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Susitna-Watana Hydroelectric Project Document
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Title:
Distribution, abundance, and habitat use by large carnivores, Study plan
Section 10.8 : Initial study report
SuWa 207
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Prepared by Alaska Department of Fish & Game and ABR, Inc. - Environmental Research &
Services
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Draft initial study report
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Series (ARLIS-assigned report number):
Susitna-Watana Hydroelectric Project document number 207
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[Anchorage : Alaska Energy Authority, 2014]
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February 2014
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Alaska Energy Authority
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Study plan Section 10.8
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Draft
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vi, 33 p.
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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)
Distribution, Abundance, and Habitat Use by
Large Carnivores
Study Plan Section 10.8
Initial Study Report
Prepared for
Alaska Energy Authority
Prepared by
Alaska Department of Fish & Game
Anchorage and Palmer, Alaska
and
ABR, Inc.—Environmental Research & Services
Fairbanks and Anchorage, Alaska
February 2014 Draft
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page i February 2014 Draft
TABLE OF CONTENTS
Executive Summary ...................................................................................................................... v
1. Introduction ....................................................................................................................... 1
2. Study Objectives................................................................................................................ 1
3. Study Area ......................................................................................................................... 2
4. Methods and Variances in 2013 ....................................................................................... 2
4.1. Bears ....................................................................................................................... 2
4.1.1. Population Estimation ..................................................................... 2
4.1.2. Downstream Surveys ...................................................................... 4
4.2. Wolf ........................................................................................................................ 5
4.2.1. Variances......................................................................................... 5
5. Results ................................................................................................................................ 5
5.1. Bears ....................................................................................................................... 6
5.1.1. Population Estimation ..................................................................... 6
5.1.2. Downstream Surveys ...................................................................... 8
5.2. Wolf ........................................................................................................................ 9
6. Discussion........................................................................................................................... 9
6.1. Bears ....................................................................................................................... 9
6.1.1. Population Estimation ..................................................................... 9
6.1.2. Downstream Surveys .................................................................... 10
6.2. Wolf ...................................................................................................................... 11
7. Completing the Study ..................................................................................................... 11
8. Literature Cited .............................................................................................................. 11
9. Tables ............................................................................................................................... 14
10. Figures .............................................................................................................................. 18
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page ii February 2014 Draft
LIST OF TABLES
Table 5.1-1. Multiple-covariate Distance-model Coefficients and Standard Errors for
Black Bears. ...........................................................................................................................14
Table 5.1-2. Multiple-covariate Mark–Recapture Model Coefficients and Standard Errors
for Black Bears. .....................................................................................................................14
Table 5.1-3. Multiple-covariate Distance-model Coefficients and Standard Errors for
Brown Bears. ..........................................................................................................................15
Table 5.1-4. Multiple-covariate Mark–Recapture Model Coefficients and Standard Errors
for Brown Bears. ....................................................................................................................15
Table 5.1-5. Number of Hair-snag Snares Set for Bears, and Samples Collected during
Each Field Survey, in 2013. ...................................................................................................16
Table 5.1-6. Number of Hair-snag Snares Set for Bears, Total Active Days, and Total
Samples Collected, by Sampling Location, 2013. .................................................................16
Table 5.1-7. Potential Salmon Spawning Areas Sampled and Not Sampled in the Middle
Susitna River Segment, 2013. ................................................................................................17
LIST OF FIGURES
Figure 3- 1. Study Area for Large Carnivores. ............................................................................. 19
Figure 5.1-1. Distance-sampling Detection Probabilities for Black Bears. .................................. 20
Figure 5.1-2. Bivariate Smoothing of x and y Coordinates, Estimating a Function of the
Number of Black Bears per km2. .......................................................................................... 21
Figure 5.1-3. Univariate Smoothing of Elevation, Estimating a Function of the Number of
Black Bears per km2. ............................................................................................................. 22
Figure 5.1-4. Bivariate Smoothing of Slope and Aspect, Estimating a Function of the
Number of Black Bears per km2. .......................................................................................... 23
Figure 5.1-5. Density Surface Map of the Estimated Number of Black Bears per km2. .............. 24
Figure 5.1-6. Density Surface Map of the Coefficient of Variation of the Predicted
Number of Black Bears per 1-km2. ....................................................................................... 25
Figure 5.1-7. Distance-sampling Detection Probabilities for Brown Bears. ................................ 26
Figure 5.1-8. Bivariate Smoothing of x and y Coordinates, Estimating a Function of the
Number of Brown Bears per km2. ......................................................................................... 27
Figure 5.1-9. Univariate Smoothing of Distance to Salmon Spawning Area, Estimating a
Function of the Number of Brown Bears per km2. ............................................................... 28
Figure 5.1-10. Univariate Smoothing of Slope, Estimating a Function of the Number of
Brown Bears per km2. ........................................................................................................... 29
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page iii February 2014 Draft
Figure 5.1-11. Density Surface Map of the Estimated Number of Brown Bears per km2. ........... 30
Figure 5.1-12. Density Surface Map of the Coefficient of Variation of the Predicted
Number of Brown Bears per km2. ......................................................................................... 31
Figure 5.1-13. Locations of Hair-snag Snares for Bears in 2013. ................................................ 32
Figure 5.1-14. Potential Salmon-Spawning Areas Identified and Sampled or Not Sampled
during Downstream Survey of Bear Use, 2013. ................................................................... 33
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page iv February 2014 Draft
LIST OF ACRONYMS, ABBREVIATIONS, AND DEFINITIONS
Abbreviation Definition
ADF&G Alaska Department of Fish and Game
AEA Alaska Energy Authority
AICc Akaike’s Information Criterion, corrected for small sample size
APA Alaska Power Authority
ARRC Alaska Railroad Corporation
CI confidence interval
CIRWG Cook Inlet Regional Working Group
CV coefficient of variation
DSM density surface model
FERC Federal Energy Regulatory Commission
GAM generalized additive model
GIS geographic information system
GMU Game Management Unit
H-T Horvitz–Thompson
ILP Integrated Licensing Process
ISR Initial Study Report
MRMCDS mark–recapture, multiple-covariate distance model
µ Estimated mode of a detection function
PRM Project river mile
Project Susitna–Watana Hydroelectric Project
RSP Revised Study Plan
SPD Study Plan Determination
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page v February 2014 Draft
EXECUTIVE SUMMARY
Study of Distribution, Abundance, and Habitat Use by Large Carnivores 10.8
Purpose The goal of this study is to obtain sufficient information to evaluate potential
Project-related effects on brown bears, black bears, and wolves. Four primary
objectives were identified in RSP Section 10.8.1: (1) Estimate the current
populations of brown bears, black bears, and wolves in the study area, using
existing data from ADF&G; (2) Evaluate bear use of streams supporting
spawning by anadromous fishes in habitats downstream of the proposed dam
that may be altered by the Project; (3) Describe the seasonal distribution of,
and habitat use by, wolves in the study area using existing data from ADF&G;
and (4) Synthesize historical and current data on bear movements and
seasonal habitat use in the study area, including the substantial body of data
gathered by radio-tracking during the 1980s.
