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Title: The optimal sampling design for littoral habitats modelling: A case study from the north-western Mediterranean
Author: Cefalì, Maria Elena
Ballesteros i Sagarra, Enric, 1958-
Riera, Joan Lluís
Chappuis, Eglantine
Terradas, Marc
Mariani, Simone
Cebrian Pujol, Emma
Keywords: Hàbitat (Ecologia)
Ecologia litoral
Teoria de models
Mediterrània (Costa)
Habitat (Ecology)
Seashore ecology
Model theory
Mediterranean Coast
Issue Date: 24-May-2018
Publisher: Public Library of Science (PLoS)
Abstract: Species distribution models (SDMs) have been used to predict potential distributions of habitats and to model the effects of environmental changes. Despite their usefulness, currently there is no standardized sampling strategy that provides suitable and sufficiently representative predictive models for littoral marine benthic habitats. Here we aim to establish the best performing and most cost-effective sample design to predict the distribution of littoral habitats in unexplored areas. We also study how environmental variability, sample size, and habitat prevalence may influence the accuracy and performance of spatial predictions. For first time, a large database of littoral habitats (16,098 points over 562,895 km of coastline) is used to build up, evaluate, and validate logistic predictive models according to a variety of sampling strategies. A regularly interspaced strategy with a sample of 20% of the coastline provided the best compromise between usefulness (in terms of sampling cost and effort) and accuracy. However, model performance was strongly depen upon habitat characteristics. The proposed sampling strategy may help to predict the presence or absence of target species or habitats thus improving extensive cartographies, detect high biodiversity areas, and, lastly, develop (the best) environmental management plans, especially in littoral environments.
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It is part of: PLoS One, 2018, vol. 13, num. 5, p. e0197234
Related resource:
ISSN: 1932-6203
Appears in Collections:Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)

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