Aligning evidence and action: The Research-to-Decision (RED) Framework to address modern environmental and ecological challenges

Esther L. Jones, Ruth King, Stuart King, Oisin Mac Aodha, Rob James Boyd, Susan Jarvis, Matthew Silk, Jenna Lawson, David Williams, Sallie Bailey, Serge Wich, Alison Johnston, Adam Butler, Hannah Worthington, Kasim Terzic, Ben C Stevenson, Eleni Matechou, Sarah C Christofides, Eduard Campillo-Funollet, Cornelia Oedekoven, Danielle V Harris, Tiago A Marques, Altea Lorenzo-Arribas, Pete A Henrys, Carolina S Marques, Rebecca Wilks, Amanda Lenzi, Rachel McCrea

Ecological and biodiversity crises are intensifying amid rapid advances in sensing, computation, and artificial intelligence (AI). AI offers unprecedented capabilities to understand complex ecological systems, provided outputs are interpretable, applied responsibly, and evaluated by domain experts. When addressing key challenges, science-policy frictions arise through different requirements relating to timelines, managing uncertainty, and accountability. We present the Research-to-Decision (RED) framework to better align these differences, using a transdisciplinary approach across the ecological research cycle. The framework is operationalised through embedding responsible AI, identifying where domain expertise should lead, where AI can accelerate research, and where AI-human synergy is essential for producing robust evidence for informed decision-making. Implementation pathways are identified: research & technology, communication & policy alignment, and assets & infrastructure, all underpinned by culture & people. AI synergy relies entirely upon human synergy, and through bridge scientists and institutional change, the RED framework can unlock capacity to tackle complex environmental challenges and support evidence-based policy development for biodiversity conservation.