Unifying Population and Landscape Ecology with Spatial Capture-recapture

Royle, J. Andrew; Fuller, Angela K.; Sutherland, Christopher

doi:10.1101/103341

Cited by 13 publications

(17 citation statements)

References 75 publications

(2 reference statements)

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“…Though SCR models with spatial covariates have been described as representing second‐order resource selection (Royle et al ), it is important to consider that such density variations can be the product of multiple demographic and behavioral factors, rather than an explicit habitat preference. Accounting for more direct sources of mortality can be a way to improve interpretation as documented for leopard ( Panthera pardus ) density in response to poaching levels (Ramesh et al ).…”

Section: Discussionmentioning

confidence: 99%

Factors associated with black bear density and implications for management

2019

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Wildlife density estimates are important to accurately formulate population management objectives and understand the relationship between habitat characteristics and a species’ abundance. Despite advances in density and abundance estimation methods, management of common game species continues to be challenged by a lack of reliable population estimates. In Washington, USA, statewide American black bear (Ursus americanus) abundance estimates are predicated on density estimates derived from research in the 1970s and are hypothesized to be a function of precipitation and vegetation, with higher densities in western Washington. To evaluate current black bear density and landscape relationships in Washington, we conducted a 4‐year capture‐recapture study in 2 areas of the North Cascade Mountains using 2 detection methods, non‐invasive DNA collection and physical capture and deployment of global positioning system (GPS) collars. We integrated GPS telemetry from collared bears with spatial capture‐recapture (SCR) data and created a SCR‐resource selection model to estimate density as a function of spatial covariates and test the hypothesis that density is higher in areas with greater vegetative food resources. We captured and collared 118 bears 132 times and collected 7,863 hair samples at hair traps where we identified 537 bears from 1,237 detections via DNA. The most‐supported model in the western North Cascades depicted a negative relationship between black bear density and an index of human development. We estimated bear density at 20.1 bears/100 km2, but density varied from 13.5/100 km2 to 27.8 bears/100 km2 depending on degree of human development. The model best supported by the data in the eastern North Cascades estimated an average density of 19.2 bears/100 km2, which was positively correlated with primary productivity, with resulting density estimates ranging from 7.1/100 km2 to 33.6 bears/100 km2. The hypothesis that greater precipitation and associated vegetative production in western Washington supports greater bear density compared to eastern Washington was not supported by our data. In western Washington, empirically derived average density estimates (including cubs) were nearly 50% lower than managers expected prior to our research. In eastern Washington average black bear density was predominantly as expected, but localized areas of high primary productivity supported greater than anticipated bear densities. Our findings underscore the importance that black bear density is not likely uniform and management risk may be increased if an average density is applied at too large a scale. Disparities between expected and empirically derived bear density illustrate the need for more rigorous monitoring to understand processes that affect population numbers throughout the jurisdiction, and suggest that management plans may need to be reevaluated to determine if current harvest strategies are achieving population objectives. © 2019 The Wildlife Society.

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Section: Discussionmentioning

confidence: 99%

Factors associated with black bear density and implications for management

2019

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“…Regardless, we caution that spatially inhomogeneous D models appear to be sensitive to misspecification of the D -covariate relationship if suitable habitats are severely fragmented and the trap array does not sample the entire range of covariate values. Considering habitat loss and fragmentation are likely to increase globally commensurate with projected human population growth [111], which may increase the use of spatially inhomogeneous D models for estimating wildlife populations that inhabit fragmented landscapes [27, 36], further investigation of this issue via simulation is warranted. Additionally, the hair subsampling protocol that we used results in reliable D estimates for bear populations identified as having spatially homogenous D [31].…”

Section: Discussionmentioning

confidence: 99%

“…Recent extensions to spatial capture-recapture models, including the incorporation of habitat and landscape covariates in the density model (i.e., spatially inhomogeneous density), can improve estimate accuracy and provide information about salient ecological relationships [22, 30–35]. The use of these models to quantify wildlife population density-habitat relationships and inform landscape connectivity is expected to increase in the future as fragmentation and loss of native habitats intensifies in many regions of the world [36]. …”

