Creating individual accessible area hypotheses improves stacked species distribution model performance

• Published in: Global ecology and biogeography Vol. 27; no. 1/2; pp. 156 - 165
• Main Authors: Cooper, Jacob C., Soberón, Jorge
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons Ltd 01.01.2018; Wiley Subscription Services, Inc
• Subjects: Accessibility; accessible area; biogeography; Communities; Community composition; community structure; Dispersal; Dispersion; ecological niche models; Ecological niches; hummingbirds; Hypotheses; Innovations; MACROECOLOGICAL METHODS; macroecological models; model validation; niches; organisms; Performance prediction; prediction; presence–absence matrix; species diversity; Species richness; stacked niche models; Training
• ISSN: 1466-822X; 1466-8238
• DOI: 10.1111/geb.12678

Aim: Stacked species distribution models (SDMs) are an important step towards estimating species richness, but frequently overpredict this metric and therefore erroneously predict which species comprise a given community. We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often-overlooked facet of ecological niche modelling. Innovation: By limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas. Main conclusions: Restricting a species' training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non-restricted stacked SDMs. These accessible area-based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to 'clip' predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.