Fraser River sockeye salmon: data synthesis and cumulative impacts

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periods showed that 4 to 6 stocks (of the 19 that have data) consistently made up about 80% ofthe run for each time period.Cross project analysis: The project also involves correlation analyses to assess correlationamong variables across projects and also comparisons among groups (run timing, year types,Fraser vs. non-Fraser, climate periods, migration routes or timing.Multivariate analyses: These include discriminant analysis or logistic regression to determinewhich stressors are best able to “discriminate” among groups of stocks with similar productivitypatterns (don’t have covariates beyond the Fraser to follow that through). Principal componentanalysis (data reduction and interpretability among predictor variables) may provide (?a betteridea of the uncertainty around relationships?).Multiple regression can assess the relative importance of each stressor as well as hypothesizedinteractions and is probably the best tool to address the question of relative and cumulativeeffects. The intent is to use an information theoretic approach: to develop candidate models, to fitmodels and compute AICs, and to assess relative weight for each model.Caveats are that while this quantitative analysis is important, it also has important limitations(e.g. stocks with no data don’t even enter into the comparisons). There is a tendency to think thatthe numbers trump everything else in terms of validity, but these should be seen as just one pieceto add to the overall weight of evidence and every effort will be made to communicate that veryclearly.Questions: These include how to deal with missing data: Is interpolation possible or will it benecessary to drop incomplete rows? Is it better to use a reduced set of years with data from morestressors or to look at a reduced set of stressors (or both)? Should the project try to come up witha status-only dataset across all stressors? For the productivity metric, which is the right responsevariable: current slope, CU status or trends? Knowing that there will be many gaps, it is proposedto use expert opinion to develop a complete data set: Is that a viable approach?Feedback was sought on redundancies and gaps (vs. what other projects were doing), priorityanalyses and additional suggestions.DiscussionJohannes: In looking at habitat loss and degradation, is 1990 to 2010 an appropriate time period?I have an accumulation of individual data sets and I don’t know how to address them, but theconceptual model starts to address the question of how to look at them.Pickard: Where is the overlap between stressors in space and time? In a more complex model,we use that with the life history model to (…?). If we have data – how to use the conceptualmodels to drive (…?) to be able to look at the relative importance of different models toexplain the productivity data. I’m not confident that it’s going to work, so we’ll take theunivariate analysis and the trends where we do have data. It won’t be conclusive but we willhave all the evidence summarized as best we can.Christensen: I like the idea of expert opinion data sets. Without complete time series, if yourestrict it to years with data it would put more weight on those that have the long time series.Hall: We have limited data on stressors for that time period, so that is future work.67
Peterman: Before you conclude from your concentration profile analysis that there has not beenmuch change in the overall stock/life history composition of Fraser sockeye, you should lookat how that profile compares with one for Bristol Bay sockeye. With cumulative probabilityplots like your concentration profile, you can have large changes that don’t show up clearlyon the graphs.Hall: Agreed. This was a very quick look at it.Routledge: Blow up the scale to see how the weak stocks are doing. But really, you could throwup your hands in despair at this task. The fundamental purpose is to understand thecumulative impacts of multiple stressors that could impact Fraser sockeye in multiple ways. Idon’t think there is much hope of answering that in a couple months. The other idea thatlooks more positive is the notion of trying to tease out effects of different stressors – additiveimpacts. That looks achievable. The other one is important too but I don’t know what we cando, so I encourage you to keep open the option of saying that we don’t really know.Hall: It’s important to take care to avoid mis-communicating the findings or over-stating thecase.Marmorek: If this big circle is the story, the portion told by quantitative data is a very smallportion. Most of it will be qualitative or expert judgment.Exploring conceptual modelsDarcy Pickard, ESSAThe draft conceptual model and spatial life history diagrams were based on information from thePSC report, plus the progress reports received to date, so they are not exhaustive. Participantswere invited to study the diagrams and to add comments regarding missing pieces or elements tosubtract. The intent is to use these diagrams to present all the information from the researchreports in one integrative way, with a focus on the factors that have the greatest explanatorypower in the productivity declines. This will be used to select model sets to test, so having everyfact at every life stage would not be useful.Blewett: Suggest using four colour bands to more clearly show the four key geographicspaces in which these things occur.Integrative workshop tasksPSC report on relative likelihood of hypothesesMarmorek explained the intent of the breakout exercise, stressing the importance of looking atthe big picture. The task was to review the conclusions of the PSC report, as summarized inTable E-1, and to consider whether or not participants agreed with their conclusions and why.Participants broke up into three groups, and their discussions were summarized on flipchartnotes).Recommended monitoring and research priorities:Participants were asked to take a few moments to individually review and comment on whetheror not they agreed with the monitoring and research priorities outlined in Table E-3.68
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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
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Table A4.3-23. SoG C1a candidate mo
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Appendix 5. Data Template User Guid
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Metric Connected to Biologically-su
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Initial Conceptual Model (Worksheet
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Commentsopen again. It is acceptabl
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Appendix 6. Workshop Report 1 (Nov.
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Table of ContentsTable of Contents
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PresentationsProductivity Dynamics
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and scope for improvement, particul
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Workshop participants pointed out t
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o Can you find the same shared tren
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generally better for Alaska sockeye
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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 326 and 327: Bristol Bay review: There are subst
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 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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