Fraser River sockeye salmon: data synthesis and cumulative impacts

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A4.3.4Analysis by regionModel: C4a1969-2004A model set was tested with the available data for brood years 1969-2004, which includedvariables for sea surface salinity (SSS), discharge and wind for QCS, and sea surface temperature(SST), SSS and discharge for SoG. While temperature is known to be important (e.g. Hinch andartins, 2011; McKinnell et al., 2011), however, this model set was performed despite not havingSST for QCS because it represents the longest time period available with a selection of variablesfor both regions. The individual models run within the model set are specified in Table A4.3-13.Table A4.3-13. Model specifications for the 1969-2004 (brood years) model set. This table shows the variablesincluded in each of the 8 models tested (i.e., M1 to M8) within this model set. Table 4.4-1 explains whichspecific data sets were used for each of these variables. “Rank of model” reflects the AIC c score showinglevel of support (#1 ranked model had the highest level of support and lowest AIC c score).Region Variable M1 M2 M3 M4 M5 M6 M7 M8QCS Salinity X X X XQCS Discharge X X XQCS Wind X XSoG Temperature X X XSoG Salinity X X X XSoG Discharge X X XRank of model 3 4 5 2 7 8 6 1The results show that the models with the most support are M8, M4, M1, then M2, with strongsupport for models M8 (SoG SST) and M4 (QCS SSS and discharge) in particular (Table A4.3-14 and Table A4.3-15). However, the AICc scores indicate that there is little difference in degreeof support among these four models (∆AICc = 2.28). For SoG during this period, temperature(M8) is more valuable for explaining the observed variability in Fraser River sockeye salmonproductivity than salinity (M7). Overall, the analysis of this time period shows that there issupport for both QCS and SoG models – the top ranked model was for SoG, the second for QCS,and the third was the global model, including both regions. These results show that for theseparticular variables, over this particular time period, there is no clear evidence of any differencebetween the explanatory value of the two regions; however, the absence of temperature data forQCS is a substantial shortcoming of this model set, and chlorophyll is not included in any model.252
Table A4.3-14. C4a candidate models ordered by AICc from best (lowest) to worst (biggest). M.ID=modelidentification, M.AIC=the estimated AIC for the model, num.obs=the total number of observations (i.e.,complete rows in the data set), num.par=the total number of fixed effects + random effects, Correction= thedifference between the AICc (corrected for small sample size compared to number of parameters) and theAIC, M.AICC= the AICc for the model, min.AICC = the smallest AICc observed within the model set,delta= the difference between the min.AICC and each M.AICC, and AICC_wts= the Akaike weight (i.e.,support) for each model.M.ID M.AIC num.obs num.par Correction M.AICC min.AICC delta AICC_wtsM8 1613.65 614 22 1.71 1615.36 1615.36 0 40.18M4 1614.87 614 23 1.87 1616.74 1615.36 1.38 20.15M1 1614.25 614 28 2.78 1617.02 1615.36 1.66 17.51M2 1615.61 614 24 2.04 1617.64 1615.36 2.28 12.82M3 1616.73 614 25 2.21 1618.95 1615.36 3.59 6.69M7 1619.46 614 22 1.71 1621.17 1615.36 5.81 2.20M5 1622.68 614 24 2.04 1624.72 1615.36 9.36 0.37M6 1626.11 614 22 1.71 1627.82 1615.36 12.46 0.08253
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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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Page 220 and 221: Change point analysis of productivi
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Page 228 and 229: Figure A3.5-3. Average chlorophyll
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Page 236 and 237: Table A3.5-5. Example of the model
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Page 242 and 243: Appendix 4. Quantitative ResultsA4.
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Page 246 and 247: Figure A4.2-3. Stock composition fo
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Page 250 and 251: A4.3 Multiple Regression ResultsA4.
Page 252 and 253: Life stage Stressor category Variab
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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
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Page 274 and 275: Model: C1a1996-2004For 1996-2004, i
Page 278 and 279: Table A4.3-23. SoG C1a candidate mo
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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
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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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