Assisted Tree Migration Can Preserve the European Forest Carbon Sink Under Climate Change

Abstract

This study investigates the potential of assisted migration (AM) to maintain the carbon sequestration capacity of European forests under climate change. By modeling scenarios for seven major European tree species, the research evaluates the impact of species and seed provenance selection on the annual above-ground carbon sink of regrowing juvenile forests.

Authors

The research paper is primarily authored by Debojyoti Chakraborty and Albert Ciceu, who played key roles in the study’s conception and analysis. Additional contributors from various institutions supported the research. For a full list, refer to the original publication.

Methodology

Modeling Approach

Utilized species distribution models (SDMs) and regional climate models (RCMs) to predict the best-suited species and seed provenances for future climates.

Scenarios

Two seed provenance sourcing scenarios were tested: ‘local seeds’ and ‘adapted seeds’ for contemporary and future climate scenarios (RCP 4.5 and RCP 8.5).

Data Analysis

Analyzed 587 range-wide provenance trials, evaluating 2,964 provenances to quantify the effects on carbon sequestration.

How AI Supports Modeling and Analysis

Artificial Intelligence (AI) plays a crucial role in enhancing the modeling and analysis processes in the study of assisted tree migration. AI techniques, particularly machine learning algorithms, are integral to the development and refinement of species distribution models (SDMs) and regional climate models (RCMs). These models are essential for predicting the climatic suitability of various tree species and seed provenances under future climate scenarios.

Data Processing and Pattern Recognition: AI algorithms can efficiently process large datasets from provenance trials and climate models, identifying patterns and correlations that might be challenging to discern manually. This capability is vital for analyzing the 587 range-wide provenance trials and evaluating 2,964 provenances.
Predictive Modeling: Machine learning models enhance the predictive accuracy of SDMs and RCMs by learning from historical climate data and species distribution patterns. This allows researchers to simulate future scenarios with greater confidence, aiding in the selection of optimal species and seed provenances for assisted migration.
Uncertainty Quantification: AI techniques help quantify uncertainties in modeling frameworks by running multiple simulations and analyzing variations in outcomes. This is crucial for understanding the potential range of impacts on carbon sequestration and forest resilience under different climate scenarios.
Optimization of Assisted Migration Strategies: AI supports the optimization of assisted migration strategies by evaluating numerous combinations of species and seed provenances, ultimately recommending those that maximize carbon sequestration and forest resilience.

By leveraging AI, researchers can enhance the robustness and reliability of their findings, providing valuable insights into the potential of assisted migration as a strategy for climate change mitigation in European forests.

Technical Aspects

Species Distribution Models (SDMs): Used to predict the climatic suitability of tree species.

Regional Climate Models (RCMs): Applied to assess future climate scenarios (RCP 4.5 and RCP 8.5).

Universal Response Functions (URFs): Developed to estimate carbon sequestration based on environmental and genetic variations.

Provenance Trials: Analyzed 587 trials to evaluate the effects of seed provenance selection on carbon sequestration.

Key Findings

Climate Change Threat: European forests’ role as a carbon sink is threatened by climate change, potentially reducing the carbon sink by 34-41% by 2061-2080 if local seed provenances are used.
Assisted Migration Benefits: Using seed provenances adapted to future climates could maintain or increase the carbon sink to 48-60 TgC per year.
Species and Provenance Selection: Replacing coniferous trees with deciduous species in many areas is recommended to enhance forest resilience.

Practical Takeaways

Strategic Species Selection: Emphasizes the importance of selecting species and seed provenances adapted to future climates.
Policy Development: Supports the development of policies for nature-based climate change mitigation strategies.
Research and Cooperation: Calls for further research and transnational cooperation to implement AM effectively.

Limitations

Data Constraints: Limited to seven main tree species common to central and northern Europe.
Model Limitations: Models focus on productivity and carbon sequestration, potentially overlooking other ecological traits and extreme climate events.
Uncertainty: Acknowledges uncertainties in modeling frameworks and data availability.

References studies on climate change impacts on tree species distributions and the role of forests in carbon sequestration. Discusses previous research on assisted migration and genetic adaptation in forest management.

Access the Paper

The full paper is available in Nature Climate Change, Volume 14, August 2024, Pages 845–852. DOI: https://doi.org/10.1038/s41558-024-02080-5.