Foraminiferal Densities and
Pore Water Chemistry in the
Indian River, Florida
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‘Smith : nian a Tosi tio
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
Foraminiferal Densities and
Pore Water Chemistry in the
Indian River, Florida
Martin A. Buzas
and Kenneth P. Severin
SMITHSONIAN INSTITUTION PRESS
Washington, D.C.
1993
¢e NUMBER
36
ABSTRACT
Buzas, Martin A., and Kenneth P. Severin. Foraminiferal Densities and Pore Water Chemistry
in the Indian River, Florida. Smithsonian Contributions to the Marine Sciences, number 36, 38
pages, 32 figures, 29 tables, 6 appendices, 1993.—Two stations were established about 10 m
apart at a depth of about | m at Link Port, Florida. One consisted of quartz sand and the other
of quartz sand with a dense stand of seagrass. At the surface of each station and at a depth of 10
cm at the grass site, four replicate samples consisting of 5 ml each were taken every fortnight
from 27 March to 6 November 1978 (17 sampling times, 204 samples). The taxa
Quinqueloculina, Elphidium, Ammonia, Bolivina, and Ammobaculites comprising 98% of the
fauna were enumerated. In addition, pore water chemistry was measured for temperature,
salinity, oxygen, pH, Eh, NH,, PO,, Si, NO,, and NO, + NO,.
General linear models were used to analyze the bare surface-grass surface, and grass
surface-grass 10 cm data sets. Foraminiferal densities were evaluated for differences between
sites, periodicity, sites x periodicity (interaction), and environmental variables.
Differences in overall density between the bare surface-grass surface sites were not
significant for the three most abundant taxa (Quinqueloculina, Elphidium, and Ammonia). At the
grass site the density for all taxa were significantly lower at 10 cm than at the surface (very few
individuals were observed at 10 cm).
Hypotheses for periodicity and interaction were significant for all taxa in all comparisons
except for Bolivina in the bare surface-grass surface analysis. At the bare surface, maximum
densities occurred in spring while at the grass surface in summer. Although densities were low
at 10 cm, no synchronization between the grass surface and 10 cm was evident.
The environmental variables were significant for all taxa in both comparisons. The
environmental variables are, however, highly correlated. To alleviate this difficulty, a principal
component analysis was performed on these variables. The first three components included all
of the 10 variables. Subsequent multiple regression of foraminiferal densities and the principal
components indicated that usually at least two components, accounting for most of the variables,
were Statistically significant. Thus, no simple relationship between pore water chemistry and
density is apparent. The very large difference in density between the grass surface and 10 cm
depth is much more strongly related to the pore water chemistry than the smaller differences
with time at the surface sites.
OFFICIAL PUBLICATION DATE is handstamped in a limited number of initial copies and is
recorded in the Institution’s annual report, Smithsonian Year. SERIES COVER DESIGN: Seascape
along the Atlantic Coast of eastern North America.
Library of Congress Cataloging-in-Publication Data
Buzas, Martin A.
Foraminiferal densities and pore water chemistry in the Indian River, Florida / Martin A. Buzas and Kenneth P. Severin.
p. cm.—(Smithsonian contributions to the marine sciences ; no. 36)
Includes bibliographical references (p. _ ).
1. Foraminifera—Florida—Indian River. 2. Protozoan populations—Florida—Indian River. 3. Population den-
sity—Florida—Indian River. 4. Pore water—Florida—Indian River. I. Severin, Kenneth P. II. Title.
III. Series.
QL368.F6B875 1993 593.1’204526323’097592-dce20 92-46076
The paper used in this publication meets the minimum requirements of the American
National Standard for Permanence of Paper for Printed Library Materials Z39.48—1984.
Contents
Page
Introductl ont: Saree eae cu Tim eM eg hie gu 1
ACKNOWICUSINEIS mere Renna erm ret tae tn ores eee ioe YS el ex sie aisle bw te 1
IMethodSieremertr ieee iitniccrm ec eh nt oeN auc ecin aco eg eS Al eee ap ads eens 1
Ja 5 oes dy co oO HO OOF GTO GENCE OR Se om eo a 1
MEAD OLALOnY A Reet ME Mee TMi aay sien s nce slave on clita. 6. ooh 8) Kulan ee locos 2
Statistical @ieecmre reruns ne In cae Rk CGO. oly ye pe ae @ secs Beng 2
BarersuntacerandsiGrassssurtace epee asiam aces meneincriel orisiiaiteom ele ss ees eee ee 3
ETVinOnMmentals Vatiableswee wae emi net ne Tuusnt eles Mn eats ee) os ae 3
Species Densities, Station Differences, Periodicity, and Environmental
Wania blest wmsnt emer aii emcee os Ge oat ina Sore eG Stale ylayite niece 10
Ouingueloculingmrest rene Tol sac ees ee ieee ten ana re ss aiden a ae kehie Yon 10
LEY ONTO: 6,5 3. 0%, Bo Gd BLOC MOR RRR nO pea are eee ri
INGO DTD. 26.5 SIS) OHO SOHO AOE ENC E O ND ATT RSI ID P an arete 11
BOliV INGE are Rea OR eA gee rs, eee eo oo ken ere eee, ys ile 11
ATTN ODAGCUIILCS tear ure Maer aL alcatel is oe Wena tte a Ue, re Wen teats y 12
GrasseSurfacerandsGrassplOlCMpusyuek sees se is cel eee ee i ee eee 13
EnviToOnmentalmvaltableSw-arwrusge Gb ry cue sl ale ten ye lie eas 13
Species Densities, Station Differences, Periodicity, and Environmental
Wanita blestese anni Segre rane ara rituals, © iin sa ewe: tivsh suse s tS
Ouingueloculinammra Rear: a rcerares ceri sieht) ae Gh ee 6 cee ete 2) ts 15
EE Didi ara aaa ie ele eee ons) east ret een Sake eel, oe eg lela ss 16
INGOT NGG, S55 SS a BR aa rar a ee cere ea 16
BOLING eR mre EM MAINE ae gids eyoci se etn oar ein eae eT sce ta Satna fend 21
PATIMODUCHINCS aa Ae ieee is coe eects foc: Siete. oe ote ee eee 22
CompanisontolsAnalyses soe ca iu ot ee tie ce ee sake eh oe 23
Companisongwitht@OthemStudies secu 6 ees ee cee es ee ese 24
AP DENGIXw ces ales SUI ACE Mime aS, ircuc-) ile. o eeeel te os etek eso) RG, vw, Be) Sakep sy 29
ZAPDENGIXG@? -BGTASSHOUTIACE pn meme tuped lcri fis ie ee ey elise ee be Gls) s Sue eto coe se 31
FAPDENGIXES Grassi OMCs seep cera cea leery ce teriol el Aue eee a nosy) Sc is see 33
PAP DENGIXEA MES ALCL SUIPACC Pw irc tie ey thes siya cies, We Cee eh we es a ee 35
PAP PENGIXE) a GLASSROULPACE ist take Lcciiionsc tat ee eos eee site elie ee See 36
XD PENGIXE Oss GIAaSSmlORGIMN naan sales es ear) Slee Secs een ael Si sas seeds si ot wi ss ueti'es ees 37
Miteraturem Citedmar spree eur iia oie vant ie ies cn et Ait) tts Moh, ote ee ee ae 38
ili
Foraminiferal Densities and
Pore Water Chemistry in the
Indian River, Florida
Martin A. Buzas
and Kenneth P. Severin
Introduction
A basic variable for ecological studies is density, the number
of individuals per volume or area. Densities of foraminiferal
species, like those of all organisms, vary in space and time.
