Dernière mise à jour
11 juin 2020
Conditions d'utilisation
Open use. Must provide the source.

Description

Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

Ressources

Informations complémentaires

Identifier
4ab13d14-6f96-41fd-96b0-b3ea45278b3d@envidat
Date de publication
4 juin 2020
Date de modification
11 juin 2020
Editeur
EnviDat
Points de contact
Langues
Anglais
Informations complémentaires
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Landing page
https://www.envidat.ch/#/metadata/spatial-modelling-of-ecological-indicator-values
Documentation
Couverture temporelle
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Couverture spatiale
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Intervalle d'actualisation
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Accès aux métadonnées
API (JSON) Télécharger XML

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