Conference Agenda

Urbanisation is one of the most significant human-induced changes to the environment. It creates novel ecosystems that are distinct in terms of biodiversity from those in non-urban areas. Berlin, the capital of Germany, is one of the largest cities in Europe and has experienced intensive urban growth since the late nineteenth century.

Numerous national, EU and international policies have set specific targets for cities to protect and enhance urban biodiversity. Prominent examples include the Kunming-Montreal Global Biodiversity Framework, the EU Biodiversity Strategy for 2030, the EU Habitats Directive, the European Green Deal, the EU Nature Restoration Regulation, the Berlin Urban Nature Pact, and the Berlin Strategy for Biodiversity 2030+.

Our project aims to quantify and visualise changes in Berlin's biodiversity, and to analyse the distribution of threatened species, in order to inform the implementation of these urban biodiversity targets. We have identified and compiled long-term population and distribution data on species, applying internationally recognised methods for temporal and spatial analyses. Specifically, for the temporal dimension we used the Living Planet Index (LPI) methodology, developed by the Zoological Society of London (ZSL) and the World Wide Fund for Nature (WWF), to analyse population trends. For the spatial analysis, we employed the Key Biodiversity Area (KBA) framework, a global standard developed by the International Union for Conservation of Nature (IUCN), and adapted it for local-scale usage to analyse spatial occurrence of threatened species. Our results indicate a population decline across different species. The identified KBAs are largely situated within already established protected areas, some are also found outside Berlin’s protected area network. This highlights the need for well-coordinated management and planning decisions. The approaches we used provide robust insights into Berlin’s biodiversity dynamics, facilitating targeted conservation strategies and urban planning decisions that align with overarching biodiversity goals.

In recent years, outdoor recreation has gained growing popularity, with a notable rise in new group-based outdoor activities. These opportunities allow people to explore and engage with nature, contributing to the economic development of local communities and, more broadly, to tourism. However, this expansion of outdoor activities raises concerns about potential negative impacts on biodiversity conservation. Scientific ecologists have introduced the concept of the “landscape of fear” to describe how wildlife, such as chamois, may be disturbed by human presence, potentially leading to altered behavior or even loss of plant and animal diversity.

At the same time, outdoor practitioners increasingly record their routes using GNSS devices, with trajectories shared as open-source data on different websites. Similarly, ecologists studying the effects of human activity on wildlife collected GNSS trajectories representing animal movement by using GPS collars. These parallel datasets open new opportunities to study spatio-temporal interactions between humans and animals.

In this presentation we will first present an overview of our research on how to study these human-animals interactions and propose solutions for sustainable outdoor recreation. Our research focuses on analyzing both human and animal trajectories to identify hotspots and coldspots which are respectively areas of frequent and less frequent co-occurrence. These identified zones first enhance human movement trajectories with ecologic data (e.g., chamois presence from April to June), and then it enriches a mobility network itself through map-matching techniques and attribute transfer, contributing thus to a trajectory-road network loop. Once the loop is finalized, the goal is to propose an algorithm that is able to suggest resilient routes which are routes that avoid temporal hotspots of human-wildlife interaction offering alternative paths that are less crowded and more environmentally sustainable.

Second, we present the first results obtained in mountain areas located in the French Alps. While our study area is currently focused on mountain environments, we argue that this approach is transferable to urban contexts, where it can support the development of sustainable alternatives in densely visited touristic areas. For example, this tool can be used by urban planners and mobility services providers to redirect tourist flows to avoid overcrowded landmarks or ecologically fragile zones, thereby reducing pressure on urban green spaces while enhancing visitor experience.

This work is part of the IntForOut research project, which aims to develop methods and tools to better quantify human pressure from sports and recreational activities on alpine ecosystems and to suggest alternative solutions for sustainable tourism and mobility.

Over the past fifty years, Brazil has experienced profound land-use changes, emerging as a global leader in agriculture. Extensive areas of natural land have been converted into pastures, croplands and forest plantations, alongside expansions in urban infrastructure and road networks. However, agricultural expansion has occurred in critical regions for biodiversity and carbon storage, many of which rank among the world’s top priorities for preserving terrestrial biodiversity and mitigating climate change. Yet, these biodiversity- and carbon-rich areas remain under pressure from agricultural expansion, as these lands are also suitable for agricultural use. Addressing this conflict is essential to achieve both profitable and sustainable land use in Brazil.

In this aspiration, this research explores how Brazil can meet its agricultural demand by 2050 while minimising impacts on biodiversity and carbon stocks. It aims to propose a range of alternative land-use pathways to meet future land demand in Brazil, with a quantification of the associated trade-offs between agro-economic development, climate change mitigation, and biodiversity preservation.

To achieve this, the research develops a spatial multi-objective optimisation approach using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimise land-use changes in Brazil between 2020 and 2050. The optimisation is conducted with respect to three objectives: biodiversity preservation, climate change mitigation, and agro-economic development. The outcome is a set of equally optimal land use configurations where gains in one objective come at the expense of another. This set, known as the Pareto front, represents the trade-offs between the competing objectives and allows for the quantification of how much improvement in one goal costs in terms of another. Each optimal land use along the front reflects the most efficient way to allocate land use based on a specific prioritisation of objectives. Finally, to guide real-world implementation, the optimised solutions are compared with land-use scenarios projected under the Shared Socioeconomic Pathway (SSPs). This comparison helps identify where and how the policies associated with these scenarios would need to be adjusted to move toward more sustainable outcomes.

The findings highlight trade-offs between agro-economic development and the environmental objectives of climate mitigation and biodiversity conservation. However, these trade-offs vary significantly depending on the socio-economic trajectory and associated levels of agricultural demand. Concentrating agricultural expansion in areas with high productivity potential, while avoiding biodiversity- and carbon-rich areas, appears to minimise these trade-offs. The comparison with the SSP scenario demonstrates that there is room for improvement in all three objectives, without much rearrangement of existing agricultural land. Overall, this research provides spatially explicit insights into how future land-use can better align with climate, biodiversity, and economic goals.

We present first results of a modified and enhanced Ukrainian instance of osmlanduse.org, an open WebGIS that fuses OpenStreetMap semantics with satellite observations to generate 10-m land-use/land-cover (LULC) maps on a biannual cycle. OSM tags are harmonized to IPCC-consistent LULUCF classes; gaps are filled via supervised learning on Sentinel-2 reflectance and, where available, Sentinel-1 backscatter. Change detection across update windows yields activity data for afforestation/deforestation, cropland–grassland transitions, wetland dynamics, and urban expansion.

The monitor is explicitly designed to serve Ukraine’s Nationally Determined Contribution (NDC) and the Paris Agreement’s Enhanced Transparency Framework. Products align with NDC sectoral coverage and IPCC 2006/2019 guidance: wall-to-wall LULUCF maps, oblast- and raion-level aggregates, and uncertainty ranges suitable for inventory tables. Combined with tiered emission factors, these activity data enable estimation of carbon sources and sinks and identification of priority zones for nature-based solutions. A policy-facing layer stack highlights Black Sea basin vulnerabilities (coastal wetlands, riparian buffers, forest edges, peatlands) and mitigation–adaptation co-benefits.