Section: Introductionmentioning

confidence: 99%

Consequences of severe habitat fragmentation on density, genetics, and spatial capture-recapture analysis of a small bear population

et al. 2017

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Loss and fragmentation of natural habitats caused by human land uses have subdivided several formerly contiguous large carnivore populations into multiple small and often isolated subpopulations, which can reduce genetic variation and lead to precipitous population declines. Substantial habitat loss and fragmentation from urban development and agriculture expansion relegated the Highlands-Glades subpopulation (HGS) of Florida, USA, black bears (Ursus americanus floridanus) to prolonged isolation; increasing human land development is projected to cause ≥ 50% loss of remaining natural habitats occupied by the HGS in coming decades. We conducted a noninvasive genetic spatial capture-recapture study to quantitatively describe the degree of contemporary habitat fragmentation and investigate the consequences of habitat fragmentation on population density and genetics of the HGS. Remaining natural habitats sustaining the HGS were significantly more fragmented and patchier than those supporting Florida’s largest black bear subpopulation. Genetic diversity was low (AR = 3.57; HE = 0.49) and effective population size was small (NE = 25 bears), both of which remained unchanged over a period spanning one bear generation despite evidence of some immigration. Subpopulation density (0.054 bear/km2) was among the lowest reported for black bears, was significantly female-biased, and corresponded to a subpopulation size of 98 bears in available habitat. Conserving remaining natural habitats in the area occupied by the small, genetically depauperate HGS, possibly through conservation easements and government land acquisition, is likely the most important immediate step to ensuring continued persistence of bears in this area. Our study also provides evidence that preferentially placing detectors (e.g., hair traps or cameras) primarily in quality habitat across fragmented landscapes poses a challenge to estimating density-habitat covariate relationships using spatial capture-recapture models. Because habitat fragmentation and loss are likely to increase in severity globally, further investigation of the influence of habitat fragmentation and detector placement on estimation of this relationship is warranted.

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“…We applied the JSMM approach to two contrasting case studies to illustrate that it is applicable to almost any kind of multispecies movement data, such as GPSbased tracking data (Reisinger et al 2018), spatial capture-recapture data based on bird ringing (Paradis et al 1998), or marking of insects (Scriven et al 2017), trapping of small mammals (Puttker et al 2012), or cameratrapping and noninvasive genetic sampling (Royle et al 2018). The JSMM approach can be applied to any likelihood-based analysis of multispecies movement data by adding a hierarchical layer that models species-specific parameters as a function of their traits and phylogenetic relationships (Eq.…”

Section: Discussionmentioning

confidence: 99%

Joint species movement modeling: how do traits influence movements?

Ovaskainen

Ramos

Slade

et al. 2019

Ecology

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Joint species distribution modeling has enabled researchers to move from species‐level to community‐level analyses, leading to statistically more efficient and ecologically more informative use of data. Here, we propose joint species movement modeling (JSMM) as an analogous approach that enables inferring both species‐ and community‐level movement parameters from multispecies movement data. The species‐level movement parameters are modeled as a function of species traits and phylogenetic relationships, allowing one to ask how species traits influence movements, and whether phylogenetically related species are similar in their movement behavior. We illustrate the modeling framework with two contrasting case studies: a stochastic redistribution model for direct observations of bird movements and a spatially structured diffusion model for capture–recapture data on moth movements. In both cases, the JSMM identified several traits that explain differences in movement behavior among species, such as movement rate increasing with body size in both birds and moths. We show with simulations that the JSMM approach increases precision of species‐specific parameter estimates by borrowing information from other species that are closely related or have similar traits. The JSMM framework is applicable for many kinds of data, and it facilitates a mechanistic understanding of the causes and consequences of interspecific variation in movement behavior.

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Unifying Population and Landscape Ecology with Spatial Capture-recapture

Cited by 13 publications

References 75 publications

Factors associated with black bear density and implications for management

Factors associated with black bear density and implications for management

Consequences of severe habitat fragmentation on density, genetics, and spatial capture-recapture analysis of a small bear population

Joint species movement modeling: how do traits influence movements?

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