Status Population density modeling for brown and black bears was developed by
ADF&G using existing survey data from 2000, 2001, and 2003. Field surveys
of bear use of salmon-spawning streams were conducted in the Middle
Susitna River Segment and its tributaries in 2013, focusing on collection of
bear hair samples for analyses of DNA and stable isotopes. Analysis of DNA
and stable isotope samples is not yet complete at this writing. ADF&G data
on wolves were reviewed and synthesized.
Study
Components
Existing data from distance-sampling surveys were used to generate
population density models and population estimates for brown and black
bears in the study area. DNA analysis of bear hairs snagged by nonlethal,
modified snares set along salmon-spawning streams was conducted to assess
the minimum number of bears using those streams and to characterize the diet
of bears using those areas. Ongoing monitoring of and historical data on
wolves was reviewed to provide background information for assessing
potential Project-related impacts on the species.
2013 Variances Researchers were unable to access some anadromous streams on Cook Inlet
Regional Working Group (CIRWG), Alaska Railroad Corporation (ARRC),
and some private lands because land access permits were not available in
2013. Therefore, some documented salmon-spawning sites in the Middle
Segment of the Susitna River were inaccessible, including all portions of the
Middle Segment upstream of PRM 146.5. These limitations on spatial
coverage of hair sampling limited the study team’s ability to estimate the
minimum population size of bears using those spawning streams, as proposed
in RSP Section 10.8.4.1.2.
Steps to
Complete the
Study
As explained in the cover letter to this draft ISR, AEA’s plan for completing
this study will be included in the final ISR filed with FERC on June 3, 2014.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page vi February 2014 Draft
Study of Distribution, Abundance, and Habitat Use by Large Carnivores 10.8
Highlighted
Results and
Achievements
ADF&G researchers observed 373 black bear groups and 153 brown bear
groups during aerial surveys in the study area in 2000, 2001, and 2003. Using
a density surface model (DSM), the study team estimated a total of 1,262
black bears and 841 brown bears in the study area. Fifty-two bear-hair snags
were deployed in 12 sampling locations (average = 49.8 days each) during
July 22–September 25, 2013. The snags collected a total of 96 hair samples
for analyses of DNA and stable isotopes.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 1 February 2014 Draft
1. INTRODUCTION
On December 14, 2012, the Alaska Energy Authority (AEA) filed with the Federal Energy
Regulatory Commission (FERC or Commission) its Revised Study Plan (RSP) for the Susitna-
Watana Hydroelectric Project No. 14241 (Project), which included 58 individual study plans
(AEA 2012a). RSP Section 10.8 described the study of Distribution, Abundance, and Habitat
Use by Large Carnivores (Large Carnivore Study). The Large Carnivore Study is a two-year
effort that combines desktop analyses of existing data on bears and wolves with new field
sampling focused on bears downstream from the proposed Watana Dam. Existing data are from
historical studies and recent and ongoing population-monitoring studies by the Alaska
Department of Fish and Game (ADF&G). New field sampling for bears is being conducted in
riparian areas along spawning streams used by anadromous fish downstream from the proposed
dam. RSP Section 10.8 described the goal, objectives, and proposed methods for data collection
regarding large carnivores, including brown bear (Ursus arctos), black bear (U. americanus), and
wolf (Canis lupus).
On February 1, 2013, FERC staff issued its study plan determination (February 1 SPD) for 44 of
the 58 studies, approving 31 studies as filed and 13 with modifications. RSP Section 10.8 was
one of the 31 studies approved with no modifications.
Following the first study season, FERC’s regulations for the Integrated Licensing Process (ILP)
require AEA to “prepare and file with the Commission an initial study report describing its
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)) This Initial
Study Report (ISR) on the Distribution, Abundance, and Habitat Use by Large Carnivores has
been prepared in accordance with FERC’s ILP regulations and details AEA’s status in
implementing the study, as set forth in the FERC-approved RSP (referred to herein as the “Study
Plan”).
2. STUDY OBJECTIVES
The goal of the study is to obtain sufficient information on three species of dominant predators
and game animals in the region—brown bear, black bear, and wolf—to use in evaluating Project-
related effects and identifying any appropriate protection, mitigation, or enhancement measures.
Four primary objectives were established for this study in RSP Section 10.8.1:
1) Estimate the current populations of brown bears, black bears, and wolves in the study
area, using existing data from ADF&G.
2) Evaluate bear use of streams supporting spawning by anadromous fishes in habitats
downstream of the proposed dam that may be altered by the Project.
3) Describe the seasonal distribution of, and habitat use by, wolves in the study area using
existing data from ADF&G.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 2 February 2014 Draft
4) Synthesize historical and current data on bear movements and seasonal habitat use in the
study area, including the substantial body of data gathered by radio-tracking during the
1980s, as a continuation of the 2012 wildlife studies (AEA 2012b).
3. STUDY AREA
As established in RSP Section 10.8.3, the study area for spatial modeling of density and
population estimation of bears encompasses a large region, including the proposed Project area
(reservoir inundation zone, access and transmission corridors, and other Project features) as well
as surrounding areas (Figure 3-1). The bear study area includes the entire area of Game
Management Unit (GMU) Subunit 13E plus parts of adjacent Subunits 13A, 16A, and 16B, to
provide a broad regional context for the analysis of bear densities.
Fieldwork conducted in 2013 under the Study Plan was limited to surveys of bear use of
spawning streams used by anadromous fishes in the Middle Susitna River Segment (Middle
River, from Project River Mile [PRM] 98 to 184) and its tributaries from the proposed Watana
Dam site as far downstream as the confluence of the Susitna River and the Chulitna River (all of
which are located within GMU Subunit 13E).
No field surveys were proposed in the Study Plan for wolves. Instead, the study involves office-
based analysis of existing ADF&G data on wolves from GMU Subunits 13E and 13A, and from
adjacent Subunits 14B, 16A, and 20A, as available.
4. METHODS AND VARIANCES IN 2013
The methods for each of the Distribution, Abundance, and Habitat use by Large Carnivores
Study components are presented in this section.
4.1. Bears
4.1.1. Population Estimation
AEA implemented the methods described in the Study Plan, with the exception of variances
explained below (Section 4.1.1.1).
A multi-faceted approach was used to address the need for current information on bears in the
Project area. Reanalysis of 1980s data and synthesis with current data from other previous or
ongoing ADF&G telemetry studies and other regional management studies provided data on bear
populations, movements, and habitat use in the study area (AEA 2012b).
In 2000, 2001, and 2003, ADF&G researchers conducted a distance-sampling survey for brown
and black bears in ADF&G’s Talkeetna study area (Becker 2001, Becker and Quang 2009), as
described in RSP Section 10.8.2.1. That 26,490-km² study area extended from the East Fork of
the Yentna River (west of the Project area) to the northeastern portion of the Susitna River
drainage and included most of the Project area, although the portion of the reservoir inundation
zone located upstream from the mouth of Kosina Creek was not covered in those surveys. The
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 3 February 2014 Draft
surveys by ADF&G in 2000, 2001, and 2003 provided the source data for the spatial density
modeling that was developed to estimate bear population sizes for this study.
