Fraser River Sockeye Fisheries and Fisheries Management - Cohen ...

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Appendix I Methods of In-season Stock Assessmentby the Pacific Salmon Commission.Pacific Salmon Commission600 - 1155 Robson StreetVancouver, B.C.V6E 1B5(604) 684-8081(604) 666-8707 (fax)To: Mike LapointeFrom: Jim CaveDate: December 7, 2010Re: Methods of In-season Stock Assessment: Test fishing CPUE and catchability.As requested, I am summarizing the methods for In-season Stock Assessment. Theseinclude run-reconstruction and run-size estimation based on test fishery catch-per-uniteffort,(CPUE) data and estimates of catchability. Note that I am not including catchestimation, stock identification or hydroacoustics methods as these are the subject ofdiscussion in other documents.OverviewRun-size estimation for Fraser River sockeye (by stock group) and pink salmonhistorically was based primarily on catch, effort, escapement and racial composition.Traditionally, most of these data for Summer and Late-runs come from commercialfisheries. However, limited commercial fishing in recent years has reduced theavailability of this source of data. Test fishing catch and CPUE data are now used moreextensively in assessing sockeye and pink salmon abundances than in previous years.These data are analyzed using a cumulative-normal model, which is described inpreviously published reports (PSC 1995, 1998). Since 2001, a Bayesian “Box-Car” orI-1
econstruction-based model developed by Bill Gazey was employed. This modelincorporated features of the cumulative-normal and cumulative-passage-to-date models,and implemented an objective method for combining the estimates from its componentmodels based on the relative uncertainty of these models. This model has since beenreplaced by a Bayesian time and density model using Winbugs software, similar inconcept to the Cumulative Normal model. This model incorporates historical informationon run-size, timing, spread of the migration and test fishing expansion line (inverse ofcatchability, see below) in the form of prior probability distributions or “Priors” as wellas in-season reconstructions of daily abundance and test fishing CPUE. Previously, aCumulative Passage model had also been employed but this model has been discontinuedas priors on timing and historical distributions make its use redundant in the currentmodel. The models described previously (PSC 1995) that used exclusively commercialcatch and effort data have also been discontinued, due primarily to the major changes infishing pattern, levels of effort and irregularity of the fisheries which provide the sourcedata for these models.The Pacific Salmon Commission conducts “Panel Approved Test Fisheries” in the marineareas “inside” Vancouver Island and in the Fraser River below Mission to collect CPUEdata and biological information on stock identification and species composition. DailyCPUE data in combination with the stock composition information and historicalestimates of catchability (q) are used to project stock abundances from the marine testfisheries. Catchability or “q” is defined as the proportion of a population removed by adefined unit of effort and the “Expansion line” is the inverse of catchability (1/q). DailyCPUE data are multiplied by expansion lines to generate estimates of abundance inmarine areas from test fisheries. The CPUE-based estimates are used until the stockspass the Mission Hydroacoustics site 6 days later in the case of Early Stuart, EarlySummer-run and Summer-run sockeye. During some years, Late-run sockeye (WeaverCreek and Harrison River sockeye) pass directly upstream, a phenomenon known as“Early Upstream Migration” (http://www.psc.org/pubs/LateRun/R-69_LateRunSockeyeConf_2009_final.pdf). In these situations the projected abundancesfrom marine test fisheries are replaced with estimates from the Mission Hydroacousticsprogram 6-8 days later. However on the 2010 and 2011 cycle years, Late-run sockeyeare dominated by Late Shuswap sockeye, the majority of which continue to delay in theStrait of Georgia. Therefore the assessment of run-size, timing and escapement to theStrait of Georgia are derived from the marine test fisheries. During these years, most ofthe escapement of Late-run stocks passes Mission too late to be used in assessments forthe management of most fisheries although these data still form the best estimate ofpotential spawning escapement.I-2
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Fraser River Sockeye Fisheriesand F
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EXECUTIVE SUMMARY1. Catch Monitorin
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implement harvest rate ceilings to
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Table of ContentsEXECUTIVE SUMMARY
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List of TablesTable 1 Annual estima
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List of FiguresFigure 1 Fraser sock
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Figure 21 Estimates of total catch,
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List of AppendicesAppendix A Statem
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used to (1) estimate catch for each
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FISHERIES HARVESTINGOverview of Fis
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Figure 1 Fraser sockeye marine migr
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Table 2 Summary of available inform
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Table 3 provides the annual estimat
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Table 4 Summary of available inform
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season update teleconference meetin
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Catch Estimation MethodsThe procedu
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catch estimates was detected using
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of precision. The overall rating fo
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1998-00 - No agreements with Sto:lo
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initiation of the AFS Agreements in
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Table 12 Comparison of annual socke
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Table 13 Annual estimates of the ha
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Canadian Commercial FisheriesThe pr
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logbook systems. In addition to the
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Table 15 Fraser River sockeye salmo
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Available catch estimates for the r
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Table 17 Summary of available infor
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anged from less than 100 to over 30
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Washington State Recreational Fishe
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equirements have been applied to se
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salmon were biopsied or tagged with
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Stellako-Late Stuart and 531 Adams)
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FISHERIES MANAGEMENTOverview of Pre
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parents of the recruits). Effective
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within the confidence limits. For e
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Ratio of Return to Forecast5.04.03.
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Ratio of Return to Forecast5.04.03.
