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Agricultural intensification has deeply altered European agroecosystems, leading to the loss of traditional farming practices and the homogenisation of agricultural landscapes in the past decades. These changes are causing dramatic losses in farmland biodiversity across the continent. Agri-Environment Measures (AEM, an umbrella term for Agri-Environmental Schemes, Ecological Focus Areas and organic farming) are the main policy instruments of the European Common Agricultural Policy to counteract this crisis, but their efficacy is recurrently questioned. A systematic monitoring and assessment of AEM efficacy at the European level is lacking, and is hindered by data gaps on biodiversity and field-level land-use/land-management practices. The overarching objectives of this thesis were (1) to improve our understanding of the varying effectiveness of AEM and (2) to develop robust biodiversity model workflows for policy impact assessment based on pre-collected biodiversity data. I combined field-level data on land use and AEM adoption from the Integrated Administration and Control System with geodata on climate, topography and land cover for three agricultural regions in Europe: the Mulde River Basin in Germany, Catalonia in Spain and South Moravia in the Czech Republic. I used synthetic biodiversity data as well as bird occurrence data to investigate drivers of variation in reported modelled biodiversity responses to AEM: (1) the use of different land-use intensity (LUI) metrics, (2) the types of AEM and their species-specific scale of effect, and (3) the structural complexity of the surrounding landscape across different regions. I first used a virtual species approach to test how to comprehensively quantify LUI, and its effects on biodiversity, in spatially-explicit models. Further, I developed open-source modelling workflows to synthesise empirical evidence across different species and regions, using pre-collected bird observations from (sub)national databases combinin, Die Intensivierung der Landwirtschaft hat die Agrarökosysteme Europas tiefgreifend verändert und in den letzten Jahrzehnten zum Verlust traditioneller Anbaumethoden und zur Homogenisierung der Agrarlandschaften geführt. Diese Veränderungen führen zu einem dramatischen Verlust der Biodiversität in der Landwirtschaft auf dem gesamten Kontinent. Agrarumweltmaßnahmen (AEM, ein Sammelbegriff für Agrarumwelt- und Klimamaßnahmen, ökologische Vorrangflächen und den ökologischen Landbau) sind die wichtigsten Instrumente der Gemeinsamen Agrarpolitik der EU, um dieser Krise entgegenzuwirken. Ihre Wirksamkeit wird jedoch immer wieder in Frage gestellt. Ein systematisches Monitoring und eine konsistente Evaluierung der Wirksamkeit von AEM auf europäischer Ebene fehlen und werden durch Datenlücken zur biologischen Vielfalt und zu Landnutzungs- und Bewirtschaftungspraktiken auf Schlagebene erschwert. Die übergreifenden Ziele dieser Dissertation waren (1) die Verbesserung unseres Verständnisses der unterschiedlichen Wirksamkeit von AEM und (2) die Entwicklung robuster Workflows für die Biodiversitätsmodellierung zur Nutzung in der Politikfolgenabschätzung auf Basis von zuvor erhobenen Biodiversitätsdaten. Ich habe Daten auf Feldebene über die Landnutzung und Implementierung von AEM aus dem Integrierten Verwaltungs- und Kontrollsystem mit Geodaten über Klima, Topographie und Landbedeckung für drei landwirtschaftliche Regionen in Europa kombiniert: das Mulde-Einzugsgebiet in Deutschland, Katalonien in Spanien und Südmähren in der Tschechischen Republik. Ich habe simulierte Biodiversitätsdaten sowie Beobachtungsdaten zum Vorkommen von Vögeln verwendet, um die folgenden ursächlichen Faktoren für festgestellte Unterschiede in den modellierten Zusammenhängen zwischen Biodiversität und AEM zu untersuchen: (1) Nutzung verschiedener Metriken zur Quantifizierung der Landnutzungsintensität (LUI), (2) Arten von AEM und ihr artspezifischer Einfluss auf verschiedenen räumlichen Skalen, und (3) s

This deliverable provides an overview of the methods and data used for developing the Farming System Archetypes (FSAs) in the five case studies - Humber, Mulde, SouthMoravia, Bačka and Catalonia. Additionally, it discusses limitations as well as problems and presents solutions. The FSAs are a generalized typology of farming systems that are assumed to have similar response to policy change. FSAs are a major component of the BESTMAP modelling architecture because they provide linkages between many aspects of the project, especially connecting the biophysical and agent-based modelling in the case studies (CS), based on local data (e.g. IACS/LPIS, for explanation see Methodology), with the modelling of policy effects at the EU level, based on FADN micro-data within the FADN regions. The FSA framework defines the main farm characteristics determined by two main dimensions: firstly farm specialization and secondly economic size, both calculated and mapped for each farm in the CSs. ‘Farmer agents’ who belong to the same FSA are then assumed to have similar decision patterns regarding the adoption of agri-environmental schemes, based on the relationships revealed in the CS agent-based models.

