Long-term Acoustic Assessment of Bats at Bismark Bridge, South Dakota for 2013-2015

Prepared for:

Bureau of Land Management

Prepared by: Dan Bachen, Braden Burkholder, Alexis McEwan, Shannon Hilty, Scott Blum, and Bryce Maxell Montana Natural Heritage Program A cooperative program of the Montana State Library and the University of Montana

June 2020

2) see ting XE

Program

Suggested Citation:

Bachen, D. A., B. O. Burkholder, A. L. McEwan, S. L. Hilty, S. A. Blum, and B. A. Maxell. 2020. Long-term acoustic assessment of bats at Bismark Bridge, South Dakota for 2013-2015. Report to Bureau of Land Management. Montana Natural Heritage Program, Helena, Montana. 19 pp.

Acknowledgments

This project was conducted with funding from the Bureau of Land Management and would not have been possible without the support of this agency and its staff. Staff at Wildlife Acoustics assisted with questions regarding the SM2Bat+ ultrasonic detector and SMX-US microphone and Kaleidoscope Pro software. Joe and Nick Szewczak provided SonoBat 4.1 software, feedback on its use, and the 2011 Humboldt State University Bat Lab’s echolocation call characteristic summaries for western and eastern U.S. bats that we used to develop the call characteristic summary for Montana bats (Appendices 6 & 7 in Maxell 2015). Bowen Deng provided technical support while processing acoustic data on Montana Tech’s high performance computing cluster. At the Montana Natural Heritage Program, Darlene Patzer assisted with grant administration and Dave Ratz assisted with downloading of weather station data from the Mesowest application programming interface.

Bismark Bridge, South Dakota - i

Table of Contents

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Bismark Bridge, South Dakota - ii

List of Tables Table 1. Species hand confirmed at the Bismark Bridge detector, by season. Species only observed previously and newly detected within the local area (50.0 km) are noted. ..........c.cccccesseceesssseeeessseeesessseeees 5

Table 2. The number of months each bat species was confirmed by hand analysis of calls identified by automated software, the number of months reviewed, and the respective successful classification rate; only-active-season data are shown ais. dAtacsccirceh cessl eee eth aa cdlevin esses nt eH 13

Table 3. Management considerations for species detected within 50.0 km of the Bismark Bridge detector. Species presence is summarized by season and include this and any previous efforts. ............ 17

Bismark Bridge, South Dakota - iii

List of Figures

Figure 1. Placement of the Bismark Bridge GeEteCtOl, ........c.cccccccccsssssscecececessesesneaeeeeecesseseaeseeeeecessessaeaeeeeeens 1 Figure 2. Photo of the detector deployment site at Bismark Bridge. .............ccccccccccecessesssseceeeeecessessnteseeeeeens 2

Figure 3. Total monthly bat passes recorded at the Bismark Bridge acoustic monitoring station. Months marked with an asterisk should be interpreted with caution as those data may not represent valid trend due to data collection for only part of that month, equipment malfunction or other issues..............00000 6

Figure 4. Average nightly activity of bats recorded at the Bismark Bridge acoustic monitoring station across the-active Seasons. .si:iten Scie atiefs ke igs A BAA ee nA hah Sins A 7

Figure 5. Average bat pass temperatures (red line) and average background temperatures (black line) across the year at the Bismark Bridge GetectOr. ..........ccccccccessssscecececeesesnsaeceeecesseseaeaeceeseseseseaaeaeesesseesessaaees 8

Figure 6. A comparison of average temperatures during bat passes (red) and average hourly background temperatures (blue) recorded at the K49B station, located 25.0 km to the south-southwest of the detector. Where the bars showing passes exceed hours, bat activity is higher than expected for this LEMP Oat Ue sO Us sons seecc 3 ccec vse eecced ccs SieOeaeaetea Sass vas dealehics sa voce en Sees cas ova weeaei oboe tu dee gee Gllad ras oes I as LTE 8

