastern
Alaska (Dahlheim and White, 2010) and in adjacent
regions to the west of the study area (Matkin et al.,
1999, 2007a; Durban et al., 2010; senior author, un-
publ. data). Additionally, the one tagged ATI transient
did not travel out of the area. All these observations
support the inference from the mark-recapture model
of high fidelity to the study area.
Satellite-monitored LIMPET tags were attached to
GOA transients in Prince William Sound on four occa-
sions for a total of 73 days of transmissions (Table 3).
One individual (AT73) was tagged on two occasions
in different years. Tagged whales traveled a total dis-
tance of 7107 km during 73 days for an average move-
150
Fishery Bulletin 1 10(2)
25 !
cA eft. op, ,o.
^ Noi° Nor Ncr scr sncsuedu
1 Department of Biology
Center for Marine Science and Technology
North Carolina State University
303 College Circle
Morehead City, North Carolina 28557
2 North Carolina State University
Department of Food, Bioprocessing,
and Nutrition Sciences
Center for Marine Science and Technology
303 College Circle
Morehead City, North Carolina 28557
Rudershausen et al.: A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
157
Stream waters off of North Carolina. Catch composi-
tion of dolphinfish, yellowfin tuna, and wahoo in this
troll fishery varies widely among vessels, seasons, and
locations in the Gulf Stream.
There is concern in the charter boat industry that
circle hook regulations (developed for and based on bill-
fish), if ever mandated outside of U.S. Atlantic billfish
tournaments, would negatively impact catch rates of
dolphinfish, yellowfin tuna, and wahoo. Chartering an
offshore fishing trip in the southeastern United States
is an expensive endeavor (~$2000/day; senior author,
personal observ. ) and reductions in catch may have eco-
nomic influences on charter fishing businesses. Success
of the offshore troll fishery relies on clientele having
a reasonable chance to catch and keep fish that are
highly valued as seafood. In North Carolina, there are
few charter captains willing to use or experiment with
circle hooks when targeting non-billfish species because
there is a widespread perception that trolling circle
hooks for non-billfish species results in reduced catch
rates, and therefore greater chances for customer dis-
satisfaction, compared with J hooks. The charter ocean
fishing industry in North Carolina includes roughly
750 vessels and receives $65 million annually in for-
hire fees (Dumas et ah, 2009). Economic ramifications
of requiring circle hooks outside U.S. Atlantic billfish
tournaments have not been quantified.
Our purpose in undertaking this study was to deter-
mine the effects of using circle hooks on catch levels of
non-billfish species in the U.S. southeastern offshore
troll fishery in comparison with catch levels with J
hooks. Mechanisms that might explain differences or
similarities in catch between hook types were also ex-
amined. Questions were the following: 1) Did predators
strike circle and J hook rigged baits at similar rates?;
2) Once struck, did circle and J hook rigged baits have
similar proportions of hook-ups?; and 3) Once hooked
up, did circle and J hook rigged baits have similar pro-
portions of retained fish (brought to the boat)?
Materials and methods
Fishing techniques workshop
In November 2007 we convened a workshop attended by
state and federal biologists, fishery managers, charter
boat captains and mates, private boat anglers, and bill-
fish tournament directors. The purpose of the workshop
was to select hook types, hook styles, rigging techniques,
and fishing techniques (see below) that could be used
to compare trolled circle and J hooks in Gulf Stream
waters off North Carolina during troll fishing days
aboard charter vessels.
Defining and selecting circle and J hooks was a cen-
tral part of the workshop. A circle hook was defined as
having the point perpendicular to the hook shank. A J
hook was defined as having the point and point shank
parallel to the hook shank. We selected circle and J
hooks that would be comparable in bend diameter (gap
between hook shank and point shank). For both hook
types, we selected barbed hooks with zero offset and
straight hook eyes (eye parallel to the hook shank).
The circular shape, hook point turned perpendicularly
toward the shank, and zero offset insured that the
circle hooks we selected conformed to the National Ma-
rine Fisheries Service definition in the current billfish
tournament regulations (Federal Register, 2006). Other
hook characteristics (hook size, hook gauge, gap width,
and shape) were selected to avoid compromising the
action and durability of the trolled dead whole fish (bal-
lyhoo [Hemiramphus brasiliensis]). Participants decided
that circle and J hooks with a gap width large enough
that allowed space between the bait and hook for hook-
ing fish but with a relatively low profile (by virtue of
the gauge of hook wire) would be most appropriate for
testing.
Bait rigging and fishing techniques
The bait rigging techniques for each non-billfish species
presented at the workshop were those used by the local
charter industry. Circle and J hooks were embedded in
ballyhoo except for directed trips for dolphinfish, when
circle hooks were rigged externally (Fig. 1). Other dif-
ferences in bait rigging and fishing techniques are
described below by species. Flook sizes and styles, leader
characteristics, and rigging techniques differed slightly
on recreational trips because these fishermen often troll
with smaller hooks and different rigging techniques
from those used by charter captains.
For charter and recreational trips targeting dolphin-
fish we used Mustad 9175 7/0 J hooks (Mustad, Gjovik,
Norway1) that were rigged inside ballyhoo; the chin
weight was affixed to 30 cm of rigging wire. We used
Eagle Claw 2004ELG 8/0 circle hooks (Eagle Claw
Fishing Tackle Co, Denver, CO) rigged externally to
the ballyhoo with a 7-g chin weight and swivel at the
top of the head, with 30-cm of rigging wire (no pin).
The leader was 1.8 m of 36 kg of monofilament attached
to the standing line with a 31-kg Sampo ball-bearing
swivel (Sampo Inc., Barneveld, NY). The leader was
attached to the hook by using a loose crimp with tag
end opposite the point (Fig. 1A). We used lever drag
reels affixed to “thirty pound-class” stand-up rods at
all locations. Reels were spooled with a 14-kg test Dia-
mond® monofilament line (Diamond Fishing Products,
Pompano Beach, FL). The drag upon strike of a fish
was set just above “free spool” (reel gear not engaged)
with the clicker in the “on” position. The drag during
the fight of a fish (regardless of species) was roughly
6.4 kg. Baits were dropped back (line allowed to come
off the spool with no drag) to missed fish (that struck)
immediately after the strike. Recreational rigging tech-
niques for dolphinfish were similar to those used on the
1 Mention of trade names or commercial companies is for
identification purposes only and does not imply endorsement
by the National Marine Fisheries Service, NOAA.
158
Fishery Bulletin 1 10(2)
charter vessel except that 1) circle and J hooks were
one size smaller.
Charter trips targeting yellowfin tuna used Mustad
7692 9/0 J hooks and Eagle Claw 2004ELG 9/0 circle
hooks rigged inside ballyhoo, with 7-g chin weights
affixed to a pin. Hook and leader were secured to the
bait with a rubberband (for wahoo see next paragraph).
The leader was 9 m of 36 kg of clear fluorocarbon leader
through which a blue and white Seawitch lure (C&H
Lures, Jacksonville, FL) with a 14-g lead head was
threaded and positioned above the eye of the hook (Fig.
IB). The leader was attached to the standing line with
a 36-kg SPRO power swivel (SPRO Corp., Kennesaw,
GA). Both hook types were attached to the leader with
a loose crimp with the tag end opposite the point. We
used Penn “50-wide” reels (Penn, Philadelphia, PA)
affixed to “fifty pound class” stand-up rods at all loca-
tions. Reels were spooled with 27-kg Diamond® line.
The drag upon strike was set at roughly 4.5 kg while
the drag during fight (regardless of fish species) was set
to roughly 6.4 kg. Baits were dropped back to missed
strikes and then only until a fish picked up the bait.
Recreational rigging techniques for yellowfin
tuna were similar except that 1) circle hooks
were the same type and style but one size
smaller, 2) J hooks were Mustad 3407, 7/0
size, 3) the fluorocarbon leader was 3.7 m
long, and 4) “thirty pound class” stand up
rods were used.
Charter trips targeting wahoo used Mus-
tad 7731A 8/0 J hooks and Eagle Claw 2004
ELG 9/0 circle hooks rigged inside ballyhoo
with a 7-g chin weight and pin that comprised
part of the wire leader. The leader was 3.7 m
of #9 (41 kg) piano wire (Fig. 1C) with hay-
wire twists for attaching leader to a hook
at one end and for forming a loop at other
end; the leader was attached to the standing
line with a 59-kg ball bearing clip swivel. As
with yellowfin tuna, a blue and white Sea-
witch lure with a 14-g lead head was thread-
ed through the leader and positioned above
the eye of the hook. The same rod and reel
types used for yellowfin tuna were used for
wahoo. Baits were dropped back to missed
strikes and then only until a fish picked up
the bait. Recreational rigging techniques for
wahoo were similar to those used for charter
fishing except that 1) circle hooks were one
size smaller; 2) J hooks were Mustad 3407,
7/0 size and 3) “thirty pound class” stand up
rods were used.
Figure 1
Circle and J hook rigging techniques and leader types used in
trolling ballyhoo for (A) dolphinfish ( Coryphaena hippurus ) on
monofilament leaders, (B) yellowfin tuna ( Thunnus albacares )
on fluorocarbon leaders, and (C) wahoo ( Acanthocybium solandri)
on single-strand wire leaders. The circle hook is the bottom hook
type in each of the three photographs.
Data collection
Circle and J hooks were trolled side-by-side
for both the charter and recreational groups.
Fishing occurred in Gulf Stream and nearby
ocean waters off North Carolina. The two
charter boats were employed in order to simu-
late a typical for-hire fishing operation in
this region. Each of the two captains and
mates used for charter trips in this study
had over 20 years experience in this fishery,
as well as experience rigging and trolling
circle hooks for billfishes. Fishing aboard a
research vessel was conducted to simulate a
recreational operation where fishermen have
no mate to coordinate the fishing operation
(i.e., to coordinate, rig, and check baits; moni-
tor and clear lines; check drags; and hook
Rudershausen et al.: A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
159
and gaff fish), but instead do these activities them-
selves. Before each charter trip, the captain and first
author determined which non-billfish species would be
targeted and adjusted the tackle class, leader, and rig
type accordingly; the first author made this determina-
tion for the recreational trips. This determination was
based on water temperature, time of year, and fish-
ing reports that indicated which species we would be
most likely to interact with. We fished monofilament,
fluorocarbon, and wire leaders a total of 6, 12, and 18
days on the charter vessels, and 18, 7, and 14 days on
the recreational vessel. There were not equal numbers
of days fished between the two user groups. At most,
one boat trip was taken per day.
On each of the two charter vessels we fished pairs
of standing rods (held by fixed rod holders) from four
positions. These four pairs were flat lines, short outrig-
gers (riggers), long riggers, and bridge poles. On windy
days, rods were not fished from the bridge because of
increased likelihood of tangles in the lines. On days di-
rected for wahoo, we used in-line planers on the flat-line
rods in order to fish baits deeper in the water column
and elicit a greater number of interactions with this
species. We randomly selected which side of the boat
(port or starboard) would receive the circle and J hook
treatment on each day of charter fishing.
We fished two pairs of lines simultaneously aboard
the research vessel. These pairs were flat lines and
poles fished from rod holders on a canopy “t-top.” For
each day of fishing on the recreational vessel, we stag-
gered hook types so that a hook type on a flat line was
on the side of the boat opposite that same hook type
on the t-top.
The three vessels used to collect data trolled at be-
tween six and seven knots (regardless of species tar-
geted). Chains of combined artificial lures consisting
of four 23-cm long squids ending with a blue and white
Hand Lure® (L&S Bait Company, Inc., Largo, FL)-
ballyhoo bait combination were deployed as teasers (no
hooks) from each vessel during the collection of data. A
chain of pink squids was deployed on the starboard side
of the boat and a chain of green squids was deployed on
the port side of the boat. Baits were medium ballyhoo
that were replaced upon washout.
We recorded fish total length (mm) when it was pos-
sible to associate a fish length with a hook type. This
was not always possible because of multiple fish being
caught and placed in fish box at nearly the same time.
Hooking location was recorded for all captured fish.
Data analysis
Four response variables were measured: numbers
caught, numbers of strikes, proportion hooked up, and
proportion retained. Numbers of fish caught reflected
the cumulative results for the strike, hook-up, and reten-
tion levels. A fish interacting with the gear in a manner
such that the line was pulled from the outrigger clip or
that engaged the reel clicker when no clip was used was
considered a strike (Prince et al., 2002). A fish that had
been hooked for 10 seconds after striking was considered
a hook-up. A retained fish was one where the leader was
touched or the fish put into the boat (“boated”). The
proportion of fish that hooked up was relative to the
number that struck (Prince et al., 2002); similarly, the
proportion of fish that were retained on the hook was
relative to the number that hooked up.
Strikes and hook-ups for fish not caught or visually
identified were included in the analysis. When the ap-
pearance of a struck bait (e.g., bite marks), water tem-
perature, time of year, fishing location, fish behavior
(jumping), and order of fish landed each day indicated
a particular species, we attributed these interactions
to that species. When these six factors did not com-
bine to indicate a particular species, these interactions
were considered to be from an unidentified species. We
allocated strikes and hook-ups from unidentified fish
to each species in the same proportion as that for fish
boated for that day of fishing. At each level of interac-
tion we found similar best fitting models for data that
excluded or included unidentified fish (Rudershausen
et al., 2010).
Generalized linear models (GLMs) were used to deter-
mine the effects of hook type (circle or J), leader type
(monofilament, fluorocarbon, or wire), species (dolphin-
fish, yellowfin tuna, or wahoo), user group (recreational
or charter), wave height, and potentially important
interactions on the numbers of fish caught and each
of three mechanisms leading to a caught fish. We con-
structed a sequence of Poisson GLMs for the numbers
caught and numbers of strikes data sets. For hook-up
and retention data, we used binomial GLMs to repre-
sent the conditional nature of the hook-up and retention
processes (e.g., the number of fish that hooked up in a
given trip was conditional on the number that struck).
In each case, hook type, leader type, species, and user
group were treated as categorical variables, whereas
wave height was treated as a continuous variable. Spe-
cies caught on days where they were not the main tar-
get were included in all analyses and are referred to as
“nondirected” species. At each level of interaction, plots
were constructed to help better visualize the relative ef-
fectiveness of circle and J hooks on directed leader types.
We collected the same response variable data for
other species that have feeding styles similar to those
of yellowfin tuna and wahoo to provide additional data
to clarify trends in relative hook-type effectiveness.
The four model sets described above were also fitted
to data sets that included blackfin tuna (Thunnus at-
lanticus), skipjack tuna (Euthynnus pelamis), and false
albacore ( Euthynnus alletteratus), which were combined
with yellowfin tuna data to form a “tuna” group (fam-
ily Scombridae, tribe Thunnini), and king mackerel
( Scomberomorus cavalla) and Spanish mackerel ( Scomb -
eromorus maculatus ), which were combined with wahoo
data to form a “mackerel” group (family Scombridae,
tribe Scomberomorini). This additional model fitting
kept dolphinfish as a single-species group.
We adopted an information-theoretic perspective to
compare the parsimony of relatively simple models
160
Fishery Bulletin 1 10(2)
that we believed would help determine relative ef-
fectiveness of each hook type at catching fish and on
mechanisms during the fish-hook interaction (strike,
hook-up, and retention). We inspected data plots to
determine factors other than hook that contributed
to variability in catch rates. Base models for each
level of fish interaction were then constructed without
hook main effects and hook interactions. For each of
these potential base models we calculated a quasi-
Akaike’s information criterion (QAIC; Burnham and
Anderson, 2002). QAIC was computed instead of AIC
because of potential over-dispersion of the data used
as the response variable in each model (Burnham and
Anderson, 2002). At each level of fish interaction, we
selected the base model with the lowest QAIC value.
The most parsimonious base models had 1) main ef-
fects (excluding hook) plus a leader-species interaction
at the catch level; 2) main effects (excluding hook)
plus leader-species and species-user interactions at
the strike level; 3) main effects (excluding hook) plus
a leader-user interaction at the hook-up level; and 4)
main effects (excluding hook) plus a species-user in-
teraction at the retention level. After the base model
was selected, we developed incrementally more com-
plex models that then included a hook effect and in-
teraction terms between hook and other factors. This
sequential model building allowed us to determine if
the main factor of interest — hook type — covaried with
other factors potentially influencing interactions with
fishes. Any models with three-way interactions also
included two-way subinteractions. QAIC( values were
then used to compare fits among all i models (includ-
ing the base model) at each level of fishing interac-
tion to help determine the combination of predictors
that best explained variation in the data. The AQAIC
value for each model was calculated as the differ-
ence between any particular model (QAIC,) and the
minimum QAIC for the best fitting model in the set
(QAICmin). The model with the QAICmin value was, for
each model set, considered to be the one representing
the data adequately with the fewest parameters; how-
ever, we regarded models that differed by <~ 4 AQAIC
as all having reasonable support (Burnham and An-
derson, 2002). We also computed Akaike weights (wt)
for each model to help gauge the relative support for
each model in the model set; the value of wt varies
between 0 and 1, with a greater value indicating that
a particular model better fits the data. See Burnham
and Anderson (2002) for equations used to compute
QAIC and wt.
Highly parameterized models often resulted in sin-
gular Hessian matrices, indicating that one or more
parameters were nonidentifiable. However, we retained
these models in each model set because our primary
goal was to obtain parsimonious predictions of how
hook type affected catch rates. In an information-the-
oretic context, over-parameterized models would simply
be penalized for requiring additional parameters to
explain the same amount of variation in the data and
therefore would be unlikely to be selected with QAIC.
The selection of base models and development of more
complex models incorporating a hook main effect and
hook interactions by using data on taxa (e.g., dolphin-
fish, “tunas,” and “mackerels”) followed the process used
for the three species. Base models at each level of fish
interaction were the same as in the species analyses
described above with the exception of the retention
level, where a model consisting of main effects (except
hook) plus a leader-species interaction best fitted the
taxa data.
We computed the relative effectiveness of circle and
J hooks (effect size) by comparing predicted circle
and J hook catch rates of dolphinfish, yellowfin tuna,
and wahoo on their respective directed leader types.
Effect size was calculated for each catch model with
a positive Akaike weight ( wi ) (see Results section).
Effect size (ES) for each of these models was com-
puted as
ES = ^-, (1)
where fix and - the predicted mean catch-per-trip
values on circle and J hooks, respectively.
Effect size theoretically ranges from zero to greater
than one. An effect size less than, equal to, or greater
than one indicates that circle hooks are less, equally,
or more effective than J hooks, respectively. The
variance (a2) about each effect size was calculated
as
where ox2 and o2 are the variances about the mean
predicted mean catch-per-trip values of circle and J
hooks. The values for user and wave were held constant
(at 0.48 and 0.79 m, respectively) when computing effect
size for the three species-leader combinations from each
aforementioned catch model. The effect size from each
model was weighted by the relative w( value. Weighted
effect size values from each model were summed to
determine an overall effect size for each of the three
species caught on its directed leader type. This model-
averaging procedure was repeated to compute overall
variance about each average effect size; model averag-
ing for variance was conducted by multiplying each
model’s variance by the squared value of the Akaike
weight (w2). Computations of predicted effect sizes
and associated variances were repeated with the data
on taxa.
For each species, we compared median lengths be-
tween hook types with a median ranks test (a=0.05).
Data were combined across leader types and user
groups for each of these size-based analyses. For each
species, we compared rates of jaw (mouth) and deep
hooking (gut, gills, or eyes) among hook types using
a chi-square square test of independence.
Rudershausen et al.: A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
161
Table t
Number of fish caught on circle and J hooks from 39 recreational and 36 charter trips trolling both hook types with natural and
combination baits offshore of North Carolina, 2006-10. Each number (no.) and percent (%) column is specific to user group (rec-
reational vs. charter) and hook type (circle vs. J ). Each column of % values adds up to 100%.
Species
Recreational
Charter
Circle
J
Circle
J
No.
%
No.
%
No.
%
No.
%
Dolphinfish (Coryphaena hippurus)
35
63.6
71
77.2
45
40.2
73
38.8
Yellowfin tuna ( Thunnus alhacares )
7
12.7
5
5.4
25
22.3
47
25.0
Wahoo ( Acanthocybium solandri )
0
0.0
1
1.1
20
17.9
22
11.7
Blackfin tuna (Thunnus atlanticus )
0
0.0
0
0.0
14
12.5
26
13.8
King mackerel ( Scomberomorus cavalla )
8
14.5
3
3.3
0
0.0
4
2.1
Barracuda (Sphyraena barracuda)
1
1.8
0
0.0
1
0.9
3
1.6
Spanish mackerel ( Scomberomorus maculatus)
0
0.0
3
3.3
0
0.0
0
0.0
False albacore ( Euthynnus alletteratus)
2
3.6
6
6.5
4
3.6
5
2.7
Greater amberjack (Seriola dumerili )
0
0.0
1
1.1
0
0.0
1
0.5
Bluefish (Pomatomus saltatrix)
0
0.0
1
1.1
0
0.0
0
0.0
Atlantic sailfish ( Istiophorus platypterus)
1
1.8
0
0.0
2
1.8
4
2.1
White marlin (Tetrapturus albidus)
0
0.0
1
1.1
0
0.0
0
0.0
Blue marlin (Makaira nigricans)
0
0.0
0
0.0
0
0.0
1
0.5
Skipjack tuna (Euthynnus pelamis)
0
0.0
0
0.0
1
0.9
2
1.1
Bullet mackerel (Auxis spp.)
1
1.8
0
0.0
0
0.0
0
0.0
Results
Catch composition
The three most abundant species captured on recre-
ational trips were dolphinfish, yellowfin tuna, and
king mackerel, which together constituted 91% of the
catch on circle hooks and 86% on J hooks. The three
most abundant species captured on charter trips were
dolphinfish, yellowfin tuna, and wahoo, which together
constituted 80% of the catch on circle hooks and 76%
on J hooks. Blackfin tuna were commonly caught on
charter trips, constituting 13% of the catch on circle
hooks and 14% of the catch on J hooks. Billfishes made
up 1% of the catch on recreational trips and 3% of the
catch on charter trips (Table 1). Pooling across both
user groups, we found that 74% of dolphinfish were
caught on monofilament leaders, 96% of yellowfin
tuna were caught on fluorocarbon leaders, and 98% of
wahoo were caught on wire leaders; that is, the vast
majority of individuals from each species were cap-
tured on the respective directed leader type. Species
identity could not be determined in 14.0% of strike
and 2.9% of hook-up interactions over the course of
the study.
Comparisons of catch and examination
of mechanisms influencing catch
Hook type influenced catch rate (Fig. 2). For the three-
species analysis of catch rate, the base model plus a
hook main effect received majority support (Table 2).
For directed leaders, J hooks caught more dolphinfish
than circle hooks for both recreational and charter
groups. Higher catches on J hooks were also observed
in the charter group for yellowfin tuna; however, there
was no clear hook effect within the recreational group
for yellowfin tuna or wahoo or charter group for wahoo.
Partial support for models containing hook-user and
hook-species interactions confirms these observations
(Table 2). The hook-leader interaction also had support
and was most obvious in the dolphinfish data where
the hook effect was not consistent across leader types
(Fig. 2). Model fitting to numbers-caught data with
taxa (i.e., dolphinfish, tunas, and mackerels) provided
similar results to those for species data (Table 2; Fig.
3); the base model plus a hook main effect received
majority support as the best fitting model and models
that included hook-user, hook-leader, and hook-species
interactions had QAIC values within four units of the
best fitting model. Tunas were caught more often on J
hooks and fluorocarbon leaders than other hook-leader
combinations. Mackerels were caught slightly more
often on J hooks than circle hooks, and most often on
wire leaders (Fig. 3).
The first mechanism contributing to catch was strike.
Hook type had little effect on strikes for each of the
three species examined (Fig. 4). No single model re-
ceived majority support when fitted to strike data for
the three species and the base model with a hook factor
received only slightly greater support than the base
model without the hook parameter (Table 3). Models
162
Fishery Bulletin 1 10(2)
Recreational Charter
Circle J Circle J
Hook type
Figure 2
Plots of the average catch per trip (±standard error) on circle hooks (open bars) and J
hooks (gray bars). Data for each species are from both directed and nondirected trips
for that species. Plots are broken down by user group (recreational [left column, panels
A-C] and charter [right column, panels D-F]) and species (dolphinfish [Coryphaena
hippurus] [A, D], yellowfin tuna [Thunnus albacares] [B, E], and wahoo [ Acanthocybium
solandri] [C, F] ). The legend denoting fill pattern for each leader type applies to all
panels. No bar for a particular hook-type + species+user-group+leader-type combination
indicates no catch.
with hook-user, hook-leader, and hook-species interac-
tions each received a relatively small amount of sup-
port. Greater numbers of strikes occurred 1) on charter
boats (owing to a greater number of rods fished), 2)
when using monofilament leaders, and 3) from dolphin-
fish than any other species. As with the three species
data, there was little difference in the average strikes
per trip between circle and J hooks for each taxa (Fig.
5). Similarly, the model that best fitted strike data for
the taxa was the base model with hook, but the base
model without hook received only slightly less support
(Table 3; Fig. 5). Models with hook-user, hook-species,
and hook-leader interactions received relatively minor
support.
The second mechanism contributing to catch was
hook-up. J hooks were more effective at hooking fish
for many user group-species combinations (Fig. 6).
Hook was a main effect in the model that best fit-
ted the proportional hook-up data (Table 4). Models
that received less support included hook-user, hook-
species, hook-leader, and hook-species + hook-user
interactions. The base model received no support.
There was a reduction of hook-ups for dolphinfish
when circle hooks were used on both recreational and
Rudershausen et al A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
163
Table 2
Candidate models fitted to catch-per-trip data for three species (dolphinfish | Coryphaena hippurus 1, yellowfin tuna I Thunnus
albacares], and wahoo \Acanthocybium solandri], and taxa (dolphinfish, tunas, and mackerels]) when trolling circle and J hooks
in Gulf Stream waters off North Carolina. Quasi-Akaike information criterion (QAIC) was used to evaluate model performance,
with the lowest value indicating the most parsimonious model. Categorical predictor variables included hook type (hook), leader
type (leader), species or taxa, and user group (user). Wave height was used as a continuous predictor variable. K= number of param-
eters for each model; ic=Akaike weight. Base models included all predictor variables with exception of hook and any hook interac-
tions; see Methods section for a full description of base models. 4QAIC values ~<4 were considered models with reasonable support.
Interaction Data type Distribution Model
K
QAIC
AQAIC
w
Catch: species Count Poisson base + hook
13
356.77
0.00
0.54
base + hook + hook*user
14
358.42
1.65
0.23
base + hook + hook*leader
15
360.49
3.72
0.08
base + hook + hook*species
15
360.90
4.14
0.07
base + hook + hook*user + hook*leader
16
361.98
5.21
0.04
base + hook + hook*species + hook*user
16
362.54
5.77
0.03
base + hook + hook*species + hook*leader
17
364.91
8.15
0.01
base + hook + hook*species + hook*leader
21
373.59
16.83
0.00
+ hook*species*leader
base
12
385.14
28.37
0.00
Catch: taxa Count Poisson base + hook
13
477.17
0.00
0.55
base + hook + hook*user
14
479.11
1.94
0.21
base + hook + hook*leader
15
480.63
3.46
0.10
base + hook + hook*taxa
15
481.14
3.97
0.07
base + hook + hook*user + hook*leader
16
482.54
5.37
0.04
base + hook + hook*taxa + hook*user
16
483.16
5.99
0.03
base + hook + hook*taxa + hook*leader
17
485.11
7.94
0.01
base + hook + hook*taxa + hook*leader
21
493.60
16.43
0.00
+ hook*taxa*leader
base
12
501.35
24.18
0.00
charter trips. This trend was most pronounced on
charter trips for all leader types (Fig. 6). The excep-
tion was a slightly greater hook-up rate for yellowfin
tuna on circle hooks than on J hooks when fishing
fluorocarbon leaders on recreational trips. For the
taxa analysis, trends in model fitting to proportional
hook-up data were similar to three species (Table
4; Fig. 7); hook was a main effect in the best fit-
ting model and it was a main effect and interaction
term in models receiving lesser support. The base
model received no support (Table 4). The addition of
mackerel data on recreational trips strengthened the
trend of greater effectiveness of J hooks in hooking
up these taxa on wire, the directed leader type for
that group (Fig. 7).
The third mechanism contributing to catch was
retention. Hook type did not appear to have a pro-
nounced effect on proportion of fish retained (Fig. 8).
For models fitted to species data, the base model re-
ceived majority support (Table 5). A base model with a
hook effect was the only other model receiving support,
but it was minor. The proportion retained on circle
hooks generally equaled (dolphinfish and yellowfin
tuna) or slightly exceeded (wahoo) those retained on J
hooks on directed leader types (Fig. 8). Proportional
retention data for the taxa also showed that retention
was high, with little to no difference between hook
types (Table 5; Fig. 9). The base model received major-
ity support and the base model with hook as a main
effect received less support. Two other models that
received minor support had hook-species and hook-user
interactions (Table 5).
Estimates of effect size on catch rates determined
from model-averaged predictions showed that J hooks
were more effective than circle hooks. This trend held
across the species and taxa levels. For the three spe-
cies, mean predicted effect size (± standard deviation
[ SD] ) for dolphinfish, yellowfin tuna, and wahoo on
directed leader types was 0.60 (0.05), 0.60 (0.07), and
0.65 (0.09), respectively (Fig. 10), meaning that circle
hooks were roughly 60% as effective as J hooks. For
the taxa groups, mean predicted effect size (±SD) for
dolphinfish, tunas, and mackerels was 0.62 (0.05),
0.62 (0.06), and 0.67 (0.08), respectively (Fig. 10).
There were no significant between-hook differences
in the distribution of lengths for dolphinfish (^2 = 0.973,
P=0.324), yellowfin tuna (^2=0.003, P=0.958), or wahoo
(^2 = 0.068, P=0.795). Thus, hook type was not size selec-
tive within a species.
The effect of hook type on deep hooking was species
dependent. Rates of deep hooking were significantly
less for dolphinfish caught on circle hooks than J hooks
(Table 6). However, there was no effect of hook type on
proportion of deep-hooked wahoo or blackfin tuna. Rates
164
Fishery Bulletin 110(2)
Recreational Charter
Hook type
Figure 3
Plots of the average catch per trip (±standard error) on circle hooks (open bars) and J
hooks (gray bars). Data for each group are from both directed and nondirected trips
for that species. Plots are broken down by user group (recreational [left column, panels
A-C], and charter [right column, panels D-F[), and taxa (dolphinfish Coryphaena hip-
purus [A, D[, tunas [B, E], and mackerels [C, Fj). The tuna group included yellowfin
tuna ( Thunnus albacares ), blackfin tuna (Thunnus atlanticus), skipjack tuna ( Euthyn -
mis pelamis ), and false albacore ( Euthynnus alletteratus). The mackerel group included
wahoo (Acanthocybium solandri), king mackerel ( Scomberomorus cavalla ), and Spanish
mackerel (Scomberomorus maculatus). The legend denoting fill pattern for each leader
type applies to all panels. No bar for a particular hook-type+taxon+user-group + leader-
type combination indicates no catch.
of deep hooking were 0% for both circle and J hooks
that caught yellowfin tuna.
Discussion
There is increased interest in requiring circle hooks in
the recreational bluewater troll fishery in the United
States. This is largely due to studies finding that circle
hooks maintain catch rates but reduce rates of deep
hooking compared with J hooks in billfishes (see Serafy
et ah, 2009, for review). In contrast, we found for non-
billfishes that observed catch rates were reduced with
circle hooks under that for J hooks in the charter group;
similar findings were found in the recreational group for
dolphinfish. Predictions of relative catch (through effect
Rudershausen et al A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
165
Recreational
Charter
Hook type
Figure 4
Plots of the average number of strikes per trip (±standard error) on circle hooks (open
bars) and J hooks (gray bars). Data for plots includes strikes from unidentified fish
later apportioned to species that could be identified. Data for each species are from both
directed and nondirected trips for that species. Plots are broken down by user group
(recreational lleft column, panels A-C] and charter I right column, panels D-F]) and
species (dolphinfish [ Coryphaena hippurus] |A, D], yellowfin tuna [ Thunnus albacares]
[B, E 1 , and wahoo ( Acanthocybium solandri ] [C, F]). The legend denoting fill pattern for
each leader type applies to all panels. No bar for a particular hook-type + species+user-
group + leader-type combination indicates no catch.
size calculations) indicate that fishermen can expect 65%
greater catches of the three species or taxa groups on J
hooks than on circle hooks. The similar findings between
the species and taxa analyses indicate that morphologi-
cal features of fish, attack styles, and hook effectiveness
are consistent among the species of the tuna group and
among the species of the mackerel group. Additionally,
the similar results when smaller tunas and mackerels
were included in the taxa analysis indicate that the inef-
fectiveness of circle hooks compared with J hooks is not
size dependent within the range of fish sizes in our study.
The similarities between our findings and prior hook
comparisons of hooks on longlines depend on the species
being considered. In a Brazilian longline fishery, Sales
et al. (2010) found a similar trend in dolphinfish catches
to that found in our study (lower catches on circle hooks
than on J hooks) but significantly more tunas caught
on circle hooks than on J hooks. The increased catch
166
Fishery Bulletin 1 10(2)
Table 3
Candidate models fitted to strike data for three species (dolphinfish \Coryphaena hippurus], yellowfin tuna [ Thunnus albacares],
and wahoo 1 Acanthocybium solandri]), and taxa (dolphinfish, tunas, and mackerels) when trolling circle and J hooks in Gulf
Stream waters off North Carolina. Quasi-Akaike information criterion (QAIC) was used to evaluate model performance, with
the lowest value indicating the most parsimonious model. Categorical predictor variables included hook type (hook), leader type
( leader), species or taxa, and user group ( user). Wave height was used as a continuous predictor variable. /f=number of parameters
for each model; w=Akaike weight. Base models included all predictor variables with exception of hook and any hook interactions;
see Methods section for a full description of base models. AQAIC values ~<4 were considered models with reasonable support.
Interaction Data type
Distribution
Model
K
QAIC
hQAIC
w
Strike: Count
Poisson
base + hook
15
979.96
0.00
0.36
species
base
14
980.17
0.21
0.33
base + hook + hook*user
16
981.89
1.93
0.14
base + hook + hook*leader
17
983.42
3.46
0.06
base + hook + hook*species
17
983.81
3.86
0.05
base + hook + hook*user + hook*leader
18
985.40
5.44
0.02
base + hook + hook*species + hook*user
18
985.75
5.79
0.02
base + hook + hook*species + hook*leader
19
987.85
7.89
0.01
base + hook + hook*species + hook*leader
23
996.39
16.43
0.00
+ hook*species*leader
B. Strike: Count
Poisson
base + hook
15
1050.57
0.00
0.40
taxa
base
14
1051.09
0.52
0.31
base + hook + hook*user
16
1052.66
2.08
0.14
base + hook + hook*taxa
17
1054.54
3.97
0.05
base + hook + hook*leader
17
1054.56
3.99
0.05
base + hook + hook*taxa + hook*user
18
1056.59
6.01
0.02
base + hook + hook*user + hook*leader
18
1056.64
6.07
0.02
base + hook + hook*taxa + hook*leader
19
1058.92
8.34
0.01
base + hook + hook*taxa + hook*leader
23
1067.40
16.83
0.00
+ hook*taxa*leader
rate of tunas on circle hooks over that for J hooks has
been observed in other longline studies (Falterman and
Graves, 2002). It is unclear what the mechanism is that
leads to higher tuna catches on longline circle hooks,
but lower tuna catches on trolled dead baits rigged with
circle hooks; it is likely that tuna ingested the bait and
hook more deeply in comparison to the actively trolled
bait in our study. Actively trolling hooks (versus passive
fishing on a longline) may be the mechanism contribut-
ing to these hook-type differences.
Most comparative studies of hooks in the dead bait
troll fishery have been designed to estimate catch-
and-release mortality in billfishes (Prince et al., 2002;
Horodysky and Graves, 2005; Graves and Horodysky,
2010). The species that we examined in this study are
not generally released; therefore, our focus was on the
influence of hook type on catch rates and the potential
mechanisms responsible for similarities or differences
in catch by hook type, rather than on postrelease mor-
tality. This was our focus because many charter boat
captains suspect that circle hooks negatively impact
catches of dolphinfish, tunas, and mackerels in the
North Carolina dead-bait troll fishery. Our results con-
firm this suspicion. Model-averaged estimates suggest
a strong negative effect of hook type on catch rates for
all three species; however, examination of the raw data
for individual species suggests that the effect of hook
type on wahoo catch may be minor. Future studies with
increased sample sizes would help to refine estimates
of species by hook-type interactions, providing greater
resolution of the importance and magnitude of hook ef-
fects for individual species. Thus, this is the first study
to find that catch rates in a dead bait troll fishery can
be negatively impacted by circle hooks. Horodysky and
Graves (2005) and Graves and Horodysky (2010) did
not provide comparisons of catch data between circle
hooks and J hooks in their hook comparative studies
on billfish.
Differences in strike, hook-up, and retention rates be-
tween hook types all have the potential to contribute to
differences in catch rates. There was little evidence for
a hook effect on strike rate; therefore, J and circle hook
rigged baits were equally attractive to these three fish
groups. Other studies that have compared hook types
in the dead bait troll fishery have not reported data on
strike rate by hook type; we recommend that this in-
formation be collected so that the specific mechanisms
responsible for potential differences in catch rate can
be determined.
The greater effectiveness of J hooks at hooking fish
once they struck generally held across the three species
and dolphinfish and the two taxa groups. Circle hooks
Rudershausen et al A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
167
Recreational
Charter
Hook type
Figure 5
Plots of the average number of strikes per trip (±standard error) on circle hooks (open
bars) and J hooks (gray bars). Data for plots include strikes from unidentified fish
later apportioned to species that could be identified. Data for each group are from both
directed and nondirected trips for that species. Plots are broken down by user group
(recreational [left column, panels A-C] and charter [right column, panels D-F]) and
taxa (dolphinfish [ Coryphaena hippurus] [A, D[, tunas [B, E], and mackerels [C, F] ) .
The tuna group included yellowfin tuna ( Thunnus albacares), blackfin tuna (Thunnus
atlanticus), skipjack tuna (Euthynnus pelamis), and false albacore (Euthynnus allet-
teratus). The mackerel group included wahoo ( Acanthocybium solandri), king mackerel
( Scomberomorus caualla), and Spanish mackerel (Scomberomorus maculatus ). The legend
denoting fill pattern for each leader type applies to ail panels. No bar for a particular
hook-type + taxon+user-group + leader-type combination indicates no catch.
are designed to hook fish if the hook rounds a corner
within the jaw area. In theory, this would be most com-
mon for fish that turn their mouth opening away from
the direction of the fishing line. However, if a fish is
not seen during a strike, if is difficult to know when
to reel the line tight (i.e., when the fish has turned).
Our workshop panel (see Methods section) argued for a
drop back for dolphinfish because this species is known
to swim with the bait in their mouth in the direction
that the line is trolled. The drop back for dolphinfish
was done to allow enough time for the dolphinfish to
turn. Even with these efforts, hook-up rates of dolphin-
fish were lower with circle hooks than J hooks for both
user groups. Prince et al. (2002) found that hook-up
168
Fishery Bulletin 1 10(2)
Recreational
Charter
D Dolphinlish ( Coryphaena hippurus)
Yellowfin tuna ( Thunnus albacares)
F Wahoo {Acanthocybium solandri)
iil
Circle
Hook type
Figure 6
Plots of the average proportion of fish that hooked up (±standard error) on circle hooks
(open bars) and J hooks (gray bars). Data for plots includes hook-ups from unidentified
fish later apportioned to species that could be identified. Data for each species are from
both directed and nondirected trips for that species. Plots are broken down by user group
(recreational [left column, panels A-C] and charter [right column, panels E-F]) and
species (dolphinfish [Coryphaena hippurus ] [A, D], yellowfin tuna [Thunnus albacares ]
[B, E], and wahoo [ Acanthocybium solandri] [C, F]). The legend denoting fill pattern for
each leader type applies to all panels. No bar for a particular hook-type + species+user-
group+leader-type combination indicates no catch.
rate on circle hooks was significantly higher than J
hooks in a dead bait troll fishery for sailfish. The abil-
ity for the angler to visually see the fish with the bait
in its mouth may allow for higher hook-ups on circle
hooks in that fishery. In contrast, fishing for yellowfin
tuna and wahoo involved using a heavy drag because
the fish are aggressive and generally hook themselves
upon striking (see Graves and Horodysky [2010] for a
similar description and approach when targeting blue
marlin). Theoretically, the circle hook should work in
this heavy-drag situation only if the fish’s mouth is at
an angle to the direction of the line when the bait is
taken into the mouth. Hook-up rates for yellowfin tuna
and wahoo were slightly higher on J hooks on charter
trips (for which we had the most data); this finding may
be a result of some strikes on circle hooks where the
mouth opening faced the direction that the bait was be-
ing trolled or because of bait rigging (see below). Graves
Rudershausen et at: A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
169
Table 4
Candidate models fitted to hook-up data for three species (dolphinfish [ Coryphaena hippurus], yellowfin tuna [ Thunnus alba-
cares I, and wahoo ( Acanthocybium solandri]), and taxa (dolphinfish, tunas, and mackerels) when trolling circle and J hooks in
Gulf Stream waters off North Carolina. Quasi-Akaike information criterion (QAIC) was used to evaluate model performance, with
the lowest value indicating the most parsimonious model. Categorical predictor variables included hook type (hook), leader type
(leader), species or taxa, and user group (user). Wave height was used as a continuous predictor variable. /C=number of parameters
for each model; w=Akaike weight. Base models included all predictor variables with exception of hook and any hook interactions;
see Methods section for a full description of base models. AQAIC values ~<4 were considered models with reasonable support.
Interaction Data type Distribution
Model
K
QAIC
AQAIC
U)
Hook-up: Proportion Binomial
base + hook
11
-1159.03
0.00
0.38
species
base + hook + hook*user
12
-1158.16
0.88
0.25
base + hook + hook*species
13
-1156.75
2.29
0.12
base + hook + hook*leader
13
-1156.53
2.51
0.11
base + hook + hook*species + hook*user
14
-1155.07
3.96
0.05
base + hook + hook*user + hook*leader
14
-1154.81
4.23
0.05
base + hook + hciok*species + hook*leader
15
-1154.75
4.28
0.04
base + hook + hook*species + hook*leader
+ species*leader + hook*species*leader
23
-1148.53
10.51
0.00
base
10
-1134.22
24.81
0.00
Hook-up: Proportion Binomial
base + hook
11
-1393.91
0.00
0.40
taxa
base + hook + hook*taxa
13
-1392.58
1.33
0.21
base + hook + hook*user
12
-1392.17
1.74
0.17
base + hook + hook*leader
13
-1390.90
3.00
0.09
base + hook + hook*taxa + hook*user
14
-1390.48
3.43
0.07
base + hook + hook*user + hook*leader
14
-1388.80
5.11
0.03
base + hook + hook*taxa + hook*leader
15
-1388.72
5.19
0.03
base + hook + hook*taxa + hook*leader
+ taxa*leader + hook*taxa*leader
23
-1385.27
8.64
0.01
base
10
-1368.17
25.74
0.00
and Horodysky (2010) did not report hook-up percentage
data for blue marlin and therefore it is unknown what
hook-up rates would be for this aggressive feeder that
is hooked upon strike.
One rigging tactic when trolling is to rig the circle
hook so that it is completely external to the bony or
fleshy portions of the bait to maximize the exposed
gap width (e.g., the hook is placed on top of the bait’s
head; Prince et ah, 2002). This placement is thought
to work best for “dropping back” to fish because the
fish have enough time to swallow the bait and the
hook (dolphinfish and billfish trolling) and turn their
body, while the exposed gap width of the circle hook
is maximized. We did not employ the external rig-
ging technique on days when yellowfin tuna or wahoo
were targeted. Hooks were rigged internally for these
two species because these species hook themselves
upon striking; drop-backs are not typically required by
charter or recreational fishers targeting these species.
An additional reason for embedding hooks in baits was
so that we could fish “combo” baits (lure and natural
bait combinations) because colored lures (skirts) elicit
more strikes than plain ballyhoo on most days for
yellowfin tuna and wahoo. The cooperating mates on
charter trips embedded the hook as close to the tail
as possible without compromising the swimming ac-
tion of the bait. Using larger circle hooks would have
increased the gap width between the point and the
point shank, potentially making hook-ups more likely,
but this change could have compromised the strike
rate by making the hook more visible to the fish or
causing the bait to wash out faster.
There was little to no hook effect at the propor-
tional retention level (caught once hooked) for dol-
phinfish, yellowfin tuna, and tunas, although there
was increased retention of wahoo and mackerels on
circle hooks and yellowfin tuna on circle hooks in the
recreational fishery. The latter result is consistent
with the findings of Prince et al. (2002) when trolling
dead baits with circle and J hooks for sailfish. The
increased retention on circle hooks relative to J hooks
has been used as a selling point for circle hooks, but
we did not find this result for the majority of species
that we caught.
