Estimating Population Abundance Using Sightability Models: R SightabilityModel Package

• Main Author: John R. Fieberg
• Format: Article in Journal/Newspaper
• Language: English
• Published: Foundation for Open Access Statistics 2012
• Subjects: abundance estimation; Horvitz-Thompson; logistic regression; sightability model; R; survey; Statistics; HA1-4737; Alces alces
• Online Access: https://doaj.org/article/48593ce715b54530ba83b93c4bcf61e6

Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlife-population 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 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.