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Susitna‐Watana Hydroelectric Project Document
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Title:
Bat distribution and habitat use, Study plan Section 10.13, Study
Completion Report SuWa 289
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ABR, Inc. - Environmental Research & Services, [Office in] Forest Grove, Oregon
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November 2015; Study Completion and 2014/2015 Implementation Reports
AEA‐identified series, if specified:
Series (ARLIS‐assigned report number):
Susitna-Watana Hydroelectric Project document number 289
Existing numbers on document:
Published by:
[Anchorage : Alaska Energy Authority, 2015]
Date published:
October 2015
Published for:
Alaska Energy Authority
Date or date range of report:
Volume and/or Part numbers:
Study plan Section 10.13
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iv, 56 pages
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Notes:
All reports in the Susitna‐Watana Hydroelectric Project Document series include an ARLIS‐
produced cover page and an ARLIS‐assigned number for uniformity and citability. All reports
are posted online at http://www.arlis.org/resources/susitna‐watana/
Susitna–Watana Hydroelectric Project
(FERC No. 14241)
Bat Distribution and Habitat Use
Study Plan Section 10.13
Study Completion Report
Prepared for
Alaska Energy Authority
Prepared by
ABR, Inc.—Environmental Research & Services
Forest Grove, Oregon
October 2015
STUDY COMPLETION REPORT BAT DISTRIBUTION AND HABITAT USE (STUDY 10.13)
Susitna–Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page i October 2015
TABLE OF CONTENTS
1. Introduction ....................................................................................................................... 1
2. Study Objectives................................................................................................................ 1
3. Study Area ......................................................................................................................... 2
4. Methods and Variances .................................................................................................... 2
4.1. Acoustic Surveys .................................................................................................... 2
4.1.1. Variances ......................................................................................... 4
4.2. Roost Surveys ......................................................................................................... 4
4.2.1. Natural Roosts ................................................................................. 4
4.2.2. Artificial Roosts .............................................................................. 5
4.2.3. Variances ......................................................................................... 5
4.3. Data Management and Analysis ............................................................................. 6
4.3.1. Variances ......................................................................................... 7
4.4. Bat Capture and Radio Telemetry........................................................................... 7
5. Results ................................................................................................................................ 8
5.1. Acoustic Surveys .................................................................................................... 9
5.1.1. General Bat Activity ....................................................................... 9
5.1.2. Temporal Comparisons ................................................................. 10
5.1.3. Spatial Comparisons ..................................................................... 11
5.2. Roost Surveys ....................................................................................................... 13
5.2.1. Natural Roosts ............................................................................... 13
5.2.2. Artificial Roosts ............................................................................ 13
5.3. Bat Capture and Radio Telemetry......................................................................... 14
6. Discussion......................................................................................................................... 14
6.1. Acoustic Monitoring ............................................................................................. 14
6.1.1. Temporal Comparisons ................................................................. 15
6.1.2. Spatial Comparisons ..................................................................... 16
6.2. Roost Surveys ....................................................................................................... 18
6.2.1. Natural Roosts ............................................................................... 18
6.2.2. Artificial Roosts ............................................................................ 18
6.3. Bat Capture and Radio Telemetry......................................................................... 19
7. Conclusions ...................................................................................................................... 19
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8. Literature Cited .............................................................................................................. 20
9. Tables ............................................................................................................................... 25
10. Figures .............................................................................................................................. 37
LIST OF TABLES
Table 4.1-1. Categorization of Acoustic Detector Stations by Habitat and Forest Structure Types,
2013–2014..................................................................................................................................... 25
Table 4.2-1.Quality Scores for Potential Cliff-Roosting Habitat, 2013. ...................................... 26
Table 5.1-1. Number and Percentage of Nights Surveyed by Acoustic Detector Stations in 2013,
and 2014. ....................................................................................................................................... 26
Table 5.1-2. Bat Activity (Bat Passes per Detector-Night) by Station and Month, 2013. ............ 28
Table 5.1-3. Bat Activity (Bat Passes per Detector-Night) by Station and Month, 2014. ............ 30
Table 5.1-4. Elevation and Minimum Distances to Water Bodies and Cliffs, by Station, 2013. . 31
Table 5.1-5. Elevation and Minimum Distances to Water Bodies and Cliffs, by Station, 2014. . 32
Table 5.1-6. Bat Activity (Bat Passes per Detector-Night) by Month and Habitat Type, 2013. .. 33
Table 5.1-7. Bat Activity (Bat Passes per Detector-Night) by Month and Forest Structure Type
for Non-Pond Habitats, 2013. ....................................................................................................... 33
Table 5.1-8. Acreage of Habitat and Vegetation Structure Types in Bat Study Area, 2013 and
2014............................................................................................................................................... 34
Table 5.1-9. Bat Activity (Bat Passes per Detector-Night) by Month and Habitat Type, 2014. .. 34
Table 5.1-10. Bat Activity (Bat Passes per Detector-Night) by Month and Vegetation Structure
Type for Non-Pond Habitats, 2014. .............................................................................................. 35
Table 5.2-1. Results of Building Searches for Artificial-Roost Surveys, 2013. ........................... 36
STUDY COMPLETION REPORT BAT DISTRIBUTION AND HABITAT USE (STUDY 10.13)
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FERC Project No. 14241 Page iii October 2015
LIST OF FIGURES
Figure 3-1. Bat Study Area for the Susitna–Watana Hydroelectric Project, 2013 and 2014. ...... 38
Figure 4.1-1. Acoustic Detector Sites Monitored for the Bat Study in 2013 and 2014. ............... 39
Figure 5.1-1. Distribution of Bat Activity Among Acoustic Detector Stations, 2013.................. 40
Figure 5.1-2. Representative Sonogram from Little Brown Bat Recorded during the Bat Study,
2013 and 2014. .............................................................................................................................. 41
Figure 5.1-3. Distribution of Bat Activity Among Acoustic Detector Stations, 2014................. 42
Figure 5.1-4. Bat Activity by Date, 2013 (error bars indicate SE). .............................................. 43
Figure 5.1-5. Bat Activity by Date, 2014 (error bars indicate SE). Note different scale than for
2013 figure. ................................................................................................................................... 44
Figure 5.1-6. Bat Activity by Hour in Relation to Sunset, 2013 (error bars indicate SE). ........... 45
Figure 5.1-7. Bat Activity by Hour in Relation to Sunset, 2014 (error bars indicate SE; note
different vertical scale than in 2013 figure). ................................................................................. 46
Figure 5.1-8. Bat Activity by Station by Habitat Type (Pond: G1, G2, G3, G4, G7, G10, G12,
G16; Stream: G6, G9, G18, G20; Cliff: G13, G14, G19; Upland: G5, G8, G11, G15, G17) in
2013 (error bars indicate SE and asterisks indicate that no bats were detected). ......................... 47
Figure 5.1-9. Bat Activity by Station in 2014 (error bars indicate SE). ....................................... 48
Figure 5.1-10. Bat Activity by Month and Habitat Type, 2013 (error bars indicate SE). ............ 49
Figure 5.1-11. Bat Activity by Month and Forest Structure Type for Non-Pond Habitats, 2013
(error bars indicate SE). ................................................................................................................ 50
Figure 5.1- 13. Distribution of Vegetation Structure Types in Bat Study Area, in Relation to
Acoustic Detector Sites, 2013 and 2014. ...................................................................................... 52
Figure 5.1-14. Bat Activity by Month and Habitat Type, 2014 (error bars indicate SE). Note
different scale than for 2013 data. ................................................................................................. 53
Figure 5.1-15. Bat Activity by Month and Vegetation Structure Type for Non-Pond Habitats,
2014 (error bars indicate SE). Note different scale than for 2013 figure. .................................... 54
STUDY COMPLETION REPORT BAT DISTRIBUTION AND HABITAT USE (STUDY 10.13)
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FERC Project No. 14241 Page iv October 2015
LIST OF ACRONYMS, ABBREVIATIONS, AND DEFINITIONS
Abbreviation Definition
ADF&G Alaska Department of Fish and Game
AEA Alaska Energy Authority
AKNHP Alaska Natural Heritage Program
APA Alaska Power Authority
cm centimeter
CF compact flash
CIRWG Cook Inlet Regional Working Group
FERC Federal Energy Regulatory Commission
ft feet
g mass
GB gravel bar
GIS geographic information system
ILP Integrated Licensing Process
ISR Initial Study Report
km kilometer
m meter
mi mile
mm millimeter
ms millisecond
NDVI Normalized Difference Vegetation Index
Project Susitna–Watana Hydroelectric Project
QA/QC quality assurance and quality control
RSP Revised Study Plan
SE standard error
SPD Study Plan Determination
WNS White-nose Syndrome
STUDY COMPLETION REPORT BAT DISTRIBUTION AND HABITAT USE (STUDY 10.13)
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FERC Project No. 14241 Page 1 October 2015
1. INTRODUCTION
The Bat Distribution and Habitat Use Study (Bat Study, for short), Section 10.13 of the Revised
Study Plan (RSP) approved by the Federal Energy Regulatory Commission (FERC) for the
Susitna–Watana Hydroelectric Project, FERC Project No. 14241, focuses on the occurrence of
bats and the distribution of habitats in which bats were detected in the Project study area.
A summary of the development of this study, together with the Alaska Ener gy Authority’s
(AEA) implementation of it through the 2013 study season, appears in Part A, Section 1 of the
Initial Study Report (ISR) filed with FERC in June 2014 for Study 10.13 (ABR 2014a). As
required under FERC’s regulations for the Integrated Licensing Process (ILP), the ISR describes
AEA’s “overall progress in implementing the study plan and schedule and the data collected,
including an explanation of any variance from the study plan and schedule” (18 CFR 5.15(c)(1)).
Since filing the ISR in June 2014, AEA has continued to implement the FERC-approved plan for
the Bat Study. For example:
Additional acoustic monitoring was conducted at 10 locations in 2014.
A bat capture and radio telemetry effort was completed in summer and fall 2014.
On October 21, 2014, AEA held an ISR meeting for the Bat Study.
In furtherance of the next round of ISR meetings and FERC’s Study Plan Determination (SPD)
expected in 2016, this report contains a comprehensive discussion of results of the Bat Study
from the beginning of AEA’s study program in 2013, through the end of calendar year 2014. It
describes the methods and results of the Bat Study and explains how all Study Objectives set
forth in the FERC-approved Study Plan have been met. Accordingly, with this report, AEA has
now completed all field work, data collection, data analysis, and reporting for this study.
