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The degree to which elevated CO2 concentrations (e[CO2]) increase the amount of carbon (C) assimilated by vegetation plays a key role in climate change. However, due to the short-term nature of CO2 enrichment experiments and the lack of reconciliation between different ecological scales, the effect of e[CO2] on plant biomass stocks remains a major uncertainty in future climate projections. Here, we review the effect of e[CO2] on plant biomass across multiple levels of ecological organization, scaling from physiological responses to changes in population-, community-, ecosystem-, and global-scale dynamics. We find that evidence for a sustained biomass response to e[CO2] varies across ecological scales, leading to diverging conclusions about the responses of individuals, populations, communities, and ecosystems. While the distinct focus of every scale reveals new mechanisms driving biomass accumulation under e[CO2], none of them provides a full picture of all relevant processes. For example, while physiological evidence suggests a possible long-term basis for increased biomass accumulation under e[CO2] through sustained photosynthetic stimulation, population-scale evidence indicates that a possible e[CO2]-induced increase in mortality rates might potentially outweigh the effect of increases in plant growth rates on biomass levels. Evidence at the global scale may indicate that e[CO2] has contributed to increased biomass cover over recent decades, but due to the difficulty to disentangle the effect of e[CO2] from a variety of climatic and land-use-related drivers of plant biomass stocks, it remains unclear whether nutrient limitations or other ecological mechanisms operating at finer scales will dampen the e[CO2] effect over time. By exploring these discrepancies, we identify key research gaps in our understanding of the effect of e[CO2] on plant biomass and highlight the need to integrate knowledge across scales of ecological organization so that large-scale modeling can represent the finer-scale mechanisms needed to constrain our understanding of future terrestrial C storage.

1. Green alder shrubs Alnus viridis increasingly overgrow European mountain pastures but hinder natural forest succession. This nitrogen-fixing, autochthonous invasive species has numerous negative effects including the loss of biodiversity and landscape aesthetic, eutrophication of soils and downstream waters, and greenhouse gas emission. Over centuries, A. viridis encroachment was impeded by grazing livestock, particularly goats. However, in modern agriculture, livestock numbers decreased on remote mountain pastures and goat farming became unprofitable. 2. A grazing experiment tested if hardy breeds of the more economically attractive livestock species, cattle and sheep can replace goats as A. viridis antagonists. On a subalpine pasture heavily encroached by A. viridis, space use and debarking (bark foraging) by Dexter cattle, Engadine sheep and Pfauen goats were analysed using GPS tracking and vegetation mapping. 3. Dexter cattle used space least evenly (Camargo evenness: cattle = 0.39; sheep = 0.52; and goats = 0.47) and preferred flat slopes and open pastures. They spent least time foraging in A. viridis stands (relative presence while foraging: cattle = 0.55; sheep = 0.76; and goats = 0.80). Dexter cattle did not debark any A. viridis, but damaged shrubs by trampling. 4. Engadine sheep visited A. viridis stands nearly as often as goats, but preferred flat slopes, short vegetation and shrub edges more clearly than goats. This sheep breed debarked significantly more A. viridis branches (average 244 branches per paddock; 7.4% of all A. viridis branches) than goats (45; 0.8%). Goats preferred mountain-ash shrubs Sorbus aucuparia covering only 2% of the shrub layer. 5. Synthesis and applications. Green alder shrubs, controlled by goats traditionally, overgrow valuable mountain pasture ecosystems today. Cattle, kept instead of goats, may slow down but not hinder shrub encroachment, because they avoid shrub stands and do not debark green alder. However, this study is the first to show that Engadine sheep are even better suited for green alder clearance than goats. Moreover, if management goals include forest re-establishment, Engadine sheep outperform goats, because they debark green alder (which hinders forest regeneration), but do not destroy elderberry—a valuable forest pioneer. Hardy breeds are an important tool to maintain biodiverse, open pastures, restore natural mountain forests and mitigate the negative environmental effects of shrub encroachment., Journal of Applied Ecology, 59 (5), ISSN:0021-8901, ISSN:1365-2664

