DOI: 10.5061/dryad.0000000j1
title: Brown bear genetic detections as the basis for an analysis of stream use in relation to spawning salmon abundance and stream morphology

description: Brown bears (Ursus arctos (Linnaeus, 1758)) are famous for exploiting
 annual pulses of Pacific salmon (Oncorhynchus sp.), but studies of how
 bears do so have largely been confined to anomalous locations where they
 congregate to feed at natural salmon migration bottlenecks. We addressed
 this knowledge gap using nine years of non-invasive genetic detections of
 brown bears along six small streams – grouped into trios along the
 northern and southern shores of Lake Aleknagik that constitute foraging
 neighborhoods where distinct bear populations prey on sockeye salmon (O.
 nerka (Walbaum, 1792)) – to test hypotheses about sex-specific patterns of
 relative stream use in relation to salmon abundance and stream morphology.
 Numbers detections per bear on a stream during the summer salmon spawning
 season were inversely proportional to water depth in the northern stream
 neighborhood; by contrast, greater in-stream salmon abundance boosted
 detection rates, and females were more likely to be detected repeatedly
 than males, in the southern stream neighborhood. Thus, bear behavior along
 small streams was consistent with optimal foraging theory, as more
 intensive use of streams in a season corresponded with greater abundance
 and accessibility (reduced stream depth) of salmon, and with the idea that
 female bears are less sensitive to humans, as these streams received a low
 but regular level of human visitation. Our findings also reveal, however,
 that the factors affecting bear movement patterns along small streams can
 vary, even among stream networks are separated by just a few km,
 underscoring the complexity of the bear-salmon relationship. Finally, we
 documented marked variability in the use of streams by individual bears,
 with some consistently visiting particular streams over the summer but
 most being detected just once, implying that impacts and reliance on
 salmon on any stream may differ considerably among individuals in brown
 bear populations.
description: <em>Field data collection</em> We
 quantified use of the six focal streams by bears during the summers of
 2013-2019 and 2021-2022 (no data were collected in 2020 because of the
 coronavirus pandemic) using a non-invasive genetic approach described in
 detail by Wirsing et al. (2018, 2020). Briefly, over the course of the
 roughly six-week salmon spawning interval, we collected hair from passing
 bears by deploying two barbed wires (one upstream and one downstream
 within the first 2 km from the mouth) crossing each stream perpendicularly
 at a height of ca. 50-55 cm from the ground (the optimum for snagging hair
 from bears stepping over or under the wire; Quinn et al. 2022). A video
 assessment revealed that bears encountering a wire deposited hair ~ 81% of
 the time (Wold et al. 2020), making this approach an effective, albeit
 conservative, means of documenting bear presence on a stream, at least in
 the vicinity of the wires. Hair samples snagged on the wires were
 collected regularly (usually every other day) in coin envelopes, stored in
 desiccant, and a subsample was sent to the Laboratory for Ecological,
 Evolutionary and Conservation Genetics (LEECG) at the University of Idaho
 for DNA analysis to assign each detection to a unique individual (see
 ‘Laboratory analysis’ section below). Given that each summer we deployed
 two hair-snagging barbed wires per stream, we could achieve up to two
 detections of an individual bear on a stream since the last wire check;
 for the purposes of this study, we defined an independent detection of an
 individual bear to be any instance over the course of a summer when hair
 collected from either wire on a given stream yielded at least one
 confirmed genotype. Accordingly, a bear could be detected a maximum of one
 time during a span between two wire checks (typically two days) on any
 stream. Consequently, over the ca. 6-week summer sampling season, the
 maximum number of detections possible for any individual bear on a stream
 was ca. 21. The six focal streams have been surveyed
 for salmon abundance for many decades as part of the University of
 Washington Alaska Salmon Program, a long-term study of salmon population
 dynamics; patterns of salmon predation by brown bears on these streams
 have been investigated since the late 1980s (Quinn et al. 2001, Quinn et
 al. 2017). These surveys, which are performed by three people walking up
 the stream while counting all live and dead salmon including those in the
 immediate riparian zone, are prone to missing some salmon that bears move
 from the stream corridor (Quinn et al. 2009) but nevertheless yield a
 standardized, quantitative index of salmon abundance that can be used for
 comparisons among streams and years and linked to bear presence. They have
 also served as the basis for careful and repeated measurements of depth
 and width for each stream; notably, these values have remained stable
 through the years included in this study (Quinn et al. 2017, unpublished
 data).   <em>Laboratory
 analysis</em> The genetic methods we used for
 this study were detailed previously (Wirsing et al. 2018, 2020). Briefly,
 DNA was extracted from snagged bear hair in a facility dedicated to low
 quality DNA samples using the DNeasy Blood and Tissue Kit (Qiagen, Inc.).