2. STUDY OBJECTIVES
The goal of the Bat Study is to collect baseline data on bats in the Project area to enable the
assessment of potential impacts on bats from development of the proposed Project.
The Bat Study objectives are established in RSP Section 10.13.1:
Assess the occurrence of bats and the distribution of habitats used by bats within the
proposed reservoir inundation zone and associated infrastructure areas for the Project.
Review geological and topographical data to assess the potential for roosting, maternity,
and hibernacula sites in the study area.
Examine suitable geological features (caves, crevices) and human-made structures
(buildings, mines, bridges) for potential use by bats as roosting sites, maternity colonies,
and hibernacula.
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FERC Project No. 14241 Page 2 October 2015
3. STUDY AREA
As established by RSP Section 10.13.3, the bat study area (Figure 3-1) encompassed the
proposed reservoir inundation zone, the proposed dam and powerhouse locations, and the
associated camp facilities area, but not the various access-road and power transmission corridor
alternatives.
4. METHODS AND VARIANCES
The methods for each component of the Bat Study are described in this section.
4.1. Acoustic Surveys
During the 2013 and 2014 study seasons, AEA implemented the acoustic survey methods
described in RSP Section 10.13.4 with the exception of variances explained below (Section
4.1.1).
Acoustic surveys of bats employed the use of echolocation detectors (Anabat® SD1 broadband
acoustic detectors; Titley Electronics, Ballina, New South Wales, Australia) to assess bat activity
patterns and habitat associations across the study area by recording the ultrasonic sounds
produced by echolocating bats. The study team deployed 20 detectors during May 25–October 7,
2013, and 10 detectors during May 15–October 11, 2014 (Table 4.1-1, Figure 4.1-1). The
deployment of detectors in 2014 was described as a study plan modification in ISR 10.13 Part C,
Section 7.1.2 (ABR 2014c), so more detail is provided under Section 4.1.1 below.
Scientists use acoustic detectors commonly for passive detection of free-ranging, echolocating
bats (O’Farrell et al. 1999). Each detector had a minimum detection range of approximately 20 m
(66 ft), with the actual range depending on air temperature, humidity, elevation, and the
frequency and intensity of echolocation calls. Microphones were housed in waterproof “bat-hats”
(EME Systems, Berkeley, California) and were secured to a section of rebar or tree, located
approximately 1–1.5 m (3–5 ft) above ground level. All associated electronic equipment for the
detectors was enclosed in waterproof plastic cases (Pelican Products, Inc., Torrance, California)
located below each microphone and a photovoltaic system (GoGreenSolar.com, Placentia,
California) was connected to each detector to provide solar power for recharging the batteries.
Sampling sites for the detectors were selected from random points generated within the study
area using a geographic information system (GIS). The random points were stratified by broad
habitat type (pond, stream, cliff, upland) based on preliminary water-body mapping and cliff
mapping prepared for other wildlife and botanical studies (Study 10.14, Surveys of Eagles and
Other Raptors, and Study 11.5, Vegetation and Wildlife Habitat Mapping in the Upper and
Middle Susitna Basin). For each broad habitat type, the study team created 200-m (656-ft)
buffers surrounding the features of interest (ponds, streams, and cliffs) and considered all
remaining habitat as uplands. The Susitna River was included in the buffers for all habitat types,
except for uplands. Sampling points were required to be within the 200-m (656-ft) buffer of each
habitat type.
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Non-pond habitat types were stratified further by vegetation structure (closed, open, and dwarf
forests and shrub types; Table 4.1-1) using the existing vegetation map prepared for the Alaska
Power Authority (APA) Susitna Hydroelectric Project in the 1980s (Kreig and Associates 1987)
because an updated vegetation map for the current Project was not yet available in 2013 or 2014.
The vegetation structure types from the Kreig and Associates (1987) map were modified slightly
by ABR (2013) to closely approximate the Level-III vegetation types of Viereck et al. (1992).
The vegetation structure types used in this study were considered to be biologically relevant to
bats because of the potential importance of structural complexity on bat activity. Closed forests
have 60–100 percent canopy cover; open forests included open (25–60 percent) and woodland
(10–25 percent) forest types; dwarf forests had at least 10 percent canopy cover of dwarf forest
trees (under 5 m [16 ft] at maturity); and the shrub type comprised at least 25 percent shrub cover
and <10 percent forest canopy cover. The shrub type also contained tiny proportions of wet
graminoid meadow (0.31 percent of total) and barrens (0.28 percent of total). Within each broad
habitat type, the study team tried to select one site in each of four vegetation structure types
(closed, open, dwarf, shrub).
The area of each habitat and vegetation structure type was measured using a GIS. In 2013, the
final sampling locations included eight pond sites, five upland sites, four stream, and three cliff
sites. In 2014, the six sites that were resurveyed included three ponds, two cliffs, and one stream,
and the four new stations on CIRWG lands included two cliff sites, one pond, and one stream.
Upland habitat types were not surveyed in 2014 because of the extremely low levels of bat
activity recorded in that habitat type in 2013. The vegetation structure classifications for non-
pond sites in 2013 included three sites in each of the four classification types (open, closed,
dwarf, and shrub). In 2014, the vegetation structure for the three non-pond sites that were
resurveyed included one shrub type and two closed forest types. For the three new non-pond
stations on CIRWG lands in 2014, the study team selected one closed and two open sites. The
study team was unable to survey the dwarf forest type or to stratify the habitat types equally by
vegetation structure in 2014 because of the limited overlap of habitat and vegetation structure
conditions on CIRWG lands. Detector stations were placed as close as possible to the primary
random sampling points. In several cases, alternative random points were used because of
discrepancies between the vegetation type at the primary sampling point and the vegetation type
identified on the 1980s vegetation map from the APA Project, which was used for sample
allocation. Difficult helicopter access at several primary points also necessitated the use of
alternative sampling locations.
At each station, the study team positioned the detector and oriented the microphone to maximize
the probability of recording echolocation call sequences (bat passes), based on the specific
characteristics of the site. Detectors were programmed to monitor the period from approximately
1 hour before sunset to 1 hour after sunrise, adjusting the duty cycle periodically, to cover the
crepuscular and nocturnal periods when bats are most active (Hayes 1997). Sunset times were
calculated for each station’s latitude, longitude, and elevation using the PyEphem Python module
3.7.5.2 (Rhodes et al. 2013). Data were recorded on 1-GB compact flash (CF) data cards. The
study team exchanged the CF cards and checked equipment approximately every 2 weeks.
Sampling covered the spring, summer, and fall seasons, encompassing the periods of parturition,
lactation, volancy of young, copulation, and possibly hibernation or migration (Gotthardt and
Coray 2005).
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FERC Project No. 14241 Page 4 October 2015
4.1.1. Variances
No variances from the acoustic survey methods described in the Study Plan were implemented in
2013. However, the lack of ground access to Cook Inlet Regional Working Group (CIRWG)
lands in the western portion of the study area in 2013 prevented acoustic sampling in some areas
that would otherwise have been included in the random allocation of sampling points.
In 2014, the study team rectified that omission by establishing four new locations on CIRWG
lands (Table 4.1-1). Six other sites that were surveyed in 2013 were resurveyed in 2014 to better
understand annual variation and to monitor areas in which bat detections were recorded in 2013,
to assist in targeting the mist-netting and telemetry effort in 2014. This additional sampling
variance was presented as a modification to the Study Plan in ISR Part C Section 7.1.2.
4.2. Roost Surveys
In 2013, AEA implemented the methods for natural and artificial roosts described in the Study
Plan with the exception of the variances explained below (Section 4.2.3). The roost surveys were
not repeated in 2014 because they were replaced by the bat capture and radio telemetry effort
designed to locate specific bat roosts, as was described as a Study Plan modification in ISR Part
C Section 7.1.2.
4.2.1. Natural Roosts
The research team used a variety of literature-based and field methods to assess the occurrence
of natural structures (caves, cliffs, trees) and their suitability as roost sites, maternity colonies, or
hibernacula in the study area in 2013. The potential occurrence of caves in the study area was
assessed by reviewing geological literature regarding the presence of suitable bedrock (e.g.,
limestone) conducive to the formation of caves.
During June 28–30, 2013, the survey team conducted an aerial survey by helicopter to examine
potential roosting habitats in cliffs and other rock structures. The team evaluated discrete cliff
sections that had been identified for Study 10.14, Surveys of Eagles and Other Raptors, by using
GIS analysis of aerial photography, digital elevation models, and remote-sensing data on plant
biomass (Normalized Difference Vegetation Index, or NDVI). Qualitative suitability scores
(Table 4.2-1) were assigned to each cliff section in the field. Where possible, cliff habitats were
examined on the ground.
Ground searches of potentially suitable tree roosts (large-diameter snags) also were conducted
during June 28–30, 2013. The tree-roost search targeted areas near inactive nests of Bald Eagles
(Haliaeetus leucocephalus) in the study area and opportunistically surveyed other possible roost
trees identified in the field. Forest inventory information was not available to assess the presence
of large-diameter dead trees as roosting habitat.
In the fall (October 4–6, 2013), additional areas were surveyed for tree roosts, including areas
near previously active Bald Eagle nests that were not accessible earlier in the season. The area
between Jay Creek and Watana Creek was surveyed to search for the potential presence of caves
in a limestone formation reported by Chapin (1918).
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FERC Project No. 14241 Page 5 October 2015
4.2.2. Artificial Roosts
The research team used a combination of office-based and field methods to evaluate human-
made structures (buildings, mines, bridges) as roost sites, maternity colonies, and hibernacula in
the study area in 2013. No bridges were present in the study area, so the search concentrated on
buildings. Before beginning the search, the study team requested permission from the
landowners via letters, emails, and telephone calls for access to building sites on private, federal,
and state lands in and near the study area. Permission was obtained for access to 11 of the 16
sites identified.
During August 11–13, 2013, the research team examined 25 structures (e.g., cabins, sheds,
outhouses) at those 11 sites for the presence of bats and any signs of use as roost sites or
maternity colonies. All structures were examined externally and some were examined internally,
but not all structures were accessible because they were locked or barricaded. The building
search was coordinated with the historical property surveys for the Cultural Resources Study (see
ISR Study 13.5). Several mining claims were identified within the bat study area; however, all of
those claims involved surface-mining methods (e.g., placer), which do not directly provide
roosting habitat, so they were not inspected if no structures were present.
During the fall roost search (October 4–6, 2013), all of the structures surveyed in mid-August
were reexamined, along with another site for which permission had not been granted previously.
The fall search focused on potential use of the structures as hibernation sites.