Background: In a pandemic, when timely and clear communication is important, visuals on social media can help citizens quickly find and understand health risk information. In recognition of visuality and social media’s value during a crisis, we investigated popular Covid-19 risk communication with visuals posted on the platform Twitter. Looking at tweet authors, their use of graphics, the preventative messages, and risk framing, our objective was to determine how visual communication on Twitter promoted WHO Covid-19 health recommendations.Methods: We sourced Twitter’s 500 most retweeted Covid-19 messages for each month from January - October 2020 using Crowdbreaks. Included tweets had to have visuals, be in English, come from verified accounts, and contain at least one of the keywords ‘covid19', 'coronavirus', 'corona', or 'covid’. Following a retrospective approach, we then performed a qualitative content analysis of the tweets’ text and visuals. Results: Most of the tweets analysed came from influencers - individuals with many followers (51%), followed by media companies (30%), and health and government institutions (15%). At the start of the pandemic, the latter two were most prevalent. Analysis of visual formats showed that photographs were most common, and the majority of tweets combined them with other graphic types (55%). 68% of tweets had text in their visual, 42% of all visuals were animated, and 26% included a URL. ‘Stay home’ and ‘wear a mask’ were the most frequently communicated Covid-19 preventative measures. 70% of tweets used risk framing (emphasising health gains or loss), and 32% had tones of critique.Conclusion: This study found that the most retweeted Covid-19 preventative measures with visuals mostly came from individuals, showing that health and government organisations were not alone in promoting preventative measures on Twitter. This stresses the important role individuals play in the dissemination of information using social media during a health crisis. The finding that more tweets used health loss framing, often combined with the emotive medium of photographs, raises concerns about persuasive tactics feeding on fear. Future research is needed to better understand this approach's consequences and its impact on public perceptions and behaviours.

This report summarizes the 23 tutorials hosted at The Web Conference 2021: nine lecture-style tutorials and 14 hands-on tutorials.

Natural estrogens such as 17α-estradiol (E2α), 17β-estradiol (E2β), estrone (E1), and estriol (E3), released to surface waters from both urban and agricultural sources, are endocrine disrupting for fish. Here, we assess the prevalence of livestock farming derived natural estrogens in tributaries and ponds in the agriculturally dominated catchment of Lake Baldegg, Switzerland. Passive samplers were deployed in the main tributary and daily time-proportional water samples were collected in five tributaries for 30 days at the beginning of the vegetation period. Furthermore, we took grab samples of 12 ponds in the catchment. Aqueous samples were liquid–liquid extracted, derivatized, and analysed with LC-MS/MS and stream water samples additionally with ERα-CALUX, a bioassay for assessing total estrogenic activity. Natural estrogens were regularly detected, with mean concentrations ranging from below the limit of detection to 0.55 ng L−1 for E2β and E1, respectively, and passive sampling and bioassay results largely confirmed these findings. Monte Carlo simulated mean natural estrogen concentrations underestimated measured ones by a factor of three to 11. An agricultural area's hydrological contribution and connectivity to surface waters seemed to be more important for the development of estrogen concentrations in streams than livestock densities in a catchment or the actual loads of slurry applied. Pond water occasionally contained natural estrogens in concentrations up to 8.6 ng L−1 for E2α. The environmental quality standards of the European Union (0.4 ng L−1 for E2β and 3.6 ng L−1 for E1) were never exceeded for longer than a day in tributaries, but E1 reached critical concentrations for aquatic organisms in ponds.

Spatial monitoring of grassland management is crucial for ecosystem assessment and the establishment of sustainable agriculture. Switzerland is covered by large areas of small structured grassland parcels differing in management practices and use intensities, making the mapping of grassland management challenging. We present a monitoring tool to map grassland management, distinguishing between mowing- and grazing practice, and between different use intensities for Swiss agroecosystems. By analyzing pixelwise spectral time series of 2015, derived from satellite imagery of the Landsat archive, we estimated the number of management events and biomass productivity. Both estimates were used to map classes of dominant management practices and use intensities following a stepwise clustering approach. The grassland management (GM) classes were evaluated relative to established spectral and topographical patterns of grassland use intensity, and in terms of spatial conformity with available regional land use data. The GM classes were also analyzed with respect to management related vegetation plot data on species diversity, as well as on indicator values for nutrient supply and management tolerance. The stepwise clustering gave three use intensity classes for each dominant management practice of grazing (pasture) and mowing (meadow). Use intensity was higher for meadows than pastures with a distinct intensity gradient for each grassland practice. The GM classes reproduced established spectral and topographical patterns of grassland use intensity, indicated by increased standard deviations (SD) of spectral time series profiles (e.g. mean SD of 0.048 for pastures and 0.054 for meadows) and lower slopes (e.g. mean slopes of 10° for pastures and 7° for meadows). The averaged spatial conformity of the GM classes with a cantonal land use map was 82% for meadows and 97% for pastures. The GM classes spatially matched with land use patterns of three subregions, e.g. with an areal proportion of 73% pasture classes for a subregion dominated by grazing. Moreover, the GM classes reproduced established vegetation patterns of grassland use intensity along the GM intensity gradient, showing a mean decrease in species richness (33%), as well as a mean increase in indicator values for nutrient supply (5%), grazing tolerance (4%), and mowing tolerance (6%).

