Using "indicator" species to model species richness: Model development and predictions

Reliable indicators of species richness (e.g., particular species), if they can be found, offer potentially significant benefits for management planning. Few efficient and statistically valid methods for identifying potential indicators of species richness currently exist. We used Bayesian-based Poisson modeling to explore whether species richness of butterflies in the Great Basin could be modeled as a function of the occurrence (presence or absence) of certain species of butterflies. We used an extensive data set on the occurrence of butterflies of the Toquima Range (Nevada, USA) to build the models. Poisson models based on the occurrence of five and four indicator species explained 88% and 77% of the deviance of observed species richness of resident and montane-resident butterfly assemblages, respectively. We then developed a test framework, including formally defined "rejection criteria," for validating and refining the models. The sensitivity of the models to inventory intensity (number of years of data) and knowledge about the potential indicators was incorporated into this evaluation phase. We conducted a test of our models by using an existing set of data on butterflies in the neighboring Toiyabe Range. Predicted values of species richness were significantly rank correlated with the observed values. Thus, the models appear to have promise for predicting species richness based on the occurrence of certain taxa.