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

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State: Public
Version: Author's accepted manuscript
Serval ID
serval:BIB_D712019FA27F
Type
Article: article from journal or magazin.
Collection
Publications
Title
Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models
Journal
Journal of Biogeography
Author(s)
D'Amen M., Dubuis A., Fernandes R.F., Pottier J., Pellisser L., Guisan A.
ISSN
1365-2699 (electronic)
ISSN-L
0305-0270
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
42
Number
7
Pages
1255-1266
Language
english
Abstract
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.
Keywords
Community ecology, functional ecology, macroecological models (MEM), SESAM framework, species distribution models (SDM), Stacked-SDM (S-SDM)
Web of science
Create date
07/01/2015 17:18
Last modification date
20/08/2019 16:56
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