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49th Annual Conference of the IAVS

49th Annual Conference of the IAVS. Large scale mapping of soil pH by plant presence/absence bioindication. J.C. Gégout, C. Piedallu, I. Seynave, J.C. Hervé AgroParisTech - INRA - IFN. The need of nutritional direct variables for plant distribution modelling.

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49th Annual Conference of the IAVS

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  1. 49th Annual Conference of the IAVS Large scale mapping of soil pH by plant presence/absence bioindication J.C. Gégout, C. Piedallu, I.Seynave, J.C. Hervé AgroParisTech - INRA - IFN

  2. The need of nutritional direct variables for plant distribution modelling • Plant distribution models are built with climatic variables and sometimes with proxies of soil resources (soil types, geology) • Nutritional variables are of major importance to plant growth and distribution • Measures of soil variables are expensive. Thus, it is difficult to gather enough measurements to make accurate maps • Is plant species bioindication useful to map nutritional soil resources ?

  3. French National Forest Inventory (IFN) Systematic sampling in forests 88 004 floristic relevés without pH measures Data EcoPlant 3 835 floristic relevés with pH measures

  4. Indicator value Response curve Pres/abs 1 1 0 0 IV Ecological variable Ecological variable EcoPlant Indicator values Response curve IV for 568 frequent plant species

  5. 8.3 0.3 0.2 6.5 5.7 3.0 0.1 6.2 0 4.0 4.9 5.9 6.9 7.9 pH Predicted pH : 6.0 Dryopteris filix-mas Melica uniflora Sambucus nigra Predicted value : mean of species IV Ellenberg (1974), ter Braak & Barendregt (1986) Bioindication with IV

  6. 88 004IFN plots with floristic inventory Mean of IV on each plot 88 004IFN plots + pH_IV pH prediction on IFN plots

  7. IDW Interpolation pH classes 3.0 - 3.5 3.5 - 4.0 4.0 - 4.5 4.5 - 5.0 5.0 - 5.5 5.5 - 6.0 6.0 - 6.5 6.5 - 7.0 7.0 - 7.5 7.5 - 8.0 8.0 - 8.5 lack of data 0 500 kms pH mapping

  8. Measure of prediction errorsR2REQM =  (1/N * (X - X2) ^ 8 7 Measured pH 6 5 4 3 3 4 5 6 7 8 Mapped pH R2 = 0.56, REQM = 0.82 Map validation 261 validation plots on a grid of 16 x 16 kms Representative of French Forest

  9. Principle A few expensive relevés with measures of variables to establish plant IV A lot of cheap relevés without measures to map variables Step Sampling Plots nb Plots variables Indicator value Plant species & measured variables Stratified according to x few Mapping Plant species Systematic large Map quality Systematic few Plant species & measured variables Variables mapping by bioindication

  10. The databases « IV », « mapping » and « quality » are constructed Acidity (pH) It is now easy to build up new maps of resources with other EcoPlant IV Nitrogen avail. (C/N) A generalisable method Towards new distribution models integrating both climate and soil direct variables IFN establish 7 000 - 10 000 new plots/year -> Increase of map resolution -> Possibility of monitoring by comparing maps of different periods

  11. Thank you !

  12. Comparison of pH and C/N Maps

  13. Validation data (88 004 plots) Nutritional and climatic model Présence Absence Climatic model Map of the potential distribution of Acer campestre Climatic model, success: 54 % Nutritional and climatic model, success: 73 %

  14. Map of the Beech potential productivity

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