Fraser River sockeye salmon: data synthesis and cumulative impacts

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Table A3.5-5. Example of the model specification document created for each model set. Each row describes a possible independent variable, including theproject it came from, the location, the life stage it is hypothesized to effect (it is possible to effect more than one life stage), the unit of measurement,dates available, and unique database name. Effect type indicates if the independent variable is to be included as a fixed effect or a random effect.Columns M1-M10 each represent a particular candidate model. The models differ in terms of which independent variables are included, as indicated bya 1 (include) or 0 (exclude).ProjectMarine ecologyMarine ecologyMarine ecologyMarine ecologyLwr Fraser Riverand Strait ofGeorgia habitatinventoryLwr Fraser Riverand Strait ofGeorgia habitatinventoryLwr Fraser Riverand Strait ofGeorgia habitatinventoryLwr Fraser Riverand Strait ofGeorgia habitatinventoryIndependentVariableRiverdischargeSea surfacesalinitySea surfacetemperatureSummerwind regimeRiverdischargeRiverdischargeSea surfacesalinitySea surfacetemperatureSubsetName Location Life stage UnitsStartYearEndYearMigration toWannock Queen rearing(Rivers Charlotte areas-Inlet) Sound Marine m3/s 1980 2004April-AugaverageJuly-AugaverageFACTORFraserdischargeMay totalJune-JulyaverageApril-AugaverageApril-AugaverageQueenCharlotteSoundQueenCharlotteSoundQueenCharlotteSoundStrait ofGeorgiaStrait ofGeorgiaStrait ofGeorgiaStrait ofGeorgiaMigration torearingareas-Marine PSU 1980 2004Migration torearingareas-MarineDegreesCelsius 1980 2004Migration torearingareas-Marine 1980 2004Migration torearingareas-Marine m3/s 1980 2004Migration torearingareas-Marine m3/s 1980 2004Migration torearingareas-Marine PSU 1980 2004Migration torearingareas- DegreesMarine Celsius 1980 2004VariableSetIDEffectTypeM1DIS_x69_QCSx_MGRR_DOM Fixed 1 1 0 1 0 0 0 0 0 0SSS_A48_QCSx_MGRR_DOM Fixed 1 1 0 1 1 0 1 0 0 0SST_A78_QCSx_MGRR_DOM Fixed 1 1 0 1 1 0 0 0 1 0WND_x28_QCSx_MGRR_DOM Fixed 1 1 0 0 0 0 0 0 0 0DIS_T05_SoGx_MGRR_DOM Fixed 1 0 1 0 0 0 0 0 0 0DIS_A67_SoGx_MGRR_DOM Fixed 1 0 1 0 0 0 0 0 0 0SSS_A48_SoGx_MGRR_DOM Fixed 1 0 1 0 0 1 0 1 0 0SST_A48_SoGx_MGRR_DOM Fixed 1 0 1 0 0 1 0 0 0 1BroodYear Random 1 1 1 1 1 1 1 1 1 1StockName Random 1 1 1 1 1 1 1 1 1 1M2M3M4M5M6M7M8M9M10220
Model selectionWe use the Burnham and Anderson (1998) approach to model selection and inference. First wecreate a list of candidate models based on biological hypotheses. Then we use Akaike’sInformation Criteria (AIC) statistic to assess the relative importance of the different candidatemodels. Burnham and Anderson (1998) promote a hypothesis-driven approach to analysis. Inhypothesis-driven analyses, the only factors that would be allowed to enter the analyses would bethose that are connected to a logical, and in this case, biologically justified hypothesis. Theyrecommend substantial effort be expended up front to generate a small number of sensible apriori hypotheses to then test with the observed data. On the other hand, in exploratory analysesmany different variables that are not necessarily justified by a hypothesis are added and removedin order to explore different relationships that might exist and develop ideas for hypotheses to beexamined in a more rigorous manner. The concern with this approach is that some variables willbe correlated simply by chance and not as a result of any underlying mechanism and often theseresults are used without further testing.The analysis methods we have used are intended to apply this criterion and exclude any variablesthat are not connected to a hypothesis. Although we have attempted to structure our analyseswithin the hypothesis-driven perspective, our final approach has been somewhere between thesecontrasting approaches for two reasons. First, we aligned observational data series with particularlife stages based on hypotheses wherever possible, but in many cases the details of theunderlying hypothesis were not known or not specified. For example, the inclusion of predatorscould likely be improved by making clearer distinctions among fish predators and alternate preythat apply to coastal migration along the continental shelf versus those that apply to the Gulf ofAlaska. Second, our original task was to perform analyses across all factors wherever the dataare actually available.Models should only be compared using AICs if the same data set was used to fit both models.This means that we need a complete data set to allow comparisons among models. Notsurprisingly data availability limited the extent of the analyses we were able to complete. Whilewe have a substantial dependent variable data set (recruits per spawner indices from 19 Fraserstocks from ~1950-2004, Peterman & Dorner Project #10), we were constrained by the smallestindependent variable data sets. Instead of one ‘grand’ analysis we had to complete the regressionanalysis on smaller subsets of stressors for which data were available. The expectation that manydata sets would be incomplete led to the request of an expert opinion based data set as well. It isimportant to recognize that each analysis can only be used to make statements about data setsthat are included within the analysis. If data from only 3 stressor categories (i.e. projects) areused, then the analysis can only tell us about the relative and cumulative impact of these 3221
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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
Page 186 and 187: ProjectNumber Project Name Variable
Page 188 and 189: ProjectNumber Project Name Variable
Page 190 and 191: A3.4 Data PreparationContractor dat
Page 192 and 193: Table A3.4-2. An example entry in t
Page 194 and 195: Table A3.4-5. Brood year adjustment
Page 196 and 197: Table A3.4-7. Example output from t
Page 198 and 199: Table A3.4-9. The table of contents
Page 200 and 201: ProjectNumberProject Variable Subse
Page 212 and 213: The objective of our approach is to
Page 214 and 215: investigated ecosystems” (Burkhar
Page 216 and 217: Figure A3.5-1. Flow diagram used to
Page 218 and 219: multiple stresses simultaneously. T
Page 220 and 221: Change point analysis of productivi
Page 222 and 223: Figure A3.5-1. This is an example o
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Page 226 and 227: exclude covariates with limited yea
Page 228 and 229: Figure A3.5-3. Average chlorophyll
Page 230 and 231: Status only data setsIn many cases
Page 232 and 233: The A-series of model sets based on
Page 234 and 235: with this report given the vast sco
Page 238 and 239: categories of stressors. We cannot
Page 240 and 241: o In some cases it may be the timin
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
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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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