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Table 20 Pre-season forecast models
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different from the straight line),
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Prediction ability of ForecastsProp
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Stock MAPEReturn Explainedby Foreca
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test fisheries are analyzed in-seas
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Inputs to the cumulative-normal mod
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eginning of the runs, but by mid-ru
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Median Absolute Percent Error (1997
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variety of hydroacoustic techniques
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enefited from improved technologies
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Post-season estimates of Escapement
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precision achieved was considerably
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m 3 /sec during the sockeye run. Fi
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Escapement TargetsOur evaluations r
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20% of the average of the 4-year se
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Table 24 Low escapement benchmarks
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Table 25 Comparison of post-season
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fisheries have been permitted to ta
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sub-dominant cycle years (e.g., 200
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SummaryLow Escapement Benchmarks (L
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Extent of OverharvestingThe estimat
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2,000,0001,800,000Early StuartCatch
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Cultus Lake Sockeye Recovery Effort
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Figure 23 Number of adult sockeye e
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Recovery ObjectivesIn anticipation
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2008 the escapement estimate was 34
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BRISTOL BAY, ALASKA SOCKEYE FISHERY
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accurate, precise, and timely, (2)
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The Bay is subjected to large tidal
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activity (Bocking and Peterman 1988
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% of total run45%40%35%30%25%20%15%
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Harvest estimation methodsTotal har
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Millions of sockeyeYearFigure 27 Hi
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management structure complements th
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1.5Togiak4.03.5NushagakMillions of
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Millions of sockeye7.06.05.04.03.02
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logical given the dynamics of the r
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1.61.4Number of fish (millions)1.21
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Pre-season forecastsAs noted above,
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8.0y = 0.79x + 1.59R² = 0.417.8Log
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12010080MAPE60402001971-1980 1981-1
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Median percent error (MPE)3020100-1
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Table 28 Average age composition of
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dampeners to reduce surface turbule
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current year’s run is accounted f
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and 1999 Revised Annexes. This inte
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Bristol Bay was in 1997 when there
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upon by managers to determine when
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STATE OF THE SCIENCECatch Monitorin
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2006) due to concerns regarding the
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RECOMMENDATIONS1. DFO needs to ensu
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LITERATURE CITEDAdkison, M.D. and R
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Beacham, T.D., M. Lapointe, J.R. Ca
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CSAS. 2007 Pre-season run size fore
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Evans, D.G., J.B. Browning and B.G.
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Hardie, D.C., D.A. Nagtegaal, K. He
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Pacific Salmon Commission. 1998. Re
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Robichaud, D., J.J. Smith, K. K. En
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Valderrama, D. and J.L. Anderson. 2
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Appendix A Statement of Work.Cohen
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All sectors3.7 The Contractor will
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“State of the Science”. Comment
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3.3 The Contractor will review and
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Appendix BList of requests to DFO b
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DFO Request 15: Recent CSAS documen
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Item Description DateRequestedReque
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Appendix CFirst Nation catch monito
Page 210 and 211: Table C-1 Summary of information re
Page 212 and 213: Table C-2 Weekly catch monitoring e
Page 214 and 215: Table D-1 Summary of information re
Page 216 and 217: community of Port Renfrew. The area
Page 218 and 219: IPrecision ( + % of the estimate)10
Page 220 and 221: Table E-2 Annual Strait of Georgia
Page 222 and 223: Table E-4 Annual Johnstone Strait e
Page 224 and 225: Table E-6 Lower Fraser River recrea
Page 226 and 227: 0 would mean that the forecast does
Page 228 and 229: the general ups and downs of the ob
Page 230 and 231: Appendix G Graphical representation
Page 232 and 233: Early Stuart Timing GroupEarly Stua
Page 234 and 235: Summer-run Timing GroupSummer run7.
Page 236 and 237: Fraser River Indicator Stocks (list
Page 254 and 255: Appendix H Pre-season forecast: sum
Page 256 and 257: ManagementGroup(IndicatorStock)MAPE
Page 258 and 259: ManagementGroup(IndicatorStock)MAPE
Page 262 and 263: Marine Gillnet Test FisheriesThe gi
Page 264 and 265: Expansion line (x1000)9080706050403
Page 266 and 267: Table I-2 Mean values for purse sei
Page 268 and 269: Figure I-5 Map of test fishing loca
Page 270 and 271: Appendix J The Hydroacoustics progr
Page 272 and 273: An SEF project was secured in 2007
Page 274 and 275: Conservation action Comments Partne
Page 276 and 277: Table K-2 Summary of actions to rec
Page 278 and 279: Table L-1 Catch and escapement of s
Page 280 and 281: Table L-3 Performance criteria of p
Page 282 and 283: Ratio of annual return to the avera
Page 284 and 285: Appendix M Reviewer comments on the
Page 286 and 287: Request #5: Sockeye allocations by
Page 288 and 289: utility of pre-season forecasts. Pr
Page 290 and 291: 3. Are there additional quantitativ
Page 292 and 293: made via tagging and experimentatio
Page 294 and 295: Page 60: The authors make extensive
Page 296 and 297: ensure the reader does not assume t
Page 298 and 299: The reported excerpts from Bradford
Page 300 and 301: (budgets, legal issues, pseudo-cons
Page 302 and 303: REVIEW 3/3Reviewer Name: Dr Sean Co
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Page 308 and 309: size.Figures showing errors for pre
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Goals can easily generate confusion
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Total rows from all these tables be
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Exploitation Rate100%90%80%70%60%50
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Exploitation RateEarly Stuart90%80%
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