The derivation of species response curves with Gaussian logistic regression is sensitive to sampling intensity and curve characteristics |
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Authors: | Christophe Coudun Jean-Claude Gégout |
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Institution: | LERFoB, UMR INRA-ENGREF 1092, Ecole Nationale du Génie Rural, des Eaux et des Forêts, 14 Rue Girardet, CS 4216, 54042 Nancy Cedex, France |
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Abstract: | We investigated quantitatively the sensitivity of plant species response curves to sampling characteristics (number of plots, occurrence and frequency of species), along a simulated pH gradient. We defined 54 theoretical unimodal response curves, issued from combinations of six values for optimum (opt = 3, 4, …, 8), three values for tolerance (tol = 0.5, 1.0, and 1.5, sensu ter Braak and Looman ter Braak, C.J.F., Looman, C.W.N., 1986. Weighted averaging, logistic regression and the Gaussian response model. Vegetatio 65, 3–11]), and three values for maximum probability of presence (pmax = 0.05, 0.20, and 0.50). For each of these 54 theoretical response curves, we built artificial binary data sets (presence/absence) to test the influence of species occurrence, frequency, or number of available plots. With real data extracted from EcoPlant, a phytoecological database for French forests Gégout, J.-C., Coudun, Ch., Bailly, G., Jabiol, B., 2005. EcoPlant: a forest sites database linking floristic data with soil characteristics and climatic conditions. J. Veg. Sci. 16, 257–260], we compared the ecological response of 50 plant species to soil pH, based first on a small data set (100 randomly sampled plots), and then based on the whole data set available (3810 plots). |
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Keywords: | Artificial data Ecological amplitude Ecological optimum EcoPlant database Forest plants Logistic regression pH Species response curve Sampling |
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