Both artificial roost searches included structures (19 in summer and 20 in fall) that were located
outside of, but near, the study area. Those additional structures were included because artificial
structures potentially suitable for bat roosting were scarce in the study area and permission could
not be obtained to examine all of the buildings in the study area.
4.2.3. Variances
In 2013, the study team opportunistically expanded roost searches to include nearby areas
outside of the study area due to the scarcity of suitable roosting structures within the study area
where permission was granted by property owners. The additional search effort expanded the
scope proposed in the Study Plan and constituted a variance.
Access to CIRWG lands, which encompassed most of the western end of the study area, was not
permitted in 2013. The lack of ground access to CIRWG lands prevented searches of potential
natural roosting habitat that would otherwise have been included in the roost surveys, resulting in
a variance from the Study Plan. The research team was unable to perform ground searches on
CIRWG lands at four Bald Eagle nest trees and one limestone area near the northern flank of
Mount Watana. No artificial structures occur on CIRWG lands within the study area. In 2014,
field effort was devoted to the targeted mist-netting and telemetry effort designed to locate
specific bat roosts, which was added as a study plan modification for the second year of field
surveys, as described in ISR 10.13 Part C, Section 7.1.2 (ABR 2014c).
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4.3. Data Management and Analysis
AEA implemented the data management and analysis methods as described in the Study Plan
with no variances.
To maintain quality assurance and quality control (QA/QC), acoustic monitoring equipment was
checked and data cards were downloaded into a database at approximately 2-week intervals to
minimize data loss from equipment failures or other factors, such as damage by animals
(primarily bears). The study lead checked the database periodically for inconsistencies and errors
and the entire database was proofed again for errors before data analysis began. All data were
stored on a network server with frequent backups to prevent loss of data.
Interpretation of bat acoustic data is subject to several important caveats. The number of “bat
passes” recorded is an index of relative activity, but may not correlate directly with numbers of
individual bats in the area being monitored. For example, 10 bat passes may represent a single
bat recorded 10 different times or may represent single passes by 10 different bats (Hayes 1997).
Activity also may not be proportional to abundance because of variability attributable to (1)
detectability (loud vs. quiet species); (2) species call rates; (3) migratory vs. foraging call rates;
and (4) attraction to or avoidance of the sampling area by bats (Kunz et al. 2007, Hayes et al.
2009). However, interpreted properly, the index of relative activity can provide useful
information on bat use by characterizing temporal (hourly, nightly, and seasonal) and spatial
(location) patterns of bat activity (Parsons and Szewczak 2009).
Echolocation sequences recorded by the detectors were processed using Anabat CFC Read
software (version 4.4u) and were analyzed with Kaleidoscope Pro software (version 2.1,
Wildlife Acoustics, Maynard, Massachusetts, USA) to detect and quantify bat passes. In
addition, the study team visually inspected all spectrogram files using AnalookW software
(version 4.1j; Corben 2011) to ensure proper species identification, based on characteristics
reported by Ober (2006) and Lausen et al. (2014). A bat pass was defined as an echolocation
sequence of ≥2 echolocation pulses with a minimum pass duration of 10 milliseconds (ms)
(Gannon et al. 2003) and each sequence separated by 5 seconds. The standard metric for
quantifying bat activity is the number of bat passes/detector-night (Kunz et al. 2007). The within-
night activity rates (hours relative to sunset) observed in this study were compared with a
probability distribution generated from 5,000 bootstrap simulations (Varian 2005). For each
simulation, the observed hourly activity rate was recorded randomly within each night and a new
average was calculated for each hour. Because so few calls were recorded in May and October,
they were excluded from the hours-relative-to-sunset analysis.
In 2013, nonparametric (Kruskal–Wallis) tests were used for statistical comparison of the spatial
and habitat differences among detectors. Kruskal–Wallis tests also were used to compare activity
rates among stations and months for periods when all 20 detectors were operational. In 2014,
formal statistical analyses of the among-station, among-habitat, and among-vegetation structure
data sets were not conducted because six of the sites in 2014 were not selected randomly.
Monitoring of those six sites was continued in 2014 to test for annual variability and to track bat
movements to assist with targeting areas for bat capture. A randomization test was used to look
for differences between years for each of the six sites and among all six sites combined because
the data were highly skewed, including many zeros and a small number of high values. For each
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simulation, the year values were randomized and the differences between nightly means for the
two years were recalculated based on the randomized years. This process was repeated 5,000
times to generate 5,000 mean differences. The mean differences between years for each site and
the overall mean were compared with the distribution of differences from the simulations. If the
actual difference was more extreme than 95 percent of the simulated differences, then the
differences between years were considered to be significant (P < 0.05). Only June–September
data were used for monthly comparisons because of the short duration of sampling that was
conducted in late May and early October. GIS software was used to measure the minimum
distance from each detector station to seven landscape features: ponds, streams, rivers, any cliffs,
and potential roosting cliffs of different value (roost quality index scores of 1, 2, and 3; see
Section 5.2.1 below). Correlations between the mean number of bat passes and the minimum
distances to these landscape features were tested using Spearman’s rank correlation. SPSS
version 18.0 analytical software was used for all statistical comparisons, assuming statistical
significance at P = 0.05 (SPSS 2009).
4.3.1. Variances
No variances from the data management and analysis methods described in the Study Plan were
necessary in 2013 or 2014.
4.4. Bat Capture and Radio Telemetry
Using the methods described in the Study Plan, the study team was unable to document roost
locations (maternity colonies, hibernacula) of bats (objective 3 above). The results of the
acoustic monitoring study documented widespread bat use of the study area, however,
demonstrating that bats roost within the study area. Little brown bats typically forage less than 3
km (1.9 mi) from roost sites (Henry et al. 2002), similar to other bats of the family
Vespertilionidae (Brigham et al. 1997b; Campbell et al. 1996), bolstering support for the
presence of bat roosts in the study area. The most efficient method for locating bat roost sites is
radio telemetry. Hence, the study team used radio telemetry to try to identify roosting locations
of bats in the study area. This Study Plan modification was presented in ISR 10.13 Part C,
Section 7.1.2 (ABR 2014c).
During July 14–26 and September 19–30, 2014, the study team deployed mist nets to capture
bats and equip them with tiny radio transmitters to determine roosting locations of bats in the
Project area (under ADFG Permit #14-153 and ADFG IACUC Protocol #2014-12). Although the
capture and handling equipment came from states in which White-nose Syndrome (WNS) had
not been detected (Montana and Oregon), the study team decontaminated all equipment
according to the latest WNS protocol (version 6.25.2012; USFWS 2015) before transporting it to
Alaska. All equipment and clothing also were decontaminated, following the same protocol,
before moving to another capture site. The start time for each mist-net session began at sunset
and the session continued until sunrise in the summer or for 5–7 hours in the fall, depending on
air temperatures. Capture efforts were suspended when air temperatures dropped below 25º F.
Mist nets were monitored continuously for the duration of each nightly trapping session, with a
maximum duration of 10 minutes between net checks. The study team used the network of
acoustic detectors at which bat activity was detected to target sites for mist netting, as well as
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identifying other potential mist-netting areas while in transit between sampling sites. Not all
mist-netting sites were located near cliff sections of high suitability.
After removal from mist nets, captured bats were placed into a paper holding bag, which were
protected from rain and cold weather. The following data were collected for each captured bat:
forearm length (mm); mass (g); sex; age (adult or juvenile, following Anthony 1988);
reproductive condition (pregnant, lactating, or scrotal); and species. The radio transmitter used
was the smallest available (Type LB-2X, mass = 0.27 g; Holohil Systems Ltd., Carp, Ontario,
Canada). The transmitter was attached to the inter-scapular area after clipping fur and applying
Perma-type surgical adhesive (Perma-Type Company Incorporated, Plainville, Connecticut).
Only bats weighing at least 6 g were candidates for transmitter attachment, for which the
transmitter and glue would represent ≤5 percent of a bat’s total mass, following standard
guidelines (Aldridge and Brigham 1988, Neubam et al. 2005). The holding time for captured bats
was no more than 1 hour, including 20 minutes of adhesive drying time after transmitter
attachment.
The study team tracked radio-tagged bats using a Telonics TR-2 receiver and H-antennas
mounted on a Robinson R-44 helicopter to identify, to the maximal extent possible, exact roost
locations using unaided vision or binoculars. Bats were tracked to rock and/or cliff faces used as
day-roosting substrates. After a cliff roost was identified, the exact location was recorded with a
GPS receiver, close-up and wide-angle photographs of the site were taken, and descriptive data
were collected, including estimated cliff height (m), estimated cliff length (m), estimated height
of roost above ground (m), approximate depth of roost location (cm), approximate crack or
crevice length of roost location (m), estimated distance to nearest water source (m), and roost
aspect.
Several biological samples were opportunistically gathered from captured bats to provide data
for an unrelated study being conducted by David Tessler (ADF&G wildlife biologist). The study
team collected tissue samples from the wing membrane with a 2-mm biopsy punch for genetic
analysis. The team also collected additional samples noninvasively for disease surveillance of
bat-borne pathogens by gently wiping a sterile cotton swab across the body, wings, and nose of
each bat.
5. RESULTS
Cumulative data developed in support of the Study Completion Report are available for
download at http://gis.suhydro.org/reports/SIR:
BAT_10_13_Acoustic_and_Habitat_2013_2014_ABR.xlsx;
BAT_10_13_Acoustic_Monitoring_2013_2014_ABR.xlsx;
BAT_10_13_Photo_Delivery_Table_2013_2014_ABR.xlsx;
BAT_10_13_Telemetry_Roost_2014_ABR.xlsx;
BAT_10_13_Data_2013_2014_ABR.gdb/BAT_2013_2014_AcousticMonitors;
BAT_10_13_Data_2013_2014_ABR.gdb/BAT_2013_2014_Habitat_ForestStructure;
BAT_10_13_Data_2013_2014_ABR.gdb/BAT_2013_2014_StudyArea;
BAT_10_13_Data_2013_2014_ABR.gdb/BAT_2014_Mist_Net_Capture;
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BAT_10_13_Data_2013_2014_ABR.gdb/BAT_2014_Mist_Net_Survey;
BAT_10_13_Data_2013_2014_ABR.gdb/BAT_Cliff_Habitat_Quality.
5.1. Acoustic Surveys
5.1.1. General Bat Activity
5.1.1.1. 2013 Sampling
In 2013, acoustic monitoring at all 20 detector stations resulted in a total of 2,767 potential
detector-nights (number of detectors multiplied by number of nights; Table 5.1-1) and usable
data were recorded on 2,660 detector-nights (96.1 percent). Data losses resulted from CF card
failures (G7, August 20–28; G9, May 25–June 2 and June 12–26), flooding during break-up of
river ice (G18, May 25–June 12), an electrical problem (G1, July 9–14), and damage caused by
bears (G7, September 18–October 6; G15, September 16–24; G18, August 6–10; G19,
September 20–28) and porcupines (G7, September 5–11).
