Using spatially‐explicit capture–recapture models to explain variation in seasonal density patterns of sympatric ursids

Abstract

Understanding how environmental factors interact to determine the abundance and distribution of animals is a primary goal of ecology, and fundamental to the conservation of wildlife populations. Studies of these relationships, however, often assume static environmental conditions, and rarely consider effects of competition with ecologically similar species. In many parts of their shared ranges, grizzly bears Ursus arctos and American black bears U. americanus have nearly complete dietary overlap and share similar life history traits. We therefore tested the hypothesis that density patterns of both bear species would reflect seasonal variation in available resources, with areas of higher primary productivity supporting higher densities of both species. We also hypothesized that interspecific competition would influence seasonal density patterns. Specifically, we predicted that grizzly bear density would be locally reduced due to the ability of black bears to more efficiently exploit patchy food resources such as seasonally abundant fruits. To test our hypotheses, we used detections of 309 grizzly and 597 black bears from two independent genetic sampling methods in spatially‐explicit capture–recapture (SECR) models. Our results suggest grizzly bear density was lower in areas of high black bear density during spring and summer, although intraspecific densities were also important, particularly during the breeding season. Black bears had lower densities in areas of high grizzly bear density in spring; however, density of black bears in early and late summer was best explained by primary productivity. Our results are consistent with the hypothesis that smaller‐bodied, more abundant black bears may influence the density patterns of behaviorally‐dominant grizzly bears through exploitative competition. We also suggest that seasonal variation in resource availability be considered in efforts to relate environmental conditions to animal density.

Spatial density estimates of Eurasian lynx (Lynx lynx) in the French Jura and Vosges Mountains

“…multi-site dimension of our study allows exploring variability in the density estimates across landscapes. Our study is yet another example of the potential of combining SCR methods and noninvasive sampling techniques to estimate abundance and density for elusive and wide-ranging species, like large carnivores(Alexander et al, 2015;Broekhuis & Gopalaswamy, 2016;Goldberg et al, 2015;López-Bao et al, 2018;Pesenti & Zimmermann, 2013;Stetz et al, 2018).When examining densities across study areas in the French Jura mountains, we found spatial variation between the three counties, with Doubs area having the lowest densities, Ain the highest densities, and Jura intermediate densities. Our density estimates were of similar magnitude to other lynx populations in Europe: 1.47 and 1.38 lynx/100 km 2 in the Northwestern Swiss Alps(Pesenti & Zimmermann, 2013), 0.58 (Štiavnica mountains) and 0.81 individuals/100 km 2 (Velká Fatra National Park) in Slovakia(Kubala et al, 2017) and 0.9 individuals/100 km 2 in the Bavarian Forest National Park in Germany(Weingarth et al, 2012).…”

“…The multi‐site dimension of our study allows exploring variability in the density estimates across landscapes. Our study is yet another example of the potential of combining SCR methods and noninvasive sampling techniques to estimate abundance and density for elusive and wide‐ranging species, like large carnivores (Alexander et al, ; Broekhuis & Gopalaswamy, ; Goldberg et al, ; López‐Bao et al, ; Pesenti & Zimmermann, ; Stetz et al, ).…”

“…In addition, individual heterogeneity in detection due to spatial variation in the distance of home ranges to the sampling devices may lead to biased density estimates (Otis et al, 1978). Spatial capture-recapture (SCR) models deal with these issues by explicitly incorporating spatial locations of detections (Borchers, 2012;Borchers & Efford, 2008;Efford, 2004;Royle & Young, 2008), and they are increasingly used to estimate densities of large carnivores (Alexander, Gopalaswamy, Shi, & Face, 2015;Broekhuis & Gopalaswamy, 2016;Goldberg et al, 2015;López-Bao et al, 2018;Pesenti & Zimmermann, 2013;Stetz, Mitchell, & Kendall, 2018).…”

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

“…Results of this work have already been incorporated into regional planning efforts to reduce road densities , Proctor et al 2018c, highlighting the value of density-habitat links when immediate conservation action is required. Finally, Stetz et al (2018) extend the density-habitat relationship to explore the effects of interspecific competition on population density, thus adding a community ecology dimension.…”

“…Finally, Stetz et al. () extend the density–habitat relationship to explore the effects of interspecific competition on population density, thus adding a community ecology dimension.…”