Towards a new generation of trait-flexible vegetation models

Berzaghi, F.; Wright, I. J.; Kramer, K.; Oddou-Mouratorio, S.; Bohn, F. J.; Reyer, Christopher P. O.; Sabaté, S.; Sanders, T. G. M.; Hartig, F.

doi:10.1016/j.tree.2019.11.006

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Towards a new generation of trait-flexible vegetation models

Authors

Berzaghi, F.
External Organizations;

Wright, I. J.
External Organizations;

Kramer, K.
External Organizations;

Oddou-Mouratorio, S.
External Organizations;

Bohn, F. J.
External Organizations;

/persons/resource/Reyer

Reyer, Christopher P. O.
Potsdam Institute for Climate Impact Research;

Sabaté, S.
External Organizations;

Sanders, T. G. M.
External Organizations;

Hartig, F.
External Organizations;

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引用

Berzaghi, F., Wright, I. J., Kramer, K., Oddou-Mouratorio, S., Bohn, F. J., Reyer, C. P. O., Sabaté, S., Sanders, T. G. M., & Hartig, F. (2020). Towards a new generation of trait-flexible vegetation models. Trends in Ecology and Evolution, 35(3), 191-205. doi:10.1016/j.tree.2019.11.006.

引用: https://publications.pik-potsdam.de/pubman/item/item_23723

要旨

Dynamic vegetation models are the main tools to assess climate change effects on terrestrial vegetation. Therefore, a realistic representation of biological processes in these models is of utmost importance. Intraspecific trait variability is ubiquitous in plants and, thus, the underlying processes causing it should be represented. Yet, trait variability is only used to a limited extent in current vegetation models. Empirical and theoretical studies make clear that intraspecific trait variability underpins evolutionary and plastic plant responses to environmental changes. We review progress towards ‘next-generation’ models that include evolutionary and plastic processes, including those explicitly representing genetic mechanisms. Modeling paradigms where plant diversity emerges mechanistically are necessary to understand both functional trade-offs (e.g., leaf and wood economics spectra) and spatial patterns of genetic and phenotypic variability as exposed by genomic and ecological data.