Fraser River sockeye salmon: data synthesis and cumulative impacts

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This project is unusual in its scope. While the response variable, ln(R/S), is available for 19stocks across B.C. and approximately 50 years of data are available for each stock, the number ofpotential covariates is very large. A total of 126 quantitative and 5 qualitative data sets wereprovided to us from the other technical reports (Table A3.4-9). We then calculated an additional32 data sets (i.e., derived variables based on the data provided) that were more appropriate forour analyses.It is possible for a single data set to be linked to (i.e., hypothesized to impact) multiple life stagesof Fraser River sockeye. In addition, there are up to 4 different age types (i.e., 4sub2, 5sub2,4sub1, and 3sub1). These links result in a total of 1058 possible covariates to include in theanalysis. However, not all covariates are available for all years and stocks. Models can only becompared when the models are fit using the same data. The implication of this is that we cannotcompare all models of interest on the full data set but instead must identify time periods withcomplete data for different subsets of the covariates.For example, there is a small subset of the covariates (e.g., sea surface salinity for the Strait ofGeorgia) that have data extending back to 1950, but there are other covariates that only have datastarting in 1996 (e.g., chlorophyll a). If we wish to compare models with these two covariates(i.e. salinity and chlorophyll), we would have to either reduce the data set to those years withdata for both covariates (i.e., limit the model to 1996-present and sacrifice the earlier data forsalinity), or exclude covariates with limited years of data (i.e., limit the model to only salinityand ignore chlorophyll a, but extend the analysis back to 1950). Choosing any particular set ofcovariates forces you to truncate longer time series to the length of the shortest data set.Choosing any particular time period forces you to limit your analyses to those covariates with aperiod of record that is sufficiently long.We chose to evaluate different time-periods independently because each time period presents adifferent trade-off between the length of the data and the number of covariates that can beincluded. Within each time-period we generated different model sets. A model set represents aset of covariates that have complete data over the chosen time-period. Within each model set,different models (i.e. combinations of variables) can be tested to determine their ability toexplain the observed variability in the dependent variable, sockeye productivity in this case.Expressed another way, a ‘model set’ is simply a suite of candidate models within a given timeperiodthat are organized to address a particular question. For example, one question of interest iswhether the set of factors affecting a particular life stage are more important than others.26
Key points:• Models may differ in the number and type of covariates, linear vs. non-linear terms, andthe presence of interaction terms.• Many models are possible, but we should only test models that have biologically justifiedhypotheses.• In order to compare the relative performance of different models using Akaike’sInformation Criterion (AIC c ), models should be fit using the same data.• Comparison of AIC c scores does not tell us the best model possible, but rather helps us tounderstand the relative support for the models we have estimated.• You need more data than parameters in order to be able to estimate the parameters.27
Page 1: April 2011technical report 6Fraser
Page 7 and 8: Stage 2: Smolt OutmigrationWe analy
Page 9 and 10: of multiple stressors and factors,
Page 11 and 12: 4.3.5 Other evidence ..............
Page 13 and 14: List of TablesTable 4.2-1. Evaluati
Page 16 and 17: List of FiguresFigure 2.3-1. Cumula
Page 18 and 19: these integrative frameworks, conve
Page 21 and 22: 2.0 Cumulative Impacts or Effects2.
Page 23 and 24: assumed that there is no potential
Page 25 and 26: classes of stressors. However, if s
Page 27: affected by many stressors over its
Page 30 and 31: examples illustrate this problem --
Page 32 and 33: 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 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 and 84: Table 4.4-2. Model specifications f
Page 85 and 86: Region Variable M1 M2 M3 M4 M5 M6 M
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
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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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Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
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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
Page 360 and 361:
Peterman: We’re lacking the big p
Page 362 and 363:
Appendix D: Table of likelihood/ hy
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Fraser River sockeye salmon: data synthesis and cumulative impacts

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