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A climate envelope model for the projection of forest carbon storage on a global scale
  • +4
  • Diego Bengochea Paz,
  • Alba Marquez-Torres,
  • João Pompeu,
  • Olivier Martin-Ducup,
  • Ferdinando Villa,
  • Carmen Köhler,
  • Stefano Balbi
Diego Bengochea Paz
National Museum of Natural Sciences

Corresponding Author:[email protected]

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Alba Marquez-Torres
BC3 Basque Centre for Climate Change
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João Pompeu
University of Sao Paulo
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Olivier Martin-Ducup
Ecologie des Forêts Méditerranéennes
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Ferdinando Villa
BC3 Basque Centre for Climate Change
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Carmen Köhler
BC3 Basque Centre for Climate Change
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Stefano Balbi
Basque Center for Climate Change
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Abstract

not-yet-known not-yet-known not-yet-known unknown Projecting how climate change will reshape global patterns of forest carbon storage is paramount to investigate the future of the global carbon cycle. Advances in machine learning and the proliferation of remote-sensing missions have stimulated the development of statistical models for the estimation of carbon storage on a global scale. However, existing models present two major limitations. First, they are not suitable for forecasting because they are trained on variables that cannot be projected. Second, they are based on large numbers of predictors, which hinders the interpretability and explainability of their results from a biological and ecological perspective. In this manuscript, we present a machine learning model for the global mapping of potential Aboveground Biomass Density that overcomes the above mentioned limitations. On the one hand, our model is based exclusively on bioclimatic variables and is therefore suitable for forecasting and interpretable from a biological perspective. On the other hand, we calibrated our model with a state-of-the-art predictor selection process to identify the optimal subset of predictors for the maximization of model accuracy. We found that a minimal model based solely on four bioclimatic variables performed the best. Furthermore, our model reproduces field measurements from experimental forest plots equally well than a state-of-the-art model trained on $190$ predictors. In conclusion, we provide a simple and computationally cheap model that can be coupled with climate projections to investigate how climate change will reshape carbon storage on a global scale.
Submitted to Ecography
Submission Checks Completed
Assigned to Editor
Reviewer(s) Assigned
09 Jul 2024Review(s) Completed, Editorial Evaluation Pending
13 Jul 2024Reviewer(s) Assigned
10 Sep 2024Editorial Decision: Revise Major
30 Oct 20241st Revision Received
01 Nov 2024Submission Checks Completed
01 Nov 2024Assigned to Editor
01 Nov 2024Review(s) Completed, Editorial Evaluation Pending
01 Nov 2024Reviewer(s) Assigned