A scalable model of vegetation transitions using deep neural networks

Area of Science:

• Ecology
• Computational Science
• Climate Change Science

Background:

• Predicting vegetation dynamics is crucial for global change management but faces scale mismatches between fine-grained data and broad decision-making needs.
• Existing models struggle to bridge the gap between detailed ecological processes and large-scale vegetation projections.

Purpose of the Study:

• To introduce a novel approach for scaling vegetation dynamics (SVD) using deep learning to predict vegetation transitions and their ecological impacts.
• To evaluate SVD's accuracy, generalizability, and computational efficiency for large-scale vegetation dynamics modeling.

Main Methods:

• Developed a deep neural network (DNN) to estimate vegetation transition probabilities between numerous states based on environmental variables.
• Trained the DNN using simulated vegetation transitions from a process-based forest landscape model under various climate scenarios.
• Quantified impacts on ecological indicators like carbon storage and biodiversity by linking them probabilistically to vegetation states.

Main Results:

• SVD accurately captured spatial and temporal vegetation dynamics, including elevational gradients and species succession.
• The model responded realistically to changing environmental conditions and demonstrated strong predictive accuracy and generalizability.
• SVD proved highly computationally efficient, outperforming existing landscape models by several orders of magnitude.

Conclusions:

• SVD is a pioneering vegetation model leveraging deep neural networks for accurate, large-scale vegetation dynamics projections.
• The approach effectively integrates fine-grained ecological process knowledge with big ecological datasets.
• SVD's efficiency and ability to use widely available data make it suitable for country- to continental-scale applications.