Bat activity was detected at 17 (85 percent) of the 20 locations sampled (Figure 5.1-1). Overall,
621 bat passes were recorded during the entire sampling period. All calls were identified as
having been made by little brown bats (Myotis lucifugus) based on the acoustic characteristics
(Figure 5.1-2) described by Ober (2006) and Lausen et al. (2014). Activity across all stations and
seasons averaged 0.23 ± 0.04 (mean ± SE) bat passes/detector-night (Table 5.1-2).
5.1.1.2. 2014 Sampling
In 2014, acoustic monitoring at all 10 detector stations resulted in a total of 1,428 potential
detector-nights (Table 5.1-1) and usable data were recorded on 1,370 detector-nights (96.0
percent). Data losses resulted from unidentified CF card or detector problems (C21, June 27–
July 7 and August 12–24; C22, September 16–26; C24, July 14–25 and September 16–26).
Bat activity was detected at all 10 locations sampled (Figure 5.1-3). Overall, 631 bat passes were
recorded during the entire sampling period. All calls were identified as having been made by
little brown bats (Myotis lucifugus) based on their acoustic characteristics (Figure 5.1-2), which
matched those described by Ober (2006) and Lausen et al. (2014). Activity across all stations and
seasons averaged 0.46 ± 0.09 (mean ± SE) bat passes/detector-night (Table 5.1-3).
Differences in bat activity between years were examined for the six sites surveyed in 2013 and
2014. All of the stations except G3 recorded fewer bat passes in 2014 than in 2013. Three of the
stations (G10, G19, and G3) showed significant differences (P < 0.05) in mean bat passes/night
between years, but the overall difference among all detector stations was not significantly
different.
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5.1.2. Temporal Comparisons
5.1.2.1. Seasonal Activity
5.1.2.1.1. 2013 Sampling
Bat activity varied substantially throughout the sampling period (Figure 5.1-4; Table 5.1-2).
Despite the variability in monthly activity, statistical differences were not detected among entire
months (June–September; H = 2.51; df = 3; P = 0.474), probably because of low statistical
power. Bat activity was recorded only sporadically until the end of June, then peaked in July
(0.47 ± 0.14 mean passes/detector-night; Table 5.1-2), declined in August (0.22 ± 0.04 mean
passes/detector-night), and increased again in September (0.29 ± 0.10 mean passes/detector-
night). Most stations recorded the greatest amount of activity in July (8 of 20 stations; 40
percent), followed by August (5 of 20 stations; 25 percent) and September (3 of 20 stations; 15
percent). Very little activity was detected in late May and early October (0.01 ± 0.01 mean
passes/detector-night for each). The spatial distribution of bat activity (number of stations with
any activity across the study area) by month followed a slightly different trend, with the most
widespread detections occurring in August (15 of 20 stations; 75 percent), followed by July (11
of 20 stations; 55 percent), and September (7 of 20 stations; 35 percent).
5.1.2.1.2. 2014 Sampling
Bat activity again varied substantially throughout the sampling period (Figure 5.1-5; Table 5.1-3)
and differed significantly among months (June–September; H = 75.0; df = 3; P < 0.001). On
May 20, two bat passes were recorded at station G6, which were the bat activity recorded from
deployment of the detector stations until July 8 (50 nights later), after which activity became
more consistent. The greatest single-night activity rates occurred in July, but the average activity
rates in July (0.88 ± 0.36 mean passes/detector-night; Table 5.1-3) and August (0.87 ± 0.17 mean
passes/detector-night) were essentially identical. September bat activity (0.41 ± 0.08 mean
passes/detector-night) was consistent, but only at approximately half of the activity levels seen in
July and August. No bat passes were recorded in June or October. Most stations recorded the
greatest amount of activity in August (7 of 10 stations; 70 percent), followed by July (2 of 10
stations; 20 percent) and September (1 of 10 stations; 10 percent). The spatial distribution of bat
activity (number of stations with any activity across the study area) by month followed a similar
trend, with the most widespread detections occurring in July (10 of 10 stations; 100 percent) and
August (10 of 10 stations; 100 percent), followed by September (6 of 10 stations; 60 percent) and
then May (1 of 10 stations; 10 percent).
5.1.2.2. Nightly Activity
5.1.2.2.1. 2013 Sampling
Bat activity within nights (expressed as mean number of bat passes per station per hour) varied
substantially among hours of the night during all months (Figure 5.1-6), with peak activity
generally occurring 1–3 hours after sunset. No bat activity was recorded in the hour before sunset
or the hour after sunrise. In June, activity peaked 1–2 hours after sunset, when significantly
greater activity occurred (mean passes/site/hour = 0.05; P < 0.01). In July, activity peaked 2–3
hours after sunset, with significantly less activity in the first hour after sunset (mean
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passes/station/hour = 0; P < 0.05), and significantly more activity 2–3 hours after sunset (mean
passes/station/hour = 0.27; P < 0.01). In August, activity peaked 1–2 hours after sunset, when
significantly more activity occurred (mean passes/station/hour = 0.08; P < 0.01). In September,
activity peaked within 2–3 hours after sunset, with significantly more activity during that time
period (mean passes/station/hour = 0.09; P < 0.01), and significantly less activity in the middle
of the night, 4–5 hours after sunset (mean passes/station/hour = 0.002; P < 0.05) and 7–8 hours
after sunset (mean passes/station/hour = 0.002; P < 0.05).
5.1.2.2.2. 2014 Sampling
As in 2013, bat activity within nights varied substantially among hours of the night during all
months (Figure 5.1-7), with peak activity generally occurring 1–3 hours after sunset. No bat
activity was recorded in the hour before sunset or the hour after sunrise. In July, activity peaked
2–3 hours after sunset, with significantly more activity occurring during this time period (mean
passes/station/hour = 0.68; P < 0.01), and significantly less activity in the first hour after sunset
(mean passes/station/hour = 0; P < 0.05). Similarly, activity in August also peaked 2–3 hours
after sunset (mean passes/station/hour = 0.37; P < 0.01) and significantly less activity occurred
in the hour after sunset (mean passes/station/hour = 0; P < 0.05). In September, activity appeared
to be the inverse of July and August, with little activity occurring within 2–3 hours after sunset
and the greatest activity occurring later in the night within 3–5 hours after sunset. Significantly
more activity occurred during those periods (3–4 hours after sunset mean passes/station/hour =
0.13, P < 0.01; 4–5 hours after sunset mean passes/station/hour = 0.14, P < 0.01), and
significantly less activity 1–2 hours after sunset (mean passes/station/hour = 0; P < 0.05).
5.1.3. Spatial Comparisons
5.1.3.1. Activity Among Stations
5.1.3.1.1. 2013 Sampling
Bat activity differed significantly among sampling stations (H = 274.16; df = 19; P < 0.001).
Station G6 recorded the greatest total amount of activity (2.02 ± 0.54 mean passes/detector-
night), more than twice as much activity as the next most active stations (G3, at 0.78 ± 0.38
mean passes/detector-night, and G16, at 0.74 ± 0.32 mean passes/detector-night; Table 5.1-2).
The next three stations in descending order of activity were G13 (0.24 ± 0.13 mean
passes/detector-night), G10 (0.21 ± 0.06 mean passes/detector-night), and G19 (0.19 ± 0.05
mean passes/detector-night). No bat activity was detected at three stations during the entire
sampling period (G5, G15, G20; Figures 5.1-1 and 5.1-8).
The elevation of detector stations above sea level ranged from 1,680 ft to 2,425 ft (Table 5.1-4).
Overall bat activity was not correlated with elevation (Spearman’s ρ = –0.008, P = 0.972),
although bat activity tended to peak at higher elevations later in the sampling period (Spearman’s
ρ = 0.474, P = 0.054).
5.1.3.1.2. 2014 Sampling
Bat activity varied among sampling stations (Figure 5.1-9). Stations G6 and C22 recorded the
greatest rates of activity, at 1.93 ± 0.73 mean passes/detector-night and 1.21 ± 0.31 mean
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passes/detector-night, respectively. The activity rates at those stations were 2–13 times greater
than were those recorded at the next most active stations (G3, at 0.45 ± 0.13 mean
passes/detector-night; G13, at 0.25 ± 0.10 mean passes/detector-night; C23, at 0.20 ± 0.06 mean
passes/detector-night; and G16, at 0.15 ± 0.09 mean passes/detector-night; Table 5.1-3). Four of
the 10 stations (C21, G10, C24, G19) recorded 0.1 bat passes/detector-night or less.
The elevation of detector stations above sea level ranged from 1,450 ft to 2,375 ft (Table 5.1-5)
and bat activity was not correlated with elevation (Spearman’s ρ = –0.236, P = 0.511). The
activity at most stations peaked in August but, contrary to the results in 2013, bat activity did not
tend to peak at higher elevations later in the sampling period.
5.1.3.2. Activity in Relation to Habitat and Vegetation Structure
5.1.3.2.1. 2013 Sampling
Bat activity varied significantly among the four broad habitat types sampled (H = 8.58; df = 3; P
= 0.035). Detector stations in stream habitats recorded the greatest level of activity (0.59 ± 0.16
mean passes/detector-night; Table 5.1-6), followed by pond habitats (0.24 ± 0.07 mean
passes/detector-night), cliff habitats (0.15 ± 0.05 mean passes/detector-night), and upland
habitats (0.004 ± 0.003 mean passes/detector-night). Bat activity at both stream and cliff sites
peaked in July, whereas activity at pond sites peaked in September (Figure 5.1-10).
Bat activity did not differ among the four types of vegetation structure sampled (H = 5.00; df =
3; P = 0.175). Detector stations in closed forest-structure types recorded the greatest level of
activity (0.77 ± 0.19 mean passes/detector-night; Table 5.1-7), followed by the shrub (0.08 ±
0.02 mean passes/detector-night), open (0.03 ± 0.01 mean passes/detector-night), and dwarf
structure types (0.002 ± 0.002 mean passes/detector-night). Activity levels in the closed and
shrub types peaked in July, whereas activity in open forests remained consistently low during the
entire study (Figure 5.1-11).
The bat study area totaled 33,124 acres (Table 5.1-8). Stratified according to the broad habitat
types sampled, the study area comprised these proportions: upland = 59.4 percent; cliff = 23.0
percent; stream = 8.0 percent; pond = 3.0 percent; and Susitna River = 6.6 (Figure 5.1-12).
