Spatial modelling of soil water holding capacity improves models of plant distributions in mountain landscapes

Détails

Ressource 1Télécharger: Cianfrani_etal2019_Plants&Soil.pdf (1069.88 [Ko])
Etat: Public
Version: de l'auteur⸱e
Licence: Non spécifiée
ID Serval
serval:BIB_DA8C63FDEC22
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Spatial modelling of soil water holding capacity improves models of plant distributions in mountain landscapes
Périodique
Plant and Soil
Auteur⸱e⸱s
Cianfrani C., Buri A., Vittoz P., Grand S., Zingg B., Verrecchia E., Guisan A.
ISSN
0032-079X
1573-5036
ISSN-L
1573-5036
Statut éditorial
Publié
Date de publication
05/2019
Peer-reviewed
Oui
Volume
438
Numéro
1-2
Pages
57-70
Langue
anglais
Résumé
Aims
The aims of this study were: 1) to test a new methodology to overcome the issue of the predictive capacity of soil water availability in geographic space due to measurement scarcity, 2) to model and generalize soil water availability spatially to a whole region, and 3) to test its predictive capacity in plant SDMs.
Methods
First, we modelled the measured Soil Water Holding Capacity (SWHC at different pFs) of 24 soils in a focal research area, using a weighted ensemble of small bivariate models (ESM). We then used these models to predict 256 locations of a larger region and used the differences in these pF predictions to calculate three different indices of soil water availability for plants (SWAP. These SWAP variables were added one by one to a set of conventional topo-climatic predictors to model 104 plant species distributions.
Results
We showed that adding SWAP to the SDMs could improve our ability to predict plant species distributions, and more specifically, pF1.8–pF4.2 became the third most important predictor across all plant models.
Conclusions
Soil water availability can contribute a significant increase in the predictive power of plant distribution models, by identifying important additional abiotic information to describe plant ecological niches.
Mots-clé
Ensemble of small weighted bivariate models, Soil water holding capacity, Habitat suitability, Predictions, Environmental niche, Topo-climate, Swiss Alps
Web of science
Financement(s)
Fonds national suisse / CR23I2_162754
Création de la notice
03/03/2019 21:58
Dernière modification de la notice
21/11/2022 8:10
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