Population estimates can be obtained from existing data collected by using complex distance
models with a new gamma-like detection function (Becker, in prep.) that is consistent with point
independence models (Borchers et al. 2006). By themselves, however, these estimates do not
allow detailed inference about the number of bears in areas potentially affected by the Project.
The addition of spatial line-transect modeling (Hedley and Buckland 2004) allowed computation
of estimates that are both more accurate and more precise. The analytical objective was to obtain
density estimates from specialized multiple-covariate, mark-resight distance models (Becker, in
prep.) along small transect sections. Combining these model results created a mark–recapture,
multiple-covariate distance model (MRMCDS) and the study team modeled these estimates with
a detailed density surface model (DSM) (Miller et al. 2013). The DSM incorporated potential
explanatory variables such as elevation, aspect, habitat, and east–west and north–south gradients
to derive a spatially explicit density model, from which the study team obtained sub-estimates
(e.g., parts of both bear survey areas that may be affected by the Project). The study team
modified spatial models of Hedley and Buckland (2004) according to recommendations by
Miller et al. (2013) to work correctly with the more complex distance models (Becker, in prep.)
that were used to model the initial bear densities. The DSM must be robust because of the
potential for nonlinearity between the spatial covariates and bear density (Miller et al. 2013) and
must also be able to accommodate sparse data.
The analytical work required writing a Geographic Information System (GIS) program to
subdivide the 1,238 35-km long transects from ADF&G’s Talkeetna study area (Becker and
Quang 2009) into 1-km long segments with a width of 450 meters (the width of the detection
model) that retained all relevant geospatial information. The study team developed an R-based
program to fit a spatial model (Miller et al. 2013) to the datasets for black bears and brown bears
and then to run this code on the datasets to obtain the population estimates. The study team
obtained covariate data from the LANDFIRE database (Wildland Fire Leadership Council 2013),
including elevation, slope, aspect, and habitat. Using a GIS, the study team overlaid a 1-km2 grid
on ADF&G’s Talkeetna study area to obtain population estimates. Spatial data from the transect
database and the LANDFIRE database used the mode of the covariate to calculate the value of
the covariate. The study team used restricted maximum likelihood scores to select the best
generalized additive model (GAM) (Wood 2006), which was used to construct the DSM. The
study team calculated GAM variance using methods detailed in Wood (2006). The study team
calculated variance of the MRMCDS model following the methods described by Innes et al.
(2002) and used a delta method (Ver Hoef 2012) to combine these variances to obtain the
variance of the DSM. The study team constructed confidence intervals (CI) using a log-normal
transformation (Borchers et al. 2006).
4.1.1.1. Variances
The Study Plan (RSP Section 10.8.2.1) proposed to use line-transect survey data collected by
ADF&G previously in both the Talkeetna study area (Becker and Quang 2009) in 2000, 2001,
and 20003, and in a 21,528-km² area spanning GMU Subunits 13A and 13B in 2003 and 2004.
The study team excluded data from GMU Subunits 13A and 13B from the analysis, however,
because of the magnitude of the computational effort required to produce the modeling results
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 4 February 2014 Draft
and population estimates, which proved to be much more time-intensive than anticipated. The
study team concluded that the spatial model of black bears in ADF&G’s Talkeetna study area
effectively modeled black bears in the Susitna River drainage, so that more information was not
required to make inferences about black bear population status in the study area. This change
deleted four black bear groups, none of which occurred on hillsides next to the Susitna River. In
2003 and 2004, the study team flew 1,221 30-km transects in GMU Subunits 13 A and 13B and
observed only 12 black bear groups, all of which were in GMU Subunit 13A. That result
indicated that extending the black bear spatial model for ADF&G’s Talkeetna study area into the
section of GMU Subunit 13A near the reservoir impoundment zone (east of Kosina Creek and
south of the Susitna River; see Figure 3-1) would still allow precise estimation of the number of
black bears in that area of marginal habitat. Of the six observations of brown bear groups in the
section of GMU Subunits 13A and 13B, all were located far from the Susitna River, the closest
being 10 miles away. Because little additional information would be added by analysis of the
information from GMU Subunits 13A and 13B, the study team concluded that its exclusion had
no appreciable effect on the ability to meet the study objectives.
4.1.2. Downstream Surveys
AEA implemented the methods described in the Study Plan, with the exception of variances
explained below (Section 4.1.2.1).
Fieldwork in 2013 focused on surveying bear use of spawning streams used by anadromous fish.
The study team conducted a survey of bear use of fish-spawning areas in the Middle River and
associated tributaries downstream from the proposed Watana Dam site to the confluence of the
Susitna and Chulitna rivers to assess the use of those resources by bears in the Project area. The
surveys were designed to obtain samples of bear hair for DNA analysis to quantify the minimum
number of bears using the downstream area and for stable-isotope analysis to characterize the
diet of bears in the sampled area. Hair-snag stations using modified, nonlethal, single-catch cable
snares (Beier et al. 2005) were deployed in documented salmon-spawning areas in the Middle
Susitna River. Snares were also deployed in several other locations where bears were regularly
seen by local river users. At each site, hair-snags were placed opportunistically along recently
used bear trails or other areas where bear sign was present. The placement of hair-snags was
constrained by land access limitations (see Section 4.1.2.1 below) and the study team avoided
areas of high human traffic by local residents, recreational users, and other researchers associated
with the Project. A river boat was used for access to the hair-snag locations.
Following initial deployment of the hair-snags, each site was checked every 11 to 14 days, for a
total of five snare check surveys. Researchers removed hair from the snares using tweezers and
placed the hair samples in paper envelopes. After all visible hair was removed, the snare was
reset and a small butane torch was used to burn any hair remnants from the snare. During the
final check (September 24–25), each snare was removed from the field.
Bear-hair samples were provided to laboratories at the University of Alaska Fairbanks for
analysis of DNA and stable isotopes. DNA and stable-isotope analyses of the hair samples will
provide information on the sex and species of bear, a minimum estimate of the number of
different individuals using the area sampled, and stable isotope signatures for diet
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AND HABITAT USE BY LARGE CARNIVORES (10.8)
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FERC Project No. 14241 Page 5 February 2014 Draft
characterization. The isotopic signature will be used to classify the proportions of the diet
composed of salmon, terrestrial meat, and vegetation (Fortin et al. 2007).
The Study Plan includes an evaluation of berry resources in the reservoir inundation zone during
the concurrent mapping efforts of the Vegetation and Wildlife Habitat Mapping Study in the
Upper and Middle Susitna Basin (Study 11.5) and the Wetland Mapping Study in the Upper and
Middle Susitna Basin Study (Study 11.7). Those mapping studies are ongoing and this study
component will be addressed after completion of the mapping on which it depends.
