Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces

• Published in: Science advances Vol. 6; no. 22; p. eaay4740
• Main Authors: Sonnewald, Maike, Dutkiewicz, Stephanie, Hill, Christopher, Forget, Gael
• Format: Journal Article
• Language: English
• Published: United States American Association for the Advancement of Science 01-05-2020
• Subjects: Ecology; Oceanography; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.aay4740

An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems.