Climate envelope models for three endangered skipper butterflies at their northern range margins in Manitoba, Canada

Climate change is accelerating biodiversity loss worldwide, intensifying pressure on already imperiled species. In Canada, several butterfly species are federally listed as endangered due in part to their narrow ranges, small and isolated populations, and dependence on rare or declining habitats, traits that heighten their vulnerability to climate change. Yet, the effects of future climate shifts on their persistence remain poorly understood. This study attempts to model the potential effects of climate change on the future extent of climatically suitable habitat for three at-risk species occurring at their northern range margins in Manitoba, Canada: the Dakota skipper (Hesperia dacotae), Poweshiek skipperling (Oarisma poweshiek), and Mottled duskywing (Erynnis martialis). Endangered species with few occurrences and restricted distributions pose unique modeling challenges, but understanding how climate change may alter their climatically suitable habitat is vital for guiding conservation efforts. To achieve these aims, ensemble climate envelope models (CEMs) were developed using six commonly used algorithms. These models were projected to future conditions using an ensemble of eight high-resolution CMIP6 climate projections for mid- (2041–2070) and late-century (2071–2100) periods, representing two shared socioeconomic pathways (SSP2-4.5 and SSP3-7.0). All ensemble models achieved strong predictive performance based on commonly used evaluation metrics. The Dakota skipper model performed best overall, likely due to more numerous and geographically spread occurrence records. Projections for all species revealed significant declines in climatic suitability across currently occupied areas under all scenarios. Only the Dakota skipper model showed newly suitable regions under future conditions, some within protected areas, offering opportunities for assisted colonisation or targeted habitat assessments. In contrast, no newly suitable areas were identified for the Poweshiek skipperling or Mottled duskywing, likely due to sparse and clustered occurrence data, which limited model generalizability. A key finding is that limited and highly localized species occurrence data can substantially influence model outputs, underscoring the need to interpret CEM results within the context of the quality and quantity of input data. The reliability of ensemble projections depends heavily on input data quality; overlooking this can distort estimates of species’ future suitability even when evaluation metrics indicate strong model performance.

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