The procedure for assigning interactions with un-
identified fish to a particular species is not ideal. For
instance, if individuals of one species generate behav-
ioral cues or are landed more readily than individuals
for another species, species assignments may be biased
toward more readily identified fish. In general, this
approach decreased our ability to detect species effects
on landing probabilities and hookup rates. However,
we expected the reduction in statistical power to be
relatively small and to affect only inferences about
170
Fishery Bulletin 1 10(2)
Recreational
Charter
Circle
D Dolphinfish ( Corvphaena hippurus)
E T unas
Mackerels
Circle
J. .
m
Hook type
Figure 7
Plots of the average proportion of fish that hooked up (±standard error) on circle hooks
(open bars) and J hooks (gray bars). Data for plots includes hook-ups from unidentified
fish later apportioned to species that could be identified. Data for each group are from
both directed and nondirected trips for that species. Plots are broken down by user group
(recreational 1 left column, panels A-C] and charter [right column, panels E-F]) and
taxa (dolphinfish [Coryphaena hippurus] [A, D], tunas [B, E], and mackerels [C, F] ).
The tuna group included yellowfin tuna ( Thunnus albacares ), blackfin tuna ( Thunnus
atianticus), skipjack tuna (Euthynnus pelamis), and false albacore (Euthynnus aliet-
teratus). The mackerel group included wahoo (Acanthocybium solandri), king mackerel
(Scomberomorus cavalla), and Spanish mackerel ( Scomberomorus maculatus). The legend
denoting fill pattern for each leader type applies to all panels. No bar for a particular
hook-type+taxon+user-group+leader-type combination indicates no catch.
species-hook interactions; main effects for hook type
remained unbiased.
If fishermen are interested in releasing dolphinfish,
our results provide evidence that released fish are not
hooked as deeply and thus have a higher likelihood of
survival if circle hooks are used. The drop-back tech-
nique that we commonly used for dolphinfish likely
led to a higher percentage of dolphinfish becoming
deep hooked with J hooks over that for the tuna and
mackerel taxa groups. The reduction in gut hooking
with circle hooks has been found in most other stud-
ies comparing circle and J hooks (Cooke and Suski,
2004). Managers should factor in the high rate of
deep hooking for J-hooked dolphinfish as they imple-
Rudershausen et al A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
171
Recreational Charter
Circle J Circle J
Hook type
Figure 8
Plots of the average proportion of fish retained (±standard error) on circle hooks (open
bars) and J hooks (gray bars). Data for each species are from both directed and non-
directed trips for that species. Plots are broken down by user group (recreational lleft
column, panels A-C] and charter [right column, panels D-F]) and species (dolphinfish
Coryphaena hippurus [A, D], yellowfin tuna \Thunnus albacares ] [B, E], and wahoo
\Acanthocybium solandri ] [C, F]). The legend denoting fill pattern for each leader type
applies to all panels. No bar for a particular hook-type+species + user-group + leader-type
combination indicates no catch.
ment new minimum size regulations for this species
in the U.S. South Atlantic (SAFMC, 2011). However,
managers should also consider that there can be a
trade-off when using circle hooks. Although rates
of deep hooking are relatively low on circle hooks,
handling time and air exposure are increased while
dislodging them from captured fish owing to their in-
herently deeper bend than J hooks (Cooke and Suski,
2004; senior author, personal observ.). Along with
outreach efforts to encourage the use of circle hooks
where appropriate, instructions should be available
on how to quickly remove the hooks with little injury
to the fish.
Circle hooks remain vaguely defined. The federal
definition of a circle hook (Federal Register, 2006) is
somewhat arbitrary. Numerous circle hooks may meet
the federal specifications, yet may not simultaneously
reduce deep hooking in billfishes and maintain catch
rates of non-billfishes. For example, some manufac-
turers advertise circle hooks with parallel or nearly
parallel point shanks and hook shanks (like a J hook),
but which simply have the tip of the point bent 90°
172
Fishery Bulletin 1 10(2)
Table 5
Candidate models fitted to retention data for three species (dolphinfish [ Coryphaena hippurus j, yellowfin tuna [Thunnus alba-
cares], and wahoo \Acanthocybium solandri]), and taxa (dolphinfish, tunas, and mackerels) when trolling circle and J hooks in
Gulf Stream waters off North Carolina. Quasi-Akaike information criterion (QAIC) was used to evaluate model performance, with
the lowest value indicating the most parsimonious model. Categorical predictor variables included hook type (hook), leader type
( leader), species or taxa, and user group ( user). Wave height was used as a continuous predictor variable. K= number of parameters
for each model; m=Akaike weight. Base models included all predictor variables with exception of hook and any hook interactions;
see Methods section for a full description of base models. AQAIC values ~<4 were considered models with reasonable support.
Interaction Datatype Distribution Model
K
QAIC
AQAIC
w
Retention: Proportion Binomial base
10
-876.22
0.00
0.63
species base + hook
11
-874.19
2.02
0.23
base + hook + hook*leader
13
-871.13
5.09
0.05
base + hook + hook*species
13
-870.76
5.46
0.04
base + hook + hook*species + hook*user
14
-869.35
6.87
0.02
base + hook + hook*user + hook*leader
14
-869.16
7.05
0.02
base + hook + hook*species + hook*leader
15
-867.10
9.12
0.01
base + hook + hook*user
12
-857.77
18.45
0.00
base + hook + hook*species + hook*leader
23
-854.71
21.51
0.00
+ species + leader hook*species*leader
Retention: Proportion Binomial base
12
-1112.66
0.00
0.53
Taxa base + hook
13
-1110.55
2.11
0.19
base + hook + hook*species
15
-1108.74
3.92
0.08
base + hook + hook*user
14
-1108.74
3.93
0.08
base + hook + hook*leader
15
-1108.54
4.12
0.07
base + hook + hook*species + hook*user
16
-1106.71
5.95
0.03
base + hook + hook*user + hook*leader
16
-1106.40
6.26
0.02
base + hook + hook*species + hook*leader
17
-1104.87
7.79
0.01
base + hook + hook*species + hook*leader
23
-1092.10
20.56
0.00
+ hook*species*leader
Table 6
Percentage of fish caught in two anatomical locations (jaw vs. “deep” [body, gill, gut, eye]) with trolled circle and J hooks. The x2
test statistic and P-value from each test of independence comparing hooking locations between hook types are presented for each
species. A x2 test was not conducted for king mackerel because of small sample size.
Circle hook J hook
Species
Jaw
Deep
Jaw
Deep
X2
P
Dolphinfish ( Coryphaena hippurus)
98.5
1.5
61.3
38.7
31.35
<0.001
Yellowfin tuna ( Thunnus albacares)
100
0
100
0
—
—
Wahoo ( Acanthocybium solandri)
100
0
91.3
8.7
1.82
0.177
Blackfin tuna (Thunnus atlanticus)
100
0
92.6
7.4
1.13
0.287
King mackerel (Scomberomorus cavalla )
100
0
66.7
33.3
toward the shank. Having discussed the structure
of the hooks with captains, Smith (2006) postulated
that a greater turn in the point shank (a point shank
that turns back towards the hook shank by >33°) re-
duces the chances for deep hooking in billfishes. This
outcome has yet to be determined with experimental
fishing and would be a useful area of future research.
We measured the angle between the point shank and
hook shank to be roughly 25 degrees for the circle
hooks we used (regardless of the size). Compared with
the circle hook styles we tested, other circle hooks with
different point shank angles that still satisfy federal
requirements may have performed better at catching
non-billfish species.
The fishing tackle industry and charter boat opera-
tors continually adapt gear and techniques to increase
Rudershausen et al A comparison between circle hook and J hook performance in the troll fisheries off North Carolina
173
T3
CD
C
03
0)
CD
o3
$
SZ
U)
o
o
•c
o
Q.
O
Q.
<
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Circle J Circle J
Hook type
Recreational
Figure 9
Plots of the average proportion of fish retained (±standard error) on circle hooks (open
bars) and J hooks (gray bars). Data for each group are from both directed and nondirected
trips for that species. Plots are broken down by user group (recreational [left column,
panels A-C] and charter [right column, panels D-F[) and taxa (dolphinfish Coryphaena
hippurus [A, D], tunas [B, E], and mackerels [C, FI). The tuna group included yellowfin
tuna ( Thunnus albacares ), blackfin tuna ( Thunnus atlanticus ), skipjack tuna ( Euthyn -
nus pelamis), and false albacore ( Euthynnus alletteratus). The mackerel group included
wahoo (Acanthocybium solandri), king mackerel ( Scomberomorus cavalla), and Spanish
mackerel ( Scomberomorus maculatus). The legend denoting fill pattern for each leader
type applies to all panels. No bar for a particular hook-type+taxon+user-group+leader-
type combination indicates no catch.
catch efficiency. There are likely untested techniques
that allow fishermen to catch non-billfish with circle
hooks more efficiently than we found in this study.
Cooke and Suski (2004) report that the choice of circle
hook size is an important consideration in order to
maximize their effectiveness. Hook size seems to be an
especially important consideration in a mixed-species
and mixed-size fishery such as the one we examined.
Hook choice (size and style) was a central topic in the
workshop we convened; in targeting each of the main
species (dolphinfish, yellowfin tuna, and wahoo), we
selected hook sizes and styles recommended by expe-
rienced offshore fishermen.
It is likely that fishermen would be more inclined
to experiment with circle hooks and novel rigging
strategies if they knew there would be a pending re-
174
Fishery Bulletin 1 10(2)
Species group
Figure 10
Mean predicted effect size (±standard deviation) of circle versus J hooks
on catch rates by species or taxa group. Dolphinfish ( Coryphaena hip-
purus) is listed twice because the predicted effect size changes slightly
in comparisons with the “tunas” and “mackerels” taxa groups. An effect
size greater than 1 indicates greater effectiveness of circle hooks than
J hooks; the opposite is true for an effect size less than 1. An effect
size equal to 1 (dashed line) indicates that the hook types are equally
effective. The mean and variance of each effect size was calculated by
using weighted model averages from each model with positive Akaike
weight (w{) at the catch level (see Materials section for details).
quirement to use them outside of Atlantic f
billfish tournaments. Industry willing- 1.2-
ness to refine rigging techniques and
fishing strategies in the face of future
hook-type regulations could help increase
experimentation with circle hooks, and
thus catch rates of non-billfish species
when trolling for them in this fishery.
The workshop we convened generated
many novel rigging and fishing tech-
niques with circle hooks, only a fraction
of which we used for the field experiment
of this project.
We urge future studies to provide catch
rates (numbers standardized to effort),
strike, hook-up, and retention data for
both hook types so that trade-offs be-
tween catch-and-release survival and
catch rates can be evaluated. In addition,
the terms used when discussing these
variables should also be standardized.
For example, the catch rate for trolled
baits as defined by Serafy et al. (2009)
equals a retention rate (caught if hooked),
but a fisherman’s interest lies in know-
ing how many fish will be caught per
trip which is the product of number of
strikes, proportion hooked, and propor-
tion retained. Without knowledge of the
first two variables, the third variable
only provides information about a hook’s
effectiveness at retaining a fish on the
line and not its overall effectiveness.
Conclusions
We examined three mechanisms that may have been
responsible for the hook effect on catch rates. These were
strike, hook-up, and retention. There was little to no
hook effect at the strike and retention levels. However,
the differences in catch rates we observed resulted from
a lower hook-up rate on circle hooks compared with J
hooks. This trend was generally consistent across analy-
ses of data on three species and on three broader taxa.
It is unknown whether a requirement to troll exclu-
sively circle hooks in the offshore fishery would have an
economic impact on either the recreational or charter
fisheries in this region. It is likely that circle hooks
need to catch fish at rates near, equal to, or higher
than J hooks to gain wider acceptance among offshore
troll fishermen (Jordan, 1999). We hope that angler
experimentation will lead to improvements in circle
hook catch rates for non-billfish species caught during
trolling operations.
Acknowledgments
This study was funded by North Carolina Sea Grant
Fishery Resource Grant awards 08-FEG-02 and 10-FEG-
06, and North Carolina Sea Grant awards E/GS-6,
FEE-1, and the Big Rock Blue Marlin Tournament. We
thank D. Kerstetter and two anonymous reviewers for
their constructive comments on the manuscript.
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2002. Model selection and inference; a practical infor-
mation-theoretic approach, 2nd ed., 488 p. Springer-
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Cooke, S. J., and C. D. Suski.
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Dumas, C. F., J. C. Whitehead, C. E. Landry, and J. H. Herstine.
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176
Distribution, movements, and habitat use
of small striped bass ( Morone saxatilis )
across multiple spatial scales
Kenneth W. Able'
Thomas M. Grothues'
Jason T. Turnure1
Donald M. Byrne2*
Paul Clerkin'
E-mail address for contact author: able@marine.rutaers.edu
' Rutgers University Marine Field Station
800 c/o 132 Great Bay Blvd.
Tuckerton, New Jersey 08087
2 New Jersey Department of Environmental Protection
Nacote Creek Research Station
P.O Box 418
Port Republic, New Jersey 08241
* Posthumous
Abstract — Distribution, movements,
and habitat use of small (<46 cm,
juveniles and individuals of unknown
maturity) striped bass (Morone saxa-
tilis) were investigated with multiple
techniques and at multiple spatial
scales (surveys and tag-recapture in
the estuary and ocean, and telemetry
in the estuary) over multiple years to
determine the frequency and dura-
tion of use of non-natal estuaries.
These unique comparisons suggest,
at least in New Jersey, that smaller
individuals (<20 cm) may disperse
from natal estuaries and arrive in
non-natal estuaries early in life and
take up residence for several years.
During this period of estuarine resi-
dence, individuals spend all seasons
primarily in the low salinity portions
of the estuary. At larger sizes, they
then leave these non-natal estuaries
to begin coastal migrations with those
individuals from nurseries in natal
estuaries. These composite observa-
tions of frequency and duration of
habitat use indicate that non-natal
estuaries may provide important
habitat for a portion of the striped
bass population.
Manuscript submitted 23 February 2011.
Manuscript accepted 10 November 2011.
Fish. Bull. 110:176-192 (2012)
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
A full understanding of the distribu-
tion, movements, and habitat use of
juvenile and other subadult striped
bass ( Morone saxatilis ) is central to
deciphering the role, impacts, and
management of this abundant and
important species in estuarine and
coastal ocean habitats. This is espe-
cially true for the populations between
Chesapeake Bay and the Gulf of
Maine where adults can be highly
migratory and are seasonal partici-
pants in coastal migrations (Chapoton
and Sykes, 1961; Boreman and Lewis,
1987; Waldman et ah, 1990; Able and
Grothues, 2007; Welsh et ah, 2007;
Grothues et ah, 2009; Mather et ah,
2010). More recently, it has become
apparent that some components of
these same populations may be resi-
dent in estuaries throughout their
life cycle (Secor, 1999; Secor and Pic-
coli, 2007; Wingate and Secor, 2007).
Despite the accumulating understand-
ing of life cycle diversity for adults (see
Secor and Kerr, 2009 for M. saxatilis
and other species), we have an incom-
plete understanding for juveniles and
other subadults (Pautzke et ah, 2010).
The conventional interpretation based
on studies of natal estuaries, includ-
ing Chesapeake Bay and other large
estuaries (Merriman, 1941; Mansueti,
1961; Waldman et ah, 1990), is that
juveniles remain in estuaries for the
first few years of life before leaving
to join the coastal migration and may
stay longer, if they are natal estuarine
residents (Secor 1999; Ashley et ah,
2000; Secor and Piccoli, 2007).
For those individuals that even-
tually become coastal migrants, the
available data suggest that the du-
ration of residency in estuaries ap-
pears to vary with location and be-
tween years, potentially relative to
year class strength and associated
density dependence (e.g., Kohlenstein,
1981), as well as size and maturity
for males and females (e.g., Kohlen-
stein, 1981; Secor and Piccoli, 2007).
For example, an early interpreta-
tion was that a mass emigration of
small individuals takes place from
Chesapeake Bay after ages 2 and 3
(Kohlenstein, 1981). More recently,
analysis with otolith microchemistry
suggests a gradual shift associated
with sexual maturation at ages 5-8
for upper Chesapeake Bay individuals
(Secor and Piccoli, 2007). In the Hud-
son River, it is estimated that emi-
gration from the estuary can occur
into adjacent Long Island Sound and
the New York Bight at ages 1 and 2
(Secor and Piccoli, 1996) or earlier by
age-0 individuals (Dovel 1992, Dun-
ning et ah, 2009).
Able et al : Distribution, movements, and habitat use of small Morone saxatilis across multiple spatial scales
177
Our understanding of the distribution, movements,
and habitat use of small striped bass is largely based
on studies that occurred before the recovery in the
late 1980s (Nichols and Miller, 1967; Clark, 1968;
Kohlenstein, 1981; Boreman and Lewis, 1987; Wool-
ey et ah, 1990; Richards and Rago, 1999). Further,
most studies have focused on large natal estuaries
such as the Hudson River (Secor and Piccoli, 1996),
Chesapeake Bay (Mansueti, 1961; Kohlenstein, 1981;
Secor, 2007), and to some extent the Delaware River
(Waldman and Wirgin, 1994; Able et ah, 2007). There
has been little emphasis on non-natal estuaries even
though small striped bass are common and even abun-
dant components of the fauna (for reviews see Able
and Fahay, 1998, 2010). Therefore, we lack a clear
understanding of their pattern of habitat use within
estuaries, duration of residency, and patterns of tim-
ing of emigration (Grothues et al., 2009). These pat-
terns are especially confounded because the sources
of small individuals in non-natal estuaries are largely
unknown.
The purpose of this article is to evaluate the distri-
bution, movements, and habitat use of small striped
bass in and adjacent to non-natal estuaries in New
Jersey and adjacent areas. We approach this evaluation
using multiple sources including information from sea-
sonal catches from trawl, seine, and gill net surveys,
tag-recapture studies, and telemetry. Most of these
data relate to a period during or after the recovery of
the population along the east coast. Further, we evalu-
ate these patterns of estuarine and coastal ocean use
at three scales: throughout the Middle Atlantic Bight
continental shelf (Cape Cod to Cape Hatteras); on the
inner continental shelf off New Jersey; and in the Mul-
lica River-Great Bay estuary in southern New Jersey.
Although the focus is on small individuals, i.e., from
young-of-the-year to sexual maturity, the duration of
this stage is sometimes difficult to define because the
age (and size) at maturity varies between sexes, popu-
lations, and even within the same estuary (see Fig. 1
in Specker et ah, 1987). We define the upper size limit
for our treatment as 46 cm total length (TL) (approx,
age 3-5 years; Merriman, 1941). In addition, there
appears to be a natural difference in the size modes of
several extensive sampling programs around this size
(see below). The rationale for using this cutoff is that it
includes the size at first maturity for some populations
and that it complements our earlier telemetry studies
of larger striped bass in the Mullica River-Great Bay
estuary (Able and Grothues, 2007; Ng et ah, 2007;
Grothues et al., 2009). Thus, those individuals <46 cm
include those likely to be resident in estuaries, such as
mature males (e.g., Wingate and Secor, 2007), but also
include those that may begin leaving estuaries to par-
ticipate in coastal migrations. For the purposes of this
article, we make a distinction, where possible, between
dispersal (from natal estuaries) by juveniles (< 20 cm)
and other individuals of unknown maturity (>20-46
cm) and dispersal by those that make (directed, an-
nual) coastal migrations.
Materials and methods
Study areas
We used three geographical areas in this study (Fig. 1):
l) continental shelf waters (to depths greater than 450
m) along the east coast of the United States between
Cape Hatteras and Cape Cod; 2) a portion of the inner
continental shelf (depths of 5.5-27.4 m) off the coast of
New Jersey; and 3) the Mullica River-Great Bay estuary
(average depth 2 m, some portions to 26 m) which is part
of the Jacques Cousteau National Estuarine Research
Reserve (JCNERR). Aspects of the geomorphology and
hydrology of each of these areas is characterized in
further detail elsewhere (Able and Fahay, 1998; 2010).
Occurrence and distribution based on surveys
Seasonal, coast-wide distributions for small ( <46 cm)
striped bass on the continental shelf were determined
with data from National Marine Fisheries Service
(NMFS) Northeast Fisheries Science Center bottom
trawl surveys (Azarovitz, 1981; Grosslein and Azarovitz,
1982) (Fig. 1, Table 1). Samples were collected on the
continental shelf at stratified random stations between
Cape Hatteras, North Carolina, and the Gulf of Maine
during fall (September-October), winter (January-Feb-
ruary) and spring (March-April) (Grosslein and Azarov-
itz, 1982; Able and Fahay, 2010). The geographical limits
of the sampling program, however, varied with season
and between years. Similar sampling effort and distribu-
tion of samples occurred in the fall (n= 7379 tows) and
spring (n =7418 tows) over the period from 1982 through
2003. The winter sampling effort was reduced in terms
of number of tows (n- 1552 tows) and geographical extent
during the years in which it occurred (1992-2003). It
was limited to the southern portion of Georges Bank
and south of Cape Cod to just north of Cape Hatteras.
In addition, the number of samples in the shallow waters
(less than 25 m) off Massachusetts and from New Jersey
to North Carolina was reduced in the winter but not in
the fall and spring. The distribution of samples over all
seasons varied with depth as well, with some less than
20 m (17%), a large portion less than 100 m (81%), fewer
between 100 and 250 m (16%) and fewer still in depths
>251 m (2%). See Able and Fahay (2010) for additional
details. An estimate of the length distribution by age of
striped bass was based on data from Mansueti (1941)
and Able and Fahay (1998) and back-calculated length
at age was based on otoliths of striped bass collected in
Delaware Bay by the New Jersey Department of Envi-
ronmental Protection (Baum1).
Distribution data for small (<46 cm) striped bass off
New Jersey were collected seasonally by otter trawl
from 1996 to 2003 by randomly selecting sites in each
of 15 sampling strata by the New Jersey Department
1 Baum, T. 2006. Personal commun. New Jersey Dep.
Environmental Protection, Nacote Creek Research Center,
Port Republic, NJ 08241.
178
Fishery Bulletin 1 10(2)
Figure 1
Collection sites for striped bass ( Morone saxatilis) within the Mid-Atlantic Bight. Striped bass were
collected by the National Marine Fisheries Service’s otter trawl survey (between Cape Cod and Cape
Hatteras), New Jersey Department of Environmental Protection’s otter trawl survey (coast of New
Jersey), and Rutgers University Marine Field Station’s estuarine-ocean beach-seine and estuarine
gillnet surveys. Stationary telemetry hydrophone and water quality data logger locations (in the
vicinity of Little Egg Inlet and the Mullica River-Great Bay estuary [inset]) are also shown. See
Table 1 for timing of sampling.
of Environmental Protection (NJDEP) (Fig. 1, Table
1). See Byrne2 and Sackett et al. (2007) for additional
details. These sites were divided into three depth stra-
ta and categorized as inshore (5. 5-9.1 m), mid-shore
2 Byrne, D. M. 1989. New Jersey trawl surveys. In Special
Report No. 17 of the Atlantic States Marine Fisheries Com-
mission (Azarovitz, T. R., J. McGurrin, and R. Seagraves,
eds.), p. 46-48. Atl. States, Marine Fish. Comm. .Woods
Hole, MA.
Able et al.: Distribution, movements, and habitat use of small Morone saxatilis across multiple spatial scales
179
(9.1-18.3 m), and offshore (18.3-27.4 m). Trawl loca-
tions were mapped with GIS (ArcGIS3, vers. 9.2, ESRI,
Redlands, CA). The entire otter trawl data matrix con-
sisted of 2872 records of catch per unit of effort (CPUE;
number of individuals per tow), average depth, date,
season (spring, April; summer, June-August; fall, Sep-
tember-October; winter, January-February), and depth
category (inshore, midshore, and offshore). Additional
collections from the surf zone adjacent to and within
the Mullica River-Great Bay estuary were collected by
seine during 1998-99 and 2004-06 (Table 1, Fig. 1).
See Taylor et al. (2007) for additional details.
In order to determine the estuarine distribution of
other small (<46 cm) striped bass in space and time, we
sampled with anchored multimesh gill nets (15 mx2.4 m
nets with five panels of five box-mesh sizes 2.5, 3.8, 5.1,
6.4, and 7.6 cm) in the Mullica River-Great Bay estuary
at several locations (Table 1, Fig. 1). Gill nets were set
(for approximately 60 min during the day) at biweekly
intervals during the spring, summer, and fall in upper
creek, creek mouth, and nearshore bay habitats. Within
each area, the position in which each net was set varied
such that no two locations were sampled twice. See Able
and Fahay (2010) for additional details.
Another sampling program was conducted with small
otter trawls between 1988-90 and 1996-2009 at a va-
riety of stations and habitats located throughout the
Mullica River-Great Bay-Inner Continental Shelf cor-
ridor (Table 1). These stations were distributed along
the salinity gradient from the ocean to tidal freshwater.
Other individuals were collected in composite surveys
in Delaware Bay with a variety of gear types and from
habitats during 1998-2006 (Table 1; Able et al., 2007;
Able and Fahay, 2010). Still others came from an exten-
sive seine survey in the Hudson River estuary (Table 1).
Tag-recapture
The tagging procedure outlined in Boreman and Lewis
(1987) for their study with American Littoral Society
(ALS) data is consistent with the protocol followed in
our study. After initial capture, code-specific loop tags
were inserted into the dorsal region of each fish and the
fish was released. Length, general capture and release
location, and date were recorded for each animal on
a supplied tagging card and mailed to ALS. The ALS
sends raw data to the National Marine Fisheries Service
in Woods Hole, Massachusetts, for processing and entry
into a long-term database (Shepherd4). We limited the
query of records to two subsets of data: 1) striped bass
initially captured in New Jersey waters and recaptured
at less than 46 cm TL along the eastern United States
coast; and 2) striped bass initially captured in nearby
3 Mention of trade names or commercial companies is for
identification purposes only and does not imply endorsement
by the National Marine Fisheries Service, NOAA.
4 Sheperd, G. 2009. Personal commun. NMFS Northeast
Fisheries Science Center, 166 Water Street, Woods Hole, MA
02543-1026.
natal estuaries (Hudson and Delaware rivers) and recap-
tured in New Jersey waters at less than 46 cm TL (Table
1). The latitude and longitude coordinates associated
with each general capture and recapture location were
assigned by ALS and NMFS by calculating the spatial
average of each location submitted by volunteer taggers.
Telemetry
We determined dynamic habitat use and movements
of small (32.4-42.5 cm fork length [ FL] ) striped bass
in the Mullica River-Great Bay estuary using acoustic
telemetry. Wireless hydrophones were moored as a series
of gates in order to determine occurrence and residency
of tagged individuals along the estuarine gradient (Fig.
1). Fishes surgically implanted with individually coded
acoustic transmitters (76.8 kHz) were detected when
they came within range (approximately 500 m) of moored
wireless hydrophones (WHS-1100, Lotek Wireless, Inc.,
St. Johns, Newfoundland, Canada) suspended at a depth
of approximately 3.2 m (see Grothues et al. [2005] for
additional details). Permanent environmental-monitor-
ing instruments in the Jacques Cousteau National Estu-
arine Research Reserve included data loggers recording
salinity, temperature, pH, and water depth (Kennish and
O’Donnell, 2002) along the estuarine gradient (Fig. 1).
In addition, mobile tracking methods were used to
determine fine-scale patterns of habitat use. In order to
spatially and temporally standardize tracking, 113-120
fixed locations were georeferenced with a global po-
sitioning system (GPS) unit in universal transverse
mercator (UTM) coordinates by using a GIS software
package (ArcGIS, vers. 9.2, ESRI) and visited with
a directional mobile hydrophone on a weekly basis
(LHP_1; Lotek Wireless). Listening range with the mo-
bile hydrophone was typically about 500 m, determined
by signal range tests. At each of the above locations,
the hydrophone was lowered 1.0 m into the water and
pointed at the four principal ordinates for 5 seconds in
each direction. When a fish was detected, its position
was triangulated by moving until a reading of 115
dB or above was detected at a gain of 15 or less (ap-
proximately 2 m from the hydrophone). Measurements
of water temperature and salinity were collected (YSI
Model 85; Yellow Springs Instruments, Inc., Yellow
Springs, Ohio), along with date, time, tag number, and
depth at each confirmed fish detection. Tracking was
not conducted when the listening range was less than
500 m (which corresponded to wind velocities greater
than 30 km/h) or on days when there was heavy rainfall
or thunderstorms. See Ng et al. (2007) and Sackett et
al. (2008) for further details on mobile tracking proto-
col. To determine patterns of seasonal habitat use in
relation to physical habitat variables, the distances of
individually tagged striped bass from emergent (marsh)
and submerged (channel) embankment edges were cal-
culated by using a GIS software package. The loca-
tions of submerged edges were derived from estuarine
bathymetry data by calculating high slope areas (i.e.,
channel edges; >2.5°) and submerged or emergent edge
180
Fishery Bulletin 1 10(2)
Summary of data
Table 1
sources for juvenile striped bass ( Morone saxatilis) examined
in the current study. See Fig. 1 for sampling locations.
General
habitat
Geographic location
Sampling
gear
Sampling frequency/duration
Ocean
Atlantic coast
Otter trawl
Fall, winter, spring/1982-2003
Atlantic coast
Tag-recapture
Fall, winter, spring, summer/1962, 1967,
1973, 1977-2009
New Jersey coast
Otter trawl
Fall, winter, spring, summer/1988-2003
Central New Jersey coast
Seine
Biweekly/June - November 1998,
May-October 1999-2000, July 2004,
May-October 2005, August-October 2006
Estuary
Mullica River-Great Bay
Otter trawl
Monthly/July and September 1988-1990,
1996-2009
Mullica River-Great Bay
Multi-mesh gill net
Monthly/August-October 2001;
Semi-monthly/May-October 2002
Mullica River- Great Bay
Seine
Biweekly/June - November 1998,
May-October 1999-2000, July 2004,
May-October 2005, August-October 2006
Mullica River-Great Bay
Acoustic telemetry
Mobile (Weekly/2006-2008)
Passive (Continuous/2006-2008)
Delaware Bay
Otter trawl/weirs
Monthly/Aprii - November 1996-2000;
May-November 2001-2005
Hudson River
Seine
July-November 1990-2009
distances were calculated as the straight-line distance
(m) to the nearest edge.
Results
Occurrence and distribution based on surveys
Small ( < 4 6 cm TL) striped bass were represented
in multiple sampling gears from multiple locations
throughout the study area (Table 1, Fig. 2). However,
individuals <20 cm (presumed age 0-1 years) were
seldom collected in the coastal ocean, including the
NMFS trawl survey between Cape Hatteras and Cape
Cod (n = 2 individuals), the NJDEP trawl survey (n = 61
individuals), and the Rutgers University Marine Field
Station (RUMFS) beach seine survey along the inner
continental shelf off New Jersey (n = 21 individuals)
despite the large number of samples. These smaller
individuals were also not abundant in estuarine seine,
gill net, or otter trawl collections within the Mullica
River-Great Bay estuary based on over 3100 samples
(Table 1, Fig. 2). Of these, individuals <20 cm were
collected only within the estuary during otter trawl
(n = 21, 3.4-19.5 cm) and gillnet (n = 1, 16.4 cm) sam-
pling. Alternatively, large numbers of small individuals
<20 cm have been collected from the Delaware River
and Hudson River estuaries, both known spawning
areas (Fig. 2, G and H). Larger juveniles (21-46 cm,
presumed age 2-5 years) were better represented in
surveys in most locations including the Mullica River-
Great Bay estuary (n = 55; Fig. 2).
The seasonal patterns of distribution were similar
regardless of the spatial scale examined. Individuals
20 to 46 cm, according to the NMFS surveys on the
continental shelf, were seldom collected in the fall
and winter (a period of restricted sampling in shallow
waters) surveys. During the spring (February-March)
they were more abundant and largely restricted to
the inner portion of the shelf according to compos-
ite collections during 1982-2003 (Fig. 3). Most were
restricted to an area from north of the Chesapeake
Bay mouth to Long Island including the coast of New
Jersey.
A similar shallow-water distribution, in space and
time, of individuals <46 cm is evident from depth strati-
fied sampling off the coast of New Jersey during all
seasons from 1988 through 2003 (Figs. 4 and 5). Both
smaller (<20 cm), although less common, and larger
(21-46 cm) individuals were most abundant in the
spring but also occurred during the winter months and
were either rare or absent in the summer and relatively
rare during the fall. Over all these seasons, both of
these size groups were most abundant in the nearshore
depth strata (5.5-9. 1 m) with a trend to decreasing
abundance with depth with the least number of collec-
tions in the offshore strata (18.3-27.4 m). During the
winter and spring the larger individuals (21-46 cm)
were found all along the coast from the mouth of Dela-
ware Bay to the tip of Sandy Hook (Fig. 5).
Able et al Distribution, movements, and habitat use of small Morone saxatilis across multiple spatial scales
181
Sampling events
Or tOWS (72)
Water depths
sampled (m)
No. of
individuals (<46 cm)
Data source
>16,000
5-481
438
National Marine Fisheries Service; Grosslein and
Azarovitz (1982); Able and Brown (2005)
>300,000 (captures);
No data
1529 (recaptures)
American Littoral Society; current study
>19,000 (recaptures)
2872
3-80
2930
New Jersey Department of Environmental Protection;
Byrne (1989); current study
526
<2
9
Able et al. (2003); current study
2328
0.35-26.0
27
Able and Fahay (2010)
599
0-8
28
Able and Fahay (2010)
243
<2
9
Able et al. (2003); current study
Mobile (80)
1-25
14
Current study
Passive (>50,000)
>15,000
1-24
5343
Nemerson and Able (2003); Able et al. (2007)
—
<2
108,445 (1-39 cm)
New York Department of Environmental Conservation
Movements determined with tag-recapture methods
Tagged and recaptured individuals revealed that they
could move from natal estuaries to the vicinity of non-
natal sources along the New Jersey coast and that those
individuals that were found along the New Jersey coast
could move to other areas. Few individuals tagged in
the nearest natal estuaries (Hudson River and vicinity,
n= 25, and Delaware River, n- 1) were recaptured along
the New Jersey coast (n= 26 total, Fig. 6A). Small striped
bass captured in neighboring natal estuaries ranged
in size from 30-46 cm before being recaptured in New
Jersey (33-46 cm). Days at liberty for fish captured in
nearby natal estuaries ranged from 13-892 (mean 276
days). Individuals tagged in or along the New Jersey
coast (n = 152 total) were recaptured throughout the
northeastern United States from northern Chesapeake
Bay (n = 4; 3%), Delaware Bay (n = 19; 13%), and Long
Island and Connecticut (n= 27; 18%), with some found
as far north as Cape Cod and Maine (re=21; 14%). The
majority of recaptures, however, occurred along the New
Jersey coast (n = 81; 53%; Fig. 6B). The time between
capture and recapture was similar for this subset of fish
(1-868 days; mean 244 days). For those fish originally
captured in New Jersey and recaptured elsewhere along
the coast, sizes were generally smaller than in the other
subset of fish analyzed and lengths ranged from 25 to 46
cm during capture and from 28 to 46 cm during recap-
ture. A relatively small number of fish were recaptured
at sizes less than 40 cm TL (n = 18; 11.8%), with all
but one of these individuals recaptured less than 100
km from their original release location in New Jersey
waters (Fig. 6B).
Estuarine habitat use determined with acoustic telemetry
From 2006 through 2008, 14 small striped bass (32.4-
42.5 cm FL) were tagged with acoustic transmitters
within the Mullica River-Great Bay estuary in south-
ern New Jersey (Tables 1 and 2, Fig. 7). Most were
consistently detected (11 of 14 individuals, 72 = 114 detec-
tions) based on mobile telemetry. An examination of
the seasonal distribution revealed consistent use of the
mesohaline portions of the river all the way up to, and
occasionally above, the freshwater-saltwater interface,
whereas fewer were found in polyhaline waters near
Little Egg Inlet (see Fig. 1). In the summer, fall, and
spring some individuals were detected downstream near
Little Egg Inlet, or in Great Bay, but during the winter
all juveniles were detected upstream in the river (Fig.
7). During December 2006, four fish (42-48 cm FL) were
tagged in the ocean off Long Beach Island (Fig. 7C). Of
these, one (code 104) moved into the estuary by way of
Main Marsh Thorofare (see Fig. 1) on December 24 and
remained there for approximately 125 days.
The use of upriver habitats (such as Lower Bank)
was evident by the temperature (Fig. 8A) and salinities
(Fig. 8B) at which tagged juvenile striped bass were
frequently detected. Juveniles inhabited the warmer
water temperatures found upstream in the summer
182
Fishery Bulletin 1 10(2)
20000
1S000
10000
5000
0
ALS tag-recaptlire
I 1 Recapture (77=14,564)
l*#g^l Capture (n=338, 666)
.11 J J3
RUMFS telemetry study (/?= 134)
Current study
Other tagged Striped bass
40 cm TL)
that may be joining the annual coastal migration. These
larger individuals were also frequently recaptured in
presumably non-natal habitats in the Gulf of Maine and
along the coasts of Connecticut and Rhode Island after
initial release in New Jersey waters. This same pattern
has been reported for age 2 and 3 fish moving into non-
natal estuaries, such as the Connecticut River (Kynard
and Warner, 1987) and in Massachusetts where 40-50
cm TL individuals (most age 2-5) apparently feed during
the summer, make coastal migrations during the fall
through spring, but return in subsequent years (Mather
et al., 2009, 2010; Pautzke et al., 2010). Certainly, other
estuarine-dependent fish species leave estuaries when
they reach a size threshold (Rountree and Able, 1992;
Potter et ah, 1997). This pattern for striped bass may
vary with sex, i.e., females are likely to leave at earlier
ages or smaller sizes, whereas males tend to remain, at
least in natal estuaries, for longer periods of time (Secor
and Piccoli, 1996).
In general, overall patterns of use of a non-natal estu-
ary and scheduling of departure appear similar between
natal and non-natal estuaries and we also suspect that
there are no major changes in these patterns before
and after the recovery of the striped bass population
in recent years (Boreman and Lewis, 1987). However,
we hasten to point out that there was little emphasis
on non-natal estuaries as secondary nursery habitat
before the recovery.
Conclusion
As we have demonstrated, non-natal estuaries are
potentially important habitat for small (20-46 cm)
striped bass. This finding may further complicate our
understanding of life cycle diversity (see Secor and
Kerr, 2009) for this species because the prior focus has
been on natal estuaries. Further, as these individu-
als from non-natal estuaries join the annual coastal
190
Fishery Bulletin 1 10(2)
migration, grow, and mature, one wonders where they
are likely to spawn. One possibility is that they will
attempt to spawn in the non-natal estuaries where they
have previously spent several months to years. This
could account for the seeming unsuccessful attempts
in the Mullica River-Great Bay estuary (Able and
Grothues, 2007; Grothues et ah, 2009). One could also
argue that these individuals may be responsible for
colonizing new spawning sites, as has previously been
suggested (Grothues et ah, 2009). Alternatively, they
may join other maturing individuals as they migrate
back to their natal rivers and streams that provided
primary nurseries. Otolith microchemistry might be the
appropriate means to distinguish the ultimate source
of individuals that use non-natal estuaries and the site
of their subsequent spawning.
Acknowledgments
Numerous individuals from the National Marine Fisher-
ies Service, New Jersey Department of Environmental
Protection-Bureau of Marine Fisheries (NJDEP-BMF),
and the Rutgers University Marine Field Station
(RUMFS) assisted in data collection, surveys, and telem-
etry. The angling skills of D. Messerschmidt and L. Webb
assisted our tagging efforts. K. Hattala (New York Dept.
Environmental Conservation) provided length data from
the Hudson River young-of-the-year survey. B. Muffley
and R. Allen (NJDEP-BMF) provided comments on
earlier drafts. Funding sources for this study were pro-
vided by the Rutgers University Institute of Marine and
Coastal Sciences (IMCS), RUMFS, the Bluefish/Striped
Bass Dynamics Research Program of Rutgers University,
and the NOAA National Marine Fisheries Service. J.
Plangere Jr. generously supported the telemetry study.
This article is contribution number 2012-4 of the Rutgers
University Institute of Marine and Coastal Sciences.
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193
Prey preference of lingcod
( Ophiodon elongatus), a top marine predator:
implications for ecosystem-based
fisheries management
Craig A. Tinus
Email address for contact author: craiq.tinus@oreaonstate.edu
Oregon State University
Department of Fisheries and Wildlife
104 Nash Hall
Corvallis, Oregon 97331-3803
Present address: Oregon Department of Fish and Wildlife
Corvallis Research Lab
28655 Hwy 34
Corvallis, Oregon 97330
Abstract — Many highly exploited eco-
systems are managed on the basis of
single-species demographic informa-
tion. This management approach can
exacerbate tensions among stakehold-
ers with competing interests who in
turn rely on data with notoriously
high variance. In this case study, an
application of diet and dive survey
data was used to describe the prey
preference of lingcod ( Ophiodon elon-
gatus) in a predictive framework on
nearshore reefs off Oregon. The ling-
cod is a large, fast-growing generalist
predator of invertebrates and fishes.
In response to concerns that lingcod
may significantly reduce diminished
populations of rockfishes ( Sebastes
spp. ), the diets of 375 lingcod on near-
shore reefs along the Oregon Coast
were compared with estimates of
relative prey availability from dive
surveys. In contrast to the transient
pelagic fishes that comprised 46% of
lingcod diet by number, rockfishes
comprised at most 4.7% of prey items.
Rockfishes were the most abundant
potential prey observed in dive sur-
veys, yet they were the least preferred.
Ecosystem-based fisheries manage-
ment (EBFM) requires information
about primary trophic relationships,
as well as relative abundance and dis-
tribution data for multiple species.
This study shows that, at a minimum,
predation relative to prey availability
must be considered before predator
effects can be understood in a man-
agement context.
Manuscript submitted 2 February 2011.
Manuscript accepted 28 November 2011.
Fish. Bull. 110:193-204 (2012).
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
Ecosystem-based fisheries manage-
ment (EBFM) has generated consider-
able interest over the last decade as
a way of better managing depressed
fisheries stocks (Pikitch et ah, 2004;
Gaichas et ah, 2010). This interest
has been in large part a reaction to
the perceived failure of traditional
single-species fisheries management
to prevent the collapse of exploited
and ancillary populations in many
systems worldwide (Dulvy et ah, 2003;
Hutchings and Baum, 2005; Myers
and Worm, 2005). One implication
of EBFM is the capacity to forecast
changes in managed populations in
reaction to fluctuations in linked
predator and prey populations. This
requires understanding what species
of interest consume in a given tempo-
ral and spatial context. An additional
consideration is that many exploited
fishes are generalist consumers and
shifts in densities and distributions
may produce complex top-down effects
(Bruno and O’Connor, 2005). These
are among numerous challenges in
gathering the information required to
describe even a subset of primary tro-
phic relationships in a dynamic system.
Prey preference is the differential
consumption of some prey types over
others given equal availability. It is
considered a fixed behavioral char-
acteristic and, as a way of forecast-
ing predation intensity on managed
stocks, has received little attention.
However, preference models may be
both useful and efficient as an exten-
sion of food web models to aid man-
agement of exploited stocks (Gaichas
et al., 2010). In this study I used an
analysis of dive survey data with
consumption data in a prey prefer-
ence model to better understand the
likely effects of a rapidly increas-
ing predator population on managed
prey. Consumption rates for gener-
alist consumers like lingcod ( Ophi-
odon elongatus Girard) may be either
positively or negatively correlated
among different prey types, or they
may be uncorrelated, and these ef-
fects can be important in actively
managed systems (Dill et ah, 2003).
If spatially and temporally transient
prey species predominate in the diet
of a resident predator, they may con-
stitute subsidies to the local preda-
tor population (Anderson and Polis,
1998). When subsidies occur there
may be a concomitant suppression of
local prey species through apparent
competition among prey types with a
common predator (Holt, 1977; Chan-
eton and Bonsall, 2000). Especially in
marine systems where trophic webs
may be poorly defined (Thompson
et ah, 2007), initial consideration of
predator-prey relationships requires
dietary analysis (Heithaus et ah,
2008). Diets of targeted fishery spe-
cies provide necessary information
for understanding food web structure,
which is an important requirement
for ecosystem-based fisheries science
194
Fishery Bulletin 1 10(2)
and management (Francis et al., 2007). However, in
addition to these basic trophic relationships, it is neces-
sary to understand the context in which prey are being
selected. The effects of predation on both predator and
prey populations change as prey densities vary.
Although EBFM requires even more information than
traditional single-species management approaches,
managers, scientists, and stakeholders make use of
less certain information both in less accessible systems
and in those that are accessible but where temporal and
spatial scales far exceed the capacity to collect local
demographic data. For these reasons identifying specific
management triggers based on comprehensive and col-
lectable information has been proposed (Samhouri et
ah, 2010) and the case made that uncertain data and
imperfect advice must be embraced, as long as they are
appropriate data (Ludwig et ah, 1993; Johannes, 1998;
Frid et ah, 2008). Challenges to the use of EBFM in-
clude “species conflicts,” where management and stake-
holder interest in one target species may interfere with
other species and often involve the assumed effects of
large generalist predator(s) on recovering high value
prey species, sometimes in and out of marine protected
areas. Examples of generalist predators involved in
management conflicts are groupers ( Epinephelus spp.