The online space has become a digital public square, where individuals interact and share ideas on the most trivial to the most serious of matters, including discussions of controversial ethical issues in science, technology and medicine. In the last decade, new disciplines like computational social science and social data science have created methods to collect and analyse such data that have considerably expanded the scope of social science research. Empirical bioethics can benefit from the integration of such digital methods to investigate novel digital phenomena and trace how bioethical issues take shape online.Here, using concrete examples, we demonstrate how novel methods based on digital approaches in the social sciences can be used effectively in the domain of bioethics. We show that a digital turn in bioethics research aligns with the established aims of empirical bioethics, integrating with normative analysis and expanding the scope of the discipline, thus offering ways to reinforce the capacity of bioethics to tackle the increasing complexity of present-day ethical issues in science and technology. We propose to call this domain of research in bioethicsdigital bioethics.

"Since the first successful application of the gene editing method based on the CRISPR/Cas-system, the technology has demonstrated great potential but also sparked a series of ethical concerns. Some of the issues are already known from earlier gene editing debates. However, the possibility of CRISPR to target genes with high accuracy and the easy application that allows a biohacker to experiment with a simple toolkit ordered online have introduced new ethical challenges. Further, thanks to preprint servers such as bioRxiv, biomedical research results are more and more accessible with little delay after an experiment was conducted. This enables researchers all over the world to participate and conduct their own experiments, making it a global endeavour. Not only does this make it difficult to monitor and regulate the technology but also speeds up the technological development significantly.CRISPR is only one of many examples of recent advancements with potentially high consequences for society at large. We think it is therefore paramount to identify new issues, understand their nature and assess their impact in a timely manner. In this paper, we propose the integration of digital methods into the toolbox of modern empirical bioethics and demonstrate their potential with two examples: We used 1) crawling and network analysis for hypothesis building, and 2) sentiment analysis to assess the public’s attitudes towards CRISPR on Twitter over a six and a half years period. "

Abandonment of pastures and successional shrub expansion are widespread in European mountain regions. Moderate shrub encroachment is perceived beneficial for plant diversity by adding new species without outcompeting existing ones, yet systematic evidence is missing. We surveyed vegetation along 24 transects from open pasture into shrubland across the Swiss Alps using a new protocol distinguishing different spatial scales, shrub cover of each plot (2 x 2 m) and larger-scale zonal cover along the transect. Data were analysed using generalized linear models of shrub cover, shrub species and environmental conditions, such as geology, aspect or soil. Most shrub communities were dominated byAlnus viridis(62% of transects) andPinus mugo(25%), and the rest by other shrub species (13%). These dominant shrub species explained vegetation response to shrub cover well, without need of environmental variables in the model. Compared to open pasture,A. viridisresulted in an immediate linear decline in plant species richness and a marginal increase in beta-diversity (maximally + 10% at 35% cover). DenseA. viridishosted 62% less species than open pasture. InP. mugo,species richness remained stable until 40% shrub cover and dropped thereafter; beta-diversity peaked at 35% cover. Hence, scatteredP. mugoincreases beta-diversity without impairing species richness. In transects dominated by other shrubs, species richness and beta-diversity peaked at 40-60% shrub cover (+ 23% both).A. viridisreduced species richness in a larger area around the shrubs thanP. mugo. Therefore, effects of shrub encroachment on plant diversity cannot be generalized and depend on dominant shrub species., Alpine Botany, 130 (2), ISSN:1664-2201, ISSN:1664-221X

The discovery of the CRISPR-Cas9-based gene editing method has opened un-precedented new potential for biological and medical engineering, sparking a growing public debate on both the potential and dangers of CRISPR applications. Given the speed of technology development, and the almost instantaneous global spread of news, it’s important to follow evolving debates without much delay and in sufficient detail, as certain events may have a major long-term impact on public opinion and later influence policy decisions. Social media networks such as Twitter have shown to be major drivers of news dissemination and public discourse. They provide a vast amount of semi-structured data in almost real-time and give direct access to the content of the conversations. Such data can now be mined and analyzed quickly because of recent developments in machine learning and natural language processing. Here, we used BERT, an attention-based transformer model, in combination with statistical methods to analyse the entirety of all tweets ever published on CRISPR since the publication of the first gene editing application in 2013. We show that the mean sentiment of tweets was initially very positive, but began to decrease over time, and that this decline was driven by rare peaks of strong negative sentiments. Due to the high temporal resolution of the data, we were able to associate these peaks with specific events, and to observe how trending topics changed over time. Overall, this type of analysis can provide valuable and complementary insights into ongoing public debates, extending the traditional empirical bioethics toolset.

Many mountain pastures consist of a mosaic of grassland and shrub communities. Ongoing changes in mountain agriculture have affected the balance between the two elements of the mosaic. In order to understand the consequences of these changes for ecosystem functioning, we studied patterns in vegetation, root structure and soil properties along transects of varying grassland-to-shrub proportions. Our hypothesis was that differences in the vegetation aboveground are accompanied by differences belowground, related to soil properties and depth. The research was conducted at a subalpine site in the Trentino region (South-eastern Alps), consisting of Nardus stricta grasslands alternating with shrub patches of Rhododendron ferrugineum. Our investigation showed that the composition of vegetation was mainly governed by R. ferrugineum cover and less by soil properties. Plant species richness peaked at low to intermediate degrees of shrub cover and composition between transects became more similar with increa...

