title: Using food-web theory to conserve ecosystems.
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.
authors.affiliation: School of Geography, Planning and Environmental Management, University of Queensland, St Lucia, Queensland 4072, Australia.
authors.forename: E
authors.initials: E
authors.lastname: McDonald-Madden
authors.affiliation: Unité de Mathématiques et Informatique Appliquées, Toulouse, INRA UR 875, BP 27 F-31326 Castanet-Tolosan, France.
authors.forename: R
authors.initials: R
authors.lastname: Sabbadin
authors.affiliation: The Nature Conservancy, Conservation Science, South Brisbane, Queensland 4101, Australia.
authors.forename: E T
authors.initials: ET
authors.lastname: Game
authors.affiliation: Centre for Applications in Natural Resource Mathematics, School of Mathematics and Physics, The University of Queensland, St Lucia, Queensland 4072, Australia.
authors.forename: P W J
authors.initials: PW
authors.lastname: Baxter
authors.affiliation: CSIRO, Ecosciences Precinct, Dutton Park, Queensland 4102, Australia.
authors.forename: I
authors.initials: I
authors.lastname: Chadès
authors.affiliation: School of Biological Sciences, University of Queensland, St Lucia, Queensland 4072, Australia.|School of Mathematics and Physics, The University of Queensland, St Lucia, Queensland 4072, Australia.
authors.forename: H P
authors.initials: HP
authors.lastname: Possingham
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D017753:Ecosystem; D020387:Food Chain