Stratified by vegetation-structure type, the non-water-body portion of the study area comprised
these proportions: open = 51.5 percent; closed = 22.7 percent; shrub = 17.6 percent; dwarf = 6.8
percent and unclassified = 1.4 percent (Figure 5.1-13).
None of the minimum distances measured to the seven landscape features (ponds, perennial
streams, rivers, any cliff, and cliff-roost quality scores of 1, 2, and 3; Table 5.1-4) were
significantly correlated with mean bat passes per detector-night (Spearman’s ρ; P > 0.05).
5.1.3.2.2. 2014 Sampling
Statistical analyses of bat activity rates and habitats (pond, stream, and cliff) were not conducted
in 2014, as was explained above in Section 4.3. Detector stations in stream habitats recorded the
greatest level of activity (1.15 ± 0.42 mean passes/detector-night; Table 5.1-9), followed by cliff
habitats (0.39 ± 0.08 mean passes/detector-night) and pond habitats (0.21 ± 0.04 mean
passes/detector-night). Bat activity at stream sites peaked in July, whereas activity at cliff sites
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peaked in September (Figure 5.1-14). Activity at pond sites remained relatively low throughout
the study period with the greatest amount of activity occurring in September.
Similarly, statistical analyses on bat activity rates and vegetation types (open, closed, and shrub)
were not conducted in 2014. Detector stations in closed forest-structure types recorded the
greatest level of activity (1.13 ± 0.28 mean passes/detector-night; Table 5.1-10), followed by the
open (0.15 ± 0.04 mean passes/detector-night) and shrub types (0.04 ± 0.02 mean
passes/detector-night). Activity levels in the closed type peaked in July, whereas activity in open
and shrub types remained consistently low during the entire study (Figure 5.1-15).
None of the minimum distances measured to the seven landscape features (ponds, perennial
streams, rivers, any cliff, and cliff-roost quality scores of 1, 2, and 3; Table 5.1-5) were
significantly correlated with mean bat passes per detector-night (Spearman’s ρ; P > 0.05).
5.2. Roost Surveys
5.2.1. Natural Roosts
The 102 discrete cliff sections identified before the field season as potential cliff-roosting habitat
were categorized into four groups during the aerial survey on June 28–30, 2013: four sections
were not suitable, 49 sections were of low suitability (quality score = 1), 33 sections were of
moderate suitability (quality score = 2), and 16 sections were of high suitability (quality score =
3) (Table 4.2-1; Figure 5.2-1). Besides cliffs, four areas near Bald Eagle nests were examined for
large-diameter snags suitable for use by roosting bats, but no suitable roosts were found. Project
researchers searched for natural caves in a limestone formation reported by Chapin (1918)
between Jay Creek and Watana Creek, but no caves were found. Despite the widespread
presence of bats revealed by acoustic monitoring, the study team was not successful in locating
any roosting locations, maternity colonies, or hibernacula in natural sites during the surveys in
2013.
5.2.2. Artificial Roosts
The study team obtained permission for access to 10 sites during the roost search in August 2013
and to 11 sites during the search in October 2013, but was unable to secure permission to visit
five other sites of interest (Table 5.2-1; Figure 5.2-2). The study team obtained permission for
access to 11 of the 16 sites of interest, including the two sites within the study area (RS 04 and
RS 09; Table 5.2-1). During August 11–13, 2013, the team investigated 25 structures (e.g.,
cabins, sheds, outhouses) at 10 sites for the presence of bats and any sign of use as roost sites or
maternity colonies. During October 4–6, 2013, the team searched the same sites and structures as
in August, plus one additional site and structure (RS 16; Table 5.2-1) for the presence of bats and
any signs of use as hibernacula. Of the 26 structures surveyed, 15 were considered to be suitable
for roosting by bats; however, no roosting bats or sign of roosting bats were found at any of the
sites or within any of the structures during either survey. Two of the 16 sites visited were located
within the bat study area (Table 5.2-1). The potential pool of candidate sites was expanded
outside the study area because of the rarity of suitable structures in the study area. Despite the
widespread presence of bats revealed through acoustic monitoring, no roosting locations,
maternity colonies, or hibernacula were located in artificial sites during the surveys in 2013.
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5.3. Bat Capture and Radio Telemetry
During the summer capture effort in 2014, the study team deployed mist nets for 304.5 mist-net-
hours (8,267 m2 mist-net-hours) during 13 nights at seven different locations. During the fall
capture effort, the team deployed mist nets for 457.1 mist-net-hours (11,946 m2 mist-net-hours)
during 12 nights at two locations. Despite these intensive mist-netting efforts, only a single little
brown bat was captured. An adult male weighing 9.1 g was captured at 03:15 on July 18 at site
G6, along a slow moving, unnamed tributary of the Susitna River, just west of Watana Creek.
After the transmitter was attached successfully, the bat was released and was tracked to day
roosts over the next 10 days.
On the first day following release, the bat roosted in a small cliff complex near the mouth of the
unnamed tributary, approximately 457 m (1,500 ft) downstream of the capture site. All but one
subsequent roost locations were in the same large cliff complex on the north side of the Susitna
River near between Project River Miles 193.7 and 195 (Figure 5.2-1). The other roost location
was occupied on the third day of tracking, 0.4 mi southeast of the large cluster of roost locations.
All roost locations were in cliff faces above a stream or river. The majority of the roost locations
(80 percent) were in a cliff section identified as having high suitability during the cliff roost
quality survey in 2013. The cliff section used on the day following release was identified as
having low suitability and the roost used on the third day of tracking was classified as having
moderate suitability. The cliff faces used for roosting by the tagged bat varied in elevation from
approximately 1,655 ft to 1,730 ft above sea level (160–330 ft in height).
6. DISCUSSION
The ecology of bats in Alaska remains largely unknown, especially in the Interior (Parker et al.
1997, AKNHP 2013). Bats were not included in the APA Project studies in the 1980s, so data on
the occurrence of bats in the upper Susitna River drainage were lacking and their status in the
Project area was essentially unknown at the time this study began. Kessel et al. (1982) reported a
single observation of a bat during their bird and mammal surveys in the early 1980s.
6.1. Acoustic Monitoring
This study revealed that activity by little brown bats was widespread across the study area,
occurring from the western end of the dam and camp facilities area almost all the way upstream
to Goose Creek, near the eastern edge of the proposed reservoir inundation zone. Seventeen of
the 20 detector stations in the study area recorded bat activity between late May and early
October in 2013 and all 10 detector stations recorded bat activity during the same time period in
2014. Nearly the same number of bat passes were recorded in each year, but only half as many
detectors were deployed in 2014 as in 2013, contributing to a higher mean activity rate in the
second year of study. The 2014 rate was likely inflated by the fact that the six stations that were
resurveyed were those at which the greatest activity levels were recorded in 2013. Those six sites
were resurveyed to elucidate annual variation in bat activity and to target reliable sites for the
mist netting and telemetry effort. Five of the six resurveyed sites actually recorded less activity
in 2014 than in 2013.
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The overall activity rates found in this study would be considered low for locations outside of
Alaska (compared to the Lower 48 states) and no other comparable studies are available for
comparison in Interior Alaska. Parker et al. (1996) documented highly variable acoustic rates
(average calls per night) of bat activity in riparian (81.0), old-growth (6.0), clearcut (2.0), and
second-growth (0.03) habitats in Southeast Alaska. Those rates are difficult to compare with the
data from this study because of timing differences (their study monitored during June–August),
the presence of four additional species in their study area, and habitat differences between the
coastal forests of Southeast Alaska and the interior forest and shrublands in this study area.
Lausen et al. (2014) conducted similar acoustic monitoring in the Northwest Territories, Canada,
but those rates also are difficult to compare with this study because Lausen only sampled during
the peak of bat activity for 22 total days in July and August; six additional species were present
in that study area; and the study design and objectives of that study differed from this study.
Nevertheless, an approximate mean of 12 bat passes per detector- night over the duration of that
study was derived from their data (Table 3 in Lausen et al. 2014). Assuming that each of the
seven species in Lausen’s study was equally represented, the activity rates in that study would be
similar to the most active station in this study.
Slough and Jung (2008) conducted bat research, including acoustic monitoring, over a 12-year
period in the Yukon, Canada. The acoustic activity they documented included three additional
species and they only reported total bat passes from sporadic, single-night surveys (not including
nights with zero detections). Similar to this study, they found tremendous variability among
detector sites, with up to 454 total bat passes in a single night (Table 2 in Slough and Jung 2008).
The greatest total activity from a single station in a single night in this study was 63 bat passes in
2013 and 83 bat passes in 2014, both recorded at station G6.
6.1.1. Temporal Comparisons
6.1.1.1. Seasonal Activity
Bats were detected during every month of the study period (late May to early October) in 2013,
but were not recorded in June or October in 2014. Parker et al. (1997) observed similar first and
last observations of the year (May and October) of bat activity near Fairbanks and suggested that
bats in Interior Alaska may not travel far to hibernate. In both years of this study, substantial
variability was evident in the monthly activity rates, but those differences were statistically
significant only in 2014. In 2013, more stations peaked in July than in any other month and
overall bat activity level in that month was roughly twice that detected in August and 1.5 times
that in September. In 2014, most stations peaked later in the year (August), although mean
activity rates across all detector stations were nearly identical in July and August. Similarly,
Tessler et al. (2014) also reported peaks of activity in July and August, based on the information
gathered in their “citizen-science” observational study. Increases in activity during these periods
may be due to increases in prey abundance or the appearance of volant (flying) juveniles. The
lack of consistent bat activity in the study area in May and early June suggests that, although bats
may be present, foraging conditions may not be favorable until late June or July. Alternatively,
the lack of consistent activity until late June and July may reflect the arrival of migrant bats.
Paradoxically, consistent bat activity was detected earlier in the late spring of 2013, when snow
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and ice cover persisted into June, than in 2014, when the Project area was free of snow and ice at
a much earlier date.