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.

Farmers are key players in actions to halt biodiversity loss from farmland. However, if farmers are to sustain biodiversity, they must first be adequately informed about biodiversity and understand its drivers. Measuring biodiversity at the farm scale is difficult because of the structural complexity of many farms, and because different aspects of diversity can be considered desirable, e.g. species richness or rarity. In this study we examined 19 grassland farms in Central Switzerland, and sampled plants, earthworms, spiders and bees using a stratified sampling design. We considered several metrics of species diversity, but found two particularly useful at farm scale: average richness (area-weighted) and farm uniqueness in terms of species identity. Average richness reflects the expected species richness in a random sample taken on the farm, and farm uniqueness is the contribution of a farm to the total species richness of all farms under study. Average richness and farm uniqueness are complementary and reflect different aspects of biodiversity. We demonstrate how combining these metrics enables tailored recommendations for enhancing species diversity on the farm.

International audience; Farmland species provide key ecological services that support agricultural production, but are under threat from agricultural intensification and mechanization. In order to design effective measures to mitigate agricultural impact, simultaneous investigations of different taxonomic groups across several regions are required. Therefore, four contrasting taxonomic groups were investigated: plants, earthworms, spiders and bees (wild bees and bumblebees), which represent different trophic levels and provide different ecological services. To better understand underlying patterns, three community measurements for each taxonomic group were considered: abundance, species richness and species composition. In four European regions, ten potential environmental drivers of the four taxonomic groups were tested and assigned to three groups of drivers: geographic location (farm, region), agricultural management (crop type, mineral nitrogen input, organic nitrogen input, mechanical field operations and pesticide applications) and surrounding landscape in a 250 m buffer zone (diversity of habitats in the surroundings, proportion of arable fields and proportion of non-productive, non-woody habitats). First, the variation in abundance, species richness and species composition from 167 arable sites was partitioned to compare the relative contribution of the three groups of drivers (geographic location, agricultural management and surrounding landscape). Second, generalized linear mixed-effects models were applied to estimate the effect of the individual explanatory variables on abundance and species richness. Our analysis showed a dominant effect of geographic location in all four taxonomic groups and a strong influence of agricultural management on plants, spiders and bees. The effect of the surrounding landscape was of minor importance and inconsistent in our data. We conclude that in European arable fields, the avoidance of mineral nitrogen and pesticides is beneficial for biodiversity, and that species protection measures should take into account regional characteristics and the community structure of the investigated taxonomic groups.

Grasslands provide multiple Ecosystem Services (ES) such as forage provision, carbon sequestration or habitat provision. Knowledge about the trade-offs between these ES is of great importance for grassland management. Yet, the outcome of different management strategies on ES provision is highly uncertain due to spatial variability. We aim to characterize the provision (level and spatial variability) of grassland ES under various management strategies. To do so, we combine empirical data for multiple ES with spatially explicit census data on land use intensities. We analyzed the variations of five ES (forage provision, climate regulation, pollination, biodiversity conservation and outdoor recreation) using data from biodiversity fieldwork, experimental plots for carbon as well as social network data from Flickr. These data were used to calculate the distribution of modelled individual and multiple ES values from different grassland management types in a Swiss case study region using spatial explicit information for 17,383 grassland parcels. Our results show that (1) management regime and intensity levels play an important role in ES provision but their impact depends on the ES. In general, extensive management, especially in pastures, favors all ES but forage provision, whereas intensive management favors only forage provision and outdoor recreation; (2) ES potential provision varies between parcels under the same management due to the influence of environmental drivers, related to topography and landscape structure; (3) there is a trade-offs between forage provision and other ES at the cantonal level but a synergy between forage provision and biodiversity conservation within the grassland categories, due to the negative impact of elevation on both ES. Information about multiple ES provision is key to support effective agri-environmental measures and information about the spatial variability can prevent uncertain outputs of decision-making processes.

Highland cattle are lighter, slower-growing and less demanding on forage than most production-oriented cattle breeds, which may affect vegetation composition. This study aimed at identifying the importance of breed-dependent impact on the composition of pasture vegetation in comparison to well-investigated factors such as site properties and grazing management. Vegetation was investigated in 50 paired pastures at 25 locations ranging from Swiss mountain areas to lowlands in southern Germany. Pastures in a pair had been grazed by either Highland cattle or a more production-oriented cattle breed for at least 5 years. Plant species composition was assessed on 150 subplots, three per pasture in areas representing different grazing intensities. Generalized linear mixed-effects models, (partial) constrained correspondence analysis and structural equation models were used for data analysis. Despite similar site conditions between the paired pastures at each location, plants on pastures of Highland cattle showed significantly lower indicator values for grazing and trampling tolerance. Both, grazing and trampling were strongly connected and had a common negative effect on plant species diversity. Moreover, Highland cattle had a direct positive influence on diversity, likely due to reduced woody plant species cover and a higher cover of epizoochoric species. This resulted in significantly higher plant species richness (alpha and gamma) on pastures of Highland cattle than those of production-oriented breeds. The observed differences in plant species richness between pastures of different grazing breeds increased with duration of adaptation, i.e. the time a pasture was grazed by a certain breed. The study demonstrates a clear impact of cattle breed on vegetation, which is consistent with the phenotypical differences of the animals. Largely overlooked, cattle breed may explain some of the frequently contrasting responses of vegetation to grazing. The findings have important implications for management decisions and breeding endeavours which go beyond mere productivity objectives. They highlight the potential of low-production Highland cattle to sustain and promote ecosystem services on species-rich, semi-natural grasslands. peerReviewed

