Estimating Population Abundance Using Sightability Models:RSightabilityModelPackage
Journal of Statistical Software
This introduction to the R SightabilityModel package is a slight modification of Fieberg (2012), published in the Journal of Statistical Software. Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlifepopulation surveys (Steinhorst and Samuel 1989) . Estimation proceeds in 2 stages: 1) sightability trials are conducted with marked individuals, and logistic regression is used to estimate the probability of detection as a function of
... n as a function of available covariates (e.g., visual obstruction, group size); 2) the fitted model is used to adjust counts (from future surveys) for animals that were not observed. A modified Horvitz-Thompson estimator is used to estimate abundance: counts of observed animal groups are divided by their inclusion probabilites (determined by plot-level sampling probabilities and the detection probabilities estimated from stage 1). We provide a brief historical account of the approach, clarifying and documenting suggested modifications to the variance estimators originally proposed by Steinhorst and Samuel (1989) . We then introduce a new R package, SightabilityModel, for estimating abundance using this technique. Lastly, we illustrate the software with a series of examples using data collected from moose (Alces alces) in northeastern Minnesota and mountain goats (Oreamnos americanus) in Washington State.