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Robust Abundance Estimation in Animal Abundance Surveys with Imperfect Detection
Citation:
BOHRMANN, T. F. AND M. Christman. Robust Abundance Estimation in Animal Abundance Surveys with Imperfect Detection. Presented at 73rd Annual Meeting of Southeastern Biologists, Athens, GA, April 04 - 07, 2012.
Impact/Purpose:
Surveys of animal abundance are central to the conservation and management of living natural resources. However, detection uncertainty complicates the sampling process of many species. One sampling method employed to deal with this problem is depletion (or removal) surveys in which animals are sequentially removed (and not replaced) from a closed subunit of the population. Information obtained in such a survey is translated into estimates of total population abundance via a statistical abundance estimator, of which there are many choices. However, any reasonable abundance estimator deals explicitly are either from the class of “design-based” estimators or “model-based” estimators, each having strengths and weaknesses. In this talk, we describe those strengths and weaknesses in terms of abundance estimation, and further present a new hybrid abundance estimator which draws from the strengths of both model-based and design-based estimators. We show that the coherent combination of these two frameworks yields a useful, flexible and yet robust total abundance estimator. We apply the estimator in the context of a simulation study based on annual depletion surveys of Chesapeake Bay blue crab abundance, and we compare the performance of our hybrid estimator with a typical fully model-based estimator. Although applied to a depletion data set, we discuss the utility of our estimator for other surveys in which animal detection rates are estimated, such as mark-recapture surveys.
Description:
Surveys of animal abundance are central to the conservation and management of living natural resources. However, detection uncertainty complicates the sampling process of many species. One sampling method employed to deal with this problem is depletion (or removal) surveys in which animals are sequentially removed (and not replaced) from a closed subunit of the population. Information obtained in such a survey is translated into estimates of total population abundance via a statistical abundance estimator, of which there are many choices. Abundance estimators are generally either from the class of design-based estimators or model-based estimators, each having strengths and weaknesses. In this talk we present a new hybrid abundance estimator which draws from the strengths of both model-based and design-based estimators. We show that the coherent combination of these two frameworks yields a useful, flexible and yet robust total abundance estimator. We apply the estimator in the context of a simulation study based on annual depletion surveys of Chesapeake Bay blue crab abundance, and we compare the performance of our hybrid estimator with a typical fully model-based estimator.