[Ault et ah, 2006; Coleman and Koenig, 2010]), red
snapper ( Lutjanus campechanus [Wells et ah, 2008;
Cowan et ah, 2010]), cod ( Gadus morhua [Link and
Garrison, 2002]), and striped bass ( Morone saxatilis
[Paolisso, 2002; Walter et ah, 2003]).
Marine reserves are becoming more widely consid-
ered as a management tool for protecting a portion of
breeding populations as interest in EBFM increases.
However, in addition to providing a refuge from fishing
mortality, marine reserves can enhance local popula-
tions of large, resident, top-level predators (Martell
et ah, 2000; McClanahan and Arthur, 2001). Among
possible effects of a local increase in predator biomass
is a decrease in a particular prey type (Graham et ah,
2003). For example, this kind of interaction has been
proposed for lingcod predation on rockfishes (Sebastes
spp.) within marine reserves (Beaudreau and Essing-
ton, 2007; 2009) and both are major targets of com-
mercial fisheries.
The following case study exemplifies necessary con-
siderations for EBFM. Lingcod are targeted by both
recreational and commercial fishermen along the west
coast of North America. The 2000 stock assessment of
lingcod from British Columbia to northern California
estimated biomass at 11% of precommercial exploitation
levels (Jagielo, et ah1) and management substantially
reduced fishing mortality to allow recovery of this stock.
By 2006, lingcod stocks were declared fully recovered by
the Pacific Fisheries Management Council. Lingcod are
1 Jagielo, T. H., F. R. Wallace, and Y. W. Cheng. 2003. Assess-
ment of lingcod (Ophiodon elongatus). Amendment 16-2:
Rebuilding plans for darkblotched rockfish, Pacific ocean
perch, canary rockfish, and lingcod. Environmental impact
statement and regulatory analysis, 129 p. Pacific Fishery
Management Council, Portland, OR.
large (up to 152 cm total length [TL] and 59 kg) and
fast growing. They are relatively site-attached, demer-
sal, generalist predators, found on shallow northeastern
Pacific rocky reefs. They roam across both rocky habitat
and soft-bottom over distances of at least hundreds of
meters, yet they demonstrate a high degree of site fidel-
ity for time scales of at least weeks to months (Jagielo,
1990; Smith et ah, 1990; Mathews, 1992; Yamanaka
and Richards, 1993; Jagielo, 1999; Starr et ah, 2004).
Although lingcod population dynamics have been
studied from a fisheries perspective, very little is un-
derstood about how this predator affects the structure
of fish populations and assemblages on rocky reefs.
A previous study of diet and habitat associations of
demersal fishes on nearshore reefs along the Oregon
Coast revealed that 282 adult lingcod had consumed 27
identifiable species of fish and invertebrates. Of those
134 prey items, no adult rockfishes were found and the
contribution to total biomass by all rockfish prey was
less than one percent (Steiner, 1979). However, no prior
lingcod studies have described diet in relation to prey
abundance. In order to assess differential selection, and
thus characterize which prey types will most likely be
selected, there must be an estimate of prey availability
relative to consumption (Manley et al., 2002). The goal
of this study was to describe the diet of adult lingcod off
the coast of Oregon, to characterize relative patterns of
consumption of transient and resident prey species by
lingcod, and describe whether or not preference, defined
as the differential consumption of one prey type over
others in relation to availability, was evident. Specifi-
cally, by using lingcod diet and prey abundance esti-
mates off the coast of Oregon, I addressed the following
questions: 1) Do lingcod prefer particular prey species,
and 2) do lingcod preferentially target rockfishes? The
answers to these questions were yes and no, respec-
tively. This information can be used to more effectively
manage a reserve system where both predator and prey
populations are the focus of conservation efforts.
Materials and methods
Study area
The nearshore zone off Oregon is generally exposed, has
relatively high wave energy, and is influenced by long-
shore currents. I sampled lingcod from two nearshore
subtidal sites along the coast of Oregon: one south of
Newport, referred to as site 1 (44°31'N lat.; 124°08/W
long.), and another south of Coos Bay, referred to as
site 2 (43°16'N; 124°25'W) (Fig. 1). Both sites comprised
high relief rocky reef, rocky flats, cobble, and sand at
depths of 20 to 50 m. The reefs varied from small pin-
nacles encompassing <10 m2 to large boulder fields and
bedrock flats that may exceed one km2 in area. The area
of exposed rock changes on temporal scales of months
to decades, however, sand transport is greatest during
the stormy winter months and relatively stable during
the summer (Kulm et al., 1968).
Tinus Prey preference of Ophiodon elongatus, a top marine predator
195
Prey availability
Prey availability was compared with observed
prey consumption to evaluate prey preference.
Lingcod are highly generalized visual preda-
tors and visual surveys provide an estimate
of relative prey density within a visual field. I
evaluated prey availability with dive surveys 44°30'0"N
in the areas where lingcod were collected for
gut analyses (Starr et al., 2010). Dive surveys
were conducted from a relatively small boat
equipped with standard electronics. Ocean
conditions had to be sufficiently benign for
both safe boat handling and diver deploy-
ment and recovery. Weather conditions were 44°0'0"N
a limiting factor for dive surveys. In general,
combined seas (wave and swell height) of less
than two to three meters and wind velocities of
less than 20 knots are necessary. Additionally,
fog and strong currents at times prohibited
safe dive and boat operation near shallow
reefs. A single dive survey consisted of a single 43°30'0"n
100x4 m visual-count transect (Bohnsack,
1996) during daylight between 1000 and 1500
hours. I conducted surveys at site 1 in Janu-
ary and June 2004, and in June 2005 (three
surveys total), and at site 2 in January and
October 2004, and in June (three surveys) and
September 2005 (six surveys total) (Table 1).
The exact locations of transects were deter- 43 00 N
mined haphazardly from the surface by drop-
ping a weighted line in an area as close as
possible to where fishing for lingcod occurred
and where depths were sufficiently shallow so
that single dive surveys could be completed
within one scuba dive (<35 m). Visibility was
variable but was always sufficient to identify fish
within two meters of the transect line, and fishes
and invertebrates were approachable. I surveyed
three basic habitat types within each transect: high-
relief rocky reef, boulder mixed with cobble, and
broken shell mixed with sand. I quantified the relative
abundance of potential prey within the foraging range
of lingcod, estimating age groups of rockfishes (year
1, 1-2, 3+) from estimated total lengths. During dives
I estimated fish lengths by comparing them against
objects of similar shape and color of known lengths
at various distances. I observed only adult lingcod on
rocky reef habitat. Relative prey availability between
sites 1 and 2 were compared by one-way analysis of
similarity (ANOSIM; Clark, 1993). The ordination,
associated tests, and species accumulation curves were
produced with PRIMER analytical software (vers. 6.1.6,
PRIMER-E Ltd., Plymouth, U.K.2) by using an included
ANOSIM method (Clark and Gorley, 2006). Additionally,
a rank concordance test of prey category abundance was
2 Mention of trade names or commercial companies is for
identification purposes only and does not imply endorsement
by the National Marine Fisheries Service, NOAA.
124°30'0"W 124WW 123°30'0"W 123 WW
Figure 1
Map of study region (inset) and study sites (1 and 2) within
the Oregon nearshore zone where stomach samples of lingcod
(Ophiodon elongatus) were collected and dive surveys of potential
prey were conducted.
conducted for sites 1 and 2 (Sokal and Rohlf, 1995). For
comparisons of two groups, t-tests were used unless a
Shapiro-Wilk test for normality, equal variance, or both,
failed, in which case a Mann-Whitney rank sum test was
used. The Michaelis-Menten equation (MME) was used
to generate species accumulation curves to evaluate how
quickly the number of new species became asymptotic
(curve stability) with additional sampling effort (Willott,
2001; Williams et al., 2007). The beta value for the MME
represents the number of samples required to detect 50%
of the total number of species, or groups.
Dietary composition
Multiple anglers using lines with a single hook and attrac-
tor on a chartered recreational fishing vessel in July (19
and 17 fish in two sampling trips), August (12 fish), and
September (12 fish) of 2003 (one trip each) collected a
total of 60 lingcod at site 1. The lingcod collected at Site
196
Fishery Bulletin 1 10(2)
2 were by a commercial fisherman in the months of May
(21 fish), June (48 fish), and October (59 fish) of 2004,
and May (49 fish), June (45 fish), August (46 fish), and
October (40 fish) of 2005 (Table 1). The commercial gear
used was a “dingle-bar” — an iron bar trolled just off the
seafloor with a set of three rubber jigs with large hooks
and an attractor. An additional set of three jigs with
hooks was trolled mid-water (about 10-20 m off-bottom).
When multiple lingcod hit the jigs, they generally did so
simultaneously on both the bottom and mid-water sets.
In the first year, lingcod stomachs were labeled,
placed in cloth bags, and preserved in ethanol. In
subsequent years, stomachs were labeled, wrapped
in cheesecloth, packed in ice, and examined within
24 hours. The number and identity of items in each
stomach were identified to the lowest possible taxon.
When the identification of a prey fish was not possible
from external characteristics, I attempted to identify
the prey by otoliths or skeletal elements (or both). A
second, blind reading of a subsample of otoliths and
skeletal elements was done by a recognized expert who
confirmed prior determinations. When possible, beaks
were used to estimate size and infer species of octopus
by comparison with other samples that were identified
to species from external characteristics.
Observed consumption provides a description of a
local prey base if sample sizes are large enough to cap-
Table 1
Dates and locations of dive surveys and stomach collec-
tions for diet samples of lingcod (Ophiodon elongatus ) in
the nearshore zone off the Oregon Coast. Site 1 is located
south of Newport, OR, and site 2, south of Coos Bay, OR.
An X indicates that data were collected. Data used for
prey preference analysis are within-season.
Dive survey Diet samples
Site
Date
X
1
16/07/03
X
1
23/07/03
X
1
25/08/03
X
1
24/09/03
X
1
09/01/04
X
2
22/01/04
X
2
24/05/04
X
1
09/06/04
X
2
24/06/04
X
2
05/10/04
X
2
22/10/04
X
2
13/05/05
X
2
03/06/05
X
2
08/06/05
X
1
22/06/05
X
2
26/06/05
X
2
27/06/05
X
2
17/08/05
X
2
28/09/05
X
2
20/10/05
ture the diversity within a population and incorporate
representative temporal and spatial scales. Although
lingcod are highly generalized, the incidence of new
prey types in gut samples was asymptotic with in-
creasing sample size. The MME was used to generate
a species-accumulation curve and test for sufficiency of
sampling effort. A rank concordance test of prey cat-
egory abundance was conducted for sites 1 and 2 (Sokal
and Rohlf, 1995). For comparisons of two groups, /-tests
were used unless a Shapiro-Wilk test for normality or
equal variance test (or both) failed, in which case a
Mann-Whitney rank sum test was used. Additionally,
one-way analysis of variance (ANOVA) was used to test
for differences among sampling trips for differences in
stomach fullness and for differences in consumption of
transient and resident prey types.
Stomach content data were analyzed by frequency of
occurrence, %Fo - (n-100)/Ns; and percentage of prey,
%N = (n'-100)/Afp; where n=the number of stomachs
containing a particular prey type, Ns=the total number
of lingcod stomachs examined, n'=the total number of
individuals of a particular prey type, and Wp=the total
number of prey items (Hyslop, 1980).
Prey-preference model
A preference model describes the relative selection
of resources in relation to the availability of those
resources. If a particular prey type is selected more or
less frequently than would be predicted by relative avail-
ability, that prey type is said to be either preferred or
avoided relative to other prey types. The general formu-
lation of the preference model (Johnson, 1980) is as fol-
lows. Let r - be the rank of some measure of consumption
of prey component (i) by an individual predator (J) and
s(/ be the rank of an observed measure of the availability
of prey component (i) to individual predator (j). The indi-
vidual differences in these ranks, ttJ = rtJ - sy , are then
averaged across animals to indicate the relative prefer-
ence of all prey types across all predators, as given in
Equation 1 below. The advantage of this nonparametric
approach is that information about prey preference can
be gleaned from imperfect field data. The use data and
availability data are ranked for each animal and even
if a particular prey type is not observed, those data can
be used in the analysis. If a known prey type was not
observed, the availability of that particular prey type
would be considered low by comparison with other prey
types in the analysis.
I = Z(rv-sy)/J- (1)
Results
Prey availability
Observed fish abundance was overwhelmingly dominated
(over 90%) by demersal rockfishes ( Sehastes spp.) at both
Tinus: Prey preference of Ophiodon elongatus, a top marine predator
197
sites. Aggregations of black rockfish ( S . melanops )
typically exceeded 100 individual adults and were
the most common rockfish species. There was no
evidence of a group effect between sites 1 and 2
(one-way ANOSIM, global /? = — 0. 115; significance
of sample stat. = 64.2%). An MME for species accu-
mulation indicated sampling effort was sufficient
to achieve a stable asymptotic curve (Smax=17.94;
/3= 0.72). In the pooled data, black rockfish were
41.1% of a total of 2640 fish recorded in nine dive
surveys. When Sebastes species were aggregated
into a single prey category (demersal rockfishes)
there was no difference in mean abundance
between sites (two-sided (-test, P=0.84, df=7) or
of lingcod abundance between sites (Mann-Whitney
(7=26.5, P=1.0). There was a mean of 177.7 (stan-
dard error [SE ] = 14 . 8 ) demersal rockfish and 4.3
(SE = 0.33) lingcod observed at Site 1 and a mean
of 148.8 (SE = 37.8) rockfish and 3.8 (SE = 0.98)
lingcod at site 2. A rank concordance test of prey
common to both sites was significant with respect
to abundance of potential prey species between
sites (Kendall’s rank concordance test, PcO.Ol,
n = 16, s = 1.91). Striped surfperch (Embiotoca late-
ralis) and yellowtail rockfish (S. flavidus ) were
recorded only at site 1, whereas canary rockfish
(S. pinniger) were observed only at site 2. Besides
those species, the sites did not differ with respect
to either the presence of potential prey species or relative
abundance by genus, with the exception of significantly
more sculpins (family Cottidae) at site 1 than site 2
(Mann-Whitney (7=18.0, P=0.3). Geographic ranges of
all species in this study are known to overlap both sites.
The smallest lingcod sampled or observed on a reef
was 42 cm TL. Lingcod may be retained by the fishery
at 61 cm TL and larger. Although undersized lingcod
were sampled by special permit at both sites, the com-
mercially caught samples were biased toward larger
lingcod and most of the lingcod sampled were within
a relatively narrow size range, likely because the local
lingcod population was rebuilding and was dominated
demographically by only a few cohorts. Lingcod juve-
niles settle onto a variety of habitats but were not ob-
served on reefs. This is not surprising because lingcod
are periodically cannibalistic, as shown in this and
other studies.
Dietary composition
Of the 60 lingcod stomachs sampled at site 1, 12 were
empty and 48 contained prey that were aggregated into
10 categories. At site 2, of the 315 lingcod stomachs
sampled, 177 were empty and 138 contained the same
10 prey categories plus Pacific sandlance ( Ammodytes
hexapterus) as a major prey item, as well as market
squid ( Loligo opalescens ), Pacific lamprey (Lampetra
tridentata), and northern anchovy (Engraulis mordax)
as minor items (Table 2). Because both the number of
samples and sampling effort was much greater at site
2, it was expected that more prey types were found in
c 50
a Site 1 empty
d Site 1 non-empty
■ Site 2 empty
■ Site 2 non-empty
30 -
t L t ■■
45 50 55 60 65 70 75 80 85 90 95 >95
Lingcod total length (cm)
Figure 2
Lingcod (Ophiodon elongatus) total length (cm) and frequency
of occurrence for empty and nonempty stomachs sampled at
sites 1 and 2 off Oregon, 2003-05. Gear types were: multiple
fishermen with single hooks and lines at site 1 (gray), and a
commercial fisherman with multiple hooks and lines for site
2 (black). Lingcod under the legal limit for total length (<61
cm) were retained by permit at both locations, but were propor-
tionately more abundant among sampled lingcod at site 1, and
proportionately more lingcod stomachs were empty at site 2.
lingcod from there (see Bock, 1987). There were pro-
portionately fewer empty stomachs among captured
lingcod, and lingcod were smaller on average at site 1
than at site 2 (Fig. 2). Among prey categories common
to both sites, there were significantly more Pacific her-
ring (Clupea pallasii) consumed by lingcod sampled at
site 1 than at site 2 (two-sided (-test, P=0.01, df=373).
A rank concordance test was significant, indicating that
prey consumption by category did not differ between
sites (Kendall’s rank concordance test, P<0 .01, n = 15,
s=2.03) and therefore the data were pooled for the pref-
erence analysis.
After sites were pooled, there were 21 identified spe-
cies aggregated into 14 ecologically similar prey catego-
ries. Among the 342 prey items found in 375 stomachs
(50.4% of lingcod stomachs were empty) major prey
items were Pacific herring, Pacific sandlance, unidenti-
fied fishes, two-spotted octopus ( Octopus bimaculatus),
and pandalid shrimps ( Pandalus spp.). All other prey
groups, including rockfishes, each comprised less than
five percent of the total gut contents (Fig. 3). A MME
for species accumulation indicated that sampling effort
was sufficient (Smax=14.91; /3= 17. 2 1 ). Of the prey items
that were measurable to total length, 14 were confirmed
to be rockfishes. The largest of those was 28 cm (the on-
ly potential adult), and none was estimated to be more
than three years old based on published length-at-age
curves (Love et al., 2002). Of the identified young-of-
year rockfishes, five were of the “black-spot” group and
one was a stripetail (S. saxicola). Nearly all rockfishes
identified to species were S. melanops and less than
two years old as inferred by length (Love et al., 2002).
198
Fishery Bulletin 110(2)
Table 2
Prey found in stomachs of 375 lingcod ( Ophiodon elongatus) collected off Oregon, where n is the number of stomachs containing
a particular prey type and ri is the total number of individuals of a particular prey type; %F0 is the frequency of occurrence, and
%N is the percentage of prey items. The preference rank for each of 14 aggregated prey categories is also provided, where l=most
preferred prey and 14 = least preferred prey.
Prey species
n
n
%F0
%N
Preference rank
Transient and pelagic fishes
Lampetra tridentata
i
i
0.27
0.31
8
Engraulis mordax
2
2
0.53
0.62
6
Clupea pallasii
37
109
9.87
33.64
7
Merluccius productus
7
8
1.87
2.47
4
Ammodytes hexapterus
15
49
4
15.12
2
Skates and flatfishes (soft bottom)
3
Raja spp.
1
1
0.27
0.31
Hippoglossus stenolepis
1
1
0.27
0.31
Citharichthys sordidus
3
3
0.8
0.93
Parophrys vetulus
1
1
0.27
0.31
Platichthys stellatus
2
2
0.53
0.62
unidentified flatfishes
5
5
1.33
1.54
Reef-dwelling fishes
Rockfishes
Sebastes melanops
6
6
1.6
1.85
13,14
Sebastes saxicola
1
1
0.27
0.31
unidentified rockfishes
Greenlings
7
9
1.87
2.78
12
Hexagrammos decagrammus
3
3
0.8
0.93
11
Ophiodon elongatus
2
2
0.53
0.62
Unidentified fishes
28
33
7.47
10.19
Sculpins
Hemilepidotus hemilepidotus
3
3
0.8
0.93
10
Scorpaenichthys marmoratus
2
2
0.53
0.62
Unidentified sculpins
9
9
2.4
2.78
Invertebrates
Octopus
Octopus bimaculatus
26
30
6.93
9.26
9
Octopus dofleini
5
5
1.33
1.54
Loligo opalescens
3
3
0.8
0.93
5
Pandalus spp.
23
27
6.13
8.33
1
Cancer magister
2
6
0.53
1.85
The dominant prey type was Pacific herring (%F=9.87,
%A = 33.64), a transient and pelagic species. Other prey
types were clustered and far less dominant in the di-
et (Fig. 4). Among sampling periods, empty stomachs
ranged from 8-81% (mean 56% empty, n = 10 sampling
periods, SE = 5.7). Among sampling months, May-Oc-
tober, the presence of consumed prey among lingcod
was unpredictable, regardless of the sampling month
(ANOVA, Fx 9=1.77, P=0.22) and consumption of resi-
dent prey appeared to be independent of consumption
of transient prey (ANOVA, F1 9=2.46, jP=Q.15).
There were 41 unidentified prey items, 33 of which
were confirmed not to be rockfishes. Lingcod eat parts
of animals they cannot swallow whole by tearing prey
apart (e.g., Pacific giant octopus; personal observ.) and
are thus not considered gape-limited with respect to
prey preference. Larger lingcod consumed larger prey
(Fig. 5) but not distinctly different prey types (adjusted
coefficient of determination r2=0.29, one-way ANOVA
F, 71 = 30.3, P < 0.01, n= 12 measurable prey items). Typi-
cally, a single prey item (but as many as 17) was found
in a stomach containing prey, and among those stom-
achs containing more than one prey item, as many as
four different species were found.
Numerically, 52% of prey were transient and pe-
lagic, 4% were associated with soft-bottom seafloors,
44% were demersal reef-dwelling species, and of the
latter, half were invertebrates. The importance of
macroinvertebrates among local prey species is dif-
ferent from what was found in previous studies. Sand
Tinus Prey preference of Ophiodon elongatus, a top marine predator
199
Figure 3
Proportional relative availability (dark bars) of potential prey as determined
from nine dive surveys off Oregon for sites 1 and 2 combined, and propor-
tional consumption (light bars) of prey by lingcod (Ophiodon elongatus) for
sites 1 and 2 combined. The lack of overlap between realized consumption
and relative availability indicates that lingcod were not preferentially
consuming the most apparently abundant prey types, which were rock-
fishes ( Sebastes spp.).
consistently occurred in lingcod stomachs containing
both octopus and shrimps, but never with flatfishes of
any species. This pattern suggests that these lingcod
did not forage for flatfishes directly over the seafloor,
but were eating them in the water column. Because
lingcod were captured on mid-water lures, they are
apparently capable of foraging in the pelagic as well
as the benthic zones.
Prey preference
Analysis of identified prey in the pooled data showed
that prey selection was not proportional to availability
(Johnson’s preference, F13 132 = 943, P<<0.001). Rock-
fishes were significantly “avoided” among prey categories
(Waller-Duncan [1969] multiple comparisons, P =0.01,
n = 145). In order of preference, adult rockfishes were
ranked last followed by subadult rockfishes (Fig. 6).
Preference ranking also indicated that transient and
pelagic prey (Pacific herring and Pacific sandlance) were
among the most preferred prey. The January surveys
could not be temporally matched with consumption data
and therefore were excluded from this analysis, as were
empty stomachs. However, because of the inherent tem-
poral and spatial patchiness of transient prey, as well as
the difficulty in comparing very different types of prey,
it was not possible to differentiate prey preference ranks
among Pacific herring, Pacific sandlance, shrimps, and
octopus. Other categories fell between these extremes
(Fig. 6).
Discussion
These data indicate that lingcod off the coast of Oregon
1) are highly generalized predators of both fish and
invertebrates in multiple habitats; 2) select prey dis-
proportionately to prey abundance; and 3) do not dif-
ferentially target rockfish as prey. Rockfishes may not
be preferred because, unlike any other identified prey
items, they have robust, venomous spines (Smith and
Wheeler, 2006). In this case, experimental manipulation
of predator and prey densities at meaningful temporal
and spatial scales is not possible. For this reason it
is necessary to use consumption and relative density
estimates in a static model to find evidence of an effect.
If consumption is very low relative to prey abundance,
as is the case with predation on rockfishes, then any
200
Fishery Bulletin 1 10(2)
direct effects on the population dynamics of either are
unlikely to be strong.
There is incomplete spatial and temporal overlap
between prey availability and consumption data sets
and the variance may be greater than it otherwise
would be because the dive surveys are disjunct. Still,
the MME beta value and asymptotic curve stability
of the combined surveys suggest that the heterogene-
ity of available prey can be detected with this level
of effort and that the data are representative at this
spatial and temporal scale. The prey availability data
are not intended to reflect regional abundance. When
a prey type such as Pacific herring is ranked low with
respect to availability relative to rockfishes, it suggests
rockfishes have more constant (less patchy) temporal
and spatial overlap with lingcod. In this way the po-
tential for encounter is much higher between lingcod
and rockfishes.
Large, highly generalized predators eat many differ-
ent prey types and often do so infrequently, and there-
fore sample sizes must be relatively large to adequately
capture the heterogeneity of the consumption data (e.g.,
Kingsford, 1992). With 375 samples, the dietary data
reported here describe the relative abundance of prey
categories in the diet of lingcod over a limited geo-
graphical area during half the year. However, Steiner
(1979) collected summer and winter stomach samples
and did not show an increase in lingcod consumption
of rockfishes in winter and the number of samples col-
lected appears to have captured the heterogeneity in
the consumption data. The primary sources of error in
these data include potential misidentification of prey
and undefined rates of egesting stomach contents. Ad-
ditionally, the digestion rates for free-living lingcod are
unknown. Although they do reflect the relative temporal
distributions of different prey types, the data from dive
surveys were biased by both the spatial and temporal
patchiness of transient prey, and by asymmetric sam-
pling accuracy among habitats for prey types that were
difficult to observe. However, rockfishes are highly ob-
servable and there was clearly a strong negative prefer-
ence (or avoidance) for rockfishes than for all other prey
types. Hydro-acoustic tracking studies of black rockfish
have shown they move less than a few hundred meters
over periods of months (Parker et al., 2008).
The gape-limitation hypothesis predicts that prey-
size selection is consistent with optimal diet theory at
the lower bound and the physical constraint of mouth
size at the upper bound (Schmitt and Holbrook, 1984)
and can be useful for predicting foraging behavior in
fish (e.g., Persson et al., 1996). Larger lingcod tend to
consume larger prey, but the gape-limitation hypothesis,
or size-spectrum hypothesis (Scott and Murdoch, 1983),
is not particularly useful for predicting prey selection
in these animals because all sizes of lingcod eat small
prey and lingcod consume parts of larger prey. Gape-
limitation does not effectively predict which prey species
or functional groups adult lingcod of different sizes will
prefer to consume, nor do these data show a distinct
shift to larger prey with increasing lingcod size.
In relatively long-lived generalist predators such
as lingcod, dietary sampling at temporal scales
over two years may be required for meaningful
patterns in consumption to emerge. The variance
in consumption by local predators of transient
prey is high and may be independent of regional
prey abundance. If consumption of resident prey is
relatively even over time, the resident prey types
may provide a maintenance resource and more
ephemeral prey may provide sporadic opportuni-
ties for enhanced growth and reproduction. Addi-
tionally, indirect effects can be important to the
distribution of predators. Besides direct consump-
tion, risk effects (modification of prey distribution
or behavior because of a perceived predation risk)
may have an important influence on community
structure (Creel and Christianson, 2007; Madin
et al., 2010).
There is concern that lingcod predation may re-
duce the efficacy of marine reserves in the recov-
ery of some overfished populations of rockfishes.
In a recent study that addressed this issue in
Puget Sound, Washington, Beaudreau and Essing-
ton (2007) found that in 560 lingcod (<30-108 cm
TL) sampled inside and outside marine reserves,
6.8% of the total number of prey items were rock-
fishes. All individual rockfish identified to species
were Puget Sound rockfish (S. emphaeus ) and
0.4% of all prey were confirmed to be other spe-
cies of Sebastes. The Puget Sound rockfish is a
%Fn
Figure 4
Percentage of lingcod ( Ophiodon elongatus) prey (%N) as a
function of frequency of occurrence in the diet (%F0). Prey
categories are as follows: TP=transient-pelagic fishes (pre-
dominantly Pacific herring [Clupea pallasii]); SF=skates ( Raja
spp.) and flatfishes; S = sculpins (family Cottidae); R=rockfishes
( Sebastes spp.); G=greenling (family HexagrammidaeXinclud-
ing cannibalism by lingcod); 1= invertebrates; 0 = other (includ-
ing uncategorized, unidentified fishes). TP, for example, was
both a relatively large percentage of the overall diet, and also
commonly occurred as a prey type among lingcod sampled.
Tinus Prey preference of Ophiodon elongatus, a top marine predator
201
very small species that matures in 1-2 years. It
is a schooling species and is often found in high
densities. It is not fished either recreationally or
commercially and thus is not the focus of recovery
efforts. The largest measurable rockfish in Beau-
dreau and Essington’s (2007) study was 16.6 cm.
Combined with the results from Steiner (1979)
and this study, lingcod of any size rarely prey on
larger-body roekfishes. Beaudreau and Essington
(2007) state that model results suggest intensive
lingcod fishing is likely to disproportionately al-
leviate predation pressure on larger roekfishes.
However, combined empirical evidence from this
study and the two studies cited immediately above
does not support this assertion.
Of all prey items found in this study, only one
was a potentially reproductive rockfish and it
apparently had been ingested within 24 hours
of capture. This ratio simplifies to less than one
adult rockfish consumed per adult lingcod per
year, whereas the dive surveys revealed an aver-
age of 40 adult roekfishes living in the vicinity
of each lingcod. If these ratios are representa-
tive, they suggest that lingcod predation is not a
primary source of mortality for nearshore adult
roekfishes off the coast of Oregon. Nor do lingcod
appear to be a primary source of mortality of
juvenile or young-of-the-year roekfishes because
they were only slightly more likely than adult
roekfishes to be eaten by lingcod. Hobson et al.
(2001) found predation by black rockfish, blue
rockfish, and kelp greenling was the primary
source of mortality for postsettlement juvenile
roekfishes in northern California.
50
40 •
40 50 60 70 80 90 100
Lingcod length (cm)
Figure 5
Lingcod ( Ophiodon elongatus) total length (cm) versus prey
length for each of 73 measurable prey items. The largest
prey item was a Pacific giant octopus (Octopus dofleini)
estimated at 70 cm in length that two individual lingcod,
72 cm and 93 cm respectively, appeared to have each eaten
half. This prey item is represented by two data points, each
with an assigned prey length value of 35 cm. The slope
of the regression line (adj. R2 = 0.29) is influenced by the
three largest prey items. Dashed lines represent the 95%
confidence interval.
Conclusions
The results of this study show that lingcod are highly
generalized predators that consume a broad variety of
prey in terms of taxa, body form, and habitat. Lingcod
are mobile, opportunistic, ambush predators that do
not appear to be individually specialized. On the basis
of the number of empty stomachs, they frequently go at
least several days without eating, indicating there may
be large differences between local prey abundance and
prey availability (see Menge, 1972; Kelly, 1996). Better
information is required on foraging range in relation
to differences in habitat and prey availability to better
understand lingcod foraging behavior as it relates to
prey density. Nevertheless, this study strongly indicates
that lingcod do not pose a threat to rockfish populations.
EBFM requires more information than single-species
management approaches. In data poor systems, and
particularly those that are difficult to access, higher
echelon data describing interactions among both tar-
geted and nontargeted species will be very difficult to
develop. However, this study shows that untested as-
sumptions about trophic relationships may lead to coun-
terproductive management decisions, particularly with
respect to large predatory species (Baum and Worm,
2009). Marine reserves can be an effective manage-
ment tool for the conservation and recovery of exploited
and other species, and particularly so where species of
particular interest have relatively site-attached adult
populations. In these cases trophic relationships, es-
pecially among resident and transient species, are a
critical uncertainty and these relationships can only be
fully understood through both consumption and relative
prey availability measures. In this case, a preference
index provides much more information about the likely
result of fluctuations in predator and prey populations
than would be the case with diet data alone.
Acknowledgments
This study was funded in part by a Cooperative Institute
for Marine Resource Studies (CIMRS) grant. I thank
R. Cross, D. Edge, M. Hixon, and J. Miller for their
helpful suggestions in the preparation of this manu-
script; S. Reimers for help with sample identification;
B. Gallagher, P. Heikkila, S. Heppell, K. Schultz, and
S. Theberge for help with sample collection; E. Gilbert
202
Fishery Bulletin 1 10(2)
6 1
5 -
O
▲
▲
o
O Other resident reef dwelling
A Transient
□ Subadult rockfishes
■ Adult rockfishes
O
o
o
□
-1
1 2 3 4 5 6 7
9 10 11 12 13 14
Prey category rank
Figure 6
Mean relative prey “preference” (Johnson’s [19801 - t) of 186
lingcod ( Ophiodon elongatus ) captured at two sites off Oregon
containing prey from 14 prey categories, listed from most
preferred (rank 1) to least preferred (rank 14). Values with
the same underline are not significantly different (P>0.05,
Waller-Duncan multiple comparison). Rank numbers identify
prey categories as listed in Table 1. Both juvenile and adult
rockfishes ( Sebastes spp.) were significantly avoided as prey
types in comparison with all other prey groups.
for help with Figure 1; three anonymous reviewers; and
Captain J. W. Cheesman (1962-2011).
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205
Variations in eastern North Pacific demersal fish
biomass based on the U.S. west coast
groundfish bottom trawl survey (2003-2010)
Email address for contact author: aimee.kellertS) noaa.gov
National Oceanic and Atmospheric Administration
National Marine Fisheries Service
Northwest Fisheries Science Center
Fishery Resource Analysis and Monitoring Division
2725 Montlake Blvd E.
Seattle, Washington 98112
Abstract-— In response to declining
biomass of Northeast Pacific ground-
fish in the late 1990s and to improve
the scientific basis for management
of the fishery, the Northwest Fish-
eries Science Center standardized
and enhanced their annual bottom
trawl survey in 2003. The survey was
expanded to include the entire area
along the U.S. west coast at depths
of 55-1280 m. Coast-wide biomass
and species richness significantly
decreased during the first eight years
(2003-10) of this fishery-independent
survey. We observed an overall ten-
dency toward declining biomass for
62 dominant taxa combined (fishery
target and nontarget species) and
four of seven subgroups (including
cartilaginous fish, flatfishes, shelf
rockfishes, and other shelf species),
despite increasing or variable biomass
trends in individual species. These
decreases occurred during a period of
reduced catch for groundfish along the
shelf and upper slope regions relative
to historical rates. We used informa-
tion from multiple stock assessments
to aggregate species into three
groups: 1) with strong recruitment,
2) without strong recruitment in 1999,
and 3) with unknown recruitment
level. For each group, we evaluated
whether declining biomass was pri-
marily related to depletion (using year
as a proxy) or environmental factors
(i.e., variation in the Pacific Decadal
Oscillation). According to Akaike’s
information criterion, changes in
aggregate biomass for species with
strong recruitment were more closely
related to year, whereas those with no
strong recruitment were more closely
related to climate. The significant
decline in biomass for species with-
out strong recruitment confirms that
factors other than depletion of the
exceptional 1999 year class may be
responsible for the observed decrease
in biomass along the U.S. west coast.
Manuscript submitted 3 June 2011.
Manuscript accepted 29 November 2011.
Fish. Bull. 110:205-222 (2012).
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
Aimee A. Keller (contact author)
John R. Wallace
Beth H. Horness
Owen S. Hamel
Ian J. Stewart
Over the past 20 years, a number of
changes have occurred in the North-
east Pacific groundfish fishery with
low abundance observed for multiple
species (Field and Fox, 2006; Levins
et ah, 2006). Historically catch and
annual exploitation rates increased
from the 1950s through the 1980s
and then declined rapidly. Between
1999 and 2002, nine important fish
stocks in the eastern North Pacific
off the U.S. west coast were declared
overfished, at which time the Pacific
Fishery Management Council (PFMC)
introduced a series of regulatory mea-
sures to reduce fishing pressure. Man-
agement actions included reducing
total allowable catch and fleet size,
and closure of large areas of the upper
continental shelf to fishing (PFMC,
2008a). In response to manage-
ment concerns the NOAA Northwest
Fisheries Science Center (NWFSC)
also expanded and standardized the
annual west coast groundfish bottom
trawl survey to provide enhanced
scientific information for managers.
Since 2003, the Northwest Fisheries
Science Center (NWFSC) has con-
ducted a comprehensive fishery-inde-
pendent bottom trawl survey covering
the entire coast from the U.S. -Canada
to the U.S.-Mexico borders, at depths
of 55 to 1280 m (Keller et ah, 2008).
This groundfish survey follows
strict sampling protocols with stan-
dardization of vessels, fishing gear,
deployment methods, catch sampling
practices, and geographic extent from
2003 onward (Stauffer, 2004).
Here we summarize variations in
biomass indices for species collected
during the 2003-10 fisheries-inde-
pendent west coast groundfish bot-
tom trawl survey. We evaluate if an
observed decline in biomass of demer-
sal fish (target and nontarget species)
from 2003 through 2010 can be at-
tributed primarily to recruitment (i.e.,
depletion after strong recruitment
events for multiple species in the late
1990s) or to climate variability (i.e.,
poor environmental conditions).
The 2003-10 survey time series
covers a period within the Califor-
nia Current system characterized by
1) reduced catch and exploitation of
groundfish species relative to histori-
cal rates (Worm et ah, 2009; Hilborn
et ah, in press); 2) the population ef-
fects of a very strong 1998-99 year
class observed for many west coast
groundfish species (e.g.. Pacific hake
[Merluccius productus ], English sole
[Parophrys vetulus], http://www.
pcouncil.org/. accessed June 2011);
and 3) a phase shift in the Pacific
Decadal Oscillation (PDO), an El Ni-
no-like pattern of Pacific climate vari-
ability linked to productivity (Mantua
et ah, 1997). The PDO is detected as
warm or cool surface waters in the
western Pacific Ocean, north of 20°N,
that shift phases on a scale of about
206
Fishery Bulletin 110(2)
10 to 30 years. During a “warm” or “positive” phase,
part of the eastern Pacific Ocean warms and productiv-
ity of waters off the U.S. west coast declines; during a
“cool” or “negative” phase, the opposite pattern occurs
(Schwing et ah, 2009).
The aim of this study was to evaluate the importance
of depletion after strong recruitment versus environ-
mental effects on declining biomass observed during
groundfish surveys in the western U.S. shelf system.
We used data from 24 stock assessments conducted
since 2005 (http://www.pcouncil.org/. accessed Septem-
ber 2011). With information contained in the assess-
ments we separated 62 dominant species into three
groups: those with strong recruitment during the late
1990s-early 2000s, those without a strong recruitment
during this period, and those with unknown year-class
strength. For each group and the overall biomass indi-
ces for all groups we evaluated regression models be-
tween demersal fish biomass (2003 through 2010) along
the U.S. west coast versus year (as a proxy for gradual
depletion after recruitment of exceptional year classes
to the fishery) and the PDO index, an ecosystem-level
indicator of climate variability. For each comparison,
the most appropriate model for describing the relation-
ship with biomass was determined. A similar analysis
was undertaken for species richness. We additionally
present information on frequency of occurrence (number
of positive hauls) and depth distribution by species.
Materials and methods
Survey design and methods
The NWFSC conducted annual bottom trawl surveys of
groundfish resources off the U.S. West Coast using stan-
dardized procedures from 2003 through 2010 (Keller et
ah, 2008). Surveys occurred May through October from
the area off Cape Flattery, Washington (lat. 48°10'N),
to the U.S. -Mexico border (lat. 32°30'N) at depths of
55-1280 m (Fig. 1). The entire geographic extent of
the survey was covered twice each year by two west
coast commercial fishing vessels (20 to 28 m in length)
per pass. Each year sampling extended from late May
through late July for the first period and mid-August
through late October for the second. A stratified random
sampling design was used, in which the surveyed region
was subdivided into -13,000 cells of equal area (1.5 nmi
longitude by 2.0 nmi latitude) (Fig. 1). An average of 700
primary cells was randomly selected each year, strati-
fied by geographic location and depth. The geographic
allocation was based on a simple north-south division
at 34°30'N lat. (Point Conception, California) with 80%
of the effort in the northern portion of the survey and
20% in the southern range. The survey area was further
stratified into depth zones as follows: north of Point
Conception, 40% of the cells were in the shallow depth
zone (55-183 m), 30% at mid-depths (184-549 m), and
30% in the deep stratum (550-1280 m); and south of
Point Conception, 25% were in the shallow depth zone,
45% at mid-depth, and 30% in the deep stratum. Four
chartered west coast fishing vessels were assigned an
equal portion of stations to sample per year except in
2004 when only three vessels were used.
Vessels were equipped with customized Aberdeen-
style nets with a small mesh (3.8 cm stretched measure)
liner in the codend, a 25.9-m headrope, and a 31.7-m
foot rope. All fishing operations were conducted in strict
compliance with national and regional protocols de-
tailed in Stauffer (2004). Simrad Integrated Trawl In-
strumentation (ITI , Kongsberg Simrad Mesotech Ltd.,
Port Coquitlam, B.C., Canada1) was used to monitor
and record net performance and position for each haul.
A differential global positioning system (DGPS) naviga-
tion unit (Northstar 500, Northstar Technologies, Ac-
ton, MA) was used to monitor towing speed during each
haul. Standard survey haul positions were estimated
from DGPS data — generally the mid-point between the
net touchdown and net liftoff positions. Average net
speed over ground and distance fished were calculated
from the position data for the trawl and actual bottom
time (Keller et ah, 2008).
Samples were collected by trawling within the ran-
domly selected cells (Fig. 1) for a target fishing time of
15 minutes at a target speed of 1.13 m sec 1 (2.2 knots).
All fish and invertebrates were sorted to species (or the
lowest possible taxon), and then weighed by using an
electronic, motion-compensated scale (Marel, Reykjavik,
Iceland). Abundance was not analyzed in this study be-
cause not all individuals were counted. Total abundance
is estimated from biomass and the two cannot be con-
sidered independent without analysis of the variability
of mean weights. That analysis is beyond the scope of
the present study. Near bottom temperature (°C) and
depth (m) were measured during each trawl with an
SBE 39 temperature and pressure recorder (Sea-Bird
Electronics, Inc., Bellevue, WA) attached to the head
rope. Mean tow depths were computed as the average
of all depth recordings from the center 80% of the trawl
duration (net touch down to lift off). Only tows judged
to be acceptable (based on postcollection analysis of
bottom contact, net performance, and other metrics;
Stauffer, 2004) were included in the data analyses.
Analyses of catch
To limit this analysis to the most reliably sampled
species, we initially examined catch for 310 individual
fish species summed over the 2003-10 period. When
graphed by species in order of descending catch, no
obvious break was apparent and therefore we included
all demersal species with an overall catch greater
than 450 kg. This break point included the 62 most
abundant species in the survey and incorporated 45 of
the demersal groundfish species present in the Pacific
Fishery Management Council Pacific Coast groundfish
1 Mention of trade names or commercial companies is for
identification purposes only and does not imply endorsement
by the National Marine Fisheries Service, NOAA.
Keller et al : Variations in eastern North Pacific demersal fish biomass, 2003-10
207
Newport
Cape Flattery
f>art Franciscc
Morro Bay
0 25 50
Pt Conception
Los Angeles
Newport Bead-
NWFSC West Coast
Groundfish Survey
(2003-2010)
| 1-5 samples
7“W
Figure 1
Geographical extent of the Northwest Fisheries Science Center’s West Coast Groundfish Bottom Trawl Survey and the
location of stations (shaded) trawled one to five times from 2003 through 2010. (A) Stations off Washington and Oregon.
(B) Stations off northern California. (C) Southern California stations. The Cowcod ( Sebastes levis ) Conservation Area
in Southern California was excluded from the experimental design because it is closed to fishing for groundfish species.
fishery management plan (PFMC, 2008b), as well as
two important benthic invertebrates and 15 non-fished
species. The break point also represented a greater
than 10% difference in catch relative to the next most
abundant species. Species-specific catch per unit of
effort (CPUE, kg ha-1) was calculated for each tow
on the basis of area swept. Area swept was computed
from the mean net width for each tow multiplied by the
distance fished. Mean CPUE was calculated for each
stratum (depth and geographic region, including those
with zero catch), by species. Species-specific biomass
indices (6, in metric tons [t] ) were computed by multi-
plying the mean CPUE by the appropriate stratum area
and then summing the strata biomasses. The estimate
of the variance of the biomass was the sum of the vari-
ances of the strata involved:
Var(b) = X ^(VariCPUE, ) x A?,
with n equal to the number of strata, and A equal
to the area of each stratum (km2). Standard errors
(SE) for the annual species-specific biomass indices
were calculated using standard statistical techniques
(Cochran, 1977).
To examine variation in biomass over time, species
groups were initially designated on the basis of either
taxonomy or depth (e.g., shelf and slope rockfishes).
Subsequently we examined relationships between bio-
mass and both year and the PDO index using groups
designated by the presence, absence, or unknown oc-
currence of a strong recruitment during the mid to
late 1990s and early 2000s. In all cases biomass was
summed over the appropriate group and variance was
calculated as previously described. For those species
included in the 2003-10 biomass analyses, we also cal-
culated mean depth (m) by averaging station values
weighted by catch for each species in each year (Hsieh
et al., 2008).
