Estimating Wildlife Density as a Function of Environmental Heterogeneity Using Unmarked Data

Thomas Connor,William Tripp,William T Bean,Asa Donahue,Jessica Camarena,Daniel Sarna-Wojcicki,Wildlife Division,Justin Brashares,B J Saxon,Luke Macaulay,Emilio Tripp

doi:10.3390/rs14051087

Thomas Connor, William Tripp + Show 9 more

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https://doi.org/10.3390/rs14051087

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Abstract

Recent developments to spatial-capture recapture models have allowed their use on species whose members are not uniquely identifiable from photographs by including individual identity as a latent, unobserved variable in the model. These ‘unmarked’ spatial capture recapture (uSCR) models have also been extended to presence-absence data and modified to allow categorical environmental covariates on density, but a uSCR model, which allows fitting continuous environmental covariates to density, has yet to be formulated. In this paper, we fill this gap and present an extension to the uSCR modeling framework by modeling animal density on a discrete state space as a function of continuous environmental covariates and investigate a form of Bayesian variable selection to improve inference. We used an elk population in their winter range within Karuk Indigenous Territory in Northern California as a case study and found a positive credible effect of increasing forb/grass cover on elk density and a negative credible effect of increasing tree cover on elk density. We posit that our extensions to uSCR modeling increase its utility in a wide range of ecological and management applications in which spatial counts of wildlife can be derived and environmental heterogeneity acts as a control on animal density.

Highlights

In recent decades, camera traps have become increasingly used by researchers and managers to monitor wildlife species and populations [1]
Our modifications of unmarked spatial capture recapture (uSCR) models allow for their estimation of continuous environmental covariate effects on wildlife population density
USCR models comes with distinct advantages over other unmarked abundance modeling frameworks and our extensions increase their utility, it is important to consider what modeling framework is most appropriate for a given detector array and species [4]

Summary

Introduction

Camera traps have become increasingly used by researchers and managers to monitor wildlife species and populations [1]. For species whose individuals are uniquely identifiable through photographs, camera trapping data have been extended to population estimation through capture-recapture statistical methods [3]. Camera trapping data have been used to estimate the populations of species for which individuals are not uniquely identifiable through photographs with statistical methods that model spatial counts or the presence/absence of species [4]. A relatively recent and potentially powerful addition to the suite of unmarked spatial presence/count models is the unmarked spatial capture recapture (uSCR) model, which estimates population density through individual captures and recaptures across sites where individual identity is an unknown latent variable [5]. A key advantage of uSCR models over other unmarked abundance models is that population density can be directly estimated through the modeling of individual activity centers across a defined state space, as opposed

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