MIGCLIM: Predicting plant distribution and dispersal in a changing climate

Détails

ID Serval
serval:BIB_8DE468F170E9
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
MIGCLIM: Predicting plant distribution and dispersal in a changing climate
Périodique
Diversity and Distributions
Auteur(s)
Engler R., Guisan A.
ISSN
1366-9516
Statut éditorial
Publié
Date de publication
2009
Peer-reviewed
Oui
Volume
15
Numéro
4
Pages
590-601
Langue
anglais
Résumé
Many studies have forecasted the possible impact of climate change on plant distribution using models based on ecological niche theory. In their basic implementation, niche-based models do not constrain predictions by dispersal limitations. Hence, most niche-based modelling studies published so far have assumed dispersal to be either unlimited or null. However, depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under- or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of "potentially suitable" and "potentially colonisable" habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed MIGCLIM, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. MIGCLIM implements various parameters, such as dispersal distance, increase in reproductive potential over time, barriers to dispersal or long distance dispersal. Several simulations were run for two virtual species in a study area of the western Swiss Alps, by varying dispersal distance and other parameters. Each simulation covered the hundred-year period 2001-2100 and three different IPCC-based temperature warming scenarios were considered. Our results indicate that: (i) using realistic parameter values, the future potential distributions generated using MIGCLIM can differ significantly (up to more than 95% decrease in colonized surface) from those that ignore dispersal; (ii) this divergence increases both with increasing climate warming and over longer time periods; (iii) the uncertainty associated with the warming scenario can be nearly as large as the one related to dispersal parameters; (iv) accounting for dispersal, even roughly, can importantly reduce uncertainty in projections.
Mots-clé
Cellular automata, climate change, dispersal modelling, dynamic niche-based models, GLM, plant species distribution
Web of science
Open Access
Oui
Création de la notice
05/12/2008 13:01
Dernière modification de la notice
20/08/2019 15:51
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