Cessation of agricultural management and subsequent natural forest succession has been the primary land use change in the Southern Alps over the past 50years. It is generally assumed that early stages of succession host more plant species than grazed pastures, but that this richness is partly lost as the density of woody species increases. Based on vegetation surveys on eight sites in the Italian Alps, we found the effects of forest succession on plant species richness to depend strongly on environmental conditions. The relationship between plant species richness and wood cover at the sites ranged from non-detectable over hump-shaped, to monotonically decreasing. Linear mixed-effects models indicate that high mean annual temperature is associated with a strong decrease in plant species richness and in the number of red-list species along the pasture-to-forest gradient. Sampling plant species composition at a range of scales allowed us to rule out artefacts caused by modified species–area relationships as a consequence of changes in wood cover. Multi-scale sampling also indicated that the primary loss of plant species richness by forest succession is in plant species with low abundance. Our data further allow assessment of the risk of species loss in mountain grasslands in the Southern Alps, which is highest on sites with higher mean annual temperature. These areas should receive concentrated attention and support for biodiversity conservation.

The study target was to assess the usefulness of the OECD test guideline 307 for the veterinary pharmaceutical ivermectin. Laboratory microcosm studies were conducted to investigate the aerobic and anaerobic transformation of ivermectin in soils from three locations in Europe (York, Madrid and Tastrup) and an artificial soil. The reason to include an artificial soil in the study was to understand the exposure potential of ivermectin in a parallel eco-toxicological study with non-target organisms in this soil for a longer duration. Three kinetic models (first-order (SFO), availability-adjusted first-order (AAFO) and bi-exponential first-order (BFO)) were applied to fit the observed transformation dynamics and to derive dissipation times. Dissipation rates were highly dependent on the tested soils. Under aerobic conditions, dissipation was remarkably faster in the three natural soils tested (DT 50 = 16.1–36.1 d) than in the artificial soil (DT 50 > 500 d). Furthermore, a clear increase in DT 50 values was seen when the temperature was lowered from 20 to 6 °C. The results indicated that dissipation in soils with comparably strong sorption and low degrees of desorption (i.e. the York soil and to some extent the Tastrup soil) were best described by the AAFO model. While dissipation in the Madrid soil which had a lower sorption coefficient and a higher degree of reversibility of sorption could be satisfactorily described with the SFO model. Our data further showed that no significant dissipation occurred under anaerobic conditions.

Field experiments that expose terrestrial ecosystems to climate change factors by manipulations are expensive to maintain, and typically only last a few years. Plant biomass is commonly used to assess responses to climate treatments and to predict climate change impacts. However, response to the treatments might be considerably different between the early years and a decade later. The aim of this data analysis was to develop and apply a method for evaluating changes in plant biomass responses through time, in order to provide a firm basis for discussing how the ‘short-term’ response might differ from the ‘long-term’ response. Across 22 sites situated in the northern hemisphere, which covered three continents, and multiple ecosystems (grasslands, shrublands, moorlands, forests, and deserts), we evaluated biomass datasets from long-term experiments with exposure to elevated CO2 (eCO2), warming, or drought. We developed methods for assessing biomass response patterns to the manipulations using polynomial and linear (piecewise) model analysis and linked the responses to site-specific variables such as temperature and rainfall. Polynomial patterns across sites indicated changes in response direction over time under eCO2, warming, and drought. In addition, five distinct pattern types were confirmed within sites: ‘no response’, ‘delayed response’, ‘directional response’, ‘dampening response’, and ‘altered response’ patterns. We found that biomass response direction was as likely to change over time as it was to be consistent, and therefore suggest that climate manipulation experiments should be carried out over timescales covering both short- and long-term responses, in order to realistically assess future impacts of climate change.

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.

