Shedding light on vegetation dynamics

Plants are of central importance to terrestrial ecosystems and play a key role in the global carbon cycle. In 2017, the IIASA Evolution and Ecology Program showed how the inclusion of evolutionary and eco-physiological principles enables improved vegetation models.

Hundreds of thousands of species of plants currently cover the surface of the earth. They are essential for terrestrial ecosystems and the ecosystem services they provide. By producing oxygen while absorbing atmospheric carbon dioxide, plants form an integral part of the global carbon cycle. With human activities now rapidly changing the conditions to which plant species are adapted, there is an urgent need to understand how changing environmental conditions will affect vegetation cover across the globe.

Broadly speaking, current models that aim to predict plant diversity fall into either of two classes. These are neutral models of biodiversity that predict vegetation structure by assuming that community composition arises through random processes, or niche-based models that predict vegetation structure by assuming competition between plant species. Until now, only the neutral models have been able to give reasonable predictions of species diversity, with the niche-based models typically giving rise to only a few coexisting species.

To show how enhanced biological realism can improve predictions of plant diversity and potentially reconcile these two approaches, researchers from the Evolution and Ecology Program developed an eco-evolutionary vegetation model that builds on established eco-physiological and evolutionary principles [1]. By moving beyond the often simplistic assumptions of traditional niche-based models, the study showed how plant diversity can be predicted from knowledge of local environmental conditions. Moreover, by accounting for evolution in two important functional traits, a large neutral range of trait combinations emerges across which species have equal fitness, arguably reconciling neutral and niche-based theory.

Examples of successional vegetation dynamics under two different environmental settings, corresponding to temperate forests (left panel) and tropical rain forests (right panel). Early fast-growing species quickly establish themselves following a disturbance and are later outcompeted by slower-growing, more efficient species. All shown species—or plant functional types—are not just assumed from the outset, but emerge intrinsically as a predictive and empirically testable result of the model.

As the compositions of plant communities arise intrinsically from the model, rather than being assumed from the outset, the novel approach taken in this study (i.e., working from eco-evolutionary first principles) has the potential to improve species-conservation efforts, land-use policies, and forest-management practices. In addition, it can also help to improve contemporary dynamic global vegetation models that are used to predict the impacts of global and regional climate change. The potential value is especially large for improving predictions of future vegetation structures under environmental conditions that do not currently exist anywhere on earth. The IIASA cross-cutting project on Dynamic vegetation models: The next generation is capitalizing on these opportunities.


[1] Falster DS, Brännström Å, Westoby M, Dieckmann U (2017). Multi-trait successional forest dynamics enable diverse competitive coexistence. Proceedings of the National Academy of Sciences of the USA 114: 2719–2728.


  • Department of Biological Sciences, Macquarie University, Australia
  • Department of Mathematics and Mathematical Statistics, Umeå University, Sweden

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