Geographic changes in foraminiferal species densities have
been documented between all marine environments from
marshes to the abyss. Large differences in space like those
between a marsh and the abyss or the Arctic and the tropics are
easily recognized. A general qualitative correlation between
observed species densities and the environment is easily
accepted as an explanation for these changes. Similarly,
differences over vast amounts of geologic time are easily
recognized, and explained by the interplay of evolution and the
environment. As we decrease the scale of our observations in
space and time, however, and, at the same time, increase our
effort to achieve quantitative results, differences in species
densities and their explanation become much more difficult.
Nevertheless, densities do vary over a matter of meters and
within a time scale measured in weeks, months, or years. The
present study is an analysis of quantitative measurements of
species densities and environmental variables observed at two
stations about 10 m apart which were sampled every fortnight
for 9 months.
During 1978 the chemistry group of the Harbor Branch
Oceanographic Institution, Ft. Pierce, Florida monitored 10
pore water chemistry variables on a continual basis at two sites
(stations) about 10 m apart at a depth of about 1 m in the Indian
River (a shallow lagoon of nearly normal marine salinity on the
central east coast of Florida). One station was located on bare
quartz sand, the other on quartz sand with a covering of
Martin A. Buzas, Department of Paleobiology, National Museum of
Natural History, Smithsonian Institution, Washington, D.C. 20560.
Kenneth P. Severin, Department of Geology and Geophysics,
University of Alaska-Fairbanks, Fairbanks, Alaska 99775-0760.
seagrass (mostly Halodule wrightii and Thalassia testidium).
We viewed their study as an ideal opportunity to conduct a
study of the foraminifera with an experimental design allowing
us to test statistically for differences in density between stations
and with time as well as for the statistical significance of 10
environmental variables.
Foraminiferal densities were also enumerated at a depth of
10 cm within the sediment at the grass station. Few living
foraminifera were observed at 10 cm and most of the water
chemistry variables exhibited a dramatic difference compared
to the measurements made at the surface. To test the efficacy of
the statistical procedures used in this study, and in others, the
same statistical analyses were employed in evaluating the
differences between the grass surface and at 10 cm as for the
two surface stations.
ACKNOWLEDGMENTS.—We thank the former Chemistry
group at the Harbor Branch Oceanographic Insititution,
especially J. Montgomery, M. Hucks, G. Peterson, M. Price,
and C. Zimmermann. In the field and laboratory K. Carle, D.
Mook, and H. Sheng were most helpful. J. Jett prepared the
tables, figures, and appendices. L.S. Collins, L.C. Hayek, and
B.K. Sen Gupta offered helpful suggestions on the manuscript.
We thank J.C. Warren for his careful copy editing. This is
contribution No. 319 from the Smithsonian Marine Station at
Link Port.
Methods
FIELD.—Two stations were established about 100 m south of
the Link Port jetty at a depth of about 1 m. The stations were
marked with four poles encompassing an area of about 1 m* so
that the same area could be re-occupied easily. The sediment at
one station consisted of bare quartz sand with a silt-clay content
of about 2%. The other was on the same substrate, but had a
dense stand of Halodule wrightii with some Thalassia
testidium. The sediment was sampled by pushing plastic coring
tubes (inner diameter 3.5 cm) into the substrate. At each station
four sediment samples were taken indiscriminately (not
statistically randomized) at each sampling time which con-
sisted of every fortnight from 27 March until 6 November 1978
(17 sampling times).
The temperature, salinity, oxygen, pH, Eh, NH, PO,, Si,
NO,, NO, + NO, were measured on pore waters by the
chemistry group of the Harbor Branch Oceanographic Institu-
tion throughout the field experiment (Montgomery et al.,
1979);
LABORATORY.—Immediately upon return to the laboratory
(within a half-hour), 5 ml of sediment was removed from each
core top, and at a depth of 10 cm. Each 5 ml sample was washed
over a 63 tum sieve and preserved in 95% ETOH. Prior to
examination for foraminifera the sample was stained overnight
in rose bengal, dried, floated in a mixture of tetrabromine and
acetone (specific gravity 2.3), and re-wet using “photo-flo” as
a wetting agent. The foraminifera were enumerated while
underwater, a procedure which facilitates the recognition of
vividly stained protoplasm. The taxa counted were Quinqueloc-
ulina (mostly Q. impressa and Q. seminulum), Elphidium
(mostly E. mexicanum and E. gunteri), Ammonia (A. beccarii),
Bolivina (B. striatula), and Ammobaculites (A. exiguus). These
taxa are sufficiently dissimilar so that they can easily be
identified under a binocular microscope and account for 98% of
the foraminiferal fauna. The second replicate sample taken at
the grass surface on 22 May 1978 was destroyed in a laboratory
accident. The mean number of individuals from the other three
replicates was used to estimate the missing data (Appendix 2).
The systematics of the taxa used here are treated by Buzas and
Severin (1982). Enumeration was made by Severin.
STATISTICAL.—We have, then, two stations: one bare sand
and the other with a stand of seagrass. For this study, the bare
surface, the grass surface, and a depth of 10 cm within the
sediment at the grass station were sampled for foraminifera.
These three sites were sampled 17 times with four replicates of
5 ml each taken for examination. There are, then, N = 68
replicates or observations at each of the three sites. The number
of individuals counted for each of five taxa and the total (the
five taxa accounted for over 98% of the total) for each replicate
is tabulated for the bare surface in Appendix 1, for the grass
surface in Appendix 2, and for grass 10 cm in Appendix 3.
We obtained the measurements made by the chemistry group
for 10 water chemistry variables at each of the three sites at
each sampling time. These measurements for the bare surface
are tabulated in Appendix 4, for the grass surface in Appendix
5, and for the grass 10 cm in Appendix 6.
The data were divided into two sets for statistical analyses,
bare: surface-grass surface, and grass surface-grass 10 cm. For
each analysis we wished to test the following hypotheses;
difference between sites (bare surface vs. grass surface or grass
surface vs. grass 10 cm), differences with time (periodicity),
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
different periodicities at each site, and the significance of the
environmental variables. To accomplish this, we constructed a
general linear model (GLM) similar to the one used by Buzas
et al. (1977). In matrix notation the GLM is written as
@ : x = LZ’ B + e
(n x 1) (nxq) (qx 1) (n x 1)
where x, the “dependent” variable is a vector of observed
species densities for the n = 136 observations (n, =n, = 68), Z’
is a matrix of q “independent” variables, the composition of
which will be discussed below, B is a vector of q parameters
“regression coefficients” explaining the observations, and
e is a vector of “errors” or “residuals,” assumed to have a
normal distribution. The original counts x were transformed to
In(x + 1) to make the data more Normal and stabilize the
variance.
