Linking Spatiotemporal Biological Data to Predict Harmful Algal Blooms

Summary

Harmful Algal Blooms (HABs) pose significant threats to health and economies, yet predictive models are inadequate. This research by Los Alamos National Laboratory is developing a machine learning algorithm that integrates spatiotemporal biological, physical, and chemical data to improve HAB prediction, focusing on Lake Erie. The project is currently in progress, having processed extensive genomic data from nearly 4,000 samples and identified initial correlations between taxa, with plans to incorporate these biological profiles into the predictive model.

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Source Document

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