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Worm, Boris 1; Email Address: bworm@dal.ca Barbier, Edward B. 2 Beaumont, Nicola 3 Duffy, J. Emmett 4 Folke, Carl 5,6 Halpern, Benjamin S. 7 Jackson, Jeremy B. C. 8,9 Lotze, Heike K. 1 Micheli, Fiorenza 10 Palumbi, Stephen R. 10 Sala, Enric 8 Selkoe, Kimberley A. 7 Stachowicz, John J. 11 Watson, Reg 12; Affiliation: 1: Department of Biology, Dalhousie University, Halifax, NS, Canada B3H 4J1. 2: Department of Economics and Finance, University of Wyoming, Laramie, WY 82071, USA. 3: Plymouth Marine Laboratory, Plymouth PL1 3DH, UK. 4: Virginia Institute of Marine Sciences, Gloucester Point, VA 23062-1346, USA. 5: Department of Systems Ecology, Stockholm University, Stockholm, SE-106 91 Sweden. 6: Beijer International Institute of Ecological Economics, Royal Swedish Academy of Sciences, SE-104 05, Stockholm, Sweden. 7: National Center for Ecological Analysis and Synthesis, Santa Barbara, CA 93101, USA. 8: Center for Marine Biodiversity and Conservation, Scripps Institution of Oceanography, La Jolla, CA 92093-0202, USA. 9: Smithsonian Tropical Research Institute, Box 2072, Balboa, Republic of Panama. 10: Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA. 11: Section of Evolution and Ecology, University of California, Davis, CA 95616, USA. 12: Fisheries Centre, University of British Columbia, Vancouver, BC, Canada V6T 1Z4.; Source Info: 11/3/2006, Vol. 314 Issue 5800, p787; Subject Term: MARINE biodiversity; Subject Term: BIOTIC communities; Subject Term: OCEAN; Subject Term: BIODIVERSITY; Subject Term: MARINE animals; Subject Term: WATER quality; Subject Term: FISHERIES; Subject Term: MARINE organisms; Subject Term: BODIES of water; NAICS/Industry Codes: 112511 Finfish Farming and Fish Hatcheries; Number of Pages: 4p; Document Type: Article

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Human-dominated marine ecosystems are experiencing accelerating Loss of populations and species, with largely unknown consequences. We analyzed local experiments, long-term regional time series, and global fisheries data to test how biodiversity loss affects marine ecosystem services across temporal and spatial scales. Overall, rates of resource collapse increased and recovery potential, stability, and water quality decreased exponentially with declining diversity. Restoration of biodiversity, in contrast, increased productivity fourfold and decreased variability by 21%, on average. We conclude that marine biodiversity loss is increasingly impairing the ocean's capacity to provide food, maintain water quality, and recover from perturbations. Yet available data suggest that at this point, these trends are still reversible. [ABSTRACT FROM AUTHOR]
Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Bayesian belief networks (BBNs) are useful tools for modeling ecological predictions and aiding resource-management decision-making. We provide practical guidelines for developing, testing, and revising BBNs. Primary steps in this process include creating influence diagrams of the hypothesized \"causal web\" of key factors affecting a species or ecological outcome of interest; developing a first, alpha-level BBN model from the influence diagram; revising the model after expert review; testing and calibrating the model with case files to create a beta-level model; and updating the model structure and conditional probabilities with new validation data, creating the final-application gamma-level model. We illustrate and discuss these steps with an empirically based BBN model of factors influencing probability of capture of northern flying squirrels (Glaucomys sabrinus (Shaw)). Testing and updating BBNs, especially with peer review and calibration, are essential to ensure their credibility and reduce bias. Our guidelines provide modelers with insights that allow them to avoid potentially spurious or unreliable models. (English) [ABSTRACT FROM AUTHOR]
