SightabilityModel

Contains code for the SightabilityModel Package

Versions and installation

• Github To install the latest development version from Github, install the newest version of the devtools package; then run

devtools::install_github("jfieberg/SightabilityModel", dependencies = TRUE,
                        build_vignettes = TRUE)

Features

Estimates abundance, density, and ratio of variables in simple random sample or stratified random sample block design often used in studying ungulate populations. Can also be used with Domain Stratification (Heard et al., 2008) but SE will have to be estimated using bootstrapping.

In a typical study, the survey area is divided into strata and each stratum divided into blocks. Not all blocks need be the same area. Aerial surveys of selected blocks find groups of animals and the number of animals in the group is recorded, often divided by sex (bulls and cows) and age (calve and mature).

A key problem is that not all animals in a group are seen because sightability is < 1. A separate study of a known number of animal (e.g. radio collared) is used to estimate a sightability model. The actual sightability study data or the fitted mode from a sightability model using logistic regression is used to correct the observed counts in the study. The corrected (for sightability) counts are used to estimate density, abundance or ratios (e.g. bulls to cows).

References

Fieberg, J. 2012. Estimating Population Abundance Using Sightability Models: R SightabilityModel Package. Journal of Statistical Software, 51(9), 1-20. URL https://doi.org/10.18637/jss.v051.i09

Heard, D.C. A.B.D. Walker, J.B. Ayotte, and G.S. Watts. 2008. Using GIS to modify a stratified random block survey design for moose. Alces 44: 11-116.

Steinhorst, Kirk R. and Samuel, Michael D. 1989. Sightability Adjustment Methods for Aerial Surveys of Wildlife Populations. Biometrics 45:415–425.

Wong, C. 1996. Population size estimation using the modified Horvitz-Thompson estimator with estimated sighting probabilities. Dissertation, Colorado State University, Fort Collins, USA.