TripleBird: Projecting Biodiversity Futures to Guide Nature-Positive Decisions

Long-term effective management of ecosystems, such as forests, requires integrating ecological principles with diverse societal demands and values. Yet, such an approach is hampered by a disconnect between large-scale biodiversity monitoring data and the practical needs of decision-makers. There is an urgent need for predictive tools that can project biodiversity consequences of different management and policy actions. To address this challenge, the TripleBird project has been launched to develop integrated, science-based decision-support tools, building on long-term avian monitoring data to provide actionable knowledge for ecosystem management. The project's architecture integrates three core pillars. Its foundation is BirdTwin, a digital twin of German and European bird communities builts on large-scale and long-term monitoring data. BirdFutures then utilizes this digital twin to co-design biodiversity scenarios with stakeholders, allowing for the evaluation of policy interventions, conservation, and restoration activities effects on bird species and communities. Finally, BirdBusiness ensures these scientific insights are translated into usable applications for practitioners in business, finance, and politics, such as for sustainability reporting, target setting, and transition planning. This presentation will introduce the TripleBird and provide a critical starting point to align scientific model development with practitioner needs. At the heart of the TripleBird project is a deep commitment to co-production, moving beyond traditional science communication to foster a collaborative and iterative dialogue with stakeholders. Through continuous engagement, we will ensure our methods and outputs are driven by real-world needs. This collaborative process is essential to (1) clearly identify priority management objectives across different sectors, (2) co-define biodiversity indicators that are not only scientifically robust but also decision-relevant and practical to implement, and (3) incorporate invaluable local knowledge to ground-truth and refine our large-scale ecological models. Our ultimate goal is to provide the robust, transparent, and credible projections of biodiversity consequences that are an essential prerequisite for designing and implementing effective pathways towards a nature-positive future.