Fraser River sockeye salmon: data synthesis and cumulative impacts

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migration, and spawning. Three additional models were tested within the model set: a “global”model, including all available variables for 1969-2001; an aggregate model of the freshwater lifestages preceding ocean-entry; and, an aggregate model of all three marine life stages. Thesemodels were then tested using the same data set to determine which models have the greatestamount of relative support (i.e., strongest ability to explain the observed variation in sockeyeproductivity across years and stocks).The results show that the aggregate marine model (M10) has the greatest relative level ofsupport. Model M10 includes many more variables than the next two models. The AICc criterionthat is used to assess the relative level of support for different models rewards models which do abetter job of explaining the variation in the response variable (in our case sockeye stockproductivity), but penalizes models for including more variables to explain this variation, sinceeach additional variable requires another model parameter to be estimated. A model with highexplanatory power from only a few variables would receive a strong AICc score (AICc scoresare like golf; lower is better). The fact that model M10 achieves a high relative level of supportdespite having so many variables indicates that the extra variables are informative for explainingvariation in sockeye productivity, relative to the other models included in Table 4.7-2.Model M10 represents an aggregate of several life stages with the marine phase. We are alsointerested in which marine life stages appear to be most important. The models of individual lifestages with the highest level of support are coastal migration (M4) and the return to the Fraser(M6). Although coastal migration ranks higher, the results show that there is roughly equalsupport for these two models, suggesting that both of these life stages are important in explainingthe variability in the productivity of Fraser River sockeye salmon. However, the analysis scoresalso indicate that both of these models have marginal support relative to the aggregate marinemodel (M10), suggesting that there is a definite benefit to using all of the marine variables toexplain variation in sockeye productivity. These results imply that factors within the marinephase are more important relative to the available freshwater factors in explaining the patternsobserved in Fraser River sockeye salmon over the 1969-2001 brood years.92
Table 4.7-2. Variables included in each model used to test the relative importance of different life-history stages over brood years 1969-2001.Variables in thedata set are organized by life history stage. The M2 and M8 models are empty because there were no variables applicable to those life history stagesavailable for the period of 1969-2001; however, they are maintained here in order for consistency in naming models among different time frames. LFR= lower Fraser River. SoG = Strait of Georgia.VariablesModelsM1 M2 M3 M4 M5 M6 M7 M8 M9 M10Life stageStressor category(i.e. CohenCommissionTechnical Report)Variable DescriptionGlobalIncubation toLake RearingOutmigrationCoastalMigrationGrowth inNorth PacificReturn to theFraserUpstreamMigrationSpawningAggregateFreshwaterAggregateMarineSmolt outmigration Contaminants Qualitative water quality index. Stock specific. X X XSmolt outmigrationFreshwater habitatconditionsSpring air temperature CU specific X X XSmolt outmigrationHabitat conditions inLFR & SoGDischarge, SoG, April total X X XSmolt outmigrationHabitat conditions inLFR & SoGDischarge SoG, May total X X XSmolt outmigrationHabitat conditions inLFR & SoGLower Fraser total dredge volume X X XSmolt outmigrationPredators/Double Crested Cormorant and Common Merganser: Quantile basedAlternative Prey aggregate estimateX X XCoastal migrationHabitat conditions inLFR & SoGSoG discharge , June-July average (Fraser River) X X XCoastal migrationHabitat conditions inLFR & SoGSoG discharge, May average (Fraser River X X XCoastal migrationHabitat conditions inLFR & SoGSea surface salinity. SoG, April-Aug average. X X XCoastal migrationHabitat conditions inLFR & SoGSea surface temperature. SoG, April-Aug average. X X XCoastal migration Marine conditions QCS discharge, June-Sept average (Wannock River) X X XCoastal migration Marine conditions Sea surface salinity. QCS, April-Aug average. X X XCoastal migration Marine conditions Summer wind regime. QCS. X X XCoastal migrationPredators/Alternative Prey5 marine bird species: Quantile based aggregate estimate X X XCoastal migrationPredators/Alternative PreyArrowtooth flounder biomass X X X93
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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
Page 57 and 58: Freshwater predators on juvenile so
Page 59 and 60: copper, iron, mercury and silver).
