Fraser River sockeye salmon: data synthesis and cumulative impacts

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Bristol Bay review: There are substantial differences between the Fraser and Bristol Bayfisheries. The latter has fewer fisheries, gear types and sectors, less mixed-stock fishing issues,very small sport and First Nations subsistence harvests and few in-river fisheries. Area managershave very timely and accurate data on catch and escapement, clearly-defined escapement goalsthat vary little between years and unambiguous authority to open and close fisheries.Their pre-season forecasts in some cases are not as good but they rely mostly on in-seasonmanagement. Their performance in meeting escapement targets has been pretty good in terms ofstaying above minimum escapement levels and usually in the range close to the upper target.Information/evidence gaps: These include uncertainties around en route losses. Given thepotential scale of the latter (greater than total escapement or total catch in some years), this isprobably much more of an issue than the amount that they are off with the estimates of catch andescapement, which are not perfectly monitored but pretty reliable estimates. The numbers for enroute losses are best guess based on very limited and questionable data.DiscussionStaley: Where the quality of data is unknown, it may be unknowable or simply unknown becauseno one has done the work. You should distinguish between the two.English: For commercial fisheries, there are aerial surveys and reported catch rates. You can getreasonable estimates of effort for some gear types and catch rates. There is no estimate ofprecision and no verification but a good proportion of the fleet is reporting. We could try toverify the data but the challenge is getting the information from FOS (DFO’s FisheriesInformation System) in time for this project.Staley: Re the pre-season forecasts, it’s surprising that the performance of the forecast for totalFraser sockeye run size is not significantly better than that for individual stocks.English: The median absolute percent error was lowest for the overall Fraser run.Peterman: The r-squared values in the bar graph “significance of forecast vs. return relationship”seem high.English: The data were from a document that shows pre-season and post-season numbers.Peterman: The pre-season data may actually be in-season estimates, so check that those areactually the pre-season forecast numbers because I’ve never seen a correlation that high forforecasting on the Fraser.That could also be that it’s based on data going back to the 1950s, not just the last 30 years.Cox: How are en route losses calculated?English: For some, it’s based on Mission counts against spawning ground counts minus catch.For some recent years, estimates are based on telemetry data.Cox: How does that figure in calculating recruits per spawner?English: Total returns are based on catch, escapement and en route mortality.McKinnell: Highly cyclic stocks have high r-squared because of the contrast between very highand very low years.37
English: That’s certainly the case for Shuswap. There is more detail in the report, whichdefinitely shows that Shuswap has the biggest error and the highest r-squared.McKinnell: Few or none of the pre-season forecast methods consider ocean conditions.English: They are based on stock recruit relationships, though there is some additionalconsideration. 2006 was a good example: the models suggested a good return for Quesnel butthere was concern over the small size of outgoing smolts and the return was indeed dismal.But basically one of several stock recruit models are used. Managers don’t pay muchattention to the forecasts – they rely on in-season estimates to manage fisheries.Marmorek: It’s important to think about how the information will be used: pre-season forecastsare used to modify expectations but in-season information is used to manage harvest.Status of DFO Management and ScienceEdwin Blewett, CounterpointThis presentation covers three projects that focused on the period from 1985 to 2009. The firstevaluated DFO management of Fraser sockeye against stated objectives; the second detailedscience and research expenditures on Fraser sockeye (not an evaluation); and the third assessedDFO’s ability to carry out applied sockeye research.1. Evaluate DFO management of Fraser sockeye against stated objectivesThis study evaluated management against stated objectives, with a focus on objectives relating tostock management. PSC objectives were interpreted as DFO objectives and expenditures of otherorganizations like NSERC were included but not evaluated.Method: The approach was to identify stated management objectives, define evaluationindicators and the data required to inform them, then submit information requests (those startedin July and significant data gaps remain, but we are proceeding with what we have). Furthersteps included organizing the data, evaluating it, interviewing key contacts (time will not likelyallow for this step) and writing up the results.DFO’s stated objectives were sourced from key agreements and policy documents, plus annualsalmon IFMPs (Integrated Fisheries Management Plans), and grouped under nine headings:conservation, international, First Nations, stock management, fleet management, economic,allocation, process and programs. Not all objectives were included in the evaluation: the focuswas on retaining those objectives: most useful to the Commissioner, focused on stockmanagement, specific to Fraser sockeye, on “ends” vs. “means” objectives, on specific (vs.general) objectives and on the biggest impacts and effects.Information requirements: A contract was issued to an Ottawa consultancy, BMB DataConsulting Services, for the massive job of extracting Fraser sockeye expenditures from overallmanagement expenditures from 2005/06 – 2009/10 for these projects. The information availabledoesn’t include FTEs, but it does include salaries.2. Fraser sockeye science/ research expendituresThis study details but does not evaluate expenditures by DFO and other agencies on Frasersockeye science and research by program and activity, with much of the information derivedfrom the earlier-noted contract. The BMB data are for a much shorter period than ours. Noexpenditure data other than the BMB was available, however.38
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April 2011technical report 6Fraser
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Stage 2: Smolt OutmigrationWe analy
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of multiple stressors and factors,
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4.3.5 Other evidence ..............