Restricted Q models containing s parameters are constructed
by equating the appropriate individual or groups of Bto 0. The
sum of squares of the residuals, e’e, for each model is a scalar
and is estimated by a least squares solution (Buzas et al., 1977,
give the equations). Restricted models can be compared to the
general model by the ratio
(en — een )r Gs)
TG oA ae (q-s\(n-q)
een / Maiq)
The mglh program of “SYSTAT” was used to calculate the sum
of squares of the residuals for each model, and these residuals
were then used to calculate the F-ratio given above. The results
of the analysis are most easily displayed in the standard ANOVA
table.
The composition of the matrix Z’ of the @ model is given in
Table 1. The vector z, is composed of 1’s so that B, is a
constant, Z, contrasts the difference between sites by assigning
+1 to one site and —1 to the other. The vectors z, and z, are
made up of sin (m x 7/3) and cos (m x 77/3), respectively, where
m = l,...,17. The vectors z; and z, are composed of sin (m x
7/6) and cos(m xX 1/6), respectively. These vectors are
components of a periodic regression (Bliss, 1958) and account
for a possible overall periodicity in the observations. Figures 1
and 2 illustrate these vectors over the period of our observa-
tions. The possibility exists that the two sites may exhibit
periodicity, but that the periodicity differs at the two sites. The
interaction vectors Z, = Z, X Z3, Zz = Z, X Z4, Zy = Z, X Zs, and 29
= Z, X Z, account for this. We have, then, 8 vectors to examine
the possible periodicity in our data. Had we constructed
instrumental variables to examine the differences between the
17 sampling times and their interaction, we would have
required 32 vectors, and made the model much more
complicated. Finally, vectors z,, through Z,, contain the water
chemistry variables completing the Z’ matrix for the GLM.
NUMBER 36
SIN (m x 17/3)
COS (mx m/ 3)
—)
TABLE 1.—Composition of the Z’ matrix.
a vector of units
+1 for bare surface, —1 for grass surface
sin (m x 7/3), m=1,...,17
cos (m x 7/3), m=1,... ,17
sin (m x 7/6), m=1,...,17
cos (m x 1/6), m=1,...,17
Z, XZ,
Z, XZ,
Z, XZ;
Z, XL,
temperature
salinity
oxygen
pH
Eh
1978
FIGURE 1.—1/3 periodicity.
cS
zB
~
fw
Zz
Nn
-1
2
Lb Ie, hb 4 % 4, Le, (a) t,
gj Cy © & G € C>, g
to, % ‘b Ny
10
5
9 9 4,
4 4 4 7%, Sy
“i, |% | % | | |, Sue
Fo, a © %,, py ® &y
% Gp p “,
1978 im
FIGURE 3.—Temperature measurements in °C.
are presented in Table 2. No significant difference was
observed between stations, but, once again, differences with
time were highly significant. Very low to zero values were
recorded in the spring and fall with maxima at both stations in
the summer.
PO, values are plotted in Figure 9 and ANOVA results are
presented in Table 2. No statistical difference was observed
between stations or with time. The measured values were
generally very low to zero with the exception of the bare station
in September which appears to be an outlier.
The measured Si values are plotted in Figure 10 and the
ANOVA results are presented in Table 2. No statistical difference
was observed between stations, but differences with time were
significant. Zero values were recorded at both stations in
NUMBER 36
12
GRASS SURFACE GRASS SURFACE
A A
Lt =28.79 Ne = 4.47
35 $ = 335 10 Oo = 1.8
>
z= z
5 i
5 —_—
= Z
a ©
BARE SURFACE 12 BARE SURFACE
nw
40 i =29.68 ye ‘
fi 2
G = 4.48 Ane
6 =2.89
>
=
= iS
4 co)
- >
n ~
~
—
1978
FIGURE 4.—Salinity measurements in °/oo.
FIGURE 5.—Oxygen measurements in mg-at/I.
pH
pH
9.0
GRASS SURFACE
ft =8.01
nw
0 =0.24
1978
FIGURE 6.—pH measurements.
Eh
Eh
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
GRASS SURFACE
fi = -4.06
G = 78.29
200
150
100
50
0
-50
-100
-150
-200
-250
q Sy (9)
te, Uy
e, te eS + o)
% Kz %, “te, ep
1978 ‘2
300
BARE SURFACE
fi = 6.09
6 = 12.38
250
9
August; however, values generally increase from March to
November.
NO, values are plotted in Figure 11 and the ANOVA results
are presented in Table 2. Significant differences were again
noted only with time. In general, values are low with maxima
in summer and fall at the bare station and spring, summer, and
fall at the grass station. Both stations had minima values from
late August until early October.
NO, + NO, values are plotted in Figure 12, and ANOVA
results are presented in Table 2. No significant differences were
observed between stations or with time. An unexplainably high
value was recorded in April at the grass station.
Table 3 shows the correlation coefficients between the
environmental variables. Temperature and NH, are positively
correlated with one another and negatively with oxygen, pH,
and Eh which are all positively correlated with one another.
Consequently, any hypothesis concerning the significance of a
particular variable on species density is not independent of the
others.
A way to avoid this difficulty is to transform the original
variables to principal components. Principal component analy-
sis is a technique which produces a succinct parsimonious
summarization of many correlated (non-zero covariance)
variables by transforming the original variables to independent
(zero covariance) variables called principal components. An
additional advantage of the technique is that the first principal
component will account for most of the variability in the data,
the second less, and so on (Seal, 1964). Eigenvalues were
calculated from the correlation matrix (Table 3) using the
SYSTAT statistical package. The first three eigenvectors account
for 63.43% of the variability. The factor score coefficients
(standardized vectors which when multiplied by the original
standardized variables produce the principal components)
indicate that all the environmental variables contribute substan-
tially to the first three principal components (Table 4). The
coefficients (Table 4) indicate that the first principal compo-
nent (PC1) accounting for 31% of the variability contrasts
TABLE 3.—Correlation matrix for chemical variables for bare surface and grass surface. 0.05 level is underlined.
200
ioe)
S
~ 150
+
oS
>
100
50
» 1s)
4, te 4 %, G 1%, %, ©
oy % Tp No tp ey “e, on
%,
1978
FIGURE 12.—NO, + NO, measurements in j1g-at/I.
