Linking resource selection and mortality modeling for population estimation of mountain lions in Montana

Robinson, Hugh S.; Ruth, Toni K.; Gude, Justin A.; Choate, David M.; DeSimone, Rich; Hebblewhite, Mark; Kunkel, Kyran; Matchett, Marc R.; Mitchell, Michael S.; Murphy, Kylie; Williams, Jake Ryland

doi:10.1016/j.ecolmodel.2015.05.013

Cited by 28 publications

(39 citation statements)

References 43 publications

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“…Our survival analysis estimated a non‐harvest mortality rate (1 − natural survival) of 0.117 for 2‐year‐old animals and 0.141 for older animals, similar to those reported for other hunted populations in Utah (0.126; Stoner et al ), Montana (0.113; Robinson et al ), and North Dakota (0.107; Johnson et al ). The harvest rate for adult animals ranged from 0.213 to 0.314 during 2004–2018, higher than that reported in Oregon (0.126–0.154; Clawson ) but similar to rates reported in southern Idaho and northwestern Utah (0.223–0.254; Laundré et al ), Washington (0.04–0.46; Cooley et al ), and North Dakota (0.291–0.535; Johnson et al ).…”

Section: Discussionsupporting

confidence: 82%

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

et al. 2019

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Directly monitoring abundance of cryptic species, such as mountain lions (Puma concolor), over large areas is a challenge for wildlife managers because traditional population estimation techniques may be impractical and expensive. We generated annual estimates of mountain lion abundance in Arizona, USA, for 2004–2018 by employing statistical population reconstruction methods, which use available age‐at‐harvest data and auxiliary information such as estimated survival rates, harvest probabilities, and hunter effort. Using PopRecon 2.0 software, we estimated that the statewide abundance of all mountain lions including kittens ranged from 1,848 (95% CI = 650–3,046) to 4,661 (95% CI = 393–9,030) during 2004–2018. Abundance for subadults and adults was more stable and precisely estimated, ranging from 1,166 (95% CI = 622–1,709) to 1,715 (95% CI = 872–2,558). Our results suggest a stable statewide mountain lion population. This approach provides a practical and cost‐effective option for monitoring Arizona's mountain lion population, and will improve the ability of managers to monitor the population annually to respond to changes in abundance and to evaluate factors that influence mountain lion abundance. © 2019 The Authors. Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

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Section: Discussionsupporting

confidence: 82%

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

et al. 2019

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“…11.2.1, Royle et al 2013); where μ( s , β) returns the expected density of activity centers at location s given the covariate values C and the parameter estimates β. The RSF was developed statewide in Montana using radiotelemetry (VHF and GPS) data from 1980 to 2012, using a generalized linear mixed‐effects model (Robinson et al 2015). The RSF was developed following a used‐available design at the second‐order, home range scale (Johnson 1980), corresponding well to the ecological selection process for individual activity distributions.…”

Section: Methodsmentioning

confidence: 99%

“…The RSF was estimated by comparing 18,695 GPS telemetry locations from 85 individual lions to availability at the state‐wide scale, and then validating it using withheld data from 142 VHF and GPS collared lions, as well as harvest locations from 1988 to 2011. Winter mountain lion habitat was a positive function of southerly aspects, intermediate elevations and slopes, forested areas, and areas far from agriculture or human development (Robinson et al 2015). The RSF model validated very well using out‐of‐sample telemetry data (Spearmans rho = 0.95).…”

Section: Methodsmentioning

confidence: 99%

“…Wildlife managers need reliable methods to monitor mountain lion population trends to manage harvest, minimize conflicts with humans and balance mountain lion densities with ungulate management objectives. Traditional approaches to estimate mountain lion abundance have focused on marking and counting individual lions, a method that is labor‐intensive and expensive, and often minimum counts of individuals are treated as a true census for management purposes (Robinson et al 2015). The resources required by these traditional methods have limited the spatial scope and utility of the resulting estimates for population management (Stoner et al 2006, Quigley and Hornocker 2010), and minimum counts of known individuals underestimate true population sizes.…”

Section: Introductionmentioning

confidence: 99%

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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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“…We considered topography (elevation, slope, aspect and ruggedness), vegetation (seven vegetation types), cover (canopy at base height) and edge density as biologically relevant covariates for puma habitat selection analyses (e.g. Elbroch et al , ; Robinson et al , ; Kusler et al , ).…”

Section: Methodsmentioning

confidence: 99%

Habitat selection when killing primary versus alternative prey species supports prey specialization in an apex predator

et al. 2019

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Many predators specialize on one or several prey species that they select from the range of potential prey. Predator specialization on primary versus alternative prey is driven in part by encounter rates with prey and a predator’s habitat selection. Although habitat selection changes with behavioural state, this has not been well‐recognized in the resource selection function (RSF) literature to date, often because auxiliary data on the predator’s behavioural states (e.g. hunting) are absent. We monitored habitat selection of pumas Puma concolor in a multi‐prey system in northern California, where pumas specialized on black‐tailed deer Odocoileus hemionus columbianus. We employed multiple RSF analyses on different datasets to test the following three hypotheses: (1) Pumas utilize habitats in proportion to their availability; (2) Pumas select specific habitat features when killing black‐tailed deer, their primary prey; (3) Pumas do not select distinct habitats from those identified under hypothesis 1 when killing alternative prey. We found that pumas in our study selected for specific habitats and habitat features in general, but that their selection was more pronounced when killing black‐tailed deer. In summer, kill sites of deer were associated with rugged terrain, but gentle slopes and northerly aspects. In winter, pumas killed deer at low elevations, on gentle slopes and on northerly and westerly aspects. Overall, evidence suggested that pumas tracked their primary prey across seasonal migrations, which were short in distance but resulted in pronounced changes in elevation. When killing alternative prey, pumas showed little evidence of habitat selection, suggesting they may kill alternative prey opportunistically. Our results hold implications for how data should be partitioned when modelling baseline habitat selection of predators, hunting habitat selection and predation risk for prey species, as well as for how we model ecological processes such as apparent competition.

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Linking resource selection and mortality modeling for population estimation of mountain lions in Montana

Cited by 28 publications

References 43 publications

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

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

Habitat selection when killing primary versus alternative prey species supports prey specialization in an apex predator

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