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Aim We predict fine-scale species richness patterns at large spatial extents by linking a systematic sample of vascular plants with a multitude of independent environmental descriptors.

Location Switzerland, covering 41,244 km² in central Europe.

Methods Vascular plant species data were collected along transects of 2500-m length within 1-km² quadrats on a systematic national grid (n = 354), using a standardized assessment method. Generalized linear models (GLM) were used to correlate species richness of vascular plants per transect (SR_t) with three sets of variables: topography, environment and land cover. Regression models were constructed by the following process: reduction of collinearity among variables, model selection based on Akaike’s information criterion (AIC), and the percentage of deviance explained (D²). A synthetic model was then built using the best variables from all three sets of variables. Finally, the best models were used in a predictive mode to generate maps of species richness (SR_t) at the landscape scale using the moving window approach based on 1-km² moving windows with a resolution of 1 ha.

Results The best explanatory model consisted of seven variables including 14 linear and quadratic parameters, and explained 74% of the deviance (D² = 0.742). Used in a predictive mode, the model generated maps with distinctive horizontal belts of highest species richness at intermediate altitudes along valley slopes. Belts of higher richness were also present along rivers and around large forest patches and larger villages, as well as on mountains.

Main conclusions The approach involved using consistent samples of species linked to information on the environment at a fine scale enabled landscapes to be compared in terms of predicted species richness. The results can therefore be applied to support the development of national nature conservation strategies. At the landscape scale, belts of high species richness correspond to steep environmental gradients and associated increases in local habitat diversity. In the mountains, the belts of increased species richness are at intermediate altitudes. These general belt-like patterns at mid-elevation are found in all model parameterizations. Other patterns, such as belts along rivers, are visible in specific parameterizations only. Thus we recommend using several sets of parameters in such modelling studies in order to capture the underlying spatial complexity of biodiversity.

Wohlgemuth, T., Nobis, M. P., Kienast, F., & Plattner, M. (2008). Modelling vascular plant diversity at the landscape scale using systematic samples. Journal of Biogeography, 35(7), 1226–1240. https://doi.org/10.1111/j.1365-2699.2008.01884.x

Species rarefaction curves have long been used for estimating the expected number of species as a function of sampling effort and they represent a powerful tool for quantifying the diversity of an area from local (α-diversity) to regional scale (β- and γ-diversity). Nonetheless, sampling species based on standard plant inventories represents a cost expensive approach. In this view, remotely sensed information may be straightforwardly used for predicting species rich sites. In this paper, we present spectral rarefaction, i.e., the rarefaction of reflectance values derived from satellite imagery, as an effective manner for predicting bio-diverse sites. We tested this approach in ten biogeographical subregions in Switzerland. Plant species data were derived from the Swiss ‘Biodiversity Monitoring’ programme (BDM), which represents species richness of Switzerland at the landscape scale by a systematic sample of 520 quadrats of 1 km × 1 km. Seven Landsat ETM+ images covering the whole study area were acquired. Species and spectral rarefaction were built and results were compared by Pearson correlation coefficient considering several sampling efforts (as measured by the number of sampled quadrats). Local α-diversity showed a similar pattern considering the ten biogeographical subregions while β- and γ-diversity showed higher values for regions in the Alpine arc and lower values for plateau regions and Jura mountains on the strength of the higher ecological (and spectral) variability of the former areas. Meanwhile, positive correlations between species and spectral richness values were significant only after a certain amount of area was accumulated, thus indicating a scale dependence of the fit of satellite and species data. With this paper, we introduce spectral rarefaction as an effective tool in quantifying diversity at a range of spatial scales. Obviously, the achieved results should be viewed as an aid to plan field survey rather than to replace it. We propose to use worldwide available remotely sensed information as a driver for field sampling design strategies.

Rocchini, D., Wohlgemuth, T., Ghisleni, S., Chiarucci, A. (2008). Spectral rarefaction: Linking ecological variability and plant species diversity. Community Ecology, 9(2), 169–176. https://doi.org/10.1556/ComEc.9.2008.2.5

Species richness is the most widely used biodiversity metric, but cannot be observed directly as, typically, some species are overlooked. Imperfect detectability must therefore be accounted for to obtain unbiased species-richness estimates. When richness is assessed at multiple sites, two approaches can be used to estimate species richness: either estimating for each site separately, or pooling all samples. The first approach produces imprecise estimates, while the second loses site-specific information.

In contrast, a hierarchical Bayes (HB) multispecies site-occupancy model benefits from the combination of information across sites without losing site-specific information and also yields
occupancy estimates for each species. The heart of the model is an estimate of the incompletely observed presence–absence matrix, a centrepiece of biogeography and monitoring studies. We illustrate the model using Swiss breeding bird survey data, and compare its estimates with the widely used jackknife species-richness estimator and raw species counts.

Two independent observers each conducted three surveys in 26 1-km² quadrats, and detected 27–56 (total 103) species. The average estimated proportion of species detected after three surveys was 0·87 under the HB model. Jackknife estimates were less precise (less repeatable between observers) than raw counts, but HB estimates were as repeatable as raw counts. The combination of information in the HB model thus resulted in species-richness estimates presumably at least as unbiased as previous approaches that correct for detectability, but without costs in precision relative to uncorrected, biased species counts.
Total species richness in the entire region sampled was estimated at 113·1 (CI 106–123); species detectability ranged from 0·08 to 0·99, illustrating very heterogeneous species detectability; and species occupancy was 0·06–0·96. Even after six surveys, absolute bias in observed occupancy was estimated at up to 0·40.

Synthesis and applications. The HB model for species-richness estimation combines information across sites and enjoys more precise, and presumably less biased, estimates than previous approaches. It also yields estimates of several measures of community size and composition. Covariates for occupancy and detectability can be included. We believe it has considerable potential for monitoring programmes as well as in biogeography and community ecology.

Kéry, M., & Royle, J. A. (2008). Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. Journal of Applied Ecology, 45(2), 589–598. https://doi.org/10.1111/j.1365-2664.2007.01441.x

The Swiss Biodiversity Monitoring Scheme (BDM) records the diversity of butterflies on 482 systematically distributed transects of 2.5 km length all over the country. The first standardized overview of the species’ diversity was achieved with the completion of the first recording period (2003–2007). In total, 188 butterfly species were recorded. Mean species diversity per transect was 32.1. Mean species diversity was highest in the Alps (39.0 species per kilometer-square), whereas it was lowest in the Plateau Area (18.8 species). The BDM contributes important data on the spatial-temporal occurrence of common and moderately rare species. It is not designed to monitor rare or local species. The systematic survey contributes important faunistic data especially for the higher altitudes. To exemplify the results, three species (Aphantopus hyperantus, Cynthia cardui and Coenonympha arcania) are discussed in detail.

Altermatt, F., Birrer, S., Plattner, M., Ramseier, P., Stalling, T. (2008). Erste Resultate zu den Tagfaltern im Biodiversitätsmonitoring Schweiz. Entomo Helvetica 1: 75-83.