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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

Vascular plants are helpful indicators for the ecological quality. In the context of the Ecological Quality Ordinance they are already used in the utilized agricultural area. Recently, criteria to assess the ecological quality of summer pastures have been discussed. ART has identified the vascular plant taxa, which best represent species richness, the occurrence of Red List species and the occurrence of target and character species following the environmental objectives for Swiss alpine pastures. More than 3500 vegetation relevés were analysed to constitute an indicator list containing 63 taxa. Based on this indicator list and an appreciation by botanical experts the share of alpine pasture reaching floristic quality is currently estimated to be approximately 50 percent.

Lüscher, G., & Walter, T. (2009). Indikatoren für Ökoqualität im Sömmerungsgebiet. Agrar Forschung 16 (5): 146-151.

Species richness is the most common biodiversity metric, although typically some species remain unobserved. Therefore, estimates of species richness and related quantities should account for imperfect detectability. Community dynamics can often be represented as superposition of species-specific phenologies (e.g., in taxa with well-defined flight [insects], activity [rodents], or vegetation periods [plants]). We develop a model for such predictably open communities wherein species richness is expressed as the sum over observed and unobserved species of estimated species-specific and site-specific occurrence indicators and where seasonal occurrence is modeled as a species-specific function of time. Our model is a multispecies extension of a multistate model with one unobservable state and represents a parsimonious way of dealing with a widespread form of “temporary emigration.” For illustration we use Swiss butterfly monitoring data collected under a robust design (RD); species were recorded on 13 transects during two secondary periods within ≤7 primary sampling periods. We compare estimates with those under a variation of the model applied to standard data, where secondary samples are pooled. The latter model yielded unrealistically high estimates of total community size of 274 species. In contrast, estimates were similar under models applied to RD data with constant (122) or seasonally varying (126) detectability for each species, but the former was more parsimonious and therefore used for inference. Per transect, 6–44 (mean 21.1) species were detected. Species richness estimates averaged 29.3; therefore only 71% (range 32–92%) of all species present were ever detected. In any primary period, 0.4–5.6 species present were overlooked. Detectability varied by species and averaged 0.88 per primary sampling period. Our modeling framework is extremely flexible; extensions such as covariates for the occurrence or detectability of individual species are easy. It should be useful for communities with a predictable form of temporary emigration where rigorous estimation of community metrics has proved challenging so far.

Kéry, M., Royle, J. A., Plattner, M., & Dorazio, R. M. (2009). Species richness and occupancy estimation in communities subject to temporary emigration. Ecology, 90(5), 1279–1290. https://doi.org/10.1890/07-1794.1

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.

Steinmann, K., Linder, H. P., & Zimmermann, N. E. (2009). Modelling plant species richness using functional groups. Ecological Modelling, 220(7), 962–967. https://doi.org/10.1016/j.ecolmodel.2009.01.006