In 2013, mean activity rates tended to peak later in the year at higher elevation sites. For
instance, activity peaked in September at four stations that were located at some of the highest
elevations sampled. In 2014, this pattern was less apparent. In both years, a pulse of activity
occurred near the end of September and in early October, after which no bats were recorded for
the remainder of the study. Those pulses of activity may indicate prehibernation behavior
(McGuire and Guglielmo 2009), premigratory behavior, or migrating bats moving through the
study area. Station G3 experienced peak activity levels in September in both 2013 (103 bat
passes) and 2014 (48 bat passes). Bats may have been active at higher elevation sites later in the
year to take advantage of cooler temperatures to maximize energy savings during torpor or
simply to follow the availability of insect prey. In the Northwest Territories, Lausen et al. (2014)
found the greatest species diversity and capture success at the lowest elevation site (820 ft), in
contrast to reduced bat activity and capture success at higher elevation sites (>1,968 ft). The
effect of elevation on bat distribution is more pronounced at high-latitude locations such as
Alaska (Parker et al. 1997); hence, the relatively high elevation of the sites sampled in this study
(1,450–2,375 ft) may have contributed to low bat activity rates and capture success.
6.1.1.2. Nightly Activity
Bat activity was recorded only between sunset and sunrise, despite beginning sampling 1 hour
before sunset and ending 1 hour after sunrise. Most of the activity probably occurred during
periods of low light or of darkness, but the study team did not specifically measure the amount of
light and the calculation of sunset time did not account for topography. Some bat activity was
detected within the first hour after sunset during relatively bright periods, as has been observed at
more northerly latitudes in Alaska (Parker et al. 1997). The majority of bat activity observed in
this study occurred within 2–4 hours after sunset, among the darkest hours of the night, for most
(June–August) of the months sampled. Outside of these months, when more hours of darkness
were available, bats were not necessarily most active during the darkest periods of the night. The
limited data recorded in June, when the fewest hours of darkness were available, suggested that
bats were most active 1–2 hours after sunset, when darkness can minimize risk from avian
predators (Rydell and Speakman 1995) and reduce competition from avian competitors
(Speakman et al. 2000). Significantly fewer bat calls were detected in the first hour after sunset
in July, which also suggested avoidance of foraging during relatively light periods, similar to
another study in southeast Alaska (Loeb et al. 2014). With increasing hours available for
foraging in September, a bimodal distribution (two peaks) in the pattern of activity became
evident, as has been documented in other studies (Kunz 1973, Erkert 1982, Taylor and O’Neil
1988, Maier 1992, Hayes 1997).
6.1.2. Spatial Comparisons
6.1.2.1. Activity Among Stations
Bat activity varied considerably among the detector stations. In both years, station G6 recorded
1.5 to 2 times more activity than the next most active stations (G3 in 2013 and C22 in 2014).
Station G6 was located adjacent to a pool of slow-moving water in an unnamed stream course
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between Deadman and Watana creeks, which appeared to provide excellent foraging habitat for
bats. In addition, Station G6 was located approximately 1 mile upstream from a “highly suitable”
section of cliff, where the male bat radio-tagged in July 2014 roosted consistently. Some of the
metrics generated from this study were certainly affected by the large number of bat calls
recorded at Station G6, but this single station did not exert undue influence on the overall trends
observed across the remaining detector stations, with one exception. The results of the habitat
and vegetation structure analyses in 2013 appeared to have been driven primarily by Station G6.
6.1.2.2. Activity in Relation to Habitat and Vegetation Structure
Bat activity rates varied among habitat types between years, but most habitat types (pond,
stream, cliff) had at least one station with substantial activity. The greatest amount of bat activity
occurred in habitat types associated with water (streams and ponds). Similarly, the cliff habitat
station that recorded the second greatest activity rate during this study also was associated with
water, being located on the Susitna River. Association of bats with water has been widely
documented. Slough and Jung (2008) recorded the highest activity rates in riparian and lacustrine
habitats in the Yukon and Loeb et al. (2014) found a similar result in Southeast Alaska. Riparian
habitats are known to provide important foraging and drinking areas for insectivorous bats
(Grindal et al. 1999). It is likely that cliff habitats provide the major source of roosting
opportunities in the study area because of the paucity of other roost structures (caves, trees,
human-made structures). Detectors located in and near cliff habitats recorded an intermediate
level of bat activity and detectors located in upland habitat types recorded the least amount of bat
activity among habitat types. In view of the apparent lack of suitable roost trees, upland habitats
probably do not provide many resources needed by bats in the study area.
The mean activity levels of bats detected among the habitat types sampled in this study were
inversely proportional to their spatial extent on the landscape. Pond and stream habitats
composed only 11.5 percent of the total acreage of the study area, but represented 84.3 percent of
all recorded bat activity in 2013. Most of the remaining activity (15.2 percent) in 2013 occurred
in cliff habitats, which constituted 19.7 percent of the study area acreage. It is inappropriate to
compare habitats or vegetation structure types in 2014 because the six sites that were resurveyed
in that year were not selected randomly.
The mean activity rates of the little brown bats detected in this study were not influenced
significantly by vegetation structure type. Little brown bats are considered to be foraging
generalists because they have the ability to glean insects from slow-moving water, to fly at
intermediate speeds through forested habitats, and to employ aerial pursuit (Adams 2003).
Studies of little brown bats and morphologically and ecologically similar species have produced
mixed results when evaluating the effect of habitat structural complexity (i.e., clutter) on bat
activity. Brigham et al. (1997a) reported lower bat activity rates in highly cluttered habitats,
whereas Jung et al. (2012) found increased bat activity in more structurally heterogeneous (i.e.,
more cluttered) environments and Sleep and Brigham (2003) reported no significant relationship
between bat activity and clutter. In this study, bats were most active in the closed forest structure
type, which was the most complex or cluttered habitat. Loeb et al. (2014) found a similar trend in
Southeast Alaska and speculated that denser canopies may offer less light penetration and more
darkness, which may provide more protection from predators. The activity rate in the closed
forest structure type in this study was driven largely by the single station that recorded the most
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bat activity during the entire study (G6). Despite the high acreage of the open forest structure
type (51.5 percent) in the study area, detectors located in open forests recorded only 3.4 percent
of the overall bat activity in 2013. Dwarf forest provided few resources needed by bats in the
study area, in view of the fact that the lowest activity rate detected among all habitat and
vegetation structure types in 2013 was recorded in that structure type.
6.2. Roost Surveys
6.2.1. Natural Roosts
The most likely natural roosting habitats available in the bat study area are the cracks and
crevices in the extensive cliffs along the Susitna River. Almost half (48 percent) of the 102 cliff
sections that the study team mapped in 2013 along the Susitna River and its major tributaries in
the study area were classified as moderately or highly suitable roosting habitat. Little brown bats
are widely known to use rock crevices as day roosts (Barbour and Davis 1969, Adams 2003,
Lausen and Barclay 2006, Foresman 2012) elsewhere in North America. A study of bats in the
Yukon documented only a few natural roosts in trees or rock crevices, including a rock crack in
Miles Canyon on the Yukon River near Whitehorse, which served as a maternity colony for little
brown bats; rock crevices above Pine Lake; and behind the exfoliating bark of a fire-killed white
spruce (Picea glauca) (Slough and Jung 2008). Randall et al. (2014) tracked three adult male
little brown bats to mostly natural roosts (cliffs and trees), in contrast to females, which only
roosted in buildings. Other studies suggest that female bats use natural roosts (mostly in the form
of trees) in northwestern Canada (Crampton and Barclay 1998, Olson and Barclay 2013). Few
trees were found in the study area in 2013 that were considered suitable as roosting habitat and
no bats were found roosting behind the bark of those trees.
In addition to cliffs and trees, the study team searched for limestone formations in and near the
study area in an attempt to locate caves, but none were found. The available sources of geologic
data for the study area contained conflicting information about the presence of limestone. The
preliminary geologic map produced for the Project (see ISR Study 4.5, Geology and Soils Study)
details the presence of some limestone in the bat study area, but no caves were discovered by the
Geology team (M. Bruen, Geology and Soils Study Lead, personal communication).
6.2.2. Artificial Roosts
The human-made structures searched in and near the bat study area included buildings associated
with seasonal mining or hunting camps, old trapper cabins from the 1930s, and modern, well-
maintained cabins. Although more than half (58 percent) of the structures examined were
considered to have potential as roost sites, no bats or bat sign were found at any of the structures.
Several owners of cabins above tree-line (at Clarence Lake) stated that they had never seen bats
at their cabins in the decades they have owned those properties. While it is possible that bats
escaped detection during the artificial roost searches in this study, nearly all structures were
surveyed twice. Hence, given the paucity of buildings in the bat study area and their apparent
lack of use as roosts, it is probable that bats are using natural roost sites in the study area.
Most roost sites documented in the Yukon by Slough and Jung (2008) were maternity colonies in
buildings and the vast majority of roosts reported to the Alaska Department of Fish and Game
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(ADF&G) in Southcentral Alaska have been in buildings (D. Tessler, ADF&G, personal
communication). Randall et al. (2014) observed female bats roosting solely in artificial structures
while males used mostly natural structures (trees and cliffs), likely due to the rigid temperature
requirements for females during pregnancy and lactation. Because of the much greater likelihood
of detecting bats in structures visited frequently by humans, however, it is difficult to evaluate
the proportional use of artificial roosts in relation to natural roosts.
6.3. Bat Capture and Radio Telemetry
Despite concerted efforts by the study team to capture bats with mist nets during summer and fall
2014, only a single male bat was captured and radio-tagged. That bat yielded 10 days of roosting
information for midsummer. The first roost location used after the bat was released was in a cliff
section just downstream of the capture location. That cliff section was mapped as having low
suitability in the 2013 cliff survey. The bat may have roosted in that location because of the close
proximity to the capture site and the lack of darkness to find more appropriate roost sites farther
from the capture site. All subsequent relocations of the bat were in cliff sections above the
Susitna River about 1 mile from the capture site. This result supports other studies documenting
that little brown bats typically forage less than 3 km (1.9 mi) from roost sites (Henry et al. 2002),
similar to other vespertilionid bats (Brigham et al. 1997b; Campbell et al. 1996). The majority
(90 percent) of relocations occurred in a cliff section classified as having high suitability in the
2013 cliff survey, whereas the remaining 10 percent were in a cliff classified as having moderate
suitability. Cliffs identified as high suitability contained more vertical and horizontal cracks and
greater depth of cracks than did cliffs classified as moderate suitability. Even though the study
team was unable to identify the exact roosting locations of the tagged bat, it was possible to
detect general changes in roost-site locations within the cliff sections by radio-tracking from a
helicopter.
In general, the study area offers very little roosting habitat in the form of tree roosts and human-
made structures. The study team was unable to locate any bats roosting in trees or human-made
structures in the study area. After expending considerable effort attempting to document roost
sites in the study area, the study team confirmed that the cliffs above the Susitna River offered
the most likely roosting locations for little brown bats. This information will be valuable for
assessing potential impacts in the Project license application. The relatively high elevation of the
study area may have contributed to the low capture success (Lausen et al. 2014) in this
investigation. Despite the limited success of the capture efforts in 2014, this effort was the first
of its kind to occur in Interior Alaska (D. Tessler, ADF&G, personal communication) and
provides useful information for bat management in Alaska.
