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To evaluate progress on political biodiversity objectives, biodiversity monitoring provides information on whether intended results are being achieved. Despite scientific proof that monitoring and evaluation increase the (cost) efficiency of policy measures, cost estimates for monitoring schemes are seldom available, hampering their inclusion in policy programme budgets., Empirical data collected from 12 case studies across Europe were used in a power analysis to estimate the number of farms that would need to be sampled per major farm type to detect changes in species richness over time for four taxa (vascular plants, earthworms, spiders and bees). A sampling design was developed to allocate spatially, across Europe, the farms that should be sampled., Cost estimates are provided for nine monitoring scenarios with differing robustness for detecting temporal changes in species numbers. These cost estimates are compared with the Common Agricultural Policy ( CAP) budget (2014-2020) to determine the budget allocation required for the proposed farmland biodiversity monitoring., Results show that the bee indicator requires the highest number of farms to be sampled and the vascular plant indicator the lowest. The costs for the nine farmland biodiversity monitoring scenarios corresponded to 0·01%-0·74% of the total CAP budget and to 0·04%-2·48% of the CAP budget specifically allocated to environmental targets., Synthesis and applications. The results of the cost scenarios demonstrate that, based on the taxa and methods used in this study, a Europe-wide farmland biodiversity monitoring scheme would require a modest share of the Common Agricultural Policy budget. The monitoring scenarios are flexible and can be adapted or complemented with alternate data collection options (e.g. at national scale or voluntary efforts), data mobilization, data integration or modelling efforts. [ABSTRACT FROM AUTHOR]

Summary: 1. Ellenberg’s indicator values scale the flora of a region along gradients reflecting light, temperature, continentality, moisture, soil pH, fertility and salinity. They can be used to monitor environmental change. 2. Ellenberg values can be extended from central Europe, for which they were defined, to nearby parts of Europe. Given a database of quadrat samples, they can be repredicted by a simple algorithm consisting of two-way weighted averaging, followed by local regression. 3. A database of British samples was assembled from two large surveys. Ellenberg values were repredicted. 4. Except for the indicator of continentality, the correlation of repredicted and original values was in the range 0·72 (light) to 0·91 (moisture). The continentality indicator could not be adequately repredicted by the algorithm, and is unusable in Britain. 5. Discrepancies between original and repredicted values can be attributed to various causes, including wrong original values, differing ecological requirements in Britain and central Europe, biased sampling of the British range of habitats, and the occurrence of small plants in shaded or basic microhabitats within well illuminated or predominantly acid quadrats. 6. The repredicted values were generally reliable, but a small proportion was clearly wrong. Wrong values were due to either inadequate sampling of species’ realized niches in Britain or sampling with quadrats that were too large and included species that were not close associates. [ABSTRACT FROM AUTHOR]

Farmland is a major land cover type in Europe and Africa and provides habitat for numerous species. The severe decline in farmland biodiversity of the last decades has been attributed to changes in farming practices, and organic and low-input farming are assumed to mitigate detrimental effects of agricultural intensification on biodiversity. Since the farm enterprise is the primary unit of agricultural decision making, management-related effects at the field scale need to be assessed at the farm level. Therefore, in this study, data were collected on habitat characteristics, vascular plant, earthworm, spider, and bee communities and on the corresponding agricultural management in 237 farms in 13 European and two African regions. In 15 environmental and agricultural homogeneous regions, 6-20 farms with the same farm type (e.g., arable crops, grassland, or specific permanent crops) were selected. If available, an equal number of organic and non-organic farms were randomly selected. Alternatively, farms were sampled along a gradient of management intensity. For all selected farms, the entire farmed area was mapped, which resulted in total in the mapping of 11 338 units attributed to 194 standardized habitat types, provided together with additional descriptors. On each farm, one site per available habitat type was randomly selected for species diversity investigations. Species were sampled on 2115 sites and identified to the species level by expert taxonomists. Species lists and abundance estimates are provided for each site and sampling date (one date for plants and earthworms, three dates for spiders and bees). In addition, farmers provided information about their management practices in face-to-face interviews following a standardized questionnaire. Farm management indicators for each farm are available (e.g., nitrogen input, pesticide applications, or energy input). Analyses revealed a positive effect of unproductive areas and a negative effect of intensive management on biodiversity. Communities of the four taxonomic groups strongly differed in their response to habitat characteristics, agricultural management, and regional circumstances. The data has potential for further insights into interactions of farmland biodiversity and agricultural management at site, farm, and regional scale. [ABSTRACT FROM AUTHOR]