Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/20940

Title: How well can models predict changes in species distributions? A 13-year-old otter model revisited
Authors: Areias-Guerreiro, Joana
Mira, António
Barbosa, A. Márcia
Keywords: generalized linear models
model performance
model evaluation
model extrapolation
discrimination
calibration
Lutra lutra
Issue Date: 2016
Publisher: Associazione Teriologica Italiana
Citation: Areias-Guerreiro J., Mira A. & Barbosa A.M. (2016) How well can models predict changes in species distributions? A 13-year-old otter model revisited. Hystrix - Italian Journal of Mammalogy, in press
Abstract: 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 environmental, human spatial and variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent Spanish otter survey, carried out a decade later and after a significant expansion of the otter 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 published 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.
URI: http://hdl.handle.net/10174/20940
Type: article
Appears in Collections:CIBIO-UE - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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