Temperature Salinity Oxygen
Temperature 1.00
Salinity —0.14 1.00
Oxygen —0.68 0.07 1.00
pH —0.36 0.03 0.69
Eh —Or92 —0.08 0.69
NH, 0.40 0.29 0.48
PO, 0.14 0.24 0.03
Si —0.06 0.39 —0.05
NO, —0.15 —0.04 —0.08
NO, + NO, —0.26 0.24 0.15
pH
1.00
0.44
-0.44
0.03
—0.33
0.14
0.10
Eh NH, PO, Si NO, NO,+NO,
1.00
0.52 1.00
0.07 ~0.03 1.00
0.04 0.04 0.13 1.00
0.09 0.32 ~0.05 0.21 1.00
~0.06 0.02 -0.07 0.23 0.12 1.00
10
TABLE 4.—Factor score coeffecients for chemical variables for bare surface and
grass surface.
Chemical
variable
Factor
PC2(18%)
PC1(31%) PC3(14%)
Temperature
Salinity 0.01 ~0.45 0.04
Oxygen 0.29 0.00 0.04
pH 0.24 0.07 ~0.19
Eh
10000 GRASS SURFACE
QUINQUELOCULINA
ft = 254.68
Aa
© = 350.65
1000
100
MEAN DENSITIES
1978
10000
BARE SURFACE
QUINQUELOCULINA
fi = 328.59
an
6 = 585.57
1000
100
MEAN DENSITIES
%,
ee,
1978
FIGURE 13.—Mean number of individuals of Quinqueloculina per 5 ml of
sediment (density).
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
temperature and NH, with oxygen, pH, and Eh. The second
principal component (PC2) accounting for 18% of the
variability consists mainly of salinity, Si, and NO, + NO,,
although PO,, NH,, and Eh also contribute and a line of
demarkation is not as clear cut as for PCl. The coefficients
(Table 4) indicate that the third principal component (PC3)
accounting for 14% of the variability consists mainly of NO,,
Si, and temperature, but again there is no dramatic demarka-
tion. The most highly correlated variables (Table 3) are all
concentrated on PC1.
SPECIES DENSITIES, STATION DIFFERENCES, PERIODICITY,
AND ENVIRONMENTAL VARIABLES.—Quinqueloc-
ulina: Quinqueloculina was the most abundant taxon making
up about 75% of the total living foraminifera at the surface
stations. A plot of the mean densities observed at the bare
and grass surface at the two stations is shown in Figure 13.
The ANOVA table for six hypotheses is shown in Table 5. We
recall that each hypothesis is formulated by equating the
desired B to zero. For example, the model used to test for
station differences deletes B,, for m/3 periodicity and inter-
action 6, = 6, = B= B, = 0, and so on. Table 5 indicates that all
hypotheses except for station differences are significant. The
overall mean density at the bare station is higher than at the
grass station, but is not statistically significant at the chosen
0.05 level. Figure 13 shows that the bare station had high
densities in spring while the grass station had high densities in
summer. The environmental variables are significant as a
group. Because they are not independent, testing for the
significance of each individually is risky. Nevertheless, using
the standard errors of the ty s from the @ model for calculating
confidence limits indicates oxygen, Eh, PO,, Si, and NO, +
NO, are significant. Again, emphasizing that the variables are
not independent, the ease of calculation using “SYSTAT” made
the calculation of simple regressions for each of the variables
vs. density irresistible. The results shown in Table 6 indicate
the regressions for oxygen, pH, PO,, Si, and NO, are
significant. A way of avoiding the correlations between
variables is to calculate a multiple regression using principal
components instead of the original variables. Because the
TABLE 5.—Statistical analysis of GLM for Quinqueloculina for bare surface
and grass surface.
Sum of
squares
Variability on
account of
Stations 1.86 1 1.86 3.64 0.06
1/3 periodicity 22.53 4 5.63 11.04 0.00
and interaction
1/6 periodicity 27.50 4 6.88 13.38 0.00
and interaction
1/3 interaction 9.88 2 4.94 9.68 0.00
7/6 interaction 20.45 2 10.23 20.05 0.00
Environmental 37.34 10 3.73 7.32 0.00
variables
Residual 0.51
NUMBER 36
TABLE 6.—Values of F-ratio’s for simple regressions on species densities and
environmental variables at bare surface and grass surface. (+ indicates
signficant (.05 level) positive value of B; — significant negative value of B.)
Environmental
variables
Temperature
principal components are orthogonal, each hypothesis is
independent and the analysis is similar to a one-way ANOVA.
The results of an analysis on the log densities of Quinqueloc-
ulina and the first three PC’s of the environmental variables are
shown in Table 7. The overall F-ratio is significant and the test
for the significance of each PC, which are independent,
indicates that PC1 and PC3 are significant at the 0.05 level
while PC2 is nearly so. We recall from Table 4 that
temperature, oxygen, pH, Eh, and NH, all contribute nearly
equally to the first PC, and the third PC is weighed heavily on
temperature, Si, and NO,. Thus, the analysis using principal
components requires seven of the 10 environmental variables
for two PC’s and 10 for three to explain the results. All of the
above analyses indicate that the identification of one or two
variables as solely significant for the observed densities is
impossible.
Elphidium: Elphidium constitutes about 14% of the total
living population and mean densities at the bare and grass
stations are plotted in Figure 14 and the results of the GLM
TABLE 7.—Regression of Quinqueloculina and principal components for bare
surface and grass surface.
Coefficient Standard t
error
Variable P(2 tail) R2=0.11
Constant
PCI
PC2
PC3
0.20 0.09 2.28 0.02
—0.17 0.09
Regression
Residual
132
11
analysis is presented in Table 8. Although the mean density at
the bare station is again higher than at the grass station, it is not
Statistically significant. The 2/3 periodicity and interaction
hypotheses are significant, but the 7/6’s are not. Figure 14
indicates a spring high at the bare station, but no pronounced
summer high at the grass station as was observed with
Quinqueloculina. The group of environmental variables are
significant, and the ty s for Eh, PO,, NO,, and NO, + NO, were
significant. Regressions of density vs. each individual environ-
mental variable yielded a significant F-ratio for temperature,
salinity, oxygen, pH, and Si (Table 6). The results of
multiple-regression of Elphidium densities and the first three
PC’s of the environmental variables are shown in Table 9. The
first two PC’s are significant and these account for all of the
environmental variables except NO,. Here, we note that
individual tests of the ty s from the @ ri10deI and individual ty s
from simple regressions do not agree testifying to the difficulty
encountered when variables are highly correlated.
Ammonia: Ammonia makes up about 8% of the total living
population and mean densities at the bare and grass stations are
plotted in Figure 15. The results of the GLM analysis are
presented in Table 10. The overall mean density is slightly
higher at the bare station, but not significantly so. The 7/3
periodicity and interaction hypotheses are significant and
Figure 15 indicates an early spring maximum at the bare station
while both stations have summer and fall maxima. Environ-
mental variables are significant as a group and the B’s for Eh,
NO,, and NO, + NO, were significant. Individual simple
regressions on density vs. environmental variables yielded
significant F-ratios for salinity and NO, (Table 6). The results
of the multiple regression on densities of Ammonia and the
PC’s of the environmental variables are shown in Table 11. The
results present us with a small dilemma because the F-ratio for
the overall analysis has a probability of 0.06 which is slightly
above our chosen level, and, therefore, is not significant. On the
other hand, PC3 is significant (Table 11). If we choose to
regard the third principal component as significant, then
temperature, Si, and NO, are the major contributors, especially
NO, (Table 4). The F-ratio for environmental variables in the @
model, while significant, is the smallest encountered for any of
the five taxa analyzed. Ammonia appears, then, to be the least
influenced by the 10 variables measured in this study.
