secrdesign-package: Spatially Explicit Capture-Recapture Study Design

In secrdesign: Sampling Design for Spatially Explicit Capture-Recapture

Spatially Explicit Capture–Recapture Study Design

Description

Tools to assist the design of spatially explicit capture–recapture studies of animal populations.

Details

Package:	secr
Type:	Package
Version:	2.8.2
Date:	2023-03-11
License:	GNU General Public License Version 2 or later

The primary use of secrdesign is to predict by Monte Carlo simulation the precision or bias of density estimates from different detector layouts, given pilot values for density and the detection parameters lambda0/g0 and sigma.

Tools are also provided for predicting the performance of detector layouts without simulation, and for optimising layouts to meet various criteria, particularly expected counts.

The simulation functions in secrdesign are:

make.scenarios	generate dataframe of parameter values etc.
run.scenarios	perform simulations, with or without model fitting
fit.models	fit SECR model(s) to rawdata output from run.scenarios
predict.fittedmodels	infer `real' parameter estimates from fitted models
select.stats	collect output for a particular parameter
summary.selectedstatistics	numerical summary of results
plot.selectedstatistics	histogram or CI plot for each scenario

Fig. Core simulation functions in secrdesign (yellow) and their main inputs and outputs. Output from the simulation function run.scenarios() may be saved as whole fitted models, predicted values (parameter estimates), or selected statistics. Each form of output requires different subsequent handling. The default path is shown by solid blue arrows.

Other functions not used exclusively for simulation are:

Enrm	expected numbers of individuals n, re-detections r and movements m
En2	expected number of individuals detected at two or more detectors
minnrRSE	approximate RSE(D-hat) given sample size (n, r) (Efford and Boulanger 2019)
GAoptim	optimization of detector placement using genetic algorithm (Durbach et al. 2021)
costing	various cost components
saturation	expected detector saturation (trap success)
scenarioSummary	applies Enrm, minnrRSE, and other summaries to each scenario in a dataframe
optimalSpacing	optimal detector spacing by rule-of-thumb and simulation RSE(D-hat)

A vignette documenting the simulation functions is available at secrdesign-vignette.pdf. An Appendix in that vignette has code for various examples that should help get you started.

Documentation for expected counts is in secrdesign-Enrm.pdf. Another vignette secrdesign-tools.pdf demonstrates other tools. These include the optimalSpacing function, for finding the detector spacing that yields the greatest precision for a given detector geometry, number of sampling occasions, density and detection parameters.

Help pages are also available as ../doc/secrdesign-manual.pdf.

Author(s)

Murray Efford [e-mail]

References

Durbach, I., Borchers, D., Sutherland, C. and Sharma, K. (2021) Fast, flexible alternatives to regular grid designs for spatial capture–recapture. Methods in Ecology and Evolution 12, 298–310. DOI 10.1111/2041-210X.13517

Efford, M. G., and Boulanger, J. (2019) Fast evaluation of study designs for spatially explicit capture–recapture. Methods in Ecology and Evolution, 10, 1529–1535. DOI: 10.1111/2041-210X.13239

See Also

make.grid, sim.popn, sim.capthist, secr.fit

secrdesign documentation built on March 31, 2023, 10:25 p.m.