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.
Last edited: 15 September 2016
27 Mar 2017 - 30 Mar 2017
Falster D, Brännström Å, Westoby M, & Dieckmann U (2017). Multitrait successional forest dynamics enable diverse competitive coexistence. Proceedings of the National Academy of Sciences 114 (13): 2719-2728. DOI:10.1073/pnas.1610206114.
Terrer Moreno C, Vicca S, Hungate BA, Phillips RP, Reich PB, Franklin O, Stocker BD, Fisher JB, et al. (2017). Response to Comment on “Mycorrhizal association as a primary control of the CO 2 fertilization effect”. Science 355 (6323): p. 358. DOI:10.1126/science.aai8242.
Franklin O, Johansson J, Dewar RC, Dieckmann U, McMurtrie RE, Brannstrom Å, & Dybzinski R (2012). Modeling carbon allocation in trees: A search for principles. Tree Physiology 32 (6): 648-666. DOI:10.1093/treephys/tpr138.
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