Improving estimates of population status and trend with superensemble models. Anderson, S. C., Cooper, A. B., Jensen, O. P., Minto, C., Thorson, J. T., Walsh, J. C., Afflerbach, J., Dickey-Collas, M., Kleisner, K. M., Longo, C., Osio, G. C., Ovando, D., Mosqueira, I., Rosenberg, A. A., & Selig, E. R. FISH AND FISHERIES, 18(4):732-741, WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, JUL, 2017. doi abstract bibtex Fishery managers must often reconcile conflicting estimates of population status and trend. Superensemble models, commonly used in climate and weather forecasting, may provide an effective solution. This approach uses predictions from multiple models as covariates in an additional ``superensemble'' model fitted to known data. We evaluated the potential for ensemble averages and superensemble models (ensemble methods) to improve estimates of population status and trend for fisheries. We fit four widely applicable data-limited models that estimate stock biomass relative to equilibrium biomass at maximum sustainable yield (B/BMSY). We combined these estimates of recent fishery status and trends in B/BMSY with four ensemble methods: an ensemble average and three superensembles (a linear model, a random forest and a boosted regression tree). We trained our superensembles on 5,760 simulated stocks and tested them with cross-validation and against a global database of 249 stock assessments. Ensemble methods substantially improved estimates of population status and trend. Random forest and boosted regression trees performed the best at estimating population status: inaccuracy (median absolute proportional error) decreased from 0.42 -0.56 to 0.32 -0.33, rank-order correlation between predicted and true status improved from 0.02 - 0.32 to 0.44 - 0.48 and bias (median proportional error) declined from - 0.22 - 0.31 to - 0.12 - 0.03. We found similar improvements when predicting trend and when applying the simulation-trained superensembles to catch data for global fish stocks. Superensembles can optimally leverage multiple model predictions; however, they must be tested, formed from a diverse set of accurate models and built on a data set representative of the populations to which they are applied.
@article{ ISI:000404555100008,
Author = {Anderson, Sean C. and Cooper, Andrew B. and Jensen, Olaf P. and Minto,
Coilin and Thorson, James T. and Walsh, Jessica C. and Afflerbach, Jamie
and Dickey-Collas, Mark and Kleisner, Kristin M. and Longo, Catherine
and Osio, Giacomo Chato and Ovando, Daniel and Mosqueira, Iago and
Rosenberg, Andrew A. and Selig, Elizabeth R.},
Title = {{Improving estimates of population status and trend with superensemble
models}},
Journal = {{FISH AND FISHERIES}},
Year = {{2017}},
Volume = {{18}},
Number = {{4}},
Pages = {{732-741}},
Month = {{JUL}},
Abstract = {{Fishery managers must often reconcile conflicting estimates of
population status and trend. Superensemble models, commonly used in
climate and weather forecasting, may provide an effective solution. This
approach uses predictions from multiple models as covariates in an
additional ``superensemble{''} model fitted to known data. We evaluated
the potential for ensemble averages and superensemble models (ensemble
methods) to improve estimates of population status and trend for
fisheries. We fit four widely applicable data-limited models that
estimate stock biomass relative to equilibrium biomass at maximum
sustainable yield (B/BMSY). We combined these estimates of recent
fishery status and trends in B/BMSY with four ensemble methods: an
ensemble average and three superensembles (a linear model, a random
forest and a boosted regression tree). We trained our superensembles on
5,760 simulated stocks and tested them with cross-validation and against
a global database of 249 stock assessments. Ensemble methods
substantially improved estimates of population status and trend. Random
forest and boosted regression trees performed the best at estimating
population status: inaccuracy (median absolute proportional error)
decreased from 0.42 -0.56 to 0.32 -0.33, rank-order correlation between
predicted and true status improved from 0.02 - 0.32 to 0.44 - 0.48 and
bias (median proportional error) declined from - 0.22 - 0.31 to - 0.12 -
0.03. We found similar improvements when predicting trend and when
applying the simulation-trained superensembles to catch data for global
fish stocks. Superensembles can optimally leverage multiple model
predictions; however, they must be tested, formed from a diverse set of
accurate models and built on a data set representative of the
populations to which they are applied.}},
Publisher = {{WILEY}},
Address = {{111 RIVER ST, HOBOKEN 07030-5774, NJ USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Anderson, SC (Reprint Author), Univ Washington, Sch Aquat \& Fishery Sci, Seattle, WA USA.
