How well can models predict changes in species distributions? A 13-year-old otter model revisited


Species distribution and ecological niche models are increasingly used in biodiversity management and conservation. However, one thing that is important but rarely done is to follow up on the predictive performance of these models over time, to check if their predictions are fulfilled and maintain accuracy, or if they apply only to the set in which they were produced. In 2003, a distribution model of the Eurasian otter (Lutra lutra) in Spain was published, based on the results of a country-wide otter survey published in 1998. This model was built with logistic regression of otter presence-absence in UTM 10 km2 cells on a diverse set of  nvironmental, human and spatial variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent otter survey, carried out a decade later and after a significant expansion of the otter distribution area in this country. Despite the time elapsed and the evident changes in this species’ distribution, the model maintained a good predictive capacity, considering both discrimination and calibration measures. Otter distribution did not expand randomly or simply towards vicinity areas, but specifically towards the areas predicted as most favourable by the model based on data from 10 years before. This corroborates the utility of predictive distribution models, at least in the medium term and when they are made with robust methods and relevant predictor variables.


generalized linear models, model performance, model evaluation, model extrapolation, discrimination, calibration, Lutra lutra

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Copyright (c) 2016 Joana Areias-Guerreiro, António Mira, A. Marcia Barbosa

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Hystrix, the Italian Journal of Mammalogy ISSN 1825-5272 (electronic version) 0394-1914 (printed version) Impact Factor (2016) 1.479, CiteScore (2016) 3.51.
Published by Associazione Teriologica Italiana
Creative Commons LicenseWorks published in Hystrix are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.