Figure 7. The cumulative sum of wind speeds recorded at the K49B station during bat passes. The speed at which 95% of all activity occurs at or below is highlighted in red........ ce eecssceceessececeeceeeceecaeeeeseneeeeseaaes 9

Figure 8. A comparison of background windspeeds recorded at the K49B station (blue) and those recorded during bat passes (red). Where the bars showing passes exceed hours, bat activity is higher than expected for this Wind Sp@@d DIN. ........ccccccccccesesssssececeeecessessaeseeeescesseseeaeeeeeessesseeaeeeeeesseeseaeaeeeeeessensees 9

Figure 9. Hourly changes in background barometric pressure at the K49B station (blue) compared to changes in pressure when bat passes were recorded (red) at the Bismark Bridge detector. Where the bars showing passes exceed hours, bat activity is higher than expected for this pressure change bin.....10

Figure 10. A comparison of hours with and without precipitation for bat passes (red) and all nighttime hours (blue) during the active season as recorded at the SBFS2 station, located 15.7 km to the southeast of the detector. Where the bars showing passes exceed hours, bat activity is higher than expected for this’ Precipitation DIN. se2 Sch. serecchedsceatee ats Rech ccesa soa arene de tesaas ors eee ee cna vane cheba eater aaa waned ets 11

Figure 11. Percent of bat passes (red) and background hours (blue) at various moon illumination categories (0% = no illumination and 100% = full moon) and with the moon above and below the horizon. Where the bars showing passes exceed hours, bat activity is higher than expected for this moon horizom/irMminatiOm: iM sieccceceecesvcacbervedecesebeausdevaepevsetecedbegscitesse' vavedeGocinecieds saceetcinecevassddder tinedevevadendbsineess 12

Figure 12. Bat passes through the deployment period identified to species using SonoBat 4.1. Note that these species identification are only suggestions and should only be used to assess general trends for species for which the classifier WOrKS Well. ...........cccssssccccececeesessseeceeecesesseaeaeeeceesseeseaaaeeeeeeeseeseasaeeeeeeeseeseea 14

Bismark Bridge, South Dakota - iv

Background

We established a long-term, ultrasonic acoustic monitoring site on BLM lands in northwestern South Dakota (Figure 1), following deployment and maintenance protocols in Maxell (2015). The detector was placed adjacent to a small lentic waterbody (Figure 2). The surrounding area was forested and not rugged (flat topography).

Figure 1. Placement of the Bismark Bridge detector.

A SM2Bat+ detector with a SMX-US microphone was deployed on 22 Oct 2013 and decommissioned on 27 Jun 2015, for a total of 614 nights deployed. Throughout the recording period the detector functioned well, recording on 613 nights or 99.8% of the time. In total this unit collected data over 21 months and did not meet our minimum of 2 years of deployment for analysis of long-term trends.

Bismark Bridge, South Dakota - 1

Figure 2. Photo of the detector deployment site at Bismark Bridge.

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Methods

Bat Detector Deployment

Across the acoustic network, detectors were placed at locations to maximize species diversity and bat activity through placement near features important for bats such as roosts, foraging areas, and waterbodies suitable for drinking. We assessed potential sites based on: (1) open water for as much of the year as possible; (2) rock outcrops and trees that might be used as roosts by bats; (3) southern solar exposure that would allow a solar panel to charge a battery even during the winter; (4) year-round accessibility; and (5) a low likelihood of vandalism. At all sites, a detector/recorder unit and microphone were deployed. The microphones at all operational sites in 2015 were upgraded to SMX-U1 microphones (Wildlife Acoustics Inc., Maynard, MA). The detector/recorder was deployed, monitored, and maintained with the equipment, supplies, settings, and protocols listed in Montana’s Bat and White- Nose Syndrome Surveillance Plan and Protocols 2012- 2016 (Maxell 2015).

