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Wednesday, January 27 • 10:40am - 11:00am
Reducing Effects of Dispersal On The Bias of 2-Sample Mark-Recapture Abundance Estimators

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AUTHORS: James N. McNair*, Annis Water Resources Institute - Grand Valley State University; Carl R. Ruetz III, Annis Water Resources Institute - Grand Valley State University; and Ariana Carlson, Annis Water Resources Institute - Grand Valley State University

ABSTRACT: Obtaining accurate estimates of fish abundance at the reach scale is an important task in managing stream fisheries. One of the commonest procedures used for this purpose worldwide is the 2-sample mark-recapture method. This closed-population method assumes there is no dispersal into or out of the study reach between samples (spatial closure), an assumption likely to be violated when block nets cannot be used or are not fully effective. Open-population methods permitting dispersal are available but often are infeasible in management applications. The 2-sample mark-recapture method therefore continues to be widely used, though violation of the spatial-closure assumption can result in substantially biased abundance estimates. We review effects of several dispersal scenarios on bias of the classical Dahl-Petersen-Lincoln abundance estimator with deterministic sampling, including some new results. We also outline new results showing the dual effects of dispersal and sampling variation on bias of Chapman’s estimator with stochastic sampling. We show that when the spatial-closure assumption is violated, abundance estimates can be biased upward, biased downward, or unbiased, depending on the rate and pattern of dispersal, true abundance, capture probabilities, and other factors, and that bias typically differs markedly between estimates of study-reach abundance and total population size. Finally, we propose and illustrate (with simulation results) a practical modification of the standard sampling scheme used in 2-sample mark-recapture studies with electrofishing that, when feasible, can reduce or eliminate bias effects of dispersal.

Wednesday January 27, 2016 10:40am - 11:00am EST
Gerald Ford