Skip to main content

The evolution, complexity and diversity of models of long-term forest dynamics

Cite this dataset

Bugmann, Harald; Seidl, Rupert (2022). The evolution, complexity and diversity of models of long-term forest dynamics [Dataset]. Dryad.


1.  To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.

2.  We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.

3.  Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.

4.  Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers’ preferences, and the ‘prevailing spirit of the epoch’. The lack of firm theories, laws and ‘first principles’ in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation.

5.  Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity – with the exception of global models – can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.


The collection and processing of the dataset are fully described in the paper.

Usage notes

Microsoft Excel to open the data compilation

R or R Studio to load and execute the script


European Union, Award: 101001905