Many aspects of the equipment and site selections influenced the detection of a bat echolocation call and the quality of the resulting recording. These included sensitivity of the individual microphone, temperature, humidity, wind speed, and frequency, amplitude, distance, and directionality of echolocation calls emitted by bats (Parsons and Szewczak 2009, Agranat 2014). The energy of sounds spreading in all directions diminishes by one fourth for every doubling of distance because the surface area of a sphere is related to the square of its radius. Furthermore, higher frequency sounds are diminished over shorter distances because of atmospheric absorption (Parsons and Szewczak 2009, Agranat 2014). Testing of the SMX-US microphone used through June 2015 across the acoustic network indicated that bats emitting frequencies in the range of 20 kHz should be detected at distances of 24 to 33 meters from the microphone while those emitting frequencies in the range of 40 kHz should be detected at distances of 18 to 22 meters (Agranat 2014). These distances are the radii of the relevant spheres of detection around microphones when they are at full sensitivity. However, we know that sensitivity varied over time by an unknown magnitude because some precipitation and freezing events permanently reduced the sensitivity. In 2015 the microphones at active detectors were upgraded to the SMX-U1 microphone, which increased the quality of recorded calls and reduced the effect of adverse weather on microphone sensitivity over time. Due to this change in hardware, comparisons between data collected before and after June 2015 should be made with caution as the different models of microphone may affect the number of calls and species detected. Where applicable, individual reports for each unique equipment configuration were produced to minimize any interpretation errors.

Data Management & Call Analyses

Acoustic file recordings, in both original WAC and processed WAV formats, are stored in the Montana Bat Call Library which is housed on a series of 20-40 terabyte Drobo 5D storage arrays at the Montana State Library as well as a secondary offsite location to protect against catastrophic loss. Acoustic analysis results, temperature files, weather station data, and solar and lunar data were all processed and combined within SQL database tables in accordance with the general workflow pattern for data management and analysis outlined in the text and in Appendices 8-10 of Maxell (2015). Bat call sequences were analyzed with the goal of definitively identifying individual species presence by month and individual species’ minimum temperatures of activity in accordance with the Echolocation Call

Bismark Bridge, South Dakota - 3

Characteristics of Montana Bats and Montana Bat Call Identification materials in Appendices 6 and 7 of Montana’s Bat and White-Nose Syndrome Surveillance Plan and Protocols 2012- 2016 (Maxell 2015).

Weather Station Data

Weather station data were downloaded using the Mesowest application programming interface (API) as outlined in Appendix 9 of Maxell (2015). Temperature, wind speed, solar, and precipitation data were downloaded from weather stations across the regions. Distance from the detector to the station varied by site and data type. All data from weather stations were averaged by hour and associated with all call sequences recorded within this hour bin for use in our analyses.

Solar and Lunar Data

Solar and lunar data were calculated for all hours of detector deployment using the Python package ephem (3.7.6.0), which uses well established numeric routines to produce high precision astronomy computations (see Appendix 10 of Maxell 2015). The underlying code produces results nearly identical to data available from the U.S. Naval Observatory (Astronomical Applications Department). Precise times for sunrise, sunset, moonrise, Moonset, and percent illumination at the detector were calculated based on latitude, longitude, and date. It should be noted that local topography is not incorporated into any of these calculations. Therefore, the exact timing of these events on the ground may differ slightly from those produced by this model but should typically be within a few minutes unless local terrain differs greatly from the modeled horizon (e.g. if the site is at the bottom of a canyon).

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Results

Species at Site

During the deployment period, 3,375 call sequences were recorded at the Bismark Bridge detector. Of those, 1,830 (54.2%) were auto-identified to species and 253 were fully reviewed by hand. Of the 48 species-months with calls auto-identified to 8 different species, 26 species-months (54.2%) were

confirmed by hand review for 6 species(Table 1).

Table 1. Species hand confirmed at the Bismark Bridge detector, by season. Species only observed previously and newly detected within the local area (50.0 km) are noted.

