A flapping good model: Collaborative approaches to machine learning for biodiversity

Mott MacDonald showcase an innovative cross-disciplinary framework revolutionising habitat suitability modelling. Using advanced machine learning combined with ecological expertise, they’ve delivered a more robust, cost-effective survey and mitigation strategy for a major UK railway project.

With 1 million species threatened globally and 69% of UK species in decline, traditional approaches aren’t enough. Learn how their integrated INLA and CircuitScape methodology identifies critical habitat corridors, reduces project risks and costs, and delivers measurable benefits for both the client and nature.