Incorporating uncertainty in predictive species distribution modelling

• Published in: Philosophical transactions of the Royal Society of London. Series B. Biological sciences Vol. 367; no. 1586; pp. 247 - 258
• Main Authors: Beale, Colin M., Lennon, Jack J.
• Format: Journal Article
• Language: English
• Published: England The Royal Society 19.01.2012
• Subjects: Bioclimate Envelope; Climate change; Climate Impacts; Climate models; Demography; Distribution Model; Ecological modeling; Ecosystem; Genetic Variation; Information relevance; Modeling; Models, Theoretical; Niche Model; Parametric models; Population Dynamics; Process-Based Model; Review; Spatial Analysis; Spatial models; Species; Statistical relevance model; Stochastic Processes
• ISSN: 0962-8436; 1471-2970; 1471-2970
• DOI: 10.1098/rstb.2011.0178

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.