A cross-regional analysis of red-backed shrike responses to agri-environmental schemes in Europe

Agri-Environmental Schemes (AES) are the main policy tool to counteract farmland biodiversity declines in Europe, but their biodiversity benefit varies across sites and is likely moderated by landscape context. Systematic monitoring of AES outcomes is lacking, and AES assessments are often based on field experiments encompassing one or few study sites. Spatial analysis methods encompassing broader areas are therefore crucial to better understand the context dependency of species' responses to AES. Here, we quantified red-backed shrike (Lanius collurio) occurrences in relation to AES adoption in three agricultural regions: Catalonia in Spain, the Mulde River Basin in Germany, and South Moravia in the Czech Republic. We used pre-collected biodiversity datasets, comprising structured and unstructured monitoring data, to compare empirical evidence across regions. Specifically, in each region we tested whether occurrence probability was positively related with the proportion of grassland-based AES, and whether this effect was stronger in simple compared to complex landscapes. We built Species Distribution Models using existing field observations of the red-backed shrike, which we related to topographic, climatic, and field-level land-use information complemented with remote sensing-derived land-cover data to map habitats outside agricultural fields. We found a positive relationship between AES area and occurrence probability of the red-backed shrike in all regions. In Catalonia, the relationship was stronger in structurally simpler landscapes, but we found little empirical support for similar landscape-moderated effects in South Moravia and the Mulde River Basin. Our results highlight the complexity of species' responses to management across different regional and landscape contexts, which needs to be considered in the design and spatial implementation of future conservation measures

A cross-regional analysis of red-backed shrike responses to agri-environmental schemes in Europe

Agri-environmental schemes (AES) are the main policy tool to counteract farmland biodiversity declines in Europe, but their biodiversity benefit varies across sites and is likely moderated by landscape context. Systematic monitoring of AES outcomes is lacking, and AES assessments are often based on field experiments encompassing one or few study sites. Spatial analysis methods encompassing broader areas are therefore crucial to better understand the context dependency of species’ responses to AES. Here, we quantified red-backed shrike ( Lanius collurio ) occurrences in relation to AES adoption in three agricultural regions: Catalonia in Spain, the Mulde River Basin in Germany, and South Moravia in the Czech Republic. We used pre-collected biodiversity datasets, comprising structured and unstructured monitoring data, to compare empirical evidence across regions. Specifically, in each region we tested whether occurrence probability was positively related with the proportion of grassland-based AES, and whether this effect was stronger in simple compared to complex landscapes. We built species distribution models using existing field observations of the red-backed shrike, which we related to topographic, climatic, and field-level land-use information complemented with remote sensing-derived land-cover data to map habitats outside agricultural fields. We found a positive relationship between AES area and occurrence probability of the red-backed shrike in all regions. In Catalonia, the relationship was stronger in structurally simpler landscapes, but we found little empirical support for similar landscape-moderated effects in South Moravia and the Mulde River Basin. Our results highlight the complexity of species’ responses to management across different regional and landscape contexts, which needs to be considered in the design and spatial implementation of future conservation measures

Farm structure and environmental context drive farmers’ decisions on the spatial distribution of ecological focus areas in Germany

Context: Ecological Focus Areas (EFAs) were designed as part of the greening strategy of the common agricultural policy to conserve biodiversity in European farmland, prevent soil erosion and improve soil quality. Farmers receive economic support if they dedicate at least 5% of their arable farmland to any type of EFA, which can be selected from a list of options drawn up at the European Union level. However, EFAs have been criticized for failing to achieve their environmental goals and being ineffective in conserving farmland biodiversity, mainly because they are not spatially targeted and because they promote economic rather than ecological considerations in farm management decisions. Objectives: We used a spatially explicit approach to assess the influence of farm and field context as well as field terrain and soil conditions on the likelihood of whether or not a particular EFA type was implemented in a field. Methods: We used a multinomial model approach using field-level land use and management data from 879 farms that complied with the EFA policy in 2019 in the Mulde River Basin in Saxony, Germany. Geospatial environmental information was used to assess which predictor variables (related to farm context, field context or field terrain and soil conditions) increased the probability of a field being assigned to a particular EFA. We tested the hypothesis that productive EFAs are more often implemented on fields that are more suitable for agricultural production and that EFA options that are considered more valuable for biodiversity (e.g. non-productive EFAs) are allocated on fields that are less suitable for agricultural production. Results: We found that farms embedded in landscapes with a low proportion of small woody features or nature conservation areas mainly fulfilled the EFA policy with productive EFAs (e.g. nitrogen fixing crops). Conversely, farms with a higher proportion of small woody features or nature conservation areas were more likely to adopt non-productive EFAs. As predicted, large and compact fields with higher soil fertility and lower erosion risk were assigned to productive EFAs. Non-productive EFAs were placed on small fields in naturally disadvantaged areas. EFA options considered particularly beneficial for biodiversity, such as fallow land, were allocated far away from other semi-natural or nature protection areas. - Conclusions Our results highlight that the lack of spatial targeting of EFAs may result in EFA options being assigned to areas where their relative contribution to conservation goals is lower (e.g. farms with higher shares of protected areas) and absent in areas where they are most needed (e.g. high intensity farms). To ensure that greening policies actually promote biodiversity in European agriculture, incentives are needed to encourage greater uptake of ecologically effective measures on intensively used farms. These should be coupled with additional measures to conserve threatened species with specific habitat requirements

