Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

Détails

ID Serval
serval:BIB_1C9D06B69CE8
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Climate-based empirical models show biased predictions of butterfly communities along environmental gradients
Périodique
Ecography
Auteur(s)
Pellissier L., Pradervand J.N., Pottier J., Dubuis A., Maiorano L., Guisan A.
ISSN
0906-7590
Statut éditorial
Publié
Date de publication
2012
Peer-reviewed
Oui
Volume
35
Numéro
8
Pages
684-692
Langue
anglais
Résumé
A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.
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Création de la notice
05/10/2011 14:10
Dernière modification de la notice
20/08/2019 13:53
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