Bolivina: Bolivina constitutes about 1% of the total living
population and mean densities at the bare and grass stations are
plotted in Figure 16. The results of the GLM analysis are shown
in Table 12. Although the differences in densities between
stations are small, they are, nevertheless, statistically signifi-
cant, and the highest density is at the grass station. None of the
hypotheses for periodicity are significant. There does appear to
be a decrease in density over the course of sampling, but the
fluctuations in density shown in Figure 16 are small compared
to those considered previously (note the difference in the scale
of the ordinate). The environmental variables are significant as
12
1000
GRASS SURFACE
MEAN DENSITIES
1000 BARE SURFACE
ELPHIDIUM
(i = 80.96
G = 145.58
MEAN DENSITIES
xy)
1978 2
FIGURE 14.—Mean number of individuals of Elphidium per 5 ml of sediment
(density).
a group, and the B’s for salinity and Si were significant. F-ratios
for simple regressions are significant for salinity, PO,, Si, and
NO, + NO, (Table 6). Multiple regression on densities of
Bolivina and the first three PC’s of the environmental variables
are shown in Table 13. Only PC2 consisting mostly of salinity,
Si, and NO, + NO, (Table 4) is significant. The relationship
with salinity and to a lesser extent with Si are notable for this
species.
Ammobaculites: Ammobaculites also constitutes about 1%
of the total living population and mean densities for the bare
and grass stations are plotted in Figure 17. The results of the
GLM analysis are shown in Table 14. The hypothesis for
station differences is significant with the highest densities
occurring at the grass surface. The 1/3 periodicity and
interaction hypotheses are significant, and Figure 17 indicates
the now familiar spring maximum was observed at the bare
station after which time the densities remained very low
(Appendix 1). At the grass surface the densities increased
overall during the sampling duration (Appendix 2). The
hypothesis for the environmental variables is significant. The
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
TABLE 8.—Statistical analysis of GLM for Elphidium for bare surface and grass
surface.
Variability on Sum of df Mean F
account of squares square ve)
Stations 0.88 1 0.88 1.78 0.18
1/3 periodicity 8.28 4 2.07 4.21 0.00
and interaction
1/6 periodicity 2.94 4 0.74 1.49 0.21
and interaction
1/3 interaction 3.73 2 1.87 3.79 0.03
1/6 interaction 2.91 2 1.45 2.95 0.06
Environmental 28.43 10 2.84 5.78 0.00
variables
Residual 57.04 116 0.49
TABLE 9.—Regression of Elphidium and principal components for bare surface
and grass surface.
Coefficient Standard t
error
Variable P(2 tail) R?=0.18
Constant 0.07 50.73
PCl 0.21 0.07 3.05 0.00
PC2 —0.30 0.07 —4,32 0.00
PC3 0.07 —0.40
Analysis of variance
Source Sum of df Mean F-ratio P
squares square
Regression 18.25 3 6.08 9.39 0.00
Residual 85.54 132 0.65
B's of the @ model for the variables pH, PO,, Si, NO,, and
NO, + NO, were significant. The F-ratios for temperature,
salinity, oxygen, and NH, were significant for simple regres-
sions on density and environmental variables (Table 6). The
results of a multiple regression on the densities of Ammobacu-
lites and the first three PC’s of the environmental variables are
shown in Table 15. The first two PC’s are significant indicating
that all the variables except for NO, (Table 4) are important
contributors. Again, we note the inconsistencies obtained by
testing the environmental variables individually.
In summary, we recognize no station differences for the three
most abundant taxa. For the rare species Bolivina and
Ammobaculites which together constitute only about 2% of the
total living population, densities are higher at the grass station.
All of the taxa except for Bolivina exhibited periodicity.
Quinqueloculina, Elphidium, and Ammonia all showed high
densities in spring at the bare surface station and in summer at
the grass surface. Bolivina exhibited an overall decreasing
density from spring onward at both stations while Ammobacu-
lites increased at the grass surface station and decreased at the
NUMBER 36
1000 GRASS SURFACE
AMMONIA
Ht =27.54
Aa
© = 29.74
wn
=
E 100
7)
r4
=
a
& 410
=
1
4
Y,
‘p % Oe Lb,
{9 ©, ©;
xe <2 eI SS KS
Sy ep
1978
1000 BARE SURFACE
AMMONIA
aA =
iu = 33.94
6 = 29.78
3
MEAN DENSITIES
S
1978
FIGURE 15.—Mean number of individuals of Ammonia per 5 ml of sediment
(density).
bare surface. The environmental variables were significant as a
group for all taxa, however, individual variables cannot be
evaluated with confidence.
Grass Surface and Grass 10 cm
ENVIRONMENTAL VARIABLES.—The recorded temperature
values are plotted in Figure 18, and the results of a two-way
ANOVA testing for differences between surface and 10 cm and
with time are shown for temperature and all the other
environmental variables in Table 16. The hypothesis testing for
differences in mean temperature between the surface and 10 cm
depth is not significant, however, the hypothesis testing for
time is. As everyone knows, the water is warmer in the summer
and cooler in spring and fall. A very high temperature was
recorded in June and a very low one in August at 10 cm.
The measured salinity values are shown in Figure 19. Only
the hypothesis for time is significant. Salinities were highest in
13
TABLE 10.—Statistical analysis of GLM for Ammonia for bare surface and
grass surface.
Variability on Sum of df Mean F p(F)
account of squares square
Stations 0.06 1 0.06 0.13 0.72
1/3 periodicity 11.29 4 2.82 5.98 0.00
and interaction
1/6 periodicity 4.24 4 1.06 2.24 0.07
and interaction
1/3 interaction 6.38 2 3.19 6.76 0.00
1/6 interaction 1.94 2 0.97 2.05 0.13
Environmental 16.60 10 1.66 3.52 0.00
variables
Residual 54.70 116 0.47
TABLE 1 1.—Regression of Ammonia and principal components for bare surface
and grass surface.
Standard
Variable Coefficient P(2 tail) R?2=0.06
error
Constant 3.14 0.07 45.92 0.00
PC1 -0.01 0.07 -0.16 0.87
PC2 -0.12 0.07 -1.72 0.09
PC3 0.15 0.07 2.18 0.03
Sum of
squares
4.92 3 1.64 2.58 0.06
Regression
Residual
spring except for the first observation in March at 10 cm. Both
stations experienced a dip in salinity in August.
The recorded oxygen values are shown in Figure 20. The
mean square for differences between surface and 10 cm is large
and highly significant, and the mean square for time, although
relatively much smaller, is nearly significant. Figure 20
illustrates and Appendix 6 tabulates zero recordings for oxygen
during spring and summer at 10 cm making the difference
between the surface and 10 cm (depth hypothesis) so dramatic.