 Genotypes were generated for each sample in a multiplex polymerase chain
 reaction (PCR) using 10 nuclear DNA microsatellite loci and a sex locus;
 each sample was amplified two to four times to detect genotyping errors.
 Consensus genotypes were achieved following the rule that each allele
 needed to be observed twice per locus, and that a sample had to contain a
 consensus genotype at eight or more loci to be included in the matching
 analysis using the software Genelex (Peakall and Smouse 2006). Each
 sample, all from brown bears, was assigned to a specific bear along with
 information on its sex.  
 <em>Statistical analysis</em>     
 Of the 223 individual brown bears detected over 9 years of
 sampling spanning an 10-year interval, 13 (5.8%) were detected in both
 stream neighborhoods, and of these only 3 (1.3%) switched neighborhoods
 (i.e., crossed the lake or outlet river) during a summer. Thus, though
 switching occurs, its rarity, particularly during the same summer, led us
 to consider the two steam trios as functionally distinct systems for the
 purposes of analysis. Accordingly, we modeled bear detection data from
 each stream neighborhood, north and south, separately. For each
 neighborhood, analyses were conducted using generalized linear mixed
 effects models (GLMM), with a negative binomial distribution built with
 the lme4 package in RStudio (initial Poisson models were over dispersed;
 Bates et al. 2015). The response variable was bear capture count, or the
 number of wire check (i.e., two-day) intervals during which a bear was
 detected on a given stream during a given summer. We included covariates
 for sex (female or male), mean stream depth (m; fixed across years),
 stream width (m; fixed across years), log-transformed in-stream salmon
 abundance (one estimate per stream per year), and log-transformed
 neighborhood salmon abundance (i.e., total salmon abundance in the other
 two streams of the neighborhood; one estimate per stream per year) as
 fixed effects in the global models for both analyses. We tested for the
 inclusion of random effects in negative binomial models for both
 neighborhoods, and likelihood-ratio tests (LRT) indicated that the
 treating year (χ2 (1) = 14.54, p = 0.0001) and bear ID (χ2 (1) = 63.55, p
 &lt; 0.0001) as random effects improved model fit for the north
 neighborhood. Bear ID also improved model fit for the south neighborhood
 (χ2 (1) = 70.83, p &lt; 0.0001), whereas year did not (χ2 (1) = 1.22,
 p = 0.27). We chose, however, to eliminate bear ID as a random effect from
 both analyses. The structure of the bear ID variable, given that most
 bears were detected once during the study, resulted in random-effect
 variance estimates being nearly zero (i.e., little variation across random
 effect levels) when added to our model sets (Bates et al. 2015, Matuschek
 et al. 2017). Global models for both neighborhoods thus included all
 predictor variables with year being treated as a fixed effect in the south
 and a random effect in the north. Candidate models were ranked according
 to differences in Akaike’s Information Criterion (∆AIC), and we considered
 models with a ΔAIC ≤ 2 relative to the top model to be equally supported
 (Burnham & Anderson, 2002). Within the set of competitive models
 for each neighborhood, we used the model(s) with the fewest variables as
 the basis for inference under the principle of parsimony (Aho et al.
