Modeling multi-scale resource selection for bear rubs in northwestern Montana

Henderson, Matthew J. Morgan; Hebblewhite, Mark; Mitchell, Michael S.; Stetz, Jeffrey B.; Kendall, Katherine C.; Carlson, Ross T.

doi:10.2192/ursus-d-14-00026.1

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…It is worth noting that the sections dominated by birches had the highest rubbing densities. RT densities in the Cantabrian Mountains are higher than those recorded by Henderson et al (2015) in conifer forests of the northwest United States, where they compared RT abundances between developed trails and roads (1.0 ± 1.1 RTs/km; n = 30) and game trails (0.8 ± 1.1 RTs/km; n = 30). In several Kamchatka valleys dominated by stone birch, Seryodkin (2014) reported very variable abundances (from 40 RTs/km to 0.4 RTs/km) and RTs groupings in short sections of the footpath (7 RTs in 20 m or 5 RTs in 8 m).…”

Section: Discussionmentioning

confidence: 99%

“…The pool of RTs in a given area provides a communication network through which individuals exchange information; this pool can be used over time for generations ( Green and Mattson 2003 ; Clapham et al 2013 ; Morgan Henderson et al 2015 ). RTs commonly are located in the proximity of foot trails or unpaved roads that facilitate the transit of bears ( Lloyd 1979 ; Green and Mattson 2003 ; Sato et al 2014 ), with trail-oriented rubbing marks ( Green and Mattson 2003 ), or at forest edges ( Green and Mattson 2003 ; Puchkovskiy 2009 ).…”

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confidence: 99%

“…Studies on RTs have focused mainly on North American grizzlies Ursus arctos horribilis ( Green and Mattson 2003 ; Clapham et al 2013 ; Morgan Henderson et al 2015 ), with a few also in Russia ( Puchkovskiy 2009 ; Puchkovskiy et al 2012 ) and Japan ( Sato et al 2014 ); however, these studies considered large continuous populations. To our knowledge, such a study has not previously been carried out in smaller, vulnerable populations, nor in populations living in areas dominated by deciduous forest.…”

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confidence: 99%

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Rubbing behavior of European brown bears: factors affecting rub tree selectivity and density

González-Bernardo

Bagnasco

Bombieri

et al. 2021

Journal of Mammalogy

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Scent-mediated communication is considered the principal communication channel in many mammal species. Compared with visual and vocal communication, odors persist for a longer time, enabling individuals to interact without being in the same place at the same time. The brown bear (Ursus arctos), like other mammals, carries out chemical communication, for example, by means of scents deposited on marking (or rub) trees. In this study, we assessed rub tree selectivity of the brown bear in the predominantly deciduous forests of the Cantabrian Mountains (NW Spain). We first compared the characteristics of 101 brown bear rub trees with 263 control trees. We then analyzed the potential factors affecting the density of rub trees along 35 survey routes along footpaths. We hypothesized that: (1) bears would select particular trees, or tree species, with characteristics that make them more conspicuous; and (2) that bears would select trees located in areas with the highest presence of conspecifics, depending on the population density or the position of the trees within the species’ range. We used linear models and generalized additive models to test these hypotheses. Our results showed that brown bears generally selected more conspicuous trees with a preference for birches (Betula spp.). This choice may facilitate the marking and/or detection of chemical signals and, therefore, the effectiveness of intraspecific communication. Conversely, the abundance of rub trees along footpaths did not seem to depend on the density of bear observations or their relative position within the population center or its border. Our results suggest that Cantabrian brown bears select trees based on their individual characteristics and their location, with no influence of characteristics of the bear population itself. Our findings can be used to locate target trees that could help in population monitoring.