The measured pH values are plotted in Figure 21. The depth
hypothesis is significant, and the pH is always lower at 10 cm
(Figure 21, Appendix 5, 6). Even at 10 cm, however, only one
reading (November) recorded a value below 7.
Eh values are plotted in Figure 22. The depth hypothesis is
significant. Values of Eh at 10 cm are always negative, and
usually highly so, while the values at the surface fluctuate from
positive to negative with a slightly negative mean (Figure 22,
Appendix 5, 6).
14
100 GRASS SURFACE
BOLIVINA
ft = 7.26
=7.98
i
S
MEAN DENSITIES
100
BARE SURFACE
BOLIVINA
ft = 3.38
G = 3.82
MEAN DENSITIES
1978
FIGURE 16.—Mean number of individuals of Bolivina per 5 ml of sediment
(density).
NH, values are plotted in Figure 23. The depth hypothesis is
significant. The values at 10 cm are usually two orders of
magnitude greater than at the surface (Appendix 5, 6). Two
zero values were recorded at 10 cm, one in April and one in
October. We suspect these anomalies are due to problems with
instrumentation.
PO, values are plotted in Figure 24. The hypothesis for depth
is significant. The values for PO, were very low at the surface,
and one or two orders of magnitude higher at 10 cm (Appendix
5, 6). A very high value was recorded at 10 cm in June.
Si values are plotted in Figure 25 and the hypothesis for
depth is significant. The values for Si at the surface are an order
of magnitude smaller than at 10 cm (Appendix 5, 6). At 10 cm
zero values were recorded in April, August, and October.
Except for the zero value in August, the pattern is very similar
to that observed for NH, at 10 cm.
NO, values are plotted in Figure 26. The hypothesis for
depth is significant, and values for NO, are low. At 10 cm very
low values were recorded in all months except October.
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
TABLE 12.—Statistical analysis of GLM for Bolivina for bare surface and grass
surface.
Variability on Sum of df Mean p(F)
account of squares square
Stations 4.03 1 4.03 7.45 0.01
1/3 periodicity 4.38 4 1.09 2.02 0.10
and interaction
1/6 periodicity 3:33 4 0.83 1.54 0.20
and interaction
7/3 interaction 1.25 2 0.62 eS 0.32
1/6 interaction 0.36 2 0.18 0.33 0.72
Environmental 42.21 10 4.22 7.80 0.00
variables
Residual 62.77 116 0.54
TABLE 13.—Regression of Bolivina and principal components for bare surface
and grass surface.
Standard
Variable Coefficient t P(2 tail) R2=0.28
error
Constant 1.38 0.07 19.10 0.00
PCl 0.04 0.07 0.47 0.64
PC2 -0.52 0.07 -7.14 0.00
PC3 0.01 0.07 0.19 0.85
Analysis of variance
Source Sum of df Mean F-ratio P
squares square
Regression 36.34 3 12.11 17.07 0.00
Residual 93.66 132 0.71
NO, + NO, values are plotted in Figure 27. There is no
significant difference with depth or with time. Except for one
measurement at the surface in April (Figure 27, Appendix 5) all
the measurements were low.
The two-way ANOVA’s for environmental variables at the
bare surface vs. grass surface showed most significant
differences are with time (Table 2). In contrast, the analysis for
grass surface vs. grass 10 cm shows most of the significant
differences are with depth (Table 16). Moreover, the F-ratios
for the latter are usually much higher. We have, then, a situation
where, except for temperature, salinity, and NO, + NO,, there
are much larger differences than at the surface stations.
The environmental variables are highly correlated as shown
in Table 17. High positive values occur between oxygen and
Eh, oxygen and NO,, pH and Eh, Eh and NO,, NH, and POQ,,
NH, and Si, and PO, and Si. High negative values occur
between oxygen and NH,, oxygen and PO,, oxygen and Si, pH
and NH,, pH and Si, Eh and NH, Eh and PO,, and Eh and Si.
The correlations differ from those at the bare and grass surface
NUMBER 36
100
GRASS SURFACE
AMMOBACULITES
MEAN DENSITIES
100
BARE SURFACE
AMMOBACULITES
MEAN DENSITIES
1
q,
“p,
%
%, %
Np tp
1978
FIGURE 17.—Mean number of individuals of Ammobaculites per 5 ml of
sediment (density).
(Table 3) in that PO,, Si, and NO, now are highly correlated so
that the matrix of correlation coefficients has many more high
values. To succinctly summarize the environmental variables,
and remove the covariance between them, a principal compo-
nent analysis was calculated on the correlation matrix. The first
three eigenvalues account for-about 70% of the variability. The
factor score coefficients are given in Table 18. The first vector
accounting for 45% of the variability has high values for
oxygen, pH, Eh, NH, PO,, Si, and NO,. The second vector
accounting for 15% of the variability has the highest values for
salinity and NO, + NO,, and the third accounting for 10% of
the variability has a high value for temperature. Thus, all of the
water chemistry variables are accounted for by using the first
three principal components, and PC1 accounts for all of them
except temperature, salinity, and NO, + NO, which, interest-
ingly, are the variables without significant differences with
depth (Table 16).
SPECIES DENSITIES, DEPTH DIFFERENCES, PERIODICITY, AND
15
TABLE 14.—Statistical analysis of GLM for Ammobaculites for bare surface
and grass surface.
Variability on Sum of df Mean F p(F)
account of squares square
Stations 21.59 1 21.59 44.79 0.00
m/3 periodicity 8.56 4 2.14 4.44 0.00
and interaction
7/6 periodicity 3.13 4 0.78 1.62 0.17
and interaction
1/3 interaction 4.90 2 2.45 5.08 0.01
1/6 interaction 0.08 2 0.04 0.08 0.92
Environmental 18.26 10 1.83 3.79 0.00
variables
Residual 55.87 116 0.48
TABLE 15.—Regression of Ammobaculites and principal components for bare
surface and grass surface.
Coefficient Standard t
error
Variable P(2 tail) R?=0.06
Constant i
PC1 0.18 0.08 Zale 0.03
PC2 0.17 0.08 2.07 0.04
PC3
Sum of df
Source F-ratio P
squares square
Regression 7.97 3 2.66 3.04 0.03
Residual 115.46 132 0.88
ENVIRONMENTAL VARIABLES.—Quinqueloculina: Quin-
queloculina mean densities are plotted in Figure 28, and
analysis by the GLM is shown in Table 19. As noted earlier, the
densities for Quinqueloculina at the grass surface exhibited a
summer maximum in July and August (Appendix 2). At 10 cm
maxima were observed in March and November. The large
increase in density observed in the summer at the surface is not
reflected at 10 cm.
