Model-based stratifications for enhancing the detection of rare ecological events

A common concern when designing surveys for rare species is ensuring sufficient detections for analytical purposes, such as estimating frequency on the landscape or modeling habitat relationships. Strict design-based approaches provide the least biased estimates but often result in low detection rates of rare species. Here, we demonstrate how model-based stratification can improve the probability of detecting five rare epiphytic macrolichens (Nephroma laevigatum, N. occultum, N. parile, Lobaria scrobiculataa, and Psuedocyphelaria rainierensis) in the Pacific Northwest. We constructed classification tree models for four more common lichens (L. oregana, L. pulmonaria, P. anomala, and P. anthraspis) that are associated with the rare species, then used the models to generate strata for sampling for the five lichen species considered rare. The classification tree models were developed using topographic and bio-climatic variables hypothesized to have direct relationships to the presence of the modeled lichen species. When the expected detection rates using the model-based stratification approach was tested on an independent data set, it resulted in two- to fivefold gains in detection compared to the observed detection rates for four of the five tested rare species.