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

Science Advances
Maike SonnewaldGael Forget

Abstract

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.

References

Feb 7, 2007·IEEE Transactions on Neural Networks·Abd-Krim Seghouane, Shun-Ichi Amari
Sep 17, 2014·Environmental Management·James M Omernik, Glenn E Griffith
Mar 10, 2016·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Ian T Jolliffe, Jorge Cadima
Oct 21, 2017·Nature Communications·Mark J CostelloChhaya Chaudhary
Mar 22, 2019·Briefings in Bioinformatics·John J DziakLars S Jermiin

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Citations

Dec 21, 2020·Global Change Biology·Stephanie DutkiewiczUlf Riebesell

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Methods Mentioned

BETA
size fractionation
PCA

Software Mentioned

SAGE
DBSCAN
SNE
PACE

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