Refine
Document Type
- Article (3)
Language
- English (3)
Is part of the Bibliography
- yes (3)
Keywords
Institute
- Fakultät WiSo (2)
- Fakultät AuL (1)
1. Flower strips are a fundamental part of agri-environment schemes (AESs) introduced by the European Union to counteract the loss of biodiversity and related ecosystem services in agricultural landscapes. Although vegetation composition of the strips is essential for most fauna groups, comprehensive studies analysing vegetation development and influencing factors are rare.
2. From 2017 to 2019, we investigated the vegetation composition of 40 perennial wildflower strips (WFSs) implemented in 2015 or 2016, and 20 cereal fields without WFS across Saxony-Anhalt, Germany. We analysed environmental factors on plot (cover of grasses, shading, soil fertility) and four landscape-scale levels (habitat diversity, proportion of WFS and open habitats). The provision of nectar and pollen resources was estimated by the newly developed Pollinator Feeding Index (PFI). All strips had been implemented by farmers as AES with species- rich seed mixtures comprising 30 native forbs.
3. In all study years, forb species richness, cover and related nectar and pollen supply were much higher on WFSs than on controls, confirming the effectiveness of this AES. Although sown native forbs contributed the most to the high PFI values, spontaneously established forbs expanded the total range of species considerably, especially in winter and spring. While sown forb communities remained similar over time, spontaneous forbs showed a higher species turnover. Altogether, shading and grass cover had the greatest negative effect on the performance of the sown forbs. Landscape variables had only minor effects and were inconsistent in their importance across scale levels and years.
4. Synthesis and applications. Successfully established perennial wildflower strips (WFSs) sown with species-rich native seed mixtures provided a forb-rich and diverse vegetation throughout the AES funding period of 5 years. By supplying feeding resources for pollinators under various landscape situations, WFSs have significant potential to promote farmland biodiversity and related ecosyste services. We recommend the mandatory use of species-rich wildflower mixtures for perennial flower strips and to avoid their creation in heavily shaded field edges. Advisory services for farmers are necessary to prevent failures in WFS implementation and management and to improve their ecological effectiveness.
Land cover change is a dynamic phenomenon driven by synergetic biophysical and socioeconomic effects. It involves massive transitions from natural to less natural habitats and thereby threatens ecosystems and the services they provide. To retain intact ecosystems and reduce land cover change to a minimum of natural transition processes, a dense network of protected areas has been established across Europe. However, even protected areas and in particular the zones around protected areas have been shown to undergo land cover changes. The aim of our study was to compare land cover changes in protected areas, non-protected areas, and 1 km buffer zones around protected areas and analyse their relationship to climatic and socioeconomic factors across Europe between 2000 and 2012 based on earth observation data. We investigated land cover flows describing major change processes: urbanisation, afforestation, deforestation, intensification of agriculture, extensification of agriculture, and formation of water bodies. Based on boosted regression trees, we modelled correlations between land cover flows and climatic and socioeconomic factors. The results show that land cover changes were most frequent in 1 km buffer zones around protected areas (3.0% of all buffer areas affected). Overall, land cover changes within protected areas were less frequent than outside, although they still amounted to 18,800 km2 (1.5% of all protected areas) from 2000 to 2012. In some parts of Europe, urbanisation and intensification of agriculture still accounted for up to 25% of land cover changes within protected areas. Modelling revealed meaningful relationships between land cover changes and a combination of influencing factors. Demographic factors (accessibility to cities and population density) were most important for coarse-scale patterns of land cover changes, whereas fine-scale patterns were most related to longitude (representing the general east/west economic gradient) and latitude (representing the north/south climatic gradient).
Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a–nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic–eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.