The effect of spatially marginal localities in modelling species niches and distributions

Abstract

AbstractAimWe introduce and evaluate the potential effect of spatially marginal localities (specifically those protruding into unsuitable regions), in overestimating species niches and distributions when using ecological niche models (ENMs).LocationNorth‐western South America.MethodsWe built an ENM for the Caribbean spiny pocket mouse (Heteromys anomalus) using MaxEnt and climatic variables. This species typically inhabits extensive tropical forests but can also range into drier habitats through patches of mesic vegetation. We ranked occurrence records according to the suitability value they received, and retrieved habitat information from collectors' field notes and the literature to determine whether those receiving lower values correspond to spatially marginal localities protruding into unsuitable regions. We then built a model excluding a subset of such localities and compared its geographic and environmental prediction with that of the original model.ResultsModels differed substantially in their estimates of suitability. The original model resulted in an overly extensive prediction, considering as suitable hot and dry regions dominated by xerophytic vegetation. Records receiving the lowest suitability values in this model corresponded mainly to captures in patches of mesic forest surrounded by thorn scrub or savannas. The model calibrated without such records restricted suitability mostly to regions characterized by the typical habitat of the species.Main conclusionsWhen it is not possible to use variables that are more proximal or have finer resolutions, we recommend building complementary models that, together, can provide a more realistic estimate of the species' niche and corresponding geographic distribution. Jointly interpreting these models, researchers may better differentiate between areas harbouring typical habitat and those where the species can be found only if locally favourable conditions exist. Such a distinction is of relevance for a wide range of applications relying on inferences obtained from ENMs.