Quantitative prediction of the distribution and abundance of Vaccinium myrtillus with climatic and edaphic factors
Authors: Coudun, Christophe; Gégout, Jean-Claude
Source: Journal of Vegetation Science, Volume 18, Number 4, August 2007 , pp. 517-524(8)
Publisher: Opulus Press
Abstract:Question: Can the distribution and abundance of Vaccinium myrtillus be reasonably predicted with soil nutritional and climatic factors?
Location: Forests of France.
Methods: We used Braun-Blanquet abundance/dominance information for Vaccinium myrtillus on 2905 forest sites extracted from the phyto-ecological database EcoPlant, to characterize the species ecological response to climatic and edaphic factors and to predict its cover/abundance at the national scale. The link between cover/abundance of the species and climatic (65 monthly and annual predictors concerning temperature, precipitation, radiation, potential evapotranspiration, water balance) and edaphic (two predictors: soil pH and C:N ratio) factors was investigated with proportional odds models. We evaluated the quality of our model with 9830 independent relevés extracted from Sophy, a large phytosociological database for France.
Results: In France, Vaccinium myrtillus is at the southern limit of its European geographic range and three environmental factors (mean annual temperature, soil pH and C:N ratio) allow prediction of its distribution and abundance in forests with high success rates. The species reveals a preference for colder sites (especially mountains) and nutritionally poor soils (low pH and high C:N ratio). A predictive map of its geographic range reveals that the main potential habitats are mountains and northwestern France. The potential habitats with maximal expected abundance are the Vosges and the Massif central mountains, which are both acidic mountains.
Conclusions: Complete niche models including climate and soil nutritional conditions allow an improvement of the spatial prediction of plant species abundance at a broad scale. The use of soil nutritional variables in distribution models further leads to an improvement in the prediction of plant species habitats within their geographical range.
Document Type: Research Article
Publication date: 2007-08-01
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