Using food-web theory to conserve ecosystems

McDonald-Madden, E., Sabbadin, R., Game, E.T., Baxter, P.W.J., Chades, I. and Possingham, H.P. (2016) Using food-web theory to conserve ecosystems. Nature Communications, 7 . doi:10.1038/ncomms10245


Author McDonald-Madden, E.
Sabbadin, R.
Game, E.T.
Baxter, P.W.J.
Chades, I.
Possingham, H.P.
Title Using food-web theory to conserve ecosystems
Journal name Nature Communications   Check publisher's open access policy
ISSN 2041-1723
Publication date 2016-01-18
Year available 2016
Sub-type Article (original research)
DOI 10.1038/ncomms10245
Open Access Status DOI
Volume 7
Total pages 7
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Subject 1600 Chemistry
1300 Biochemistry, Genetics and Molecular Biology
3100 Physics and Astronomy
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.
Formatted 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.
Keyword Multidisciplinary Sciences
Science & Technology - Other Topics
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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