 2014). Covariates in these models were considered significant if the 95%
 confidence interval (CI) for their coefficient did not overlap zero, and
 odds ratios were calculated for significant covariates as a measure of
 effect size. For both neighborhood models, we deemed prediction 1 to be
 supported if a significant coefficient indicated that odds of detection on
 a stream increased with salmon abundance, prediction 2 to be supported if
 greater salmon abundance in nearby streams (i.e., neighborhood abundance)
 significantly reduced the odds of detection on a focal stream, prediction
 3 to be supported if the odds of detection increased with reduced stream
 depth and/or width, and prediction 4 to be supported if females had
 significantly greater odds of detection on a stream than males.
description: --- ## Title of Dataset: FULL_Bear_hair_detections.2012.2022_2 # Access
 this dataset on Dryad: DOI:10.5061/dryad.0000000j1 Dataset contents
 include BearID (from DNA analysis), capture count (i.e., times a bear was
 detected), year of data collection, stream sampled, focal stream salmon
 abundance, neighborhood salmon abundance (this was abundance in the
 adjacent streams and not the focal stream within a neighborhood trio), sex
 (male or female), stream depth (m), stream width (m), and multiple columns
 indicating if one,or both, wires were contacted by a given bear.
 Non-invasive genetic detections of individual brown bears along salmon
 spawning streams were used to explore hypotheses about sex-specific
 patterns of relative stream use in relation to salmon abundance and stream
 morphology. Bear detections were modeled separately for northern and
 southern stream trios of Lake Aleknagik using generalized linear models
 and generalized linear mixed models with capture count as the response
 metric. We found partial support for optimal foraging and sexual
 dimorphism hypotheses, with support differing between the two stream
 neighborhoods, north and south. Numbers of individual brown bear
 detections during the summer salmon spawning season were inversely
 proportional to water depth in the northern neighborhood, whereas in the
 south neighborhood greater in-stream salmon abundance boosted detection
 rates and females were more often detected repeatedly than males. ##
 Investigators Annika K. McFeely, University of Washington,
 [akmmcfeely@gmail.com](mailto:akmmcfeely@gmail.com) Dr. Clint Robins,
 University of Washington, [crobins4@uw.edu](mailto:crobins4@uw.edu) Dr.
 Jennifer R. Adams, University of Idaho, EMAIL Dr. Lisette P. Waits,
 University of Idaho, EMAIL Dr. Thomas P. Quinn, University of Washington,
 [tquinn@uw.edu](mailto:tquinn@uw.edu) Dr. Aaron Wirsing, University of
 Washington, [wirsinga@uw.edu](mailto:wirsinga@uw.edu) contact person: Dr.
 Aaron Wirsing ## Description of the data and file structure The dataset is
 structured for analyses that assess how stream morphology, changes to
 salmon runs, and brown bear demographics impact bear detection on small
 salmon spawning streams in southwest Alaska. All depth measures are in
 meters, and salmon abundance represents the number of individual fish
 (live + dead) as estimated from annual surveys of each focal stream that
 occur during the summer sockeye salmon spawning interval (July-August) and
 are then converted into a standardized index of relative abundance per
 stream for comparison. Included in the data are columns for: Bear ID,
 Year, Stream, Stream depth, Stream width, In-stream salmon abundance,
 Total adjacent stream salmon abundance with the neighnorhood
 (titled:Other_neighborhood_abundance), Neighborhood (2-level categorical),
 Sex, Capture count, as well as data on how many wires were on a stream in
 a given year and which wires where "hit" by bears. ##
 Sharing/Access information Links to other publicly accessible locations of
 the data: NA Data was derived from the following sources: All data apart
 from individual identification via DNA analysis was obtained in the field.
 DNA analysis was conducted at the Laboratory for Ecological, Evolutionary
 and Conservation Genetics (LEECG) at the University of Idaho. ##
 Code/Software R (R version 4.3.2 (2023-10-31) code for analyzing brown
 bear detections along small sockeye spawning streams in SW AK. Directions
 for code workflow, model sets, and plots included in the separate R
 script. Statistical packages used in the script include: lme4 MASS
 maptools MuMIn AICcmodavg DHARMa dplyr sjPlot unmarked data.table dplyr
 car lattice ggeffects ggplot2 
 
 **Script** Brown-Bear-Detection-Analysis_McFeely_et_al_2025.R **Dataset**
 FULL_Bear_hair_detections.2012.2022_2.csv