Species Seasonal Acoustically | Acoustically | Observed New Presence Detected in | Detected in | Previously, Species

Active Winter not Detected Season Season

Big Brown Bat (Eptesicus Confirmed Yes Yes

fuscus) Year-round

Silver-haired Bat Confirmed Yes Yes

(Lasionycteris noctivagans) Year-round

Eastern Red Bat (Lasiurus Migratory Yes Yes

borealis)

Hoary Bat (Lasiurus cinereus) | Migratory Yes Yes

Western Small-footed Myotis | Confirmed Yes Yes

(Myotis ciliolabrum) Year-round

Little Brown Myotis (Myotis Confirmed Yes Yes

lucifugus) Year-round

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General Patterns of Bat Activity

The patterns of activity recorded at the Bismark Bridge acoustic monitoring station were generally consistent with overall average bat activity patterns recorded across the regional network of acoustic detectors (Figure 3). During the active season, activity increased through the spring onto summer, peaked in June with an average of 843 calls recorded, and decreased in the fall. A monthly average of 198 calls were recorded between April and October. Activity during the winter was limited, with an average of 1 calls per month between November and March. March had the least activity, with an average of 1 calls recorded.

2000

= wn J oO

1000

Monthly Total of Bat Passes

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Figure 3. Total monthly bat passes recorded at the Bismark Bridge acoustic monitoring station. Months marked with an asterisk should be interpreted with caution as those data may not represent valid trend due to data collection for only part of that month, equipment malfunction or other issues.

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Timing of Bat Activity

During the active season (April to October), some level of bat activity was evident throughout most of the nighttime hours. Activity often peaked immediately after sunset or close to sunrise. However, the pattern of activity varied across this period (Figure 4), likely in response to seasonal changes in the length of each night, prey availability, and physiological needs of the animals. Over the winter, the pattern of activity was less clearly tied to sunrise and sunset in most cases.

° oo

S a

Proportion of Monthly Passes ‘>

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OFM OM TO-OMNO ONO OR NOTION OOK COIN C O ONO TOP- MO Om NCTSOOr-0ONO ON TW OPr-OMO- ND a = =

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Apr May Jun Jul Aug Sep Oct Hour after Sunset

Figure 4. Average nightly activity of bats recorded at the Bismark Bridge acoustic monitoring station across the active season.

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Temperature and Bat Activity

Throughout the study, average bat pass temperatures were generally higher than or equal to ambient nighttime background temperatures recorded at the detector (Figure 5). Bat calls were recorded at temperatures ranging from 2.7 to 29.5°C during the active season and -5.4 to 19.6°C during the winter season. Similarly, the distribution of temperatures recorded at the K49B station, located 25.0 kilometers to the south-southwest of the detector, that were associated with bat passes was significantly higher than the distribution of background temperatures (Figure 6). Thus, bats consistently restricted their activity to warmer time periods from the range of background temperatures available.

20

10

wn

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Average Nighttime Temperature (°C)

Oct

dan Feb Mar Apr

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5 8 2 6

2013 2014 2015

May Aug Sep Oct Nov Dec Jan Feb Mar Apr May dun

Figure 5. Average bat pass temperatures (red line) and average background temperatures (black line) across the year at the Bismark Bridge detector.

O Hours Ey Passes

0.8

Proportion o n

°o f

-15 -10 -5 0 5 10 15 20 25 30 -25 -20 -15 -10 -5 0 Active Winter

wn o = wn Ny o

Temperature Bin (°C) Figure 6. A comparison of average temperatures during bat passes (red) and average hourly background temperatures (blue)

recorded at the K49B station, located 25.0 km to the south-southwest of the detector. Where the bars showing passes exceed hours, bat activity is higher than expected for this temperature bin.

