Representation of plant diversity in territory models: an evolutionary approach to make “functional types” more functional

This article was originally published here

Glob Chang Biol. 2021 Dec 29. doi: 10.1111/gcb.16040. Online ahead of print.


Plants are essential mediators of terrestrial mass and energy fluxes, and their structural and functional characteristics have profound impacts on local and global climate, biogeochemistry, biodiversity and hydrology. Yet Earth System Models (ESMs), our most powerful tools for predicting human effects on the coupled biosphere-atmosphere system, simplify the incredible diversity of terrestrial plants into a handful of crude categories of “functional types”. of plants” (PFT) that often fail to capture ecological dynamics such as the distribution of biomes. Including more realistic functional diversity is a recognized goal for ESMs, but there is currently no consistent and widely accepted way to add diversity to models, i.e. to determine which new PFT add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and build on recent ecological and evolutionary findings to present an evolutionary-based functional type approach to further disaggregate functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes over evolution, reveals that the evolutionary relationship is a powerful framework for summarizing functional similarities and differences between the types of plants. We advocate that plant functional types based on evolutionarily dominant lineages (“lineage functional types”) will provide an ecologically defensible, tractable, and evolutionary framework for representing plant diversity in next-generation ESMs, with the potential to improve the parameterization, process representation and model referencing. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

PMID:34964527 | DO I:10.1111/gcb.16040

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