Accurate modelling of changes in freshwater supplies is critical in an era of increasing human demand, and changes in land use and climate. However, there are concerns that current landscape-scale models do not sufficiently capture catchment-level changes, whilst large-scale comparisons of empirical and simulated water yield changes are lacking. Here we modelled annual water yield in two time periods (1: 1985–1994 and 2: 2008–2017) across 81 catchments in England and validated against empirical data. Our objectives were to i) investigate whether modelling absolute or relative change in water yield is more accurate and ii) determine which predictors have the greatest impact on model accuracy. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Annual Water Yield model. In this study, absolute values refer to volumetric units of million cubic metres per year (Mm3/y), either at the catchment or hectare level. Modelled annual yields showed high accuracy as indicated by the low Mean Absolute Deviation (MAD, based on normalised data, 0 is high and 1 is low accuracy) at the catchment (1: 0.013 ± 0.019, 2: 0.012 ± 0.020) and hectare scales (1: 0.03 ± 0.030, 2: 0.030 ± 0.025). But accuracy of modelled absolute change in water yield showed a more moderate fit on both the catchment (MAD = 0.055 ± 0.065) and hectare (MAD = 0.105 ± 0.089) scales. Relative change had lower accuracy (MAD = 0.189 ± 0.135). Anthropogenic modifications to the hydrological system, including water abstraction contributed significantly to the inaccuracy of change values at the catchment and hectare scales. Quantification of changes in freshwater provision can be more accurately articulated using absolute values rather than using relative values. Absolute values can provide clearer guidance for mitigation measures related to human consumption. Accuracy of modelled change is related to different aspects of human consumption, suggesting anthropogenic impacts are critically importa

Developing spatially-targeted policies for farmland in the European Union (EU) requires synthesized, spatially-explicit knowledge of agricultural systems and their environmental conditions. Such synthesis needs to be flexible and scalable in a way that allows the generalization of European landscapes and their agricultural potential into spatial units that are informative at any given resolution and extent. In recent years, typologies of agricultural lands have been substantially improved, however, agriculturally relevant aspects have yet to be included. We here provide a spatial classification approach for identifying archetypal patterns of agri-environmental potential in Europe based on machine-learning clustering of 17 variables on bioclimatic conditions, soil characteristics and topographical parameters. We improve existing typologies by (a) including more recent biophysical data (e.g. agriculturally-important soil parameters), (b) employing a fully data-driven approach that reduces subjectivity in identifying archetypal patterns, and (c) providing a scalable approach suitable both for the entire European continent as well as smaller geographical extents. We demonstrate the utility and scalability of our typology by comparing the archetypes with independent data on cropland cover and field size at the European scale and in three regional case studies in Germany, Czechia and Spain. The resulting archetypes can be used to support spatial stratification, upscaling and designation of more spatially-targeted agricultural policies, such as those in the context of the EU's Common Agricultural Policy post-2020.

The Group on Earth Observations Biodiversity Observation Network (GEO BON) was established to promote and provide guidance toward a global observation network on biodiversity and ecosystems for decision makers and the scientific community. Here we comment on three key challenges in the development and implementation of monitoring schemes and indicators of ecosystem services (ES): (1) combining ES observations, data and methods across scales; (2) identifying operational ES metrics that consider the interactions between people and ecosystems; and (3) integrating the diversity of socio-cultural values and knowledge into monitoring activities. We discuss these challenges with the goal to stimulate the ES research community to help tackle these focus areas in ES monitoring.

Understanding biodiversity changes in time is crucial to promptly provide management practices against diversity loss. This is overall true when considering global scales, since human-induced global change is expected to make significant changes on the Earth's biota. Biodiversity management and planning is mainly based on field observations related to community diversity, considering different taxa. However, such methods are time and cost demanding and do not allow in most cases to get temporal replicates. In this view, remote sensing can provide a wide data coverage in a short period of time. Recently, the use of Rao's Q diversity as a measure of spectral diversity has been proposed in order to explicitly take into account differences in a neighbourhood considering abundance and relative distance among pixels. The aim of this paper was to extend such a measure over the temporal dimension and to present an innovative approach to calculate remotely sensed temporal diversity. We demonstrated that temporal beta-diversity (spectral turnover) can be calculated pixel-wise in terms of both slope and coefficient of variation and further plotted over the whole matrix / image. From an ecological and operational point of view, for prioritisation practices in biodiversity protection, temporal variability could be beneficial in order to plan more efficient conservation practices starting from spectral diversity hotspots in space and time. In this paper, we delivered a highly reproducible approach to calculate spatio-temporal diversity in a robust and straightforward manner. Since it is based on open source code, we expect that our method will be further used by several researchers and landscape managers.

There is increasing interest in the use of economic valuation of ecosystem goods and services for a wide variety of purposes. These include relatively familiar uses in project appraisal and more novel applications in advocacy, performance tracking and accounting in public and private settings. Decision makers who use valuation information need to understand the background, strengths and weaknesses of these approaches. The methods have a strong foundation in economic theory and offer a rapidly growing evidence base, improving ability to evaluate a broad range of ecosystem goods and services. Nevertheless, there are theoretical and practical limitations that need to be understood and kept in mind when interpreting results. In this paper, we briefly review the economic valuation methods and situate them in their historical and theoretical contexts. We assess the main critiques, attempts at resolving them, and implications for the usefulness of the methods in different contexts. We examine the main barriers and opportunities for wider uses of valuation evidence, and draw conclusions on the appropriate role of valuation in future, as a tool for aiding reflection and deliberation processes.

Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the us, Mathematical Physics