Predicting the potential distribution of plant species in an alpine environment

Abstract

The relationships between the distribution of alpine species and selected environmental variables are investigated by using two types of generalized linear models (GLMs) in a limited study area in the Valais region (Switzerland). The empirical relationships are used in a predictive sense to mimic the potential abundances of alpine species over a regular grid. Here, we present the results for the alpine sedge Carex curvula ssp. curvula. The modelling approach consists of (1) a binomial GLM, including only the mean annual temperature as explanatory variable, which is adjusted to species presence/ absence data in the entire study area; (2) a logistic model restricted to stands occurring within the a priori defined temperature range for the species -which allows ordinal abundance data to be adjusted; (3) the two species-response functions combined in a GIS to generate a map of the species' potential abundance in the study area; (4) model predictions filtered by the classes of the qualitative variables under which the species never occur.Such a stratified approach used to better fit the variability within the optimal altitudinal zone for the species. Removing stand descriptions from altitudes too high or too low, where the species is unlikely to occur, enhances the global modelling performance by allowing the identification of important environmental variables only retained in the second model.The model evaluation is finally carried out with the γmeasure of association in an ordinal contingency table. It shows that abundance is satisfactorily predicted for C. curvula.

Evaluating resource selection functions

“…Typically, a mid-point cut-off level of 0.5 is the default for creating a classification table. Even with presence/absence data, the cut-off is seldom equal to 0.5 and one must select a threshold cut-off level (Guisan et al, 1998). Classification accuracy is also sensitive to the relative frequency (prevalence) of observations of the species within the sample as threshold cut-off levels are varied.…”

“…Remote-sensing classifications frequently use Kappa as a measure of accuracy assessment (Congalton, 1991;Lillesand and Kiefer, 1994). As in a confusion matrix, a threshold value for prediction must be identified, as illustrated in the Guisan et al (1998) model for the distribution of an alpine plant. Under situations where one might consider some errors in the classification table to be less or more important, one can use a weighted Kappa, controlling the seriousness of each possible disagreement (Cohen, 1968).…”

Generalized linear and generalized additive models in studies of species distributions: setting the scene

“…Examples include the use of regression analyses to predict the distribution of tree and shrub species (Austin et al, 1983(Austin et al, , 1990Lenihan, 1993;Franklin, 1998;Guisan et al, 1999), of herbaceous species (Guisan et al, 1998;Guisan and Theurillat, 2000), of aquatic plant species (Lehmann, 1998), of terrestrial animal species (Pereira and Itami, 1991;Augustin et al, 1996;Manel et al, 1999;Guisan and Hofer, 2001;Jaberg and Guisan, 2001;Zimmermann and Breitenmoser, 2002), of birds (Manel et al, 1999(Manel et al, , 2000, of aquatic animal species (invertebrates; Manel et al, 2000), of plant communities (Zimmermann and Kienast, 1999), or of structural vegetation types (Brown, 1994;Frescino et al, 2001). At a higher level of complexity, these approaches have also been used to investigate the distribution of plant (Currie and Paquin, 1987;Margules et al, 1987;Pausas, 1994;Heikkinen, 1996;Wohlgemuth, 1998) and animal diversity (Owen, 1989;Currie, 1991;Fraser, 1998).…”

Edaphic influences of ophiolitic substrates on vegetation in the Western Italian Alps

“…The low proportion (22.6%) of the inertia explained by the model is due to the disorder typical of ecological systems (Chiarucci et al 2001), associated with a weak differentiation in species composition or to the omission of important environmental properties (e.g., bare soil, hydrography, winter snow depth, etc.) which deeply influence vegetation in mountain ecosystems (Guisan et al 1998).…”