ISSN: 1578-665X | e-ISSN: 2014-928X An international journal devoted to the study and conservation of animal biodiversity, open-access, free for authors, driven by a fast-paced editorial process that includes assessment by experts. It is published twice a year.

Costs of detection bias in index-ased population monitoring

Moore, C. T.  Kendall, W. L. 


Managers of wildlife populations commonly rely on indirect, count-based measures of the population in making decisions regarding conservation, harvest, or control. The main appeal in the use of such counts is their low material expense compared to methods that directly measure the population. However, their correct use rests on the rarely-tested but often-assumed premise that they proportionately reflect population size, i.e., that they constitute a population index. This study investigates forest management for the endangered Red-cockaded Woodpecker (Picoides borealis) and the Wood Thrush (Hylocichla mustelina) at the Piedmont National Wildlife Refuge in central Georgia, U.S.A. Optimal decision policies for a joint species objective were derived for two alternative models of Wood Thrush population dynamics. Policies were simulated under scenarios of unbiasedness, consistent negative bias, and habitat-dependent negative bias in observed Wood Thrush densities. Differences in simulation outcomes between biased and unbiased detection scenarios indicated the expected loss in resource objectives (here, forest habitat and birds) through decision-making based on biased population counts. Given the models and objective function used in our analysis, expected losses were as great as 11%, a degree of loss perhaps not trivial for applications such as endangered species management. Our analysis demonstrates that costs of uncertainty about the relationship between the population and its observation can be measured in units of the resource, costs which may offset apparent savings achieved by collecting uncorrected population counts.

Key words

Wildlife surveys, Detection bias, Opportunity costs, Optimization, Uncertainty, Decision making

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Índex de Volume 27.1 (2004)