Fraser River sockeye salmon: data synthesis and cumulative impacts

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Model: B4bBrood years: 1969-2001We repeated the comparison by stressor category with abundance of pinks included as anadditional stressor. The same stressor category models were tested with the addition of pinksalmon as its own stressor category model. The results from this set of analyses show that thepink salmon model (M8) and the global model (M1) appear as the strongest two models. Theyhave similar AICc’s (∆AICc of 2.8) and because they are substantially different models (21versus 52 parameters), the interpretation is that they provide legitimately competing models toexplain the patterns observed in Fraser River sockeye salmon. The predators/alternate preymodel (M5) also receives some support while the lower Fraser /Strait of Georgia stressors (M7)rank forth. The fact that the pink salmon model does well is in some ways not surprising: there isa scientifically supported hypothesis, with good data for the attribute that the hypothesis relatesto, and the connection is specific to a life-history stage, whereas many of the project groupingsdo not have the most appropriate data. Given the discussion above about whether or notparticular data are representative of the factors of interest, and the idea that it may be morejustified to organize data around life-history stages with biologically based boundaries ratherthan projects with sometimes arbitrary boundaries, it makes sense that the pink salmon modelwould do well compared to other project-based models where those considerations are not asclearly met.Overall, we do not believe that organizing the data by project is as useful of an approach as isorganizing the data by life-history stage. The boundaries are arbitrary and many of thecategories are lacking in data. In this model set, none of the models had definitively highersupport than the global model which included all available variables. By contrast, when theexact same data were organized by life-history stage, several of the models achieved a higherlevel of support than the global model.248
Table A4.3-11. B4b 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 1141.75 463 21 2.10 1143.84 1143.84 0 68.30M1 1133.22 463 52 13.44 1146.66 1143.84 2.82 16.68M5 1142.77 463 32 4.91 1147.68 1143.84 3.84 10.02M7 1146.23 463 27 3.48 1149.70 1143.84 5.86 3.65M6 1151.37 463 20 1.90 1153.27 1143.84 9.43 0.61M4 1151.12 463 24 2.74 1153.86 1143.84 10.02 0.46M2 1154.06 463 21 2.10 1156.15 1143.84 12.31 0.15M3 1154.22 463 21 2.10 1156.32 1143.84 12.47 0.13249
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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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Page 216 and 217: 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.
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 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
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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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