Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France
Article first published online: 25 MAY 2006
Journal of Biogeography
Volume 33, Issue 10, pages 1750–1763, October 2006
How to Cite
Coudun, C., Gégout, J.-C., Piedallu, C. and Rameau, J.-C. (2006), Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. Journal of Biogeography, 33: 1750–1763. doi: 10.1111/j.1365-2699.2005.01443.x
- Issue published online: 25 MAY 2006
- Article first published online: 25 MAY 2006
- Climatic variables;
- logistic regression;
- soil nutritional variables;
- spatial autocorrelation;
- species distribution modelling
Aim To estimate the relative importance of climate and soil nutritional variables for predicting the distribution of Acer campestre (L.) in French forests.
Methods 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.
Results 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.
Main conclusion Soil nutritional variables may improve the performance of fine-scale (grain) plant species distribution models.