Data compatibility and system interoperability are fundamental for crosswalks and collaboration between domains. The most frequently used references for information sharing are time and location. In order to understand the requirements, fundamental processes, and core information concepts of a domain, a comprehensive, but standardised documentation is needed. In spatial data infrastructures models presented in Unified Modelling Language (UML) are widely used to facilitate the uptake of standards and valorise best practices of various communities. The European agricultural decision makers must deal with many components described in the Common Agricultural Policy (CAP) in order to optimize data integration and achieve transparency. Geospatial information plays a key role in the implementation of this policy, which comprises the establishment and maintenance of the Integrated Administration and Control System (IACS). In the past the IACS of the MS has principally served a single high level business case: to run a correct administration of the CAP. However, the recent reform and the synergies with environmental and societal policies create increasing expectations for IACS, which impact upon system interoperability and data usability. These objectives can be achieved by establishing a framework that is standard based, allows flexible extensions, and that supports efficient implementation and information exchange between the stakeholders. This paper presents the development process and describes the structure of a domain model we propose for IACS. This standard driven model was designed to translate requirements into technical elements, ensuring interoperability, and providing flexible extensions at the same time. For this purpose we set-up a formal requirement model, formalized use cases, and integrated ISO/TC 211 and INSPIRE UML based class diagrams. As such, this paper proposes a methodology to help guide how the policy can be implemented. The importance of traceability from IACS and third party business rules to the information concepts was proven by simulation runs. The developed modelling approach yielded a reference for conformance testing, indicated critical points of potential errors, and highlighted the impact of eventual changes. Storing all concepts and implementation options of IACS in a unique framework helps to eliminate redundant efforts, provides a strong basis for developing various applications, underpins interoperability with other domains and enhances transparency of the CAP.JRC.H.4-Monitoring Agricultural Resource

This review synthesizes historical and contemporary research on wildlife–vehicle collisions and roadkill, outlining its evolution from early documentation to modern road ecology. It discusses how early efforts in North America and Europe that quantified animal casualties and developed standardized methodologies formed current studies that use advanced geospatial tools, citizen science, and artificial intelligence to analyze spatiotemporal patterns. We examine key ecological, methodological, and economic impacts of roadkill on wildlife populations and human safety, highlighting the role of road density, vehicle speed, and seasonal factors. The framework presented also underscores a commitment to sustainability by integrating environmental conservation with infrastructural development and socio-economic resilience. The review details various mitigation strategies, from fencing and wildlife crossings to dynamic signage, and evaluates their effectiveness in reducing mortality rates, thereby supporting sustainable development in transportation infrastructure and wildlife management. It also identifies research gaps and outlines future directions, advocating for integrated, multidisciplinary approaches to improve wildlife conservation, infrastructure planning, and public awareness in the context of rapidly expanding road networks.

The present work presents the development of a moose movement model to explore the value of wildlife mitigation structures and examine how hypothetical changes in land use patterns could alter wildlife habitats at landscape scales. Collisions between vehicles and animals pose a threat to humans and wildlife populations, the most dangerous collisions being with moose. Migrations of moose are generally predictable and habitat-dependent. Here, we use GIS-based simulations of moose movements to examine road-related habitat fragmentation around the main highways A1 and A2 in Lithuania. From forest data, we develop a moose habitat suitability map. Then, by running multiple simulation iterations, we generate potential moose pathways and statistically describe the most efficient potential long-range movement routes that are based on the principles of habitat utilization. Reflecting the probabilities of cross-highway moose movement, ranks are assigned to all 1 km highway segments, characterizing them in terms of their likelihood of moose movement, and thus identifying discrete migration corridors and highway crossing zones. Bottlenecks are identified through simulation, such as where sections of wildlife fencing end without highway crossing structures, thereby creating a ‘spillover’ effect, i.e., moose moving parallel to the highway, then crossing. The tested model has proven the prognostic capacity of the tool to foresee locations of moose-vehicle collisions with high accuracy, thus allowing it to be a valuable addition to the toolbox of highway planners.