Estimating population abundance using sightability models: R SightabilityModel package

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,sightability model,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.