Over the past 60 years, natural habitats have been affected by various anthropogenic pressures. However, little is known about how these pressures have influenced the species composition of whole ...floras across large areas. We used a large database of the Czech flora to assess broad-scale temporal trends in temperate European flora over the last 60 years. We extracted over 4.6 million occurrence records of 1912 species collected over the past six decades and analysed the changes in species occurrence frequency over time using dynamic occupancy models within a Bayesian framework that accounted for various biases in the data. Five main patterns of temporal change were revealed. The increasing species were supported by different environmental changes that peaked at different periods. Competitively strong, nutrient-demanding generalist species that successfully colonize new and highly disturbed habitats supported by eutrophication and anthropogenic disturbances strongly increased in 1961–1980. Shade-tolerant species of less disturbed habitats increased between 1981 and 2000, indicating an effect of habitat abandonment, and thermophilous species began to spread in the last 20 years, reflecting rising temperatures. Competitively strong species of less frequently disturbed habitats with higher moisture and nutrient requirements and low light requirements increased gradually over the last six decades. In contrast, specialized species of nutrient-poor habitats with low colonization and competitive ability, associated with more frequent but less severe disturbances, steadily decreased due to the ongoing decline of habitat quality after the cessation of traditional management, and many of them have been included in the national Red List.
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•Multiple factors contributed to the changes in the Czech flora in different periods•Eutrophication and anthropogenic disturbances caused species increases in 1961–1980•Abandonment of traditional management contributed to species increase in 1981–2000•Thermophilous species have begun to spread since 2000, reflecting global warming•Cessation of traditional management was the main cause of species decline
Urban areas have developed under anthropogenic impact and hence feature strongly altered environmental conditions that influence today’s plant species distribution. Generally, urban areas are rich in ...plant species, but on smaller scales richness can vary considerably. Our study aim is to identify areas of high species richness and to assess richness distribution patterns in the city of Hamburg by analyzing a floristic mapping dataset on the scale of 1 km². Differences in plant species richness were analyzed between three urbanization zones. With multiple regression analyses, we tested effects of urban structure, habitat, and environmental conditions on the distribution of species richness measures. Total species richness per 1 km² was 274 ± 60 on average and differed only slightly between the urbanization zones. It increased with habitat diversity and decreased with Ellenberg indicator values (EIV) for nutrients (multiple R² = 0.31). Proportion of non-native species increased with mean annual temperature and decreased with EIVs for moisture (multiple R² = 0.72), while proportion of endangered species increased with EIVs for moisture and decreased with EIVs for nutrients (multiple R² = 0.66). Proportion of thermophilic species (multiple R² = 0.58) could be explained by mean annual temperature. The emerging patterns probably differ from those in other cities due to the central port harboring a particular flora. Besides the expected high proportions of non-native species, high proportions of endangered species were also found in this area. Our results contribute to identifying drivers of biodiversity in cities and can thus be used to develop measures for the conservation of urban biodiversity.
Assessment of spatial variability in the long-term urban heat island (UHI) is severely restricted by coverage and availability of measurements. This also limits the opportunities to analyse its ...relation to surface characteristics. In this study we therefore introduce a new proxy dataset derived from floristic mapping. The basic assumption is that the species composition in an area reflects climatologic conditions of a certain time period. Ellenberg indicator values for temperature (EITs) were processed to summarise the overall temperature preferences of the occurring plant species. The EITs showed a clear heat island pattern, were highly correlated with existing measurements, and showed increased values in densely built urban classes. Hence, they are considered suitable as UHI proxies. Further, they were related to a large number of typical UHI predictors. The normalised difference vegetation index (NDVI) showed the strongest correlation with the derived pattern and was comparatively robust towards cloud contamination. Urban morphology also explained a noticeable proportion of the overall variance. All predictors explained more than 2/3 of the overall spatial variability, while the redundancy was high. Therefore, the predictors at least allow qualitative statements about the differential exposure to heat-related risks in cities.
Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low ...frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid-based habitat occurrence from grid-based plant species distribution data in a meso-scale analysis. The study was carried out over two spatial extents: Germany and Bavaria. Two simple models were set up to examine the number of characteristic plant species needed per grid cell to predict the occurrence of four selected habitats (species data from FlorKart, http://www.floraweb.de). Both models were calibrated in Bavaria using available information on habitat distribution, validated for other federal states, and applied to Germany. First, a spatially explicit regression model (generalized linear model (GLM) with assumed binomial error distribution of response variable) was obtained. Second, a spatially independent optimization model was derived that estimated species numbers without using spatial information on habitat distribution. Finally, an additional uncalibrated model was derived that calculated the frequencies of 24 habitats. It was validated using NATURA2000 habitat maps. Using the Bavarian models it was possible to predict habitat distribution and frequency from the co-occurrence of habitat-specific species per grid cell. As the model validations for other German federal states were successful, the models were applied to all of Germany, and habitat distribution and frequencies could be retrieved for the national scale on the basis of habitat-specific species co-occurrences per grid cell. Using the third, uncalibrated model, which includes species distribution data only, it was possible to predict the frequencies of 24 habitats based on the co-occurrence of 24% of formation-specific species per grid cell. Predicted habitat frequencies deduced from this third model were strongly related to frequencies of NATURA2000 habitat maps. It was concluded that it is possible to deduce habitat distributions and frequencies from the co-occurrence of habitat-specific species. For areas partly covered by habitat mappings, calibrated models can be developed and extrapolated to larger areas. If information on habitat distribution is completely lacking, uncalibrated models can still be applied, providing coarse information on habitat frequencies. Predicted habitat distributions and frequencies can be used as a tool in nature conservation, for example as correction factors for species frequencies, as long as the species of interest is not included in the model set-up.
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