208
Fishery Bulletin 1 10(2)
Recruitment versus PDO indices
Stock assessments for 24 of the 62 species in the analyses
have been published by the PFMC since 2005 (available
at http://www.pcouncil.org/groundfish/stock-assess-
ments/safe-documents/2011-safe-document/, accessed
September 2011) and provide information on the number
of recruits by year. We examined stock assessments for
each species to determine whether strong recruitment
events occurred during the period from the mid to late
1990s through 2002. Annual recruitment strength is
generally modeled in the assessments as random devia-
tions about a stock- recruitment (S-R) relationship. These
deviations and the central tendency of the S-R curve are
informed by all other sources of available information
(i.e., observed lengths, weights, age, and trend informa-
tion from fishery-dependent and independent sources)
and will reflect predation intensity, climate, and other
influences (Methot, 2011). For this analysis, we defined
strong recruitment as 1.7-5 times greater than the
average recruitment during the 10 to 14 years before
the most recent assessment.
We subsequently subdivided the 62 species included
in our study into three groups: those with strong re-
cruitment during the late 1990s, those without strong
recruitment during this period, and those with un-
known recruitment levels. We summed the biomass
indices for all species within each group and the overall
biomass indices for all three groups and regressed these
summed values versus year. We reasoned that declining
biomass indices would be more tightly tied to time for
those species with elevated recruitment as the resulting
exceptionally strong cohorts declined due to natural and
fishing-induced mortality in the early 2000s.
Biomass indices for the aggregated subgroups and
overall were also compared with the PDO, a widely
used index of climate variability for the California Cur-
rent system. The PDO is an index based on patterns
of variation in sea surface temperature of the North
Pacific from 1900 to the present (Mantua et al., 1997;
Schwing et ah, 2009). Although derived from sea sur-
face temperature data, the PDO index is well correlated
with other environment factors, including sea level pres-
sure, winter air temperature, wind shear, and precipi-
tation, as well as other Pacific climate indices (ENSO
[El Nino-Southern Oscillation] and MEI [multivariate
ENSO index]). For comparison with the annual survey
data, monthly PDO values were averaged annually
(November to October) to include the survey period each
year (Mantua2).
Species richness
Coast-wide estimates of species richness were calcu-
lated as area-weighted mean number of fish species
taken per trawl sample. Estimates were stratified by
survey year (2003-10), depth (55-183 m, 184-549 m.
2 Mantua, N. 2010. Personal commun. Dep. Atmospheric
Sciences, Univ. Washington, Seattle, WA 98195.
and 550-1280 m), and geographic region (one degree
latitudinal increments from 32° to 49°N) for all fish spe-
cies. Estimates were built upon the number of distinct
fish species reported for each trawl sample. Mean species
counts were determined for each stratum and weighted
by the proportion of stratum area within the total area.
Annual species richness estimates were computed as
the sum of these area-weighted species counts within
the area of interest (per depth range or coast-wide) and
survey year. Species richness variance within each area
was similarly estimated as the sum of stratum vari-
ances weighted by their associated squared proportion
of stratum area within the total area. Standard errors of
the mean were computed as the square root of the ratio
of the variance estimate to the stratum count for each
area (i.e., within a specific depth stratum or coast-wide).
We compared species richness over time by regressing
against year and also evaluated the relationship between
species richness and the annual Pacific Decadal Oscilla-
tion (PDO) index. In both cases, regression analyses by
depth strata and overall depth were undertaken
Statistical analyses
For the biomass data we examined individual species,
and present for comparison several aggregate groups
formed by summing species coast-wide biomass indices
(metric tons, t). For each species, regression analy-
sis was used to initially investigate the relationship
between annual biomass indices and year. To account
for the large number of tests conducted, a sequential
Bonferroni correction with a significance level of 0.05
was applied to the data (Peres-Neto, 1999). Grouping
data for later analyses (initially by depth or taxonomic
group to examine trends over time for aggregated data
and subsequently by the presence, absence, or unknown
occurrence of exceptionally large year classes after
recruitment) resulted in fewer tests and no Bonferroni
correction was applied. Results for biomass and species
richness were statistically compared with year and the
PDO index by linear and multiple regression (GLM)
by using SAS for Windows (SAS Institute, Inc., Cary,
North Carolina). To stabilize the variance, the natural
logarithm of the response variable was used in the
regression models; however, even after the transforma-
tion, annual variance estimates were highly variable
for some species. Regressions weighted by the variance
estimate of the annual values were therefore used to
examine interactions between annual biomass indices
and species richness versus year, PDO values, or both
(Draper and Smith, 1981).
The Akaike information criterion (AIC) was used to
choose between competing models (i.e., recruitment,
environmental variability or both) when comparing
biomass values, summed by groups, versus year and the
PDO index (Sakamoto et al., 1986). For each group, the
best model was selected on the basis of the smallest AIC
value ( AlCmin ). A similar comparison was done between
species richness versus year, the PDO index, and both
year and the PDO index. To determine whether a model
Keller et al. : Variations in eastern North Pacific demersal fish biomass, 2003-10
209
other than the best model was plausible, the difference
in AIC values for each model was calculated as
A=A 1C r A ICm m.
Models with At < 2 are considered equivalent to the
best model ( AICmin ) and candidate models with At >10
are highly unlikely to be plausible alternatives for the
best model. Candidate models with Al between 3 and 7
have less support than the best model (Burnham and
Anderson, 2002).
Results
Biomass
Between 2003 and 2010, 5271 trawls were successfully
conducted as part of the groundfish survey with an
annual average of 659 trawls yr-1 (range: 505 to 722
trawls; Fig. 1). Although an average of 265 individual
fish taxa were identified each year (range: 252- 310),
the 60 demersal fishes and two benthic invertebrates
included in this analysis comprised greater than 99%
of the total catch. Annual biomass indices (t) for the 62
individual species revealed variable trends over time
(2003-10) (Figs. 2 and 3). Six species exhibited signifi-
cant (P<0.001) increases in biomass indices over time
(Fig. 2A), 20 species displayed significant (P<0.05) or
near significant (P<0.10) negative trends (Fig. 3, Califor-
nia skate [Raja inornata] and pygmy rockfish [Sebastes
wilsoni] not shown), and 36 species exhibited nonsig-
nificant trends over time. Representative examples for
species with no significant trends are shown for the
most abundant species within each group (Fig. 2B).
Regardless of trends, both target and nontarget species
occurred in each group.
Mean, minimum, and maximum depth (m), and total
numbers of positive hauls over the eight year study
are shown for the 62 individual species included in
the analyses (Table 1). Catch for these 62 species was
initially partitioned into seven groups based on tax-
onomy and depth (in order of decreasing biomass): flat-
fish (30%), other shallow to mid-depth species (20%),
shelf rockfish (15%), sharks, skates, and ratfish (13%),
other deep water species (9%), thornyheads (8%), and
slope rockfish (5%), to examine trends over time. The
weighted mean depths for shallow to mid-depth spe-
cies was <500 m, and the weighted average for deep
water species was >650 m. Shelf rockfish occurred at
average depths ranging from 101 to 209 m, whereas
slope rockfish were somewhat deeper (226-456 m). In
general, rockfish were encountered in fewer hauls than
other subgroups.
Despite variations in biomass indices at the species
level, four of the seven groups initially examined here
(with the exception of slope rockfish, thornyheads, and
other deep water species) and overall biomass indi-
ces decreased significantly (P<0.05) over time (Fig. 4).
Overall aggregate coastwide biomass indices for all 62
species decreased approximately 60% from 2,308,207
t in 2003 to 1,384,391 t in 2010. However the lowest
biomass (1,373,473 t) was recorded in 2008 (the year of
the lowest PDO and also possibly a good recruitment
year) followed by slight increases in 2009 and 2010.
Recruitment versus PDO indices
Our initial analyses based on biomass indices for spe-
cies grouped taxonomically or by depth indicated that
deepwater species, such as thornyheads and slope rock-
fish, did not significantly decrease over time (Fig. 4).
However because some species within these groups
either decreased significantly or displayed decreasing
trends (Figs. 2 and 3), we included all 62 species when
we separated demersal catch into categories based on
recruitment. Our examination of 24 stock assessment
models revealed that 13 species had large recruitment
events occurring primarily in 1999 (arrowtooth flounder
[Atheresthes stomias], English sole, Pacific hake, sable-
fish [ Anoplopoma fimbria], bocaccio [Sebastes paueispi-
nis ], chilipepper rockfish [Sebastes goodie ], splitnose
rockfish [Sebastes diploproa], but occasionally some-
what earlier (petrale sole [Eopsetta jordani ], longspine
thornyhead [Sebastolobus altivelis]), or later (Dover sole
[Microstomas pacificus], greenstriped rockfish [Sebastes
elongates ], darkblotched rockfish [Sebastes crameri ],
Pacific ocean perch [Sebastes alutus]). Eleven additional
species assessed since 2005 did not display significantly
larger individual recruitment levels during the period
examined (spiny dogfish [Squalus acanthias], longnose
skate [Raja rhina], lingcod [Ophiodon elongatus], black-
gill rockfish [Sebastes melanostomus], canary rockfish
[Sebastes pinniger], greenspotted rockfish [Sebastes
chlorostictus], shortbelly rockfish [Sebastes jordani[,
widow rockfish [Sebastes entomelas] yelloweye rockfish
[Sebastes ruberrimus], yellowtail rockfish [Sebastes flavi-
dus], shortspine thornyheads [Sebastolobus alascanus]).
Stock assessments have not yet been conducted on the
remaining 38 species included in our analyses.
During the 1999-2010 period, when depletion of a
strong year class by fisheries was expected to result in
declining biomass trends, we observed variable trends
in the PDO index (Fig. 5). Changes in the PDO index
from 1999 to 2010 indicate that average climate in the
California Current system gradually shifted from cool
(1999-2001) to warm (2003-06) and back to cool ( 2007—
10) conditions (Fig. 5). We summed biomass for each of
the three groups previously described and overall (all
species combined) and regressed aggregate biomass
(natural log transformed) versus year and annual PDO
indices (Fig. 6). We noted significant (P<0.001) or near-
significant (P=0.06) inverse relationships for all groups
and overall versus year (Fig. 6). For species with strong
recruitment events in 1999, the trend in biomass over
time was increasingly downward, whereas for those
with no or unknown recruitment there was a tendency
for biomass to increase in recent years (Fig. 6). All
groups also demonstrated significant relationships with
the annual PDO indices (Fig. 6); however, the greatest
Fishery Bulletin 1 10(2)
A Increasing trends
250.000
200.000
150.000
100.000
50,000
0
longspine thornyhead
( Sebastolobus altivelis )
ft ft ft
ft ft
B Nonsignificant trends
Dover sole
( Microstomus pacificus)
Pacific grenadier
12 ooo ( Embassichthys bathybius)
yellowtail rockfish
(Sebastes flavidus)
40.000
20.000
0
3000 snakehead eelpout
( Lycenchelys crotalinus)
20,000
15.000
10.000
5000
Pacific cod
(Gadus macrocephalus)
ft i
a i .
SB -01 , L
2003 2004 2005 2006 2007 2008 2009 2010
Figure 2
(A) Increasing and (B) nonsignificant trends in annual, coast-wide demersal
biomass indices (in metric tons, t) for 12 taxa caught in the Northwest Fisheries
Science Center’s West Coast Groundfish Bottom Trawl Survey, 2003-10. Taxa
are ranked from highest (top) to lowest (bottom) biomass for each category,
and standard errors are shown. All six species exhibiting significant (P<0.05)
or near-significant (P<0.10) increases in biomass indices over time are shown;
however representative examples of species with nonsignificant trends (36 spe-
cies) are shown only for the most abundant taxa for each subgroup described
in Table 1.
Keller et al. : Variations in eastern North Pacific demersal fish biomass, 2003-10
211
350.000
280.000
210,000
140.000
70.000
0
200.000
150.000
100.000
50.000
0
75.000
50.000
25.000
0
75.000
50.000
25.000
Pacific hake
( Merluccius productus )
ft
&
c
tj ft a
& x
LI L
C=B
□
15,000
10,000
4000
3000
2000
1000
Ai
sablefish
(Anoplopoma fimbria)
A a, £
lingcod
( Ophiodon elongatus)
Hi,
spotted ratfish
(Hydrolag us collief)
n n
o LI Li U U
ft A
bocaccio
( Sebastes paucispinis)
fi a
black eelpout
( Lycodes diapterus)
ft ft i ft 4 i ft
350.000
280.000
210,000
140,000
70.000
0
180.000
135,000
90.000
45.000
0
75.000
50.000
25.000
50.000
40.000
30.000
20.000
10,000
0
15.000
10.000
'5000
0
3000
2000
spiny dogfish
( Squalus acanthias)
□ 0 fi 0 => ft
sharpchin rockfish
(Sebastes zacentrus)
rex sole
(Glyptocephalus zachirus)
ft ft
darkblotched rockfish
( Sebastes cramen)
fi a i fi a
white croaker
(Genyonemus lineatus)
0 ■ »-*
2003 2004 2005 2006 2007 2008 2009 20 1 0
redbanded rockfish
(Sebastes babcocki )
250.000
200.000
150.000
100.000
50.000
0
125.000
100.000
75.000
50.000
25.000
0
60.000
40.000
20.000
40.000
30.000
20.000
10,000
0
6000
4000
chilipepper rockfish
( Sebastes goodei)
shortbelly rockfish
( Sebastes jordani)
ft . i £ ft
English sole
(Parophrys vetutus )
o u u u
giant grenadier
(Albatrossia pectoralis )
A A A
4
flathead sole
( Hippoglossoides elassodon)
ft 1
a s a
2000
1500
1000
500
curlfin sole
(Pteuronichthys decurrens)
2003 2004 2005 2006 2007 2008 2009 2010
2003 2004 2005 2006 2007 2008 2009 201 0
Figure 3
Decreasing trends in demersal biomass indices in metric tons (t) (±standard error) for 18 of 20 taxa caught
in the Northwest Fisheries Science Center’s West Coast Groundfish Bottom Trawl Survey, 2003-10. With
the exception of California skate and pygmy rockfish, all species exhibiting significant (P<0.05) or near-
significant (P<0.1Q) decreases in biomass indices over time are shown.
amount of variation in biomass was explained by the
PDO indices for those species with no strong recruit-
ment during the late 1990s. We used AIC to determine
which model (i.e., based on year, PDO indices or com-
bined) provided the best fit to the data for each group
(Table 2). For species with strong recruitment, the re-
gression of biomass versus year had the minimum AIC
value; for species without strong recruitment the model
incorporating PDO indices provided the best fit. For
both other groups (unknown recruitment and overall),
212
Fishery Bulletin 1 10(2)
Table 1
Common name, scientific name, and group designations according to taxonomy or depth for 62 dominant demersal species col-
lected during the 2003 to 2010 Northwest Fisheries Science Center West Coast Groundfish Bottom Trawl Surveys. Mean, mini-
mum, and maximum depth, and the total number of positive hauls per species are shown for the 2003 to 2010 period. Mean values
are weighted by catch per unit of effort to accurately reflect average depth.
Depth (m)
Hauls
Common name Scientific name mean min. max. (No. of hauls)
Sharks, skates, ratfish
big skate
California skate
filetail cat shark
longnose skate
sandpaper skate
spiny dogfish
spotted ratfish
Flatfish
arrowtooth flounder
curlfin sole
Dover sole
English sole
flathead sole
Pacific sanddab
petrale sole
rex sole
slender sole
Shallow to mid-depth water (<500 m)
bigfin eelpout
black eelpout
Dungeness crab
lingcod
Pacific cod
Pacific hake
plainfin midshipman
sablefish
white croaker
Rockfish — shelf
bocaccio
canary rockfish
chilipepper
greenspotted rockfish
greenstriped rockfish
halfbanded rockfish
pygmy rockfish
redstripe rockfish
rosethorn rockfish
sharpchin rockfish
shortbelly rockfish
squarespot rockfish
stripetail rockfish
swordspine rockfish
widow rockfish
yelloweye rockfish
yellowtail rockfish
Raja binoculata
104
Raja inornata
107
Parmaturus xaniurus
489
Raja rhina
265
Bathyraja kincaidii
284
Squalus acanthias
171
Hydrolagus colliei
185
Atheresthes stomias
208
Pleuronichthys decurrens
94
Microstomus pacificus
382
Parophrys vetulus
130
Hippoglossoides elassodon
146
Citharichthys sordidus
107
Eopsetta jordani
136
Glyptocephalus zachirus
235
Lyopsetta exilis
209
Lycodes cortezianus
343
Lycodes diapterus
470
Cancer magister
139
Ophiodon elongatus
137
Gadus macrocephalus
133
Merluccius productus
281
Porichthys notatus
109
Anoplopoma fimbria
495
Genyonemus hneatus
85
Sebastes paucispinis
159
Sebastes pinniger
139
Sebastes goodei
167
Sebastes chlorostictus
146
Sebastes elongatus
156
Sebastes semicinctus
115
Sebastes wilsoni
138
Sebastes proriger
158
Sebastes helvomaculatus
207
Sebastes zacentrus
209
Sebastes jordani
170
Sebastes hopkinsi
101
Sebastes saxicola
175
Sebastes ensifer
156
Sebastes entomelas
195
Sebastes ruberrimus
149
Sebastes flavidus
141
56
332
668
56
792
517
113
1224
472
57
1227
2934
59
1173
1631
57
1143
1737
56
1241
2521
58
992
1726
56
440
473
56
1246
4205
56
480
1957
62
346
332
56
491
1554
56
541
2059
56
937
3147
57
830
2564
57
1095
1418
82
1143
896
56
835
1633
56
417
1553
56
285
273
56
1213
2775
56
464
656
57
1268
3320
56
181
302
56
333
263
57
264
340
56
464
656
62
348
303
64
474
1299
57
440
378
64
268
95
66
271
120
65
447
420
76
455
307
71
406
405
59
203
80
59
436
1076
100
236
30
64
399
182
66
250
107
60
343
315
continued
Keller et al : Variations in eastern North Pacific demersal fish biomass, 2003-10
213
Table 1 (continued)
Depth (m)
Hauls
(No. of hauls)
Common name
Scientific name
mean
min.
max.
Rockfish — slope
aurora rockfish
Sebastes aurora
456
129
814
689
bank rockfish
Sebastes rufus
267
101
499
91
blackgill rockfish
Sebastes melanostomus
406
133
647
264
darkblotched rockfish
Sebastes crameri
226
84
538
920
Pacific ocean perch
Sebastes alutus
279
87
715
344
redbanded rockfish
Sebastes babcocki
269
84
550
398
rougheye rockfish
Sebastes aleutianus
291
124
798
253
splitnose rockfish
Sebastes diploproa
287
68
559
1107
Thornyhead rockfish
longspine thornyhead
Sebastolobus altiuelis
767
104
1271
1844
shortspine thornyhead
Sebastolobus alascanus
611
67
1268
2590
Deep (>650 m)
brown cat shark
Apristurus brunneus
672
82
1241
1717
California slickhead
Alepocephalus tenebrosus
909
477
1268
1097
deepsea sole
Embassichthys bathybius
874
276
1271
1116
giant grenadier
Albatrossia pectoralis
943
443
1271
838
grooved Tanner crab
Chionoecetes tanneri
782
64
1271
1431
Pacific flatnose
Antimora microlepis
931
262
1271
890
Pacific grenadier
Coryphaenoides acrolepis
915
313
1271
994
roughtail skate
Bathyraja trachura
941
107
1271
622
snakehead eelpout
Lycenchelys crotalinus
893
63
1264
783
twoline eelpout
Bothrocara brunneum
861
343
1271
898
Table 2
Akaike information criterion (AIC), P, coefficient of determination (r2), and zf, for different models fitting annual biomass indices
versus year, Pacific Decadal Oscillation (PDO) values, and both variables for three subgroups of demersal species determined by
the occurrence of strong recruitment during the late 1990s (with recruitment: « = 13, without recruitment: ra=ll, and unknown:
n = 38) and overall (n = 62). The best model for each group is determined by the minimum AIC value (!) within each category, with
lower AIC values indicating a better fit. Additionally, when Al values are <7 (2) there is some support for the alternative model.
Similar results are shown for species richness subdivided by depth strata (shallow 55-183 m; mid-depth 184-549 m; deep
550-1280 m and overall 55-1280 m).
With year
With PDO
With
year and PDO
Models:
AIC
P
r2
AIC
P
r2
AIC
P
r2
4
Biomass
with recruitment
-8.91
0.007
0.87
-4.8
0.02
0.62
-1.7
0.008
0.93
7.2
without recruitment
2.8
0.002
0.81
-0.6J
0.001
0.84
5.6
0.01
0.91
6.22
unknown recruitment
0.8
0.07
0.45
-5.1
0.01
0.70
-8.71
0.0002
0.99
9.5
all
-6.3
0.001
0.85
-7.5
0.002
0.81
-1 1.97
0.0001
0.99
5.62
Richness
shallow
23.1
0.15
0.34
15. 77
0.009
0.71
18.4
0.10
0.75
7.4
mid-depth
17.5
0.06
0.49
12. 6;
0.01
0.68
16.1
0.12
0.73
4.92
deep
18.7
0.28
0.19
16.0
0.20
0.20
12. 81
0.05
0.84
5.92
all
15.9
0.08
0.45
7.61
0.004
0.80
13.4
0.08
0.79
8.3
214
Fishery Bulletin 1 10(2)
13.2
12 8
12.4
12.0
11.6
11.2
sharks, skates, ratfish
L
t
y=-0.131x+275
^=0.79
*
13.5
13.3
13.1
12.9
flatfish
y=-0.027x+66
^=0 58
13.5
13.0
12.5
12.0
115
% 11.0
to
>/>
03
E 13.5
o
CD
13 0
12.5 -
shelf rockfish
i
4
y=-0 18x+373
r*=0.89
others shallow-mid
y=-0 138X+290
^=0.89
12.0
* J
12.4
119
114
10.9
10.4
12.5
12.4
12.3
12.2
12.1
slope rockfish
ttti.
i
^=0 003
thornyheads
JtM
1 I y=0.027x-41 1
I ^=0 59
12.3
12.1
119
others deep
I * *
it tu
^=0.0004
1 1 7 4
2002 2004 2006 2008 2010
14 8
14 6
14 4
14.2 -I
total groundfish
y=-0 091x+198
^=0.93
14.0
2002 2004 2006 2008
2010
Figure 4
Expanded coast-wide biomass indices (expressed as the natural log in metric
tons, [t] ) and standard errors as a function of time (year) for 62 dominant
demersal taxa collected during the 2003-10 groundfish survey, subdivided
into seven subgroups (Table 1), and summed overall. Regression equations for
subgroups with significant decreasing trends (P<0.05) over time and coefficients
of determination (r2) are displayed.
the minimum AIC value occurred for models incorporat-
ing both year and PDO indices combined (Table 2). The
A- for the four biomass models indicate that none of the
models are equivalent to the best model; however, the
observed A- <7 for two of the four groups suggest some
support for alternative models (Table 2).
Species richness
Species richness indices incorporated all fish collected
during the 2003-10 surveys, including rare deep-water
species and those not normally associated with the
bottom. Regressions between species richness and year
indicate near significant negative trends for mid-depth
(P=0.06) and overall (P=0.08), but insignificant rela-
tionships for shallow and deep depth strata (Fig. 7).
With the exception of the deep depth stratum (P=0.20),
significant positive relationships (P<0.01) were observed
between species richness and the PDO. The number of
fish species present within two depth strata (shallow and
mid-depth) and overall increased during the warm PDO
phase. Additionally, species richness indices declined
within increasing depth strata (Fig. 7).
Based on minimum AIC values, the models which
provided the best fit to changes in species richness
during the survey period were either models incor-
porating only PDO indices (shallow, mid-depth, and
overall) or models incorporating both year and PDO
values (deep), but not models based solely on year
(Table 2).
Keller et al : Variations in eastern North Pacific demersal fish biomass, 2003-10
215
Discussion
The Pacific Coast groundfish fishery is exceed-
ingly complex to manage because a wide range of
species (90+), including a number of overfished
and rebuilding stocks, are caught with the same
trawl gear. Beginning in 2000, the PFMC ini-
tiated a series of measures designed to reduce
catch along the west coast, including fleet reduc-
tions, closed areas, and catch restrictions. These
measures were initiated in direct response to
nine stocks being declared overfished from 1999
to 2002. Landed catch from 1980 through 2010
shows that groundfish harvests off Washington,
Oregon, and California were significantly lower
(rockfishes, flatfishes, sablefish) or relatively con-
stant (Pacific hake, thornyheads) in recent years
relative to historical rates (Fig. 8). This period of
decreased catch corresponds to the implementa-
tion of the PFMC’s management plan. And yet,
despite much reduced fishing effort (and landings),
overall survey indices of groundfish biomass off
the western United States declined from 2003 through
2010. The PFMC’s management directives also coincided
with the period following expansion of the NWFSC’s
West Coast Goundfish Bottom Trawl Survey to annu-
ally include both the shelf region, as well as the upper
continental slope waters along the entire coast (U.S.-
Canada to U.S.-Mexico). We used data from this fishery-
independent survey combined with information from 24
stock assessments since 2005 to attempt to unravel the
causes for the decline in biomass indices despite strict
adherence to fishery management plans.
Our results indicated that from 2003 through 2010
individual groundfish stocks along the U.S. west coast
responded in varying ways to the newly imposed man-
agement measures, with many of the overfished spe-
cies of concern exhibiting increases in spawning stock
biomass (PFMC, 2008a). However, despite improve-
ments in individual stocks there has been a gradual
decline in overall groundfish biomass, measured as the
sum of the biomass indices for 60 abundant groundfish
and two benthic invertebrate species, as well as in
major groundfish groups (e.g., sharks, flatfishes, and
rockfishes). Twenty of the 62 species described here
significantly decreased in observed biomass from 2003
through 2010, whereas 6 species significantly increased.
Of the remaining 36 species, 20 exhibited decreas-
ing trends and 16 exhibited increasing trends. These
changes indicate that the decline in biomass is not
attributable to just a few species. Similar declines in
Northwest Atlantic fish stocks have previously been
attributed to a variety of factors including overfish-
ing and environmental effects (Haedrich and Barnes,
1997). Hilborn et al. (in press) noted that the dramatic
decline in catch can be interpreted in two ways: as an
indication of collapsing stocks caused by overfishing,
or as a demonstration that management has effectively
reduced catch to prevent overfishing of sensitive species.
However, the continued decline in overall observed bio-
03
13
C
C
03
O
Q
CL
1 5
1 0
0 5
0 0
-0 5
-1 0
-1 5
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 5
Annual variation in the Pacific Decadal Oscillation index (PDO)
from January 1999 through December 2010 as it fluctuated
between cool negative and warm positive phases. Annual
values are the mean of the 12 months beginning in November
of the year preceding the start of each annual survey and
ending in October with the completion of the annual survey.
mass along the west coast suggests that reduced catch
in itself may not be sufficient to prevent biomass from
further decreasing when environmental conditions are
poor. Hsieh et al. (2008) further concluded that fishing
pressure reduces the resilience of exploited populations
facing negative climatic effects.
By chance our study occurred during a period after
the emergence of a particularly strong 1999 year class
for many groundfish species inhabiting the California
Current system (Haltuch and Hicks, 2009) and a pe-
riod of changing environmental conditions as indicated
by variability in the annual PDO index. The observed
declining survey trends are consistent with natural
and fishing-induced mortality estimated for the 1999
cohort in many stock assessments, especially flatfishes,
sablefish, and Pacific hake (Schirripa, 2007; Haltuch
and Hicks, 2009; Stewart et al., 2011). However, the
prevalence of this large year class among many west
coast groundfish and its gradual depletion over the sur-
vey period (2003-10) may not be entirely responsible for
the dramatic decline in overall biomass estimated with
data from our west coast fishery-independent survey.
The decline in biomass may additionally be tied to
environmental conditions. The annual PDO index, a
measure of climate variability, declined from a high
value at the start of our standardized survey (2003)
to low and negative values near the end of the series
examined (2007-10). Numerous studies correlate shifts
in the abundance and distribution of marine fish to
oscillations in ocean conditions (Francis et al., 1998;
Beamish et ah, 1999; Hare et ah, 1999). Within the
northern California Current system, changes in salmon
production (Mantua et ah, 1997), landed sardine catch
(Smith and Moser, 2003; Norton et al., 2008), and Pa-
cific hake distribution (Benson et al., 2002) are linked
to decadal-scale fluctuations in climate. Hollowed et al.
(2001) further reported that production of commercial
fish stocks (32 pelagic fish and groundfish species) in
216
Fishery Bulletin 1 10(2)
14.1
13.9
13.7
13 5
13.3
with recruitment
t
y=-0.059x+1 31
r>=0.87
14 1
13.9
13.7
13.5
13.3
with recruitment
# y=0.159x+13.7
^=0.62
i
13.4 without recruitment
13.0
12.6
i
12 2 y=-0 124X+262 ^ I "
r*=0.81 T
11.8
Hi
13.4
13.0
12.6
12.2 #
*
11.8
without recruitment
13.6
unknown recruitment
13.6
13.4
13.4
13.2
13.2
13.0
*
13.0
12.8
y=-0.046x+106 *
12.8
12 6
^=0.45
12.6
14 8
total
14.8
14.6
i
14.6
14.4
Vj.
’ V*.
14,4
14.2
T
y=-0.066x+147 •
14.2
14.0
^=0.85 "
14.0
2002 2004 2006 2008 2010
If
a
.1
' • y=0 41x+12.5
^=0.84
" y=0.186x+13.0
r==0.70
total
■I
Year
• #- " « y=0.209x+14.3
I -- " r*=0.81
1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
PDO
Figure 6
Expanded coast-wide biomass indices (natural log of biomass, [ t] ) and standard
errors as a function of year and annual Pacific Decadal Oscillation (PDO) values
for 62 dominant demersal taxa collected during the 2003-10 groundfish survey
subdivided into three subgroups (with, without, and unknown recruitment)
and summed overall (total). Regression equations for subgroups with signifi-
cant (P<0.05) or near significant (P<0.10) trends are displayed in the figure.
Significance levels (P) are listed in Table 2.
the North Pacific responded to climatic shifts. Major
changes in coastal ocean productivity of the eastern
Pacific are correlated with phase changes in the PDO
and cold eras are associated with enhanced productiv-
ity. Warm periods are correlated with low productivity
in the California current system (Brodeur and Ware,
1992; Hare and Francis, 1995) and the observed de-
crease in coastwide biomass could reflect the decline in
biomass during the most recent warm period (2003-06)
as productivity levels drop.
Results indicate that not only for individual species
but also for species grouped taxonomically or by depth
the change in biomass over time (2003-10) was vari-
able. For the slope rockfish group (aurora rockfish [Se-
bastes aurora], bank rockfish [Sebastes rufus], blackgill
rockfish, darkblotched rockfish, Pacific ocean perch,
redbanded rockfish, [Sebastes babcocki], rougheye rock-
fish [Sebastes aleutianus], and splitnose rockfish) and
the deepwater species group (mean depth >650 m)
composed of brown cat shark ( Apristurus brunneus),
Keller et al Variations in eastern North Pacific demersal fish biomass, 2003-10
217
shallow
55-183 m
shallow
55-183 m
in
a>
O
CD
CL
CO
18
i i
♦ *
18
16
* # * *
16
^=0.34
14
14
18
mid-depth
184-549 m
18
*
16
*
16
T"
14
y=-0.200x+417.2
14
d=0 49
12
12
15
deep
15
550-1280 m
14
i , } i
14
13
i * i
T I
13
12
1
r2=0 19
t *
12
11
11
18
all depths
18
55-1280 m
16
16
14
i
"r~
14
y=-0 160x+336 3
12
r*=0.45
12
2002 2004 2006 2008 2010
* i
y=1 086x+17 0
r*=0 71
mid-depth
184-549 m
^"1" *
y=0.757x+15.5
r'=0 68
deep
550-1280 m
1 i
f
all depths
55-1280 m
r*=0 20
y=0 691x+15 0
r>=0.80
-1 -0 5 0 0 5 1 1.5
Year
PDO
Figure 7
Coast-wide estimates of species richness (± standard error) by depth (shallow
55-183 m; mid 184-549 m; deep 550-1280 m; all depths combined 55-1280
m) versus year and annual Pacific Decadal Oscillation (PDO) values, 2003-10.
Regression equations for relationships with significant (P<0.05) trends are
displayed. Coefficients of determination (r2) are shown here for the regression
equations while significance levels ( P ) are shown in Table 2.
California slickhead ( Alepocephalus tenebrosus), deep-
sea sole (Embassichthys bathybius), giant grenadier
( Albatrossia pectoralis ), Pacific flatnose ( Antimora mi-
crolepis), Pacific grenadier ( Coryphaenoides acrolepis ),
roughtail skate (Bathyraja trachura), snakehead eel-
pout ( Lycenchelys crotalinus), twoline eelpout (Bothro-
cara brunneum), and grooved Tanner crab ( Chionoece -
tes tanneri), no significant relationship with time was
detected over the survey period. Thornyheads were the
sole group to exhibit an overall positive correlation
with time (with higher biomass occurring in recent
years). Shallow to mid-water species (mean depth <500
m) consisting of groups composed of cartilaginous fish,
flatfish, shelf roekfish, or a mixed subgroup of fish, and
overall biomass all significantly declined over time.
These declines occurred despite the classification of the
west coast fishery as having the lowest overall exploita-
tion rates of ten ecosystems examined by Worm et al.
(2009) and multiple management measures introduced
to reduce catch.
218
Fishery Bulletin 1 10(2)
80,000
60,000
40,000
20,000
Rockfish
■o— Flatfish
■*— Sablefish
o Thornyheads
400,000
300,000
200,000
100,000
B
-° Pacific hake
- Total groundfish
1980
1985
1990
1995
Year
2000
2005
2010
Figure 8
Landed catch (in metric tons, t) for (A) rockfish ( Sebastes spp.), flatfish,
sablefish ( Anoplopoma fimbria ), and thornyheads (Sebastolobus spp.),
and (B) total groundfish and Pacific hake (Merluccius productus), off
Washington, Oregon, and California from 1980 through 2010. Data
were obtained from the Pacific Fisheries Information Network: http://
pacfin.psmfc.org/pacfin pub/data.php (accessed September 2011). Note
the different scales for overall catch and Pacific hake (0-400,000 t) and
for the remainder of the species (0-80,000 t).
To evaluate the decline in overall groundfish biomass
indices off the western United States, despite much re-
duced fishing effort, we investigated two potential and
perhaps overlapping factors: depletion after strong re-
cruitment and environmental effects. Decreases in total
biomass indices occur within an ecosystem when catch
is greater than net population growth. To evaluate the
contribution of these factors to the observed decline in
biomass, we separated the species examined here into
subgroups based on the presence or absence of strong
recruitment during the late 1990s. We used information
contained in 24 stock assessments to assign species
to subgroups with strong recruitment (primarily in
1999), without strong recruitment, and with unknown
recruitment. We developed regressions models for each
subgroup and overall versus year, annual PDO indices,
and including both variables. We assumed that year
was a good proxy for depletion of exceptionally strong
cohorts as they recruited to the groundfish fishery in
subsequent years (Haltuch and Hicks, 2009). For mul-
tiple species with highly successful recruitment events
in 1999, there has been a gradual decline in biomass
since 2003. Regression models for total biomass of these
species were best fitted by time, whereas regression
models for those species without strong recruitment
were best fitted to variation in climate as measured by
annual PDO indices. Our analysis also indicated that
for models comparing summed biomass for groups with
Keller et at. Variations in eastern North Pacific demersal fish biomass, 2003-10
219
unknown recruitment and overall, the best fit incorpo-
rated both year and PDO indices combined. We think it
is important to note that for all subgroups, particularly
the group without species with known strong recruit-
ment events, there is still a significant decrease in
biomass indices over time. This decrease indicates that
the depletion of an exceptionally strong 1999 year class
for multiple species is not the only factor contributing
to decreasing biomass of west coast demersal fish spe-
cies. Despite the overall decline in biomass observed
in recent years, very few of the species examined here
are considered overfished (NMFS, 2009) and therefore a
loss of yield cannot be inferred. However, the continued
decline in overall observed biomass from 2003 to 2010,
despite enactment of multiple management measures
and reduced catch, emphasizes the need for the state
of the ecosystem to be considered when setting catch
limits. During periods of low ocean productivity, a pre-
cautionary approach is advised.
During the NWFSC bottom trawl survey, random,
rare, and very large tows of schooling rockfish are oc-
casionally taken. If a tow occurs near rocky but still
trawlable habitat when schooling rockfishes are pres-
ent, then very large catches of rockfish are possible
(Stewart, 2007). Occurrence of these large catches may
prevent detection of underlying biomass trends when
only a small number of years are available for analysis,
as with the 2003-10 survey. Although this phenomenon
appears clear for canary and widow rockfish, for spe-
cies like sharpchin and redbanded rockfish, it is harder
to clearly separate large rare tows from an increase
or decrease in biomass, and certainly random large
catches and biomass trends may be occurring at the
same time (Fig. 3).
The higher productivity associated with the cool PDO
phase in the California Current system may have re-
sulted in another strong recruitment event in 2008
(Ralston3) and if cool conditions continue, the associated
higher productivity could promote enhanced growth
and survival of groundfish. Our data indicate that the
return to cool conditions in 2007-10 was followed by a
slight increase in overall biomass in 2010 — an increase
that suggests a time lag between cool conditions and
increased demersal biomass within the California Cur-
rent. Although many rockfish species are long lived and
exhibit highly variable recruitment, previous studies
have additionally indicated that both rockfish recruit-
ment and juvenile growth respond to broad indicators
of productivity, and that juvenile abundance is corre-
lated with large-scale oceanographic events such as El
Niho-Southern Oscillation and the PDO (Ainley et ah,
1993; Laidig et ah, 2007). For now we find our results
interesting and recognize that the value of the ground-
fish survey increases with each annual increment and
over time will provide additional information to unravel
these relationships.
3 Ralston, S. 2011. Personal commun. National Marine
Fisheries Service, Southwest Fisheries Science Center, Fish-
eries Ecology Division. Santa Cruz, CA 95060.
The best models to describe the variation in species
richness (restricted to fish only) included the PDO indi-
ces (shallow, mid-depth, and overall) or a combination
of the PDO indices and year (deep depths) but were not
based on year alone. Species richness was positively
correlated with the annual PDO index indicating that
more species were present within the survey area and,
in particular, at shallow and mid-depth strata during
the warm phase of the PDO. Tolimieri and Levin (2006)
and Tolimieri (2007) examined patterns of diversity in
groundfish assemblages in relation to depth (200-1200
m) and latitude (33-37° N) along the U.S. Pacific Coast.
They found, as we did, that species richness declined
with depth (Fig. 7), but they did not examine changes
over time or in relation to climate indices. However,
their observation that patterns of diversity were cor-
related with temperature may partially support our ob-
servation of elevated species richness during the warm
phase of the PDO. Tolimieri (2007) points out that lati-
tude and depth are factors well known to correlate with
diversity and assemblage structure. Both species rich-
ness and biomass decreased along the U.S. west coast
for demersal groundfish as the PDO index shifted from
a warm to a cool phase. Given that the swept area per
haul remains constant, fewer species per haul are ex-
pected if the underlying population densities decrease;
however, as demonstrated above, the densities for the 62
most abundant species exhibited variable trends from
2003 through 2010.
Mueter and Litzow (2008) provided convincing evi-
dence of climate-linked changes in the distribution of
demersal fishes in the Bering Sea (1982-2006), cou-
pled with reorganization in community composition by
latitude. They observed increases in both biomass and
species richness in an area characterized by warming
temperatures. Community-wide patterns indicated that
taxa shifted northward and also were captured with
increasing frequency at shallower stations from 1982 to
2006. Interestingly, they noted that mean species rich-
ness significantly increased within the portion of the
survey area termed the “cold-pool” as it warmed over
time, in a similar relationship to that observed here.
Species richness along the U.S. west coast was elevated
during the warmer phase of the PDO and lower during
the cool phase. Changes in species richness are most
likely caused by movement of species in response to
environmental conditions (Trenkel and Cotter, 2009).
Understanding the mechanisms underlying the ob-
served relationships between biomass indices, species
diversity, depletion, and climate remains a challenge.
However, Brodeur et al. (2008) recently related varia-
tions in the abundance of dominant ichthyoplankton in
the northern California Current to oceanic and climatic
indices, thus providing a link between climate and
recruitment success. Both larval concentrations and
diversity varied on a semidecadal basis in conjunction
with fluctuations in the PDO. Zheng and Kruse (2006)
found some evidence that recruitment variation in east-
ern Bering Sea crabs may be related to climate forcing,
although interaction with groundfish predators likely
220
Fishery Bulletin 1 10(2)
contributed to recruitment success as well. Brodeur
and Ware (1992) and Sugimoto and Tadokoro (1997)
both demonstrated changes in zooplankton biomass and
distribution coupled with changes in ocean conditions,
and Lenarz et al. (1995) reported reduced primary pro-
ductivity and zooplankton biomass, coupled with poor
rockfish recruitment off the west coast during El Nino
events. Recruitment success of salmon within the north-
ern California Current system has previously been tied
to fluctuations in the PDO index (Mantua et ah, 1997).
Hollowed et al. (2001) examined the timing of the PDO
and ENSO and correlated changes in both with recruit-
ment success in groups such as flatfish and gadoids,
but further study is needed to confirm their findings.
Like others, they caution that additional factors could
also affect biomass, species richness, and distributional
changes such as density-dependent habitat selection,
timing of migrations, changes in local currents, catch-
ability, and shifts between nursery and feeding grounds
(Swain et al., 1994; Delworth et al., 1997; Attrill and
Power, 2002; Magill and Sayer, 2002). The interactions
among climate variability, currents, and the seasonal
strength of upwelling and downwelling is particularly
interesting given our prior research where we directly
linked changes in demersal biomass and species diver-
sity to depressed oxygen levels along the Oregon shelf
(Keller et al., 2010).
At the species level, changes appear driven by cli-
mate-induced variation in primary and secondary
productivity and recruitment (Beaugrand et al., 2003;
Steingrund and Gaard, 2005), although the nature of
the relationship has not been deciphered. We recognize
that the relatively short time series examined may in-
crease the likelihood that the results are spurious. The
observed tight correlations between total and grouped
groundfish biomass indices and the PDO are expected if
the underlying relationship results from reduced popu-
lation growth due to poor environmental conditions or
if environmental conditions, such as phase shifts in
regional climate (Mantua et al., 1997; Hollowed et ah,
2001; Castonguay et al., 2008) are also coupled with pe-
riodic strong recruitment events, such as the emergence
of the 1999 year class.
Natural mortality for species with relatively high
natural mortality rates could play an additional role
in declining biomass indices. However, for rockfish with
low natural mortality rates, it is likely that growth
would be more important than mortality during most of
the time period. The relationship between the PDO and
biomass indices may also be due to changes in catch-
ability or selectivity, rather than to actual population
changes. A decrease could be due to higher selection
of younger fish (i.e., to peak selection around age 4 or
5 and a decline afterwards). Although understanding
the mechanisms supporting the relationships observed
here remains problematic, our results demonstrate the
importance of incorporating environmental conditions
in management decisions. Despite enactment of highly
effective management measures and the occurrence of
periodic strong recruitment, biomass indices declined
as oceanographic conditions changed throughout much
of the survey period.
Acknowledgments
The authors are indebted to the captains and the crew
of the chartered fishing vessels Excalibur, Ms. Julie ,
Noah’s Ark, Raven , B. J. Thomas, and Sea Eagle for
providing at-sea support. We especially thank the West
Coast Groundfish Bottom Trawl Survey team (V. Simon,
M. Bradburn, K. Bosley, D. Kamikawa, V. Tuttle, J.
Buchanan, and J. Harms) for their skill and dedication
in collecting high quality data for the groundfish survey
and C. Whitmire for preparing GIS charts.
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223
A note on the von Bertalanffy
growth function concerning
the allocation of surplus energy
to reproduction
Shuhei Ohnishi (contact author)1
Takashi Yamakawa2
Hiroshi Okamura3
Tatsuro Akamine4
Email address for contact author: ohnishi @scc.u-tokai.ac. ip
1 School of Marine Science and Technology
Tokai University
Shimizu, Shizuoka 424 8610, Japan
2 Graduate School of Agricultural and Life Sciences
The University of Tokyo
Bunkyo, Tokyo 1 13-8657, Japan
3 National Research Institute of Far Seas Fisheries
Fisheries Research Agency
Yokohama, Kanagawa 236-8648, Japan
4 National Research Institute of Fisheries Science
Fisheries Research Agency
Yokohama, Kanagawa 236-8648, Japan
Abstract— We propose an extended
form of the von Bertalanffy growth
function (VBGF), where the allocation
of surplus energy to reproduction is
considered. Any function can be used
in our model to describe the ratio of
energy allocation for reproduction to
that for somatic growth. As an exam-
ple, two models for energy allocation
were derived: a step-function and a
logistic function. The extended model
can jointly describe growth in adult
and juvenile stages. The change in
growth rate between the two stages
can be either gradual or steep; the
latter gives a biphasic VBGF. The
results of curve fitting indicated
that a consideration of reproduc-
tive energy is meaningful for model
extension. By controlling parameter
values, our comprehensive model gives
various growth curve shapes ranging
from indeterminate to determinate
growth. An increase in the number of
parameters is unavoidable in practical
applications of this new model. Addi-
tional information on reproduction
will improve the reliability of model
estimates.
Manuscript submitted 17 February 2011.
Manuscript accepted 5 December 2011.
Fish. Bull. 110:223-229(2012).
