The Earth’s biodiversity and the ecosystem services it provides are declining rapidly because of an increasing use of natural (finite) resources to meet human needs and environmental change affecting biodiversity dynamics. Truly trans-disciplinary solutions are needed for a sustainable development of ecosystem services. Collaboration and knowledge transfer across paleo biology, climatology, global ecology, evolution, biogeography, water sciences, computer science, statistics, and economics has to be mustered for scientists, resources managers and policy makers to counter ecosystem function collapse and ecosystem services loss. We propose a novel framework that closes the implementation gap, in which long-term dynamics of biodiversity, abiotic properties, and ecosystem functions are reconstructed using an unprecedented integration of biochemical and environmental fingerprinting of biological archives spanning centuries. The long-term dynamics obtained from this fingerprinting are then placed in a machine learning pipeline to identify cause-effect relations between environmental change and biodiversity dynamics. Predictive models are tested by hindcasting, and then used to accurately forecast the future of ecosystem services and their socio-economic impact under different climate change scenarios. The framework provides accessible tools to practitioners to translate cutting-edge research into practical solutions for environmental management and practice.