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

Sonnewald, Maike; Dutkiewicz, Stephanie; Hill, Christopher; Forget, Gael

doi:10.1126/sciadv.aay4740

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

2020
By Sonnewald, Maike ; Dutkiewicz, Stephanie ; Hill, Christopher ; ...
Source: Science Advances, 6(22)

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Journal Title:

Science Advances
Personal Author:

Sonnewald, Maike ; Dutkiewicz, Stephanie ; Hill, Christopher ; Forget, Gael

Sonnewald, Maike ; Dutkiewicz, Stephanie ; Hill, Christopher ; Forget, Gael Less -
NOAA Program & Office:

CIMES (Cooperative Institute for Modeling the Earth System)
Description:

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.
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Classification Ecoregions Framework Ocean Science & Technology - Other Topics World
Source:

Science Advances, 6(22)
DOI:

https://doi.org/10.1126/sciadv.aay4740
Document Type:

Journal Article
Funding:

Grant no. NA18OAR4320123
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Submitted
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urn:sha256:78f6f8488d5530047f184f8bfb1db3888dafcff50431c3260b67686e6e03520e
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https://repository.library.noaa.gov/view/noaa/30676/noaa_30676_DS1.pdf
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Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces

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