Quantifying agricultural land-use intensity for spatial biodiversity modelling: implications of different metrics and spatial aggregation methods

Abstract Context Agricultural intensification is a major driver of farmland biodiversity declines. However, the relationship between land-use intensity (LUI) and biodiversity is complex and difficult to characterise, not least because of the difficulties in accurately quantifying LUI across heterogeneous agricultural regions. Objectives We investigated how the use of different LUI metrics and spatial aggregation methods can lead to large variations in LUI estimation across space and thus affect biodiversity modelling. Methods We used three spatial aggregation methods (square, hexagonal, and voronoi grids) to calculate ten commonly used LUI metrics describing three LUI dimensions: land use, land management and landscape structure. Using a virtual species approach, we compared how LUI values sampled at biodiversity monitoring sites vary across different metrics and grids. We modelled the distribution of three virtual species using Generalised Additive Models to test how omitting certain LUI dimensions from the models affected the model results. Results The density distributions of LUI values at the presence points of the virtual species were significantly different across metrics and grids. The predefined species-environment relationships characterising the environmental niches of two out of three virtual species remained undetected in models that omitted certain LUI dimensions. Conclusions We encourage researchers to consider the implications of using alternative grid types in biodiversity models, and to account for multiple LUI dimensions, for a more complete representation of LUI. Advances in remote sensing-derived products and increased accessibility to datasets on farm structure, land-use and management can greatly advance our understanding of LUI effects on biodiversity

What is the healing time of stage II pressure ulcers? Findings from a secondary analysis

Pressure ulcers (PrUs) remain a concern for clinicians, patients, caregivers, and researchers. Although data on prevalence and incidence are available, as well as evidence-based prevention and management intervention, PrU healing time is underreported. Objective: The objective of this study was to evaluate the healing time of Stage II PrUs. Methods: Secondary analysis of data collected from a multicenter randomized clinical trial was undertaken. Patients (a) with a Stage II PrU, (b) older than 18 years, and (c) who had given informed consent were included. The endpoints of the study were complete re-epithelialization of the PrU measured with the Pressure Ulcer Scale for Healing Tool 3.0 and the healing time. A network of 46 healthcare centers located in northern Italy participated in the study. Results: Two hundred seventy patients with an average age of 83.9 years (95% confidence interval [CI], 82.71-85.10) were recruited. Among 270 Stage II PrUs included, 153 lesions healed (56.7%), whereas 74 (27.4%) were still present after 10 weeks of follow-up. For 43 lesions (15.9%), the follow-up evaluation was interrupted because of patient death or transfer to units not included in the study. The PrUs healed on an average of 22.9 days (95% CI, 20.47-25.37 days), with a median of 18 days. The average healing time for PrUs of less than 3.1 cm2 was significantly shorter (19.2 days; 95% CI, 16.6-21.8) compared with those 3.1 cm2 or greater (31.0 days; 95% CI, 26.4-35.6 days) (P = .000). Conclusions: To achieve complete re-epithelialization in Stage II PrUs, it takes approximately 23 days. This is quite a long time if we consider that pressures of only 60 to 70mmHg for between 30 and 240 minutes are needed to cause tissue damage. On average, a small ulcer heals 12 days faster compared with those with a surface of 3.1 cm2 or greater. Copyright \ua9 2015 Wolters Kluwer Health | Lippincott Williams & Wilkins