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
The von Bertalanffy growth function
(VBGF) has been used to analyze
somatic growth data in a wide range
of studies. It is now commonly put into
practice to partially reparameterize
the VBGF parameters to avoid their
covariation and to ensure statistical
accuracy (Quinn and Deriso, 1999).
Although variations in the growth
rate influenced by extrinsic envi-
ronmental fluctuations have been
examined in many studies, we pro-
pose that intrinsic physiological dy-
namics are also of great importance.
The interaction between growth rate
and sexual maturation has often
been debated in life history studies
(Roff, 1984; Beverton, 1992; Jensen,
1996). Mathematical treatments for
representing the switch in energy
allocation between growth and re-
production have been introduced to
discuss optimal life-history strategies
(Roff, 1983; Kozlowski, 1992, 1996;
Kozlowski and Teriokhin, 1999).
When the energy budget of fishes
has been quantified, dynamics of the
energy distribution between growth
and reproduction have often been
considered in simulated scenarios
(Jorgensen and Fiksen, 2006; Pec-
querie et ah, 2009).
The effects of reproductive energy
should also be important for practi-
cal curve fitting studies. A biphasic
growth model derived by connect-
ing two independent VBGFs at an
arbitrary age is often employed for
curve fitting (Soriano et ah, 1992;
Porch et ah, 2002; Araya and Cu-
billos, 2006; Quince et ah, 2008a,
2008b; Alos et ah, 2010; Tribuzio et
ah, 2010). Although a biphasic VBGF
is one approach used to account for
inflections in growth and is similar
to the higher-parameter model (Sch-
nute and Richards, 1990), results of
model selection based on the Akaike
information criterion (AIC; Akaike,
1973) often indicate that the bipha-
sic VBGF is a more suitable model
than the original monophasic VBGF
(Porch et ah, 2002; Araya and Cubil-
los, 2006; Tribuzio et ah, 2010). The
better fit implies that the delay in
growth due to a reallocation of en-
ergy may be detected as a change in
the growth trajectory.
224
Fishery Bulletin 1 10(2)
Day and Taylor (1997) and Czarnot^ski and Kozlowski
(1998) identified the lack of an explicit formula for the
reproductive process in the VBGF. Although the bipha-
sic VBGF is an empirical approach, a deductive model
that can incorporate both growth and reproduction
should be developed to help to understand the process of
energy allocation and to improve curve fit. In this study,
we begin with an extension of the VBGF with respect
to a continuous change in energy allocation. We also
present an application of curve fitting and model selec-
tion. An overview of changes in growth-curve shapes
is subsequently shown. Finally, we briefly discuss the
features of our model.
Methods
We start with the general form of VBGF given by
= hw2/> - kw, (1)
dt
where w, t, h, and k are body weight, age, and coefficients
of anabolism and catabolism, respectively. The right
hand side of Equation 1 is the total production rate of
surplus energy.
If we consider the reallocation of surplus energy for
reproduction. Equation 1 can be expanded as
— + c- = hw2/3-kw. (2)
dt dt
Two newly introduced terms, f and c, denote the cumula-
tive energy investment for reproduction until age t and
the conversion factor of reproductive energy to body
weight, respectively. Note that f is not equivalent to the
weight of the gamete (i.e., eggs or spermatozoa). Equa-
tion 2 is equivalent to the exoskeleton growth model
(Ohnishi and Akamine, 2006) in that energy allocation
to activities or appendages unrelated to somatic growth
are explicitly described.
Suppose w=fH3, where l is body length and /3 is a con-
stant proportionality coefficient. Dividing dw/dt=3pl2dl/
dt by each side of Equation 2 and substituting w=f3l3
yields the following equation:
ctt
dt
dw df 'l
+ c — —
dt dt )
K(L-l),
(3)
where K=k/3 and loa(=hk~1j3~1/3) is the asymptotic length.
Let p be the ratio (0trn), where 0tm )
Equation 7 represents the time delay to attain a cer-
tain body size in t>tm due to change in energy alloca-
tion. Consequently, the growth curve becomes biphasic,
combining two independent VBGFs.
The alternative model assumes that pit) changes
continuously throughout an individual’s lifetime. In
particular, an S-type curve that has an inflection point
around t=tm is suitable for describing a change in pit)
due to sexual maturation. Let pit) be p(D = u/(l+exp(-
ait-tm))) as a general logistic curve such that the ana-
lytical solution for Tit) is given by
T(t) = (l-o)(t-t„)- (g)
where u and a are the upper limit of the allocation rate
in reproductive energy and the rapidity of maturation,
respectively. The logistic function converges to a step-
function when a—> By inspection, Equation 7 is a
special case of Equation 8.
The solution for Equation 5 is complicated when
pspil) (or p^piw)) such that
|0£i,<" ,9)
The explicit solution for / is a biphasic VBGF when pit)
is a step-function that has discontinuous switching at
Ohmshi et al.: The von Bertalanffy growth function concerning the allocation of surplus energy to reproduction
225
Table 1
Parameter estimates for two types of von Bertalanffy growth functions (VBGFs). Both types of VBGF have three common
parameters: asymptotic length (/„), growth coefficient (K), and initial condition of age (t0). Additional parameters, namely age
at maturity (tm), the upper limit of the allocation rate in reproductive energy (u), and rapidity of maturation (a), were used in
the extended model. Values within parentheses show the square root of the variance of the estimates derived from the matrix
inverse of the Hessian matrix. The 4AIC shows the relative difference of the Akaike information criterion (AIC) value compared
with the minimum AIC.
Type of VBGF
L
K
^0
V
a
AIC
4AIC
Conventional model
260.72
0.34
-0.45
—
—
—
26881.3
20.8
(1.51)
(0.01)
(0.04)
Extended model
463.01
0.15
-0.77
3.41
0.79
1.01
26860.5
0
(49.08)
(0.01)
(0.14)
(0.35)
(0.05)
(0.21)
the boundary of mature
“size.” In most
cases,
however,
Results
it is not easy to obtain an explicit solution, as shown in
Equation 6, owing to the complexity of the integrand in
Equation 9.
Allocated reproductive energy can be derived as fol-
lows by rearranging Equation 4 with the condition
p^pit) as
(i-PMEf-PWf.
Substituting dwldt=3fH2dlldt and Equation 5 into this
equation, one obtains
f = 3-frW(L-D,
f = ^~ J'p(s)/2 {L-l)ds.
(10)
(11)
Equation 10 represents the instantaneous reproduc-
tive energy at age t. Equation 11 shows the cumulative
investment of reproductive energy until age t. Thus,
changes in two quantities (body size in Eq. 6 and energy
investment in Eq. 10) are treated in an extended VBGF.
We fitted the growth curve in Equations 6 and 8 to
individual measurements in length-at-age as Ll(i = 1,...,
AD, where N is the total number of samples. Param-
eters were estimated by minimizing the residual sum of
squares ofS=X, iL-lf2. The numerical optimization for
S was accomplished by using the quasi-Newton method
(BFGS algorithm) in “optim( )” with R statistical soft-
ware (R Development Core Team, 2011). The comparison
between this model and the original monophasic VBGF
was based on the AIC value as follows: AIC=ATogS+20,
where 8 is the number of free parameters.
We used measurement data on willowy flounder
( Tanakius kitaharai) males collected by bottom-trawl
surveys in the coastal area of Fukushima Prefecture,
Japan, from 2004 to 2006. The sample size was n=2169.
Age ranged from 1.38 to 14.30 years and length ranged
from 113 to 298 mm (standard length). Otoliths were
used to determine yearly ages and dates of birth were
assigned as January 1st.
Results for curve fitting and model selection are sum-
marized in Table 1 and Figure 1. As shown in Table 1,
the AIC difference (AAIC=20.8) between the two types of
VBGF suggests that the trajectory given by the extended
model more appropriately described lifetime growth.
This result implies that a consideration of reproductive
energy can be meaningful for model extensions. The
variance of two common parameters (i.e., and t0) in the
extended model was larger than that in the conventional
VBGF (Table 1).
Twelve types of energy allocation schedules, pit), and
the corresponding somatic growth (in length /) based
on different combinations of parameter values in Equa-
tion 8 (u = Q.4, 0.6, 0.8, 1.0 and a- 1, 3, 100) are shown
in Figure 2. The behavior of df/dt and f describing the
energy investment in reproduction is shown in Figure 3.
When v = 0, the growth curve is identical to the origi-
nal VBGF (Fig. 2). Although somatic growth curves
generated by lower v values (i.e., v = 0.4, Fig. 2, A-C) do
not differ substantially from the original VBGF, there
are distinctive differences for shapes with higher v
values (i.e., u = 0.8, 1.0, Fig. 2, G-L). In these cases, the
somatic growth of the adult and juvenile stages can be
clearly distinguished. Gradual but steady growth after
maturation is typical with indeterminate growth (Fig.
2, A-I). We can see a continuous phase shift of inde-
terminate growth in Figure 2. When u = 1.0, the growth
rate after maturation converges to zero because most
surplus energy is devoted to reproduction, generating
more determinate growth (Fig. 2, J-L).
The variation in a leads to a difference in the degree
of continuity of growth rate during the sexual matura-
tion transition period (Fig. 2, A, D, G, and J vs. Fig.
2, C, F, I, and L). The curves given by sufficiently high
a (a = 100) represent biphasic VBGF resulting from an
abrupt change in growth rate around age tm (Fig. 2, C,
F, I, and L).
In Figure 3, an apex can be found on the convex
shape of df/dt, and the height and degree of curvature
changes according to the values of v and a. An increase
in the value of v raises the reproductive investment df!
226
Fishery Bulletin 1 10(2)
Figure 1
Two types of growth curve estimated with the conventional von Bertalanffy growth
function (A) and with the extended von Bertalanffy growth function (B) for individual
measurement data of the willowy flounder ( Tanakius kitaharai ) male (n = 2169).
dt and shifts the maximum df/dt to older ages. When
u = 1.0 (Fig. 3, J-L), df/dt converges to a constant value
after maturation as a result of determinate growth and
constant surplus energy, defined by Equation 2. Lower
a values show a slower initial rise in df/dt around tm
(Fig. 3, A, D, G, and J), whereas higher a values yield
a steeper initial rise in df/dt around tm (Fig. 3, C, F,
I, and L).
Discussion
A notable feature of our mode! is that energy allocation
can be quantified by the arbitrary functional form p(-).
The introduction of p(-) provides a unified platform to
treat the trade-off between somatic growth and repro-
duction. The extended model can jointly describe adult
and juvenile growth. The change in growth rate between
the two stages can be either gradual or steep, with the
latter case showing a biphasic VBGF. By controlling the
value of p(-), our comprehensive model yields various
shapes of growth curves that range from indeterminate
to determinate growth. Therefore, our model can be
used for life history studies, as well as practical curve
fitting studies. When allocation dynamics are not fully
described by a simple model, such as seen in Equation
8, additional parameters beyond a, u, and tm, or a par-
ticularly designed form ofp(-) would be useful for further
model development.
The extended VBGF in Equation 5 can theoretically
incorporate an unlimited number of parameters. How-
ever, an increase in the number of free parameters
in p(-) will be disadvantageous for model estimation
because the functional form of p(-) does not directly
appear in the age-length relationship. Increases in the
variance of estimates imply instability due to curve
fitting (Table 1). Therefore, it is necessary to consider
methods of overcoming the trade-off between an in-
creased number of parameters and estimation stability.
Data sets other than those for length-at-age data will
be useful for estimating the parameters in p(-) because
the dynamics of p(-) are readily apparent in the behav-
ior of df/dt (Fig. 3) rather than in length (Fig. 2). We
expect that the robustness of this estimation will be
improved by means of a combined likelihood-function
(Martin and Cook, 1990; Eveson et ah, 2004) described
by two heterogeneous relationships: length-at-age and
reproductive energy-at-age.
Our model development has similarities to that of
Lester et al. (2004). Both studies explicitly give a
growth function that can quantify a delay in somatic
growth due to reproductive energy allocation. Lester
et al. (2004) initially assumed a linear function of pre-
mature growth in length and derived the conventional
VBGF by introducing an intensive energy investment
at postmature ages. Additionally, Lester et al. (2004)
assumed that the ratio of gonad to body weight at
postmature ages is constant. This assumption causes
the linear function to yield a delay in growth after
maturation equivalent to that yielded with the VBGF.
Alternatively, our model derivation started from the
VBGF. Additional hypotheses for model formulation
other than ours and those of Lester et al. (2004) are
possible. Hence, the adequacy of these assumptions for
model derivation must be evaluated with a wide range
of practical applications.
Ohnishi et al.: The von Bertalanffy growth function concerning the allocation of surplus energy to reproduction
227
A ( 0 4 . 1 ) B ( 0.4 , 3 ) C ( 0 4 . 100 )
Age (years)
Figure 2
Variation in somatic growth due to differences in energy allocation (A-L).
Thick, thin, and broken lines correspond to growth in length lit), growth
in length lit) with u = 0 (i.e., the original von Bertalanffy growth function),
and the energy allocation function pit), respectively. The numerical values
in parentheses correspond to the combination of parameters r(=0.4, 0.6, 0.8,
1.0) and a( = l, 3, 100) employed in pit). The parameters used for all cases
include the following: fl=1.0, c = 1.0, A = 0.15, 7^ = 1.0, t0= 0, and tm = 5.
Acknowledgments
The authors would like to thank to Y. Narimatsu of
Tohoku National Fisheries Research Institute, Fisher-
ies Research Agency, who provided us with the willowy
flounder data for analysis. The authors also express
our gratitude to the anonymous reviewers who provided
valuable, insightful suggestions for the improvement of
the manuscript.
228
Fishery Bulletin 1 10(2)
o
0
N
in
tt>
3
CD
l3
0 5 10 15 20 25 300 5 10 15 20 25 300 5 10 15 20 25 30
Age (years)
Figure 3
Variation in variables related to reproduction that result from differences in energy
allocation (A-L). Thick, thin, and broken lines correspond to the instantaneous
reproductive energy df/dt, cumulative energy investment fit), and the energy alloca-
tion function pit), respectively. The numerical values in parentheses correspond to
the combination of parameters i>(=0.4, 0.6, 0.8, 1.0) and a( = l, 3, 100) employed in
pit). The parameters used for all cases include the following: /3= 1.0, c = 1.0, /f=0.15,
Z„=1.0, t0=0 , and tm = 5.
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Abstract — Gillnet mesh selectivity
parameters were estimated for juve-
nile blacktip sharks ( Carcharhinus
limbatus) by using length data from
an experimental fishery-independent
gillnet survey in the northeastern
Gulf of Mexico. Length data for 1720
blacktip sharks were collected over 17
years (1994-2010) with seven mesh
sizes ranging from 7.6 to 20.3 cm.
Four selectivity models, a normal
model assuming fixed spread, a
normal model assuming that spread
is proportional to mesh size, a log-
normal model, and a gamma model
were fitted to the data by using the
SELECT (share each length’s catch
total) method. Each model was run
twice under separate assumptions
of 1) equal fishing intensity; and 2)
fishing intensity proportional to mesh
size. The normal, fixed-spread selec-
tivity curve where fishing intensity is
assumed to be proportional to mesh
size provided the best fit to the data
according to model deviance estimates
and was chosen as the best model.
Results indicate that juvenile blacktip
sharks are susceptible as bycatch in
some commercial gillnet fisheries.
Manuscript submitted 23 March 2011.
Manuscript accepted 23 December 2011.
Fish. Bull. 110:230-241 (2012).
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
Gillnet selectivity for juvenile blacktip sharks
( Carcharhinus limbatus )
Ivy E. Baremore (contact author)1
Dana M. Bethea1
Kate I. Andrews2
Email address for contact author: Ivy. Baremore (Sjnoaa.qov
1 Panama City Laboratory
Southeast Fisheries Science Center
National Marine Fisheries Service
National Oceanic and Atmospheric Administration
3500 Delwood Beach Rd
Panama City, Florida 32408
2 Beaufort Laboratory
Southeast Fisheries Science Center
National Marine Fisheries Service
National Oceanic and Atmospheric Administration
101 Pivers Island Road
Beaufort, North Carolina 28516-9722
In the late 1980s, a gillnet fishery
for sharks developed in the Atlantic
Ocean off the coasts of Florida and
Georgia (Trent et ah, 1997). Fishing
area varied with seasons, and shark
drift gillnet vessels operated in near-
shore waters between 4.8 and 14.4
km offshore, ranging from West Palm
Beach, Florida (~26°46'N), to Alta-
maha Sound, Georgia (~31°45'N). A
variety of methods were used to deploy
gillnets, including drifting the net on
the surface (Trent et ah, 1997), strik-
ing around a school of sharks (Carlson
and Baremore1), and anchoring the
net to the bottom (Carlson and Bethea,
2007). Fishermen targeted a variety of
coastal species of sharks, from black-
tip sharks (Carcharhinus limbatus)
to Atlantic sharpnose sharks (Rhi-
zoprionodon terraenouae ) depending
on market conditions and fishery clo-
sures. Over the last 10 years, the size
and scope of the commercial shark
gillnet fishery has decreased primar-
1 Carlson, J. K., and I. E. Baremore.
2003. The directed shark gillnet fish-
ery: catch and bycatch 2003, NOAA Sus-
tainable Fisheries Division Contribution
PCB-03/07, 8 p. Panama City Labora-
tory, National Marine Fisheries Service,
Panama City, Florida. (Available from
http://www.sefsc.noaa.gov/labs/panama/
documents/observer documents/gillnet/
SDG2003.pdf. accessed December 2011.]
ily owing to regulations that restrict
gear, fishing areas, and trip limits for
sharks. In 2008, Amendment 2 to the
Consolidated Atlantic Highly Migra-
tory Species Fishery Management
Plan (NMFS, 2008) limited landings
of large coastal sharks to 33 sharks
per trip. The high cost of fuel and
low market value for shark meat, in
conjunction with these regulations,
caused most commercial fishermen
in the U.S. south Atlantic Ocean to
abandon the gillnet fishery for sharks.
Although shark-targeted gillnet
trips are currently rare in the U.S.
Atlantic Ocean, blacktip sharks are
still caught as bycatch in other gill-
net fisheries that target species such
as Spanish mackerel ( Scomberomorus
maculatus ) and king mackerel (S. ca-
valla) (Passerotti et ah, 2010; Thorpe
and Frierson, 2009). These coastal
teleost gillnet fisheries are expansive,
and had more than 65 active fishing
vessels in 2010. 2 The fishing locations
of these vessels span the U.S. east
coast throughout the range of the
blacktip shark.
The blacktip shark is a cosmopoli-
tan species, ranging from Massachu-
2 Southeast Fisheries Science Center
Coastal Fisheries Logbook, available
at http://www.sefsc.noaa.gov/fisheries/
reporting.htm. accessed March 2011.
Baremore et at: Gillnet selectivity for |uvenile Carcharhinus limbatus
231
setts throughout the Gulf of Mexico in U.S. coastal wa-
ters (McEachran and Fechhelm, 1998). Juvenile blacktip
sharks use nursery areas such as bays and nearshore
habitats during spring and summer months (Castro,
1993; Heupel and Hueter, 2002). Because of their range
and life history characteristics, juvenile blacktip sharks
are likely to encounter commercial gillnets.
Gillnet selection curves are a useful way to represent
the retention probabilities of different mesh sizes for a
given species of fish. Retention probability by gillnets
is usually considered to be dome-shaped and can be
described by the equation
where r-(Z-) is the retention probability that a fish of
length l in size class i is caught by mesh size j, and n and
a represent the mean and spread of the curve (Millar
and Fryer, 1999). However, the selection curve may be
skewed because of snagging, rolling, or entangling of
animals, and can result in a gamma or lognormal curve
(Millar and Fryer, 1999).
Generally, selectivity can be measured in two ways:
directly and indirectly (Millar and Fryer, 1999). Direct
experiments are performed on a population for which
the size distribution is known, and size selection is
calculated by comparison of the population with the
catch distributions. Indirect, or comparative, experi-
ments are more common and usually involve simulta-
neously fishing gillnets of differing mesh sizes with
equal effort.
Commercial fishing gear selectivity curves are in-
corporated into modern stock assessment models,
and changes in the parameters have the potential
to impact the assessed status of the stock (Maunder,
2002). Size selectivity is used in the estimation of the
length-frequency of a stock, estimation of fishing-in-
duced mortality, and in age-based assessment models
(Millar and Fryer, 1999). Although important for the
stock assessment models, fishery-independent selec-
tivity models are rare for many large shark species
(McAuley et al., 2007). Selectivity for bycatch species
is also becoming an important issue in stock assess-
ment, but direct estimates are likewise rare for most
fisheries. The goal of this study is to determine the
relationship between gillnet mesh size and selectivity
for juvenile blacktip sharks using fishery-independent
data.
Materials and methods
Sampling
Data necessary for indirect calculation of gillnet mesh
selectivities were obtained from the Gulf of Mexico
Shark Pupping and Nursery (GULFSPAN) survey,
which is a fishery-independent gillnet survey of coastal
shark populations in the northeastern Gulf of Mexico
(Carlson and Brusher, 1999). Catch data for C. lim-
batus were collated over 17 years (1994-2010) from
five bay systems in northwest Florida: St. Andrew Bay,
Crooked Island Sound, St. Joseph Bay, the gulf side
of St. Vincent Island, and Apalachicola Bay (Fig. 1).
Six gillnet panels of differing stretched mesh sizes
were strung together in increasing mesh size, anchored,
and fished concurrently as a single gillnet. Each panel
was 30.1 m long and 3.4 m deep (Table 1). From 1994
through 2005, stretched mesh sizes ranged from 8.9 cm
to 14.0 cm, increasing by 1.3-cm (0.5-in) intervals, with
an additional panel of 20.3 cm. In 2006, the 20.3-cm
panel was removed and a 7.6-cm panel was added ad
hoc. The largest mesh panel was removed because of
its historically low catch of juvenile small coastal shark
species, and the 7.6-cm panel was added to increase
catch of small neonatal sharks. Unless otherwise indi-
cated, all mesh sizes reported in the present study are
stretched mesh sizes.
Sampling occurred each year from late March
through October. Net set locations within bay sys-
tems were randomly chosen over a variety of habitat
and depth combinations. The majority of sets were
short (<1 hr) as a means of reducing mortality, es-
pecially when water temperatures were above 25°C.
However, some nets were soaked for longer periods
of time, depending on the research priorities at the
time. Captured sharks were removed from the net,
their sex was determined, and they were measured
for fork length (FL) on a rigid measuring board in a
straight line from the tip of the nose to the fork in
the tail. Sharks in poor condition were sacrificed for
research projects and those in good condition were
tagged and released. Maturity state was determined
by clasper calcification for males, internal examina-
tion for sacrificed female sharks, and released females
were considered to be mature when greater than 115
cm FL (Carlson et al., 2006). Sexes were combined
for data analyses.
Data analysis
Catch data were pooled by mesh size into 5-cm-FL size
bins, and the midpoint of each size class (/,) was used to
calculate a selectivity curve for each mesh size (Millar
and Holst, 1997). Four gillnet selectivity models were
fitted to the /( for each mesh size (m) (Millar and Holst,
1997), by using the SELECT (share each length’s catch
total) method (Millar and Holst, 1997; Millar and Fryer,
1999; Millar, 2003, 2010). The selection curves were
fitted to the data by using the “gillnetfunctions” package
in R statistical software (Millar, 2003, 2010; R Develop-
ment Core Team, 2009). The SELECT method applies
the method of maximum likelihood, which estimates
selectivity parameters from a general log-linear model.
The expected catch of sharks of length class i in gillnet
j is described by
vu = PArj’ (1)
232
Fishery Bulletin 1 10(2)
85°30,0’,w 84°20'0"W
Figure 1
Location of the Gulf of Mexico Shark Pupping and Nursery (GULFSPAN) survey in
northwest Florida. Sampling sites were located in St. Andrew Bay, Crooked Island
Sound, St. Joseph Bay, the gulf side of St. Vincent Island, and Apalachicola Bay.
Sampling occurred from 1994 through 2010.
Table 1
Gillnet specifications used in the Gulf of Mexico Shark Pupping and Nursery (GULFSPAN) survey 1994-2010. For all net con-
figurations, the hanging ratio (length to height ratio of the meshes) was 0.5, leadline weight was 4.5 kg, 2.3 kg of buoyancy was
used, and each panel length was 30.1 m.
Stretch mesh
size (in/cm)
Twine
size no.
No. of
meshes deep
Thickness of twine
(mm)
Breaking strength
(kg)
Years fished
3. 0/7.6
208
45
0.52
11.8
2006-2010
3. 5/8. 9
208
40
0.52
11.8
1994-2010
4.0/10.2
208
35
0.52
11.8
1994-2010
4.5/11.4
208
35
0.52
11.8
1994-2010
5.0/12.7
277
30
0.62
18.2
1994-2010
5.5/14.0
277
25
0.62
18.2
1994-2010
8.0/20.3
24
20
1.00
115.9
1994-2005
where pf = the relative fishing intensity of gillnet j:
A- = the abundance of sharks in length class i;
and
r = the selection curve for each gillnet j.
Relative fishing intensity represents fishing effort and
fishing intensity combined and is the conditional prob-
ability that a fish contacted gillnet pane! j, with the
assumption that it made single contact with the entire
combined gillnet panel (Millar, 1992). The normal,
gamma, and lognormal models observe geometric simi-
larity (mean p • and spread a, proportional to mesh size),
whereas the normal model with fixed spread is not geo-
metrically similar (mean p ■ and spread cr equal across
mesh sizes). When p; is assumed to be equal among
mesh sizes, the form of the log-linear model is as follows:
1 o g ( v J = fac to r ( /, ) + /3, fx ( m J , j ) + /J2 ■ f2 ( m J , j ) , ( 2 )
Baremore et at. Gillnet selectivity for |uvenile Carcharhmus limbatus
233
Table 2
Selectivity curves for normal, gamma, and lognormal models used to estimate gillnet selectivity for blacktip sharks (Carcharhi-
nus limbatus ): m/ is the mesh size for panel j ( j= 1-7 panels) and /( is the midpoint of length class i (i= 1-22 length classes). Rela-
tive fishing intensity is modeled separately. Equations in the right hand column are the last two terms in the log-linear model
Model
Selection curve
A /i("vO+A
Normal:
fixed spread
exp
( / \2
(, l,-k m J
2cx
2
Normal:
proportional
spread
exp
{l, ~ai mj)~
2 a2
2
Gamma:
proportional
spread
/,
(a-l)-k-m
a- 1 -
[«-!]■
/,
m
j
Lognormal:
proportional
spread
-exp
^+logl H y
log(/,)-p, - log
2d"1
^ | log (Z,.)- log
— 1 --log'
m, J 2
Table 3
Equations used to estimate the modal length of blacktip
sharks (Carcharhinus limbatus) caught with each gillnet
mesh size {mf for all four gillnet selectivity models.
Model
Mode
Normal (fixed and
proportional spread)
Mode (m])=k-mJ
Gamma
Mode (m )=(a- 1) • k-m}
Lognormal
f m \
Mode (m,)=exp(ii-a2) • —
J ^ r l ml )
where factor (l J indicates that length class is fitted as
a factor of the model, and fx(mt, j) and f2(mr j ) are the
selectivity functions of rrij and j (right hand column of
Table 2). When p is assumed to be proportional to mesh
size (logp-=logm-j, the form of the log-linear model is as
follows:
l°g ( vu ) = log (my) + factor (/,) +
Pif,(mJ,j) + l32f2(mJj).
The parameters /3; and /3, are related to the form of the
selectivity curve and are defined in Table 2. The follow-
ing assumptions were made for all models: 1) catches
were independent; and 2) gillnet panels were fished
with equal effort. The mode, or maximum selected size
for each panel, was calculated according to equations
listed in Table 3. All models were fitted to the data twice,
once under the assumption of equal fishing intensity
and again under the assumption of fishing intensity
proportional to mesh size. Overdispersion, or lack of
fit, was tested by calculating the dispersion parameter,
which is the model deviance divided by the degrees of
freedom. When the dispersion parameter is >1, data are
considered to be overdispersed.
Results
A total of 1720 blacktip sharks were measured from 1994
through 2010 (Table 4). Blacktip sharks were collected
during 14 of the 17 years of the survey. Average net soak
time was 2.67 hr (range: 0.17-23.83 hr) over 1573 sets.
Some outliers were excluded when sampling protocol
was considered to be out of the ordinary procedure. The
majority (97%) of blacktip sharks caught in all panels
were immature and less than 110 cm FL (mode = 65 cm
FL, Fig. 2). There was a general increase in the mean
size of blacktip sharks with increasing mesh size. For
the panels that were fished concurrently for all years
(8.9-14.0 cm mesh), the total sample sizes of measured
sharks were similar (Table 4).
234
Fishery Bulletin 1 10(2)
Table 4
Length distribution for all blacktip sharks ( Carcharhinus
limbatus ) caught in each gillnet mesh panel in the Gulf
of Mexico Shark Pupping and Nursery (GULFSPAN)
Survey in northwest Florida, 1994-2010.
Fork Mesh sizes (cm)
lengin
(cm)
7.6
8.9
10.2
11.4
12.7
14.0
20.
42.5
4
4
4
0
1
0
0
47.5
2
10
14
18
6
4
0
52.5
13
25
29
57
21
22
2
57.5
13
20
32
53
30
43
3
62.5
12
24
21
41
63
41
0
67.5
7
30
31
29
47
31
4
72.5
6
26
30
34
20
32
6
77.5
13
36
34
21
30
20
5
82.5
8
15
20
22
17
28
7
87.5
4
29
24
34
25
16
9
92.5
7
15
17
20
14
15
9
97.5
0
14
12
15
13
14
18
102.5
2
4
16
15
9
8
17
107.5
0
10
5
1
5
4
12
112.5
0
1
0
2
4
0
6
117.5
0
2
0
4
3
0
8
122.5
0
2
1
1
0
0
4
127.5
0
0
0
1
0
0
2
132.5
0
0
0
0
0
0
0
137.5
0
0
0
0
0
0
2
142.5
0
0
0
1
0
0
1
147.5
1
0
1
0
0
0
0
Totals
92
267
291
369
308
278
115
The normal, fixed-spread models had the lowest model
deviance overall, with the model incorporating fishing
intensity proportional to mesh size having the low-
est total model deviance (Fig. 3, Table 5). The ratio of
model deviance to degrees of freedom was higher than 1
(2.9), indicating overdispersion of the data. This result
indicates that blacktip sharks may not have behaved
independently (e.g., with schooling behavior), violating
the first assumption of independent catches. Residual
plots showed a similar degree of bias for all models
(Fig. 3), with none demonstrating markedly different
fits to the data. The biggest difference among models
was for the largest mesh (20.3 cm) for which the normal
(proportional spread), lognormal, and gamma curves
under-represented some of the smaller length classes.
The highest number of positive residuals was seen for
the smaller length classes (50-70 cm FL) in mesh sizes
11.4 cm and 12.7 cm and, to a lesser degree, the 14.0 cm
panel for all models (Fig. 3). The plots indicated that
more of the smaller individuals were caught in these
panels than predicted by the models. The largest and
smallest mesh sizes (20.3 and 7.6 cm) caught fewer of
the smallest sharks than predicted by the models. The
residuals did not indicate systematic bias in any of the
models aside from the lack of fit to the smallest size
classes (Fig. 3). Predicted selectivity curves for the
normal, fixed-spread model assuming proportional fish-
ing intensity plotted with observed length-frequencies
for each mesh size (Fig. 4) showed that the model fitted
the observed data well.
Discussion
In previous gillnet selectivity studies on
sharks, a gamma-shaped distribution has been
assumed (Carlson and Cortes, 2003; Kirkwood
and Walker, 1986; McLoughlin and Stevens,
1994; Simpfendorfer and Unsworth, 1998),
based on the specialized SELECT method
described by Kirkwood and Walker (1986).
However, a more recent study on the gillnet
selectivity for sandbar sharks C. plumbeus
(McAuley et al., 2007) found that all four
models estimated by the Millar and Holst
(1997) method provided better fits than the
Kirkwood and Walker (1986) gamma model.
Our study on blacktip sharks indicated that
the normal, fixed spread models provided the
best fit. A more limited study in North Caro-
lina (Thorpe and Frierson, 2009) found that
the normal model with spread proportional to
mesh size generally provided the best fit for
blacknose (C. acronotus), bonnethead ( Sphyrna
tiburo), and blacktip sharks. Although the
method of Kirkwood and Walker (1986) was
not employed in this study, the gamma curve
estimated by the Millar and Holst (1997)
SELECT method provided a poorer fit than
the normal and lognormal models. Therefore, it
Baremore et at: Gillnet selectivity for juvenile Carcharhinus hmbatus
235
Table 5
Gillnet selectivity parameter estimates for each model for blacktip sharks ( Carcharhinus limbatus ) in northwest Florida, 1994-
2010. All four models were run twice: first assuming fishing intensity to be equal across mesh sizes and again assuming that
fishing intensity was proportional to mesh size. Model deviance is the likelihood ratio goodness of fit, with 130 degrees of freedom
for each model.
Equal fishing intensity Proportional fishing intensity
Model
Parameters
Model deviance
Parameters
Model deviance
Normal (fixed spread)
(k, ct) = (5.98, 30.98)
411.79
(k, cr) = ( 6.94 , 34.91)
371.36
Normal (prop, spread)
(a^ a2)=(6.8Q, 10.11)
536.33
(aj, a2) = (8.11, 8.20)
553.73
Lognormal
(Pj, ol = (4.00, 0.41)
440.33
(jUj, ct) = <4.17, 0.41)
440.33
Gamma
(a, k) = ( 6.39, 1.17)
469.68
(a, k ) = ( 7 . 3 9 , 1.17)
469.68
was not necessary to test a separate method to estimate
a gamma selectivity curve.
Residual plots from all selectivity models showed
some degree of bias for the smaller (50-70 cm FL) size
classes in the 11. 4-, 12. 7-, and 14.0-cm mesh sizes. This
finding indicated that all models underestimated the
numbers of small blacktip sharks caught in these mesh
sizes, and these underestimates could be an artifact of
the sampling design of the GULFSPAN juvenile shark
survey (Carlson and Brusher, 1999). In this survey
gillnet panels were arranged in increasing order by
mesh size, and the order of panels was not randomized.
Randomization of gillnet panels is common in selectiv-
ity experiments because it is thought to reduce the
potential preference of fish for any one area of the net.
However, because fixed stations were not used, and the
nets were fished at a variety of depths, habitats, and
seasons, sampling design was probably not a factor in
the model’s lack of fit to the data. The overdispersion
of the data could be a result of the pooling of the data
into 5-cm bins, or could indicate schooling behavior by
some size classes of blacktip sharks. Shark species are
known to segregate by size and sex; therefore the cap-
ture of a cluster of similar-size blacktip sharks is likely.
Overdispersion does not necessarily affect parameter
estimation (Millar and Fryer, 1999), although an initial
model assumption may have been violated.
Although the assumption of equal catches may have
been violated, the second assumption of equal fishing
effort among gillnet panels was most likely met. The
shallow bays and estuaries sampled, along with the
length of the net (-600 m), decreased the probability of
different panels fishing in different habitats and depth
zones. Commercial gear can be several kilometers in
length, and sagging can cause the middle part of the
gear to fish in different depth strata than those at the
ends. Blacktip sharks were therefore equally likely to
encounter each panel of the GULFSPAN survey gillnet.
On occasion, adult blacktip sharks (>130 cm FL) have
been captured in the survey areas on longlines (Bethea
and Carlson3). However, larger sharks are less likely
to be caught in gillnets with mesh sizes smaller than
20 cm, and those few large sharks captured in the
smaller mesh sizes were generally entangled by roll-
ing in the gear — a phenomenon that was also noted
for finetooth sharks (C. isodon) (Carlson and Cortes,
2003). All gillnet panels, except the 20.3 cm panel, were
monofilament, and large sharks were able to break the
monofilament and escape the gear. Such cases where
larger sharks were entangled in smaller mesh sizes or
where they broke free of the net could also have affected
the lack of fit because the assumption of geometric
similarity would not stand. The occurrence of larger
sharks in small mesh sizes may have been reflected
by the high model deviances for the models (normal
proportional spread, lognormal, and gamma) where
geometric similarity of the data was assumed. However,
other than the lack of fit to the smallest size classes,
the models described the data very well, with residu-
als showing mostly equal error distribution and little
systematic bias.
Because of the change in the gear from 2005 through
2006, several attempts were made to account for a
year effect within the SELECT method. Because of
low sample sizes within years, especially for the 7.6-
and 20.3-cm panels, it was not possible to incorporate
year as a factor. For instance, a total of 92 and 115
blacktip sharks were captured by the 7.6- and 20.3-cm
panels, respectively. Although these sample sizes were
adequate for the overall model, when broken down by
year the sample sizes were in the single digits for most
size classes. The data were also separated into two
time periods (1994-2005 and 2006-10), and the SE-
LECT method was used to estimate selectivity models
for each time period. The first time period produced
reasonable results; however, no realistic solution was
found for the second time period. This could also be
due to sample sizes in the second time period. Although
3Dana M. Bethea and John. K. Carlson. 2011. Unpubl.
data. Panama City Laboratory, Southeast Fisheries Sci-
ence Center, National Marine Fisheries Service, National
Oceanic and Atmospheric Administration, 3500 Delwood
Beach Rd., Panama City, Florida 32408.
236
Fishery Bulletin 1 10(2)
Normal (fixed spread) retention curve Deviance residuals
Normal (proportional spread) retention curve
Log-normal retention curve
Deviance residuals
Deviance residuals
Figure 3
Gillnet selectivity curves and residuals estimated for blacktip sharks (Carcharhinus limbatus ) in the Gulf of Mexico
Shark Pupping and Nursery (GULFSPAN) survey in northwest Florida calculated from the normal (fixed spread),
normal (proportional spread), lognormal, and gamma distributions. The plots on the left are the estimated gillnet
selectivity curves with relative retention probability on they axis. Increasing height of the curves indicates increas-
ing mesh sizes. The plots on the right show the residuals of the models and mesh size on the y axis increases from
bottom to top. Filled circles represent positive residuals and open circles represent negative residuals. The area of
the circle is proportional to the square of the residual.
there may have been a year effect that we were unable
to account for, this is unlikely because of the nature
of the survey and the species studied. Generally, year-
to-year variability in recruitment is lower in sharks
than in teleosts because of the production of large,
well-developed young and low natural mortality (Smith
et al., 1998; Walker, 1998). The GULFSPAN survey
primarily targets juvenile sharks in nursery areas,
and the majority of the blacktip sharks captured were
juveniles. Therefore it is probable that interannual
size variability was low in the survey area for black-
tip sharks. Although this is an important factor that
could be applied to other selectivity studies with more
robust sample sizes, current stock assessment models
Baremore et al Gillnet selectivity for juvenile Carcharhmus hmbatus
237
FL (cm)
FL (cm)
Figure 4
Selectivity curves for the normal proportional spread model where fishing intensity is
assumed to be proportional to mesh size. Curves were plotted with observed data on
frequency of fork lengths (FL) for blacktip sharks ( Carcharhmus limbatus) caught in
the Gulf of Mexico Shark Pupping and Nursery (GULFSPAN) survey. Selectivity curves
and length frequencies are plotted separately by mesh size. Relative retention is the
probability that a fish of a given length class that comes into contact with that mesh
size is captured.
238
Fishery Bulletin 1 10(2)
for sharks do not include year-specific selectivity func-
tions (SEDAR, 2006).
Gillnet selectivity is more highly influenced by mor-
phological features such as girth and the presence or
absence of hard structures than it is by the length of a
fish (Reis and Pawson, 1999; Carol and Garcia Berthou,
2007). Nevertheless, straight-line measured length can
often be used as a proxy for girth in selectivity studies
because of the close direct relationship between the two
parameters (Reis and Pawson, 1999), with exceptions
for cases of unusual morphological features (e.g., in
hammerheads [Thorpe and Frierson, 2009]) or behav-
ioral response to entanglement (e.g., finetooth sharks
[Carlson and Cortes, 2003]). Many sharks in the family
Carcharhinidae share similar body shape and struc-
ture (Compagno and Niem, 1998), with girth and the
rigidity of fins acting as a limiting factor for capture
by gillnets (Carlson and Cortes, 2003; McAuley et ah,
2007). Girth-to-length relationships have been found to
be similar among related species of sharks (McLoughlin
and Stevens, 1994). It is therefore possible that selectiv-
ity curves could be family-specific rather than species-
specific for sharks. The most recent data have shown
that the normal selectivity curves may provide the best
fit for sharks in the family Carcharhinidae (McAuley
et ah, 2007; Thorpe and Frierson, 2009), indicating
that the results for blacktip sharks could be useful for
other carcharhinids of similar size. Selectivity param-
eters estimated for the blacktip shark could be used
as a proxy for other species in the same family when
species-specific selectivity estimates are unavailable.
This theory could be tested by applying this method
to other similar-size shark species for which a gillnet
selectivity curve has been estimated, and should be
pursued further as more data become available.
Thorpe and Frierson (2009) found length modes of 97
and 88 cm FL for blacktip sharks caught in mesh sizes
7.6 and 10.2 cm, respectively, whereas we estimated
modes of 46 and 62 cm FL for the same mesh sizes
(Table 6). However, Thorpe and Frierson (2009) failed
to fit a selectivity curve to the individual mesh sizes
because of the wide spread of the sparse length data.
Their study was based on a small number of samples
(n = 76) and the modes for only two mesh sizes were
estimated. The low sample size reported by Thorpe
and Frierson (2009) was likely due to the relatively
short duration of sampling, which was conducted over
a period of eight months. Additionally, Thorpe and
Frierson (2009) conducted their survey more than 1
km from shore, where the likelihood of small juveniles
coming in contact with the gear was low. Total effort
was not reported; however, catch rates were low in
all gillnet panels (<0.15 blacktip sharks caught per
hour of fishing). It is also possible that the size classes
sampled in both studies were not reflective of the true
size structure of the population because localized con-
centrations of sharks in each area that were available
to the gear probably differed. The true availability of
blacktip sharks to gillnets in different regions cannot be
known; therefore applying selectivity functions should
Baremore et al.: Gillnet selectivity for |uvenile Carcharhmus limbatus
239
be done with a proper context and with supporting
length-frequency data when possible.
Based on data from fisheries observers, the average
mesh size used from 2005 through 2010 in the commer-
cial anchored gillnet fishery in the U.S. Atlantic Ocean
was 11.1 cm, with a range of 8.5-16.0 cm (Passerotti4).
The modal length calculated by using the equation
in Table 3 for normal models indicates that blacktip
sharks approximately 77 cm FL should be most vulner-
able to the average mesh size in the commercial gillnet
fishery. When calculated by using the full range of mesh
sizes, the predicted modes range from 59 to 111 cm FL.
Average lengths of blacktip sharks measured by ob-
servers captured by commercial anchored gillnets from
2005-2010 ranged from 79 to 107 cm FL (Baremore et
al., 2007; Passerotti and Carlson, 2009, 2010; Passerotti
et al., 2010, 2011). The observed lengths are consis-
tent with the selectivity model estimated for blacktip
sharks. Blacktip sharks are born at approximately 40
cm FL and mature between 120 and 130 cm FL (5-7
yr) in the U.S. Atlantic Ocean (Carlson et al., 2006),
suggesting that the blacktip sharks most vulnerable to
commercial gear are juveniles. Juvenile blacktip sharks
use inshore nursery areas during spring and summer
months, but migrate into deeper waters in the fall and
winter (Castro, 1993; Heupel et al., 2007). Commercial
gillnet fishermen operating in states with gillnet bans
are required to fish at least 4.8 km from shore (federal
waters) in the U.S. Atlantic Ocean; therefore the small-
est juvenile blacktip sharks may not be as vulnerable
to bycatch in these areas, especially during summer
months. However, in states such as North Carolina,
which allow commercial gillnet fishing in state waters,
the potential for gear interaction with juvenile blacktip
sharks year-round is higher. Observer data show that
blacktip sharks <120 cm FL are captured in commercial
gillnet fisheries and therefore juvenile blacktip sharks
are likely affected by both offshore and inshore gillnet
fisheries.
Thorpe and Frierson (2009) reported a mortality rate
of 90.5% for blacktip sharks captured in experimental
gillnets. Although soak time was not reported, the gill-
nets and sampling protocol in their study were designed
to mimic those commonly used by commercial gillnet
fishermen in North Carolina; therefore it is probable
that juvenile blacktip sharks interacting with commer-
cial gillnets may also experience high bycatch mortal-
ity. Demographic evidence suggests that population
growth rates are more sensitive to survival of juvenile
life stages of sharks than adults (Cortes, 2002). There-
fore, modeling of the gear selectivity of gillnet fisheries,
and particularly modeling bycatch from fisheries that
have the potential to impact juveniles, is especially
important.
4 Passerotti, M. 2011. Personal commun. Panama City
Laboratory, Southeast Fisheries Science Center, National
Marine Fisheries Service, National Oceanic and Atmos-
pheric Administration, 3500 Delwood Beach Rd., Panama
City, Florida 32408.