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Section: Discussionmentioning

confidence: 99%

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Rubbing behavior of European brown bears: factors affecting rub tree selectivity and density

González-Bernardo

Bagnasco

Bombieri

et al. 2021

Journal of Mammalogy

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“…We identified 11 uncorrelated habitat variables (| r | < 0.7; Allen et al , Henderson et al ) for inclusion in the RSF that were measurable in a GIS and were potentially important for deer habitat selection at the landscape‐scale. We measured all habitat variables according to statewide 30‐m grids using the Focal Statistics (percent cover variables) or Euclidean Distance (distance variables) functions in ArcGIS 10.1.…”

Section: Methodsmentioning

confidence: 99%

“…Resource selection functions (RSFs) yield estimates proportional to the probability of use of a resource unit under a used‐available design and are typically fitted as a generalized linear model in readily available statistical analysis software (Boyce et al , Manly et al , Johnson et al ). Various presence‐only data can be used to inform the models including radio‐telemetry (Walter et al ), sign (e.g., track, scat; Holmes and Laundré , Henderson et al ), and count (e.g., aerial survey; Allen et al ) data. These models have been applied to a range of wildlife conservation and management issues involving disease transmission between sympatric species (Walter et al ), habitat suitability for isolated (Hough and Dieter ) or threatened (Williams et al ) populations, foraging habitat selection (Holmes and Laundré ), and the effects of long‐term habitat change (Anderson et al ) and anthropogenic land use (Seip et al , Sawyer et al , Crane et al ) on populations.…”

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confidence: 99%

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

2019

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Spotlight surveys for white‐tailed deer (Odocoileus virginianus) can yield large presence‐only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat‐based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence‐only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county‐level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat‐based density estimator (extrapolation) and zero‐inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence‐only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society.

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Predicting landscape-scale summer resource selection for the northern long-eared bat (Myotis septentrionalis) in Iowa

Kaminski

Poole

Clark

et al. 2019

Journal of Mammalogy

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The northern long-eared bat (Myotis septentrionalis) is currently listed as threatened under the U.S. Endangered Species Act largely due to population declines resulting from the spread of white-nose syndrome in North America. White-nose syndrome was confirmed in Iowa in 2015, emphasizing a need to closely monitor populations of M. septentrionalis statewide. We applied presence-only models to predict landscape-scale resource selection by M. septentrionalis using roost tree observations and mist net captures from various research and environmental assessment projects in Iowa (2003–2015). We used a simultaneous autoregressive (SAR) model to account for residual spatial autocorrelation in our compiled data set and estimate the proportional probability of use of summer habitats for M. septentrionalis. We estimated SAR models using four environmental predictor variables measured at two landscape scales (0.5- and 2.4-km) representative of M. septentrionalis home range sizes in North America. The SAR models resulted in high predictive fit with withheld test observations and an independent data set of acoustic detections of M. septentrionalis from recent surveys (2016–2018), indicating a significant positive relationship existed between habitat quality (as an index of selection) and distribution of M. septentrionalis at landscape scales. At both spatial scales, M. septentrionalis showed positive selection of closed canopy interior forest, bottomland hardwood forest, and total perennial stream length, whereas at the 0.5-km scale, M. septentrionalis also showed a positive association with open canopy forest. Our models indicated that up to 7.0% and 8.5% of the state was comprised of potentially suitable forested summer habitats for M. septentrionalis for 0.5- and 2.4-km scales, respectively. Our models also indicated the distribution of highly selected habitats at landscape scales in Iowa and accurately predicted independent observations of M. septentrionalis in areas of the state where no capture efforts have occurred. This study provides methods to predict landscape-scale resource selection and distribution for bats where multiple fine-scale data sources exist across broad geographic regions.

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Modeling multi-scale resource selection for bear rubs in northwestern Montana

Cited by 5 publications

References 39 publications

Rubbing behavior of European brown bears: factors affecting rub tree selectivity and density

Rubbing behavior of European brown bears: factors affecting rub tree selectivity and density

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

Predicting landscape-scale summer resource selection for the northern long-eared bat (Myotis septentrionalis) in Iowa

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