Fraser River sockeye salmon: data synthesis and cumulative impacts

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Table A4.3-21. Estimates for all fixed effects parameters included in each model, for model set C1a, QCS. Fixed effect parameters (i.e., covariates) are listed inthe rows. The estimates for each model are provided in the columns. A value of ‘NA’ indicates that that particular model did not include the covariate inthe corresponding row.ModelSet_C1a_QCS_VarName M1 M2 M3 M4 M5 M6 M7 M8 M9(Intercept) 1.93 -25.73 -20.97 -9.64 -7.13 -15.97 -6.57 -6.26 0.02CHL_M04_QCSx_MGRR_DOM 0.42 0.19 0.36 0.43 0.37 NA NA NA 0.43CHL_M05_QCSx_MGRR_DOM 0.75 -0.24 0.28 0.25 0.20 NA NA NA 0.18DIS_x69_QCSx_MGRR_DOM -0.03 0.02 NA NA NA NA NA NA NASSS_A48_QCSx_MGRR_DOM 0.60 -0.11 0.35 NA 0.25 0.35 0.29 NA NASST_A78_QCSx_MGRR_DOM -0.54 1.30 0.72 0.65 NA 0.53 NA 0.57 NAStockNameBirkenhead:Spawners -8.91 -9.46 -9.69 -9.58 -9.77 -10.05 -9.30 -9.93 -9.68StockNameBowron:Spawners -220.31 -161.60 -169.13 -164.84 -169.48 -167.13 -188.50 -165.03 -165.60StockNameChilko:Spawners -3.46 -2.81 -2.88 -2.83 -2.89 -2.92 -3.24 -2.87 -2.85StockNameCultus:Spawners -995.51 -929.65 -957.51 -936.40 -974.92 -970.81 -1005.56 -929.08 -952.29StockNameE.Stuart:Spawners -9.01 -13.55 -14.24 -13.87 -13.73 -14.62 -9.21 -14.52 -13.50StockNameFennell:Spawners -153.76 -98.27 -102.31 -99.54 -102.33 -103.16 -135.99 -100.65 -99.95StockNameGates:Spawners -30.63 -9.06 -12.16 -10.05 -12.15 -11.00 -19.23 -10.16 -10.18StockNameHarrison:Spawners -17.80 106.15 78.10 101.56 77.36 57.45 7.87 74.79 94.75StockNameL.Shuswap:Spawners -0.23 -0.29 -0.31 -0.31 -0.30 -0.29 -0.22 -0.30 -0.31StockNameL.Stuart:Spawners -5.38 -4.61 -4.83 -4.72 -4.73 -4.81 -4.90 -4.83 -4.63StockNameNadina:Spawners -9.00 -8.41 -9.25 -8.71 -9.07 -8.34 -6.60 -8.40 -8.56StockNamePortage:Spawners -123.52 -76.18 -79.88 -78.44 -78.45 -80.33 -114.96 -78.76 -77.66StockNameQuesnel:Spawners -0.76 -1.13 -1.16 -1.16 -1.14 -1.18 -0.82 -1.19 -1.14StockNameRaft:Spawners -43.22 -23.99 -25.88 -24.63 -25.29 -25.12 -34.20 -24.71 -24.24StockNameScotch:Spawners 17.45 14.77 13.85 13.96 14.23 14.84 18.31 14.63 14.12StockNameSeymour:Spawners 3.01 0.22 -0.82 -0.52 -0.83 -0.35 3.96 -0.36 -0.66StockNameStellako:Spawners -6.29 -6.19 -6.43 -6.31 -6.40 -6.28 -5.67 -6.24 -6.30StockNameWeaver:Spawners 3.64 25.55 24.30 24.76 24.17 23.72 9.21 24.09 24.50WND_x28_QCSx_MGRR_DOM 0.50 NA NA NA NA NA NA NA NA260
Table A4.3-22. Model specifications for the 1996-2004 (brood years) model set for the Strait of Georgia. This tableshows the variables included in each of the 8 models tested (i.e. M1 to M8) within this model set. Table4.4-1 explains which specific data sets were used for each of these variables. “Rank of model” reflects theAIC c score showing level of support (#1 ranked model had the highest level of support and lowest AIC cscore).Region Variable M1 M2 M3 M4 M5 M6 M7 M8 M9SoG Chlorophyll X X X X XSoG Temperature X X X X XSoG Salinity X X X X XSoG Discharge X XRank of model 3 9 8 6 2 1 5 4 7Within the SoG model set, Models M6 (SSS) and M5 (SSS and SST) have the strongest supportwithin the group (Table A4.3-23 and Table A4.3-24). M6 and M5 have very similar AICc scores(∆AICc = 1.53) and only differ by one parameter, this implies that SST is an uninformativeparameter relative to SSS (Arnold 2010). However, the ∆AICc show that there is also relativelylittle difference in support from the M1 global model (∆AICc = 2.59 and M6) despite theaddition of 5 extra parameters and therefore M1 is a legitimately competing model for SoG.Chlorophyll in SoG on its own does not appear to be an important explanatory variable for FraserRiver sockeye salmon productivity, at least over the period of 1996-2004.In summary, while the results show that QCS chlorophyll may be an important metric inexplaining the variation in sockeye salmon productivity over the period of 1996-2004, whereasQCS temperature and salinity are relatively uninformative parameters. To the contrary, withinSoG during this timeframe, salinity has strong support and the remaining parameters are found tobe uninformative, except when they are all included together in the global model. One should bevery cautious about drawing conclusions from patterns observed over such a very short period oftime, but these results do at least indicate that there may be strong regional differences in theimportance of the potential drivers examined. During the data processing steps of this project, itwas noted that the variance in chlorophyll measured in the Northern SoG was substantiallygreater than that measured in the Central SoG across all months where data were provided(Figure A3.5-3). It may be worth examining these regional differences more closely.261
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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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Page 228 and 229: Figure A3.5-3. Average chlorophyll
Page 230 and 231: Status only data setsIn many cases
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Page 236 and 237: Table A3.5-5. Example of the model
Page 238 and 239: categories of stressors. We cannot
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Page 242 and 243: Appendix 4. Quantitative ResultsA4.
Page 244 and 245: alance” over time and so this it
Page 246 and 247: Figure A4.2-3. Stock composition fo
Page 248 and 249: Figure A4.2-5. Stock composition fo
Page 250 and 251: A4.3 Multiple Regression ResultsA4.
Page 252 and 253: Life stage Stressor category Variab
Page 254 and 255: The results show strong support for
Page 256 and 257: ModelSet_A4c_VarName M1 M2 M3 M4 M5
Page 258 and 259: Table A4.3-7. Estimates for all fix
Page 260 and 261: A4.3.3Analyses by stressor category
Page 262 and 263: Table A4.3-10. Estimates for all fi
Page 264 and 265: Model: B4bBrood years: 1969-2001We
Page 268 and 269: A4.3.4Analysis by regionModel: C4a1
Page 272 and 273: suggest any underlying mechanism, b
Page 274 and 275: Model: C1a1996-2004For 1996-2004, i
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
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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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