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Circuit theory and model-based inference for landscape connectivity

Journal of the American Statistical Association

By:
and
DOI: 10.1080/01621459.2012.724647

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Abstract

Circuit theory has seen extensive recent use in the field of ecology, where it is often applied to study functional connectivity. The landscape is typically represented by a network of nodes and resistors, with the resistance between nodes a function of landscape characteristics. The effective distance between two locations on a landscape is represented by the resistance distance between the nodes in the network. Circuit theory has been applied to many other scientific fields for exploratory analyses, but parametric models for circuits are not common in the scientific literature. To model circuits explicitly, we demonstrate a link between Gaussian Markov random fields and contemporary circuit theory using a covariance structure that induces the necessary resistance distance. This provides a parametric model for second-order observations from such a system. In the landscape ecology setting, the proposed model provides a simple framework where inference can be obtained for effects that landscape features have on functional connectivity. We illustrate the approach through a landscape genetics study linking gene flow in alpine chamois (Rupicapra rupicapra) to the underlying landscape.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Circuit theory and model-based inference for landscape connectivity
Series title:
Journal of the American Statistical Association
DOI:
10.1080/01621459.2012.724647
Volume
108
Issue:
501
Year Published:
2013
Language:
English
Publisher:
Taylor & Francis
Contributing office(s):
Colorado Cooperative Fish and Wildlife Research Unit
Description:
12 p.
First page:
22
Last page:
33
Online Only (Y/N):
N
Additional Online Files(Y/N):
N