Page 61 and 62: Several analyses by MacDonald et al
Page 63 and 64: abundances (Sproat, Klukshu, Chilka
Page 65 and 66: feel reasonably confident in this c
Page 67 and 68: Nelitz et al. (2011; Table 18) foun
Page 69 and 70: in the Lower Fraser than other Fras
Page 71 and 72: stressors. Thus our conclusions hav
Page 73 and 74: There are many marine predators tha
Page 75 and 76: assumption that exposure occurs whe
Page 77 and 78: observations that are then averaged
Page 79 and 80: Johannes et al. (2011) demonstrate
Page 81 and 82: “likely” contributor to the ove
Page 83 and 84: Table 4.4-2. Model specifications f
Page 85 and 86: Region Variable M1 M2 M3 M4 M5 M6 M
Page 87 and 88: 3. survival rates within key portio
Page 89 and 90: 2011). The thermal limit hypothesis
Page 91 and 92: Historically, the majority of retur
Page 93 and 94: salinity. There is much evidence th
Page 95 and 96: declines (discussed in section 4.2
Page 97 and 98: There is indisputable evidence of t
Page 99 and 100: Figure 4.6-2. Number of years that
Page 101 and 102: correlation was statistically signi
Page 103 and 104: 4.6.7 Key things we need to know be
Page 105 and 106: al (2010). The combined effect of a
Page 107: more likely to suffer en-route and
Page 111 and 112: However, an important limitation is
Page 113 and 114: Alternative PreyVariablesModelsM1 M
Page 115 and 116: will likely have a high correlation
Page 117 and 118: 5.0 Conclusion5.1 Important Contrib
Page 119 and 120: Stage 5: Migration back to SpawnWhi
Page 121 and 122: More specifically, the extended res
Page 123 and 124: The twelve highest priority recomme
Page 125 and 126: Life stagefor FraserRiversockeyesal
Page 127 and 128: 6.0 ReferencesAinsworth, L.M., Rout
Page 129 and 130: Johannes, M.R.S., R. Hoogendoorn, R
Page 131: Schaller et al. 2007. Comparative S
Page 134 and 135: utilized to clarify the full range
Page 137 and 138: Appendix 2. Reviewer Evaluations an
Page 139 and 140: the conclusions of the Expert Panel
Page 141 and 142: Response: Figure numbers have been
Page 143 and 144: Response: This section has been com
Page 145 and 146: ased on time or other stocks) to
Page 147 and 148: them in terms of how changes in spa
Page 149 and 150: efer to some variable being include
Page 151 and 152: ... (Table xx)". Structures of thes
Page 153 and 154: Report Title: Fraser River sockeye
Page 155 and 156: show stronger correlations. Hence,
Page 157 and 158: 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
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There was widespread agreement with
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Appendix A: List of Projects and In
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Peer Reviewers: Provide constructiv
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MacDonald Environmental Sciences Lt
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Appendix C: Workshop MinutesContent
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Levy urged researchers to bear in m
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within-population, within-brood-yea
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To address potential skepticism ove
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instead of migrating directly out i
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considered a good surrogate of temp
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Bristol Bay review: There are subst
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Method: The approach included formu
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Staley: Our project needs to compar
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Levy: It will go ahead in January.
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preliminary assessment of potential
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MacDonald: We’re waiting for all
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including Shuswap and North Thompso
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and biological variables. The combi
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temperatures. Notably, it doesn’t
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Seasonal population distribution pa
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Information gaps: The most importan
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Fraser sockeye salmon habitat analy
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survey data, but they were looking
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was also shown that tagged fish ret
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English: There was extensive holdin
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periods showed that 4 to 6 stocks (
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Q/A: Researchers should also feel f
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Peterman: We’re lacking the big p
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Appendix D: Table of likelihood/ hy
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Fraser River sockeye salmon: data synthesis and cumulative impacts

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