Anderson, Sean C.; Cooper, Andrew B.; Walsh, Jessica C., Simon Fraser Univ, Sch Resource \& Environm Management, Burnaby, BC, Canada.
Jensen, Olaf P., Rutgers State Univ, Inst Marine Coastal Sci, New Brunswick, NJ USA.
Minto, Coilin, Galway Mayo Inst Technol, Marine \& Freshwater Res Ctr, Galway, Ireland.
Thorson, James T., NOAA, Natl Marine Fisheries Serv, Northwest Fisheries Sci Ctr, Fisheries Resource Anal \& Monitoring Div, Seattle, WA 98112 USA.
Afflerbach, Jamie, Univ Calif Santa Barbara, Natl Ctr Ecol Anal \& Synth, Santa Barbara, CA 93106 USA.
Dickey-Collas, Mark, Int Council Explorat Sea, Copenhagen, Denmark.
Dickey-Collas, Mark, Tech Univ Denmark DTU, DTU Aqua Natl Inst Aquat Resources, Charlottenlund, Denmark.
Kleisner, Kristin M., NOAA, Natl Marine Fisheries Serv, Ecosyst Assessment Program, Northeast Fisheries Sci Ctr, Woods Hole, MA 02543 USA.
Longo, Catherine, Marine Stewarship Council, London, England.
Osio, Giacomo Chato; Mosqueira, Iago, European Commiss, DG Joint Res Ctr, Directorate Sustainable Resources D, Unit Water \& Marine Resources D 02, Ispra, Italy.
Ovando, Daniel, Univ Calif Santa Barbara, Bren Sch Environm Sci \& Management, Santa Barbara, CA 93106 USA.
Rosenberg, Andrew A., Union Concerned Scientists, Cambridge, MA USA.
Selig, Elizabeth R., Conservat Int, Arlington, VA USA.}},
DOI = {{10.1111/faf.12200}},
ISSN = {{1467-2960}},
EISSN = {{1467-2979}},
Keywords = {{CMSY; data-limited fisheries; ensemble methods; multimodel averaging;
population dynamics; sustainable resource management}},
Keywords-Plus = {{SEASONAL CLIMATE FORECASTS; MULTIMODEL SUPERENSEMBLE; EXTINCTION RISK;
FISHERIES; SELECTION; ENSEMBLE; OCEANS}},
Research-Areas = {{Fisheries}},
Web-of-Science-Categories = {{Fisheries}},
Author-Email = {{sean.anderson@dal.ca}},
ResearcherID-Numbers = {{Dickey-Collas, Mark/A-8036-2008}},
Funding-Acknowledgement = {{Gordon and Betty Moore Foundation}},
Funding-Text = {{Gordon and Betty Moore Foundation}},
Number-of-Cited-References = {{62}},
Times-Cited = {{0}},
Usage-Count-Last-180-days = {{6}},
Usage-Count-Since-2013 = {{6}},
Journal-ISO = {{Fish. Fish.}},
Doc-Delivery-Number = {{EZ2QR}},
Unique-ID = {{ISI:000404555100008}},
OA = {{No}},
DA = {{2017-08-17}},
}
Downloads: 0
{"_id":"eQeEbpJWdsYrSKsKW","bibbaseid":"anderson-cooper-jensen-minto-thorson-walsh-afflerbach-dickeycollas-etal-improvingestimatesofpopulationstatusandtrendwithsuperensemblemodels-2017","downloads":0,"creationDate":"2017-08-17T14:08:07.473Z","title":"Improving estimates of population status and trend with superensemble models","author_short":["Anderson, S. C.","Cooper, A. B.","Jensen, O. P.","Minto, C.","Thorson, J. T.","Walsh, J. C.","Afflerbach, J.","Dickey-Collas, M.","Kleisner, K. M.","Longo, C.","Osio, G. C.","Ovando, D.","Mosqueira, I.","Rosenberg, A. A.","Selig, E. R."],"year":2017,"bibtype":"article","biburl":"http://flr-project.org/flr.bib","bibdata":{"bibtype":"article","type":"Article","author":[{"propositions":[],"lastnames":["Anderson"],"firstnames":["Sean","C."],"suffixes":[]},{"propositions":[],"lastnames":["Cooper"],"firstnames":["Andrew","B."],"suffixes":[]},{"propositions":[],"lastnames":["Jensen"],"firstnames":["Olaf","P."],"suffixes":[]},{"propositions":[],"lastnames":["Minto"],"firstnames":["Coilin"],"suffixes":[]},{"propositions":[],"lastnames":["Thorson"],"firstnames":["James","T."],"suffixes":[]},{"propositions":[],"lastnames":["Walsh"],"firstnames":["Jessica","C."],"suffixes":[]},{"propositions":[],"lastnames":["Afflerbach"],"firstnames":["Jamie"],"suffixes":[]},{"propositions":[],"lastnames":["Dickey-Collas"],"firstnames":["Mark"],"suffixes":[]},{"propositions":[],"lastnames":["Kleisner"],"firstnames":