Les réseaux de croyance bayésiens (RCB) sont des outils utiles pour faire des prédictions de nature écologique à l'aide de modèles et pour aider dans la prise de décision en aménagement des ressources. Nous fournissons des directives pratiques pour développer, tester et réviser des RCB. Les principales étapes dans ce processus incluent : la création d'un diagramme d'influences du « réseau causal » hypothétique des facteurs-clefs qui affectent une espèce ou un enjeu écologique d'intérêt; le développement d'un premier modèle RCB de niveau alpha à partir du diagramme d'influences; la révision du modèle par des experts; l'essai et la calibration du modèle à partir d'études de cas; la création d'un modèle de niveau bêta; l'actualisation de la structure et des probabilités conditionnelles du modèle à partir de données de validation et la création d'un modèle final d'application, de niveau gamma. Nous illustrons et discutons ces étapes à l'aide d'un modèle RCB empirique des facteurs qui influencent la capture des grands polatouches (Glaucomys sabrinus (Shaw)). L'essai et l'actualisation des RCB, surtout à l'aide de révisions par les pairs et de la calibration, sont essentiels pour assurer leur crédibilité et réduire les biais. Nos directives donnent aux modélisateurs des indications pour éviter les modèles potentiellement inexacts ou peu fiables.[Traduit par la Rédaction] (French) [ABSTRACT FROM AUTHOR]
Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Article
00455067
Accession Number: 24635341; Marcot, Bruce G. 1; Email Address: bmarcot@fs.fed.us Steventon, J. Douglas 2 Sutherland, Glenn D. 3 McCann, Robert K.; Affiliation: 1: USDA Forest Service, 620 S.W. Main Street, Suite 400, Portland, OR 97205, USA 2: British Columbia Ministry of Forests and Range, Bag 6000, 3333 Tatlow Road, Smithers, BC V0J 2N0, Canada 3: Cortex Consultants Inc., Suite 2a,1218 Langley Street, Victoria, BC V8W 1W2, Canada; Source Info: Dec2006, Vol. 36 Issue 12, p3063; Subject Term: BAYESIAN statistical decision theory; Subject Term: ECOLOGICAL assessment (Biology); Subject Term: CHARTS, diagrams, etc.; Subject Term: PROBABILITIES; Subject Term: FLYING squirrels; Subject Term: ENVIRONMENTAL impact analysis; Number of Pages: 12p; Illustrations: 4 Diagrams, 3 Charts, 1 Graph; Document Type: Article

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Barbier, Edward B. 1; Email Address: ebarbier@uwyo.edu Koch, Evamaria W. 2 Silliman, Brian R. 3 Hacker, Sally D. 4 Wolanski, Eric 5 Primavera, Jurgenne 6 Granek, Elise F. 7 Polasky, Stephen 8 Aswani, Shankar 9 Cramer, Lori A. 10 Stoms, David M. 11 Kennedy, Chris J. 1 Bael, David 8 Kappel, Carrie V. 12 Perillo, Gerardo M. E. 13 Reed, Denise J. 14; Affiliation: 1: Department of Economics and Finance, University of Wyoming, Laramie, WY 82071, USA 2: Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD 21613, USA 3: Department of Zoology, University of Florida, Gainesville, FL 32611, USA 4: Department of Zoology, Oregon State University, Corvallis, OR 97331, USA 5: Australian Centre for Tropical Freshwater Research, James Cook University and Australian Institute of Marine Science, Townsville, Queensland 4810, Australia 6: Aquaculture Department, Southeast Asian Fisheries Development Center, Tigbauan, Iloilo S021, Philippines 7: Environmental Sciences and Resources, Portland State University, Portland, OR 97207, USA 8: Department of Applied Economics, University of Minnesota, St. Paul, MN 55108, USA 9: Department of Anthropology, University of California, Santa Barbara, CA 93106, USA 10: Department of Sociology, Oregon State University, Corvallis, OR 97331, USA 11: Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, USA 12: National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, CA 93101, USA 13: Instituto Argentino de Oceanografía, B8000FWB Bahía Blanca, Argentina 14: Department of Earth and Environmental Sciences, University of New Orleans, New Orleans, LA 70148, USA; Source Info: 1/18/2008, Vol. 319 Issue 5861, p321; Subject Term: BIOTIC communities; Subject Term: COASTAL zone management; Subject Term: ECOSYSTEM management; Subject Term: BIODIVERSITY conservation; Subject Term: CORAL reef conservation; Subject Term: HABITAT (Ecology); Subject Term: NONLINEAR waves; Subject Term: CORAL reefs & islands; Subject Term: THAILAND; Number of Pages: 3p; Illustrations: 1 Graph; Document Type: Article

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In this introduction to the following series of papers on Bayesian belief networks (BBNs) we briefly summarize BBNs, review their application in ecology and natural resource management, and provide an overview of the papers in this section. We suggest that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions, and communicating with nonexperts about making natural resource management decisions. BBNs can be used effectively to represent uncertainty in understanding and variability in ecosystem response, and the influence of uncertainty and variability on costs and benefits assigned to model outcomes or decisions associated with natural resource management. BBN tools also lend themselves well to an adaptive-management framework by posing testable management hypotheses and incorporating new knowledge to evaluate existing management guidelines. (English) [ABSTRACT FROM AUTHOR]
