Comparing Two Species Distribution Models Using Satellite Only and Ready-Made Environmental Variables for the Dakota Skipper (Hesperia dacotae), Interlake Region of Manitoba, Canada

Abstract

The Dakota skipper, Hesperia dacotae (Skinner, 1911) [Hesperiidae, Lepidopterida] is a rare prairie obligate butterfly with an affinity for anthropogenically undisturbed, grassland habitat with diverse native flora. Persistent threats include habitat fragmentation, destruction, and degradation. These and other threats have caused precipitous population declines and local extirpation across its range. Consequentially, the Dakota skipper is currently listed as Endangered in Canada and Threatened in the United States, and the province of Manitoba. Species distribution models (SDM) are a well-known technique which attempt to predict a species distribution on a landscape. These predictions can then be used to inform conservation actions such as guiding survey effort, land acquisitions, and reintroductions. The objectives of this project were to: 1) Compare Dakota skipper models using freely available high resolution remotely sensed products to those using more traditional environmental predictors. 2) Field validate both models to identify the most accurate model using efficient and economical methods. 3) Address issues of modelling rare species to produce a robust SDM for the Dakota skipper in Manitoba. I found that SDMs built from environmental variables generated from satellite imagery performed comparably to one produced from readily available geospatial information. I also found that field validation was more accurate for evaluating SDMs than purely statistical methods. I also produced usable SDMs for the Dakota skipper in the Interlake. Implications from this study are that the advantages of satellite imagery can be leveraged to create useable SDMs to guide conservation actions. This study also further supports the need to field validate an SDM over relying on model statistical output which can be misleading.