Using food-web theory to conserve ecosystems
Identifieur interne : 001472 ( Main/Exploration ); précédent : 001471; suivant : 001473Using food-web theory to conserve ecosystems
Auteurs : E. Mcdonald-Madden [Australie] ; R. Sabbadin [France] ; E. T. Game [Australie] ; P. W. J. Baxter [Australie] ; I. Chadès [Australie] ; H. P. Possingham [Australie]Source :
- Nature Communications [ 2041-1723 ] ; 2016.
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English descriptors
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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.
Url:
DOI: 10.1038/ncomms10245
PubMed: 26776253
PubMed Central: 4735605
Affiliations:
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Mcdonald-Madden</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>School of Geography, Planning and Environmental Management, University of Queensland</institution>, St Lucia, Queensland 4072,<country>Australia</country></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Sabbadin, R" sort="Sabbadin, R" uniqKey="Sabbadin R" first="R." last="Sabbadin">R. Sabbadin</name><affiliation wicri:level="1"><nlm:aff id="a2"><institution>Unité de Mathématiques et Informatique Appliquées, Toulouse</institution>, INRA UR 875, BP 27 F-31326 Castanet-Tolosan,<country>France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Game, E T" sort="Game, E T" uniqKey="Game E" first="E. T." last="Game">E. T. 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Chadès</name><affiliation wicri:level="1"><nlm:aff id="a5"><institution>CSIRO, Ecosciences Precinct</institution>, Dutton Park, Queensland 4102,<country>Australia</country></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Possingham, H P" sort="Possingham, H P" uniqKey="Possingham H" first="H. P." last="Possingham">H. P. Possingham</name><affiliation wicri:level="1"><nlm:aff id="a6"><institution>School of Biological Sciences, University of Queensland</institution>, St Lucia, Queensland 4072,<country>Australia</country></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="a7"><institution>School of Mathematics and Physics, The University of Queensland</institution>, St Lucia, Queensland 4072,<country>Australia</country></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></analytic><series><title level="j">Nature Communications</title><idno type="eISSN">2041-1723</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Bayes Theorem</term><term>Ecosystem</term><term>Food Chain</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Chaine alimentaire</term><term>Intelligence artificielle</term><term>Théorème de Bayes</term><term>Écosystème</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Bayes Theorem</term><term>Ecosystem</term><term>Food Chain</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Chaine alimentaire</term><term>Intelligence artificielle</term><term>Théorème de Bayes</term><term>Écosystème</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="May, R M" uniqKey="May R">R. M. May</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pimm, S L" uniqKey="Pimm S">S. L. Pimm</name></author><author><name sortKey="Lawton, J H" uniqKey="Lawton J">J. H. Lawton</name></author><author><name sortKey="Cohen, J E" uniqKey="Cohen J">J. E. Cohen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Allesina, S" uniqKey="Allesina S">S. Allesina</name></author><author><name sortKey="Bodini, A" uniqKey="Bodini A">A. Bodini</name></author><author><name sortKey="Pascual, M" uniqKey="Pascual M">M. 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