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Spatial capture-recapture models for jointly estimating population density and landscape connectivity

Ecology

By:
, , ,
DOI: 10.1890/12-0413.1

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Abstract

Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on “ecological distance,” i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture–recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture–recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Spatial capture-recapture models for jointly estimating population density and landscape connectivity
Series title:
Ecology
DOI:
10.1890/12-0413.1
Volume
94
Issue:
2
Year Published:
2013
Language:
English
Publisher:
ESA
Contributing office(s):
Patuxent Wildlife Research Center
Description:
8 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Ecology
First page:
287
Last page:
294