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Wind Speed and Bat Activity

Bat activity patterns in relation to wind speed recorded at the K49B station, located 25.0 km to the south-southwest of the detector, indicate that 95% of activity was at windspeeds of 5.8 meters/second and below (Figure 7). Furthermore, bats were more active than expected at windspeeds of less than 5 meters/second (Figure 8). Due to the distance between the detector and the weather station and low bat activity in winter, the patterns shown should be interpreted cautiously (e.g. wind speed at the detector may not correlate with the measured wind speed).

c=) ° i=) ts n oo

Cumulative Proportion of Calls

wd i)

0 2 4 6 8 10 12 Wind Speed (m/s)

Figure 7. The cumulative sum of wind speeds recorded at the K49B station during bat passes. The speed at which 95% of all activity occurs at or below is highlighted in red.

O Hours

o- im Passes

Proportion o n

° te

0123 4 5 6 7 8&8 9Y 10 11 12 13 14 0123 4 5 6 F 8 9 10 11 12 13 14 15+ Active Winter

Wind Speed Bin (m/s)

Figure 8. A comparison of background windspeeds recorded at the K49B station (blue) and those recorded during bat passes (red). Where the bars showing passes exceed hours, bat activity is higher than expected for this wind speed bin.

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Barometric Pressure and Bat Activity

Nearly 43.6% of bat activity was associated with little to no change (-0.5 to +0.5 millibars) in hourly barometric pressure recorded at the K49B station, located 25.0 km to the south-southwest of the detector (Figure 9). Bat activity was approximately equal to the availability of pressure change classes in the active season. During winter, bat activity was greater than would be expected in the negative pressure change classes down to -4 millibars of change per hour. However, bat activity in the winter season is low and patterns shown may not be biologically significant.

0.8

Proportion o n

° tf.

ia Hours OH Passes _-_ -— wt ll | ll ae ——e eee | |

4 -3 -2 -1 0 1 2 3 4 <4 -3 -2 -1 0 1 2 3 4 Active Winter

Press Change Bin (mb/hour) Figure 9. Hourly changes in background barometric pressure at the K49B station (blue) compared to changes in pressure when

bat passes were recorded (red) at the Bismark Bridge detector. Where the bars showing passes exceed hours, bat activity is higher than expected for this pressure change bin.

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Precipitation and Bat Activity

At the Bismark Bridge detector, bats were notably more active than expected during precipitation events (Figure 10), which differs from trends observed across the acoustic network and during mist netting. This unexpected trend may simply be a result of the facts that: (1) nighttime precipitation events are infrequent with only precipitation documented during only 6.6% of nighttime hours; (2) the SBFS2 weather station is approximately 15.7 kilometers away and may not accurately represent precipitation at the bat detector, and (3) precipitation was coded in hourly bins while bats are capable of flight within minutes after the passage of a storm front. Thus, bat activity recorded at this acoustic detector may be relatively meaningless with regard to precipitation events recorded at the weather station.

| Hours ia Passes

0.6

Proportion

0.2

No Yes Precipitation Observed in the Last Hour

Figure 10. A comparison of hours with and without precipitation for bat passes (red) and all nighttime hours (blue) during the active season as recorded at the SBFS2 station, located 15.7 km to the southeast of the detector. Where the bars showing passes exceed hours, bat activity is higher than expected for this precipitation bin.

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Moonlight & Bat Activity

At the detector site, bats were generally more active than expected during dark periods where the moon was either below the horizon or less than half full (Figure 11).

ist Hours

= HB Passes c 2 0.1 = o a 2 oa

0.05 | |

0% 10% 20% 30% 40% S0% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 380% 90% 100% Below Above

Moon Illumination (%)

Figure 11. Percent of bat passes (red) and background hours (blue) at various moon illumination categories (0% = no illumination and 100% = full moon) and with the moon above and below the horizon. Where the bars showing passes exceed hours, bat activity is higher than expected for this moon horizon/illumination bin.

