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 su..., Data on search times for removal and quadrat surveys was collected by divers in three Minnesota lakes. Density estimates, also provided here, are discussed in detail in the Digital Repository of the University of Minnesota at https://doi.org/10.13020/655p-j357. Functions for formatting the dataset for analysis are also included., , # Data from: A framework for modeling the impacts of searcher behavior on the efficiency of abundance surveys

Contains data and R files to reproduce analyses from \"A common framework for modeling animal search: Linking foraging ecology to survey design through trade-offs between search effort and detection\"

## Description of the Data and file structure

### R files to reproduce analyses in the main manuscript:

CV_calculation.R: To reproduce the zebra mussel analyses, run this code. This file calculates optimal search times by minimizing the coefficient of variation in the estimated density of a removal survey. Reproduces Fig 3. Calls DensityEstimates.R.

CV_sensitivity.R - contains code to produce the sensitivity analysis of the zebra mussel surveys. Reproduces Fig 4. Calls DensityEstimates.R.

DensityEstimates.R: This calls the necessary functions to estimate density and model the time budget data. Also produces density estimates from the empirical survey data. More information on t...