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Robust Abundance Estimation in Animal Surveys with Imperfect Detection
Citation:
Bohrmann, T. AND M. Christman. Robust Abundance Estimation in Animal Surveys with Imperfect Detection. Presented at 2012 Joint Statistical Meetings, San Diego, CA, July 27 - August 02, 2012.
Impact/Purpose:
Presentation at the 2012 Joint Statistical Meetings, Thursday 08/02/12, in San Diego, CA.
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.
