Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models

Détails

Ressource 1Télécharger: DOI12485AM.pdf (1495.32 [Ko])
Etat: Public
Version: Author's accepted manuscript
ID Serval
serval:BIB_D712019FA27F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models
Périodique
Journal of Biogeography
Auteur(s)
D'Amen M., Dubuis A., Fernandes R.F., Pottier J., Pellisser L., Guisan A.
ISSN
1365-2699 (electronic)
ISSN-L
0305-0270
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
42
Numéro
7
Pages
1255-1266
Langue
anglais
Résumé
Aim: Modelling species at the assemblage level is required to make effective forecast of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (MEM), or by stacking of individual species distribution models (S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities.
Location: The western Swiss Alps.
Methods: Two implementations of the SESAM framework were tested: a "Probability ranking" rule based on species richness predictions and rough probabilities from SDMs, and a "Trait range" rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area and seed mass) to constraint a pool of environmentally filtered species from binary SDMs predictions.
Results: We showed that all independent constraints expectedly contributed to reduce species richness overprediction. Only the "Probability ranking" rule allowed slightly but significantly improving predictions of community composition.
Main conclusion: We tested various ways to implement the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further improving the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.
Mots-clé
Community ecology, functional ecology, macroecological models (MEM), SESAM framework, species distribution models (SDM), Stacked-SDM (S-SDM)
Web of science
Création de la notice
07/01/2015 16:18
Dernière modification de la notice
20/08/2019 15:56
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