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Nature recovery depends on turning ecological data into clear, timely, and actionable insights. In AI for Nature Recovery – Improving the Journey from Data to Decisions, Kate explores how artificial intelligence is reshaping biodiversity monitoring, conservation science, and environmental decision-making. Drawing on her own interdisciplinary research in ecological data science, she examines how sensing technologies, automated acoustic monitoring, bioacoustics, remote sensing, and machine learning can strengthen the critical data-to-decision pipeline. How can AI improve ecosystem monitoring and biodiversity assessment at scale? Where does artificial intelligence genuinely enhance conservation outcomes? And where do data gaps, bias, uncertainty, and real-world complexity still constrain its impact? This talk is hosted by the Leverhulme Centre for Nature Recovery and the Nature Network, organisations committed to exploring diverse perspectives on nature recovery from researchers and practitioners across academia, government, and NGOs. For academics, policymakers, conservation practitioners, and environmental economists, this discussion connects ecology, AI, sustainability, and governance — helping translate environmental data into smarter, evidence-based decisions for biodiversity conservation and ecosystem restoration. #NatureRecovery #Biodiversity #ConservationScience #AIforGood #EnvironmentalPolicy #EcologicalData #MachineLearning #Sustainability #EnvironmentalEconomics #EcosystemRestoration #Bioacoustics #NatureBasedSolutions