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Bayesian inference in camera trapping studies for a class of spatial capture-recapture models

Ecology

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Abstract

We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Bayesian inference in camera trapping studies for a class of spatial capture-recapture models
Series title:
Ecology
Volume
90
Issue:
11
Year Published:
2009
Language:
English
Publisher:
Ecological Society of America
Publisher location:
Washington, D.C.
Contributing office(s):
Patuxent Wildlife Research Center
Description:
12 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Ecology
First page:
3233
Last page:
3244
Country:
India