7. CONCLUSIONS
In 2013 and 2014, AEA completed two years of acoustic monitoring to document bat use of the
study area and surveyed the study area for bat roosts. The field work, data collection, data
analysis, and reporting for the Bat Study successfully met all study objectives in the FERC-
approved Study Plan. The results of the Bat Study are reported herein and earlier by AEA in ISR
10.13 Parts A, B, and C (ABR 2014a, 2014b, 2014c). With this report, AEA has now completed
the Bat Distribution and Habitat Use Study.
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9. TABLES
Table 4.1-1. Categorization of Acoustic Detector Stations by Habitat and Forest Structure Types, 2013–2014.
Station Habitat Type Forest Structure Type Year Surveyed
G1 Pond – 2013
G2 Pond – 2013
G3 Pond – 2013, 2014
G4 Pond – 2013
G5 Upland Shrub 2013
G6 Stream Closed 2013, 2014
G7 Pond – 2013
G8 Upland Dwarf 2013
G9 Stream Open 2013
G10 Pond – 2013, 2014
G11 Upland Open 2013
G12 Pond – 2013
G13 Cliff Closed 2013, 2014
G14 Cliff Open 2013
G15 Upland Dwarf 2013
G16 Pond – 2013, 2014
G17 Upland Closed 2013
G18 Stream Shrub 2013
G19 Cliff Shrub 2013, 2014
G20 Stream Dwarf 2013
C21 Pond – 2014
C22 Cliff Closed 2014
C23 Cliff Open 2014
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Station Habitat Type Forest Structure Type Year Surveyed
C24 Stream Open 2014
Table 4.2-1.Quality Scores for Potential Cliff-Roosting Habitat, 2013.
Quality
Score Description
Number (%) of
Cliff Sections Identified
in Study Area
0 Not suitable: no potential for bat roosts; e.g., unvegetated mud slope
with no holes, cracks, or crevices.
4 (3.9%)
1
Low suitability: no or few vertical and/or horizontal cracks or
crevices, shallow cracks approximately <2 cm deep1, vegetation
may block access.
49 (48.0%)
2
Moderate suitability: moderate number of vertical and/or horizontal
cracks or crevices present, cracks approximately 2 cm–0.5 m deep,
no vegetation blocking access.
33 (32.4%)
3
High suitability: large numbers of vertical and/or horizontal cracks or
crevices present, cracks >0.5 m deep, no vegetation blocking
access.
16 (15.7%)
Notes:
1. Similar size requirement for roost site in trees from Crampton and Barclay (1998).
Table 5.1-1. Number and Percentage of Nights Surveyed by Acoustic Detector Stations in 2013, and 2014.
2013 2014
Station
Number of
Nights in
Sampling
Period
Number of
Nights
Actually
Surveyed
Percentage
of Nights
Surveyed
Number of
Nights in
Sampling
Period
Number of
Nights
Actually
Surveyed
Percentage
of Nights
Surveyed
G1 139 133 95.7 – – –
G2 139 139 100 – – –
G3 139 139 100 150 150 100
G4 139 139 100 – – –
G5 139 139 100 – – –
G6 142 142 100 150 150 100
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2013 2014
Station
Number of
Nights in
Sampling
Period
Number of
Nights
Actually
Surveyed
Percentage
of Nights
Surveyed
Number of
Nights in
Sampling
Period
Number of
Nights
Actually
Surveyed
Percentage
of Nights
Surveyed
G7 136 101 74.3 – – –
G8 140 140 100 – – –
G9 137 113 82.5 – – –
G10 140 140 100 149 149 100
G11 138 138 100 – – –
G12 140 140 100 – – –
G13 140 140 100 149 149 100
G14 139 139 100 – – –
G15 140 131 93.6 – – –
G16 136 136 100 149 149 100
G17 136 136 100 – – –
G18 136 112 82.4 – – –
G19 136 127 93.4 149 149 100
G20 136 136 100 – – –
C21 - - - 133 109 82.0
C22 - - - 132 121 91.7
C23 - - - 134 134 100
C24 - - - 133 110 82.7
Total 2,767 2,660 96.1 1,428 1,370 95.9
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Table 5.1-2. Bat Activity (Bat Passes per Detector-Night) by Station and Month, 2013.
May June July August September October Total
Station 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3
G1 0 – 11 0 – 30 0 – 25 0.10 0.07 31 0 – 30 0 – 6 0.02 0.02 133
G2 0 – 11 0 – 30 0 – 31 0.03 0.03 31 0 – 30 0 – 6 0.01 0.01 139
G3 0 – 11 0 – 30 0.03 0.03 31 0.10 0.05 31 3.43 1.68 30 0.17 0.17 6 0.78 0.38 139
G4 0 – 11 0 – 30 0 – 31 0.19 0.11 31 0 – 30 0 – 6 0.04 0.02 139
G5 0 – 11 0 – 30 0 – 31 0 – 31 0 – 30 0 – 6 0 – 139
G6 0 – 11 0.20 0.14 30 5.84 2.23 31 2.23 0.72 31 1.03 0.36 30 0 – 9 2.02 0.54 142
G7 0 – 8 0 – 30 0.23 0.09 31 0.05 0.05 22 0 – 10 – – 0 0.08 0.03 101
G8 0 – 10 0 – 30 0 – 31 0 – 31 0.03 0.03 30 0 – 8 0.01 0.01 140
G9 – – 0 0 – 13 0.10 0.07 31 0.03 0.03 31 0 – 30 0 – 8 0.04 0.02 113
G10 0 – 10 0 – 30 0.03 0.03 31 0.26 0.13 31 0.67 0.23 30 0 – 8 0.21 0.06 140
G11 0 – 10 0 – 30 0 – 31 0.03 0.03 31 0 – 30 0 – 6 0.01 0.01 138
G12 0 – 10 0 – 30 0.13 0.08 31 0 – 31 0.03 0.03 30 0 – 8 0.04 0.02 140
G13 0 – 10 0.03 0.03 30 0.71 0.55 31 0.32 0.13 31 0 – 30 0 – 8 0.24 0.13 140
G14 0 – 9 0 – 30 0.03 0.03 31 0.06 0.04 31 0.07 0.07 30 0 – 8 0.04 0.02 139
G15 0 – 10 0 – 30 0 – 31 0 – 31 0 – 21 0 – 8 0 – 131
G16 0.11 0.11 9 0.83 0.64 30 1.55 1.25 31 0.65 0.20 31 0.20 0.12 30 0 – 5 0.74 0.32 136
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May June July August September October Total
Station 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3
G17 0 – 9 0 – 30 0 – 31 0.03 0.03 31 0 – 30 0 – 5 0.01 0.01 136
G18 0 – 2 0.06 0.06 18 0.10 0.05 31 0.04 0.04 26 0 – 30 0 – 5 0.04 0.02 112
G19 0 – 9 0 – 30 0.48 0.13 31 0.29 0.14 31 0 – 21 0 – 5 0.19 0.05 127
G20 0 – 9 0 – 30 0 – 31 0 – 31 0 – 30 0 – 5 0 – 136
Total 0.01 0.01 181 0.06 0.03 571 0.47 0.14 614 0.22 0.04 606 0.29 0.10 562 0.01 0.01 126 0.23 0.04 2,660
Notes:
1. 𝑥̅ = Mean bat activity.
2. SE = Standard error of mean.
3. n = Number of detector-nights used in analysis.
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Table 6.31-3. Bat Activity (Bat Passes per Detector-Night) by Station and Month, 2014.
May June July August September October Total
Station 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3
G3 0 - 17 0 - 30 0.06 0.04 31 0.58 0.18 31 1.60 0.57 30 0 - 11 0.45 0.13 150
G6 0.12 0.12 17 0 - 30 5.77 3.27 31 2.68 1.08 31 0.87 0.28 30 0 - 11 1.93 0.73 150
G10 0 - 17 0 - 30 0.03 0.03 31 0.39 0.10 31 0 - 30 0 - 10 0.09 0.03 149
G13 0 - 17 0 - 30 0.45 0.16 31 0.77 0.47 31 0 - 30 0 - 10 0.26 0.10 149
G16 0 - 17 0 - 30 0.42 0.42 31 0.13 0.06 31 0.20 0.14 30 0 - 10 0.15 0.09 149
G19 0 - 17 0 - 30 0.06 0.04 31 0.13 0.06 31 0 - 30 0 - 10 0.04 0.02 149
C21 0 - 1 0 - 26 0.17 0.08 24 0.28 0.11 18 0.07 0.05 30 0 - 10 0.10 0.03 109
C22 0 - 0 0 - 30 1.10 0.45 31 2.84 1.03 31 1.26 0.42 19 0 - 10 1.21 0.31 121
C23 0 - 1 0 - 30 0.16 0.09 31 0.42 0.14 31 0.30 0.17 30 0 - 11 0.20 0.06 134
C24 0 - 1 0 - 30 0.11 0.11 19 0.23 0.14 31 0 - 19 0 - 10 0.08 0.04 110
Total 0.02 0.02 105 0 - 296 0.88 0.36 291 0.87 0.17 297 0.41 0.08 278 0 0 103 0.46 0.09 1,370
Notes:
1. 𝑥̅ = Mean bat activity.
2. SE = Standard error of mean.
3. n = Number of detector-nights used in analysis.
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Table 5.1-4. Elevation and Minimum Distances to Water Bodies and Cliffs, by Station, 2013.