["Kristin","M."],"suffixes":[]},{"propositions":[],"lastnames":["Longo"],"firstnames":["Catherine"],"suffixes":[]},{"propositions":[],"lastnames":["Osio"],"firstnames":["Giacomo","Chato"],"suffixes":[]},{"propositions":[],"lastnames":["Ovando"],"firstnames":["Daniel"],"suffixes":[]},{"propositions":[],"lastnames":["Mosqueira"],"firstnames":["Iago"],"suffixes":[]},{"propositions":[],"lastnames":["Rosenberg"],"firstnames":["Andrew","A."],"suffixes":[]},{"propositions":[],"lastnames":["Selig"],"firstnames":["Elizabeth","R."],"suffixes":[]}],"title":"Improving estimates of population status and trend with superensemble models","journal":"FISH AND FISHERIES","year":"2017","volume":"18","number":"4","pages":"732-741","month":"JUL","abstract":"Fishery managers must often reconcile conflicting estimates of population status and trend. Superensemble models, commonly used in climate and weather forecasting, may provide an effective solution. This approach uses predictions from multiple models as covariates in an additional ``superensemble'' model fitted to known data. We evaluated the potential for ensemble averages and superensemble models (ensemble methods) to improve estimates of population status and trend for fisheries. We fit four widely applicable data-limited models that estimate stock biomass relative to equilibrium biomass at maximum sustainable yield (B/BMSY). We combined these estimates of recent fishery status and trends in B/BMSY with four ensemble methods: an ensemble average and three superensembles (a linear model, a random forest and a boosted regression tree). We trained our superensembles on 5,760 simulated stocks and tested them with cross-validation and against a global database of 249 stock assessments. Ensemble methods substantially improved estimates of population status and trend. Random forest and boosted regression trees performed the best at estimating population status: inaccuracy (median absolute proportional error) decreased from 0.42 -0.56 to 0.32 -0.33, rank-order correlation between predicted and true status improved from 0.02 - 0.32 to 0.44 - 0.48 and bias (median proportional error) declined from - 0.22 - 0.31 to - 0.12 - 0.03. We found similar improvements when predicting trend and when applying the simulation-trained superensembles to catch data for global fish stocks. Superensembles can optimally leverage multiple model predictions; however, they must be tested, formed from a diverse set of accurate models and built on a data set representative of the populations to which they are applied.","publisher":"WILEY","address":"111 RIVER ST, HOBOKEN 07030-5774, NJ USA","language":"English","affiliation":"Anderson, SC (Reprint Author), Univ Washington, Sch Aquat & Fishery Sci, Seattle, WA USA. Anderson, Sean C.; Cooper, Andrew B.; Walsh, Jessica C., Simon Fraser Univ, Sch Resource & Environm Management, Burnaby, BC, Canada. Jensen, Olaf P., Rutgers State Univ, Inst Marine Coastal Sci, New Brunswick, NJ USA. Minto, Coilin, Galway Mayo Inst Technol, Marine & Freshwater Res Ctr, Galway, Ireland. Thorson, James T., NOAA, Natl Marine Fisheries Serv, Northwest Fisheries Sci Ctr, Fisheries Resource Anal & Monitoring Div, Seattle, WA 98112 USA. Afflerbach, Jamie, Univ Calif Santa Barbara, Natl Ctr Ecol Anal & Synth, Santa Barbara, CA 93106 USA. Dickey-Collas, Mark, Int Council Explorat Sea, Copenhagen, Denmark. Dickey-Collas, Mark, Tech Univ Denmark DTU, DTU Aqua Natl Inst Aquat Resources, Charlottenlund, Denmark. Kleisner, Kristin M., NOAA, Natl Marine Fisheries Serv, Ecosyst Assessment Program, Northeast Fisheries Sci Ctr, Woods Hole, MA 02543 USA. Longo, Catherine, Marine Stewarship Council, London, England. Osio, Giacomo