Organizing principles for vegetation dynamics

Franklin, Oskar; Harrison, Sandy P.; Dewar, Roderick; Farrior, Caroline E.; Brännström, Åke; Dieckmann, Ulf; Pietsch, Stephan; Falster, Daniel; Cramer, Wolfgang; Loreau, Michel; Wang, Han; Mäkelä, Annikki; Rebel, Karin T.; Meron, Ehud; Schymanski, Stanislaus J.; Rovenskaya, Elena; Stocker, Benjamin D.; Zaehle, Sönke; Manzoni, Stefano; van Oijen, Marcel; Wright, Ian J.; Ciais, Philippe; van Bodegom, Peter M.; Peñuelas, Josep; Hofhansl, Florian; Terrer, Cesar; Soudzilovskaia, Nadejda A.; Midgley, Guy; Prentice, I. Colin. 2020 Organizing principles for vegetation dynamics. Nature Plants, 6 (5). 444-453. 10.1038/s41477-020-0655-x

Before downloading, please read NORA policies.

Text Organizing principles for vegetation dynamics - Nat Plants 3_acc.docx Restricted to NORA staff only Download (3MB)

Abstract/Summary

Plants and vegetation play a critical — but largely unpredictable — role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1038/s41477-020-0655-x
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	2055-026X
Additional Keywords:	ecosystem ecology, plant ecology, theoretical ecology
NORA Subject Terms:	Ecology and Environment
Date made live:	02 Jun 2020 12:18 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/527841

Actions (login required)

View Item

Document Downloads

More statistics for this item...