Abundance of rare and elusive species: Empirical investigation of closed versus spatially explicit capture–recapture models with lynx as a case study

Blanc, Laetitia; Marboutin, Éric; Gatti, Sylvain; Giménez, Olivier

doi:10.1002/jwmg.453

Cited by 63 publications

(58 citation statements)

References 31 publications

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“…We showed that the SCR framework we adopt here results in more precise estimates of density than previously published non-spatial estimates (Silveira et al, 2009) for the same data set. Better statistical performance of SCR over traditional capture-recapture models has been shown before in a simulation study (Blanc et al, 2013). Moreover, the spatial framework allows combining and sharing parameters between different data types, which further improved population estimates.…”

Section: Discussionmentioning

confidence: 89%

Combining camera-trapping and noninvasive genetic data in a spatial capture–recapture framework improves density estimates for the jaguar

Sollmann

Tôrres

Furtado

et al. 2013

Biological Conservation

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Section: Discussionmentioning

confidence: 89%

Combining camera-trapping and noninvasive genetic data in a spatial capture–recapture framework improves density estimates for the jaguar

Sollmann

Tôrres

Furtado

et al. 2013

Biological Conservation

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“…Similar methods exist within the likelihood framework Efford 2008, Efford 2014), however harvest has not been previously included within the Bayesian framework. SCR models have been applied to other species, for example, wolverines (Gulo gulo) and Lynx (Lynx lynx; Royle et al 2011, Blanc et al 2013 with potentially open harvest seasons without explicitly considering effects of ongoing harvest. Given that most large carnivore species are harvested or subject to management removals, our dead-recovery approach is likely to Our median estimates of 4.5 and 5.2 independent mountain lions/100 km 2 in the management units of interest represent the estimated density of a hunted population living sympatrically with wolves, and are higher than previously published estimates of mountain lion densities (Hornocker and Negri 2009).…”

Section: Discussionmentioning

confidence: 99%

Integrating resource selection into spatial capture‐recapture models for large carnivores

et al. 2015

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Wildlife managers need reliable methods to estimate large carnivore densities and population trends; yet large carnivores are elusive, difficult to detect, and occur at low densities making traditional approaches intractable. Recent advances in spatial capture‐recapture (SCR) models have provided new approaches for monitoring trends in wildlife abundance and these methods are particularly applicable to large carnivores. We applied SCR models in a Bayesian framework to estimate mountain lion densities in the Bitterroot Mountains of west central Montana. We incorporate an existing resource selection function (RSF) as a density covariate to account for heterogeneity in habitat use across the study area and include data collected from harvested lions. We identify individuals through DNA samples collected by (1) biopsy darting mountain lions detected in systematic surveys of the study area, (2) opportunistically collecting hair and scat samples, and (3) sampling all harvested mountain lions. We included 80 DNA samples collected from 62 individuals in the analysis. Including information on predicted habitat use as a covariate on the distribution of activity centers reduced the median estimated density by 44%, the standard deviation by 7%, and the width of 95% credible intervals by 10% as compared to standard SCR models. Within the two management units of interest, we estimated a median mountain lion density of 4.5 mountain lions/100 km2 (95% CI = 2.9, 7.7) and 5.2 mountain lions/100 km2 (95% CI = 3.4, 9.1). Including harvested individuals (dead recovery) did not create a significant bias in the detection process by introducing individuals that could not be detected after removal. However, the dead recovery component of the model did have a substantial effect on results by increasing sample size. The ability to account for heterogeneity in habitat use provides a useful extension to SCR models, and will enhance the ability of wildlife managers to reliably and economically estimate density of wildlife populations, particularly large carnivores.

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“…In our analysis, we retained only independent lynx, that is, adult lynx or emancipated individuals based on physical characteristics or previous knowledge of their age or status (from photographic evidence). We defined a capture occasion as 5 successive trap nights (Blanc et al, ), dissociating trapping events from individual photograph to avoid pseudo‐replications.…”

Section: Methodsmentioning

confidence: 99%

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

Giménez

Gatti

Duchamp

et al. 2019

Ecology and Evolution

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Obtaining estimates of animal population density is a key step in providing sound conservation and management strategies for wildlife. For many large carnivores however, estimating density is difficult because these species are elusive and wide‐ranging. Here, we focus on providing the first density estimates of the Eurasian lynx (Lynx lynx) in the French Jura and Vosges mountains. We sampled a total of 413 camera trapping sites (with two cameras per site) between January 2011 and April 2016 in seven study areas across seven counties of the French Jura and Vosges mountains. We obtained 592 lynx detections over 19,035 trap days in the Jura mountains and 0 detection over 6,804 trap days in the Vosges mountains. Based on coat patterns, we identified a total number of 92 unique individuals from photographs, including 16 females, 13 males, and 63 individuals of unknown sex. Using spatial capture–recapture (SCR) models, we estimated abundance in the study areas between 5 (SE = 0.1) and 29 (0.2) lynx and density between 0.24 (SE = 0.02) and 0.91 (SE = 0.03) lynx per 100 km2. We also provide a comparison with nonspatial density estimates and discuss the observed discrepancies. Our study is yet another example of the advantage of combining SCR methods and noninvasive sampling techniques to estimate density for elusive and wide‐ranging species, like large carnivores. While the estimated densities in the French Jura mountains are comparable to other lynx populations in Europe, the fact that we detected no lynx in the Vosges mountains is alarming. Connectivity should be encouraged between the French Jura mountains, the Vosges mountains, and the Palatinate Forest in Germany where a reintroduction program is currently ongoing. Our density estimates will help in setting a baseline conservation status for the lynx population in France.

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Abundance of rare and elusive species: Empirical investigation of closed versus spatially explicit capture–recapture models with lynx as a case study

Cited by 63 publications

References 31 publications

Combining camera-trapping and noninvasive genetic data in a spatial capture–recapture framework improves density estimates for the jaguar

Combining camera-trapping and noninvasive genetic data in a spatial capture–recapture framework improves density estimates for the jaguar

Integrating resource selection into spatial capture‐recapture models for large carnivores

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

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