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Species Activity Patterns

Identification of individual species activity patterns was hindered by relatively low and potentially inconsistent rates of auto-identification of call sequences to species (Table 4 in Maxell 2015). Only Little Brown Myotis, Western Small-footed Myotis, Silver-haired Bat, Big Brown Bat, and Hoary Bat had relatively high rates of confirmation of monthly presence, enough calls auto-identified to examine trends, and >50 percent correct auto-identification rates of call sequences of known species identity in the Montana Bat Call Library (Table 2). For those 5 species at this site with high auto-identification confirmation, potential patterns of documented activity are shown in Figure 12. However, activity patterns for these species from auto-identified call sequences should still be regarded as speculative due to a variety of issues that might cause auto-identifications to be inaccurate and/or inconsistent (Maxell 2015).

Table 2. The number of months each bat species was confirmed by hand analysis of calls identified by automated software, the number of months reviewed, and the respective successful classification rate; only active season data are shown.

Species Months Months Auto-ldentification Confirmed | Reviewed | Success Rate

Pallid Bat (Antrozous pallidus) 0 1 0.0%

Big Brown Bat (Eptesicus fuscus) 3 7 42.9%

Silver-haired Bat (Lasionycteris noctivagans) 8 8 100.0%

Eastern Red Bat (Lasiurus borealis) 1 6 16.7%

Hoary Bat (Lasiurus cinereus) 3 6 50.0%

Western Small-footed Myotis (Myotis ciliolabrum) 6 7 85.7%

Little Brown Myotis (Myotis lucifugus) 5 5 100.0%

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Bi myth i Mylu a Myev BB vyci i Lano ii Laci WB Euma ME Eptu i Bat

1500

1000

500

Monthly Total of Bat Passes

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Figure 12. Bat passes through the deployment period identified to species using SonoBat 4.1. Note that these species identification are only suggestions and should only be used to assess general trends for species for which the classifier works well.

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Discussion

At this detector we confirmed the presence of 6 species (Table 1). Of the species documented, there were 0 Species of Greatest Conservation Need (SGCN). No confirmed species are currently listed as threatened or endangered by the U. S. Fish and Wildlife Service (USFWS). The state and federal conservation or regulatory status for observed species are listed in Table 3.

Species presence and activity metrics recorded at these sites will serve as robust baseline that can be used to assess the status of populations at sites into the future. This is particularly important due to the imminent threats to bat species posed by White-Nose Syndrome (WNS) caused by the pathogenic fungus Pseudogymnoascus destructans (Pd) and wind energy development. During this deployment, 18 of 21 months recorded met our standards for quality. As such, our assessment is that sufficient data have been collected to document bat activity and species diversity at the site during this time period.

Listed Species Conservation

In South Dakota, the Northern Myotis (Myotis septentrionalis) is the only bat species listed by the USFWS as threatened or endangered. The USFWS has designated 9 counties along Montana’s eastern border and North and South Dakota as within the range of this species, and the species has been confirmed as present within three Montana counties (MTNHP 2020). This detector was deployed at a site within the range for this species. Across the recording period, the auto-classifier found 0 call sequences that had characteristics similar to those produced by Northern Myotis. As such, no further review for this species was conducted.

White-nose Syndrome

To-date, the presence of Pseudogymnoascus destructans and associated WNS have not been detected in Montana. However, Pd and WNS was detected in Washington in 2015 (WDFW, USFWS, and USGS 2016) and in South Dakota and Wyoming in 2018 (NPS 2018, WYGFD 2018). These detections and the continued spread westward into the Great Plains have increased the urgency for establishing baseline metrics to assess future impacts on resident bats. Of the 6 species detected at this site, 2 have been shown to develop WNS when exposed to Pd. These species are Big Brown Bat and Little Brown Myotis (Table 3). Additionally, Silver-haired Bat, Eastern Red Bat, and Western Small-footed Myotis have been shown to carry Pd, but not exhibit symptoms of WNS (Bachen et al. 2018, but see

White NoseSyndrome.org for most up to date information on species susceptibility). The remaining Myotis species have not been shown to carry Pd or develop WNS. Rather than indicating immunity, the lack of detections of Pd positive individuals or WNS is likely a result of their western distribution that does not overlap affected areas. As many other Myotis species are impacted by WNS, it is probably best to consider these species as susceptible until proven otherwise.

