Fraser River sockeye salmon: data synthesis and cumulative impacts

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The results show strong support for M10 (all marine life stages), with weak support for M4(Inshore Migration) and M6 (Return to the Fraser). Despite the large number of parameters (41)M10 had the lowest AICc providing strong support for this model. The next two models hadsubstantially fewer parameters but their AICc scores were 5-7 units higher than that of M10indicating some information loss as a result of the reduced number of parameters. Overall theseresults indicate that the marine component of the life stage is more important in explaining theoverall productivity than the freshwater component. However, it must be noted that there werelimited stressor data available for the freshwater component and those data available may nothave been the most appropriate data. The true interpretation of this result is that the marine dataavailable do a better job of explaining the overall productivity of Fraser sockeye, than thefreshwater data available over this time period. Estimates of effect sizes are presented in TableA4.3-5 for all models. Interpretation of the actual estimates, their magnitude and sign is difficultparticularly for very complex models with many covariates. As described in the previous section(Interpretation of results) models with only a few stressors are easier to interpret. If interested indigging into the estimates in detail, one should also obtain the estimates of variability for each ofthese parameters. These are available, but not provided in this document in the interest of space.Table A4.3-4. A4c 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_wtsM10 1127.51 463 41 8.18 1135.69 1135.69 0 88.43M4 1135.78 463 32 4.91 1140.69 1135.69 5.00 7.25M6 1139.55 463 26 3.22 1142.77 1135.69 7.08 2.56M1 1131.35 463 50 12.38 1143.73 1135.69 8.04 1.59M5 1146.65 463 21 2.10 1148.75 1135.69 13.06 0.13M8 1151.31 463 19 1.72 1153.02 1135.69 17.33 0.02M2 1151.31 463 19 1.72 1153.02 1135.69 17.33 0.02M7 1152.93 463 23 2.51 1155.44 1135.69 19.75 0.0045M3 1155.74 463 25 2.97 1158.71 1135.69 23.02 0.00089M9 1156.46 463 26 3.22 1159.68 1135.69 23.99 0.00055238
Table A4.3-5. Estimates for all fixed effects parameters included in each model, for model set A4c. Fixed effect parameters (i.e., covariates) are listed in therows. The estimates for each model are provided in the columns. A value of ‘NA’ indicates that that particular model did not include the covariate in thecorresponding row.ModelSet_A4c_VarName M1 M2 M3 M4 M5 M6 M7 M8 M9 M10(Intercept) 47.74 2.55 2.12 -9.77 1.04 1.86 4.63 2.55 2.06 -4.46CBQ_107_NULL_SMLT_DOM 0.19 NA -0.05 NA NA NA NA NA -0.10 NACBQ_108_NULL_MGRR_DOM 0.86 NA NA -0.06 NA NA NA NA NA -0.03CBQ_x91_NULL_MGRS_DOM -0.46 NA NA NA NA NA -0.27 NA NA NADIS_A67_SoGx_MGRR_DOM -1.5E-06 NA NA 2.3E-06 NA NA NA NA NA 6.4E-06DIS_T04_SoGx_SMLT_DOM 1.4E-06 NA -8.5E-06 NA NA NA NA NA -8.5E-06 NADIS_T05_SoGx_MGRR_DOM -2.1E-06 NA NA 3.6E-06 NA NA NA NA NA 3.8E-06DIS_x69_QCSx_MGRR_DOM 0.0012 NA NA 1.8E-05 NA NA NA NA NA -0.0018DRG_x45_LFRx_SMLT_DOM -1.4E-07 NA 4.4E-08 NA NA NA NA NA 5.4E-08 NAFPR_x87_NULL_MGRR_DOM -8.9E-06 NA NA -2.3E-06 NA NA NA NA NA -3.9E-06NPI_x51_NPOx_GROW_DOM -0.07 NA NA NA -0.007 NA NA NA NA -0.05PNE_x25_NULL_MGRR_DOM -0.0006 NA NA 6.4E-05 NA NA NA NA NA 5.7E-05PNE_x25_NULL_MGRS_DOM 0.0016 NA NA NA NA -6.9E-05 NA NA NA 6.6E-05PNE_x39_NULL_MGRR_DOM -0.0003 NA NA 1.2E-05 NA NA NA NA NA -5.2E-05PNE_x39_NULL_MGRS_DOM 0.0004 NA NA NA NA -8.9E-06 NA NA NA 7.5E-05PNE_x61_NULL_MGRR_DOM -0.0006 NA NA 0.0001 NA NA NA NA NA -3.6E-06PNE_x61_NULL_MGRS_DOM 0.0004 NA NA NA NA 4.5E-05 NA NA NA 0.0001PRQ_110_NULL_GROW_DOM -7.36 NA NA NA 0.73 NA NA NA NA -3.01PRQ_110_NULL_MGRR_DOM 3.61 NA NA 1.17 NA NA NA NA NA 3.24PRQ_110_NULL_MGRS_DOM -0.56 NA NA NA NA 0.38 NA NA NA 0.39SPT_x82_FWRx_SMLT_DOM 0.04 NA 0.02 NA NA NA NA NA 0.02 NASSS_A48_QCSx_MGRR_DOM -0.89 NA NA 0.13 NA NA NA NA NA -0.11SSS_A48_SoGx_MGRR_DOM 0.23 NA NA 0.07 NA NA NA NA NA 0.24SSS_A59_QCSx_MGRS_DOM -0.97 NA NA NA NA 0.09 NA NA NA 0.02SSS_A59_SoGx_MGRS_DOM 0.30 NA NA NA NA -0.15 NA NA NA -0.11SST_A48_SoGx_MGRR_DOM 0.37 NA NA 0.28 NA NA NA NA NA 0.51SST_M09_SoGx_MGRS_DOM 0.09 NA NA NA NA 0.08 NA NA NA -0.07StockNameBirkenhead:Spawners -8.63 -9.30 -9.02 -8.71 -9.24 -9.52 -9.25 -9.30 -8.88 -9.64StockNameBowron:Spawners -83.92 -98.08 -96.23 -104.29 -98.88 -95.50 -98.32 -98.08 -96.49 -103.13StockNameChilko:Spawners -1.91 -2.40 -2.33 -2.13 -2.32 -2.28 -2.45 -2.40 -2.30 -2.17239
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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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ProjectNumberProject Variable Subse
Page 204 and 205: 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
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Page 236 and 237: Table A3.5-5. Example of the model
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 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
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
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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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