Fraser River sockeye salmon: data synthesis and cumulative impacts

More documents

Recommendations

Info

suggest any underlying mechanism, but it does provide an excellent opportunity to emphasizesome of the potential limitations of this analysis and considerations that need to be kept in mind.First, it is possible due to strong regional differences, that the mechanisms that might connectSSS with sockeye salmon productivity are in fact different in the two regions. An exploration ofsome of the oceanographic and climatic variables over time shows that there are many ways inwhich the two regions appear quite distinct from each other. Second, correlational analyses findrelationships among data, but correlation does not imply causation – just because there is anegative correlation over time between SSS in SoG and productivity of sockeye salmon does notmean that there is a direct mechanism relating the two. The fact that the direction of therelationship with SSS is opposite as for QCS could imply that there is another factor that isunique to SoG, for which we do not have data in the model, that confounds the expectedrelationship between SSS and sockeye productivity. Third, it is important to consider the scale ofthe underlying measurements. In this case, the SSS data come from point measurements at twoparticular lighthouses. These data sources were chosen by Cohen Commission contractors asbeing the most representative of the two regions but it is possible that there is fine scale variationthat is lost when using a point measure as a regional index. For example, SSS measured at onelighthouse in SoG may not always reflect conditions experienced along the migration paths ofsockeye salmon or the depth at which they travel.Table A4.3-17. C3a 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_wtsM4 1136.05 423 24 3.02 1139.06 1139.06 0 45.62M5 1136.89 423 23 2.77 1139.66 1139.06 0.60 33.87M2 1137.88 423 25 3.27 1141.15 1139.06 2.09 16.04M7 1142.86 423 22 2.53 1145.39 1139.06 6.33 1.93M9 1143.81 423 22 2.53 1146.34 1139.06 7.28 1.20M1 1142.84 423 29 4.43 1147.27 1139.06 8.21 0.75M10 1146.07 423 22 2.53 1148.60 1139.06 9.54 0.39M6 1148.06 423 23 2.77 1150.83 1139.06 11.77 0.13M8 1149.92 423 22 2.53 1152.45 1139.06 13.39 0.056M3 1151.84 423 25 3.27 1155.11 1139.06 16.05 0.015256
Table A4.3-18. Estimates for all fixed effects parameters included in each model, for model set C3a. 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_C3a_VarName M1 M2 M3 M4 M5 M6 M7 M8 M9 M10(Intercept) -3.47 -4.50 7.65 -4.89 -3.34 6.81 -7.26 3.41 5.76 6.63DIS_A67_SoGx_MGRR_DOM -1.4E-06 NA -1.1E-06 NA NA NA NA NA NA NADIS_T05_SoGx_MGRR_DOM -4.0E-06 NA 1.0E-06 NA NA NA NA NA NA NADIS_x69_QCSx_MGRR_DOM 0.0026 0.0021 NA 0.0022 NA NA NA NA NA NASSS_A48_QCSx_MGRR_DOM 0.37 0.29 NA 0.30 0.31 NA 0.32 NA NA NASSS_A48_SoGx_MGRR_DOM -0.19 NA -0.02 NA NA -0.01 NA -0.04 NA NASST_A48_SoGx_MGRR_DOM 0.25 NA -0.32 NA NA -0.29 NA NA NA -0.29SST_A78_QCSx_MGRR_DOM -0.34 -0.22 NA -0.22 -0.26 NA NA NA -0.25 NAStockNameBirkenhead:Spawners -8.84 -8.78 -8.71 -8.78 -8.89 -8.71 -8.90 -8.71 -8.67 -8.71StockNameBowron:Spawners -144.67 -144.51 -141.58 -144.73 -142.39 -142.17 -148.01 -145.06 -139.23 -141.98StockNameChilko:Spawners -2.32 -2.29 -2.15 -2.29 -2.28 -2.16 -2.29 -2.17 -2.14 -2.16StockNameCultus:Spawners -87.72 -91.09 -96.10 -91.17 -94.71 -95.77 -95.40 -95.50 -95.17 -95.84StockNameE.Stuart:Spawners -2.98 -3.08 -3.06 -3.08 -3.05 -3.04 -3.26 -3.16 -3.00 -3.05StockNameFennell:Spawners -103.02 -101.63 -97.20 -101.10 -101.65 -97.28 -104.85 -99.52 -95.83 -97.21StockNameGates:Spawners -15.72 -14.71 -9.79 -14.38 -12.37 -10.10 -14.32 -11.78 -9.54 -10.03StockNameHarrison:Spawners -10.01 -8.55 6.80 -9.27 -5.98 3.90 -5.16 5.50 2.71 3.77StockNameL.Shuswap:Spawners -0.31 -0.30 -0.33 -0.29 -0.31 -0.34 -0.32 -0.35 -0.35 -0.34StockNameL.Stuart:Spawners -1.70 -1.71 -1.54 -1.71 -1.64 -1.55 -1.69 -1.58 -1.53 -1.55StockNameNadina:Spawners -11.32 -11.81 -10.74 -11.78 -11.67 -10.78 -11.65 -11.19 -11.14 -10.79StockNamePortage:Spawners -42.25 -40.91 -38.14 -40.88 -40.83 -38.58 -41.67 -37.88 -36.86 -38.58StockNameQuesnel:Spawners -0.74 -0.74 -0.74 -0.74 -0.73 -0.74 -0.77 -0.77 -0.75 -0.74StockNameRaft:Spawners -27.64 -26.85 -23.10 -26.47 -24.94 -23.52 -26.05 -25.49 -24.67 -23.53StockNameScotch:Spawners 7.97 8.71 7.56 8.87 8.40 7.48 7.29 6.32 6.98 7.48StockNameSeymour:Spawners -6.37 -6.32 -6.94 -6.27 -6.89 -6.87 -7.07 -7.27 -7.15 -6.88StockNameStellako:Spawners -5.28 -5.18 -4.88 -5.14 -5.08 -4.93 -5.33 -5.15 -4.93 -4.93StockNameWeaver:Spawners -5.35 -4.65 -4.14 -4.65 -4.52 -4.27 -4.26 -3.96 -4.11 -4.25WND_x28_QCSx_MGRR_DOM 0.02 0.04 NA NA NA NA NA NA NA NA257
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 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 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
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
Page 134 and 135:
utilized to clarify the full range
Page 137 and 138:
Appendix 2. Reviewer Evaluations an
Page 139 and 140:
the conclusions of the Expert Panel
Page 141 and 142:
Response: Figure numbers have been
Page 143 and 144:
Response: This section has been com
Page 145 and 146:
ased on time or other stocks) to
Page 147 and 148:
them in terms of how changes in spa
Page 149 and 150:
efer to some variable being include
Page 151 and 152:
... (Table xx)". Structures of thes
Page 153 and 154:
Report Title: Fraser River sockeye
Page 155 and 156:
show stronger correlations. Hence,
Page 157 and 158:
Fraser and non-Fraser sockeye popul
Page 159 and 160:
a single database for other researc
Page 161 and 162:
Response: We have removed the itali
Page 163 and 164:
evidence unique to each life stage.
Page 165 and 166:
Report Title: Data Synthesis and Cu
Page 167 and 168:
Response: We have further emphasize
Page 169 and 170:
5. I would also encourage the autho
Page 171 and 172:
events occur, it should be possible
Page 173 and 174:
I don’t have anything to add beyo
Page 175 and 176:
constitutes an exposure to human ac
Page 177 and 178:
3.3.1 has been revised to say “As
Page 179:
most likely contributors to the rec
Page 182 and 183:
A3.2.1 Integrative quantitative met
Page 184 and 185:
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 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
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
Page 224 and 225: ecruits for this analysis as we wan
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 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 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 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
Page 314 and 315: Appendix C: Workshop MinutesContent
Page 316 and 317: Levy urged researchers to bear in m
Page 318 and 319: within-population, within-brood-yea
Page 320 and 321: To address potential skepticism ove
Page 322 and 323:
instead of migrating directly out i
Page 324 and 325:
considered a good surrogate of temp
Page 326 and 327:
Bristol Bay review: There are subst
Page 328 and 329:
Method: The approach included formu
Page 330 and 331:
Staley: Our project needs to compar
Page 332 and 333:
Levy: It will go ahead in January.
Page 334 and 335:
preliminary assessment of potential
Page 336 and 337:
MacDonald: We’re waiting for all
Page 338 and 339:
including Shuswap and North Thompso
Page 340 and 341:
and biological variables. The combi
Page 342 and 343:
temperatures. Notably, it doesn’t
Page 344 and 345:
Seasonal population distribution pa
Page 346 and 347:
Information gaps: The most importan
Page 348 and 349:
Fraser sockeye salmon habitat analy
Page 350 and 351:
survey data, but they were looking
Page 352 and 353:
was also shown that tagged fish ret
Page 354 and 355:
English: There was extensive holdin
Page 356 and 357:
periods showed that 4 to 6 stocks (
Page 358 and 359:
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
show all

Fraser River sockeye salmon: data synthesis and cumulative impacts

Create successful ePaper yourself

Delete template?

Save as template?