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
Date of Award
Doctor of Philosophy (PhD)
Dr. Patrick Gerard, Committee Co-Chair
Dr. Brook Russell, Committee Co-Chair
Dr. William Bridges
Dr. Jun Luo
Dr. Beth Ross
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.
Thomas, April L., "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" (2018). All Dissertations. 2101.