Pattern-recognition ecological niche models fit to presence-only and presence-absence data

Abstract

Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern‐recognition methods as ecological niche models (ENMs). Particularly, one‐class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence‐only data. The development of one‐class methods that perform comparably to two‐class methods (for presence/absence data) would remove modelling decisions about sampling pseudo‐absences or background data points when absence points are unavailable. We studied nine methods for one‐class classification and seven methods for two‐class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade‐offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy.

One‐class models fit to presence‐only data were comparable to two‐class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One‐class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two‐classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low.

These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern‐recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern‐recognition discipline.

Department(s)

Biology

Document Type

Article

DOI

https://doi.org/10.1111/2041-210x.12222

Keywords

machine learning, potential distribution, realized distribution, species distribution model, Swiss flora

Publication Date

2014

Journal Title

Methods in Ecology and Evolution

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