Title |
Using food-web theory to conserve ecosystems
|
---|---|
Published in |
Nature Communications, January 2016
|
DOI | 10.1038/ncomms10245 |
Pubmed ID | |
Authors |
E. McDonald-Madden, R. Sabbadin, E. T. Game, P. W. J. Baxter, I. Chadès, H. P. Possingham |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 11 | 22% |
United States | 8 | 16% |
New Zealand | 3 | 6% |
United Kingdom | 3 | 6% |
Germany | 2 | 4% |
Japan | 1 | 2% |
Portugal | 1 | 2% |
Curaçao | 1 | 2% |
France | 1 | 2% |
Other | 2 | 4% |
Unknown | 16 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 26 | 53% |
Scientists | 18 | 37% |
Science communicators (journalists, bloggers, editors) | 5 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | <1% |
Japan | 2 | <1% |
Canada | 2 | <1% |
United Kingdom | 2 | <1% |
Kenya | 1 | <1% |
Brazil | 1 | <1% |
Tanzania, United Republic of | 1 | <1% |
France | 1 | <1% |
India | 1 | <1% |
Other | 5 | 1% |
Unknown | 316 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 70 | 21% |
Researcher | 58 | 17% |
Student > Master | 50 | 15% |
Student > Doctoral Student | 27 | 8% |
Student > Bachelor | 26 | 8% |
Other | 44 | 13% |
Unknown | 59 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 131 | 39% |
Environmental Science | 72 | 22% |
Biochemistry, Genetics and Molecular Biology | 10 | 3% |
Earth and Planetary Sciences | 9 | 3% |
Physics and Astronomy | 7 | 2% |
Other | 31 | 9% |
Unknown | 74 | 22% |