4.1.2.1. Variances
Researchers were unable to access Cook Inlet Regional Working Group (CIRWG) and Alaska
Railroad Corporation (ARRC) lands in 2013 because land-access agreements were not available
at the time of the study. Hence, some documented salmon spawning sites on the Middle Susitna
River were inaccessible, including all portions of the Middle River upstream of PRM 146.5 (see
Section 5.1.2 below). If access can be obtained in the next year of study, then those sites will be
added to the sampling area. Additionally, the study team avoided setting hair-snag snares on
private property. Due to concerns about human safety, it was not feasible to set snares at sites
experiencing high levels of human activity, however, so those sites will not be sampled in the
next year of study. The study team will still be able to produce a minimum estimate of the
number of bears using the spawning streams sampled over both years.
4.2. Wolf
AEA implemented the methods described in the Study Plan with no variances.
ADF&G’s Division of Wildlife Conservation stated that ongoing ADF&G monitoring work
would be sufficient to describe the distribution and habitat use of wolves (ADF&G memorandum
to AEA; November 22, 2011), so no additional field surveys were deemed necessary for the
Project. Hence, desktop analyses of existing ADF&G data are being used to meet the study
objectives for wolves.
The study team reviewed and synthesized historical reports from the original Alaska Power
Authority Susitna Hydroelectric Project (APA Project) study, where possible, with data from
other recent and current monitoring by ADF&G of wolves in GMU Subunits 13A, 13B, 13E,
14B, 16A, and 20A, as a continuation of AEA’s wildlife studies that were begun in 2012 (AEA
2012a). The study team will continue to obtain applicable data for the study area in the next year
of study.
4.2.1. Variances
No variances from the methods described for wolves in the Study Plan were necessary in 2013.
5. RESULTS
Because animal location data collected during ADF&G population surveys are restricted under
Alaska state statute (AS 16.05.815(d)), the location coordinates of the bears and wolves observed
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 6 February 2014 Draft
during the previous population surveys analyzed for this ISR (Sections 5.1.1 and 5.2 below) are
not included in the ISR data posted on the Project website.
Data developed in support of the downstream bear survey component of this study are available
for download in the following file at http://gis.suhydro.org/reports/isr:
• ISR_10_8_LCAR_Data_2013.accdb.
5.1. Bears
5.1.1. Population Estimation
In 2000, 2001, and 2003, ADF&G researchers surveyed 1,238 randomly placed transects, each
approximately 35-km long, for a total length of 42,744 km of transects in their Talkeetna study
area (Becker and Quang 2009). Areas above 5,000 feet in elevation were not considered to be
spring habitat for either bear species, so were excluded from the survey. The data obtained on
those surveys were used to develop spatial density models and population estimates for this
study.
5.1.1.1. Black Bear
ADF&G researchers detected 373 black bear groups along the transects surveyed in 2000, 2001,
and 2003, with the distances from transects to bear groups ranging from 8.4 to 711.8 m. The
study team truncated the black bear distance data (Buckland et al. 2001) at 450 m. In addition,
the study team excluded distances below 22 m, the minimum distance at which a bear can be
seen from the survey airplane (Piper PA-18 “Super Cub”) in level flight at 100 m above ground
level (E. Becker, ADF&G, unpublished data). This exclusion removed 22 black bear groups
from the analysis and left 351 black bear groups with which to build a multiple-covariate
distance model (Marques and Buckland 2003) and a subsequent mark–recapture distance model
that assumed point independence (Borchers et al. 2006). No black bears were observed above
4,600 feet in elevation, so the study team used that elevation contour to define the upper extent
of spring habitat for black bears.
Based on Akaike’s Information Criterion statistics adjusted for small sample size (AICc;
Burnham and Anderson 2002), the best multiple-covariate distance-sampling model used a two-
piece normal detection function (Becker and Christ, in prep.) that included the following
covariates: distance (required), an indicator for distance greater than the mode (required to make
the distribution gamma-like), the logarithm of search distance for detection distances greater than
µ, and pilot search type. Search distance is the distance from the transect to the GPS location
denoting the farthest location searched. The pilot search type variable divides the pilots into two
groups, with one group searching farther out than the other group. The mode of the detection
function (exp(lnµ)) was estimated to be 129.9 m (SE = 1.007) (Table 5.1-1). Estimated detection
probabilities that assumed perfect detection at 129.9 m ranged from 0.1510 to 0.9305, with a
median of 0.5501.
Based on AICc statistics (Burnham and Anderson 2002), the best multiple-covariate mark–
recapture model contained the following covariates: a cubic spline of distance, observer (pilot or
backseat), percent snow, percent cover, pilot detection group (two groups), and observer by pilot
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detection group interaction (Table 5.1-2). The model results coupled with the point independence
assumption (Borchers et al. 2006) estimates maximum detection (MR-pdot) at the mode of the
two-piece normal detection function (129.9 m) to range from 0.4195 to 0.9957, and a median of
0.9493. The estimated probability that a bear group was observed in the survey (Horvitz-
Thompson, or H-T, probability) is the product of these probabilities, ranging from 0.0976 to
0.9097, with a median of 0.4930 (Figure 5.1-1).
The study team used a DSM (Miller et al. 2013) utilizing GAMs (Wood 2006) that contain a
bivariate smoothing function of x- and y-axis locations of the centroid of the polygon (Figure
5.1-2), a smoothing function of elevation (Figure 5.1-3), and an interaction smoothing function
of slope and aspect (Figure 5.1-4) to predict the number of black bears in 1-km-long polygons
searched next to the transects. The study team used the model to predict the number of black
bears in 1-km2 cells (Figure 5.1-5). Model fit diagnostics indicated a good fit. The deviance
explained by the model was high (38.1 percent), indicating a good predictive model. The map
(Figure 5.1-5) clearly depicts substantial differences in spring distribution of black bears. Much
of the eastern part of ADF&G’s Talkeetna study area is marginal black bear habitat, with very
low densities along the Susitna River from just a few miles east of Devils Canyon up to the “Big
Bend” of the Susitna River (just upstream of the Oshetna River mouth). The primary areas of
concentration in spring were low-elevation, south-facing slopes west of the Chulitna River (west
of the Project area) and the south-facing hills just north of Talkeetna. Using this DSM, the study
team estimated that 1,262 black bears (SE = 169.0) inhabited the Project study area during 2000–
2003 (95 percent CI: 971.9, 1,638.8; coefficient of variation [CV]=13.4 percent). Figure 5.1-6
depicts a CV map expressing the degree of uncertainty for the estimate of the number of bears in
each 1-km2 cell. Due to instability of the ratio of small numbers, the CV map excludes cells that
predict 1/1000th of a bear or less per km2.
5.1.1.2. Brown Bear
ADF&G researchers detected 153 brown bear groups along the surveyed transects during the
spring surveys in 2000, 2001, and 2003, with the distance from transects to bear groups ranging
from 35.1 to 1,281 m. The study team truncated the brown bear distance data (Buckland et al.
