How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer

Détails

ID Serval
serval:BIB_F2A5299D8422
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer
Périodique
Methods in Ecology and Evolution
Auteur(s)
Scherrer D., D'Amen M., Fernandes R.F., Mateo R.G., Guisan A.
ISSN
2041-210X
ISSN-L
2041-210X
Statut éditorial
Publié
Date de publication
2018
Peer-reviewed
Oui
Volume
9
Numéro
10
Pages
2155-2166
Langue
anglais
Résumé
1. The popularity of species distribution models (SDMs) and the associated stacked species distribution models (S-SDMs), as tools for community ecologists, largely increased in recent years. However, while some consensus was reached about the best methods to threshold and evaluate individual SDMs, little agreement exists on how to best assemble individual SDMs into communities, i.e. how to build and assess S-SDM predictions.
2. Here, we used published data of insects and plants collected within the same study region to test (1) if the most established thresholding methods to optimize single species prediction are also the best choice for predicting species assemblage composition, or if community-based thresholding can be a better alternative, and (2) whether the optimal thresholding method depends on taxa, prevalence distribution and/or species richness. Based on a comparison of different evaluation approaches we provide guidelines for a robust community cross-validation framework, to use if spatial or temporal independent data are unavailable.
3. Our results showed that the selection of the “optimal” assembly strategy mostly depends on the evaluation approach rather than taxa, prevalence distribution, regional species pool or species richness. If evaluated with independent data or reliable cross-validation, community-based thresholding seems superior compared to single species optimisation. However, many published studies did not evaluate community projections with independent data, often leading to overoptimistic community evaluation metrics based on single species optimisation.
4. The fact that most of the reviewed S-SDM studies reported over-fitted community evaluation metrics highlights the importance of developing clear evaluation guidelines for community models. Here, we move a first step in this direction, providing a framework for cross-validation at the community level.

Mots-clé
community cross-validation, community modeling, ecological niche models, species distribution modeling, species distribution models, thresholding methods
Web of science
Création de la notice
05/06/2018 19:37
Dernière modification de la notice
20/08/2019 16:19
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