Through the deployment of this and other detectors across the network, we now know that winter activity is normal for many resident bat species and does not necessarily indicate the presence of Pd in the local area. At this detector we found that winter activity was in the first quartile (0-25%) of average activity recorded across network sites. As few if any call sequences were recorded over the winter, this lack of bat activity may indicate that few if any animals over winter in the local area.” We were unable to confirm the presence of any species during the winter season at this site (Table 1).

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Wind Energy Development

Tree roosting species such as the Eastern Red Bat, Hoary Bat, and Silver-haired Bat are not known to be susceptible to WNS but suffer mortality at wind farms. Of these we detected Eastern Red Bat, Hoary Bat, and Silver-haired Bat at the detector site. Due to the presence of these species, mortality due to wind energy is a concern for this area at current and future sites. These species often fly near turbines and suffer barotrauma when near the turbine blades. Due to these species low reproductive rate and long life, unmitigated wind energy development may cause precipitous declines of these species over the next 50 years (Frick et al. 2017). Wind energy may have indirect impacts on bats using this site due to mortality during migration or decreased regional populations. If development of wind energy is considered within the local area, mitigation measures should be implemented to reduce potential impacts on resident and migratory species.

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Table 3. Management considerations for species detected within 50.0 km of the Bismark Bridge detector. Species presence is summarized by season and include this and any previous efforts.

Species Seasonal Detected Detected State Federal White-Nose Wind Energy Presence Active Winter Status Status Syndrome Impacts* Season! Season2 (South Impacts? Dakota) Big Brown Bat Confirmed Yes Confirmed Infrequent (Eptesicus fuscus) | Year-round Susceptible - Mortality Mortality Documented Documented Silver-haired Bat Confirmed Yes SGCN-3 Detected - Frequent (Lasionycteris Year-round Possibly Mortality noctivagans) Susceptible Documented Eastern Red Bat Migratory Yes Detected - Frequent (Lasiurus borealis) Possibly Mortality Susceptible Documented Hoary Bat Migratory Yes No impacts Frequent (Lasiurus cinereus) Mortality Documented Western Small- Confirmed Yes Detected - No Mortality footed Myotis Year-round Likely Documented (Myotis Susceptible ciliolabrum) Little Brown Confirmed Yes Confirmed Infrequent Myotis (Myotis Year-round Susceptible - Mortality lucifugus) Mortality Documented Documented

1may indicate day roosts and/or maternity colonies present in area 2may indicate hibernaculum or other important winter habitat in area 3see review in Bachen et al. (2018) and WhiteNoseSyndrome.org 4see review in Bachen et al. (2018)

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Management Recommendations

Measures of overall bat activity near the detector, hand confirmed presence of individual species by month, and hand confirmed minimum temperatures associated with bat passes of individual species are all stable metrics upon which management recommendations can be made. However, patterns of activity of individual species resulting from automated analyses should be used with a great deal of caution due to low rates of species assignment and low or uncertain rates of accuracy of those assignments. Furthermore, it should be noted that bat activity measured during this study was made by a microphone on a nine to ten-foot mast and may not have adequately sampled the activity of high flying bats such as the Hoary Bat and Silver-haired Bat, which together with the Eastern Red Bat are the three species that have suffered approximately 75% of the documented mortalities associated with wind turbines across North America (Kunz et al. 2007). Thus, the following management recommendations avoid use of activity patterns of individual species as determined by automated analyses and instead rely on results of hand confirmed analyses, general patterns of bat activity that were recorded at the study site, and results of published studies of wind turbine impacts on bat species.

General management recommendations for species observed at project sites include:

(1) Protect potential natural roost sites by conserving large diameter trees (especially snags with loose bark), rock outcrops, cliff crevices, and caves.

(2) Maintain accessibility for underground mine entrances that bats may be using as summer or winter roosts. Install bat friendly gates if closure is required.

