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Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state

John T Delaney1, Danelle M Larson1

  • 1U.S. Geological Survey, La Crosse, Wisconsin, USA.

Conservation Biology : the Journal of the Society for Conservation Biology
|October 11, 2023
PubMed
Summary

We developed a framework to predict ecosystem shifts and provide early warnings for aquatic systems. This approach helps in restoration planning and preventing ecosystem collapse, using submersed aquatic vegetation as a case study.

Keywords:
Alto Río MississippiUpper Mississippi Riveraquatic plantsecological thresholdsecosystem stateestado del ecosistemahabitat suitabilityidoneidad de hábitatmacrophytesmacrófitasoccurrence probabilityplantas acuáticasprobabilidad de presenciasubmerged aquatic vegetationumbral ecológicovegetación acuática sumergida出现率大型植物密西西比河上游水生植物沉水植物生境适宜性生态系统状态生态阈值

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Area of Science:

  • Ecology
  • Environmental Science
  • Machine Learning

Background:

  • Ecosystem state transitions can lead to ecological devastation or restoration success.
  • Aquatic systems globally face transitions due to human land and water use changes.
  • Predicting and managing these transitions is crucial for ecosystem health.

Purpose of the Study:

  • To create a transferable conceptual framework for assessing ecosystem resilience and providing early warnings of state transitions.
  • To integrate machine learning with ecosystem state concepts for multiscale assessments.
  • To identify environmental drivers and thresholds influencing ecosystem states, specifically submersed aquatic vegetation (SAV) presence.

Main Methods:

  • Developed a conceptual framework integrating machine learning predictions with ecosystem state concepts.
  • Applied the framework to predict SAV presence at nearly 10,000 sites in the Upper Mississippi River.
  • Utilized an interpretability method to identify key environmental drivers and their response types (threshold or linear).

Main Results:

  • Achieved 89% model accuracy in predicting SAV presence without spatial bias.
  • Identified average water depth, suspended solids, substrate, and distance to nearest SAV as key habitat suitability predictors.
  • Found nonlinear, threshold-type responses of SAV presence to these environmental drivers.

Conclusions:

  • The developed framework effectively predicts ecosystem states and identifies critical environmental drivers.
  • The findings provide insights into SAV habitat suitability and inform targeted restoration strategies.
  • Multiscale outputs presented in an online dashboard aid research and restoration planning for aquatic ecosystems.