Modelling ecological niches with support vector machines

Summary The ecological niche is a fundamental biological concept. Modelling species’ niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species’ ranges. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species’ environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine‐learning algorithms can estimate the support of such high‐dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre‐processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo‐absence data and are comparable in empirical tests. Synthesis and applications . Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine‐learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species’ potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.