2001) at 540 m. In addition, the study team excluded distances below 22 m, the minimum
distance at which a bear can be seen from the survey airplane (Piper PA-18 “Super Cub”) in
level flight at 100 m above ground level (E. Becker, ADF&G, unpublished data). This exclusion
removed eight brown bear groups from the analysis and left 145 brown bear groups with which
to build a multiple-covariate distance model (Marques and Buckland 2003) and a subsequent
mark–recapture distance model that assumed point independence (Borchers et al. 2006). The
study team used the 5,000-foot contour as the upper elevational limit of brown bear habitat in
spring.
Based on AICc statistics (Burnham and Anderson 2002), the best multiple-covariate distance-
sampling model used a two-piece normal detection function (Becker and Christ, in prep.) that
included the following covariates: distance (required), an indicator for distance greater than the
mode (required to make the distribution gamma-like), and the logarithm of search distance for
detection distances greater than µ. The mode of the detection function (exp(lnµ)) was estimated
to be 127.1 m (SE = 1.007) (Table 5.1-3). Estimated detection probabilities that assume perfect
detection at 127.1 m ranged from 0.143 to 0.906, with a median of 0.589.
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Based on AICc statistics (Burnham and Anderson 2002), the best multiple-covariate mark–
recapture model contained the following covariates: observer (pilot or backseat), pilot detection
group (three groups), and observer by pilot detection group interaction (Table 5.1-4). The model
results coupled with the point independence assumption (Borchers et al. 2006) estimates
maximum detection (MR-pdot) at the mode of the two-piece normal detection function (127.1 m)
to range from 0.762 to 0.915, and a median of 0.835. The estimated probability that a bear group
was observed on the survey (H-T probability) is the product of these probabilities, ranging from
0.109 to 0.829, with a median of 0.485 (Figure 5.1-7).
The study team used a DSM (Miller et al. 2013) utilizing GAMs (Wood 2006) that contain a
bivariate smoothing function of x- and y-axis locations of the centroid of the polygon (Figure
5.1-8), a smoothing function of distance to salmon spawning location (Figure 5.1-9), a
smoothing function of slope (Figure 5.1-10), and non-vegetative habitat to predict the number of
brown bears in 1-km-long searched polygons next to the transect. The model was used to predict
the number of brown bears in 1-km2 cells (Figure 5.1-11). Model fit diagnostics indicated a
reasonable fit. The deviance explained by the model was moderate (14.6 percent), indicating a
moderate predictive model. The map (Figure 5.1-11) depicts moderate differences in spring
distribution of brown bears, with locations on moderate slopes, especially those classified as
non-vegetated, having the most bears. Due to the low slope values for valley floors, the DSM
predicts very low spring brown bear densities in those locations. Using this DSM, the study team
estimated that 841 brown bears (SE = 161.7) inhabited the study area during 2000–2003 (95
percent CI: 578.7, 1,221.5; CV=19.2 percent). Figure 5.1-12 depicts a CV map expressing the
degree of uncertainty for the estimate of the number of bears in each 1-km2 cell.
5.1.2. Downstream Surveys
A total of 52 hair-snag snares were set in 12 different sampling locations throughout the Middle
Susitna River Segment (Tables 5.1-5 and 5.1-6, Figure 5.1-13). Snares were deployed for an
average of 49.8 days (range = 11–64; SD = 18.3) between 22 July and 25 September, 2013.
Snares were deployed and removed from the field based on observed bear activity and
fluctuating water levels. Fifteen snares were deployed during snare checks after the initial
deployment because new high-activity bear areas were located. Thirteen snares were removed
earlier than planned, five because of low bear activity and eight due to flooding that occurred
during August 17–26, 2013 (Table 5.1-5).
The 52 snares were checked between one and five times each for a total 213 snare checks during
five field surveys. A total of 106 (49.8 percent) of the snares were tripped when checked and 77
samples (72.6 percent of tripped snares) were collected from 34 different snares at nine sampling
locations. Slough 21 and Fourth of July Slough produced the most hair samples (18 and 17,
respectively). The snares at Fifth of July and Fourth of July creeks were the most productive,
however, based on the total number of active snare days (sum of total days each snare was
deployed). The least productive sampling locations were Slough 10 (two samples), Lane Creek
(one sample), and McKenzie area, South Indian Slough, and Slough 8A (no samples) (Table 5.1-
6). Each of the five snare checks produced between 11 and 21 bear-hair samples, with the most
samples obtained during the August 6–7 snare check (20 samples) and the September 10–11
snare check (21 samples) (Table 5.1-5). Only two snares, located at Sloughs 20 and 21, produced
a hair sample during each snare check survey. Fifteen snares had multiple (two or three) clumps
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of hair that were collected in more than one sample envelope. Overall, a total of 96 bear-hair
samples were available for laboratory analysis.
In 2013, the study team identified 37 documented salmon spawning sites (i.e., sloughs and
tributaries) throughout the Middle Susitna River Segment that were considered to be potentially
suitable for deployment of hair snags. Due to land access constraints and areas of high human
activity (see Section 4.1.2.1 above), however, the study team was able to deploy snares at 10 of
those sites (Figure 5.1-14, Table 5.1-7). The study team also included two other sampling
locations (i.e., McKenzie area and South Indian Slough; Figure 5.1-14, Table 5.1-7) in the area
sampled, based on observations of bear activity by local residents.
Bear-hair samples were delivered to two analytical laboratories (one for DNA, one for stable
isotopes) at the University of Alaska Fairbanks and are currently being analyzed. Analytical
results are not yet available at this writing.
5.2. Wolf
Wolf numbers have varied dramatically in GMU 13 over the past half-century as a result of
predator control efforts, changing prey populations, and hunting pressure. Wolves were
considered common in the area in the 1940s but federal population control decreased their
numbers to as few as 12 wolves by 1954. Wolf numbers increased rapidly after federal wolf
control ended in 1959 and over 300 wolves were estimated to inhabit GMU 13 until the mid-
1970s. Hunting pressure kept the population to an average of 275 wolves. With the
discontinuation of land-and-shoot hunting in 1988 (with the exception of 1990–1991), the fall
wolf population increased to 525 by 1999–2000. In 2000, wolf control was initiated by the state
and in 2004 land-and-shoot hunting was allowed by permit. The wolf population was estimated
to be 254 wolves in 46 packs (6.3 wolves/1,000 km²) in 2007 (Schwanke 2009). The fall
population increased to approximately 300 in 2010 before decreasing to 204 in 2011, although
the latter population estimate may have undercounted wolf numbers due to poor snow conditions
(ADF&G 2013). Annual harvest in GMU 13, including removal due to public wolf control
efforts, has ranged from 81 to 159 from 2008 to 2011 (ADF&G 2013). More wolf harvest occurs
in alpine tundra where wolves are more visible and easier to track (Schwanke 2009).
6. DISCUSSION
6.1. Bears
6.1.1. Population Estimation
The analysis described in Sections 4.1.1 and 5.1.1 of this study complete the objective to
estimate the populations of black and brown bears in the study area using existing data from
ADF&G.
