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We present a new indicator taxa approach to the prediction of climate change effects on biodiversity at the national level in Switzerland. As indicators, we select a set of the most widely distributed species that account for 95% of geographical variation in sampled species richness of birds, butterflies, and vascular plants. Species data come from a national program designed to monitor spatial and temporal trends in species richness. We examine some opportunities and limitations in using these data. We develop ecological niche models for the species as functions of both climate and land cover variables. We project these models to the future using climate predictions that correspond to two IPCC 3rd assessment scenarios for the development of ‘greenhouse’ gas emissions. We find that models that are calibrated with Swiss national monitoring data perform well in 10-fold cross-validation, but can fail to capture the hot-dry end of environmental gradients that constrain some species distributions. Models for indicator species in all three higher taxa predict that climate change will result in turnover in species composition even where there is little net change in predicted species richness. Indicator species from high elevations lose most areas of suitable climate even under the relatively mild B2 scenario. We project some areas to increase in the number of species for which climate conditions are suitable early in the current century, but these areas become less suitable for a majority of species by the end of the century. Selection of indicator species based on rank prevalence results in a set of models that predict observed species richness better than a similar set of species selected based on high rank of model AUC values. An indicator species approach based on selected species that are relatively common may facilitate the use of national monitoring data for predicting climate change effects on the distribution of biodiversity.

Pearman, P. B., Guisan, A., & Zimmermann, N. E. (2011). Impacts of climate change on Swiss biodiversity: An indicator taxa approach. Biological Conservation, 144(2), 866–875. https://doi.org/10.1016/j.biocon.2010.11.020

The Global Index of Vegetation-Plot Databases (GIVD) is a metadatabase of vegetation databases worldwide that was initiated by an international Steering Committee in 2010 and that is hosted on a server in Greifswald. GIVD aims at providing a better overview on the growing number of vegetationplot databases and increasing their accessibility for overarching analyses. In this article, we analyse which data from central Europe (including the Benelux countries) are available in GIVD. On 20 March 2011, 1.35 million of the total 2.45 million registered relevés originated from one of the covered twelve countries. With more than 600,000 digitally available relevés, corresponding to a density of 18 km–2 , the Netherlands are globally leading in this respect.

Jansen, F., Dengler, J., Glöckler, F., Chytry, M., Ewald, J., Oldeland, J., & Schaminée, J.H.J. (2011). Die mitteleuropäischen Datenbanken im Global Index of Vegetation-Plot Databases (GIVD). Tuexenia 31: 351-367.

The relationship between plant diversity and topographic variability in agricultural landscapes was investigated, with the aim of determining whether sampling landscape units of 1 km² (LUs) across a gradient of topographic variability is more efficient than a random design for assessing the range of biodiversity in climatically and biogeographically homogenous areas called sub-regions. Representative plant species data from the Swiss biodiversity monitoring programme were analyzed covering a broad environmental gradient of four altitudinal belts and seven biogeographic regions. The focus of the study laid on agricultural areas but the whole dataset was as well analyzed to put the agricultural LUs in a general context.

Plant species lists of LUs were used to calculate two diversity components: ECOrichness, the number of ecological plant types per LU (as a measure of beta diversity) and AGROrichness, the number of species of conservation importance for agriculture. Mixed regression models were used to analyse the effects of topographic variability on the two plant diversity components, including sub-regions (areas with the same combination of altitudinal belt and biogeographic region) as random factor. These analyses were performed for the whole dataset (419 LUs within 22 sub-regions) and for the focal subset of 187 agricultural LUs within 13 sub-regions.

ECOrichness increased significantly with topographic variability for both the general and the agricultural dataset. The partial correlations within the sub-regions revealed consistent trends for the agricultural LUs but some inconsistencies for the whole dataset. For the monitoring of agricultural LUs the sampling along a gradient of topographic variability is therefore suggested as an efficient means for assessing the range of plant species diversity within sub-regions. Compared to other measures of landscape heterogeneity like habitat heterogeneity, sampling LUs along topographic variability is cheap and easily applied and it was demonstrated to work over large environmental gradients.

Hofer, G., Bunce, R. G. H., Edwards, P. J., Szerencsits, E., Wagner, H. H., & Herzog, F. (2011). Use of topographic variability for assessing plant diversity in agricultural landscapes. Agriculture, Ecosystems & Environment, 142(3–4), 144–148. https://doi.org/10.1016/j.agee.2011.04.011

The species–area curve is generated by niche-related factors and stochastic factors like neutral processes or dispersal. Even though the use of environmental variables is widespread to predict the spatial distribution of species richness, it remains difficult to distinguish the relative importance of habitat heterogeneity and the area effect on total species richness. In our study, we used different types of species–area curves to disentangle the habitat heterogeneity effect and the area effect on vascular plant species richness. We generated three types of sample rarefaction curves: (1) a randomly aggregated rarefaction curve, (2) a rarefaction curve for which areas of similar habitat types were aggregated and (3) a rarefaction curve, for which areas of dissimilar habitat types were aggregated. These analyses were made on three data sets separately with different grain sizes to investigate if this had an effect on the observed pattern. The classification of the habitat types was based on three environmental variables (mean annual temperature, mean moisture index and the slope of the terrain). A consistent pattern of sample rarefaction curves was found with all three data sets. While the aggregation of dissimilar habitat types showed the highest species accumulation rates and saturation levels, the lowest accumulation rates and saturation levels were found when similar habitat types were aggregated. Depending on the grain size, the habitat heterogeneity effect accounted for 20–30% to the total species richness. However, this effect was not statistically significant. The results indicate, that effects of niche related factors on the species–area curve are scale dependent and that effects related to the area are at least as important in explaining the species richness.

Steinmann, K., Eggenberg, S., Wohlgemuth, T., Linder, H. P., & Zimmermann, N. E. (2011). Niches and noise—Disentangling habitat diversity and area effect on species diversity. Ecological Complexity, 8(4), 313–319. https://doi.org/10.1016/j.ecocom.2011.06.004