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List of TablesTable 4.2-1. Evaluati
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List of FiguresFigure 2.3-1. Cumula
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these integrative frameworks, conve
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2.0 Cumulative Impacts or Effects2.
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assumed that there is no potential
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classes of stressors. However, if s
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affected by many stressors over its
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examples illustrate this problem --
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possibly even negligible component
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Figure 3.3-1. The conceptual model
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3.3.3 Life history perspective/appr
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additional analyses to gain further
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Figure 3.3-3. Flow diagram used to
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This project is unusual in its scop
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4.0 Results, Synthesis and Discussi
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Peterman and Dorner (2011) have thr
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Figure 4.1-3. Estimates of long ter
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Figure 4.1-5. A) Total run size of
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Figure 4.1-6. Aggregate returns to
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o were generally worse during the l
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Freshwater predators on juvenile so
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copper, iron, mercury and silver).
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Several analyses by MacDonald et al
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abundances (Sproat, Klukshu, Chilka
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feel reasonably confident in this c
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Nelitz et al. (2011; Table 18) foun
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in the Lower Fraser than other Fras
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stressors. Thus our conclusions hav
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There are many marine predators tha
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assumption that exposure occurs whe
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observations that are then averaged
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Johannes et al. (2011) demonstrate
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“likely” contributor to the ove
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Table 4.4-2. Model specifications f
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Region Variable M1 M2 M3 M4 M5 M6 M
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3. survival rates within key portio
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2011). The thermal limit hypothesis
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Historically, the majority of retur
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salinity. There is much evidence th
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declines (discussed in section 4.2
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There is indisputable evidence of t
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Figure 4.6-2. Number of years that
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correlation was statistically signi
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4.6.7 Key things we need to know be
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al (2010). The combined effect of a
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more likely to suffer en-route and
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Table 4.7-2. Variables included in
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However, an important limitation is
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Alternative PreyVariablesModelsM1 M
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will likely have a high correlation
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5.0 Conclusion5.1 Important Contrib
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Stage 5: Migration back to SpawnWhi
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More specifically, the extended res
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The twelve highest priority recomme
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Life stagefor FraserRiversockeyesal
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6.0 ReferencesAinsworth, L.M., Rout
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Johannes, M.R.S., R. Hoogendoorn, R
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Schaller et al. 2007. Comparative S
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utilized to clarify the full range
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Appendix 2. Reviewer Evaluations an
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the conclusions of the Expert Panel
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Response: Figure numbers have been
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Response: This section has been com
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ased on time or other stocks) to
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them in terms of how changes in spa
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efer to some variable being include
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... (Table xx)". Structures of thes
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Report Title: Fraser River sockeye
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show stronger correlations. Hence,
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Fraser and non-Fraser sockeye popul
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a single database for other researc
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Response: We have removed the itali
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evidence unique to each life stage.