Minimum Distance (ft)
Station Elevation
(ft) Pond Stream1 River
Cliff
Quality
Score > 0
Cliff
Quality
Score = 12
Cliff
Quality
Score = 23
Cliff
Quality
Score = 34
G1 2,362 0 1,086 2,408 6,724 9,988 6,724 7,629
G2 2,425 97 3,478 3,487 10,384 13,065 10,384 10,655
G3 2,242 140 1,589 3,568 8,961 12,198 8,961 10,364
G4 2,230 31 1,856 3,170 1,676 8,460 1,676 8,777
G5 2,388 1,477 4,004 7,432 3,506 5,787 3,506 3,517
G6 1,829 2,899 12 5,906 1,417 1,417 7,380 6,910
G7 2,047 441 1,657 4,447 2,309 2,309 7,874 7,787
G8 2,042 4,651 1,519 10,663 5,694 5,694 18,765 10,108
G9 1,869 3,010 2 21,533 4,715 7,468 4,715 21,353
G10 2,031 0 3,542 3,276 2,903 2,903 10,371 8,622
G11 1,748 7,334 685 293 1,931 1,931 4,025 4,113
G12 1,795 20 928 1,490 626 626 668 2,416
G13 1,680 6,523 1,688 210 603 3,231 603 7,594
G14 1,920 3,072 141 10,406 103 1,083 103 13,275
G15 1,711 8,790 1,069 618 1,560 1,560 4,477 2,792
G16 1,751 33 232 724 5,267 5,267 6,112 17,729
G17 1,827 8,405 4,131 740 4,882 4,882 6,468 24,487
G18 1,876 2,501 475 50 472 12,392 1,596 472
G19 1,968 9,331 72 131 336 25,377 14,501 336
G20 1,716 7,749 2,452 811 1,452 1,452 6,233 2,146
Notes:
1. Perennial Stream.
2. Cliff Quality Score 1 = Low Suitability.
3. Cliff Quality Score 2 = Moderate Suitability.
4. Cliff Quality Score 3 = High Suitability.
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Table 5.1-5. Elevation and Minimum Distances to Water Bodies and Cliffs, by Station, 2014.
Minimum Distance (ft)
Station Elevation
(ft) Pond Stream1 River
Cliff
Quality
Score > 0
Cliff
Quality
Score = 12
Cliff
Quality
Score = 23
Cliff
Quality
Score = 34
G3 2,242 140 1,589 3,568 8,961 12,198 8,961 10,364
G6 1,829 2,899 12 5,906 1,417 1,417 7,380 6,910
G10 2,031 0 3,542 3,276 2,903 2,903 10,371 8,622
G13 1,680 6,523 1,688 210 603 3,231 603 7,594
G16 1,751 33 232 724 5,267 5,267 6,112 17,729
G19 1,968 9,331 72 131 336 25,377 14,501 336
C21 2,375 0 2,953 4,261 5,125 5,765 5,125 5,813
C22 1,450 4,461 5,151 0 58 249 58 3,306
C23 1,528 6,700 4,357 0 431 3,165 4,719 431
C24 1,588 2,089 73 502 900 900 16,394 1,307
Notes:
1. Perennial Stream.
2. Cliff Quality Score 1 = Low Suitability.
3. Cliff Quality Score 2 = Moderate Suitability.
4. Cliff Quality Score 3 = High Suitability.
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Table 5.1-6. Bat Activity (Bat Passes per Detector-Night) by Month and Habitat Type, 2013.
Pond Stream Cliff Upland Total
Month 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3
May 0.01 0.01 81 0 – 22 0 – 28 0 – 50 0.01 0.01 181
June 0.10 0.08 240 0.08 0.05 91 0.01 0.01 90 0 – 150 0.06 0.03 571
July 0.25 0.16 242 1.51 0.59 124 0.41 0.19 93 0 – 155 0.47 0.14 614
August 0.18 0.04 239 0.60 0.21 119 0.23 0.07 93 0.01 0.01 155 0.22 0.04 606
September 0.59 0.24 220 0.26 0.10 120 0.02 0.02 81 0.01 0.01 141 0.29 0.10 562
October 0.02 0.02 45 0 – 27 0 – 21 0 – 33 0.01 0.01 126
Total 0.24 0.07 1,067 0.59 0.16 503 0.15 0.05 406 0.004 0.003 684 0.23 0.04 2,660
Notes:
1. 𝑥̅ = Mean bat activity.
2. SE = Standard error of mean.
3. n = Number of detector-nights used in analysis.
Table 5.1-7. Bat Activity (Bat Passes per Detector-Night) by Month and Forest Structure Type for Non-Pond Habitats,
2013.
Open Closed Dwarf Shrub Total
Month 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3
May 0 – 19 0 – 30 0 – 29 0 – 22 0 – 100
June 0 – 73 0.08 0.05 90 0 – 90 0.01 0.01 78 0.02 0.01 331
July 0.04 0.03 93 2.18 0.80 93 0 – 93 0.19 0.05 93 0.60 0.21 372
August 0.04 0.02 93 0.86 0.26 93 0 – 93 0.11 0.05 88 0.26 0.07 367
September 0.02 0.02 90 0.34 0.13 90 0.01 0.01 81 0 – 81 0.10 0.04 342
October 0 – 22 0 – 22 0 – 21 0 – 16 0 – 81
Total 0.03 0.01 390 0.77 0.19 418 0.002 0.002 407 0.08 0.02 378 0.23 0.05 1,593
Notes:
1. 𝑥̅ = Mean bat activity.
2. SE = Standard error of mean.
3. n = Number of detector-nights used in analysis.
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Table 5.1-8. Acreage of Habitat and Vegetation Structure Types in Bat Study Area, 2013 and 2014.
Habitat Type
Vegetation
Structure Type Pond Stream Cliff Upland Susitna
River Total
Closed 9 612 2,212 3,642 – 6,475
Open 309 1,472 2,720 10,213 – 14,714
Dwarf 3 90 421 1,426 – 1,939
Shrub 432 269 273 4,067 – 5,042
Water 272 133 1,962 0 2,169 4,536
Unclassified 3 77 13 326 – 419
Total 1,027 2,653 7,601 19,674 – 33,124
Table 6.3. Bat Activity (Bat Passes per Detector-Night) by Month and Habitat Type, 2014.
Pond Stream Cliff Total
Month 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3
May 0 – 52 0.11 0.11 18 0 – 35 0.02 0.02 105
June 0 – 116 0 – 60 0 – 120 0 – 296
July 0.17 0.11 117 3.62 2.06 50 0.44 0.13 124 0.88 0.36 291
August 0.35 0.07 111 1.45 0.56 62 1.04 0.30 124 0.87 0.17 297
September 0.47 0.16 120 0.53 0.18 49 0.30 0.10 109 0.41 0.08 278
October 0 – 41 0 – 21 0 – 41 0 – 103
Total 0.21 0.04 557 1.15 0.42 260 0.39 0.07 553 0.46 0.09 1370
Notes:
1. 𝑥̅ = Mean bat activity.
2. SE = Standard error of mean.
3. n = Number of detector-nights used in analysis.
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Table 5.1-10. Bat Activity (Bat Passes per Detector-Night) by Month and Vegetation Structure Type for Non-Pond
Habitats, 2014.
Open Closed Shrub Total
Month 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3 𝒙̅1 SE2 n3
May 0 – 2 0.06 0.06 34 0 – 17 0.04 0.04 53
June 0 – 60 0 – 90 0 – 30 0 – 180
July 0.14 0.07 50 2.44 1.12 93 0.06 0.04 31 1.36 0.60 174
August 0.32 0.10 62 2.10 0.53 93 0.13 0.06 31 1.18 0.27 186
September 0.18 0.11 49 0.63 0.16 79 0 – 30 0.37 0.09 158
October 0 – 21 0 – 31 0 – 10 0 – 62
Total 0.15 0.04 244 1.13 0.28 420 0.04 0.02 149 0.63 0.15 813
Notes:
1. 𝑥̅ = Mean bat activity.
2. SE = Standard error of mean.
3. n = Number of detector-nights used in analysis.
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Table 5.2-1. Results of Building Searches for Artificial-Roost Surveys, 2013.
Site ID Number of Structures
Searched
Number of Structures
with Roost Potential Bat Sign Observed?
RS 011 – – –
RS 02 1 0 No
RS 03 1 0 No
RS 042 1 0 No
RS 051 – – –
RS 061 – – –
RS 07 1 0 No
RS 08 1 1 No
RS 092 5 4 No
RS 10 4 2 No
RS 111 – – –
RS 12 5 4 No
RS 13 5 3 No
RS 14 1 0 No
RS 151 – – –
RS 163 1 1 No
Total 26 15
Notes:
1. Access permission not received.
2. Within Bat Study Area.
3. Searched in fall only.
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10. FIGURES
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Figure 3-1. Bat Study Area for the Susitna–Watana Hydroelectric Project, 2013 and 2014.
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Figure 4.1-1. Acoustic Detector Sites Monitored for the Bat Study in 2013 and 2014.
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Figure 5.1-1. Distribution of Bat Activity Among Acoustic Detector Stations, 2013.
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Figure 6.31-2. Representative Sonogram from Little Brown Bat Recorded during the Bat Study, 2013 and 2014.
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Figure 5.1-3. Distribution of Bat Activity Among Acoustic Detector Stations, 2014.
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Figure 5.1-4. Bat Activity by Date, 2013 (error bars indicate SE).
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Figure 6.31-5. Bat Activity by Date, 2014 (error bars indicate SE). Note different scale than for 2013 figure.
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Figure 6.31-6. Bat Activity by Hour in Relation to Sunset, 2013 (error bars indicate SE).
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Figure 6.31-7. Bat Activity by Hour in Relation to Sunset, 2014 (error bars indicate SE; note different vertical
scale than in 2013 figure).
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Figure 6.31-8. Bat Activity by Station by Habitat Type (Pond: G1, G2, G3, G4, G7, G10, G12, G16; Stream:
G6, G9, G18, G20; Cliff: G13, G14, G19; Upland: G5, G8, G11, G15, G17) in 2013 (error bars indicate SE
and asterisks indicate that no bats were detected).
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Figure 5.1-9. Bat Activity by Station in 2014 (error bars indicate SE).
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Figure 6.31-10. Bat Activity by Month and Habitat Type, 2013 (error bars indicate SE).
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Figure 5.1-11. Bat Activity by Month and Forest Structure Type for Non-Pond Habitats, 2013 (error bars
indicate SE).
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Figure 5.1-12. Distribution of Habitat Types in Bat Study Area, in Relation to Acoustic Detector Sites, 2013 and 2014.
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Figure 5.1- 13. Distribution of Vegetation Structure Types in Bat Study Area, in Relation to Acoustic Detector Sites, 2013 and 2014.
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Figure 6.31-14. Bat Activity by Month and Habitat Type, 2014 (error bars indicate SE). Note different scale
than for 2013 data.
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Figure 5.1-15. Bat Activity by Month and Vegetation Structure Type for Non-Pond Habitats, 2014 (error bars
indicate SE). Note different scale than for 2013 figure.
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Figure 5.2-1. Mist-Net Sites in 2014 and Cliffs used by Radio-tagged Bat in 2014, in Relation to Cliff Habitats Surveyed in 2013.
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Figure 5.2-2. Locations of Buildings Searched on Artificial-Roost Surveys, in Relation to Acoustic Detector Sites, 2013. While CIRWG lands were not accessed in 2013, there are no known structures on CIRWG lands within the study area.