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

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A new unsupervised learning method identifies over a hundred marine ecological provinces using plankton data. These findings aid in understanding nutrient control on marine ecosystems and improve model interpretation for better monitoring.

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Area of Science:

  • Marine ecology
  • Ecological modeling
  • Unsupervised machine learning

Background:

  • Marine ecosystems exhibit complex community structures influenced by nutrient dynamics.
  • Existing methods for defining marine ecological provinces often lack objectivity and scalability.
  • Understanding spatial patterns in plankton communities is crucial for ecosystem assessment.

Purpose of the Study:

  • To develop and apply an unsupervised learning method for identifying global marine ecological provinces.
  • To define robust aggregated eco-provinces (AEPs) for improved ecosystem model interpretation.
  • To explore the influence of nutrient supply on marine community structure using the defined AEPs.

Main Methods:

  • The Systematic Aggregated Eco-province (SAGE) method was employed, utilizing plankton community structure and nutrient flux data.
  • t-stochastic neighbor embedding (t-SNE) was used for dimensionality reduction to handle non-Gaussian data covariance.
  • Density-based spatial clustering of applications with noise (DBSCAN) identified over a hundred initial eco-provinces.
  • A connectivity graph with ecological dissimilarity defined robust aggregated eco-provinces (AEPs).

Main Results:

  • Over a hundred distinct eco-provinces were identified globally.
  • Robust aggregated eco-provinces (AEPs) were objectively defined by nesting the initial eco-provinces.
  • The study explored the control of nutrient supply rates on community structure within the defined AEPs.

Conclusions:

  • The developed SAGE method provides unique and interpretable marine ecological provinces and AEPs.
  • These novel classifications aid in the interpretation of complex ecosystem models.
  • The findings facilitate marine ecosystem model intercomparison and enhance monitoring capabilities.