A framework for modeling the impacts of adaptive search intensity on the efficiency of abundance surveys

Abstract

When planning abundance surveys, the impact of search intensity on the quality of the density estimates is rarely considered. We constructed a time-budget modeling framework for abundance surveys using principles from optimal foraging theory. We link search intensity to the number of sample units surveyed, searcher detection probability, the number of detections made, and the precision of the estimated population density. This framework allowed us to determine how a searcher should behave to produce optimized density estimates. Using data collected from quadrat and removal surveys of zebra mussels (Dreissena polymorpha) in central Minnesota, we applied this framework to evaluate potential improvements. We found that by tuning searcher behavior, density estimates from removal surveys of zebra mussels could be improved by up to 60% in some cases, without changing the overall survey time. Our framework also predicts a critical population density where the best survey method switches from removal surveys at low densities to quadrat surveys at high densities, consistent with past empirical work. In addition, we provide simulation tools to apply this form of analysis to a number of other commonly used survey designs. Our results provide insights into how to improve the performance of many survey methods in high-density environments by either tuning searcher behavior or decoupling the estimation of population density and detection probability.