Imperial College London

Professor Iain Colin Prentice

Faculty of Natural SciencesDepartment of Life Sciences (Silwood Park)

Chair in Biosphere and Climate Impacts
 
 
 
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Contact

 

+44 (0)20 7594 2354c.prentice

 
 
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Location

 

1.1Centre for Population BiologySilwood Park

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Summary

 

Publications

Citation

BibTex format

@article{Franklin:2020:10.1038/s41477-020-0655-x,
author = {Franklin, O and Harrison, S and Roderick, D and Farrior, C and Brannstrom, A and Ulf, D and Pietsch, S and Falster, D and Wolfgang, C and Loreau, M and Wang, H and Makela, A and Rebel, K and Meran, E and Schymanski, S and Ravenskaya, E and Stocker, B and Zaehle, S and Manzoni, S and van, Oijen M and Wright, I and Ciais, P and van, Bodegom P and Penuelas, J and Hofhansl, F and Terrer, C and Soudzilouskaia, N and Midgley, G and Prentice, IC},
doi = {10.1038/s41477-020-0655-x},
journal = {Nature Plants},
pages = {444--453},
title = {Organizing principles for vegetation dynamics},
url = {http://dx.doi.org/10.1038/s41477-020-0655-x},
volume = {6},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Franklin,O
AU - Harrison,S
AU - Roderick,D
AU - Farrior,C
AU - Brannstrom,A
AU - Ulf,D
AU - Pietsch,S
AU - Falster,D
AU - Wolfgang,C
AU - Loreau,M
AU - Wang,H
AU - Makela,A
AU - Rebel,K
AU - Meran,E
AU - Schymanski,S
AU - Ravenskaya,E
AU - Stocker,B
AU - Zaehle,S
AU - Manzoni,S
AU - van,Oijen M
AU - Wright,I
AU - Ciais,P
AU - van,Bodegom P
AU - Penuelas,J
AU - Hofhansl,F
AU - Terrer,C
AU - Soudzilouskaia,N
AU - Midgley,G
AU - Prentice,IC
DO - 10.1038/s41477-020-0655-x
EP - 453
PY - 2020///
SN - 2055-026X
SP - 444
TI - Organizing principles for vegetation dynamics
T2 - Nature Plants
UR - http://dx.doi.org/10.1038/s41477-020-0655-x
UR - http://hdl.handle.net/10044/1/79094
VL - 6
ER -