@article{ ISI:000382599600013,
Author = {Kell, Laurence T. and Kirnoto, Ai and Kitakado, Toshihide},
Title = {{Evaluation of the prediction skill of stock assessment using hindcasting}},
Journal = {{FISHERIES RESEARCH}},
Year = {{2016}},
Volume = {{183}},
Pages = {{119-127}},
Month = {{NOV}},
Abstract = {{A major uncertainty in stock assessment is the difference between models
   and reality. The validation of model prediction is difficult, however,
   as fish stocks can rarely be observed and counted. We therefore show how
   hindcasting and model-free validation can be used to evaluate multiple
   measures of prediction skill. In a hindcast a model is fitted to the
   first part of a time series and then projected over the period omitted
   in the original fit. Prediction skill can then be evaluated by comparing
   the predictions from the projection with the observations. We show that
   uncertainty increased when different datasets and hypotheses were
   considered, especially as time-series of model-derived parameters were
   sensitive to model assumptions. Using hindcasting and model-free
   validation to evaluate prediction skill is an objective way to evaluate
   risk, i.e., to identify the uncertainties that matter. A hindcast is
   also a pragmatic alternative to hindsight, without the associated risks.
   While the use of multiple measures helps in evaluating prediction skill
   and to focus research onto the data and the processes that generated
   them. (C) 2016 Elsevier B.V. All rights reserved.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Kell, LT (Reprint Author), ICCAT Secretariat, C Corazon de Maria 8, Madrid 28002, Spain.
   Kell, Laurence T., ICCAT Secretariat, C Corazon de Maria 8, Madrid 28002, Spain.
   Kirnoto, Ai, Fisheries Res Agcy, Bluefin Tuna Resources Div, Natl Res Inst Far Seas Fisheries, 5-7-1 Orido, Shimizu, Shizuoka 4248633, Japan.
   Kitakado, Toshihide, Tokyo Univ Marine Sci \& Technol, Fac Marine Sci, Dept Marine Biosci, Minato Ku, 5-7 Konan 4, Tokyo 1088477, Japan.}},
DOI = {{10.1016/j.fishres.2016.05.017}},
ISSN = {{0165-7836}},
EISSN = {{1872-6763}},
Keywords = {{Abundance indices; Cross-validation; Projection; Retrospective analysis;
   Stock assessment; Taylor diagrams}},
Keywords-Plus = {{ATLANTIC BLUEFIN TUNA; POPULATION ANALYSIS; ASSESSMENT MODELS;
   MANAGEMENT; FISHERIES; UNCERTAINTY; CATCH; PERFORMANCE; CHALLENGES;
   FRAMEWORK}},
Research-Areas = {{Fisheries}},
Web-of-Science-Categories  = {{Fisheries}},
Author-Email = {{Laurie.Kell@iccat.int}},
Funding-Acknowledgement = {{Fisheries Research Agency, Japan}},
Funding-Text = {{This study does not necessarily reflect the views of ICCAT and in no way
   anticipates the Commission's future policy in this area. Ai Kimoto was
   supported by the overseas research program of the Fisheries Research
   Agency, Japan. The authors would also like to thank the reviewers and
   the editor, Andre Punt, who made many suggestions which greatly improved
   the manuscript.}},
Number-of-Cited-References = {{51}},
Times-Cited = {{2}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{9}},
Journal-ISO = {{Fish Res.}},
Doc-Delivery-Number = {{DV0HW}},
Unique-ID = {{ISI:000382599600013}},
OA = {{No}},
DA = {{2017-08-17}},
}

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The validation of model prediction is difficult, however, as fish stocks can rarely be observed and counted. We therefore show how hindcasting and model-free validation can be used to evaluate multiple measures of prediction skill. In a hindcast a model is fitted to the first part of a time series and then projected over the period omitted in the original fit. Prediction skill can then be evaluated by comparing the predictions from the projection with the observations. We show that uncertainty increased when different datasets and hypotheses were considered, especially as time-series of model-derived parameters were sensitive to model assumptions. Using hindcasting and model-free validation to evaluate prediction skill is an objective way to evaluate risk, i.e., to identify the uncertainties that matter. A hindcast is also a pragmatic alternative to hindsight, without the associated risks. While the use of multiple measures helps in evaluating prediction skill and to focus research onto the data and the processes that generated them. (C) 2016 Elsevier B.V. All rights reserved.","publisher":"ELSEVIER SCIENCE BV","address":"PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS","language":"English","affiliation":"Kell, LT (Reprint Author), ICCAT Secretariat, C Corazon de Maria 8, Madrid 28002, Spain. Kell, Laurence T., ICCAT Secretariat, C Corazon de Maria 8, Madrid 28002, Spain. Kirnoto, Ai, Fisheries Res Agcy, Bluefin Tuna Resources Div, Natl Res Inst Far Seas Fisheries, 5-7-1 Orido, Shimizu, Shizuoka 4248633, Japan. 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We therefore show how\n hindcasting and model-free validation can be used to evaluate multiple\n measures of prediction skill. In a hindcast a model is fitted to the\n first part of a time series and then projected over the period omitted\n in the original fit. Prediction skill can then be evaluated by comparing\n the predictions from the projection with the observations. We show that\n uncertainty increased when different datasets and hypotheses were\n considered, especially as time-series of model-derived parameters were\n sensitive to model assumptions. Using hindcasting and model-free\n validation to evaluate prediction skill is an objective way to evaluate\n risk, i.e., to identify the uncertainties that matter. A hindcast is\n also a pragmatic alternative to hindsight, without the associated risks.\n While the use of multiple measures helps in evaluating prediction skill\n and to focus research onto the data and the processes that generated\n them. (C) 2016 Elsevier B.V. All rights reserved.}},\nPublisher = {{ELSEVIER SCIENCE BV}},\nAddress = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},\nType = {{Article}},\nLanguage = {{English}},\nAffiliation = {{Kell, LT (Reprint Author), ICCAT Secretariat, C Corazon de Maria 8, Madrid 28002, Spain.\n Kell, Laurence T., ICCAT Secretariat, C Corazon de Maria 8, Madrid 28002, Spain.\n Kirnoto, Ai, Fisheries Res Agcy, Bluefin Tuna Resources Div, Natl Res Inst Far Seas Fisheries, 5-7-1 Orido, Shimizu, Shizuoka 4248633, Japan.\n Kitakado, Toshihide, Tokyo Univ Marine Sci \\& Technol, Fac Marine Sci, Dept Marine Biosci, Minato Ku, 5-7 Konan 4, Tokyo 1088477, Japan.}},\nDOI = {{10.1016/j.fishres.2016.05.017}},\nISSN = {{0165-7836}},\nEISSN = {{1872-6763}},\nKeywords = {{Abundance indices; Cross-validation; Projection; Retrospective analysis;\n Stock assessment; Taylor diagrams}},\nKeywords-Plus = {{ATLANTIC BLUEFIN TUNA; POPULATION ANALYSIS; ASSESSMENT MODELS;\n MANAGEMENT; FISHERIES; UNCERTAINTY; CATCH; PERFORMANCE; CHALLENGES;\n FRAMEWORK}},\nResearch-Areas = {{Fisheries}},\nWeb-of-Science-Categories = {{Fisheries}},\nAuthor-Email = {{Laurie.Kell@iccat.int}},\nFunding-Acknowledgement = {{Fisheries Research Agency, Japan}},\nFunding-Text = {{This study does not necessarily reflect the views of ICCAT and in no way\n anticipates the Commission's future policy in this area. 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