Using food-web theory to conserve ecosystems
File(s)
Published version
Author(s)
Possingham, HP
McDonald-Madden, E
Type
Journal Article
Abstract
Food-web theory can be a powerful guide to the management of complex ecosystems.
However, we show that indices of species importance common in food-web and network
theory can be a poor guide to ecosystem management, resulting in significantly more
extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial
Optimization to find optimal management strategies for a wide range of real and hypothetical
food webs. This Artificial Intelligence approach provides the ability to test the performance of
any index for prioritizing species management in a network. While no single network theory
index provides an appropriate guide to management for all food webs, a modified version of
the Google PageRank algorithm reliably minimizes the chance and severity of negative outcomes.
Our analysis shows that by prioritizing ecosystem management based on the network-wide
impact of species protection rather than species loss, we can substantially improve
conservation outcomes.
However, we show that indices of species importance common in food-web and network
theory can be a poor guide to ecosystem management, resulting in significantly more
extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial
Optimization to find optimal management strategies for a wide range of real and hypothetical
food webs. This Artificial Intelligence approach provides the ability to test the performance of
any index for prioritizing species management in a network. While no single network theory
index provides an appropriate guide to management for all food webs, a modified version of
the Google PageRank algorithm reliably minimizes the chance and severity of negative outcomes.
Our analysis shows that by prioritizing ecosystem management based on the network-wide
impact of species protection rather than species loss, we can substantially improve
conservation outcomes.
Date Issued
2016-01-18
Date Acceptance
2015-11-23
Citation
Nature Communications, 2016, 7
ISSN
2041-1723
Publisher
Nature Publishing Group
Journal / Book Title
Nature Communications
Volume
7
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0
International License. The images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise
in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
International License. The images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise
in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
License URL
Subjects
MD Multidisciplinary
Publication Status
Published
Article Number
10245