Analyses of national mountain lion harvest indices yield ambiguous interpretations

Elbroch, L. Mark; O'Malley, Connor; Robinson, Hugh S.

doi:10.1002/2688-8319.12150

Cited by 2 publications

(1 citation statement)

References 28 publications

(62 reference statements)

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“…Although the terms hunting and harvest are typically used interchangeably, we define hunting as the pursuit or search for mountain lions, while harvest specifically refers to the lethal take of a mountain lion. There has been an overall increase in juvenile harvest reported across the western United States (Elbroch et al., 2022 ), which influences recruitment and impacts a population's age structure (Cooley, Wielgus, Koehler, & Maletzke, 2009 ; Logan & Runge, 2021 ; Newby et al., 2013 ; Robinson et al., 2008 ; Stoner et al., 2006 ). Harvest pressure and habitat quality have also been shown to influence population dynamics (Andreasen et al., 2012 ; Lindzey et al., 1992 ).…”

Section: Introductionmentioning

confidence: 99%

Impacts of management practices on habitat selection during juvenile mountain lion dispersal

Randolph,

Young,

Stoner

et al. 2024

Ecology and Evolution

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Dispersal is a complex series of movements before an individual establishes a home range. Animals must travel and forage in unfamiliar landscapes that include anthropogenic risks such as road crossings, harvest, and urban landscapes. We compare dispersal behavior of juvenile mountain lions (Puma concolor) from two geographically distinct populations in California and Nevada, USA. These two sites are ecologically similar but have different management practices; hunting is permitted in Nevada, whereas mountain lions are protected in California. We used GPS‐collar data and net‐squared displacement analysis to identify three dispersal states: exploratory, departure, and transient home range. We then compared each dispersal state of the two mountain lion populations using an integrated step selection analysis (iSSA). The model included explanatory variables hypothesized to influence one or more dispersal states, including distance to forest, shrub, water, hay and crop, developed lands, and four‐wheel drive roads, as well as elevation and terrain ruggedness. Results revealed consistent habitat selection between sites across most landscape variables, with one notable exception: anthropogenic covariates, including distance to developed land, distance to hay and crop, and distance to four‐wheeled drive roads, were only statistically significant on modeled habitat selection during dispersal in the population subject to hunting (i.e., Nevada). Results suggest that hunting (pursuit with hounds resulting in harvest) and non‐lethal pursuit (pursuit with hounds but no harvest allowed) increase avoidance of anthropogenic landscapes during dispersal for juvenile mountain lions. By comparing populations, we provided valuable insights into the role of management in shaping dispersal behavior.

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Section: Introductionmentioning

confidence: 99%

Impacts of management practices on habitat selection during juvenile mountain lion dispersal

Randolph,

Young,

Stoner

et al. 2024

Ecology and Evolution

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Machine learning allows for large-scale habitat prediction of a wide-ranging carnivore across diverse ecoregions

O’Malley,

Elbroch,

Zeller

et al. 2024

Landsc Ecol

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Context Resource selection functions are powerful tools for predicting habitat selection of animals. Recently, machine-learning methods such as random forest have gained popularity for predicting habitat selection due to their flexibility and strong predictive performance. Objectives We tested two methods for predicting continental-scale, second-order habitat selection of a wide-ranging large carnivore, the mountain lion (Puma concolor), to support continent-wide conservation management, including estimating abundance, and to predict habitat suitability for recolonizing or reintroduced animals. Methods We compared a generalized linear model (GLM) and a random forest model using GPS location data from 476 individuals across 20 study sites in the western USA and Canada and remotely-sensed landscape data. We internally validated models and examined their ability to correctly classify used and available points by calculating area under the receiver operating characteristics (AUC). We performed leave-one-out (LOO) out-of-sample tests of predictive strength on both models. Results Both models suggested that mountain lions select for steeper slopes, areas closer to water, and with higher normalized difference vegetation index (NDVI), and against variables associated with human impact. The random forest model (AUC = 0.94) demonstrated that mountain lion habitat can be accurately predicted at continental scales, outperforming the traditional GLM model (AUC = 0.68). Our LOO validation provided similar results (x̄ = 0.93 for the random forest and x̄ = 0.65 for the GLM). Conclusions We found that the added flexibility of the random forest model provided deeper insights into how individual covariates impacted habitat selection across diverse ecosystems. Our LOO analyses suggested that our model can predict mountain lion habitat selection in unoccupied areas or where local data are unavailable. Our model thus provides a tool to support discussions and analyses relevant to continent-wide mountain lion conservation and management including estimating metapopulation abundance.

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Analyses of national mountain lion harvest indices yield ambiguous interpretations

Cited by 2 publications

References 28 publications

Impacts of management practices on habitat selection during juvenile mountain lion dispersal

Impacts of management practices on habitat selection during juvenile mountain lion dispersal

Machine learning allows for large-scale habitat prediction of a wide-ranging carnivore across diverse ecoregions

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