Estimating Animal Abundance by Employing an External Experiment to Account for Detection and Count Bias with an application to Wintering Ducks in the Mississippi Alluvial Valley

A valid concern when structuring an aerial survey of wildlife populations is the presence of visibility bias. Many studies attempt to correct for visibility bias by including additional parameters in estimators for wildlife abundance. Often these parameters are estimated through data collected during the aerial survey. Some, how-ever, have suggested using an external visibility experiment to estimate parameters being used to adjust for visibility bias. This work considers a bias adjusted estimator, proposed by Pearse et al. (2008), in which the bias correction parameters are esti-mated via an external visibility experiment using decoys in place of the live animals. The bootstrap method was then used to find the standard error of this estimate. We propose a second bootstrap approach to obtain an estimate of the standard error. We find that both implementations of the bootstrap work equally well, although the computations involved may dictate the most feasible choice in a specific case. We then evaluate the performance of the estimator through the use of a simulation study of an artificial population. We find that the use of an external experiment can pro-duce reasonable results and identify some situations where it can produce under or over estimates of the population