Optimizing the bioindication of forest soil acidity, nitrogen and mineral nutrition using plant species
2016
Pinto, Paulina E. | Dupouey, Jean-Luc | Hervé, Jean-Christophe | Legay, Myriam | Wurpillot, Stephanie | Montpied, Pierre | Gégout, Jean-Claude
Soil moisture and nutritional characteristics are frequently assessed using plant species and community bioindication, e.g., the Ellenberg system of species indicator values. This method, based on complete inventories of plant species present in plots, is time-consuming, which could prevent its general use for forest or other natural land management. Our aim was to determine the impact of a reduction in the time spent to carry out a floristic inventory on the quality of soil characteristic assessment using plant bioindication. We compared the measurements of soil pH-H2O (pH), organic carbon to total nitrogen ratio (C:N) and base saturation (BS) in the 0–5cm soil layer of 470 plots with the same variables estimated from floristic inventories of increasing duration, using plant indicator values (IV) from the EcoPlant database. The performance of predictions was evaluated by the square of the linear correlation coefficient between measured and predicted values (R2) and the root mean square error (RMSE) of predictions.The number rather than the percentage of total plot species used for the estimations was determinant for the prediction of soil pH quality. Performance of bioindication of pH, BS and C:N reached the maximum R2 using the first 20–25 species recorded per plot, corresponding to a 14-min-long floristic inventory in comparison to a mean of 28min spent to carry out a complete floristic inventory. A precision of prediction of 80% of the maximal precision was obtained after 4–5min (6–12 inventoried species) for the three studied variables. These results are independent of the nutritional capability of the soils and were similar at the national and local scales. In order to estimate soil nutritional resources by plant bioindication, it is feasible to significantly reduce the time spent on floristic inventories and, thus, their cost. This is especially useful when the goal is to map the soil quality for decision-making in forest management.
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