Data from: A framework for modeling the impacts of searcher behavior on the efficiency of abundance surveys

When planning abundance surveys, the impact of search effort 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 effort to the number of sample
units surveyed, searcher detection probability, the number of detections
made, and the precision of the estimated resource density. This framework
allowed us to determine how a surveyor should behave to produce optimal
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. By tuning searcher
behavior, we find that density estimates from removal surveys of zebra
mussels could be improved by up to 60% in some cases, without changing the
overall survey effort. 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. 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 resource density
and detection probability.