Improving selection of indicator species when detection is imperfect

Abstract

Indicator species are widely adopted in conservation programs due to their time- and cost-effectiveness, and the indicator value method is a popular approach for selecting species that are indicative of particular site classifications. The indicator value method does not explicitly incorporate detection probability into its abundance or occupancy estimates. We present an approach based on N-mixture models for estimating indicator values with improved accuracy. Simulations with two site classifications demonstrate the importance of accounting for imperfect detection and how ignoring it can alter indicator values. Our simulations also illustrate the sampling conditions under which use of N-mixture models achieves reduced bias in indicator values relative to naïve estimates based on counts and the assumption of perfect detection. Naïve estimates produced greater bias at nearly all probabilities of detection, and exhibited greater sensitivity to low probability of detection in preferred sites compared with N-mixture estimates. Differences between the naïve estimate and the N-mixture estimate were most pronounced when detection probability in the preferred site type was lower than 0.4. A case study with 11 species of songbirds demonstrated modest associations between most species and their “preferred” habitats, and 3 species in which preferred habitats differed between the naïve and N-mixture model approaches. When studying less common species and in instances where detection probability varies strongly among site classifications, we recommend using the approach based on N-mixture models to improve selection and inference regarding indicator species.