Summary • In grasslands, the loss of structural carbon (C) from nonharvested plant parts is a primary C source for the soil. The amount of input depends not only on the size of structural C pools but also on their loss rates. • In the field, we examined the effects of elevated atmospheric partial pressures of CO2 (pCO2) and nitrogen (N) supply on pool size and rates of structural C loss in stubble and roots of perennial ryegrass (Lolium perenne) by using multiple-pulse labelling and steady-state labelling. • Stubble retained structural C for roughly half the time it was retained in roots. Elevated pCO2 combined with low N supply enlarged the pools of roots and stubble. These conditions also stimulated the rate of structural C loss from stubble and, thus, the amounts available for further transformation. • The potential of multiple-pulse labelling as a field technique is highlighted. The stimulation of structural C loss from stubble by elevated pCO2 at low N provides a missing link between increased C assimilation and low yield response and indicates a potentially higher input of structural C into the soil.

1. The effects of neighbours on the growth of individual plants are fundamental to dynamics in plant populations and can be described by means of mathematical functions, so-called competition kernels, in formal spatiotemporal models. Little is known about the form and components such functions should have. 2. We evaluate some properties of kernel functions using data on the growth of Arabidopsis thaliana plants in replicated, even-aged stands of many individuals. Because of the essential non-independence of plant growth in stands, we employed a Bayesian hierarchical modelling approach to estimate values and uncertainties of kernel parameters in location-dependent models of interacting plants. 3. During the experiment plant size and a simple measure of neighbourhood crowding became strongly correlated, plants tending to be small where local crowding was intense, indicating that local competition was an important process in the growth of the plants. 4. Competitive interactions between plants of different sizes were strongly asymmetric, the larger individual acquiring a disproportionately greater share of resources. Competition increased with plant size and attenuated rapidly at distances of a few centimetres, but the exact shape of the attenuation function was less important. 5. Kernel functions with the same kind of structural features were similar in their predictive ability. However, a simple zone-of-influence model, based on overlap of pairs of individuals, with competition favouring the larger individual, was arguably the most parsimonious. 6. Neighbourhood competition in stands of even-aged plants may be successfully captured with relatively simple kernel functions. The results should inform and enhance the formal theory of spatiotemporal plant population and community dynamics. Bayesian hierarchical modelling is a powerful tool with which to analyse complex, spatially dependent data, and has potential as a widely applicable statistical approach for plant ecology.

Wild bees, spiders, earthworms and plants contribute considerably to biodiversity in grasslands and fulfil vital ecological functions. They also provide valuable services to agriculture, such as pollination, pest control and maintenance of soil quality. We investigated the responses of wild bees, spiders, earthworms and plants to geographic location, agricultural management and surrounding landscape variables using a dataset of 357 grassland fields within 88 farms in six European regions. Regions and taxonomic groups were selected to have contrasting properties, in order to capture the multiple facets of European grasslands. Geographic location alone had a dominant effect on the fauna and flora communities. Depending on the taxonomic group, various agricultural management and surrounding landscape variables alone had an additional significant effect on observed species richness, rarefied species richness and/or abundance, but it was always small. Bee species richness and abundance decreased with increasing number of mechanical operations (e.g. cutting). Observed spider species richness and abundance were unrelated to measured aspects of agricultural management or to surrounding landscape variables, whereas rarefied species richness showed significant relations to nitrogen input, habitat diversity and amount of grassland habitats in the surroundings. Earthworm abundance increased with increasing nitrogen input but earthworm species richness did not. Observed plant species richness decreased with increasing nitrogen input and increased when there were woody habitats in the surroundings. Rarefied plant species richness decreased with mechanical operations. Investigating multiple regions, taxonomic groups and aspects of fauna and flora communities allowed identifying the main factors structuring communities, which is necessary for designing appropriate conservation measures and ensuring continued supply of services. Wildbienen, Spinnen, Regenwürmer und Pflanzen machen einen bedeutenden Teil der Biodiversität in landwirtschaftlich genutztem Grünland aus und bilden eine wichtige Grundlage für ökologische Dienstleistungen. Dazu gehören z.B. Bestäubung, biologische Schädlingsbekämpfung und der Erhalt der Bodengesundheit. Wir untersuchten, inwiefern die vier taxonomischen Gruppen von der geografischen Lage, von Bewirtschaftungs- und von Umgebungsfaktoren abhängig sind. In die Studie gingen Daten aus sechs europäischen Regionen ein, die in 88 landwirtschaftlichen Betrieben auf insgesamt 357 Mähwiesen und Weiden erhoben wurden. Die Regionen und taxonomischen Gruppen wurden gezielt ausgewählt, um eine möglichst breite Vielfalt im europäischen Agrargrünland abzudecken. Die geografische Lage beeinflusste die Artengesellschaften am stärksten. Je nach taxonomischer Gruppe hatten verschiedene Bewirtschaftungs- und Umweltfaktoren zusätzlich einen signifikanten, aber kleinen Effekt auf den beobachteten Artenreichtum, den rarefizierten Artenreichtum und/oder die Abundanz. Bei den Bienen nahmen der Artenreichtum und die Abundanz mit der Anzahl maschineller Bearbeitungen (z.B. Schnitt) pro Jahr ab. Weder der beobachtete Spinnenartenreichtum noch die Spinnenabundanz waren abhängig von den erhobenen Bewirtschaftungs- oder Umgebungsfaktoren. Der rarefizierte Spinnenartenreichtum hingegen stand im Zusammenhang mit dem Stickstoffeintrag, der Habitatvielfalt und dem Grünlandanteil in der Umgebung. Bei den Regenwürmern erhöhte sich die Abundanz mit dem Stickstoffeintrag, nicht aber der Artenreichtum. Der beobachtete Artenreichtum der Pflanzen nahm mit dem Stickstoffeintrag ab und mit dem Gehölzanteil in der Umgebung zu. Auf den rarifizierten Pflanzenartenreichtum hatte die Anzahl maschineller Bewirtschaftungen zusätzlich einen negativen Effekt. Die Untersuchung von mehreren Regionen, taxonomischen Gruppen und Aspekten von Artengesellschaften erlaubte es, wichtige Einflussfaktoren auf Artengesellschaften zu erkennen. Diese Resultate können dazu beitragen wirksame Massnahmen für den Erhalt der Biodiversität und die Sicherstellung der ökologischen Leistungen zu erarbeiten.

