Modeling Forest Growth Under Current and Future Climate

Abstract

Abstract Purpose of Review Forests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time. This work provides a systematic in-depth analysis of both established and emerging theories as found in the literature, exploring their integration into modern forest growth modeling with a special focus on new approaches, as implemented in 18 forest growth models which vary in their structure, objectives, and overarching goals. Recent Findings Forest modeling requires a deep understanding of forest growth theories driven by multiple interacting processes. Over time, numerous eco-physiological theories have been developed to predict forest growth under both current and future climatic conditions via dynamic vegetation models. While some were established in the past, new approaches continue to emerge, refining the complexity, predictive accuracy, and practical applicability of models. This ongoing evolution has resulted in models that are theoretically diversified but also increasingly relevant for real-world case studies dealing with both anthropogenic and natural disturbances. Machine learning, trained on increasingly large datasets, is emerging as a powerful complement to traditional forest models. Rather than replacing process-based approaches, it can be combined with them in hybrid frameworks that integrate mechanistic understanding with data-driven flexibility. This combination improves predictive performance, extends model applicability, and supports more robust decision-making in forest management. Summary Amid the ongoing’chicken-and-egg’ debate on whether photosynthesis drives growth or growth drives photosynthesis, our review synthesizes key interconnected theories, including Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth. By integrating these perspectives, we offer a clear and comprehensive overview of the main frameworks governing growth and resource allocation in plants. As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.

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