Blacktip sharks are a commercially exploited species
in U.S. waters, and the stock status in the Atlantic
Ocean and Gulf of Mexico is assessed by the National
Marine Fisheries Service on a regular basis (NMFS,
2002; SEDAR, 2006). Bycatch estimates for blacktip
sharks are available from observer data (Passerotti
et al., 2010), and fishing intensity of the Spanish and
king mackerel gillnet fisheries has been previously es-
timated (SEDAR 2008; 2009). These fishery-dependent
data, along with selectivity curves provided by this
study, can be used by assessment scientists to estimate
the selectivity of blacktip sharks caught as bycatch by
commercial gillnet fisheries in the U.S. Atlantic Ocean.
Bycatch data are equally as important as primary catch
data for stock assessment models (NMFS5; SEDAR,
2006), though often more difficult to attain because
bycatch is generally discarded at sea. This study pro-
vides valuable information for assessment scientists and
managers tasked with estimating the size structure of
blacktip sharks caught by commercial gillnet fisheries.
Conclusions
Juvenile blacktip sharks are caught as bycatch in com-
mercial gillnet fisheries in the U.S. Atlantic Ocean,
although the impact on the population has not been
assessed. The results from this study showed that gill-
net selectivity for juvenile blacktip sharks caught in
the fishery-independent survey was best described by a
normal selectivity curve with fixed spread and with fish-
ing intensity proportional to mesh size. Because many
commercial gillnet fisheries use mesh sizes similar to
those used to produce these results, it may be possible
to estimate the length frequencies of juvenile blacktip
sharks influenced by these coastal fisheries. Selectiv-
ity estimates may also be applicable to other sharks
of similar size for which species-specific information
is unavailable. Future studies should focus on fishery-
dependent gillnet selectivity estimates to determine if
selectivity changes with gear, location, or target species.
Acknowledgments
We thank everyone at the NOAA Fisheries Panama
City Laboratory who aided in field work and gear
maintenance. We would especially like to thank the
numerous volunteers and unpaid interns who gave
countless hours collecting data over the years. J. Carl-
son reviewed a version of the manuscript and gave
useful comments. E. Cortes imparted expertise on the
subject of gillnet selectivity and M. Passerotti provided
5 NMFS (National Marine Fisheries Service). 2002. Stock
assessment of large coastal sharks in the U.S. Atlantic Ocean
and Gulf of Mexico: final meeting report of the 2002 shark
evaluation workshop. Contribution report 02-03-177, 64
p. Sustainable Fisheries Div., National Marine Fisheries
Service, NOAA, Silver Spring, MD.
240
Fishery Bulletin 1 10(2)
information on commercial gillnet fisheries. The com-
ments of three anonymous reviewers also added to the
overall quality of the article. Sharks were collected
under Florida Fish & Wildlife Conservation Commis-
sion special activity licenses 02R-075, 03SR-075A,
04SR-075 and 08SR-075.
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Population structure of pink salmon
(Oncorhynchus gorbuscha ) in British Columbia
and Washington, determined with microsatellites
E-mail address for contact author: Terry. Beacham@dfo-mpo.Qcc
1 Fisheries and Oceans Canada
Pacific Biological Station
3190 Hammond Bay Road
Nanaimo, B C., Canada V9T 6N7
2 Fisheries and Oceans Canada
417-2nd Avenue West
Prince Rupert, B C Canada V8J 1G8
3 Pacific Salmon Commission
600-1155 Robson Street
Vancouver, B C., Canada V6E 1B5
Abstract — Population structure of
pink salmon ( Oncorhynchus gorbus-
cha) from British Columbia and Wash-
ington was examined with a survey
of microsatellite variation to describe
the distribution of genetic variation.
Variation at 16 microsatellite loci was
surveyed for approximately 46,500
pink salmon sampled from 146 loca-
tions in the odd-year broodline and
from 116 locations in the even-year
broodline. An index of genetic differ-
entiation, Fst, over all populations
and loci in the odd-year broodline was
0.005, with individual locus values
ranging from 0.002 to 0.025. Popula-
tion differentiation was less in the
even-year broodline, with a ^st value
of 0.002 over all loci, and with individ-
ual locus values ranging from 0.001
to 0.005. Greater genetic diversity
was observed in the odd-year brood-
line. Differentiation in pink salmon
allele frequencies between broodlines
was approximately 5.5 times greater
than regional differentiation within
broodlines. A regional structuring
of populations was the general pat-
tern observed, and a greater regional
structure in the odd-year broodline
than in the even-year broodline. The
geographic distribution of microsatel-
lite variation in populations of pink
salmon likely reflects a distribution
of broodlines from separate refuges
after the last glaciation period.
Manuscript submitted 3 October 2011.
Manuscript accepted 5 January 2012.
Fish. Bull. 110:242-256(2012)
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
Terry D. Beacham (contact author)1
Brenda McIntosh'
Cathy MacConnachie1
Brian Spilsted2
Bruce A. White3
Pink salmon ( Oncorhynchus gor-
buscha) spawn in more than 700
rivers in British Columbia (Aro and
Shepard, 1967), and the distribution
of spawning populations varies in odd-
numbered years and even-numbered
years. Spawning occurs primarily
only in odd years in the Fraser River
drainage, mainly in even years on
the Queen Charlotte Islands, and in
both years in central and northern
coastal areas of British Columbia
(Neave, 1952). Because virtually all
pink salmon mature at two years of
age (Bilton and Ricker, 1965), two dis-
tinct broodlines in the species have
developed (odd-year and even-year),
with virtually no gene flow between
the broodlines. This reproductive iso-
lation of the broodlines has resulted
in reported differences in body size
(Godfrey, 1959), morphological char-
acteristics (Ricker, 1972; Beacham,
1985), timing of spawning (Aro and
Shepard, 1967; Dyagilev and Markev-
ich, 1979), and genetic differentiation
(Aspinwall, 1974; Beacham et al.,
1988). Evaluation of the genetic pop-
ulation structure both between and
within broodlines in British Columbia
and Washington form the basis of the
current study.
Estimation of genetic population
structure has been a key area of
research in salmon assessment and
management. Identification of a ge-
netically distinct group of populations
in the distribution of a species is a
key step in conserving and maintain-
ing genetic diversity. Genetically dis-
tinct populations or regional groups of
populations (stocks) were determined
through surveys of genetic variation
to evaluate the population structure
of a species (Shaklee and Bentzen,
1998). Identification of genetically
distinct groups in the distribution of
pink salmon in British Columbia and
Puget Sound may lead to the conser-
vation of genetic diversity by fisheries
and resource management. An evalu-
ation of genetic variation is effective
in describing the population struc-
ture of salmonids, is a key part in the
elucidation of management units or
conservation units for a species and
can be applied to manage fisheries
exploiting specific stocks of salmon.
Determination of genetic population
structure is an important part in de-
veloping a genetically based method
for estimation of stock composition.
Allozymes were initially the key set
of genetic markers used in evaluat-
Beacham et al : Population structure of Oncorhynchus gorbuscha in British Columbia and Washington, determined with microsatellites
243
ing population structure in pink salmon in British Co-
lumbia (Beacham et ah, 1988; Shakiee et ah, 1991)
and elsewhere in the Pacific Rim distribution of the
species (Aspinwall, 1974; Varnavskaya and Beacham,
1992; Shakiee and Varnavskaya, 1994; Noil et ah, 2001;
Hawkins et ah, 2002). Analysis of allozyme variation
provided the general pattern of marked differentiation
between the broodlines, but identification of fine-scale
regional structure was limited, although estimation of
stock composition of samples from mixed-stock fisheries
was conducted (Beacham et ah, 1985; Shakiee et ah,
1991). Development of DNA markers has led to new
avenues of research for using genetic variation in de-
fining population structure. Mitochondrial DNA varia-
tion again showed strong differentiation between the
broodlines, but differentiation among regional groups
of pink salmon was limited (Brykov et ah, 1996, 1999;
Churikov and Gharrett, 2002). However, Golovanov et
ah (2009) noted that differentiation among even-year
populations was higher than among odd-year popula-
tions in the northern Sea of Okhotsk region.
Microsatellites are reported to be useful for evaluat-
ing fine-scale population structure in salmonids (Banks
et ah, 2000) and have been used to evaluate large-
scale and regional variation in chum salmon (O. keta)
(Beacham et ah, 2009). Initial applications of micro-
satellite variation to evaluate individual identification
and population structure were reported by Olsen et ah
(1998, 2000a). However, surveys of population variation
were quite limited in these studies, and no comprehen-
sive evaluation of variation at microsatellites has been
conducted for pink salmon. A survey of microsatellite
variation over a broader geographic range of pink salm-
on distribution would likely be valuable for evaluating
population structure.
In the current study, we outline the microsatellite-
based population structure of pink salmon in British
Columbia and Washington as an initial step in evalu-
ating whether higher resolution in estimation of stock
composition may be possible when compared with esti-
mates previously derived with aliozymes. This objec-
tive was accomplished by analyzing variation at 16
microsatellite loci to evaluate relationships in popula-
tion structure of pink salmon, as well as by analyzing
regional differences in allelic variation. The distribu-
tion of genetic diversity among broodlines, regions, and
populations was estimated in the study, as well as the
stability of population structure.
Materials and methods
Collection of DNA samples and laboratory analysis
Tissue samples were collected from mature pink salmon.
Samples were preserved in 95% ethanol, and sent to the
Molecular Genetics Laboratory at the Pacific Biologi-
cal Station of Fisheries and Oceans Canada. DNA was
extracted from the tissue samples by using a variety
of methods, including a chelex resin protocol outlined
Table 1
Microsatellite loci surveyed in pink salmon ( Oncorhyn-
chus gorbuscha) and their associated annealing and
extension temperatures and times (seconds), as well as
the number of cycles used in polymerase chain reaction
amplifications.
Locus
Annealing
Extension
Cycles
OkilO
53°C/30s
70°C/30s
39
OkilOl
53°C/45s
68°C/30s
40
OnelOl
50°C/30s
70°C/30s
39
Onel02
50°C/30s
70°C/30s
39
Onel04
50°C/30s
70°C/30s
36
Onel09
55°C/30s
70°C/30s
34
Onelll
55°C/30s
70°C/30s
34
Onell4
50°C/30s
70°C/45s
38
Ots213
52°C/45s
72°C/60s
38
Ots7e
51°C/30s
72°C/30s
35
OtsG253b
60°C/45s
72°C/45s
35
OtsG311
50°C/45s
68°C/45s
34
OtsG68
50°C/30s
70°C/30s
36
Ssa407
60°C/30s
70°C/30s
39
Ssa408
60°C/45s
70°C/45s
40
Ssa419
50°C/30s
70°C/30s
40
by Small et al. (1998), a Qiagen 96-well DNeasy®1 pro-
cedure (Qiagen, Mississauga, Ontario), or a Promega
Wizard SV96 Genomic DNA Purification system (Pro-
mega, Madison, WI). Once extracted DNA was avail-
able, surveys of variation at 16 microsatellite loci were
conducted: OkilO (Smith et a!., 1998), OkilOl (Beacham
et ah, 2011), OtsG68, OtsG253b, OtsG311 (Williamson
et ah, 2002), Ots213 (Greig et ah, 2003), Ots7e (Wright
et ah, 2008), OnelOl, Onel02, Onel04, Onel09 , Onelll ,
Onell4 (Olsen et ah, 2000b), Ssa407 , Ssa408, Ssa419
(Cairney et ah, 2000)
In general, polymerase chain reaction (PCR) DNA
amplifications were conducted by using a DNA En-
gine Cycler Tetrad2 (BioRad, Hercules, CA) in 6-pL
volumes consisting of 0.15 units of Taq polymerase,
1-pL of extracted DNA, lxPCR buffer (Qiagen), 60 pM
each nucleotide, 0.40 pM of each primer, and deionized
water. The thermal cycling profile involved one cycle
of Taq activation for 15 minutes at 95°C, followed by a
denaturation cycle of 30 seconds at 94°C, with anneal-
ing and extension conditions for each locus as outlined
in Table 1. PCR fragments were initially size fraction-
ated in denaturing polyacrylamide gels with an ABI 377
automated DNA sequencer, and genotypes were scored
by Genotyper, vers. 2.5 software (Applied Biosystems,
Foster City, CA) by using an internal lane sizing stan-
dard. Later in the study, microsatellites were size frac-
tionated in an ABI 3730 capillary DNA sequencer, and
1 Mention of trade names or commercial companies is for
identification purposes only and does not imply endorsement
by the National Marine Fisheries Service, NOAA.
244
Fishery Bulletin 1 10(2)
Table 2
Pink salmon (Oncorhynchus gorbuscha) spawning regions, number of populations sampled, years when sampling occurred, aver-
age number of fish sampled per population per year over all populations within a region, and average total number of fish
sampled (AO per population within the region for 146 odd-year and 116 even-year populations in 15 geographic regions (Fig. 2).
Listing of populations in each region as well as allele frequencies for all population samples surveyed in this study are available
at the Molecular Genetics Laboratory website http://www.pac.dfo-mpo.gc.ca/science/facilities-installations/pbs-sbp/mgl-lgm/
data-donnees/index-eng.htm. Values shown are averages with the ranges of samples sizes in parentheses.
Average annual Average population
Region
Populations
Years
population sample size
size (A0
Washington (odd)
12
1995-2009
110
(44_498)
257
(98-755)
Fraser River-upper (odd)
9
1987-2009
95
(69-102)
201
(100-463)
Fraser River-lower (odd)
6
1987-2009
87
(50-100)
245
(98-463)
East Coast Vancouver Island (odd)
10
1987-2009
108
(85-219)
173
(85-397)
South Coast British Columbia (odd)
13
1987-2009
105
(25-200)
169
(38-390)
Central Coast British Columbia (odd)
59
2003-2009
111
(2-227)
164
(32-394)
Skeena River (odd)
10
2003-2007
163
(67-229)
228
(123-393)
North Coast British Columbia (odd)
25
2003-2009
151
(9-233)
211
(87-381)
Queen Charlotte Islands (odd)
2
2005
210
(200-219)
210
(200-219)
East Coast Vancouver Island (even)
2
2006-2008
83
(50-113)
124
(85-163)
South Coast British Columbia (even)
11
2002-2010
119
(27-237)
162
(47-452)
Central Coast British Columbia (even)
50
2002-2010
96
(5-202)
133
(18-312)
Skeena River (even)
7
2002-2006
162
(63-228)
208
(100-381)
North Coast British Columbia (even)
25
2002-2010
113
(3-215)
190
(24-425)
Queen Charlotte Islands (even)
21
2002-2006
135
(32-200)
176
(32-364)
genotypes were scored by GeneMapper software, vers.
3.0 (Applied Biosystems) by using an internal lane siz-
ing standard. Allele identification between the two se-
quencers was standardized by analyzing approximately
600 individuals on both platforms and converting the
sizing in the gel-based data set to match that obtained
from the capillary-based set.
Data analysis
All annual samples available for a location were com-
bined to estimate population allele frequencies, as is
recommended by Waples (1990). Each population in each
broodline at each locus was tested for departure from
Hardy-Weinberg equilibrium of genotypic frequencies by
using the software Genetic Data Analysis (GDA; Univ.
of Connecticut, Storrs, CT). Critical significance levels
for simultaneous tests were evaluated by using Bon-
ferroni adjustment for each broodline separately (odd-
year broodline: 0.05/146 = 0.00034, even-year broodline
0.05/116 = 0.00043; Rice, 1989). Weir and Cockerham’s
(1984) Fst estimates for each locus over all populations
were calculated with FSTAT, vers. 2. 9. 3. 2 (Goudet,
1995). The significance (P< 0.05) of the multilocus FST
value over all samples was determined by jackknifing
over loci. Populations were combined into 14 regional
groups in order to develop a practical method to display
mean pairwise FST values between regions, as well as
the mean number of alleles observed per locus in each
region. These 14 regional groups were constructed from
the 15 regional groups outlined in Table 2 by combin-
ing the upper and lower Fraser River regions into a
single region, with other regions remaining as outlined
in Table 2. Broodlines were separated in odd-year and
even-year spawning lines. Geographic areas were out-
lined in Figure 1. The 14 regional groups correspond
to the geographic areas and broodlines as follows: 1)
Washington odd-year; 2) Fraser River (upper+lower)
odd-year; 3) east coast Vancouver Island odd-year; 4)
south coast British Columbia odd-year; 5) central coast
British Columbia odd-year; 6) Skeena River odd-year;
7) north coast British Columbia odd-year; 8) Queen
Charlotte Islands odd-year; 9) east cCoast Vancouver
Island even-year; 10) south coast British Columbia even-
year; 11) central coast British Columbia even-year; 12)
Skeena River even-year; 13) north coast British Colum-
bia even-year; 14) Queen Charlotte Islands even-year.
Individual populations remained discrete within these
larger regional groups for determination of pairwise
Fst values.
Genotypic disequilibrium and potential genetic link-
age among loci were tested with GDA with 1500 itera-
tions per test. The number of pairs of loci exhibiting
potential linkage was summed for the 146 populations
sampled in the odd-year broodline and 116 popula-
tions sampled in the even-year broodline (Table 2).
Statistical significance was evaluated by using a Bon-
ferroni adjustment as outlined previously.FSTAT was
used to measure the “allelic richness” (allelic diversity
standardized to a sample size of 240 fish per region)
for the 14 regional groups of populations. Computa-
tion of the number of alleles observed per locus was
carried out with GDA. Cavalli-Sforza and Edwards
(CSE) chord distance (1967) was used to estimate
Beacham et al. : Population structure of Oncorhynchus gorbuscha in British Columbia and Washington, determined with microsatellites
245
Figure 1
Map of British Columbia and northern Washington coasts indicating the general geographic regions
where pink salmon (Oncorhynchus gorbuscha) from 146 odd-year and 116 even-year populations
were surveyed, with the regions listed in Table 2. The regions depicted in the figure are the fol-
lowing: 1 Washington; 2 lower Fraser River; 3 upper Fraser River; 4 east coast Vancouver Island
(ECVI); 5 southern British Columbia; 6 central coast; 7 Skeena River; 8 north coast; and 9 Queen
Charlotte Islands. In 6 of these regions (ECVI, southern British Columbia mainland, central coast,
Skeena River, north coast, Queen Charlotte Islands), pink salmon spawn in both even and odd
years. Because 6 regions have both broodlines present, the map encompasses 12 regional groups of
populations when even and odd populations are separated. The other 3 regional groups (upper and
lower Fraser River, and Washington) have pink salmon only in odd years. This summary accounts
for the 15 regional groups outlined in Table 2.
genetic distances among all populations. An unrooted
neighbor-joining tree based upon CSE was generated
with NJPLOT (Perriere and Gouy, 1996). Bootstrap
support for the major nodes in the tree was evalu-
ated with the CONSENSE program from PHYLIP
based upon 500 replicate trees (Felsenstein, 1993).
The distribution of genetic variation in pink salmon
was evaluated with a gene diversity analysis with
the analysis structured between broodlines, among
regions within broodlines, and among populations
within regions. The analysis was conducted with GDA,
which will support a maximum of three nested levels
of variation in addition to the error mean square. All
populations outlined in Table 2 were included in the
analysis. Allele frequencies for all location samples
surveyed in this study are available at the Molecular
Genetics Laboratory website http://www.pac.dfo-mpo.
gc.ca/science/facilities-installations/pbs-sbp/mgl-lgm/
data-donnees/index-eng.htm.
Results
Variation within populations
Variation was displayed in the number of observed
alleles at the 16 microsatellite loci surveyed in the
study. The fewest number of alleles was observed at
Ots7e (12 alleles odd-year broodline, 13 alleles even-
year broodline), and the greatest number of alleles
was observed at OkilO (85 alleles odd-year broodline,
83 alleles even-year broodline) (Table 3). Heterozy-
gosities were generally above 90%, with notable excep-
tions observed at Ots7e (both broodlines) and Onelll
(odd-year only). Genotypic frequencies at all 16 loci
surveyed typically conformed to those expected under
Hardy-Weinberg equilibrium (HWE) for populations in
both broodlines. Greater overall population differen-
tiation was observed in the odd-year broodline ( Fsr =
0.005) than in the even-year broodline ( FST =0.002).
246
Fishery Bulletin 1 10(2)
Table 3
Number of alleles per locus, an index of genetic differentiation FST (standard deviation in parentheses), expected heterozygosity
(He), observed heterozygosity (H0), and percent significant Hardy-Weinberg equilibrium (HWE) tests for 16 microsatellite loci
among 146 odd-year and 116 even-year pink salmon (Oncorhynchus gorbuscha) populations.
Locus
Number of alleles
1
ST
He
H0
HWE
Odd-year broodline
OkilO
85
0.004
(0.000)
0.94
0.94
0.0
OkilOl
83
0.002
(0.000)
0.97
0.96
0.7
OnelOl
52
0.003
(0.000)
0.96
0.95
0.7
One 102
32
0.006
(0.001)
0.92
0.91
0.0
One 104
40
0.007
(0.001)
0.95
0.94
0.0
Onel09
32
0.005
(0.001)
0.90
0.90
0.0
Onelll
36
0.012
(0.001)
0.72
0.71
0.0
Onell4
49
0.003
(0.000)
0.96
0.95
2.1
Ots213
69
0.003
(0.001)
0.97
0.95
2.8
Ots7e
12
0.025
(0.003)
0.45
0.45
0.0
OtsG253b
46
0.005
(0.001)
0.95
0.94
1.4
OtsGSll
34
0.009
(0.001)
0.89
0.88
1.4
OtsG68
34
0.004
(0.001)
0.93
0.93
0.0
Ssa407
73
0.006
(0.001)
0.94
0.94
0.0
Ssa408
69
0.002
(0.000)
0.97
0.95
6.2
Ssa419
66
0.003
(0.000)
0.96
0.93
7.5
Total
0.005
(0.001)
Even-year broodline
OkilO
83
0.001
(0.000)
0.94
0.93
1.8
OkilOl
73
0.002
(0.000)
0.97
0.96
0.9
OnelOl
65
0.001
(0.000)
0.96
0.95
1.8
One 102
35
0.002
(0.000)
0.93
0.93
0.0
Onel04
36
0.004
(0.000)
0.92
0.92
0.0
Onel09
29
0.003
(0.000)
0.91
0.90
0.0
Onelll
38
0.004
(0.001)
0.92
0.91
2.6
Onell4
42
0.002
(0.000)
0.96
0.95
0.0
Ots213
71
0.002
(0.000)
0.97
0.97
5.1
Ots7e
13
0.005
(0.001)
0.58
0.58
1.8
OtsG253b
48
0.002
(0.000)
0.95
0.94
3.5
OtsG311
34
0.003
(0.000)
0.93
0.91
0.0
OtsG68
32
0.003
(0.000)
0.93
0.93
0.0
Ssa407
77
0.002
(0.000)
0.96
0.93
2.6
Ssa408
69
0.002
(0.000)
0.97
0.95
1.8
Ssa419
66
0.002
(0.000)
0.97
0.95
2.6
Total
0.002
(0.000)
Individual locus FST values ranged from 0.002 to 0.012
for the odd-year broodiine, and between 0.001 and
0.005 for the even-year broodline. For the odd-year
broodline, individual pairs of loci displayed poten-
tial linkage of between 0% and 28% ( Ssa408 and
Ssa419) of the populations surveyed. In the even-year
broodline, individual pairs of loci displayed potential
linkage of between 0% and 13% ( Ssa407 and Ssa419 )
of the populations surveyed. Potential linkage among
loci was higher in the Ssa series of loci than that
observed in the other microsatellites, but different
patterns were observed within the broodlines. Poten-
tial linkage among the Ssa loci was not judged to
be at a level that required removal from subsequent
analyses.
The number of alleles observed displayed variation
among the regional groups of pink salmon surveyed.
With the number of alleles observed standardized to
a sample size of 240 individuals per region for both
broodlines, the odd-year broodline populations with
the fewest number of observed alleles originated from
Washington (486 alleles), whereas populations with the
greatest number of alleles originated from the north-
ern coastal region of British Columbia (551 alleles)
(Table 4). In the even-year broodline, populations with
the fewest numbers of alleles originated from the east
Beacham et al Population structure of Oncorhynchus gorbuscho in British Columbia and Washington, determined with microsatellites
247
Table 4
Mean number of alleles observed per locus at 16 microsatellite loci for pink salmon (Oncorhynchus gorbuscha) from 14 geographic
broodline areas standardized to a sample size of 240 fish per geographic area. Regional groups (see Table 2), are as follows: 1)
Washington odd-year, 2) Fraser River (upper-i- lower) odd-year, 3) east coast Vancouver Island odd-year, 4) south coast British
Columbia odd-year, 5) central coast British Columbia odd-year, 6) Skeena River odd-year, 7) north coast British Columbia odd-
year, 8) Queen Charlotte Islands odd-year, 9) east coast Vancouver Island even-year, 10) south coast British Columbia even-year,
11) central coast British Columbia even-year, 12) Skeena River even-year, 13) north coast British Columbia even-year, 14) Queen
Charlotte Islands even-year. The two Fraser River regions in Table 2 were combined for the analysis.
Locus
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Odd
Even
OkilO
30.77
33.14
41.59
40.84
47.21
40.53
48.42
44.81
26.93
31.14
38.21
36.03
43.31
35.67
40.91
35.21
OkilOl
50.95
53.09
54.73
55.30
55.83
51.38
54.65
55.79
49.88
48.04
51.90
52.11
55.65
48.74
53.96
51.05
OnelOl
35.30
37.33
35.93
37.70
36.39
36.18
36.36
35.78
35.78
36.66
39.15
36.55
39.41
39.42
36.37
37.83
Onel02
22.73
22.25
23.22
23.45
24.44
20.79
24.40
23.99
23.94
25.73
25.53
26.08
26.16
25.68
23.16
25.52
Onel04
30.29
31.81
30.85
31.78
30.64
28.23
31.10
30.49
29.89
27.95
30.04
24.63
30.40
27.40
30.65
28.39
One 109
17.31
18.00
17.51
19.13
19.62
20.18
20.23
18.90
16.00
18.00
18.60
18.54
19.33
16.98
18.86
17.91
Onelll
19.79
21.45
21.52
21.81
21.01
19.73
21.24
20.54
21.97
24.05
25.27
22.29
23.51
22.40
20.88
23.25
Onell4
37.24
39.89
38.61
38.84
37.30
34.99
36.19
35.26
30.00
32.23
34.74
35.46
35.78
34.58
37.29
33.80
Ots213
43.25
42.92
45.32
49.90
51.23
50.38
51.77
50.54
43.91
50.37
50.34
48.99
51.27
46.15
48.16
48.51
Ots7e
5.79
5.90
7.00
6.43
7.68
7.12
7.80
6.48
4.00
5.84
5.82
6.17
7.27
4.92
6.77
5.67
OtsG311
16.65
15.91
17.97
18.32
20.50
20.44
20.24
21.25
20.00
21.41
21.06
18.75
21.49
19.69
18.91
20.40
OtsG68
24.95
25.11
25.12
24.44
26.38
27.07
26.01
23.80
20.98
21.94
21.99
20.99
25.23
21.14
25.36
22.04
OtsG253b
33.96
33.88
34.08
35.64
33.92
31.77
34.03
32.57
32.83
34.17
35.74
34.44
36.26
33.12
33.73
34.43
Ssa407
27.87
27.35
31.41
31.74
33.37
31.83
32.28
25.33
47.90
48.86
52.42
51.36
51.01
49.25
30.15
50.13
Ssa408
52.54
53.04
54.75
51.62
52.89
49.94
54.09
52.31
49.90
48.73
54.88
52.09
55.73
51.32
52.65
52.11
Ssa419
36.33
40.19
49.00
46.13
53.07
47.58
52.36
50.93
48.00
46.94
47.97
44.46
50.95
44.42
46.95
47.12
Total
485.69
501.24
528.60
533.04
551.46
518.14
551.17
528.78
501.90
522.06
553.65
528.92
572.73
520.90
524.76
533.36
coast of Vancouver Island (502 alleles), and those popu-
lations with the greatest number of alleles originated
from the northern coastal region of British Columbia
(573 alleles). The greatest difference in number of al-
leles between the broodlines was observed at the locus
Ssa407, with an average of 30 alleles observed at the
locus in the odd-year populations, and 50 alleles ob-
served in the even-year populations (P<0.01). Within
the odd-year broodline, lower numbers of observed al-
leles of Washington populations, and to some extent
Fraser River populations, compared with other regional
groups of populations, were concentrated in the OkilO
and Ssa419 loci (Table 4).
Distribution of genetic variance
Gene diversity analysis of the 16 microsatellites sur-
veyed was used to evaluate the distribution of genetic
variation between two broodlines, among regions (15
regions, Table 2) within broodlines, and among popula-
tions within regions (146 odd-year, 116 even-year popu-
lations). The amount of variation within populations
ranged from 85.1% ( Onelll ) to 99.8% ( OkilOl , OnelOl ),
and averaged 98.1% across all loci (Table 5). Variation
between the two broodlines accounted for 1.5% of total
observed variation and was the largest source of varia-
tion after within-population variation. Variation among
regions within broodlines was the next largest source
of variation and accounted for 0.3% of total observed
variation. Variation among populations within regions
accounted for 0.2% of total observed variation. Differen-
tiation between the broodlines was approximately three
times greater than any combined regional or population
source of variation. Significant broodline differentia-
tion in allele frequencies was observed in 13 of the 16
loci surveyed, with the greatest difference observed at
Onelll , which was nine times larger than that observed
at any other locus (Table 5). For the geographic range of
populations surveyed in the study, broodline differences
contributed more to differentiation of allele frequencies
than any regional or population source of differentiation.
Population structure
Regional genetic differentiation was observed among
pink salmon populations sampled in the different geo-
graphic regions. As expected, the largest differences in
genetic differentiation were observed between regional
groups of populations when compared with populations
in the alternate broodline (regional FST values ranging
from 0.014 to 0.036) (Table 6). In the odd-year brood-
line, the largest average population differentiation
was observed in comparisons of populations originat-
ing from Washington compared with populations from
British Columbia (regional FST values ranging from
0.011 to 0.016) (Table 6). Within British Columbia,
pink salmon in the odd-year broodline originating from
the Fraser River were the most genetically distinct
248
Fishery Bulletin 1 10(2)
Table 5
Hierarchical gene-diversity analysis of regional FST values for 262 populations of pink salmon (Oncorhynchus gorbuscha) within
15 regions (9 regions in odd-year broodline, 6 regions in even-year broodline) for 16 microsatellite loci, with the regions outlined
in Table 2. **P<0.01 *P<0.05
Locus
Within
populations
Among populations
within regions
Among regions
within broodlines
Between
brood lines
OkilO
0.9967
0.0014"
0.0017”
0.0002
OkilOl
0.9976
0.0013"
0.0009"
0.0002
OnelOl
0.9976
0.0012"
0.0009"
0.0003
One 102
0.9942
0.0021"
0.0023"
0.0014*
Onel04
0.9782
0.0021"
0.0045"
0.0152”
Onel09
0.9902
0.0021"
0.0025"
0.0052”
Onelll
0.8507
0.0022"
0.0057"
0.1414“
Onell4
0.9944
0.0013"
0.0013"
0.0030”
Ots213
0.9952
0.0019"
0.0012"
0.0017"
Ots7e
0.9207
0.0056"
0.0111"
0.0626”
OtsG253b
0.9955
0.0019”
0.0023"
0.0003
OtsG311
0.9772
0.0023"
0.0047"
0.0158"
OtsG68
0.9896
0.0019”
0.0020"
0.0065**
Ssa407
0.9930
0.0018"
0.0034”
0.0018*
Ssa408
0.9942
0.0013"
0.0009"
0.0036’*
Ssa419
0.9949
0.0016"
0.0012”
0.0023"
Total
0.9806
0.0019"
0.0027"
0.0148"
Table 6
Mean pairwise FST values averaged over 16 microsatellite loci from 14 regional groups of pink salmon (Oncorhynchus gorbuscha)
outlined in Table 2 that were sampled at 262 locations (146 odd-year, 116 even-year) in British Columbia and Washington. Com-
parisons were conducted between individual populations in each region. Values in bold on the diagonal represent comparisons
among populations within each region. ^st values are listed above the diagonal, and standard deviations are shown below the
diagonal. RC is region code, and codes are as follows: 1) Washington odd-year, 2) Fraser River odd-year, 3) east coast Vancouver
Island odd-year, 4) southern coast British Columbia odd-year, 5) central coast British Columbia odd-year, 6) Skeena River odd-
year, 7) north coast British Columbia odd-year, 8) Queen Charlotte Islands odd-year, 9) east coast Vancouver Island even-year,
10) southern Coast British Columbia even-year, 11) central coast British Columbia even-year, 12) Skeena River even-year, 13)
north coast British Columbia even-year, 14) Queen Charlotte Islands even-year.
RC
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
0.011
0.011
0.013
0.011
0.013
0.016
0.014
0.015
0.036
0.034
0.032
0.032
0.031
0.034
2
0.010
0.001
0.009
0.006
0.007
0.010
0.008
0.010
0.029
0.027
0.026
0.025
0.025
0.028
3
0.009
0.003
0.006
0.006
0.006
0.008
0.006
0.008
0.025
0.023
0.021
0.021
0.020
0.023
4
0.009
0.002
0.003
0.004
0.003
0.006
0.004
0.006
0.025
0.023
0.021
0.020
0.020
0.024
5
0.009
0.002
0.003
0.002
0.001
0.003
0.001
0.003
0.019
0.017
0.015
0.015
0.014
0.018
6
0.009
0.003
0.003
0.003
0.002
0.001
0.004
0.007
0.019
0.017
0.015
0.015
0.014
0.018
7
0.008
0.003
0.003
0.003
0.002
0.002
0.001
0.003
0.019
0.017
0.015
0.015
0.014
0.018
8
0.009
0.001
0.003
0.003
0.001
0.003
0.002
0.003
0.022
0.020
0.018
0.019
0.018
0.021
9
0.010
0.003
0.006
0.004
0.003
0.003
0.003
0.003
0.008
0.005
0.004
0.006
0.005
0.007
10
0.009
0.003
0.006
0.004
0.003
0.003
0.003
0.003
0.002
0.003
0.002
0.004
0.002
0.005
11
0.009
0.003
0.006
0.004
0.002
0.002
0.002
0.002
0.002
0.003
0.001
0.003
0.001
0.004
12
0.008
0.002
0.005
0.004
0.002
0.002
0.002
0.002
0.002
0.003
0.002
0.002
0.002
0.006
13
0.009
0.002
0.006
0.004
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.002
0.001
0.004
14
0.009
0.002
0.006
0.004
0.002
0.002
0.002
0.002
0.003
0.002
0.001
0.002
0.001
0.002
Beacham et al.: Population structure of Oncorhynchus gorbuscha in British Columbia and Washington, determined with microsatellites
249
group of populations (regional FST values ranging from
0.006 to 0.010). The least differentiation was observed
between populations from the northern and central
coastal regions of British Columbia (F ST= 0.001). In
the even-year broodline, the largest regional genetic
differentiation was observed between populations from
the east coast of Vancouver Island and those from the
Queen Charlotte Islands (FST= 0.007), whereas the least
differentiation between regional groups of populations
was observed between the northern and central coastal
regions of British Columbia (Fsr=0.001) (Table 6).
Two distinct lineages of pink salmon were observed
in the cluster analysis, and they were clearly based
on whether pink salmon spawned in odd-numbered or
even-numbered years. All odd-year populations clus-
tered together with 100% bootstrap support, as did
all even-year populations (Fig. 2). Within the odd-year
broodline, a Washington group of populations was well
supported (Washington populations clustered together
in 83% of dendrograms evaluated). Within Washing-
ton, further geographic subdivision was observed, with
populations from the Strait of Juan de Fuca (Gray
Wolf River, Dungeness River) clustering with the Hood
Canal hatchery population in 100% of dendrograms
evaluated. The Hood Canal hatchery population was
the most genetically distinct population included in
the survey (Fig. 2). The three remaining populations
from Hood Canal (Dosewallips River, Hamma Hamma
River, and Duckabush River), in addition to the Gray
Wolf River, Dungeness River, and Hood Canal hatchery
populations, were well separated from other popula-
tions in Washington, clustering together in 100% of
dendrograms evaluated. Pink salmon populations from
the Fraser River in southern British Columbia were
a well-defined geographic cluster — all 15 populations
clustered together in 98% of dendrograms evaluated.
Furthermore, populations in the upper portion of the
drainage were separated from those populations in the
lower portion of the drainage, with upper populations
clustering together in 98% of dendrograms evaluated.
Populations from the central portion of the east coast
of Vancouver Island (Quinsam River, Puntledge River,
Oyster River, Big Qualicum River, and Nanaimo River)
clustered together in 100% of dendrograms evaluated,
as did 98% of populations from the northern portion
of the east coast of Vancouver Island (Keogh River,
Quatse River, Cluxewe River). Populations from the
northern portion of the South Coast of British Colum-
bia (Kakweiken River, Lull Creek, Ahta Creek, Heydon
River, Glendale River) constituted a well-defined group
(96% of dendrograms evaluated). Those in the southern
portion of the south coast (Cheakamus River, Ashlu
River, Mamquam River, Squamish River, Indian River)
were not well supported, but displayed some affinity
to each other in the cluster analysis (Table 6). All 10
odd-year populations sampled from the Skeena River
formed a distinct regional group (50% bootstrap sup-
port). Populations sampled from the central coast and
north coast regions of British Columbia did not cluster
into distinct geographic units. Although some sepa-
ration was observed, genetic differentiation between
populations in the two regions was limited, and this
was reflected in the lack of consistency in population
clustering in the dendrograms evaluated.
Cluster analysis of the populations sampled in the
even-year broodline revealed a general lack of consisten-
cy in geographically based clustering of the populations.
The only exception was observed for the Skeena River
drainage, where all seven populations sampled clus-
tered together in 97% of dendrograms evaluated. There
was some evidence for a weak association for 19 of 21
populations from the Queen Charlotte Islands, but the
cluster was not well supported (22% bootstrap support).
As with the odd-year broodline, populations sampled
from the central coast and north coast regions of Brit-
ish Columbia did not cluster into distinct geographic
units (Fig. 2), which again reflected the overall lack of
genetic differentiation (Fsr=0.001) between populations
in the two regions.
Discussion
In the current study of microsatellite variation in pink
salmon, approximately 46,500 individuals were surveyed
from 146 odd-year and 116 even-year populations, 16
microsatellites were analyzed encompassing 812 alleles,
and 12-85 alleles were identified per locus. Sample size
ranged from 18 to 755 individuals per population, with at
least 100 individuals sampled in 127 of the 146 odd-year
populations, and 92 of the 116 even-year populations.
Only six odd-year and seven even-year populations had
fewer than 40 individuals surveyed. With a range in the
number of individuals sampled per population, sampling
errors may have influenced the estimated allele frequen-
cies within populations, particularly for populations with
fewer than 40 individuals sampled. If sampling errors
are large in estimation of allele frequencies, there is a
potential for these errors to obscure genetic relationships
among related populations. Kalinowski (2005) reported
that loci with larger numbers of alleles produced esti-
mates of genetic distance with lower coefficients of varia-
tion than loci with fewer numbers of alleles, without
requiring larger sample sizes from each population.
Given the results from the cluster analysis, variation in
the number of individuals sampled per population likely
did not result in misidentification of genetic relationships
among populations.
Inferences from the genetic relationships of popula-
tions surveyed in our study were dependent upon ac-
curate determination of population allele frequencies.
Microsatellite alleles differ in size, but alleles of the
same size at a locus in geographically separate popula-
tions may not have the same origin as a result of size
homoplasy. Convergent mutations in different lineages
may produce alleles of the same size, with the result
that there may be greater differentiation among lin-
eages than revealed by analysis of size variation alone.
However, with over 800 alleles observed across all loci
in the study, the large amount of variation present
250
Fishery Bulletin 1 10(2)
Kwakusdis even
Kainet even
Crane Bay even
Skowquiltz even
— Salmon Bay even
1 lartley Bav even
Kilasoo even
Borrowman even
— Evelyn even
— Barnard even
Kitkiata even
— West Arm even
— Neckas even
Amoup even
— Turn even
— Clatse even
Nekite even
- Martin even
0.002
Central
Keogh even
— Embiey even
-Wakeman even
— Big Qualicum even
— Phillips even
- Clearwater even
rfi
Kakweiken even
— Ahta even
Kvvalate even
Glendale even
Klinaklini even
-Clear even
Ahnuhati even
South / ECV1
— Ambaek even
— Atnarko even
Kcmano even
Clyak even
- Chuckwalla even
Dallery even
—Green even
Khutze even
■ Kxngeal even
100
Even
— Bel owe even
- Wilauks even
IJliancc even
Hankin even
Head even
- Stewart even
-Elcho even
- Kiltuish even
"Crag even
Bullock Channel even
Miiton even
- Quartcha even
Spi Her even
Skedans even
Centra! / North
-Johnston even
- Security even
QC1
I Central
Lignite even
Datlaman even
Mamin even
Yakoun even
Gregory even
— Kano Inlet even
Celestial even
T asu even
- Browns Cabin even
Big Goose even
Windy Bay even
— Copper even
Pallant even
Tarundl even
- 1 lonna even
Decna even
— Echo 1 Iarbour even
QCI
Salmon even
- La I Iou even
-Stumaun even
— Sandy Bay even
— Shaw even
-Gilttoyees even
Tlell even
- Bish even
— Duthie even
Central / North
QCI
I Central
-McNichol even
Ensheshese even
— Mouse even
— Little Tsamspanaknok even
— Big Tsamspanaknok even
— Dogfish even
Crow Lagoon even
North
Figure 2
Neighbor-joining dendrogram of Cavalli-Sforza and Edwards (1967)
chord distance for 146 odd-year and 116 even-year populations of pink
salmon (Oncorhynchus gorbuscha ) surveyed at 16 microsatellite loci.
Bootstrap values at major tree nodes indicative of a regional group
of populations display the percentage of 500 trees where populations
beyond the node clustered together. QCI = Queen Charlotte Islands
and ECVI = east coast Vancouver Island.
Beacham et at: Population structure of Oncorhynchus gorbuscha in British Columbia and Washington, determined with microsatellites
251
Quaal even
Kwinamass even
Killope even
Lizard even
Kincolith even
Gill even
Lachmaeh even
Toon even
Kateen even
— Mussel even
Kleanza even
Kalum even
Kitwanga even
— Babine even
— Bulklcy even
Kispiox even
Lakelse even
Khutzeymateen even
Larch even
Chambers even
Cedar even
Kitimat even
Silver even
Oona even
Kumealon even
Pa-aat even
Central / North
Skccna
North
Alpha even
W 1 1 auks odd
i oo
Odd
Illiance odd
— Gilttoyees odd
— Foch odd
Cedar odd
Skowquiltz odd
— Kitimat odd
— Kiltuish odd
— Bish odd
Weewante odd
— Union odd
Toon odd
— Tsimitack odd
— Copper odd
— Tlellodd
Central / North
Central
Central / North
QCI
- Stewart odd
Alpha odd
— Sandy Bay odd
— Crow Lagoon odd
Kxngeal odd
-—Head odd
— Han kin odd
- Kumealon odd
lit
- Spiller odd
- Oona odd
-Silver odd
LalTou odd
— Stumaun odd
— Lizard odd
Dogfish odd
Khutzeymateen odd
-Chambers odd
— Mouse odd
it
— Kwinamass odd
— Quaal odd j
— Eagle Bay odd II
— Ensheshese odd
— L.Tsamspanaknok odd
Big Tsamspanak
Crag odd
— Larch odd
— Lachmaeh odd
— Kateen odd
— Evelyn o
Kitkiata odd
Amoup odd
- Martin odd
North
Central / North
Central
North
- Tyler odd
Nais odd
- Kainet odd
East Arm odd
- Hartley Bay odd
- West Arm odd
-Gill odd
- Barnard odd
— Checnis Lake odd
- Nckite odd
Cooper Inlet odd
— Kwakusdis odd
Central
Figure 2 (continued)
252
Fishery Bulletin 1 10(2)
Central
Central / South
Central
EC VI
South
Mamma Mamma odd
Grev Wo if odd
Dungencss odd
Lower Fraser
Upper Fraser
Washington
I iood Canal odd
Figure 2 (continued)
Beacham et al: Population structure of Oncorhynchus gorbuscha in British Columbia and Washington, determined with microsatellites
253
at these loci largely compensates for size homoplasy
(Estoup et al., 2002), and therefore the pattern of popu-
lation structure identified was unlikely to be obscured
or distorted by size homoplasy.
The distribution of genetic variance indicated that
differences in allele frequencies between the brood-
lines were about three times greater than differences
for all regional and population sources of variation,
but that regional differences in allele frequencies were
only about 1.4 times larger than differences among
populations within regions. Previous analyses of an-
nual variation in allele frequencies within populations
have indicated that this source of variation can be as
large as differences among populations within regions
(Golovanov et al., 2009). Although not specifically il-
lustrated in the current study because of limitations of
the software to facilitate a four-level nested analysis
of variance, similar results (not shown) were observed
in the current study. The larger importance of annual
variation within populations relative to population dif-
ferentiation compared with other Pacific salmon species
likely reflected the reduced population differentiation
observed in pink salmon relative to that observed in
other salmon species (Beacham et al., 2009, 2011).