The study team concluded that the interpretation of the DSM coefficients is biologically sound.
This fact, coupled with the relatively high explained deviance (38.1 percent) and the much lower,
more uniform CVs for black bears (Figure 5.1-6) than those calculated for brown bears, indicates
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solid inferences about the spring distribution of black bears can be made from this model. The
DSM can be used to obtain estimates of subpopulations of black bears in specific portions of the
study area.
The brown bear DSM had fewer observations (145 groups) than did the black bear DSM (351
groups) and is therefore less accurate. The interpretation of the DSM coefficients makes
biological sense for most of the parameter coefficients, with the exception of increasing brown
bear density at distances far (60–80 km) from salmon-spawning locations. The study team
suspects that the latter part of this smoothing function (Figure 5.1-9) is acting as a surrogate for
an unknown variable that was not considered in the analysis. The study team considered distance
from paved roads, but that variable was not significant when added to the DSM. The bivariate
smoothing of the x- and y-axis location data put a modeling emphasis on the southwestern
portion of the study area (Figure 5.1-8) and left the concentrations of brown bears in the
northeastern portion unexplained. It is that section of the study area that benefits from adding the
covariate of distance from salmon-spawning location. The study team surmised that brown bears
were overestimated in the northeastern portion of the study area, but the overall model is better
with distance to salmon-spawning locations than without it. The lower explained deviance (14.6
percent) is indicative of poorer modeling results, mainly at higher elevations, based on the CV
map (Figure 5.1-2).
6.1.2. Downstream Surveys
The use of modified, nonlethal, break-away snares as hair snags proved to be effective at
collecting a reasonable number of samples of bear hairs in 2013. Compared with a similar study
conducted in an area of higher bear density in southeastern Alaska (Beier et al. 2005), the
percentage of snares tripped in this study was lower (49.8 percent compared to 65.0 percent), but
a greater proportion of the snares tripped in this study produced hair samples (72.6 percent
compared to 45.5 percent).
The low number of samples obtained during the late August check was likely the result of high
water in the Susitna River that reduced salmon abundance and reduced the number of functional
snares. Approximately 11 snares showed signs of having been flooded during that high-water
event and the study team removed three snares due to sustained high water levels. Bears
appeared to be more abundant in tributaries of the Susitna River than in sloughs. The three
stream locations averaged 2.33 samples per snare (n = 9 snares), whereas the slough locations
averaged 0.95 samples per snare (n = 41 snares). This difference may have resulted from better
fishing success and greater activity by bears in the clearer, higher-gradient waters of the
tributaries than in the silty, lower-gradient waters of the sloughs.
Due to land-access constraints and high levels of human activity at some sites (see Section
4.1.2.1 above), the study team was unable to achieve the extent of spatial coverage of the study
area that was desired in 2013, which reduced the area in which the study team can produce an
estimate of minimum population size. Black and brown bears are highly territorial and tend to
use the same high-quality foraging areas throughout a season (Barnes 1990). For this reason, the
study team will concentrate on expanding the number of sampling locations in the next year of
study to include more of the areas not sampled in 2013 (see Table 5.1-7). Doing so will increase
the number of unique samples and provide better information on bear abundance and diet
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throughout more of the study area. Continued monitoring is important to identify annual
variation in the diet of bears. For example, Miller (1987) observed a prevalence of berries in
black bear scats along sloughs of the Middle Susitna River, but predicted that salmon constituted
an important buffer food during years when berry crops fail.
6.2. Wolf
The study team has made progress in summarizing the knowledge of trends in wolf distribution
and population in the study area based on previous studies and current reports summarizing
ADF&G research and monitoring activities. That effort will continue in the next year of study to
achieve the study objective.
7. COMPLETING THE STUDY
[As explained in the cover letter to this draft ISR, AEA’s plan for completing this study will be
included in the final ISR filed with FERC on June 3, 2014.]
8. LITERATURE CITED
ADF&G (Alaska Department of Fish and Game). 2012. Anadromous Waters Catalog. Alaska
Department of Fish and Game. Anchorage, AK.
ADF&G. 2013. Annual report (February 2013) to the Board of Game on intensive management
for moose with wolf predation control in Unit 13. ADF&G Division of Wildlife
Conservation, Palmer. 11 pp.
AEA (Alaska Energy Authority). 2012a. Revised Study Plan: Susitna-Watana Hydroelectric
Project FERC Project No. 14241. December 2012. Prepared for the Federal Energy
Regulatory Commission by the Alaska Energy Authority, Anchorage. Available online:
http://www.susitna-watanahydro.org/study-plan.
AEA. 2012b. W-S1: Big-game movement and habitat use study for the Susitna–Watana
Hydroelectric Project, FERC Project No. 14241. Draft final version (March 21, 2012).
Alaska Energy Authority, Anchorage.
Barnes, V.G., Jr. 1990. The influence of salmon availability on movements and range of brown
bears on southwest Kodiak Island. Bears: Their Biology and Management 8: 305-313.
Barrett, B. M., F. M. Thompson, S. N. Wick. 1985. Adult salmon investigations May–October
1984. Prepared for Alaska Power Authority. Alaska Department of Fish and Game.
Anchorage, AK.
Becker, E. F. 2001. Brown bear line-transect technique development. Federal Aid in Wildlife
Restoration, Research Performance Report, 1 July 1999–30 June 2000. Grant W-27-3,
Study 4.30. Alaska Department of Fish and Game, Juneau.
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Becker, E. F. (in prep.) Aerial distance sampling with unmodeled heterogeneity and a gamma-
like detection function.
Becker, E. F., and A. M. Christ. (In prep.) A gamma-like detection model for double-observer
distance-sampling surveys that accommodates unmodeled heterogeneity.
Becker, E. F., and P. X. Quang. 2009. A gamma-shaped detection function for line-transect
surveys with mark–recapture and covariate data. Journal of Agricultural, Biological, and
Environmental Statistics 14: 207–223.
Beier, L. R., S. B. Lewis, R. W. Flynn, G. Pendleton, and T. V. Schumacher. 2005. A single-
catch snare to collect brown bear hair for genetic mark–recapture studies. Wildlife Society
Bulletin 33: 766–773.
Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas.
2001. Introduction to Distance Sampling. Oxford University Press, New York, NY.
Borchers, D. L., J. L. Laake, C. Southwell, and C. G. M. Paxton. 2006. Accommodating
unmodeled heterogeneity in double-observer distance-sampling surveys. Biometrics 62:
372–378.
Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multimodel Inference. 2nd ed.
Springer, New York, NY. 488 pp.
Fortin, J. K., S. D. Farley, K. D. Rode, and C. T. Robbins. 2007. Dietary and spatial overlap
between sympatric ursids relative to salmon use. Ursus 18: 19–29.
Hedley, S. L., and S. T. Buckland. 2004. Spatial models for line-transect sampling. Journal of
Agricultural, Biological, and Environmental Statistics 9: 181–199.
