secrdesign-package: Spatially Explicit Capture--Recapture Study Design
Description
Tools to assist the design of spatially explicit capture--recapture studies of animal populations.
Arguments
Author
Murray Efford murray.efford@otago.ac.nz
Details
| Package: | secr |
| Type: | Package |
| Version: | 2.9.2 |
| Date: | 2024-09-27 |
| 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.
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