Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species

Abstract

Effective conservation of rare species requires reasonable knowledge of population locations. However, surveys for rare species can be time-intensive and therefore expensive. We test a methodology using stacked species distribution models (S-SDMs) to efficiently discover the greatest number of new rare species’ occurrences possible. We used S-SDMs for 22 rare plant species in southern Ontario, Canada to predict the best survey locations among individual 1-ha cells. For each cell, we weighted distribution model outputs by accuracy and species rarity to create an efficiency value. We used these efficiency values as an index to determine the locations of our field surveys. We conducted field surveys in multi-species cells, “MSC” (areas with high predicted efficiency for multiple species) and single species cells, “SSC” (areas with high probability for only one species) to determine the relative efficiency of a multi-species survey approach. MSC were more than twice as likely as SSC to have at least one rare plant species discovered. Efficiency ranks were also useful in directing surveyors toward incidental discoveries of other rare species that were not modeled. Our technique of using S-SDMs can help direct surveys to more efficiently find rare species occurrences.