Modeling Forest Dynamics and Tree Growth Responses

Modelling forest dynamics and tree growth responses integrates ecological theory, statistical inference and computational methods to project changes in forest structure, composition and function over time. Approaches range from process‐based models that simulate physiological processes and disturbance regimes under varying climatic scenarios to empirical and machine‐learning models that relate tree growth rates and demographic processes to stand conditions and environmental drivers. Key objectives include estimating carbon sequestration potential, assessing regeneration and mortality patterns, evaluating stability thresholds and informing adaptive management strategies. Advances in high‐resolution remote sensing, enhanced algorithms and long‐term inventory data have improved the fidelity of projections, enabling more reliable predictions of forest resilience, productivity and ecosystem service provision under global change.

Research from Nature Portfolio

Recent studies have applied evolutionary optimisation to ensemble learning, using a genetic algorithm to calibrate random forest models for dynamic estimation of forest carbon sink intensity across mountainous landscapes. This work demonstrated substantially improved accuracy in predicting above‐ground carbon sinks and clarified the combined influence of precipitation, temperature and solar radiation on carbon uptake. In parallel, theoretical advances have incorporated stochastic differential equations with heavy‐tailed Lévy noise to explore forest state stability under extreme perturbations. Analysis of a tropical rain forest revealed that rare but large disturbances can precipitate abrupt transitions between forest and savanna states, offering a novel framework for assessing tipping points in ecosystem dynamics.

Research from all publishers

A multi‐decadal analysis of national forest inventory data has characterised ingrowth dynamics of broadleaved and coniferous species along climatic gradients, revealing a slowdown in regeneration rates and shifts towards more climate‐adapted species in European mountain forests. Statistical models implemented in a forest simulator project continued declines in ingrowth without intervention, highlighting the need for adaptive planting strategies. Complementary work in managed Central European stands has applied survival analysis to differentiate natural mortality from removals, demonstrating that sanitary cuttings should be treated as mortality events to avoid underestimation of stand health declines and improve long‐term monitoring.

Modeling Forest Dynamics and Tree Growth Responses publication trend

The graph below shows the total number of articles in modeling forest dynamics and tree growth responses across all publications each year (not limited to Nature Index journals).

Technical terms

Dynamic model: A computational framework that simulates temporal changes in forest structure or processes in response to internal interactions and external drivers.

Genetic algorithm: An optimisation heuristic inspired by natural selection, used to identify optimal model parameters through iterative selection, crossover and mutation.

Random forest: An ensemble machine‐learning method that constructs multiple decision trees for regression or classification, reducing overfitting and improving predictive performance.

Stochastic differential equation: A mathematical equation incorporating random perturbations to describe the probabilistic evolution of system states over time.

Ingrowth: The process by which new saplings reach a defined diameter threshold, entering the measurable tree population.

Survival analysis: A suite of statistical techniques for modelling the time until an event of interest, such as tree mortality, while accounting for censored observations.

References

1. Soil and climate‐dependent ingrowth inference: broadleaves on their slow way to conquer Swiss forests. Ecography (2024).
2. “Mortality, or not mortality, that is the question …”: How to Treat Removals in Tree Survival Analysis of Central European Managed Forests. Plants (2024).
3. Estimating forest aboveground carbon sink based on landsat time series and its response to climate change. Scientific Reports (2025).
4. Metastability for discontinuous dynamical systems under Lévy noise: Case study on Amazonian Vegetation. Scientific Reports (2017).