Chato; Mosqueira, Iago, European Commiss, DG Joint Res Ctr, Directorate Sustainable Resources D, Unit Water & Marine Resources D 02, Ispra, Italy. Ovando, Daniel, Univ Calif Santa Barbara, Bren Sch Environm Sci & Management, Santa Barbara, CA 93106 USA. Rosenberg, Andrew A., Union Concerned Scientists, Cambridge, MA USA. Selig, Elizabeth R., Conservat Int, Arlington, VA USA.","doi":"10.1111/faf.12200","issn":"1467-2960","eissn":"1467-2979","keywords":"CMSY; data-limited fisheries; ensemble methods; multimodel averaging; population dynamics; sustainable resource management","keywords-plus":"SEASONAL CLIMATE FORECASTS; MULTIMODEL SUPERENSEMBLE; EXTINCTION RISK; FISHERIES; SELECTION; ENSEMBLE; OCEANS","research-areas":"Fisheries","web-of-science-categories":"Fisheries","author-email":"sean.anderson@dal.ca","researcherid-numbers":"Dickey-Collas, Mark/A-8036-2008","funding-acknowledgement":"Gordon and Betty Moore Foundation","funding-text":"Gordon and Betty Moore Foundation","number-of-cited-references":"62","times-cited":"0","usage-count-last-180-days":"6","usage-count-since-2013":"6","journal-iso":"Fish. Fish.","doc-delivery-number":"EZ2QR","unique-id":"ISI:000404555100008","oa":"No","da":"2017-08-17","bibtex":"@article{ ISI:000404555100008,\nAuthor = {Anderson, Sean C. and Cooper, Andrew B. and Jensen, Olaf P. and Minto,\n Coilin and Thorson, James T. and Walsh, Jessica C. and Afflerbach, Jamie\n and Dickey-Collas, Mark and Kleisner, Kristin M. and Longo, Catherine\n and Osio, Giacomo Chato and Ovando, Daniel and Mosqueira, Iago and\n Rosenberg, Andrew A. and Selig, Elizabeth R.},\nTitle = {{Improving estimates of population status and trend with superensemble\n models}},\nJournal = {{FISH AND FISHERIES}},\nYear = {{2017}},\nVolume = {{18}},\nNumber = {{4}},\nPages = {{732-741}},\nMonth = {{JUL}},\nAbstract = {{Fishery managers must often reconcile conflicting estimates of\n population status and trend. Superensemble models, commonly used in\n climate and weather forecasting, may provide an effective solution. This\n approach uses predictions from multiple models as covariates in an\n additional ``superensemble{''} model fitted to known data. We evaluated\n the potential for ensemble averages and superensemble models (ensemble\n methods) to improve estimates of population status and trend for\n fisheries. We fit four widely applicable data-limited models that\n estimate stock biomass relative to equilibrium biomass at maximum\n sustainable yield (B/BMSY). We combined these estimates of recent\n fishery status and trends in B/BMSY with four ensemble methods: an\n ensemble average and three superensembles (a linear model, a random\n forest and a boosted regression tree). We trained our superensembles on\n 5,760 simulated stocks and tested them with cross-validation and against\n a global database of 249 stock assessments. Ensemble methods\n substantially improved estimates of population status and trend. Random\n forest and boosted regression trees performed the best at estimating\n population status: inaccuracy (median absolute proportional error)\n decreased from 0.42 -0.56 to 0.32 -0.33, rank-order correlation between\n predicted and true status improved from 0.02 - 0.32 to 0.44 - 0.48 and\n bias (median proportional error) declined from - 0.22 - 0.31 to - 0.12 -\n 0.03. We found similar improvements when predicting trend and when\n applying the simulation-trained superensembles to catch data for global\n fish stocks. Superensembles can optimally leverage multiple model\n predictions; however, they must be tested, formed from a diverse set of\n accurate models and built on a data set representative of the\n populations to which they are applied.