The current study indicates that the largest deter-
minant of population structure of pink salmon in Brit-
ish Columbia and Washington was year of spawning
(odd or even), with a distinct separation of the two
broodlines. With the odd-year broodline, regional dif-
ferentiation was stronger in the southern portion of the
distribution of populations, with the greatest population
differentiation within a region observed in Washing-
ton. The Hood Canal hatchery population (also known
as Hoodsport hatchery 47°23'37"N, 123°08'54"W) was
the most distinct, even though this population was
derived from adults returning to the Dungeness River
and Dosewallips River in 1953 (Hard et al., 1996). The
distinctiveness of this population was reflected in allelic
frequency differentiation. For example, the frequency
of the Onel023U allele was 0.54 in the Hood Canal
hatchery population, 0.34 in the Gray Wolf River popu-
lation, and <0.20 in all other Washington populations.
Additionally, the frequency of Onel09133 was 0.33 in the
Hood Canal hatchery population, but <0.20 in all other
Washington populations. All populations from drainages
entering Hood Canal (Hood Canal hatchery, Dosewal-
lips River, Duckabush River, Hamma Hamma River) or
the Strait of Juan de Fuca (Dungeness River, Gray Wolf
River) were distinct from those on the eastern side of
Puget Sound (Snohomish River, Stillaguamish River,
Skagit River, Green River, Puyallup River). Genetic
separation of Strait of Juan de Fuca populations and
Hood Canal populations from those in eastern Puget
Sound was initially described by Shaklee et al. (1991) in
an analysis of allozyme variation. However, as described
by Shaklee et al. (1991), the Nooksack River population,
located in the northeastern section of Puget Sound and
nearest to the border with Canada, clustered with Hood
Canal and Strait of Juan de Fuca populations, rather
than with geographically closer populations on the east
side of Puget Sound. Similar results were observed in
the current study. Shaklee et al. (1991) suggested that
the genetic similarity of the Nooksack River population
to that of Hood Canal populations was a consequence of
a 1977 transfer of fertilized eggs from the Hood Canal
hatchery to a tributary of the Nooksack River and a
reduction of the native population due to habitat degra-
dation. As Shaklee et al. (1991) outlined, this enhance-
ment effort may have caused a genetic change in the
characteristics of this population that has persisted
over time (Hard et al., 1996).
Fraser River populations were separate from those
in southern British Columbia (east coast of Vancouver
Island, south coast mainland) and Washington, confirm-
ing the results from the previous analysis of allozyme
variation reported by Beacham et al. (1988) and Shak-
lee et al. (1991). In the Fraser River drainage, some
separation was observed between populations spawning
upstream from the Fraser River canyon (southern limit
approximately 175 km upstream from the mouth) from
those spawning downstream of the canyon. Genetic
separation between upriver and downriver populations
had also been had been outlined previously by Beacham
et al. (1988) and Shaklee et al. (1991). Similar genetic
separation between upper drainage and lower drain-
age populations has been observed in coho salmon (O.
kisutch) (Beacham et al., 2011) and reflects geographic
separation between the two groups of populations.
In northern British Columbia, odd-year broodline
populations in the Skeena River drainage were separate
from those farther south in the central coastal region of
British Columbia and from those farther north on the
northern coastal region of British Columbia in a simi-
lar pattern to that outlined by Beacham et al. (1988).
Similar differentiation was also observed in the even-
year broodline, with Skeena River drainage populations
distinct from other populations in northern British
Columbia. Some differentiation was observed in the
current study between even-year broodline pink salmon
populations from the Queen Charlotte Islands and other
regions in northern British Columbia (central coast,
Skeena River, north coast) ( FST =0.004-0.006); differen-
tiation of populations from the Queen Charlotte Islands
had also been observed by Beacham et al. in 1988.
Studies of population structure in Pacific salmon are
a useful initial step in developing and applying genetic
variation to the problem of estimating stock composition
in mixed-stock salmon fisheries. The key to successful
application of genetic variation to estimation of stock
composition centers around whether or not there is a
regional basis to population structure. This is a key
consideration because a regionally based population
structure is generally required for genetic stock identi-
fication estimation, with the assumption that the por-
tion of the mixed-stock sample derived from unsampled
populations is allocated to sampled populations from the
same region. With this assumption, the cost and com-
plexity of developing a baseline for stock composition
analysis is reduced, and refinements in estimated stock
compositions are possible as the baseline is enhanced
254
Fishery Bulletin 1 10(2)
in stages. For the odd-year broodline, applications in
southern British Columbia would appear to be possible
if fishery management objectives are to separate pink
salmon of Washington, Fraser River, and southern Brit-
ish Columbia origin. Finer subdivision of stock composi-
tion estimation, particularly in the Washington region,
may be possible, as separation of Hood Canal and Strait
of Juan de Fuca populations from those in Puget Sound
may be practical.
Studies of population structure in pink salmon
have revealed some consistent patterns. The great-
est differentiation observed in population structure
has been consistently reported to occur between the
two broodlines, whether in Asia or North America
(Beacham et al., 1988; Kartavtsev, 1991; Varnavs-
kaya and Beacham, 1992; Zhivotovsky et ah, 1994;
Salinenkova et ah, 2006; Golovanov et ah, 2009). In
Asia, studies have indicated that genetic differen-
tiation among populations is greater in the even-year
broodline than in the odd-year broodline (Hawkins et
ah, 2002; Golovanov et ah, 2009). In North America,
the reverse situation occurs, with population differ-
entiation among populations greater in the odd-year
broodline than in the even-year broodline (Beacham et
ah, 1988; Gharrett et ah, 1988; current study). These
findings support the concept of two main refugia oc-
cupied by pink salmon during the most recent Pleisto-
cene Era glaciation some 10,000 years ago (Aspinwall,
1974). The even-year broodline may have survived the
glaciation in a northern refugium (Aspinwall, 1974).
Once the glaciation ended, the even-year broodline
dispersed from the northern refugium, colonizing
southern regions more recently than northern ones.
Conversely, the odd-year broodline may have occupied
a southern refugium during the Pleistocene Era gla-
ciation (McPhail and Lindsey, 1970), and dispersed
northward, with northern populations derived more
recently than southern ones. As populations closer to
the refugium have had greater time to accumulate
genetic mutations and thus display greater population
differentiation, the current pattern of broodline and
population differentiation is consistent with dispersal
from a northern refugium for the even-year broodline
(greater population genetic differentiation in even-year
broodline) and dispersal from a southern origin for the
odd-year broodline (greater population differentiation
in odd-year broodline). Additionally, embryonic sur-
vival of the even-year broodline has been reported to
be higher than that of the odd-year broodline in a cold
(4°C) incubation environment, with higher alevin and
fry growth of the even-year broodline also observed
in the cold incubation environment (Beacham and
Murray, 1988). Greater suitability of the even-year
broodline to a colder environment is also illustrated
by the spawning distributions of the broodlines in
North America, with the even-year broodline in very
low abundance from the southern portion of the range
(Fraser River, Washington) and the odd-year broodline
in low abundance in western Alaska. Alternatively,
Krkosek al. (2011) suggested that the distribution of
even-year and odd-year populations result from densi-
ty-dependent mortality caused by interactions between
the broodlines. However, it seems difficult to account
for genetic population structure observed in pink salm-
on as a result of broodline interactions.
Conclusion
The level of differentiation observed among the pink
salmon populations within broodlines surveyed in the
current study was considerably less than in other species
of Pacific salmon. Sockeye salmon (O. nerka) typically
display high levels of genetic differentiation ( FST =0.097,
14 loci, average 30 alleles per locus, 299 populations)
(Beacham et al., 2006), with the other species displaying
levels of genetic differentiation ranging between sockeye
salmon and pink salmon. The low level of differentia-
tion observed in pink salmon may be a result of a more
recent colonization history (Hawkins et al., 2002), but
may also be a result of straying among local populations
within regions. As pink salmon juveniles spend little
time in fresh water after fry emergence, imprinting on
natal streams may not be as strong as in other species,
and as a result may stray more upon returning spawn-
ing migrations (Quinn, 1993). Chum salmon ( O . keta )
juveniles spend similar amounts of time in fresh water
as pink salmon, and population differentiation in the
species is higher only than pink salmon ( FST =0.033,
14 loci, average 57 alleles per locus, 380 populations)
(Beacham et al., 2009). The low level of genetic differ-
entiation observed in pink salmon population structure
likely reflects higher levels of straying among popula-
tions during spawning than those observed for other
Pacific salmon species.
Acknowledgments
A very substantial effort was undertaken to obtain the
pink salmon samples used in this study. We thank vari-
ous staff of Fisheries and Oceans Canada (DFO), the
Pacific Salmon Commission (PSC), and the Washington
Department of Fish and Wildlife (WDFW) for sample
collection, as well as First Nations staff. We acknowl-
edge those within the Kitasoo Fisheries Program who
sampled pink salmon, as well as the Gitxsan Watershed
Authority, the Kitselas and Kitsumkalum field staff,
the Skeena Fisheries Commission, the Nisga’a First
Nation, the Haida Fisheries Program, and the crew
of the Canadian Coast Guard Vessel Arrow Post. L.
Fitzpatrick drafted the map. C. Wallace assisted in the
analysis. Funding for the study was provided by DFO
and the PSC.
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257
Abstract — We evaluated measures
of bioelectrical impedance analysis
(BIA) and Fulton’s condition factor
(K) as potential nonlethal indices
for detecting short-term changes in
nutritional condition of postsmolt
Atlantic salmon ( Salmo salar). Fish
reared in the laboratory for 27 days
were fed, fasted, or fasted and then
refed. Growth rates and proximate
body composition (protein, fat, water)
were measured in each fish to evalu-
ate nutritional status and condition.
Growth rates of fish responded rap-
idly to the absence or reintroduction
of food, whereas body composition
(% wet weight) remained relatively
stable owing to isometric growth in
fed fish and little loss of body con-
stituents in fasted fish, resulting in
nonsignificant differences in body
composition among feeding treat-
ments. The utility of BIA and Ful-
ton’s K as condition indices requires
differences in body composition. In
our study, BIA measures were not sig-
nificantly different among the three
feeding treatments, and only on the
final day of sampling was K of fasted
vs. fed fish significantly different.
BIA measures were correlated with
body composition content; however,
wet weight was a better predictor of
body composition on both a content
and concentration (% wet weight)
basis. Because fish were growing
isometrically, neither BIA nor K was
well correlated with growth rate. For
immature fish, where growth rate,
rather than energy reserves, is a more
important indicator of fish condition,
a nonlethal index that reflects short-
term changes in growth rate or the
potential for growth would be more
suitable as a condition index than
either BIA measures or Fulton’s K.
Manuscript submitted 16 February 2011.
Manuscript accepted 10 January 2012.
Fish. Bull. 110:257-270 (2012).
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
Evaluation of bioelectrical impedance analysis
and Fulton's condition factor as nonlethal techniques
for estimating short-term responses
in postsmolt Atlantic salmon ( Salmo salar )
to food availability
Elaine M. Caldarone (contact author)1
Sharon A. MacLean1
Beth Sharack2
Email address for contact author elaine.caldarone@ noaa.gov
' Narragansett Laboratory
Northeast Fisheries Science Center
National Marine Fisheries Service
National Oceanic and Atmospheric Administration
28 Tarzweil Drive
Narragansett, Rhode Island 02882
2 J. j. Howard Laboratory
Northeast Fisheries Science Center
National Marine Fisheries Service
National Oceanic and Atmospheric Administration
74 McGruder Road
Highlands, New Jersey 07732
Populations of Atlantic salmon ( Salmo
salar) are broadly distributed across
the North Atlantic Ocean, repro-
ducing in coastal rivers of Iceland,
Europe, northwestern Russia, and
northeastern North America. Histori-
cally, native Atlantic salmon ranged
throughout New England waters, but
by the late 1880s, they were extir-
pated from many of the rivers. Cur-
rently, native populations exist only in
central and southeast Maine (North-
east Fisheries Science Center, http://
www.nefsc.noaa.gov/sos, accessed July
2011). A significant decline in these
populations during the 1990s resulted
in the listing of the Gulf of Maine
Distinct Population Segment (DPS) as
endangered under the United States
Endangered Species Act (Federal Reg-
ister, 2009). Hatchery-based restora-
tion of salmon to this area began in
1970 and continues today. Although all
life-stages have been released to the
rivers, recent emphasis has focused on
releasing fry and smolts produced in
the hatchery from field-caught adults
from the DPS region. Once smolts
reach the sea, they must adapt to an
environment and food source radi-
cally different from their freshwater
habitats. To evaluate the success of
these restoration efforts, managers
need tools to assess whether hatchery-
reared fish are thriving in the natural
environment and to assess condition of
the postsmolt Atlantic salmon popula-
tion as a whole.
Growth rate and fat content are key
measures of condition in fish. During
a fish’s early life-history stage, rapid
growth rates increase the probability
of survival and recruitment, primar-
ily through decreased vulnerability
to predation and starvation (see re-
view by Fonseca and Cabral, 2007).
Fat, the primary energy repository
in marine fish, increases when food
intake exceeds metabolic needs and
decreases during food-limited times
when it provides energy for main-
tenance, growth, and reproduction
(Shulman and Love, 1999; Jobling,
2001). Growth rates of fish caught in
the wild are often estimated by us-
ing nucleic acid analysis (the ratio of
RNA to DNA) or otolith microstruc-
ture analysis (Chambers and Miller,
258
Fishery Bulletin 110(2)
1995). The former requires removal of a plug of muscle
tissue (MacLean et al., 2008), and the latter requires
removal of an otolith, a lethal procedure. Direct mea-
surement of fat content is also lethal, requiring chemi-
cal analysis of a sacrificed fish. Identifying a minimally
invasive, nonlethal index that could estimate growth
rate or body composition in postsmolt salmon would al-
low restoration managers to evaluate the condition of
field-captured fish.
We chose to evaluate two nonlethal techniques which
had the potential to reflect fish condition: bioelectrical
impedance analysis (BIA) and Fulton’s condition factor
(hereafter, also called Fulton’s K). BIA is a technique
which has been applied to humans and other mammals
as a means to estimate nutritional status and body
composition (Baumgartner et al., 1988; Marchello and
Slanger, 1994; Schwenk et al., 2000; Barbosa-Silva et
al., 2003). Recently, BIA has been used to estimate body
composition content in fish (Cox and Hartman, 2005;
Pothoven et al., 2008; Hanson et al., 2010). For BIA,
a small, portable, battery-operated instrument is used
to generate a mild alternating current between two
sets of electrodes that have been placed on the subject.
The resulting voltage drop is recorded as resistance
( R ) and capacitive reactance ( Xc ) in series. When an
alternating current passes around a cell, R can be af-
fected by extracellular water (good conductor) and fat
(poor conductor). When a constant signal frequency
is applied, a geometrical system can be modeled as a
cylinder (conductor volume = pL2/R, where L is length)
(Lukaski et al., 1985). With this conductor volume ap-
proach, predictive equations have been constructed to
estimate water content and fat-free mass in humans
(Lukaski et al., 1985; Chumlea et al., 2002), and water-,
fat-, protein-, and ash-content in fish (Cox and Hart-
man, 2005; Pothoven et al., 2008; Hanson et al., 2010).
Impedance values have also been used to calculate a
condition index (phase angle, arctangent Xc/R converted
to degrees) in both humans and fish (Schwenk et al.,
2000; Barbosa-Silva et al., 2003; Cox and Heintz, 2009).
In an organism, Xc is a measure of the phase shift that
results from an electrical charge being momentarily
stored in the double phospholipid layer of a cell mem-
brane. When cells die, Xc drops to zero; phase angles
thus range from zero (zeroXc, all cells dead) to 90° (ze-
ro R). In humans, lower phase angle values have been
associated with conditions such as reduced survival in
HIV-infected patients, and malnutrition (Schwenk et
al., 2000; Barbosa-Silva et al., 2003). In fish, signifi-
cant decreases in phase angles have been observed in
juvenile rainbow trout ( Oncorhynchus mykiss) and brook
trout ( Salvelinus fontinalis) after three weeks of fasting,
and in juvenile Chinook salmon (O. tshawytscha) after
eight weeks of fasting (Cox and Heintz, 2009). In those
studies, fish were repeatedly measured over a period
of weeks with no mortalities associated with the BIA
procedure — an attribute that made it attractive for our
application.
Fulton’s K (weight/length3) (Ricker, 1975) is a widely
used fish condition index based on morphometries (e.g.,
Anderson and Gutreuter, 1983; Stevenson and Woods
Jr., 2006), measurements that can be obtained easily
in the field. This index is based on the assumption that
within a cohort, individuals with higher K values (more
rotund fish) contain more energy reserves (fat and pro-
tein), and thus are in better condition than those with
lower K values.
The response time of a condition index can be af-
fected by factors such as water temperature, life-stage,
season, and species (Busacker et al., 1990). Cox and
Heintz (2009) measured phase angles in food-deprived
rainbow trout and brook trout on a weekly basis, and
in food-deprived Chinook salmon intermittently for 13
weeks. Because our field recaptures of hatchery-reared
postsmolts occur two to three weeks after release, we
measured response of BIA measures and K to varying
food availability every 3-4 days throughout a 3-week
time period. Thus the objectives of our study were 1) to
assess and validate the relations between two nonlethal
condition indices (BIA measures and Fulton’s condi-
tion factor) and two measures of nutritional condition
(growth rate and body composition); and 2) to determine
the short-term response time (days to a few weeks) of
these measures to varying food availability.
Materials and methods
Smolts used in this study were progeny of field-caught
Atlantic salmon from the Penobscot River, Maine, which
had been spawned at Craig Brook National Fish Hatch-
ery, East Orland, Maine, and reared at the Green Lake
National Fish Hatchery, Ellsworth, Maine, for 13-15
months. Randomly selected smolts (52-113 g, 16-21
cm) were anesthetized in buffered tricaine methane
sulfonate (MS-222, 150 mg/L) and implanted intra-
muscularly with a passive integrated transponder tag
(PIT tag, Biomark, Boise, ID1) to permit identification
of individuals. The smolts were then returned to the
hatchery tank to allow time for full recovery, resumption
of feeding, and removal of any tagging-related mortali-
ties. Twenty-five days later the fish were transported
to the University of Rhode Island’s Blount Aquarium
facility in Narragansett, Rhode Island, where they were
randomly placed into two aerated, flow-through tanks
(360-L capacity) initially filled with freshwater trucked
from the hatchery. Over a period of five to six hours,
freshwater was gradually replaced with sand-filtered
seawater (10°C, 30 ppt). During the subsequent three
weeks, while the fish were recovering from the transfer
and acclimating to seawater, the water temperature was
gradually raised to 12°C. During this period fish were
fed to satiation twice per day with a commercial feed
(Corey Optimum Hatchery Feed for Salmonids, Corey
Nutrition Co., Fredericton, NB, Canada) supplemented
with freeze-dried krill ( Euphausia pacifica, Aquatic
1 Mention of trade names or commercial companies is for
identification purposes only and does not imply endorsement
by the National Marine Fisheries Service, NOAA.
Caldarone et al.: Nonlethal techniques for estimating responses of postsmolt Salmo solar to food availability
259
Table 1
Sampling schedule for Atlantic salmon ( Salmo salar ) postsmolts reared at 12°C under three feeding regimens (fed; fasted; fasted
then refed) in order to obtain a range of nutritional condition and growth rates. Nonlethal condition indices (Fulton’s condition
factor \K\ and bioelectrical impedance analysis [BIA] measures) and two measures of nutritional condition (wet-weight based
growth rate and proximate body composition) were determined for each fish. The refed group was fasted for 11 days and then fed.
Numbers listed are number of fish sampled.
Sampling day and feeding regimen
Day 0
Day 3
Day 7
Day 11
Base-line
Fed
Fasted
Refed
Fed
Fasted
Refed
Fed
Fasted
Refed
Fed
Fasted
Refed
Weight, length
5
24
24
22
4
4
0
4
4
0
4
4
22
(Fulton’s K)
BIA measures
5
24
24
0
4
4
0
4
4
0
4
4
0
Proximate body
5
0
0
0
4
4
0
4
4
0
4
4
0
composition
Sampling day and feeding regimen
Day 15
Day 19
Day 23
Day 27
Fed
Fasted
Refed
Fed
Fasted
Refed
Fed
Fasted
Refed
Fed
Fasted
Refed
Weight, length (Fulton’s K)
4
4
5
4
4
5
4
4
5
0
0
7
BIA measures
4
4
5
4
4
5
4
4
5
0
0
7
Proximate body composition
4
4
5
4
4
5
4
4
5
0
0
7
Eco-Systems, Inc., Apopka, FL). Twenty-five days after
the initial seawater transfer, when the now postsmolts
appeared to be acclimated and feeding well, the experi-
ment commenced (day 0).
Throughout the experiment, water temperature in
each tank was recorded hourly with a HOBO® data log-
ger (Onset Computer Corp., Bourne, MA), and ammonia
levels and salinity were tested weekly. Water tempera-
tures averaged 12.0°C, standard deviation (SD) = 0.2;
salinity averaged 31 ppt, SD = 1; and the photoperiod
was 15 hours of light to 9 hours of dark. Two-thirds
of each tank surface was covered with black plastic
to provide a low-light refuge, and the remaining third
was exposed to overhead fluorescent lighting covered
with red plastic. All experiments were conducted in
accordance with guidelines established by the Institu-
tional Animal Care and Use Committee (IACUC) at the
University of Rhode Island.
Sampling protocols
Day 0 sampling On day 0, five fish were randomly
selected and sacrificed to provide baseline body compo-
sition data. To obtain a range of nutritional condition
levels, all remaining postsmolts were subdivided into
three different feeding treatments (tanks); fed, fasted,
and fasted then refed. The fed treatment (re = 24) was
continually fed, the fasted treatment (re =24) received
no food, and the fasted, then refed treatment (re =22)
received no food for 11 days followed by feeding for
16 days. Individuals were anesthetized with buffered
MS-222 (150 mg/L) in chilled (12°C) seawater, blotted
dry, measured for initial weight (wet weight, WW, near-
est 0.1 g) and fork length (FL, nearest 0.1 cm), and the
PIT tag number and any gross external abnormalities
were noted. BIA measurements were taken on all fish
assigned to the fed and fasted treatments. Fish assigned
to the fasted, then re fed treatment had their fork length
and wet weight recorded at the start of their fasting
(day 0), and again on day 11 when they were fed for the
remainder of the experiment. Total handling time per
fish for wet weight, fork length, and BIA measurements
was no more than 30 seconds.
During daylight hours, fish in the fed treatment and
refed group were provided freeze-dried krill ad libitum
from a belt feeder. On days fish were sampled, no food
was provided until after sampling was completed (-1200).
Days 3-27 sampling In order to determine the response
time of BIA measures and K to the three feeding treat-
ments, and to construct predictive equations for body
composition, fish were sampled and sacrificed every
three to four days over a 23-27 day period. On day 3
and every 4 days thereafter, 4 fish each from the fed
and fasted treatments were sacrificed. On day 15 and
every 4 days thereafter, 5 fish from the refed group were
sacrificed and sampled, except on the final day when 7
fish were sacrificed (Table 1).
260
Fishery Bulletin 1 10(2)
All fish were killed by an overdose of buffered MS-
222 (300 mg/L) in chilled (12°C) seawater. The fish
were immediately blotted dry, measurements of wet
weight, fork length, and BIA (in that order) were taken,
and PIT tag numbers and external abnormalities were
noted. Internal temperatures (muscle and stomach)
were determined by inserting an instant-read digital
thermometer into the dorsal musculature of the fish,
and down the esophagus into the stomach. Total sam-
pling time for each fish was ~1.5 min. Each fish was
then dissected, its liver removed, its gut evacuated, and
sex and maturity status was determined. Livers and
carcasses were wrapped separately in aluminum foil
before being vacuum-sealed in plastic bags and stored
frozen at -80°C until subsequent analysis.
Body composition analysis Liver and carcass wet
weights were determined to the nearest 0.1 mg before
being freeze-dried to a constant weight and reweighed.
Each dried sample was ground in a Foss Tecator®
Cyclotec 1093 sample mill (FOSS, Hilerqd, Denmark)
and stored at -20°C in glass scintillation vials under
nitrogen gas until further analysis. Total water ( TWa )
was calculated by subtracting total dry weight (liver
dry weight plus carcass dry weight, DW) from total wet
weight.
Freeze-dried carcasses were analyzed for proximate
body composition (protein, fat, ash) by an indepen-
dent laboratory (A&L Great Lakes Laboratory, Fort
Wayne, IN) by using Association of Official Analytical
Chemists international certified methods and were
reported to us on a percent DW basis. Nitrogen was
determined by using a LECO nitrogen combustion ana-
lyzer (LECO Corp., St. Joseph, MI; Dumas method),
protein was calculated by multiplying nitrogen values
by 6.25 (Jones, 1931), fat was obtained with a 4-hr
ether reflux extraction, and ash was determined after
combustion at 600°C for 2-4 hr. Body composition (g)
(total amount of each proximate body constituent) were
calculated from percent dry weight concentrations by
dividing the independent laboratory values by 100 and
multiplying by the total dry weight. To conform to the
format most often reported in the literature, percent
dry-weight-based concentrations were converted to a
percent wet-weight-based concentration by dividing
body composition (g) by wet weight and multiplying by
100 (body composition [%WW]).
Liver lipids, which are often mobilized first during
fasting (Love, 1970; Black and Love, 1986), were mea-
sured separately in order to detect changes more easily.
Liver lipid content was determined in-house by using a
modification of Folch et al. (1957). Entire freeze-dried
livers were first extracted by ultrasonic homogenization
with 2:1 methylene chloride:methanol solvent (20 mL/g
tissue), then back extracted with aqueous 0.1 M KC1
and centrifuged to remove water, methanol, and water-
soluble and water-insoluble tissue components by phase
separation. The remaining nonaqueous fractions were
evaporated to remove methylene chloride, and the non-
volatile lipid residue was weighed on a Mettler® AE240
balance (nearest mg, Mettler-Toledo, Inc., Columbus,
OH). Individual livers were not analyzed for protein or
ash because of their small size; therefore body composi-
tion values were obtained for carcass water, liver water,
total water (liver water plus carcass water), carcass fat,
liver fat, total fat (liver fat plus carcass fat), carcass
protein, and carcass ash.
Growth-rate calculations
Individual instantaneous wet-weight based growth rates
were calculated with the following formula (Ricker,
1979):
growth rate (per d) = (In WWl2-\nWWtl)l(t2- tj ) , (1)
where WW = the wet weight of an individual at time t
(day).
Growth rates for fish in the fed and fasted treat-
ments were calculated from day 0 (t;) until the day
they were sacrificed ( t2 ). For the fasted portion of the
fasted, then refed treatment, growth rates were cal-
culated from day 0 (t7) until day 11 (<2); for the refed
portion, growth rates were calculated from the first
day of refeeding (day 11, t:) until the day they were
sacrificed (t2). Growth rates calculated over intervals
of less than five days were excluded from our analyses
because minimal weight changes over those short time
intervals, combined with the inherent variability of
measuring wet weight, resulted in inaccurate growth
rate estimates.
BIA measurement protocol and BIA measures
BIA measurements were determined with a Quantum-
X® (RJL Systems, Point Heron, MI) four-electrode single
frequency (800 pA, 50 KHz) analyzer. Needle electrode
probes were constructed in-house according to Cox and
Hartman (2005). For each probe, two 12-mmx28-gauge
electroencephalographic (EEG) needles (Grass Telefac-
tor, West Warwick, RI) were mounted in balsam wood
1 cm apart and with 0.5 cm of the needle exposed. The
fish were placed on their right sides on a nonconduc-
tive board. The detector electrode of the anterior probe
was inserted midway between the posterior edge of
the operculum and the leading edge of the dorsal fin,
and midway between the base of the dorsal fin and the
lateral line. The signal electrode of the posterior probe
was inserted at the leading edge of the adipose fin,
and midway between the base of the adipose fin and
the lateral line. Serial R, serial Xc, and the distance
between the inside (detector) electrodes, were recorded
for each fish.
All BIA measures were initially calculated by using
both their series and parallel forms. Results of statis-
tical analyses indicated that the parallel forms were
more highly correlated to the independent variables.
For this reason, as well as the instrument manufac-
turer’s recommendation that the parallel forms most
Caldarone et al Nonlethal techniques for estimating responses of postsmolt Sa/mo salar to food availability
261
closely approximate the real electrical values of biologi-
cal tissue (RJL Systems, http://www.rjlsystems.com/
docs/bia_info/principles/, accessed April 2008), only
the parallel forms of the BIA measures are discussed
and reported.
Serial R and Xc values were transformed to their
parallel equivalents with the following formulas:
Rpar(Q) = R + Xc2/ R, (2)
XcpJQ) = Xc + R2/ Xc . (3)
Because R and Xc are dependent upon the distance the
current must travel ( D , distance between the electrodes
in cm), the BIA instrument manufacturer advises that
when these variables are used in prediction equations,
the effect of this distance must be accounted for (RJL
Systems, http://www.rilsvstems.com/docs/bia info/prin-
ciples/. accessed April 2008). We therefore also calcu-
lated standardized Rpar and Xcpar values by dividing
Kpar ™*Xcpar by D(RpJD,XcpJD).
Conductor volumes were calculated by using the fol-
lowing formulas:
R„ar conductor volume = D2/R„„r , (4)
pdi pdi
Xc conductor volume = Z)2/Xc .. . (5)
pdr r
Capacitance (a measure of the electrical storage ca-
pacity) and impedance (a measure of the opposition to
the flow of electrical current) were calculated using the
following formulas:
capacitance (pF) = 1 x 1012/(2tt •50000»Xcpar), (6)
impedance (Q) = sqrt((Rpar2) + ( Xcpar 2)) , (7)
where 50,000 is the frequency applied by the BIA instru-
ment in Hertz.
In order to conform to values previously reported in
the literature, phase angles were calculated with Xc
and R in their series form:
phase angle (°) = arctan (Xc //? ) • 180/zr, (8)
where Xc and R are the vertical and horizontal axis,
respectively. The arctangent of the ratio will yield the
angle of the impedance vector in radians, which is then
converted to degrees by multiplying by 180/7T. A series-
based phase angle is equal to 90° minus the parallel-
based phase angle.
Impedance measurements are negatively related to
temperature (van Marken Lichtenbelt, 2001). Water
temperature, room temperature, and internal fish tem-
peratures (mean muscle temperature=13.4°C, SD = 0.9;
mean stomach temperature = 12.6°C, SD = 1.0) were con-
stant in our study and therefore not included as fac-
tors in our analyses. Unless otherwise noted, the term
“BIA measures” refers collectively to i?par, Xcpar, Rp.JD ,
Xcpar/D, phase angle, /?par and XcpaT conductor volumes ,
capacitance, and impedance.
Fulton's K
Fulton’s condition factor (K) was calculated with the
following formula (Ricker, 1975):
K = 100 • WW/FL3 , (9)
where WW (in g) and FL (in cm) are values from the day
the fish was sacrificed.
Data analysis
Within treatments A Dunnett two-tailed /-test with
final FL as a covariate was used to detect changes
in body composition (% WW), BIA measures, and K,
within the fed and fasted treatments and the refed
group. Baseline values (day 0) were specified as the
control for all variables except BIA measures, where
day-3 fed values were the specified controls.
Between treatments A two-way multivariate analysis
of covariance (MANCOVA) for unbalanced design
was used to compare body composition (%WW) (total
fat concentration, TF%; total water concentration,
TWa%\ carcass protein concentration, CP%), BIA
measures, growth rate, and K between the three
feeding treatments (fed; fasted; fasted, then refed)
and sampling times, with final FL as the covariate.
When interactions were significant, feeding treat-
ment was nested in day and follow-up comparisons
were examined by using Tukey’s HSD multiple range
tests. Because we found no significant differences
in percent liver fat between any of the treatments
or days, and liver fats comprised <1% of total fats
(range: 0.39-0.99%), only total fat values were used
in all analyses.
Prediction models Prediction models for body compo-
sition expressed as both content (total fat, TF\ TWci;
carcass protein, CP), and concentration ( CP% , TF%,
TWa%), and growth rate were developed. We used an
information-theoretic approach for small sample sizes
(Akaike’s information criterion, AICc) to select the
“best-fit” models (Wagenmakers and Farrell, 2004).
Because we had no prior knowledge of the variables
or combination of variables that would be the best
predictors of the dependent variables, all nine BIA
measures plus the interaction of Rpar, and Xc with
D were tested, along with WW, FL, and K. Testing 14
independent variables generated a large number of
models for each dependent variable, with many models
significant at the PcO.0001 level. For brevity, only the
top three most parsimonious models (as indicated by
the smallest AICc values) are reported and discussed.
Correlations Pearson product-moment correlations were
used to investigate the relations between body compo-
sition (both g and %WW), and WW, BIA measures, K,
and growth rate. All statistical analyses were carried
out with SAS software vers. 9.1 (SAS Inst., Inc., Cary,
262
Fishery Bulletin 1 10(2)
Table 2
Proximate body composition (% whole-body wet weight), capacitance, and % change in wet weight of Atlantic salmon ( Salmo
sala r) postsmolts reared at 12°C under 3 feeding regimens (fed; fasted; fasted then refed). The refed group was fasted for 11 days
and then fed. TF%= total fat concentration; TWa%= total water concentration; CP%=carcass protein concentration. Capacitance
(pF) is a measure of the electrical storage capacity of cells. Percent change in wet weight was calculated by subtracting the initial
wet weight of a fish on day 0 (fed and fasted treatments) or day 11 (fasted then refed treatment, day they were fed) from its wet
weight on the day it was sacrificed, expressed as a percent of its initial wet weight. Asterisks (*) in the body composition columns
indicate a significant difference from baseline values, and in the capacitance column they indicate a significant difference from
day 3 fed values (Dunnett two-tailed t-test, P<0.05). Values are means with standard deviation (SD) in parentheses. Mean initial
wet weight of all fish was 76.0 g (SD = 12.6, [no. of fish sampled=74|). NA=not available. n=number of fish sampled.
Feeding regimen
Sampling day
n
TF%
TWa%
CP%
Capacitance
% change in wet weight
Baseline
0
5
7.1 (1.1)
73.2 (1.2)
17.6(0.5)
NA
0
Fed
3
4
7.4 (1.8)
73.9(1.5)
17.0 (0.4)
2275 (322)
NA
7
4
6.3 (1.4)
74.1 (1.3)
17.4 (0.7)
2057 (66)
1.2 (1.9)
11
4
5.9 (0.9)
74.9(1.3)
16.9(0.7)
2012 (333)
9.0 (2.3)
15
4
5.5 (0.8)
74.9 (1.0)
17.4 (0.4)
2144 (150)
15.5 (6.5)
19
4
6.2 (1.1)
73.8(1.3)
17.8(0.3)
2024 (113)
25.9 (5.0)
23
4
6.7 (2.0)
73.9(2.6)
17.6(0.7)
2024 (126)
20.7 (7.2)
Fasted
3
4
7.9 (1.2)
72.6 (1.1)
17.0 (1.0)
2112 (268)
NA
7
4
6.4 (1.3)
74.8 (1.6)
16.6 (0.4)
2042 (156)
-2.7 (0.8)
11
4
6.0 (1.0)
74.5(1.2)
16.9(0.2)
1861 (180)*
-4.2 (0.9)
15
4
5.2 (0.6)*
75.9 (1.1)*
17.0 (1.0)
1919 (152)*
-4.6 (1.4)
19
3
5.3 (1.6)
75.8 (1.6)*
17.3 (0.2)
1988 (175)
-7.3 (2.5)
23
4
4.9 (1.1)*
76.5(1.5)*
16.2 (1.1)
1744 (297)*
-7.3 (2.5)
Fasted, then refed
15
5
6.5 (2.1)
74.8 (2.8)
17.3 (0.9)
2005 (228)
NA
19
5
5.7 (0.8)
75.7 (1.2)
16.8 (0.7)
1876 (151)
4.0 (4.5)
23
5
5.8 (0.8)
74.7 (0.9)
17.4 (0.7)
1978 (136)
17.5 (2.7)
27
7
5.8 (0.8)
75.1 (0.7)
17.2 (0.5)
1953 (74)
15.5 (5.9)
NC). Unless otherwise stated, the level of significance
was set at P<0.05.
Results
General observations
At the start of the experiment we observed frayed or
eroded dorsal fins in -78% of the fish, and fraying of
the pelvic fin in -12% of the fish. During the experi-
ment there was no change in the frequency of these
abnormalities and there were no mortalities; however,
one severely emaciated, moribund fish was excluded
from the data set. None of the fish had well developed
gonads and therefore all were considered to be imma-
ture. Because sex did not emerge as a significant factor
in any of the statistical analyses, it was not included
in the data set.
Within two weeks after their transfer to seawater,
the postsmolts were actively feeding and appeared to
be acclimated to the seawater and their surroundings.
On day 0, wet weight ranged from 43 to 132 g, and fork
length from 18 to 23 cm; size distributions of fish were
not significantly different between feeding treatments
(fed mean=76 g, SD = 12; fasted mean = 75 g, SD = 13;
fasted then refed mean = 80 g, SD = 4).
On day 0 we encountered instability problems with
our handmade probes, which were resolved by the end
of the day. Because we were not completely confident in
our BIA measurements that day, we have not included
these initial BIA values in any of our analyses. Cur-
rently, BIA needle-electrodes are not manufactured and
must be made in-house. Standardized manufactured
probes would be necessary if BIA is to be routinely
used.
Within-treatment effects
Over the course of the experiment, changes in fish
weight and body composition (%WW) were observed
within a treatment. On the final day of the experiment,
fish in the fed treatment had increased in weight by
14-28%, whereas fasted fish had lost 5-10% of their
weight (Table 2). Fish in the fasted, then refed treat-
ment lost an average of 4% in weight during their 11
days without food, and gained 10-21% after 16 days of
refeeding. Within each of the 3 feeding treatments, CP%
(Table 2) and CA% (not shown) remained fairly constant.
Changes in TF% (decreasing) and TWa% (increasing)
occurred in fasted fish only, and differences from base-
line values became significant after 15 d of fasting (Table
2). Within a treatment and sampling day, TF% was the
most variable of the body components, averaging an
Caldarone et al : Nonlethai techniques for estimating responses of postsmolt Salmo solar to food availability
263
18% coefficient of variation (CV) compared to 3.7% for
CP% and 1.9% for TWa%. In continually fasted fish only,
changes in two BIA measures from day-3 fed values were
observed: capacitance significantly decreased beginning
on day 11 (Table 2) and impedance increased on day 23
only (not shown).
Between- treatment effects
Results of the MANCOVA indicated that of the vari-
ables tested (body composition [%-WW], BIA measures,
growth rate, K), only growth rate (PcO.OOOl, df=13) and
K (P=0.0009, df= 16) revealed significant differences due
to feeding treatment. Results of Tukey’s HSD multiple
range tests indicated that beginning on day 11 and con-
tinuing until the end of the experiment, growth rates
(negative) of the continually fasted fish were statisti-
cally significantly slower than the fed treatment (Fig.
1A). Eight days after refeeding (day 19), growth rates
of the refed group were significantly faster than those
of the continually fasted treatment. On day 19 the fed
treatment also had faster growth rates than those of
the refed group, whereas the relation was reversed on
day 23. On day 23, K values were significantly smaller
in the continually fasted fish than those of the fed fish
(Fig. IB). There was a trend of higher mean phase angle
values in the fed treatment than those in the fasted
treatment (Fig. 1C), but the values between the feeding
treatments were not statistically different owing to high
variability within a day’s sample.
Prediction models
Body composition (g), /? and Xcpar measured in the
postsmolts encompassed a range of values (Table 3).
The best predictor models for body composition (g) all
contained fff and FL as independent variables, with
some models also including BIA measures or K (Table
4). In these models, K can be viewed as an interaction
term between fW and FL (i.e., a size-related variable).
The models for TWa and CP had high predictive capabili-
ties (coefficient of determination (r2)>0. 98), whereas the
model for TF was less so (r2 range: 0.74-0.76). Adding
any of the BIA measures to models containing only size
or size-related independent variables (size-based-only
models) increased the explanatory capabilities by <1.5%.
The best predictor models for body composition
(%WW) also contained size-related variables (WW or
FL, and often K) (Table 4). The models for CP% ad-
ditionally contained two or three BIA measures. Add-
ing the BIA measures to a size-based-only CP% model
increased the explanatory capabilities by <3.4%.
The best predictor models for growth rate included
size-related variables plus two BIA measures (Table 4).
Adding the two BIA measures to size-based-only models
increased the predictive capability of the growth equa-
tions by 18-22%.
Models with AAICc values <2 are considered to be
equally probable to the “best fit” model (Burnham and
Anderson, 2002). Based on the AAICc and r2 values,
(A) Instantaneous wet-weight-based growth rate (per d);
(B) Fulton’s condition factor (K= 100»final wet weight/
fork length3); and (C) phase angle (arctangent reactance/
resistance converted to degrees) of laboratory-reared
Atlantic salmon (Salmo salar) postsmolts measured
over 27 days. Values are mean (±standard deviation
[ SD I ) for each sampling day. Fish were either fed ad
libitum , fasted, or fasted until day 11 and then fed for the
remainder of the experiment (refed). Within a sampling
day, food treatments sharing a common superscript or
without superscripts do not differ significantly (Tukey’s
HSD multiple range tests). For fed and fasted fish n = 4
for each sampling day. For refed fish n = 22 for day 11,
n = 5 for days 15, 19, and 23, and n = 7 for day 27.
264
Fishery Bulletin 1 10(2)
little difference was evident between the top three mod-
els for each dependent variable measured. Statistical
significance of all models was high (P<0.0001); however,
the predictive capability of the body composition content
(g) models (74-99%) was much higher than the con-
centration (%WW) and growth rate models (all <50%),
with the TF% models having the lowest explanatory
capability (<33%).
Correlations
Body composition (g) values were most highly correlated
with WW (Table 5), although CP and TWa were also well
correlated with Xcpar conductor volume (Table 5). CP and
TWa were highly positively correlated with each other,
and TF and TWa were less so; thus the majority of the
changes in WW would be from the relation of water to
protein, not water to fat. The ratio of water to protein
averaged 4.4 (SD = 0.2), which is very similar to the value
reported by Breck (2008) from other studies.
Variables most highly correlated with TF% (posi-
tively) and TWa% (negatively) were WW and K (Table
5), whereas CP% and growth rate were most correlated
with WW. TF%, TWa%, and CP% had no or low corre-
lation with growth rate, indicating that the fish were
growing isometrically and not storing fat (Table 5).
Overall, correlations of body composition (%WW) were
much lower than correlations of body composition (g)
with WW, BIA measures, and K.
Discussion
Treatment effects
Feeding treatment (fed; fasted; fasted then refed)
clearly impacted weight-based growth rates of post-
smolt salmon. Growth rates of fed fish (mean=0. 0078/d;
range: -0.0018 to 0.0139/d) were significantly faster
than those of fasted fish (mean = -0. 0036/d; range:
-0.0020 to -0.0053/d) and fell within ranges reported
for other laboratory studies using similarly aged Atlantic
salmon postsmolts (Handeland et ah, 2000; Jobling et
al., 2002; Bendiksen et al., 2003; Sissener et al., 2009).
Growth rates of fish responded quickly to the absence
or reintroduction of food; decreasing after seven days
of fasting, and increasing eight days after refeeding.
Wilkinson et al. (2006) reported a similar response time
for growth rates of Atlantic salmon smolts after 15 days
of fasting (their first sampling date) and seven days of
refeeding. Somatic growth rate in immature fish is an
important index of condition because faster growers
are considered to have a higher probability of survival
(Lundvall et al., 1999; Craig et al., 2006; Fonseca and
Cabral, 2007). Rapid growth rate, which results in larger
individuals, is also thought to be critical for over-winter
survival (Beamish and Mahnken, 2001). The widespread
importance of rapid growth rates during early life his-
tory stages is demonstrated in a meta-analysis of 40 fish
studies (Perez and Munch, 2010) where 77% of estimated
Table 3
Mean, standard deviation (SD) in parentheses, and range
of body composition (g) and bioelectrical impedance anal-
ysis measures (BIA; parallel resistance [i?par] and reac-
tance [Xcpar] ) of postsmolt salmon (Salmo salar) reared
at 12°C and three feeding regimens in order to obtain a
range of nutritional condition. Fish weight ranged from
43 to 132g and length from 18 to 23 cm (no. of fish sam-
pled=74).
Variable Mean Range
Total water content (TWa) (g)
60.0
(11)
CO
CO
-92.4
Carcass protein content (CP) (g)
13.7
(3.1)
6.4-
-24.3
Total fat content (TF) (g)
5.1
(1.8)
1.8-
-12.7
Carcass ash content (CA) (g)
1.9
(0.4)
1.0-
-3.0
Rpar(tl)
408
(24)
340-
-480
Xcpar<£>)
1613
(178)
1204-
-2437
selection differentials favored a larger fish size during
this stage.
During our study there was little impact of feeding
treatment on the body composition (%WW) of postsmolt
salmon. The reason for this relative constancy among
feeding treatments was two-fold: 1) isometric growth
in fed fish resulted in stable body composition (%WW)
throughout a range of growth rates; and 2) there was
little loss of proximate body constituents in fasting fish.