Innes, S., M. P. Heide–Jorgensen, J. L. Laake, K. L. Laidre, H. J. Cleator, P. Richard, and R. E.
A. Stewart. 2002. Surveys of belugas and narwhals in the Canadian High Arctic in 1996.
NAMMCO Sci. Publ. 4: 169–190.
Link, M. R., B. Nass, J. J. Smith, D. Robichaud, S. Burril, K. English, S. Crawford, J. T. Priest,
and M. J. Nemeth. 2013. Adult salmon distribution and habitat utilization study. Susitna-
Watana Hydroelectric Project (FERC No. 14241), Draft report prepared for Alaska
Energy Authority by LGL Alaska Research Associates, Inc. Anchorage.
Marques, F. F. C., and S. T. Buckland. 2003. Incorporating covariates into standard line-transect
analyses. Biometrics 59: 924–935.
Miller, D. L., M. L. Burt, E. A. Rexstad, and L. Thomas. 2013. Spatial models for distance-
sampling data: recent developments and future directions. Methods in Ecology and
Evolution 4: 1001–1010.
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Miller, S. D. 1987. Susitna Hydroelectric Project final report, big game studies: Vol. VI—Black
bear and brown bear. Report by Alaska Department of Fish and Game, Anchorage, for
Alaska Power Authority, Anchorage. 276 pp.
R2 Resource Consultants, Inc. 2013. Synthesis of Existing Fish Population Data. Prepared for
Alaska Energy Authority, Susitna-Watana Hydroelectric Project, by R2 Resource
Consultants, Inc., Anchorage, AK.
Schwanke, R. A. 2009. Unit 13 wolf management report. Pages 93–103 in P. Harper, editor.
Wolf management report of survey and inventory activities, 1 July 2005–30 June 2008.
Alaska Department of Fish and Game, Juneau.
Ver Hoef, J. M. 2012. Who invented the delta method? The American Statistician 66: 124–127.
Wildland Fire Leadership Council. 2013. LANDFIRE: Landscape Fire and Resource
Management Planning Tool. Available online: http://www.landfire.gov.
Wood, S. N. 2006. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC
Press, New York, NY.
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9. TABLES
Table 5.1- 1. Multiple-covariate Distance-model Coefficients and Standard Errors for Black Bears.
Parameter Point Estimate Standard Error
Intercept 3.556 0.069
Ind(distance > µ) –11.275 3.414
Ind(distance > µ) ln(SearchDist.) 2.016 0.086
PilotSearch 0.399 0.0256
lnµ 4.867 0.007
Notes:
Intercept = y-intercept of the detection function.
Ind(distance > µ) = Detection-function parameter for distances > µ; zero otherwise.
Ind(distance > µ) ln(SearchDist.) = Detection-function parameter for natural log of Search Distance when distance >
µ; zero otherwise.
PilotSearch = Detection-function parameter for the group of pilots that tend to search farther away from the transect.
lnµ = Natural log of the estimated mode of the detection function.
Table 5.1- 2. Multiple-covariate Mark–Recapture Model Coefficients and Standard Errors for Black Bears.
Parameter Point Estimate Standard Error
Intercept 0.606 0.624
bs(distance 1) 3.746 1.475
bs(distance 2) –1.366 0.986
bs(distance 3) 0.673 1.053
Observer 0.031 0.175
(% Cover/20) –0.546 0.112
(% Snow/20) –0.373 0.098
Pilot-2Grp 2.460 0.768
Observer x Pilot-2Grp –1.389 0.413
Notes:
Intercept = y-intercept of the logistic regression model.
bs(distance 1) = Parameter estimate of the slope of the cubic-spline model of distance at the first node.
bs(distance 2) = Parameter estimate of the slope of the cubic-spline model of distance at the second node.
bs(distance 3) = Parameter estimate of the slope of the cubic-spline model of distance at the third node.
Observer = A parameter that shifts the y-intercept for data collected by the backseat observer.
(% Cover/20) = A logistic regression parameter representing the effect of (percent vegetative cover within 10 m of
the bear, divided by 20).
(% Snow/20) = A logistic regression parameter representing the effect of (percent snow within 10 m of the bear,
divided by 20).
Pilot-2Grp = A logistic regression parameter representing pilots who are more efficient at detecting black bears.
Observer × Pilot-2Grp = An interaction parameter between backseat observers and pilot groups.
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Table 5.1- 3. Multiple-covariate Distance-model Coefficients and Standard Errors for Brown Bears.
Parameter Point Estimate Standard Error
Intercept 3.838 0.085
Ind(distance > µ) –14.701 11.049
Ind(distance > µ) ln(SearchDist.) 2.658 0.293
lnµ 4.845 0.022
Notes:
Intercept = y-intercept of the detection function.
Ind(distance > µ) = Detection function parameter for distances > µ; zero otherwise.
Ind(distance > µ) ln(SearchDist.) = Detection function parameter for natural log of Search Distance when distance >
µ; zero otherwise.
lnµ = Natural log of the estimated mode of the detection function.
Table 5.1- 4. Multiple-covariate Mark–Recapture Model Coefficients and Standard Errors for Brown Bears.
Parameter Point Estimate Standard Error
Intercept –2.6158 1.101
Observer 1.517 0.638
Pilot-3Grp 1.955 0.616
Observer x Pilot-3Grp –1.053 0.338
Notes:
Intercept = y-intercept of the logistic regression model.
Observer = A parameter that shifts the y-intercept for data collected by the backseat observer.
Pilot-3Grp = Treating the 3-pilot groups as ordinal, this logistic regression parameter represents the slope effect of
pilot group.
Observer × Pilot-3Grp = An interaction parameter between backseat observers and pilot groups.
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Table 5.1- 5. Number of Hair-snag Snares Set for Bears, and Samples Collected during Each Field Survey, in 2013.
Survey Dates Number of Snares Set Number of Snares Removed Number of Snares Deployed Number of Snares Checked Number of Hair Samples Collected
July 22–24 37 0 37 0 –
August 6–7 8 4 41 37 20
August 15–16 7 0 48 41 11
August 27–28 0 3 45 48 11
September 10–11 0 3 42 45 21
September 24–25 0 42 0 42 14
Table 5.1- 6. Number of Hair-snag Snares Set for Bears, Total Active Days, and Total Samples Collected, by Sampling
Location, 2013.
Project
River Mile Sampling Location
Number of
Snares Set Total Active Snare Days1 Total Samples Collected
113.7 Oxbow Slough 7 418 6
117.2 Lane Creek 2 50 1
119.4 McKenzie Area 2 50 0
127.3 5th of July Creek 4 194 12
128.7 Slough 8A 1 11 0
134.3 4th of July Creek 3 144 8
134.4 4th of July Slough 10 423 17
136.3 Slough 9A 3 189 5
137.1 Slough 10 7 359 2
141.2 South Indian Slough 2 38 0
143.6 Slough 20 4 225 8
145.2 Slough 21 7 426 18
Notes:
Total active snare days is equal to the total number of days between initial deployment and removal for each snare at
that sampling location.
