DOI: 10.5061/dryad.hhmgqnkm7
title: Data from: A framework for modeling the impacts of searcher behavior on the efficiency of abundance surveys

description: 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.
description: Data on search times for removal and quadrat surveys was
 collected by divers in three Minnesota lakes. Code is provided to format
 the dataset, estimate density, and reproduce all analyses in the
 manuscript.
description: # 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" ## Change log Version July 16, 2024 - minor edits to
 README ## 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. RemovalSearch.R - this file is used to calculate
 the optimal search time and time to complete a transect for the removal
 survey. Reproduces Fig 1 and Fig 2. ZebraFuncs.R: This contains functions
 to read in datasets, format data for analysis, and estimate zebra mussel
 densities. Also contains functions to calculate the coefficient of
 variation for removal and quadrat surveys given time budget information
 and the target density distribution. ### Data files to reproduce analsyses
 in the main manuscript: Missing data are entered as NA Encounters -
 Quadrats.xlsx: This contains information on each detection event in the
 quadrat surveys for each of the three lakes. Data columns, in order, are
 time that detection occurred, date of detection, name of the lake, the
 observer who made the detection, the distance along the transect that
 quadrat was at, the number of mussels in the quadrat, any substrate the
 mussels were attached to, divers comments, and the number of mussel
 clusters in the quadrat. Encounters - Removal.xlsx: This contains
 information on each detection event in the removal surveys for each of the
 three lakes. Data columns, in order, are time that detection occurred,
 date of detection, name of the lake, the observer who made the detection,
 the transect number the detection occurred on, the distance along the
 transect that detection occurred at, the number of mussels in the
 detection event, the length and width of the cluster, the substrates that
 mussels are attached to, and any additional diver comments. Encounters -
 Distance.xlsx: This contains information on each detection event in the
 distance surveys for each of the three lakes. Note that distance surveys
 were not used in the associated paper but are included for completeness.
 Data columns, in order, are time that detection occurred, date of
 detection, name of the lake, the observer who made the detection, the
 transect number the detection occurred on, the distance along the transect
 that detection occurred at, the distance from the transect the detection
 occurred at, the side of the transect the detection occurred at, the
 number of mussels in the detection event, the length and width of the
 cluster, the substrates that mussels are attached to, and any addition
 diver comments. Transect datasheets.xlsx: Contains information about each
 transect completed in the study. Data columns, in order, are the transect
 type conducted, the lake the transect was conducted in, the date the
 transect was completed, the divers on the transect, the name of the
 primary observer and the name of the secondary observer (if relevant), the
 length of the transect, the unique number of the transect, the GPS
 coordinates at the start of the transect, compass bearing fo the transect
 line, time taken to survey the habitat along the transect, time take to
 survey the transect, clarity along the transect. ### Files for simulation
 Tutorial.pdf - This tutorial runs through three different examples using
 distance sampling, occupancy analysis, and n-mixture models using
 simulation tools. Presents the code in MRDS_halfnorm_example.R,
 UnmarkedSims_occu_example.R, and Unmarked_pcount_example.R. Sim_funcs.R -
 Contains functions needed to simulate datasets and find optimal survey
 time using the MRDS and unmarked packages. MRDS_halfnorm_example.R -
 Example calculating the optimal search time using the halfnormal distance
 function in the MRDS package. Relies on Sim_funcs.R.
 UnmarkedSims_occu_example.R - Example calculating the optimal search time
 using the occupancy model in the unmarked package. Relies on Sim_funcs.R.
 UnmarkedSims_pcount_example.R - Example calculating the optimal search
 time using the  distance fuction in MRDS. Relies on Sim_funcs.R.