Dynamic vegetation models: the next generation

Dynamic Global Vegetation Models (DGVMs) are invaluable for understanding the biosphere. However, as currently implemented by the international research community, these models suffer from a challenging accumulation of uncertainty. This project aims to address this problem by developing the foundations of a new generation of models centered on a “missing law” – adaptation and optimization principles rooted in natural selection.

While the versatility of DGVMs is increasing as new processes and variables are added, their accuracy is impaired by the accumulation of uncertainty, especially in the absence of overarching principles controlling their concerted behavior.

Using a “missing law” – adaptation and optimization principles rooted in natural selection – to constrain relationships between traits, researchers can vastly reduce the number of uncertain parameters in ecosystem models. Recent research developments have shown that optimization- and trait-based models of gross primary production can be both simpler and more accurate than current models based on functional types (Wang et al., 2016), and that observed vegetation structures and distributions of plant traits can be predicted with new eco-evolutionary models (Franklin et al., 2012; Falster et al., 2017).

Building on these innovations as a springboard, this project aims to operationalize these concepts in an international, multidisciplinary, IIASA-coordinated working group, led by experts in the following key areas: eco-evolutionary theory, systems analysis, vegetation modeling, and DGVM applications. Complemented by matching in-house research, international collaborations will be facilitated by a series of IIASA workshops intended to culminate in an international conference.

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Last edited: 15 September 2016