Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France

To estimate the relative importance of climate and soil nutritional variables for predicting the distribution of Acer campestre (L.) in French forests. France. We used presence/absence information for A. campestre in 3286 forest plots scattered all over France, coupled with climatic and edaphic data...

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Bibliographic Details
Published in:Journal of biogeography 2006-10, Vol.33 (10), p.1750-1763
Main Authors: Coudun, Christophe, Gégout, Jean-Claude, Piedallu, Christian, Rameau, Jean-Claude
Format: Article
Language:English
Subjects:
Biogeography
Climate models
Climatic variables
Ecological modeling
EcoPlant
Edaphic factors
Forest ecology
Forest soils
Life Sciences
logistic regression
logit analysis
Modeling
Plants
soil nutritional variables
spatial autocorrelation
Species
species distribution modelling
Vegetation
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Summary:To estimate the relative importance of climate and soil nutritional variables for predicting the distribution of Acer campestre (L.) in French forests. France. We used presence/absence information for A. campestre in 3286 forest plots scattered all over France, coupled with climatic and edaphic data. More than 150 climatic variables (temperature, precipitation, solar radiation, evapotranspiration, water balance) were obtained using a digital elevation model (DEM) and a geographical information system (GIS). Six direct soil variables (pH, C/N ratio, base saturation rate, concentrations of calcium, magnesium and potassium) were available from EcoPlant, a phytoecological data base for French forests. Using a forward stepwise logistic regression technique, we derived two distinct predictive models for A. campestre; the first with climatic variables alone and the second with both climatic and edaphic variables. The distribution of A. campestre was poorly modelled when including only climatic variables. The inclusion of edaphic variables significantly improved the quality of predictions for this species, allowing prediction of patches of presence/absence within the study region. Soil nutritional variables may improve the performance of fine-scale (grain) plant species distribution models.
ISSN:0305-0270
1365-2699
DOI:10.1111/j.1365-2699.2005.01443.x