International audience; Organic farming is promoted to reduce environmental impacts of agriculture, but surprisingly little is known about its effects at the farm level, the primary unit of decision making. Here we report the effects of organic farming on species diversity at the field, farm and regional levels by sampling plants, earthworms, spiders and bees in 1470 fields of 205 randomly selected organic and nonorganic farms in twelve European and African regions. Species richness is, on average, 10.5% higher in organic than nonorganic production fields, with highest gains in intensive arable fields (around +45%). Gains to species richness are partly caused by higher organism abundance and are common in plants and bees but intermittent in earthworms and spiders. Average gains are marginal +4.6% at the farm and +3.1% at the regional level, even in intensive arable regions. Additional, targeted measures are therefore needed to fulfil the commitment of organic farming to benefit farmland biodiversity.

[1] A distributed hydrological model was used to simulate the distribution of fast runoff formation as a proxy for critical source areas for herbicide pollution in a small agricultural catchment in Switzerland. We tested to what degree predictions based on prior knowledge without local measurements could be improved upon relying on observed discharge. This learning process consisted of five steps: For the prior prediction (step 1), knowledge of the model parameters was coarse and predictions were fairly uncertain. In the second step, discharge data were used to update the prior parameter distribution. Effects of uncertainty in input data and model structure were accounted for by an autoregressive error model. This step decreased the width of the marginal distributions of parameters describing the lower boundary (percolation rates) but hardly affected soil hydraulic parameters. Residual analysis (step 3) revealed model structure deficits. We modified the model, and in the subsequent Bayesian updating (step 4) the widths of the posterior marginal distributions were reduced for most parameters compared to those of the prior. This incremental procedure led to a strong reduction in the uncertainty of the spatial prediction. Thus, despite only using spatially integrated data (discharge), the spatially distributed effect of the improved model structure can be expected to improve the spatially distributed predictions also. The fifth step consisted of a test with independent spatial data on herbicide losses and revealed ambiguous results. The comparison depended critically on the ratio of event to preevent water that was discharged. This ratio cannot be estimated from hydrological data only. The results demonstrate that the value of local data is strongly dependent on a correct model structure. An iterative procedure of Bayesian updating, model testing, and model modification is suggested.

The veterinary parasiticide ivermectin was selected as a case study compound within the project ERAPharm (Environmental Risk Assessment of Pharmaceuticals). Based on experimental data generated within ERAPharm and additional literature data, an environmental risk assessment (ERA) was performed mainly according to international and European guidelines. For the environmental compartments surface water, sediment, and dung, a risk was indicated at all levels of the tiered assessment approach. Only for soil was no risk indicated after the lower tier assessment. However, the use of effects data from additional 2-species and multispecies studies resulted in a risk indication for collembolans. Although previously performed ERAs for ivermectin revealed no concern for the aquatic compartment, and transient effects on dung-insect populations were not considered as relevant, the present ERA clearly demonstrates unacceptable risks for all investigated environmental compartments and hence suggests the necessity of reassessing ivermectin-containing products. Based on this case study, several gaps in the existing guidelines for ERA of pharmaceuticals were shown and improvements have been suggested. The action limit at the start of the ERA, for example, is not protective for substances such as ivermectin when used on intensively reared animals. Furthermore, initial predicted environmental concentrations (PECs) of ivermectin in soil were estimated to be lower than refined PECs, indicating that the currently used tiered approach for exposure assessment is not appropriate for substances with potential for accumulation in soil. In addition, guidance is lacking for the assessment of effects at higher tiers of the ERA, e.g.;for field studies or a tiered effects assessment in the dung compartment. Integr Environ Assess Manag 2010;6567587. © 2010 SETAC.

