A non-technical overview of spatially explicit capture–recapture models
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Most capture–recapture studies are inherently spatial in nature, with capture probabilities depending on the location of traps relative to animals. The spatial component of the studies has until recently, however, not been incorporated in statistical capture–recapture models. This paper reviews capture–recapture models that do include an explicit spatial component. This is done in a non-technical way, omitting much of the algebraic detail and focussing on the model formulation rather than on the estimation methods (which include inverse prediction, maximum likelihood and Bayesian methods). One can view spatially explicit capture–recapture (SECR) models as an endpoint of a series of spatial sampling models, starting with circular plot survey models and moving through conventional distance sampling models, with and without measurement errors, through mark–recapture distance sampling (MRDS) models. This paper attempts a synthesis of these models in what I hope is a style accessible to non-specialists, placing SECR models in the context of other spatial sampling models.
KeywordsSpatially explicit capture–recapture Spatial sampling Measurement error Capture function Plot sampling Distance sampling
I would like to thank Andy Royle for inviting me to present this work at the 2009 EURING Meeting, Tiago Marques and Len Thomas for useful feedback on an earlier draft, which led to a much improved manuscript, and to the anonymous reviewers for making suggestions that improved the accessibility and readability of the manuscript.
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