All of the hypotheses tested by the GLM were significant
(Table 19). The mean square for differences with depth is very
large reflecting the two orders of difference in magnitude of the
mean density between the surface and 10 cm. The set of
environmental variables are significant and in the @ model B’s
for oxygen, Eh, and NH, were significant. Simple regressions
on density vs. environmental variables indicate the F-ratios for
all variables except temperature are significant (Table 20). The
results of a multiple regression on the densities of Quinqueloc-
ulina and the first three PC’s of the environmental variables are
16
TABLE 16.—Analysis of variance for chemical variables on grass surface and
10 cm.
Variable | Source PENS ap rea p(F)
squares
Temperature | depth 0.54 1 0.54 0.12 0.74
time 273.64 16 17.10 3512 0.01
residual 73.63 16 4.60
Salinity depth By PA ana 522 2.83 0.11
time 311.96 16 19.50 9.64 0.00
residual 32°35); 16 2.02
Oxygen depth 87.68 1 87.68 30.77 0.00
time 96.21 16 6.01 21 0.07
residual 45.59 16 2.85
pH depth 2.950 2.95 45.09 0.00
time 1.03 16 0.06 0.98 0.52
residual 1.05 16 0.07
Eh depth 469412.50 1 469412.50 109.31 0.00
time 107511.88 16 6719.49 1.57 0.19
residual 68712.00 16 4294.50
NH, depth 1148940.80 1 1148940.80 32.13 0.00
time 601797.43 16 37612.34 1.05 0.46
residual 572067.08 16 35754.19
PO, depth 12189.21 1. 12189.21 8.61 0.01
time 22394.25 16 1399.64 0.99 0.51
residual 22652.76 16 1415.80
Si depth 1477313.29. = -1-—-«:1477313.29 41.34 0.00
time 635755.12 16 39734.70 1.11 0.42
residual 571797.45 16 35737.34
NO, depth 0.11 1 0.11 7.82 0.01
time 0.19 16 0.01 0.85 0.62
residual 0:23— 16 0.01
NO, + NO, depth 293323) atl 2933.23 1.33 0.27
time 35026.37 16 2189.15 0.99 0.51
residual 35275.14 16 2204.70
shown in Table 21. The first and third PC’s are significant
indicating that only NO, + NO, did not contribute substantially
to the significance of the analysis. The F-ratios for the simple
regressions are much larger than for the bare surface and grass
surface (compare Tables 6 and 20). Similarly, the F-ratio and
R? for the multiple regression using the PC’s is much larger for
the grass surface and grass 10 cm than for the bare surface and
grass surface (compare Tables 7 and 21). This is, of course, a
reflection of the very large differences between densities and
environmental variables between the surface and 10 cm. The
signs of the factor score coefficients for PCI (Table 18), the
correlation matrix (Table 17), and the B’s of the simple
regressions (Table 20) show that the analysis contrasts oxygen,
pH, Eh, NO, with NH,, PO,, Si.
Elphidium: Elphidium mean densities at the grass surface
and grass 10 cm are plotted in Figure 29. The summer
maximum observed at the surface for Quinqueloculina was not
observed for Elphidium (Appendix 2). Minima at the surface
were observed in June and November which is similar to the
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
GRASS SURFACE
40 As
ft =26.61
S = 2.96
=)
i=
<<
o
je)
i*I
=
ea}
=
40 GRASS 10 CENTIMETERS
nw
LU = 26.86
nw
So = 3.60
j=)
=
<
$2
&
(5
=
ica)
»
ty
ch
‘a
Sy
1978 4 ?
FIGURE 18.—Temperature measurements in °C.
pattern of Quinqueloculina (Appendix 2). At 10 cm a
maximum occurred in March due to high number of individuals
in two of the four replicates (Appendix 3). Once again,
however, the densities at 10 cm were very low, averaging less
than two individuals.
Table 22 indicates the largest mean square is for the
hypothesis contrasting depth. The 7/3 periodicity with interac-
tion is significant while the 1/6 is not. The set of environmental
variables are significant, and individual B’s of the @ model for
Eh and NH, were significant. F-ratios of all variables except
temperature and salinity are significant for simple regressions
(Table 20). The results of a multiple regression on the densities
of Elphidium and the first three PC’s of the environmental
variables indicate that all three PC’s, and, consequently, all the
environmental variables contribute substantially (Table 23).
Ammonia: Ammonia densities for the grass surface and
grass 10 cm are plotted in Figure 30. A maximum density was
NUMBER 36
GRASS SURFACE
[it = 28.79
35 G = 335
>
(>
_—
Zz
S|
=
nn
GRASS 10 CENTIMETERS
ft =29.62
35 6 = 321
30
>
‘=
a
Z
=
=
nN
25
20
ty, ) ie % %, %
q # Tp Ay, a Co,
SO Xe ¢ Pe.
1978
FIGURE 19.—Salinity measurements in °/oo.
OXYGEN
OXYGEN
GRASS SURFACE
A
UL =4.47
cas
O =1.83
Ty 4 %, y q, Lp
q B Cp_ (“A Ch,
% e Ve tp %, “2p,
>|
“
1978
GRASS 10 CENTIMETERS
ft =1.26
G =2.35
1978
iw)
FIGURE 20.—Oxygen measurements in mg-at/1.
18
pH
pH
GRASS SURFACE
ft =8.01
6 = 0.24
GRASS 10 CENTIMETERS
“a
UW =7.42
6 =0.27
Se
Pe Nene al see eel
1978
FIGURE 21.—pH measurements.
Eh
Eh
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
GRASS SURFACE
fl = -4.06
6 = 78.29
200
100
0
-100
-200
-300
-400
dp a7 %, %, 4, Ye Gg, 4,
| |) | el See ee
% > % 4 %,
ane. %
1978
200
GRASS 10 CENTIMETERS
ft = -239.06
100 S= 69.89
0
-100
-200
-300
-400
4 4 4b %, Y oe S (a) 4
cK Sl MM Ie RG IIS. 2, |S
& o Sp y 7) “py,
@, 77) ©,
bp, ep
1978
FIGURE 22.—Eh measurements in mV.
NUMBER 36 19
300
GRASS SURFACE GRASS SURFACE
1000 ff = 4.28 ft =0.54
6 = 8.05 250 G =0.57
100 200
oj ¢
= 150
10
100
50
1
q o Y 4, S, ca) 4,
“4, % I. Go % es, 0
op
> “Gp p %,
1978
GRASS 10 CENTIMETERS a
“aw
ft =371.93 GRASS 10 CENTIMETERS
6 =270.74 ft =38.41
nw
a ah) 6 = 53.06
100 S
[<0
foe)
=
Zs
10
1
wy | a ty, % % 1%, |% Q %
7 27 7 C . ©. 7) y g S, fa) 4
lip %, ip Np ep G as % Le ty Ip tp *, %, 4, %, On, Ve
% ‘ “ “Ny e, ey, Go| % ME Ne
SI Ry
Date g RY RY
‘ Y 9
(1978) Ni RS g £ s
eS ¥ € $ €
Ny ® y ~ % «9
28 Aug 163 19 14 0 5 208
196 17 10 2 2 248
196 3 14 1 2 221
185 33 4 Z 6 247
11 Sep 117 31 11 1 12 178
77 24 11 1 11 128
76 18 20 0 4 127
95 41 19 1 6 164
25 Sep 208 24 18 0 16 270
162 29 3 0 17 217
131 18 22 0 9 187
199 20 15 2 a 255
9 Oct 152 33 27 3 16 239
159 19 25 g) 16 227
145 17 19 0 10 207
67 10 4 0 8 108
23 Oct 224 25 30 il 22 329
304 81 56 5 18 480
197 34 54 3 19 321
238 25 37 7 16 336
6 Nov 115 20 33 2 14 208
19 17 19 0 1 57
17 6 6 0 3 39
17 9 5 2 9 55
Appendix 3
Grass 10 cm
Number of individuals per 5 ml of sediment.