Predicting discharge in ungauged catchments or contaminant movement through soil requires knowledge of the distribution and spatial heterogeneity of hydrological soil properties. Because hydrological soil information is not available at a European scale, we reclassified the Soil Geographical Database of Europe (SGDBE) at 1:1 million in a hydrological manner by adopting the Hydrology Of Soil Types (HOST) system developed in the UK. The HOST classification describes dominant pathways of water movement through soil and was related to the base flow index (BFI) of a catchment (the long-term proportion of base flow on total stream flow). In the original UK study, a linear regression of the coverage of HOST classes in a catchment explained 79% of BFI variability. We found that a hydrological soil classification can be built based on the information present in the SGDBE. The reclassified SGDBE and the regression coefficients from the original UK study were used to predict BFIs for 103 catchments spread throughout Europe. The predicted BFI explained around 65% of the variability in measured BFI in catchments in Northern Europe, but the explained variance decreased from North to South. We therefore estimated new regression coefficients from the European discharge data and found that these were qualitatively similar to the original estimates from the UK. This suggests little variation across Europe in the hydrological effect of particular HOST classes, but decreasing influence of soil on BFI towards Southern Europe. Our preliminary study showed that pedological information is useful for characterising soil hydrology within Europe and the long-term discharge regime of catchments in Northern Europe. Based on these results, we draft a roadmap for a refined hydrological classification of European soils.

Studies of animal behavior are crucial to understanding animal-ecosystem interactions, but require substantial efforts in visual observation or sensor measurement. We investigated how classifying behavioral states of grazing livestock using global positioning data alone depends on the classification approach, the preselection of training data, and the number and type of movement metrics. Positions of grazing cows were collected at intervals of 20 seconds in six upland areas in Switzerland along with visual observations of animal behavior for comparison. A total of 87 linear and cumulative distance metrics and 15 turning angle metrics across multiple time steps were used to classify position data into the behavioral states of walking, grazing, and resting. Five random forest classification models, a linear discriminant analysis, a support vector machine, and a state-space model were evaluated. The most accurate classification of the observed behavioral states in an independent validation dataset was 83%, obtained using random forest with all available movement metrics. However, the state-specific accuracy was highly unequal (walking: 36%, grazing: 95%, resting: 58%). Random undersampling led to a prediction accuracy of 77%, with more balanced state-specific accuracies (walking: 68%, grazing: 82%, resting: 68%). The other evaluated machine-learning approaches had lower classification accuracies. The state-space model, based on distance to the preceding position and turning angle, produced a relatively low accuracy of 64%, slightly lower than a random forest model with the same predictor variables. Given the successful classification of behavioral states, our study promotes the more frequent use of global positioning data alone for animal behavior studies under the condition that data is collected at high frequency and complemented by context-specific behavioral observations. Machine-learning algorithms, notably random forest, were found very useful for classification and easy to implement. Moreover, the use of measures across multiple time steps is clearly necessary for a satisfactory classification.

Background In order to understand the impact of grazing livestock on pasture ecosystems, it is essential to quantify pasture use intensity at a fine spatial scale and the factors influencing its distribution. The observation and analysis of animal activity is greatly facilitated by remote tracking technology and new statistical frameworks allowing for rapid inference on spatially correlated data. We used these advances to study activity patterns of GPS-tracked cows in six summer-grazing areas in the Swiss Alps that differed in environmental conditions as well as livestock management. Results Recorded GPS positions were assigned to the activities of grazing, resting, and walking, and were discretized on a regular grid. Regression models with spatially structured effects were fitted to the spatial activity patterns using Integrated Nested Laplace Approximation. They indicated that terrain slope, forage quality, and stocking rate were the primary factors determining cow activity in the six study areas. Terrain slope significantly reduced livestock activity in five of the six areas and sparse forage availability significantly reduced grazing in all areas. In three areas, grazing pressure imposed by the pasture rotation was observable in the grazing pattern. Insolation, distance to the shed, and distance to water were less important for cow activity. In addition to the main factors identified across all study areas, we found effects operating only in individual areas, which were partly explained by specific environmental and management characteristics. In study areas with few paddocks, environmental variables exerted a stronger control on livestock activity than in areas with a short stocking period per paddock. Conclusions The data demonstrated that a strict pasture rotation with short stocking periods is necessary to influence livestock activity, and hence potential effects on ecosystem processes. Without grazing management, livestock activity is primarily determined by the environment. Such insight is indispensable for studying relationships between grazing animals and ecosystem characteristics, and for developing management strategies to optimize ecosystem services. The analysis also highlighted the need for an appropriate statistical treatment of bio-logging data, since various estimates were biased if spatial autocorrelation was ignored. Electronic supplementary material The online version of this article (doi:10.1186/s40462-015-0053-6) contains supplementary material, which is available to authorized users.