Verlässliche Daten über unsere Lebens­grundlage

We consider the species-area problem in ecology. The aim is to estimate the total number of species in a large area by extrapolating a species-area curve. Starting with a stationary random field as the background process that is decisive of species occurrence, we argue that asymptotically, the number of unseen species and the species number increment will decrease hyperbolically with increasing area. In particular, stationarity of the background process leads to a fast hyperbolic rate. Some vascular plant species richness data from Switzerland are used to motivate discussions.

Ghosh, S. (2009). The unseen species number revisited. Sankhya: the Indian Journal of Statistics 71-B: 137-150.

The number of species recorded on Red Lists in Central European countries is high and includes several species that already have disappeared. This suggests that the total species number is declining in these countries. However, besides disappearing species there are species immigrating into new areas, either due to human help or due to natural area expansion, as well as formerly extinct species that are remigrating. Regional extinction of some species therefore does not necessarily lead to a decrease in total species number. The study analysed the influence of extincion and of immigration on total species number in Switzerland for the last 107 years and for several taxonomic groups (mammals without bats, breeding birds, reptiles, amphibians, fish, cyclostomes, butterflies, grashoppers and dragonflies). During this period total species number clearly increased (+ 19 species). This increase is mainly due to species that immigrated autonomously from other European countries. Most of them are wetland inhabitants.

Martinez, N., Küttel, M., & Weber, D. (2009). Deutliche Zunahme wildlebener Tierarten in der Schweiz seit 1900. Naturschutz und Landschaftsplanung 41(12): 375-381.

Aim Land use and climate are two major components of global environmental change but our understanding of their simultaneous and interactive effects upon biodiversity is still limited. Here, we investigated the relationship between the species richness of neophytes, i.e. non-native vascular plants introduced after 1500 AD, and environmental covariates to draw implications for future dynamics under land-use and climate change.

Location Switzerland, Central Europe.

Methods The distribution of vascular plants was derived from a systematic national grid of 1 km² quadrates (n = 456; Swiss Biodiversity Monitoring programme) including 1761 species, 122 of which were neophytes. Generalized linear models (GLMs) were used to correlate neophyte species richness with environmental covariates. The impact of land-use and climate change was thereafter evaluated by projections for the years 2020 and 2050 using scenarios of moderate and strong changes for climate warming (IPCC) and urban sprawl (NRP 54).

Results Mean annual temperature and the amount of urban areas explained neophyte species richness best, with a high predictive power of the corresponding model (cross-validated D2 = 0.816). Climate warming had a stronger impact on the potential increase in the mean neophyte species richness (up to 191% increase by 2050) than ongoing urban sprawl (up to 10% increase) independently from variable interactions and model extrapolations to non-analogue environments.

Main conclusions In contrast to other vascular plants, the prediction of neophyte species richness at the landscape scale in Switzerland requires few variables only, and regions of highest species richness of the two groups do not coincide. The neophyte species richness is basically driven by climatic (temperature) conditions, and urban areas additionally modulate small-scale differences upon this coarse-scale pattern. According to the projections climate warming will contribute to the future increase in neophyte species richness much more than ongoing urbanization, but the gain in new neophyte species will be highest in urban regions.

Nobis, M. P., Jaeger, J. A. G., & Zimmermann, N. E. (2009). Neophyte species richness at the landscape scale under urban sprawl and climate warming. Diversity and Distributions, 15(6), 928–939. https://doi.org/10.1111/j.1472-4642.2009.00610.x

Aim To analyse the effects of simultaneously using spatial and phylogenetic information in removing spatial autocorrelation of residuals within a multiple regression framework of trait analysis.

Location Switzerland, Europe.

Methods We used an eigenvector filtering approach to analyse the relationship between spatial distribution of a trait (flowering phenology) and environmental covariates in a multiple regression framework. Eigenvector filters were calculated from ordinations of distance matrices. Distance matrices were either based on pure spatial information, pure phylogenetic information or spatially structured phylogenetic information. In the multiple regression, those filters were selected which best reduced Moran's I coefficient of residual autocorrelation. These were added as covariates to a regression model of environmental variables explaining trait distribution.

Results The simultaneous provision of spatial and phylogenetic information was effectively able to remove residual autocorrelation in the analysis. Adding phylogenetic information was superior to adding purely spatial information. Applying filters showed altered results, i.e. different environmental predictors were seen to be significant. Nevertheless, mean annual temperature and calcareous substrate remained the most important predictors to explain the onset of flowering in Switzerland; namely, the warmer the temperature and the more calcareous the substrate, the earlier the onset of flowering. A sequential approach, i.e. first removing the phylogenetic signal from traits and then applying a spatial analysis, did not provide more information or yield less autocorrelation than simple or purely spatial models.

Main conclusions The combination of spatial and spatio-phylogenetic information is recommended in the analysis of trait distribution data in a multiple regression framework. This approach is an efficient means for reducing residual autocorrelation and for testing the robustness of results, including the indication of incomplete parameterizations, and can facilitate ecological interpretation.

Kühn, I., Nobis, M. P., & Durka, W. (2009). Combining spatial and phylogenetic eigenvector filtering in trait analysis. Global Ecology and Biogeography, 18(6), 745–758. https://doi.org/10.1111/j.1466-8238.2009.00481.x