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Machine learning applied to global scale species distribution models.

Alba Fuster-Alonso1,2, Jorge Mestre-Tomás3,4, Jose Carlos Baez5,6

  • 1Renewable Marine Resources Department, Institute of Marine Sciences (ICM)-CSIC, Barcelona, 08003, Spain. afuster@icm.csic.es.

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Summary
This summary is machine-generated.

Bayesian Additive Regression Trees (BART) accurately forecast marine turtle distributions under climate change. This machine learning method offers a reliable alternative for global species distribution modeling, especially with limited data.

Keywords:
BARTEnvironmental changeGlobal scaleLong-term predictionMachine learningMarine turtlesSimulationSpatial distributions

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Area of Science:

  • Ecology
  • Machine Learning
  • Climate Change Biology

Background:

  • Species Distribution Models (SDMs) are crucial for understanding marine species' historical and future ranges.
  • Climate change poses a significant challenge, necessitating accurate predictions of species' range shifts.

Purpose of the Study:

  • To apply Bayesian Additive Regression Trees (BART) for estimating and forecasting global marine turtle distributions under climate change scenarios.
  • To assess habitat suitability and identify key environmental predictors for marine turtles.
  • To evaluate BART's performance against other SDM methods.

Main Methods:

  • Utilized Bayesian Additive Regression Trees (BART), a non-parametric machine learning algorithm.
  • Modeled individual marine turtle species and their functional group.
  • Conducted simulation studies with contrasting species distribution scenarios (cosmopolitan, persistent).
  • Tested BART's sensitivity to pseudo-absence data and compared it with MaxEnt and Generalized Additive Models (GAMs).

Main Results:

  • BART demonstrated slightly superior overall performance compared to MaxEnt and GAMs.
  • BART showed enhanced accuracy and more stable sensitivity and specificity, particularly when handling pseudo-absence data.
  • Identified key environmental predictors influencing marine turtle habitat suitability.

Conclusions:

  • Bayesian Additive Regression Trees (BART) is a reliable and accurate method for global-scale, long-term species distribution modeling in marine ecosystems.
  • BART is particularly effective in scenarios with limited or uncertain data, such as pseudo-absences.
  • The study provides valuable insights into predicting marine turtle responses to climate change.