Predictive mapping of alpine grasslands in Switzerland: Species versus community approach

Abstract

Abstract. Separate logistic regression models were developed to predict the distribution and large‐scale spatial patterns of dominant graminoid species and communities in alpine grasslands. The models are driven by four bioclimatic parameters: degree‐days of growing season (basis 0 °C), a moisture index for July, potential direct solar radiation for March, and a continentality index. Geology and slope angle were used as a surrogate for nutrient availability and soil water capacity. The bioclimatic parameters were derived from monthly mean temperature, precipitation, cloudiness and potential direct solar radiation. The environmental parameters were interpolated using a digital elevation model with a resolution of 50 m. The vegetation data for model calibration originate from field surveys and literature. An independent test data set with samples from three different climatic zones was used to test the model. The degree of coincidence between simulated and observed patterns was similar for species and communities, but the κ‐values for communities were generally higher (κ= 0.539) than for species (mean individual κ= 0.201). Information on land use was detected as a major factor that could significantly improve both the species and the community model. Nevertheless, the climatic factors used to drive the model explained a major part of the observed patterns.

Transitions in social complexity along elevational gradients reveal a combined impact of season length and development time on social evolution

“…To interpolate this variable, we averaged the number of days above 168C for each meteorological station and related this to elevation using both linear and quadratic terms. We then projected the model using a digital elevation model at a resolution of 25 m. We investigated whether spatial autocorrelation remained in the residuals and used an inverse distance-weighted approach to interpolate residual errors (see [40] for further details). Finally, we summed modelled values and interpolated residual layers to obtain a climatic map of the number of days above 168C in Switzerland.…”

Variation in habitat suitability does not always relate to variation in species' plant functional traits

“…In Guisane we used: slope, topographic position, growing degree-days (5.568), moisture index during the growing season, temperature of the coldest month, and annual solar radiation from the meteorological model Aurelhy (Benichou & Le Breton 1987) at 50 Â 50 m. In Anzeindaz we used: slope, topographic position, mean annual temperature, mean annual solar radiations, and mean annual moisture index at 25 Â 25 m (Zimmermann & Kienast 1999 We fitted models using the information-theory approach based on all possible sub-models (2 number of candidate variables ) for a set of explanatory variables (see the electronic supplementary material). Inference from more than one single 'optimal' model allows the resulting habitat suitability to be the average from all possible candidate models weighted by their weights of evidence (see the electronic supplementary material).…”

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).…”