mS
| td
As
~
54
100
11
20
10
11
16
16
29
15
15
19
53
17
14
15
32
33
18
24
27
22
14
<
27 Mar
10 Apr
24 Apr
8 May
22 May
5 Jun
19 Jun
3 Jul
17 Jul
31 Jul
14 Aug
SMITHSONIAN CONTRIBUTIONS TO THE MARINE SCIENCES
34
Appendix 3.—Continued.
Date
(1978)
27 Mar
10 Apr
24 Apr
8 May
22 May
5 Jun
19 Jun
3 Jul
17 Jul
31 Jul
14 Aug
28 Aug
11 Sep
25 Sep
9 Oct
23 Oct
6 Nov
Temperature
24.5
26.5
23.5
27.0
28.3
32.0
27.0
30.0
31.0
30.0
31.0
30.5
30.5
29.5
24.0
24.0
23.0
Salinity
32.0
32.5
32.5
3S
39.0
35.0
33.0
32.0
30.5
28.0
20.0
26.0
28.0
26.0
25.5
26.0
27.0
Oxygen
7.4
9.1
10.2
0.4
1.8
0.6
Sal
0.4
3.3
4.4
23
2.4
3.4
5.4
6.0
3:3
4.9
pH
8.63
8.70
8.75
7.50
8.30
7.50
8.10
MSS
7.50
8.30
8.08
8.21
8.06
8.12
TOM
8.00
8.35
Appendix 4
Bare Surface
Pore water chemistry.
Eh
35
NH,
0.1425
0.0920
0.1149
0.1204
21.2810
95.9300
2.1058
65.8357
1.4197
0.1717
21.3100
0.1721
1.8610
0.1638
0.1790
1.2926
3.1530
PO,
0.7354
0.2202
0.0320
0.5373
0.0412
0.8373
0.0560
1.1206
0.0524
0.1915
1.6034
0.5327
2.2904
16.4580
0.0742
0.4990
1.8657
Si
13.3037
26.7167
7.1574
11.5527
49.3007
70.7123
29.4110
32.1760
68.8000
55.7310
51.5230
0.0000
104.0400
66.4087
133.5466
105.5033
69.4803
NO,
0.0721
0.0609
0.0113
0.0218
0.0229
0.3944
0.3152
0.3722
0.3134
0.2811
0.2359
0.0224
0.0215
0.0225
0.0218
0.3724
0.6236
NO, + NO,
4.3109
3.0221
4.9155
27.1770
3.8981
4.5691
3.3008
47.3260
0.1385
1.3133
0.5315
0.0622
0.0668
0.1841
0.0844
0.6684
1.8702
Date
(1978)
27 Mar
10 Apr
24 Apr
8 May
22 May
5 Jun
19 Jun
3 Jul
17 Jul
31 Jul
14 Aug
28 Aug
11 Sep
25 Sep
9 Oct
23 Oct
6 Nov
Temperature
22.0
24.5
22.8
27.0
26.5
29.0
25.2
29.5
28.0
29.0
30.0
31.0
29.5
28.5
23:5
23.3
23.0
Salinity Oxygen
32.5
31.0
33.0
31.0
33.0
31.0
32.0
29.0
28.0
31.0
22.0
26.0
25.5
25.5
24.5
28.0
26.5
pH
8.03
8.25
8.35
8.00
8.15
7.60
8.00
7.50
7.74
8.08
8.17
8.21
8.03
8.16
7.72
8.00
8.19
Appendix 5
Grass Surface
Pore water chemistry.
Eh
=A
105
=
-56
-67
=1128
185
S115
38
-50
-47
= 12
=33
-37
52
43
50
36
NH,
0.1425
0.0920
0.9383
0.1204
7.8668
33.7027
0.1486
5.5559
0.0000
4.6810
6.9164
0.1721
2.9416
0.3979
0.1790
4.6589
4.2475
PO,
0.8665
0.1329
0.1563
0.0391
0.0412
0.3404
0.0560
0.4846
0.0000
0.4182
0.9911
0.3936
1.1962
0.4561
0.5758
0.7639
2.2476
Si
13.7943
22.6467
12.0900
14.7607
32.5660
50.9217
1.3497
69.2380
70.8490
43.1990
16.9515
0.0000
98.0553
72.2520
138.5800
106.3733
68.6020
NO,
0.0549
0.0535
0.2704
0.0218
~ 0.0229
0.1105
0.1303
0.2146
0.0313
0.1782
0.1416
0.0224
0.0215
0.0225
0.0218
0.5176
0.5030
NO, + NO,
20.8870
3.2316
275.4266
5.3229
10.0146
Date
(1978)
27 Mar
10 Apr
24 Apr
8 May
22 May
5 Jun
19 Jun
3 Jul
17 Jul
31 Jul
14 Aug
28 Aug
11 Sep
25 Sep
9 Oct
23 Oct
6 Nov
Temperature
24.2
23.8
23°)
27.2
27.2
28.5
25.5
36.2
27.7
27.8
21.0
30.0
31.0
29.0
24.0
26.5
23.5
Salinity Oxygen
27.0
30.7
33.0
33.5
34.5
3335
32.5
32.0
30.5
30.5
24.0
26.5
27.5
27.5
26.0
2IES
27.0
pH
7.71
7.67
7.60
7.30
7.45
7.50
7.60
7.52
Use)
7.44
7.62
7.46
7.49
7.49
7.35
7.04
6.57
Appendix 6
Grass 10 cm
Pore water chemistry.
Eh
—196
—235
Se
—286
—289
~307
—331
—312
—282
—288
— 27)
—223
HH
—225
—199
—33
—176
Si
NH,
96.2270
0.0000
285.6600
312.6400
463.4599
836.8533
929.6766
745.4833
518.7266
387.2833
198.0666
233.1100
417.0299
470.0866
240.5966
0.0000
187.9799
PO,
12.2930
0.0000
28.0770
26.0440
29.3460
51.8203
32.1800
70.4513
46.8573
36.5710
19.7187
13.1470
25.0397
26.2023
12.2607
0.0000
22.9157
583.6932
515.6699
688.2266
659.5033
616.1066
572.8933
559.7299
785.4966
461.1333
0.0000
324.8800
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