(3) When removing bat colonies from buildings or other structures follow current best practices, including waiting until the late fall and winter to seal entry points and placing bat houses to compensate for elimination of the roost.

(4) Reduce structural complexity of vegetation (e.g., short stature grasslands) and availability of standing waters in proximity to wind turbines or other human structures that might represent a threat to bats or where bats are undesired.

(5) In safe environments, maintain lotic or lentic waterbodies to provide habitat for foraging and drinking.

(6) If wind turbines are installed in the region, set turbine cut-in speeds to > 6.0 m/sec between April and October — especially important in July during peak bat activity when young are newly flighted, and August, September, and October when migratory species are passing through and local bats are swarming and breeding. Feather wind turbine blades, making them parallel to wind direction, when wind speeds are <6 m/sec to reduce risk of barotrauma during times of relatively high bat activity.

(7) Report dead bats of any species found in the winter or spring to Montana Fish Wildlife and Parks or Montana Natural Heritage Program personnel. Animals found dead during these seasons may have contracted WNS and should be tested as part of Montana’s Passive WNS surveillance protocol.

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Literature Cited

Agranat, |. 2014. Detecting bats with ultrasonic microphones: understanding the effects of microphone variance and placement on detection rates. Unpublished white paper. Wildlife Acoustics, Maynard, MA. 14 pp.

Armitage, D. W. and H. K. Ober. 2010. A comparison of supervised learning techniques in the classification of bat echolocation calls. Ecological Informatics 5(6): 465-473.

Bachen, D. A., A. L. McEwan, B. O. Burkholder, S. L. Hilty, S. A. Blum, and B. A. Maxell. 2018. Bats of Montana: Identification and Natural History. Report to Montana Department of Environmental Quality. Montana Natural Heritage Program. Helena, MT. 111 pp.

Clement, M. J., T. J. Rodhouse, P. C. Ormsbee, J. M. Szewczak and J. D. Nichols .2014. Accounting for false-positive acoustic detections of bats using occupancy models. Journal of Applied Ecology 51(5): 1460-1467.

Frick W. F., J. F. Pollock, A. C. Hicks, K. E. Langwig, D. S. Reynolds, G. G. Turner, C. M. Butchkoski, and T. H. Kunz. 2010. An emerging disease causes regional population collapse of a common North American bat species. Science 329: 679-682.

Maxell, B. A. Coordinator. 2015. Montana bat and White-Nose Syndrome surveillance plan and protocols 2012-2016. Montana Natural Heritage Program. Helena, MT. 185 pp.

Montana Natural Heritage Program. 2020. Animal point observation database. Montana Natural Heritage Program. Helena, MT. Accessed June 2020.

National Park Service Midwest Regional Office. 2018. Fungus that causes White-Nose Syndrome in bats detected in South Dakota for the first time. News release. May 31, 2018.

Parsons, S., and J. M. Szewczak. 2009. Detecting, recording, and analyzing the vocalization of bats. Pages 91-111 in Kunz, T. H. and S. Parsons (eds.). Ecological and behavioral methods for the study of bats (2"4 edition). Johns Hopkins University Press, Baltimore, MD. 901 pp.

Redgwell, R. D., J. M. Szewczak, G. Jones and S. Parsons. 2009. Classification of echolocation calls from 14 species of bat by support vector nachines and ensembles of neural networks. Algorithm 2(3): 907-924.

S. A. Scott, P. C. Ormsbee, and J. M. Zinck. 2008. Field identification of Myotis yumanensis and Myotis lucifugus: a morphological evaluation. Western North American Naturalist, 68(4), 437-443.

Washington Department of Fish and Wildlife, U.S. Fish and Wildlife Service, and U.S. Geological Survey. 2016. Bat with white-nose syndrome confirmed in Washington state. News release. March 31, 2016.

Wyoming Game and Fish Department. 2018. Fungus that causes White-Nose Syndrome in bats detected in Wyoming for first time. News release. June 1, 2018.

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