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Table 5.1- 7. Potential Salmon Spawning Areas Sampled and Not Sampled in the Middle Susitna River Segment, 2013.
Project River Mile Salmon Spawning Area1 Sampled in 2013? Reason for Not Sampling
105.1 Whisker's Creek & Slough No High level of human activity
110.5 Chase Creek No High level of human activity
113.7 Oxbow Slough Yes –
114.9 Slash Creek No Access to ARRC land restricted
115.0 Gash Creek No Access to ARRC land restricted
117.2 Lane Creek Yes Limited access due to ARRC land
117.2 Slough 8 No Access to ARRC land restricted
120.2 McKenzie Creek No Access to ARRC land restricted
121.4 Little Portage Creek No Access to ARRC land restricted
124.4 Deadhorse Creek No Access to ARRC land restricted
124.4 Tulip Creek No No bear sign observed
125.7 Slough 8B No No bear sign observed
127.3 Fifth of July Creek Yes –
128.1 Skull Creek No Access to ARRC land restricted
128.7 Slough 8A Yes Limited due to high human activity
131.5 Slough 9 No Difficult to access by boat
132.5 Slough 9B No No bear sign observed
134.1 Sherman Creek No Access to ARRC land and private property restricted
134.3 Fourth of July Creek Yes –
134.4 Fourth of July Slough Yes –
136.3 Slough 9A Yes –
137.1 Slough 10 Yes –
138.7 Slough 11 No High level of human activity
140.1 Gold Creek No High level of human activity
142.1 Indian River No High level of human activity
142.3 Slough 17 No High level of human activity
142.5 Slough 18 No High level of human activity
143.6 Slough 20 Yes –
145.2 Slough 21 Yes –
148.3 Jack Long Creek No Access to CIRWG lands restricted
152.3 Portage Creek No Access to CIRWG lands restricted
155.9 Cheechako Creek No Access to CIRWG lands restricted
160.5 Chinook Creek No Access to CIRWG lands restricted
164.8 Devil Creek No Access to CIRWG lands restricted
179.3 Fog Creek No Access to CIRWG lands restricted
184.6 Tsusena Creek No Access to CIRWG lands restricted
Notes:
1. Current and historic salmon spawning areas and bear concentration areas documented by one or more of the
following references: Barrett et al. (1985), Miller (1987), ADF&G (2012), Link et al. (2013), R2 Resource
Consultants, Inc. (2013).
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10. FIGURES
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Figure 3- 1. Study Area for Large Carnivores.
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AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-1. Distance-sampling Detection Probabilities for Black Bears.
Top Right: Black bear detection probabilities calculated assuming perfect detection at the mode of the detection
function (unadjusted probabilities).
Left: Mark and recapture probabilities for various covariate levels calculated at the mode distance, 129.9 m (MR
Pdot at µ).
Bottom Right: The final estimated H-T probability of bear detection, which is the product of the unadjusted
probability and the MR Pdot at µ.
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AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-2. Bivariate Smoothing of x and y Coordinates, Estimating a Function of the Number of Black
Bears per km2.
The color scheme uses whitish-yellow to depict the areas that black bears prefer, followed by orange, and then red.
The black dots represent the centroid of the transect segments containing the data. Median values for the other DSM
covariates are used in this plot.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-3. Univariate Smoothing of Elevation, Estimating a Function of the Number of Black Bears per
km2.
The y-axis represents the contribution of the smoothing function on elevation to a function of black bears per km2.
Median values for the other DSM covariates are used in this plot. The bottom of the graph depicts a density plot of
the amount of data collected for each elevation.
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Figure 5.1-4. Bivariate Smoothing of Slope and Aspect, Estimating a Function of the Number of Black Bears
per km2.
Bivariate smoothing of slope and aspect is represented by the number on the contour line. The color scheme uses
whitish-yellow to depict the highest concentrations of black bears, followed by orange, and then red. The smoothing
function used a cyclic cubic regression spline to ensure that the estimates at aspects of 0 and 360 degrees were the
same. Visualize this graph as a tube, by making a cylinder out of the aspect axis. Median values for the other DSM
covariates are used in this plot.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE, AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-5. Density Surface Map of the Estimated Number of Black Bears per km2.
Gray-blue areas represent areas above 4600 ft in elevation, which are not considered to be spring habitat for black bears.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE, AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-6. Density Surface Map of the Coefficient of Variation of the Predicted Number of Black Bears per 1-km2.
Gray-blue areas represent either areas above 4600 ft elevation, which are not considered to be spring habitat for black bears, or cells that predict 1/1000th of a bear
or less per km2. Black and gray dots represent estimated number of black bears from the distance-sampling models, with darker dots indicating more bears.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-7. Distance-sampling Detection Probabilities for Brown Bears.
Top Right: Brown bear detection probabilities calculated assuming perfect detection at the mode of the detection
function (unadjusted probabilities).
Left: Mark and recapture probabilities for various covariate levels calculated at the mode distance, 129.9 m (MR
Pdot at µ).
Bottom Right: The final estimated H-T probability of bear detection, which is the product of the unadjusted
probability and the MR Pdot at µ.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 27 February 2014 Draft
Figure 5.1-8. Bivariate Smoothing of x and y Coordinates, Estimating a Function of the Number of Brown
Bears per km2.
Color scheme uses whitish-yellow to depict the highest concentrations of brown bears, followed by orange, and then
red. Black dots represent the centroid of the transect segments containing the data. Median values for the other DSM
covariates are used in this plot.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-9. Univariate Smoothing of Distance to Salmon Spawning Area, Estimating a Function of the
Number of Brown Bears per km2.
The y-axis represents an estimate of a function of brown bears per km2. The bottom of the graph depicts a density
plot of the amount of data collected for each distance. Median values for the other DSM covariates are used in this
plot.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-10. Univariate Smoothing of Slope, Estimating a Function of the Number of Brown Bears per km2.
The y-axis represents an estimate of a function of brown bears per km2. Median values for the other DSM covariates
are used in this plot.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE, AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-11. Density Surface Map of the Estimated Number of Brown Bears per km2.
Gray-blue areas represent areas above 5000 ft in elevation, which are not considered spring habitat for brown bears.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE, AND HABITAT USE BY LARGE CARNIVORES (10.8)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
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Figure 5.1-12. Density Surface Map of the Coefficient of Variation of the Predicted Number of Brown Bears per km2.
Gray-blue areas represent areas above 5000 ft in elevation, which are not considered to be spring habitat for brown bears. Black and gray dots represent estimated
number of brown bears from the distance-sampling models, with darker dots indicating more bears.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE,
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Figure 5.1-13. Locations of Hair-snag Snares for Bears in 2013.
INITIAL STUDY REPORT STUDY OF DISTRIBUTION, ABUNDANCE, AND HABITAT USE BY LARGE CARNIVORES (10.8)
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Figure 5.1-14. Potential Salmon-Spawning Areas Identified and Sampled or Not Sampled during Downstream Survey of Bear Use, 2013.