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Combining inferences from models of capture efficiency, detectability, and suitable habitat to classify landscapes for conservation of threatened bull trout

Conservation Biology

By:
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Abstract

Effective conservation efforts for at-risk species require knowledge of the locations of existing populations. Species presence can be estimated directly by conducting field-sampling surveys or alternatively by developing predictive models. Direct surveys can be expensive and inefficient, particularly for rare and difficult-to-sample species, and models of species presence may produce biased predictions. We present a Bayesian approach that combines sampling and model-based inferences for estimating species presence. The accuracy and cost-effectiveness of this approach were compared to those of sampling surveys and predictive models for estimating the presence of the threatened bull trout ( Salvelinus confluentus ) via simulation with existing models and empirical sampling data. Simulations indicated that a sampling-only approach would be the most effective and would result in the lowest presence and absence misclassification error rates for three thresholds of detection probability. When sampling effort was considered, however, the combined approach resulted in the lowest error rates per unit of sampling effort. Hence, lower probability-of-detection thresholds can be specified with the combined approach, resulting in lower misclassification error rates and improved cost-effectiveness.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Combining inferences from models of capture efficiency, detectability, and suitable habitat to classify landscapes for conservation of threatened bull trout
Series title:
Conservation Biology
Volume
17
Issue:
4
Year Published:
2003
Language:
English
Contributing office(s):
Forest and Rangeland Ecosystem Science Center
Description:
p. 1070-1077
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Conservation Biology
First page:
1070
Last page:
1077