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Report Title: Data Synthesis and Cu
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Response: We have further emphasize
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5. I would also encourage the autho
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events occur, it should be possible
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I don’t have anything to add beyo
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constitutes an exposure to human ac
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3.3.1 has been revised to say “As
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most likely contributors to the rec
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A3.2.1 Integrative quantitative met
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Table A3.3-1. A summary of data set
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ProjectNumber Project Name Variable
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ProjectNumber Project Name Variable
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A3.4 Data PreparationContractor dat
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Table A3.4-2. An example entry in t
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Table A3.4-5. Brood year adjustment
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Table A3.4-7. Example output from t
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Table A3.4-9. The table of contents
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ProjectNumberProject Variable Subse
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The objective of our approach is to
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investigated ecosystems” (Burkhar
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Figure A3.5-1. Flow diagram used to
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multiple stresses simultaneously. T
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Change point analysis of productivi
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Figure A3.5-1. This is an example o
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ecruits for this analysis as we wan
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exclude covariates with limited yea
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Figure A3.5-3. Average chlorophyll
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Status only data setsIn many cases
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The A-series of model sets based on
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with this report given the vast sco
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Table A3.5-5. Example of the model
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categories of stressors. We cannot
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o In some cases it may be the timin
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Appendix 4. Quantitative ResultsA4.
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alance” over time and so this it
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Figure A4.2-3. Stock composition fo
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Figure A4.2-5. Stock composition fo
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A4.3 Multiple Regression ResultsA4.
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Life stage Stressor category Variab
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The results show strong support for
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ModelSet_A4c_VarName M1 M2 M3 M4 M5
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Table A4.3-7. Estimates for all fix
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A4.3.3Analyses by stressor category
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Table A4.3-10. Estimates for all fi
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Model: B4bBrood years: 1969-2001We
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A4.3.4Analysis by regionModel: C4a1
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suggest any underlying mechanism, b
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Model: C1a1996-2004For 1996-2004, i
Page 276 and 277: Table A4.3-21. Estimates for all fi
Page 278 and 279: Table A4.3-23. SoG C1a candidate mo
Page 281 and 282: Appendix 5. Data Template User Guid
Page 283 and 284: Metric Connected to Biologically-su
Page 285 and 286: Initial Conceptual Model (Worksheet
Page 287 and 288: Commentsopen again. It is acceptabl
Page 289 and 290: Appendix 6. Workshop Report 1 (Nov.
Page 291 and 292: Table of ContentsTable of Contents
Page 293 and 294: PresentationsProductivity Dynamics
Page 295 and 296: and scope for improvement, particul
Page 297 and 298: Workshop participants pointed out t
Page 299 and 300: o Can you find the same shared tren
Page 301 and 302: generally better for Alaska sockeye
Page 303 and 304: Relative Likelihood of Alternative
Page 305: There was widespread agreement with
Page 308 and 309: Appendix A: List of Projects and In
Page 310 and 311: Peer Reviewers: Provide constructiv
Page 312 and 313: MacDonald Environmental Sciences Lt
Page 314 and 315: Appendix C: Workshop MinutesContent
Page 316 and 317: Levy urged researchers to bear in m
Page 318 and 319: within-population, within-brood-yea
Page 320 and 321: To address potential skepticism ove
Page 322 and 323: instead of migrating directly out i
Page 324 and 325: considered a good surrogate of temp
Page 328 and 329: Method: The approach included formu
Page 330 and 331: Staley: Our project needs to compar
Page 332 and 333: Levy: It will go ahead in January.
Page 334 and 335: preliminary assessment of potential
Page 336 and 337: MacDonald: We’re waiting for all
Page 338 and 339: including Shuswap and North Thompso
Page 340 and 341: and biological variables. The combi
Page 342 and 343: temperatures. Notably, it doesn’t
Page 344 and 345: Seasonal population distribution pa
Page 346 and 347: Information gaps: The most importan
Page 348 and 349: Fraser sockeye salmon habitat analy
Page 350 and 351: survey data, but they were looking
Page 352 and 353: was also shown that tagged fish ret
Page 354 and 355: English: There was extensive holdin
Page 356 and 357: periods showed that 4 to 6 stocks (
Page 358 and 359: Q/A: Researchers should also feel f
Page 360 and 361: Peterman: We’re lacking the big p
Page 362 and 363: Appendix D: Table of likelihood/ hy
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Fraser River sockeye salmon: data synthesis and cumulative impacts

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