When excess energy in young fish is directed primar-
ily toward isometric growth, differences in growth rate
cannot be discerned from body composition (%WW). For
this reason, indices based on body composition (%WW)
may not be the best metrics for assigning nutritional
status and condition in immature fish.
Generally, fish are well adapted to fluctuations in
the food supply — a scenario they encounter often in
the wild (Jobling, 2001). Atlantic salmon and other
fish can respond to instances of food deprivation by
reducing oxygen consumption, resulting in a decreased
rate of fat and protein catabolism (Beamish, 1964;
Metcalfe, 1998; O’Connor et al., 2000). Our results
indicate that fasted postsmolts used only a small por-
tion of fat for their metabolic needs during the 3-week
experiment. The combination of isometric growth in
the fed fish, and short-term food deprivation period
in the fasting fish, resulted in nonsignificant differ-
ences in body composition (%WW) among the feeding
treatments.
The utility of BIA as a condition index is based on
differences in proportions of body constituents trans-
lating into measurable differences in impedance when
an electric current is applied. Because body composi-
tion (%WW) of the postsmolts did not differ among the
feeding treatments, it was not surprising that none
of the nine BIA measures differed among the feeding
treatments. Within the fasted treatment, capacitance
values did decrease with increasing time fasted, pos-
sibly reflecting the decline in fat concentration in this
Caldarone et al Nonlethal techniques for estimating responses of postsmolt Sa/mo sa/ar to food availability
265
Table 4
Coefficients and Akaike’s second-order information criterion for small sample sizes (AICc) for the top 3 most parsimonious regres-
sion models for proximate body composition (expressed as g and % wet weight) and growth rate of postsmolt Atlantic salmon
( Salmo salar) reared at 12°C under 3 feeding regimens in order to obtain a range of nutritional condition and growth rates.
tyW=wet weight (g); FL = fork length (cm); i?par=resistance in parallel (Q); Acpar=reactance in parallel (12); Z) = distance between
bioelectric impedance detector electrodes (cm); Rpar conductor volume =D2 /Rpar\ F=Fulton’s K (100 •WW/FL3)', capacitance (pF) is
a measure of the electrical storage capacity of cells; impedance (Q) is a measure of the opposition to the flow of electrical current;
RpaJD=Rpar standardized for D\ phase angle (°)=arctangent of Xc/R converted to degrees; AAICc= difference in AICc values with
respect to the most parsimonious model. For all models PcO.OQOl. r2 = coefficient of determination.
Dependent variable n
Model
r 2
AICc
AAICc
Total fat content (TF)( g) 60
0.733 + 0.172HVW) - 0.696(FL) + 0.090 (RpJD)
0.755
-6.72
0
8.326 + 0.150( VFIV) - 0.755(FL)
6.573 + 0.173GVW) - 0.648(FL) -
0.742
-5.71
1.0
54.90 )Rpar conductor volume)
0.750
-5.49
1.2
Carcass protein 65
content (CP)(g)
13.794 + 0.260(WWO - 0.825 (FL) - 4.453(F)
28.269 + Q.285(WW) — 1.131(FL) - 0.002 (capacitance) -
0.985
-119.18
0
0.002 (impedance) - 6.62(F4
0.986
-117.97
1.2
2.261+0.2151 WW) - 0.234 (FL) - 0.0005(capacficmce)
0.985
-117.16
2.0
Total water 67
-12.187 + 0.616( WW) + 1.109(FL)
0.993
-3.20
0
content (TWa Mg)
-30.19 + 0.54KWW) + 2.002(FL) + 6.210(F)
0.993
-2.93
0.3
-7.791 + 603(WW) + 1.087(FL) - 0.055 (RpJD)
0.993
-2.35
0.8
Total fat concentration 60
15.272 + 0.099( WW) - 0.842 (FL)
0.326
9.81
0
( TF% )
-1.568 + 0.28 (WW) + 5.70(F)
0.324
10.02
0.2
Carcass protein 65
8.086 + 0.330IFL) + 7.910(F)
32.185 + 0.044( WW) - 0.218 (FL) -
0.318
10.55
0.7
concentration (CP%)
0.004 (capacitance) - 0.004 [impedance)
27.318 + 0.026(WW) - 0.003 (capacitance) -
0.503
-83.17
0
0.004 (impedance)
28.090 + 0.036( WW) + 0.041 (Rpl,r/D) -
0.483
-82.96
0.2
0.004 (capacitance) - 0.005 {impedance)
0.498
-82.52
0.6
Total water 67
86.901 - 0.044( WW) - 9.180(F)
0.499
20.00
0
concentration ( TWa% )
97.183 - 0.522(FL) - 12.553(F)
0.496
20.35
0.3
Instantaneous wet -weight 56
based growth rate
60.435 - 0.154IWW) - 1.310(FL)
-0.6866 - 0.0016( WW ) + 0.0219(FL) +
0 . 3882 ( R par conductor volume) +
0.485
21.86
1.9
(per day)
0.0025 (RpJD) + 0.1931(F)
-0.1087 + 0.0033(FL)-0.00003(capacfiance)
0.481
-582.29
0
+ 0.00374 (phase angle) + 0.0560(F)
-0.2156 + 0.0034IFL) + 0.00003(impecfance) +
0.453
-581.85
0.4
0.0031 (phase angle) + 0.0578(F)
0.444
-580.98
1.3
group. However, when the whole data set was exam-
ined, capacitance and fat concentration (range: 4-10%)
were only somewhat correlated (coefficient of correlation
[r] = 0.41, P<0.005).
Isometric growth is assumed for Fulton’s K, and dif-
ferences in the weight-length relation are interpreted
as an indication of stored energy. Because the posts-
molts were growing isometrically with little energy
storage, Fulton’s K was unable to distinguish between
fast and slow growers within the fed treatment, and K
values of fed fish were significantly higher than those
of fasted fish only on the final day of sampling (day
23). Generally, Fulton’s K tends to have a long tempo-
ral response (weeks to months) (Busacker et al., 1990).
The relations among a fish’s wet weight, water weight,
protein weight, and fat weight may explain this lag
time. The wet weight of a fish is highly related to wa-
ter weight (as was observed by Sutton et al. [2000] in
Atlantic salmon parr), and water weight is much more
strongly associated with protein weight than fat weight
(20-40x more) (Breck, 2008). Therefore during the
early stages of fasting, when fat stores are used first
(Shulman and Love 1999, Jobling 2001), changes in a
fish’s wet weight may be fairly subtle, but once a fish be-
gins to use protein for energy, water loss (and thus wet
weight loss) would accelerate. In our study, mean fat
concentration in fasted fish decreased slightly with time
while mean protein concentration remained constant.
Within the fasted treatment there was a decreasing
trend in mean K values, but owing to high variability
266
Fishery Bulletin 110(2)
Table 5
Pearson product-moment correlations (r) between (A) proximate body composition (g) or (B) wet-weight-based proximate body
composition or instantaneous wet-weight-based growth rate of Atlantic salmon ( Salma salar) post-smolts, and wet weight, bioelec-
trical impedance analysis (BIA) measures and Fulton’s K. i?par=resistance in parallel; Xcpar= reactance in parallel; D = distance
between BIA detector electrodes; Rpar and Xcpar conductor volume =D2 /Rpgr and D2 lXcpar, respectively; phase angle (“^arctan-
gent of Xc/R converted to degrees; capacitance is a measure of the electrical storage capacity of cells; impedance is a measure
of the opposition to the flow of electrical current; Fulton’s A=(100»wet weight/fork length3). Boldface type highlights highest
correlation between body composition or growth rate, and wet weight, BIA measures, and Fulton’s K. * P<0.050, ** P<0.005, ***
P0.95) and total water and protein content in
their fish. The relation of total fat to conductor volume
was equally high in the brook trout (r2=0.96; Cox and
Caldarone et al.: Nonlethal techniques for estimating responses of postsmolt Sa/mo salar to food availability
267
Hartman, 2005) but was not significant in the steelhead
(r2 = 0.02; Hanson et al., 2010). Pothoven et al. (2008)
observed significant relations between conductor volume
and total fat or dry mass in yellow perch ( Perea flaves-
cens), walleye ( Sander vitreus), and lake whitefish ( Core -
gonus clupeaformis), with r2 values ranging from 0.62 to
0.93. In our study, we also found conductor volume to be
highly correlated with carcass protein content and total
water (r=0.93 and 0.95, respectively) but more weakly
correlated with total fat (r=0.74). Pothoven et al. (2008)
suggested that significant correlation between published
conductor volumes and body composition (g) is most
likely due to biases imposed by the distance between the
electrodes. The consistent placement of BIA electrodes
on each fish results in the numerator of the conductor
volume equation becoming a proxy for the size of the fish,
and fish size is highly correlated with body contents (i.e.,
the larger the animal, the greater its total fat, water,
protein, and ash content). In both our study (protein,
fat, water) and Pothoven et al. (2008; yellow perch, fat
and dry weight; walleye, fat), wet weight had a stron-
ger relation to body composition (g) than did conductor
volume. Neither Cox and Hartman (2005) nor Hanson
et al. (2010) reported correlation results between wet
weight and body composition (g). It would be valuable
to know whether wet weight could estimate total fat,
protein, and water content equally weil or better than
BIA conductor volume in their studies as well.
Prediction modeis
In our study, the most parsimonious models for predict-
ing body composition (g) all contained wet weight and
fork length, and frequently a weight-length interaction
term (Fulton’s K). Adding any combination of the nine
BIA measures to size-based-only models increased the
explanatory capabilities by less than 2%. Bosworth and
Wolters (2001) estimated carcass fat content in channel
catfish ( Ictalurus punctatus) using wet weight, R, and
Xc as the predictor variables, resulting in an r2 = 0.75.
They determined that adding the BIA measures to a
model containing wet weight only increased the pre-
dictive capability by 71%; however, R and Xc in their
model had not been corrected for the distance between
the electrodes. Because these impedance measurements
are highly dependent upon the distance the current
must travel between the electrodes, they must be stan-
dardized to distance when used in prediction equations
(RJL Systems, http://www.rjlsystems.com/docs/bia_info/
principles/, accessed April 2008) or they simply become
proxies for length. Therefore the Bosworth and Wolters
(2001) carcass fat content model essentially contains
size-related only variables — and size is highly correlated
with content values.
When body constituents are expressed as concentra-
tions (e.g., percent wet weight), the values are less size-
dependent. Pothoven et al. (2008) examined the capabil-
ity of 4 models (3 of which contained BIA measures) to
predict percent total fat in 3 fish species. Interestingly,
all of the variables used in their models were size re-
lated (body mass, total length, conductor volume, Rpar
and Xc|)ar uncorrected for distance between electrodes).
In their study, fat concentration within a species encom-
passed a range of values (yellow perch: 2. 7-8. 7%; wall-
eye: 6.0-18.2%; lake whitefish: 2.4-14.7%), but overall
predictive capability of the most parsimonious model for
each species was low (r2 range: 0.17-0.53).
In our study, all of the body composition (%WW) mod-
els also had low predictive capabilities, with less than
50% of the variability explained by any of the top 3
models. Our most parsimonious models for TF% and
TWa% contained only size variables (Table 4), whereas
the CP% and growth rate models contained both size
and either 2 or 3 BIA measures (Table 4). In the CP%
models there was little added value of BIA measures
to models containing only size variables (explanatory
capabilities increased by <3.4%), and in the growth-rate
models, adding two BIA measures did increase the r2 of
size-based-only models by -20%. However, the overall
predictive capability of the growth-rate models was still
less than 50%.
In a recent study by Hartman et al. (2011), highly
predictive models for estimating percent dry weight in
coastal bluefish (Pomatomus saltatrix) were constructed
by using BIA measures. The most highly predictive
models for their fish (15°C, r2=0.86; 27°C, r2 = 0.91) con-
tained either phase angle or capacitance, plus RID and
Xc/D. Ideally, standardizing R and Xc by the distance
between the electrodes (D) would eliminate the effect
of size on the impedance values; however, in our study
we determined that even after standardization, RpaJD
and Xcpar/D were still highly correlated with size (wet
weight, r>0.91, Table 5B). Correlations between size
(wet weight) and candidate BIA predictor variables were
not reported in the coastal bluefish study, and size (wet
weight or length) was not tested as a predictor variable
in any of the models.
In our study, size continually emerged as a significant
variable in all of the body composition models. To our
knowledge, no experiment has been conducted which
controls the effect of size well enough to determine the
actual contribution of BIA measures to the estimation
of body composition independent of size. Until this is
better understood, it will remain unclear to what extent
BIA measures can improve upon size-only estimates of
body composition.
Phase angle
The use of BIA-estimated phase angle as a measure
of fish condition has been proposed by Cox and Heintz
(2009). They concluded that in the 5 species they stud-
ied, fish with phase angles >15° were in better condition
than those with phase angles <15°. Caution should be
used before universally applying this 15°cut-off value.
In their own study, Cox and Heintz (2009) observed
12° phase angles in field-caught Pacific herring ( Clupea
pallasii) with mass-specific energy contents of 7.15 kJ/g,
and 15° phase angles in Pacific herring caught four
months later with energy contents of 5.02 kJ/g. They also
268
Fishery Bulletin 1 10(2)
reported phase angles >15° in large juvenile rainbow
trout which had experienced a 9% loss of body weight
after four weeks of food deprivation (Cox and Heintz,
2009). In our study, we found a low correlation (r= 0.37)
between growth rate and phase angle. On the basis of
our results and those of Cox and Heintz (2009), we feel
it is unlikely that a single phase angle value can cor-
rectly distinguish good from poor condition fish in all
applications, and that the cut-off value will be influenced
by factors such as life-stage, age, reproductive status,
species, and temperature.
In the Cox and Heintz (2009) study, the time frame
necessary to clearly differentiate fasted from fed labo-
ratory fish by phase angle varied by species and size.
Significant differences between feeding treatments were
observed in small juvenile rainbow trout after 14 days
of fasting, in large juvenile rainbow trout after 21 days,
in juvenile brook trout after 28 days, and in juvenile
Chinook salmon after >77 days. This large range of
response times may reflect differences in fat reserves
in the test animals. If fat reserves do affect response
time, then more than just a decline in fat content would
be necessary to elicit a decrease in phase angle. A de-
crease in protein content (with its concomitant water
loss) may also be necessary for a significant decline
in phase angle. If this is true, then the response time
of K to food deprivation may be similar to (or possibly
better than) that of phase angle. On the final sampling
day of our study, K values, not phase angle, were sig-
nificantly different between the fasted and fed fish. It
would be interesting to know what the response time
of K values were in the Cox and Heintz (2009) food-
deprivation study.
Conclusions
The use of BIA as a proximate body composition esti-
mator or fish condition index is relatively new. As with
any condition index, its validity must be established for
the specific application in which it will be used. Field
personnel are seeking a nonlethal index that can reflect
the condition of Atlantic salmon postsmolts 2 to 3 weeks
after the fish are released from the hatchery, when the
majority of postsmolts are captured on targeted trawl
surveys. We designed an experiment to evaluate the
utility of BIA and Fulton’s condition factor as indices of
condition during that time-frame. Results of our study
indicated that 1) growth rates of postsmolts responded
rapidly to the withholding and re-introduction of food;
2) fed postsmolts grew isometrically; and 3) 3 weeks of
withholding food is not sufficient time to elicit signifi-
cant declines in proximate body constituents in fasting
postsmolts. This combination of isometric growth in fed
fish and short-term starvation period in fasting fish
resulted in nonsignificant differences in body composi-
tion (%WW) among the feeding treatments (fed; fasted;
fasted, then refed). The utility of BIA and Fulton’s K as
condition indices depends upon detecting differences in
proportions of body constituents. During our study, BIA
measures were not significantly different among the 3
feeding treatments, and only on the final day of sam-
pling was K in fasted fish significantly less than in fed
fish. Our study has demonstrated that neither BIA nor
Fulton’s K would be an appropriate choice of index 1) to
reflect short-term changes (weeks rather than months)
in postsmolt condition, or 2) to monitor fish condition
during a life-stage where excess energy is primarily
directed toward isometric growth rather than energy
storage. We propose that a methodology that measures
growth rate (directly or indirectly) would be a more
suitable condition index for isometric growth life-stages.
We will be reporting on the utility of two potential
growth-rate indices in Atlantic salmon postsmolts in a
later publication.
Results from our study also supported conclusions
by Pothoven et al. (2008) that simple measures of size
were better predictors of body composition than BIA
measures. Additionally, we observed that Fulton’s K
responded more quickly to food deprivation than BIA
measures, and that a single cut-off value for phase
angle, as a distinction between good and poor condition
fish, should be used with caution. BIA is an emerging
technique in fishery biology, and as such, its application
will require more research to identify its appropriate
use.
Acknowledgments
The authors would like to thank M. K. Cox for help
constructing the BIA electrodes and demonstrating the
BIA technique, E. Baker, and K. Fredrick for assistance
in aquarium setup and temperature control, M. Prezioso
and J. St. Onge-Burns for help rearing the salmon, the
University of Rhode Island Graduate School of Ocean-
ography for the use of Blount Aquarium, and anony-
mous reviewers whose suggestions greatly improved
this article.
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271
Estimation of discard mortality
of sablefish ( Anopiopoma fimbria)
in Alaska longline fisheries
Email address for contact author: chns.lunsford tat noaa.gov
1 Auke Bay Laboratories
Ted Stevens Marine Research Institute
Alaska Fisheries Science Center
National Marine Fisheries Service
National Oceanic and Atmospheric Administration
17109 Pt Lena Loop Rd.
Juneau, Alaska 99801
2 Present address:
School of Aquatic and Fishery Sciences
University of Washington
Box 355020
Seattle, Washington 98195
Abstract — -Sablefish ( Anopiopoma
fimbria) are often caught inciden-
tally in longline fisheries and dis-
carded, but the extent of mortality
after release is unknown, which
creates uncertainty for estimates of
total mortality. We analyzed data
from 10,427 fish that were tagged in
research surveys and recovered in
surveys and commercial fisheries up
to 19 years later and found a decrease
in recapture rates for fish originally
captured at shallower depths (210—
319 m) during the study, sustaining
severe hooking injuries, and sustain-
ing amphipod predation injuries. The
overall estimated discard mortality
rate was 11.71%. This estimate is
based on an assumed survival rate
of 96.5% for fish with minor hooking
injuries and the observed recapture
rates for sablefish at each level of
severity of hook injury. This esti-
mate may be lower than what actu-
ally occurs in commercial fisheries
because fish are likely not handled as
carefully as those in our study. Com-
paring our results with data on the
relative occurrence of the severity of
hooking injuries in longline fisheries
may lead to more accurate account-
ing of total mortality attributable to
fishing and to improved management
of this species.
Manuscript submitted 4 October 2011.
Manuscript accepted 22 February 2012.
Fish. Bull. 110:271-279 (2012).
The views and opinions expressed
or implied in this article are those of the
author (or authors) and do not necessarily
reflect the position of the National Marine
Fisheries Service, NOAA.
Megan M. Stachura' 2
Chris R. Lunsford (contact author)1
Cara J. Rodgveller1
Jonathan Heifetz'
For stock assessment, accurate ac-
counting of discard mortality is impor-
tant for estimating total mortality
attributable to fishing. Studies of
sablefish ( Anopiopoma fimbria) and
other fish species show that catch-
related injuries can cause delayed
mortality after a fish is discarded.
For example, sablefish laboratory
experiments have shown that the
level of physical injury, reflex impair-
ment, and behavior impairment may
be useful proxies for delayed mortal-
ity (Davis, 2005; Davis and Ottmar,
2006). Pacific halibut ( Hippoglossus
stenolepis) with more severe hook
injuries had increased mortality and
reduced growth compared to those
with less severe injuries (Kaimmer,
1994; Kaimmer and Trumble, 1998)
and were visually impaired after
exposure to simulated sunlight (Brill
et ah, 2008). For Atlantic cod ( Gadus
morhua L), injuries to the eyes, gills,
and belly were more lethal than
injuries to other anatomical parts
(Palsson et ah, 2003). After release,
Atlantic cod had inhibited activity
for 4 days, during which there was
potentially increased susceptibility to
predation and delayed mortality (Neat
et ah, 2009).
A first step in estimating discard
mortality is to estimate the propor-
tion of fish that die after being dis-
carded. Estimates of sablefish discard
mortality rates and the derivation
methods for determining these esti-
mates vary regionally and by man-
agement agency. In the southeast
Alaska sablefish stock assessment
conducted by the Alaska Depart-
ment of Fish and Game for state wa-
ters, a 25% discard mortality rate in
the Pacific halibut longline fishery
is assumed for sablefish (Dressel1).
For both trawl and longline federal
groundfish fisheries in Alaska, 100%
mortality is assumed for all sablefish
that are discarded (Hanselman et al.,
2010). In the federal Pacific Coast
sablefish stock assessment a much
lower discard mortality rate of 10%
is assumed for longline gear (Schir-
ripa, 2008).
Sablefish support one of the most
valuable fisheries in Alaska (Hiatt et
ah, 2010). The fixed gear fishery in
1 Dressel, S. C. 2009. 2006 northern
southeast inside sablefish stock assess-
ment and 2007 forecast and quota. Fish-
ery Data Series 09-50, 78 p. Alaska
Dep. Fish Game, Anchorage, AK.
272
Fishery Bulletin 1 10(2)
federal waters off Alaska is managed by a catch shares
program, where annual individual fishing quota (IFQ)
shares are allocated to fishermen, for fish that can be
caught anytime during the eight and a half month sea-
son. For fishermen with IFQs, full retention of all sable-
fish caught is required. However, sablefish are often
legally discarded in other commercial longline fisheries,
primarily in those targeting Pacific halibut and Pacific
cod (Gadus macrocephalus). In the sablefish fishery, the
practice of releasing small sablefish and retaining only
the larger fish because of the greater value per pound
of larger fish (a technique known as “highgrading”
[Davis, 2002] ) is illegal. However, because there is an
incentive to retain larger fish and not all fishing trips
are monitored, highgrading may occur.
Factors affecting discard mortality likely vary by spe-
cies, gear type, depth, and other environmental factors.
Injury location on fish has proven to be an indicator
of short and long-term discard mortality (e.g., Bar-
tholomew and Bohnsack, 2005). In Alaska, sablefish
inhabit a wide range of depths and are caught primar-
ily on longline gear, which can cause external injuries
to different areas of the body. Fish tethered to longline
gear for extended periods are subject to predation by
parasitic amphipod crustaceans. Also fish size may
affect mortality of discarded fish. The objective of our
study is to determine if the location and severity of the
hook injury, line and roller gear injury, water depth,
fish size, and the level of amphipod predation affect
the discard mortality rate in Alaskan longline fisher-
ies. To answer these questions, the recapture rates of
fish tagged and released in the marine environment
were related to each factor. In addition, an absolute
discard mortality rate was computed on the basis of the
observed severity of hooking injuries.
Materials and methods
Tagging and data collection
In 1989 and 1990, research surveys were conducted
by the National Marine Fisheries Service (NMFS),
Alaska Fisheries Science Center (AFSC) in Southeast
Alaska. In 1989, sablefish were tagged during August
and September in Chatham Strait; in 1990, sablefish
were tagged during April and May in Clarence Strait
(Fig. 1). Longline gear was fished on the bottom at
depths from 210 to 419 m with a minimum 3-hour soak
time. Gear configuration consisted of size 13/0 circle
hooks baited with squid attached to 38-cm gangions
that were secured to beckets tied in a 9.5-mm (3/8 in)
groundline at 2-m intervals. This gear configuration is
similar to that used in the commercial sablefish fishery
in Alaska. However, the Pacific halibut fishery typically
uses larger hooks (16/0). All sablefish, except those
with extremely severe injuries, were tagged with plastic
T-bar style anchor tags, and injuries were classified by
the following 4 variables: location of hook injury, sever-
ity of hook injury, severity of injuries due to amphipod
predation, and the presence of injury sustained on fins
or body from line and roller gears. Within each variable,
a categorical condition code describing the injury was
recorded (Table 1). The date of capture, capture location,
and depth of capture were also documented. Fish were
promptly released after they were measured (fork length,
nearest mm) and tagged.
To determine recapture rates of fish within each cat-
egory, tagged fish were recovered in commercial fisher-
ies and tags were returned to the AFSC for a reward
(Maloney2). Tags were also recovered during subsequent
research studies. Data for fish recaptured from the time
of tagging to June 2009 were used in our analysis (up
to 19 years at liberty).
Analysis
A logistic regression model was constructed to determine
which factors were related to significant differences in
recapture rates. The relationship between the binary,
dependent variable, Tj-, which represents whether a fish
was recaptured or not, and seven independent explana-
tory variables was estimated with the following full
model,
Logit(Yj) = a + bYr, + cL, + dD. +
eHLi + ftiS. + gAt + hG(, (1)
where a
Yn
L,
Di
HL.
HSt
A,
G(
the intercept, and b to h are estimated
model coefficients;
year of tagging (1989, 1990);
fish length at capture;
capture depth group (210-269, 270-319,
320-419 m);
location of the hook injury (cheek, upper jaw,
lower jaw, nose, throat, eye, gill);
severity of the hook injury (minor, moderate,
severe);
severity of amphipod predation injury (no
injury, <10% scale loss, >10% scale loss);
and
type of injury sustained on fins or body from
line and roller gears (no injury, fin damage,
lacerations) for fish i (Table 1).
Year can also be considered to be the effect of location
because in each year fish were tagged at different loca-
tions. All independent variables were treated as categori-
cal except for length, which was continuous. Interaction
terms were not included in the model because of the
small sample sizes available across multiple categori-
cal variables, which resulted in an inability to estimate
these interaction parameters.
Forward-stepwise model selection was performed to
simplify the model to factors that significantly improved
2 Maloney, N. E. 2002. Report to industry on the Alaska
sablefish tag program, 1972-2001. AFSC Processed Rep.
2002-01, 44 p. Alike Bay Laboratory, NMFS, NOAA, 11305
Glacier Highway, Juneau, AK 99801.
Stachura et al. : Estimation of discard mortality of Anoplopoma fimbria in Alaska longline fisheries
273
135°W 130°W
Figure I
Map of the areas in Southeast Alaska where sablefish (Anoplopoma fimbria) were tagged
during research surveys in 1989 (•) and 1990 ( + ).
model fit. The model with the minimum Akaike infor-
mation criteria (AIC) value was chosen. A Wald chi-
squared test was used to calculate the overall signifi-
cance of categorical variables with multiple coefficients.
All statistical analysis was implemented in R software,
vers. 2.11.1 (R Development Core Team, 2010) including
use of the aod package, vers. 1.2 (Lsenoff and Lancelot,
2010).
Recapture rates for categories within each variable
were calculated by dividing the number of recaptured
fish by the number of tagged fish for each category.
Absolute survival rates were calculated for each level of
hook severity on the basis of observed recapture rates
and the survival rate of a Pacific halibut with minor
hooking injuries (Kaimmer, 1994; Kaimmer and Trum-
ble, 1998; Trumble et. al., 2000). Previous studies have
determined that the expected survival of a properly
handled Pacific halibut is in the 95-98% range; a re-
leased fish with minor injuries has an estimated 96.5%
survival rate (Trumble et al., 2000). We used the Pacific
halibut estimate of survival rate as a proxy for that of
sablefish for the following reasons: these species do not
experience barotrauma as a result of rapid decompres-
sion; they co-occur in the same water temperatures,
areas, and depths; they are caught with nearly identical
gear types; and they are commonly fished by the same
fishing vessels and crew. Like Pacific halibut, sablefish
are hardy and, when handled appropriately, have high
survival rates after capture and discard. Long-term tag-
ging programs for both species provide evidence of their
hardiness (Kaimmer, 2000; Maloney2). The hardiness
of sablefish is also supported by previous research in a
laboratory setting where there was 100% survival after
60 days (Davis et al., 2001). Ours is the first dedicated
study to estimate sablefish discard mortality. Previous
estimates of Pacific halibut survival rates are the best
available data to use as a proxy for sablefish.
The average survival rate of fish with different severi-
ties of hook injury, i.e., the absolute survival rate, was
estimated on the basis of recapture rates and relative
frequency of all 3 levels of hook injury (minor, moderate,
severe) by using the methods in Kaimmer and Trumble
(1998). The overall absolute survival rate (S) of cap-
tured fish was calculated with the following formula:
S =
( R0 + R1+R2 )
{Tc + ^+T.t + NT j
Ro
To
x 0.965,
(2)
where T0, Tv and T2 and R0, R ,, and R2 are the number
of fish tagged ( T ) and recovered (R) with minor (0), mod-
erate (1) and severe hook injuries (2). Fish that were not
274
Fishery Bulletin 1 10(2)
Table 1
Description and assigned injury code for injury types and severities for sablefish ( Anoplopoma fimbria ) caught on longline gear,
for an estimation of discard mortality.
Factor Description
Hook injury location
0 Hooked in cheek or parts of operculum
1 Hooked in upper jaw: maxilla or premaxilla
2 Hooked in lower jaw: dentary (mandible)
3 Hooked in nose or snout
4 Hooked in throat
5 Hooked in eye
6 Hooked around gill or gill arches
Hook injury severity
0 Minor: small puncture, flesh not torn, no abrasion
1 Moderate: flesh torn; some abrasion; bones intact, eye orbit not punctured
2 Severe: bones torn at insertion, severed or shattered, gills hooked but no broken gill arches, hooked through
palatine into nose capsule, cheek bones shattered, hooked in throat and
bleeding but not torn
NT No tag: gill arches torn or bleeding, hook swallowed with substantial tears in throat; maxillary and premaxillary or
dentary torn off; nose or snout smashed
Amphipod predation injury
0 No injury
1 Moderate scale loss: 10% or less
2 Heavy scale loss: greater than 10%
Line and roller gear injury sustained on fins or body
0 No injury
1 Fin damage: caudal, pectoral, pelvic, dorsal, or anal fin
2 Lacerations: line markings across body
tagged because of the extreme severity of their injuries
were assumed to have 0% survival and are represented
in the equation as NT (having no tag and they were
included in the total number of fish caught when cal-
culating the recovery rate for each injury group). The
survival rate of fish in each category was calculated with
the following formula:
Sx = — x 0.965, (3)
•TCq
T
1 0
where all variables are the same as in Equation 2 and
x represents the severity of the hook injury (0, 1, or 2).
Results
A large number of sablefish were captured (10,940)
and tagged (10,508): 8838 fish were tagged during
the 1989 survey and 1670 during the 1990 survey.
A substantial number of fish were recaptured (1207
fish, 11.49% recapture rate of tagged fish) between 9
days and 19.2 years (mean = 3.4 yr, standard devia-
tion = 4.5 yr) after tagging. Because some data were
lacking for 81 fish, analyses were run with data from
10,427 fish. An additional 432 fish were captured but
not tagged because of the extreme injuries from capture
or amphipod predation (NT in Eq. 2, see Materials and
methods section).
Logistic regression model
The reduced model was chosen on the basis of the small-
est AIC value. Several parameters were found to sig-
nificantly affect recapture rates: year (which also can
be considered to be a location effect), depth, severity of
hook injury, and amphipod predation (Table 2, Fig. 2).
Fish tagged in 1989 had a lower recapture rate (11.26%)
than those tagged in 1990 (12.66%) (Table 3). Fish from
the greatest depths (320-419 m) had a greater rate
of recapture (14.33%) than fish captured at shallower
depths (210-269 m, 10.61%; 270-319 m, 10.43%; Table
3). Severity of hook injury also exhibited a significant
effect on the recapture of tagged fish (Table 2). Fish
with severe injuries had a lower recapture rate (8.49%)
than those with minor (12.05%) or moderate (11.81%)
injuries (Table 3). The confidence intervals surround-
ing the parameters for severity of injury were relatively
narrow, with the 95% confidence interval of the odds
Stachura et al Estimation of discard mortality of Anoplopoma fimbria in Alaska longline fisheries
275
Year 1990
Depth: 270-319 m
Depth: 320^119 m
Severity of hook injury: moderate
Severity of hook injury : severe
Amphipod predation: s10% scale loss
Amphipod predation: >10% scale loss
Higher mortality
Hig
<■
Lower mortality
0.5 1.0 1.5
Effect on recapture rate
2.0
Figure 2
Comparison of the effects of the variables in the final model on the recapture rate
of tagged sablefish ( Anoplopoma fimbria). The effect on recapture (circle) is the
exponent of the estimated parameter for the variable in the logistic regression
and is the odds ratio: the odds of recapture of a fish in a category compared to
the odds of recapture in the initial category of the categorical variable (year:
1989; depth: 210-269 m; severity of hook injury: minor; amphipod predation:
no predation). Horizontal lines are 95% confidence intervals for the estimate.
ratio for the effect of severe hooking injuries <1, indicat-
ing a significant negative effect on recapture (Fig. 2).
Although only a small portion of the fish sampled suf-
fered from amphipod predation, it significantly affected
recapture and was included in the final mode! (Table 2).
Fish with no observed amphipod predation had a higher
rate of recapture (11.86%) than fish with <10% scale loss
(8.44%) and fish with >10% scale loss (7.84%) owing to
amphipod predation (Table 3). The 95% confidence inter-
val of the odds ratio for the effect of less than or equal to
10% scale loss was less than 1, indicating a significant
negative effect on recapture (Fig. 2). However, there
was a high amount of variability around the estimated
parameter for the effect of >10% scale loss because of
a low number of samples (Fig. 2). The majority of fish
(51.06%) that were too severely injured to be tagged had
suffered from amphipod predation, and only 11.09% of
fish that were healthy enough to be tagged had suffered
amphipod predation.
In our study location of hook injury, fish length, and
type of gear injury did not significantly affect recap-
ture rates. Hook injuries were not in critical locations
that would likely cause mortality alone. Most injuries
were located on the cheek and upper and lower jaws
(95.53%). There were a small number of fish observed
that had hook injuries to other areas of the body (nose,
throat, eye, gill; 4.47%) (Table 3). A wide range of fish
lengths were included in our study, but length did not
Table 2
Significant effects included in the reduced logistic regres-
sion model, where the response is whether a sablefish
(Anoplopoma fimbria) was successfully recaptured after
tagging. The overall variable significance was calculated
by using a Wald chi-squared test.
Variable
t
df
P(>X2)
Intercept
571.0
1
<0.001
Year
11.6
1
<0.001
Depth (m)
34.8
2
<0.001
Severity of hook injury
12.4
2
0.002
Amphipod predation
7.7
2
0.021
have a significant effect on recapture rate. Injuries
caused by the line and roller gear also did not have a
significant effect on recapture rate. This result may be
the consequence of low statistical power because few
fish (5.58%) sustained injuries caused by gear other
than hooks (Table 3).
Survival rates
The absolute survival rate of fish in each category of
severity of hook injury was calculated with Equation 3
276
Fishery Bulletin 1 10(2)
Table 3
Number of sablefish (Anoplopoma fimbria) tagged and recaptured (number and %) by each variable. Length
variable in the analysis but is categorized here for summary purposes. The estimated absolute survival was
levels of the severity of hook injury on the basis of an assumed 96.5% survival of fish with minor injuries.
was a continuous
estimated for the
Variable
Tagged
Recaptured
Estimated
% Recaptured absolute survival %
Year
1989
8768
987
11.26
1990
1659
210
12.66
Length (cm)
<60
1280
152
11.88
60-69
5250
585
11.14
70-79
3048
360
11.81
>80
849
100
11.78
Depth (m)
210-269
3354
356
10.61
270-319
4428
462
10.43
320-419
2645
379
14.33
Hook location
Cheek
3290
396
12.04
Upper jaw
1759
199
11.31
Lower jaw
4912
559
11.38
Nose
123
14
11.38
Throat
212
17
8.02
Eye
120
11
9.17
Gill
11
1
9.09
Severity of hook injury
Minor
2963
357
12.05
96.50
Moderate
6204
733
11.81
94.63
Severe
1260
107
8.49
68.01
Extreme
Total
432
10,859
1197
11.02
0.0
88.29
Amphipod predation
No predation
9271
1100
11.86
<10% scale loss
1054
89
8.44
>10% scale loss
102
8
7.84
Gear injury
No injury
9845
1133
11.51
Fin damage
539
57
10.58
Lacerations
43
7
16.28
(Table 3). The overall absolute survival of released
sablefish was estimated, with Equation 2, to be 88.29%,
or an overall mortality rate of 11.71% (Table 3). The
absolute survival of fish with severe injuries (68.01%)
was much lower.
Discussion
Our results indicate that the severity of hook injury is
related to recapture rates for tagged sablefish. Most inju-
ries were to the cheek and jaw and not to critical areas,
such as the gills and brain. The severe injuries that
we saw likely resulted in delayed mortality following
the tagging event which would explain lower recapture
rates. The severity of an injury is likely influenced by
the technique for hook removal. Previous studies with
Pacific halibut (Kaimmer, 1994; Kaimmer and Trumble,
1998) found that the removal of the hook affects the
severity of the hook injury and, as with sablefish, sur-
vival decreased with an increase in the severity of hook
injury. Severity of hook injury is a logical parameter for
estimation of discard mortality because it significantly
affects recapture rate.
Stachura et al Estimation of discard mortality of Anoplopoma fimbria in Alaska longline fisheries
277
In our study, the location of hooking injury did not
significantly affect recapture rates. However, a large
portion of injuries occurred on the cheek and upper
and lower jaws — locations that are typically affected by
circle hooks. We likely did not have enough samples of
fish with injuries in other locations to detect the effects
of those injuries. Unlike our results, results from stud-
ies of catch-and-release of recreationaliy caught species
have indicated that hooking location was the most sig-
nificant factor in estimating mortality (reviewed in Bar-
tholomew and Bohnsack, 2005). Deep-hooking injuries
in critical locations such as the esophagus, stomach,
gills, eyes, and brain significantly increase mortality
in many species (e.g., Muoneke and Childress, 1994;
Palsson et al., 2003; Aalbers et al., 2004; Aids et al.,
2009). The circle hooks that are used in Alaska longline
fisheries usually hook fish in the mouth and injuries in
critical locations are not common (reviewed in Trumble
et al., 2000). Capture with other hook types or fishing
gears, such as trawl gear, would likely produce injuries
on other locations of the body.
Depth of capture significantly affected the recapture
rate of sablefish, which is common for other fish species.
We found a positive relationship between depth of cap-
ture and assumed survival (i.e., fish caught at shallow
depths were less likely to be recaptured). The sablefish
fishery extends to at least 800 m in many areas and
so the effect of depth on recapture rates may be even
more pronounced at depths greater than 419 m, the
maximum sampling depth in our study. The opposite
has been observed in physoclistous species due to baro-
trauma, because of organ damage caused by gas expan-
sion in the body cavity during capture (e.g., Gitschlag
and Renaud, 1994; Wilson and Burns, 1996; Collins et
al., 1999; St. John and Syers, 2005). Sablefish lack a
swim bladder, thus no correlation between mortality
and depth of capture was expected. Deeper-dwelling
fish can also have increased injuries with greater cap-
ture depths, indicating that injuries are inflicted while
fish struggle during hauling (Atlantic cod; Palsson et
al., 2003).
There are some potential explanations for why fish
caught at shallow depths had lower recapture rates.
First, sablefish caught at deeper depths (320-419 m)
could be less vigorous because of the longer retrieval
time and the increased time spent fighting the line
during retrieval and therefore they are less likely to
become injured during the landing process when out of
the water and onboard the fishing vessel. Differential
predation in the depth categories may also affect the
mortality of released sablefish, if they return to their
previous depths after release. Two major predators
of sablefish have greater concentration at shallower
depths, Pacific halibut (27-274 m; IPHC, 1998) and
Pacific sleeper sharks ( Somniosus pacificus) (Yano et
al., 2007). Second, fishing effort likely differs by depth
and therefore may affect depth-related recapture rates
of tagged fish. Data on fishing effort by depth were
not available from the Pacific halibut fishery or the
southeast Alaska state sablefish fishery and there-
fore a full examination of this supposition was not
possible.
In our study, amphipod predation was related to the
recapture rate of sablefish and was prevalent for fish
that were too severely injured to tag. Similarly, Pacific
halibut that were tethered to longlines for extended
periods suffered from amphipod predation and had a
low survival rate (Trumble et al., 2000). Fishery-specific
amphipod predation rates would need to be investigated
to accurately assess this effect on the discard mortality
of sablefish.
The year of capture significantly affected the re-
capture rate of sablefish. A greater recapture rate
was found for fish tagged in Clarence Strait in 1990
and several factors likely contributed to this differ-
ence. First, a greater proportion of fish tagged in 1990
(18.57%) were recaptured within 60 days of tagging
compared to those tagged in 1989 (7.42%). This is
likely explained by the occurrence of an Alaska De-
partment of Fish and Game sablefish survey and the
southern southeast Alaska directed sablefish fishery
both occurring within 60 days of the initial tagging
effort. Tagging conducted in Chatham Strait in 1989
occurred after both the state survey and fishery period
and therefore the grounds were not fished for nearly a
year after the tagging effort. A minimum time at liber-
ty was not used in our study because the year or loca-
tion of tagging was secondary to our primary objective
of determining the factors related to discard mortality
and estimating absolute discard mortality based on
the severity of injuries to sablefish. Second, longline
fishing is permitted in the Chatham Strait fishery,
and in Clarence Strait both longline and pot gear are
allowed. Animals can exhibit varying levels of “trap
addiction” (attraction to fishing gear) or “trap shyness”
(an aversion to the gear) depending on the gear type
(Seber, 1982). Previous tagging analyses have shown
that sablefish may be trap shy towards longline gear
within the first year after capture, likely because of
the stress incurred during the initial capture (Carlile,
et al.3). Because many of our fish were caught soon
after capture in the fishery, some of the difference in
recapture rate that we saw may be explained by the
differential recapture catch rates between pot and
longline gear types. Finally, amphipod predation was
significantly higher in 1989 (12.45%) than in 1990
(3.86%) indicating that Chatham Strait may have a
higher incidence of amphipods, which we found to be
related to a decreased recapture rate.
We calculated an absolute mortality rate for each
level of severity of hook injury. The overall mortality
rate of 11.71% is substantially lower than the 25%
mortality rate assumed for sablefish discarded in the
Pacific halibut fishery in state waters (i.e., Chatham
3 Carlile, D., B. Richardson, M. Cartwright, and V. M.
O’Connell. 2002. Southeast Alaska sablefish stock as-
sessment activities 1998-2001. Regional Information Report
IJ02-02, 86 p. Alaska Dep. Fish and Game, Douglas,
AK.
278
Fishery Bulletin 1 10(2)
and Clarence Straits; Dressel1), and the assumed 100%
mortality of sablefish caught in other target fisheries
in federal waters in Alaska (Hanselman et ah, 2010).
Applying the 11.71% mortality rate to the average
catch of sablefish discarded in federally managed hook-
and-line fisheries (491 t, 2004-09 average; Hanselman
et al., 2010), yields an annual discard mortality of
57.5 tons.
There are two reasons why our estimate of absolute
discard mortality may be lower than what occurs in
the commercial fishery. First, in our study fish were
handled carefully and released, whereas in commer-
cial fisheries we would expect a greater proportion of
moderate and severe injuries that would result in a
higher discard mortality. Second, commercial fishery
discards come from multiple fisheries that use numer-
ous gear types, most notably different hook types and
sizes. Larger hooks have been shown to result in higher
discard mortality (Trumble et al., 2000). Because the
halibut fishery in Alaska uses larger hooks than we
used in our study, a higher discard mortality rate for
sablefish would be expected in the halibut fishery. Care-
ful hook removal during release of fish could potentially
minimize discard mortality rates observed in commer-
cial fisheries.
Conclusion
In this study we examined some of the factors that affect
the discard mortality rate of sablefish in Alaskan long-
line fisheries. We found a decrease in recapture rates for
fish originally captured at shallower depths (210-319 m)
in our study, sustaining severe hooking injuries, and
sustaining amphipod predation injuries. Based on the
severity of hook injury, we estimated an overall discard
mortality rate of 11.71%. Obtaining data on the relative
occurrence of the severity of hook injuries that occur in
these fisheries is a logical next step. Such data would
allow us to extrapolate our findings more reliably and
may lead to a more accurate accounting of total mortal-
ity attributable to fishing and to improved management
of this species.
Acknowledgments
This analysis was performed while author M. Stachura
was a student intern from the University of Miami and
funded by the National Oceanic and Atmospheric Admin-
istration (NOAA) Ernest F. Hollings Undergraduate
Scholarship Program administered by Oak Ridge Associ-
ated Universities through a Cooperative Grant sponsored
by NOAA. We thank N. Maloney for managing the tag
data for the entirety of this project. We also thank E.
Varosi and J. Fujioka for help in designing hook injury
codes, and the crew of the RV Townsend Cromwell. This
manuscript has benefited from review by D. DiResta, G.
Thomas, J. Richardson, J. Murphy, K. Echave, P. Rigby,
and three anonymous reviewers.
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of figures in the original software if conversion to any
of these formats yields a degraded version.
Questions? If you have questions regarding these
guidelines, please contact the Managing Editor, Sharyn
Matriotti, at
sharyn.matriotti@noaa.gov
Questions regarding manuscripts under review should
be addressed to Julie Scheurer, Associate Editor.
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