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SpeedMind - Improving species biodiversity analyses and citizen science feedback through mining data

 

In order to conserve and manage biodiversity, we need improved understanding of essential biodiversity drivers and improved predictions of resulting biodiversity patterns in space and time. Here, we propose a novel approach based on data-mining and iterative machine learning to improve biodiversity models and to better exploit existing data as well as guide future data sampling efforts.Key milestones of the project include: a) an operative framework linking real-time data streams and a citizen science interface to iteratively model the distribution of individual species and associated spatiotemporal uncertainty patterns using machine learning and data-mining, and meta-learning to detect ecologically relevant, higher-level processes structuring biodiversity; and b) a model-based catalogue of criteria for guiding citizen scientists for improved data collection.