Dans cette introduction à la série d'articles qui suivent sur les réseaux bayésiens d'appréciation (RBA), nous donnons un bref aperçu des RBA, révisons leur application en écologie et en gestion des ressources naturelles et présentons une vue d'ensemble des articles dans cette section. Nous croyons que les RBA sont des outils utiles pour représenter l'expertise existante au sujet d'un écosystème, évaluer les effets potentiels de décisions alternatives de gestion et communiquer aux profanes les enjeux liés aux décisions associées à la gestion des ressources naturelles. Les RBA peuvent être utilisés efficacement pour représenter la part d'incertitude dans notre compréhension et la variabilité dans la réponse des écosystèmes ainsi que l'influence de l'incertitude et de la variabilité sur les coûts et les bénéfices assignés aux résultats des modèles ou sur les décisions associées à la gestion des ressources naturelles. Les outils que constituent les RBA se prêtent bien également à un cadre de gestion adaptative en formulant des hypothèses de gestion qui peuvent être testées et en incorporant de nouvelles connaissances pour évaluer les directives actuelles concernant la gestion.[Traduit par la Rédaction] (French) [ABSTRACT FROM AUTHOR]
Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

", "tags": [], "relations": {}, "dateAdded": "2011-11-17T23:32:46Z", "dateModified": "2011-11-17T23:32:46Z" } }, { "key": "T6RUFA55", "version": 1, "library": { "type": "group", "id": 60517, "name": "ESR692Paper", "links": { "alternate": { "href": "https://www.zotero.org/groups/esr692paper", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/60517/items/T6RUFA55", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/esr692paper/items/T6RUFA55", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/60517/items/TTD9T5WU", "type": "application/json" } }, "meta": { "createdByUser": { "id": 513204, "username": "mansonp", "name": "Paul Manson", "links": { "alternate": { "href": "https://www.zotero.org/mansonp", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "T6RUFA55", "version": 1, "parentItem": "TTD9T5WU", "itemType": "note", "note": "

In this introduction to the following series of papers on Bayesian belief networks (BBNs) we briefly summarize BBNs, review their application in ecology and natural resource management, and provide an overview of the papers in this section. We suggest that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions, and communicating with nonexperts about making natural resource management decisions. BBNs can be used effectively to represent uncertainty in understanding and variability in ecosystem response, and the influence of uncertainty and variability on costs and benefits assigned to model outcomes or decisions associated with natural resource management. BBN tools also lend themselves well to an adaptive-management framework by posing testable management hypotheses and incorporating new knowledge to evaluate existing management guidelines. (English) [ABSTRACT FROM AUTHOR]
Dans cette introduction à la série d'articles qui suivent sur les réseaux bayésiens d'appréciation (RBA), nous donnons un bref aperçu des RBA, révisons leur application en écologie et en gestion des ressources naturelles et présentons une vue d'ensemble des articles dans cette section. Nous croyons que les RBA sont des outils utiles pour représenter l'expertise existante au sujet d'un écosystème, évaluer les effets potentiels de décisions alternatives de gestion et communiquer aux profanes les enjeux liés aux décisions associées à la gestion des ressources naturelles. Les RBA peuvent être utilisés efficacement pour représenter la part d'incertitude dans notre compréhension et la variabilité dans la réponse des écosystèmes ainsi que l'influence de l'incertitude et de la variabilité sur les coûts et les bénéfices assignés aux résultats des modèles ou sur les décisions associées à la gestion des ressources naturelles. Les outils que constituent les RBA se prêtent bien également à un cadre de gestion adaptative en formulant des hypothèses de gestion qui peuvent être testées et en incorporant de nouvelles connaissances pour évaluer les directives actuelles concernant la gestion.[Traduit par la Rédaction] (French) [ABSTRACT FROM AUTHOR]
Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

", "tags": [], "relations": {}, "dateAdded": "2011-11-17T23:32:46Z", "dateModified": "2011-11-17T23:32:46Z" } }, { "key": "P6BJRQDD", "version": 1, "library": { "type": "group", "id": 60517, "name": "ESR692Paper", "links": { "alternate": { "href": "https://www.zotero.org/groups/esr692paper", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/60517/items/P6BJRQDD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/esr692paper/items/P6BJRQDD", "type": "text/html" } }, "meta": { "createdByUser": { "id": 513204, "username": "mansonp", "name": "Paul Manson", "links": { "alternate": { "href": "https://www.zotero.org/mansonp", "type": "text/html" } } }, "creatorSummary": "Doney", "parsedDate": "2010-06-18", "numChildren": 0 }, "data": { "key": "P6BJRQDD", "version": 1, "itemType": "journalArticle", "title": "The Growing Human Footprint on Coastal and Open-Ocean Biogeochemistry", "creators": [ { "creatorType": "author", "firstName": "Scott C.", "lastName": "Doney" } ], "abstractNote": "Climate change, rising atmospheric carbon dioxide, excess nutrient inputs, and pollution in its many forms are fundamentally altering the chemistry of the ocean, often on a global scale and, in some cases, at rates greatly exceeding those in the historical and recent geological record. Major observed trends include a shift in the acid-base chemistry of seawater, reduced subsurface oxygen both in near-shore coastal water and in the open ocean, rising coastal nitrogen levels, and widespread increase in mercury and persistent organic pollutants. 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However, the prevalence and magnitude of these interactions remain one of the largest uncertainties in projections of future ecological change. We address this uncertainty by performing a meta-analysis of 112 published factorial experiments that evaluated the impacts of multiple stressors on animal mortality in freshwater, marine and terrestrial communities. We found that, on average, mortalities from the combined action of two stressors were not synergistic and this result was consistent across studies investigating different stressors, study organisms and life-history stages. Furthermore, only one-third of relevant experiments displayed truly synergistic effects, which does not support the prevailing ecological paradigm that synergies are rampant. 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"creators": [ { "creatorType": "author", "firstName": "Kevin B.", "lastName": "Korb" }, { "creatorType": "author", "firstName": "Ann E.", "lastName": "Nicholson" } ], "abstractNote": "", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2011", "publisher": "CRC Press", "place": "New York", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "", "ISBN": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2011-11-17T23:32:46Z", "dateModified": "2011-11-17T23:32:46Z" } }, { "key": "3IAR242C", "version": 1, "library": { "type": "group", "id": 60517, "name": "ESR692Paper", "links": { "alternate": { "href": "https://www.zotero.org/groups/esr692paper", "type": "text/html" } } }, "links": { 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P.", "lastName": "Madin" }, { "creatorType": "author", "firstName": "Matthew T.", "lastName": "Perry" }, { "creatorType": "author", "firstName": "Elizabeth R.", "lastName": "Selig" }, { "creatorType": "author", "firstName": "Mark", "lastName": "Spalding" }, { "creatorType": "author", "firstName": "Robert", "lastName": "Steneck" }, { "creatorType": "author", "firstName": "Reg", "lastName": "Watson" } ], "abstractNote": "The management and conservation of the world's oceans require synthesis of spatial data on the distribution and intensity of human activities and the overlap of their impacts on marine ecosystems. We developed an ecosystem-specific, multiscale spatial model to synthesize 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems. Our analysis indicates that no area is unaffected by human influence and that a large fraction (41%) is strongly affected by multiple drivers. 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