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Estimating animal abundance is essential to natural resource management and conservation. However, the cost associated with abundance estimation can be high for populations that are difficult to sample. Researchers, particularly in fisheries management, often sample such populations
using depletion or removal surveys. Depletion surveys rely upon successive removals of animals, without replacement, to estimate abundance. These researchers also must decide on other sampling protocol, including the depletion technique, which may include depletion gear-type, vessel, or personnel.
To inform this decision, we propose a supplement to the hierarchical Bayesian models recently introduced for the analysis of depletion data. Using Bayesian sample size methodology along with hierarchical modeling, we present a method for estimating the efficiency of previously employed depletion
techniques. Using this method, the researcher can estimate the expected variability in abundance estimates for each depletion technique and apply this information to future decisions. Additionally, this method allows the estimation of expected variability for various numbers of depletion passes.
We demonstrate the methodology using a data set of Chesapeake Bay blue crab (Callinectes sapidus) depletion surveys.
Department of Biology, University of Florida, Gainesville, FL 32611, USA.
Publication date: June 24, 2012
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Published continuously since 1901 (under various titles), this monthly journal is the primary publishing vehicle for the multidisciplinary field of aquatic sciences. It publishes perspectives (syntheses, critiques, and re-evaluations), discussions (comments and replies), articles, and rapid communications, relating to current research on cells, organisms, populations, ecosystems, or processes that affect aquatic systems. The journal seeks to amplify, modify, question, or redirect accumulated knowledge in the field of fisheries and aquatic science. Occasional supplements are dedicated to single topics or to proceedings of international symposia.