Fraser River sockeye salmon: data synthesis and cumulative impacts

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SoG. This may be due to regional differences in mechanisms, the confounding impact of otherfactors that interact with sea surface properties, or issues regarding the precise location ofmeasurements versus the precise migration routes of the sockeye. We cannot offer a definitiveexplanation for why this might be the case or suggest any underlying mechanism.For 1996-2004, it was not possible to test a model set with both QCS and SoG because the timeperiod was too short for the number of variables to be included for the two regions. Thealternative approach was to develop two model sets, one for each region, to test the importanceof chlorophyll against the other variables independently within each region (Tables 4.4-4 and4.4-5). The results show that QCS chlorophyll may be an important metric in explaining thevariation in sockeye salmon productivity over the period of 1996-2004, whereas QCStemperature and salinity are relatively uninformative parameters. To the contrary, within SoGduring this timeframe, salinity has strong support and the remaining parameters are found to beuninformative, 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(Appendix 3). It may be worth examining these regional differences more closely.Table 4.4-4. Model specifications for the 1996-2004 (brood years) model set for Queen Charolotte Sound. Thistable shows the variables included in each of the 9 models tested (i.e. M1 to M9) within this model set.Table 4.4-1 explains which specific data sets were used for each of these variables. “Rank of model”reflects the AIC c score showing level of support (#1 ranked model had the highest level of support andlowest AIC c score).Region Variable M1 M2 M3 M4 M5 M6 M7 M8 M9QCS Chlorophyll X X X X X XQCS Temperature X X X X X XQCS Salinity X X X X X XQCS Discharge X XQCS Wind XRank of model 9 6 4 2 3 8 5 7 1Table 4.4-5. 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).68
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 74.4.6 ConclusionsTable 4.4-6 shows a summary of the results of the weight of evidence evaluation of potentialcontributing factors at this life stage. All of the potential factors in this life stage have plausiblemechanisms. There are almost no data on exposure for pathogens making no conclusionpossible. The evidence presented suggests that sockeye salmon in the Strait of Georgia have littledirect exposure to human activities and development, leading to a conclusion that it is unlikelythat these factors have contributed to the decline of Fraser River sockeye salmon. Sockeyesalmon have been exposed to predators, marine conditions, and climate change during this earlymarine phase. However, there has been no evidence presented on any correlations between keypredators and sockeye salmon survival. Some important predators appear to be increasing inabundance, and some potentially important alternate prey appear to be decreasing, but manyother known predators are decreasing or remaining stable. It therefore remains possible thatpredators have contributed to the observed declines in sockeye salmon. Based on plausiblemechanisms, exposure, consistency with observed sockeye productivity changes, and otherevidence, marine conditions and climate change are considered likely contributors to the longtermdecline of Fraser River sockeye salmon. Peterman and Dorner’s analyses of delayeddensity dependence were applied to total productivity over the whole life cycle. Therefore, theirconclusion that delayed density dependence is unlikely to have been a primary factor causingproductivity declines (discussed in section 4.2 for life stage 1) reflects the net effect across alllife history stages. Aquaculture was not considered in our report as the Commission Technicalreports on this potential stressor were not available, but will be considered in an addendum tothis report.69
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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
Page 34 and 35: Figure 3.3-1. The conceptual model
Page 36 and 37: 3.3.3 Life history perspective/appr
Page 38 and 39: additional analyses to gain further
Page 40 and 41: Figure 3.3-3. Flow diagram used to
Page 42 and 43: This project is unusual in its scop
Page 45 and 46: 4.0 Results, Synthesis and Discussi
Page 47 and 48: Peterman and Dorner (2011) have thr
Page 49 and 50: Figure 4.1-3. Estimates of long ter
Page 51 and 52: Figure 4.1-5. A) Total run size of
Page 53 and 54: Figure 4.1-6. Aggregate returns to
Page 55 and 56: 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: Table 4.4-2. Model specifications f
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 and 108: more likely to suffer en-route and
Page 109 and 110: Table 4.7-2. Variables included in
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
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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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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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