}},\nPublisher = {{WILEY}},\nAddress = {{111 RIVER ST, HOBOKEN 07030-5774, NJ USA}},\nType = {{Article}},\nLanguage = {{English}},\nAffiliation = {{Anderson, SC (Reprint Author), Univ Washington, Sch Aquat \\& Fishery Sci, Seattle, WA USA.\n Anderson, Sean C.; Cooper, Andrew B.; Walsh, Jessica C., Simon Fraser Univ, Sch Resource \\& Environm Management, Burnaby, BC, Canada.\n Jensen, Olaf P., Rutgers State Univ, Inst Marine Coastal Sci, New Brunswick, NJ USA.\n Minto, Coilin, Galway Mayo Inst Technol, Marine \\& Freshwater Res Ctr, Galway, Ireland.\n Thorson, James T., NOAA, Natl Marine Fisheries Serv, Northwest Fisheries Sci Ctr, Fisheries Resource Anal \\& Monitoring Div, Seattle, WA 98112 USA.\n Afflerbach, Jamie, Univ Calif Santa Barbara, Natl Ctr Ecol Anal \\& Synth, Santa Barbara, CA 93106 USA.\n Dickey-Collas, Mark, Int Council Explorat Sea, Copenhagen, Denmark.\n Dickey-Collas, Mark, Tech Univ Denmark DTU, DTU Aqua Natl Inst Aquat Resources, Charlottenlund, Denmark.\n Kleisner, Kristin M., NOAA, Natl Marine Fisheries Serv, Ecosyst Assessment Program, Northeast Fisheries Sci Ctr, Woods Hole, MA 02543 USA.\n Longo, Catherine, Marine Stewarship Council, London, England.\n Osio, Giacomo Chato; Mosqueira, Iago, European Commiss, DG Joint Res Ctr, Directorate Sustainable Resources D, Unit Water \\& Marine Resources D 02, Ispra, Italy.\n Ovando, Daniel, Univ Calif Santa Barbara, Bren Sch Environm Sci \\& Management, Santa Barbara, CA 93106 USA.\n Rosenberg, Andrew A., Union Concerned Scientists, Cambridge, MA USA.\n Selig, Elizabeth R., Conservat Int, Arlington, VA USA.}},\nDOI = {{10.1111/faf.12200}},\nISSN = {{1467-2960}},\nEISSN = {{1467-2979}},\nKeywords = {{CMSY; data-limited fisheries; ensemble methods; multimodel averaging;\n population dynamics; sustainable resource management}},\nKeywords-Plus = {{SEASONAL CLIMATE FORECASTS; MULTIMODEL SUPERENSEMBLE; EXTINCTION RISK;\n FISHERIES; SELECTION; ENSEMBLE; OCEANS}},\nResearch-Areas = {{Fisheries}},\nWeb-of-Science-Categories = {{Fisheries}},\nAuthor-Email = {{sean.anderson@dal.ca}},\nResearcherID-Numbers = {{Dickey-Collas, Mark/A-8036-2008}},\nFunding-Acknowledgement = {{Gordon and Betty Moore Foundation}},\nFunding-Text = {{Gordon and Betty Moore Foundation}},\nNumber-of-Cited-References = {{62}},\nTimes-Cited = {{0}},\nUsage-Count-Last-180-days = {{6}},\nUsage-Count-Since-2013 = {{6}},\nJournal-ISO = {{Fish. Fish.}},\nDoc-Delivery-Number = {{EZ2QR}},\nUnique-ID = {{ISI:000404555100008}},\nOA = {{No}},\nDA = {{2017-08-17}},\n}\n\n","author_short":["Anderson, S. C.","Cooper, A. B.","Jensen, O. P.","Minto, C.","Thorson, J. T.","Walsh, J. C.","Afflerbach, J.","Dickey-Collas, M.","Kleisner, K. M.","Longo, C.","Osio, G. C.","Ovando, D.","Mosqueira, I.","Rosenberg, A. A.","Selig, E. R."],"key":"ISI:000404555100008","id":"ISI:000404555100008","bibbaseid":"anderson-cooper-jensen-minto-thorson-walsh-afflerbach-dickeycollas-etal-improvingestimatesofpopulationstatusandtrendwithsuperensemblemodels-2017","role":"author","urls":{},"keyword":["CMSY; data-limited fisheries; ensemble methods; multimodel averaging; population dynamics; sustainable resource management"],"metadata":{"authorlinks":{}},"downloads":0},"search_terms":["improving","estimates","population","status","trend","superensemble","models","anderson","cooper","jensen","minto","thorson","walsh","afflerbach","dickey-collas","kleisner","longo","osio","ovando","mosqueira","rosenberg","selig"],"keywords":["cmsy; data-limited fisheries; ensemble methods; multimodel averaging; population dynamics; sustainable resource management"],"authorIDs":[],"dataSources":["WA8XYs6LQoFRqHaQt","NDGPxX9hJs9BdrF52"]}