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Predictive systems ecology

delete2013-11-22
delete113
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OA
AI
M
Matthew R. Evans *
M
Mike Bithell
S
Stephen J. Cornell
S
Sasha R. X. Dall
S
Sandra Dı́az
S
Stephen Emmott
B
Bruno Ernande
V
Volker Grimm
D
David J. Hodgson
S
Simon L. Lewis
G
Georgina M. Mace
M
Michael Morecroft
A
Aristides Moustakas
E
Eugene J. Murphy
T
Tim Newbold
K
Ken Norris
O
Owen L. Petchey
M
Matthew J. Smith
J
Justin M. J. Travis
T
Tim G. Benton
DOI:10.1098/rspb.2013.1452delete
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摘要

摘要

En 中文
Human societies, and their well-being, depend to a significant extent on the state of the ecosystems that surround them. These ecosystems are changing rapidly usually in response to anthropogenic changes in the environment. To determine the likely impact of environmental change on ecosystems and the best ways to manage them, it would be desirable to be able to predict their future states. We present a proposal to develop the paradigm of predictive systems ecology, explicitly to understand and predict the properties and behaviour of ecological systems. We discuss the necessary and desirable features of predictive systems ecology models. There are places where predictive systems ecology is already being practised and we summarize a range of terrestrial and marine examples. Significant challenges remain but we suggest that ecology would benefit both as a scientific discipline and increase its impact in society if it were to embrace the need to become more predictive.
Keyword:
modelling
systems ecology
climate change
ecosystem assessment
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期刊

P
Proceedings of the Royal Society B-Biological Sciences
IF:
3.5
论文数:
1.5W
被引数:
5.7W

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I
Ifremer
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5.4K
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H
Helmholtz Center for Environmental Research
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4.5K
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被引数: 7
U
University College London
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7.9W
论文数: 6.2W
被引数: 15.7W
N
natural environment research council (nerc)
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4.4K
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N
national university of cordoba
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被引数: 3
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Queen Mary University London
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University of Exeter
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Helmholtz Association
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论文数: 10.7W
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U
University of Liverpool
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被引数: 3.5W
U
University of Cambridge
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7.7W
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U
uk research & innovation (ukri)
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2.7W
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university of leeds
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university of london
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21.5W
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M
Microsoft
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3.0K
论文数: 2.7K
被引数: 7
M
microsoft united kingdom
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150
论文数: 121
被引数: 1
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