Eastern Bering Sea Walleye Pollock Early Survival Pathways

A Quarto synthesis of causal hypotheses and DAG alternatives for prerecruit survival to age 3.

This site summarizes causal hypotheses for eastern Bering Sea walleye pollock survival from eggs and larvae through age-0, age-1, and age-2 fish reaching age 3. The synthesis is based on the local paper set in the parent reprints folder, with web-visible citation links supplied through DOI and source URLs.

The papers often quantify recruitment at age 1 or age 2. Here, age-3 recruitment means the same cohort passing through the prerecruit filters and remaining alive and available at age 3.

Main Conclusion

Two mechanisms have the clearest support:

  1. Age-0 energy gate. Late-summer and fall condition, especially total energy, lipid reserves, and access to lipid-rich copepods and euphausiids, strongly affects overwinter survival to age 1 (Heintz et al. 2013; Siddon et al. 2013; Sogard and Olla 2000; Sigler et al. 2016).
  2. Age-1 predation gate. Juvenile survival after the first winter is strongly affected by spatial overlap with adult pollock, arrowtooth flounder, and other predators, with age-1 mortality particularly important (Mueter et al. 2006; Spencer et al. 2016).

Temperature, sea ice, stratification, summer wind mixing, and transport matter because they modify these gates. Average temperature alone is a useful proxy only when the active pathway is unresolved (Smart et al. 2012; Petrik et al. 2015; Gann et al. 2016).

Integrated Causal Map

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flowchart TB
  SSB["Spawning stock<br/>biomass"] --> EG["Egg<br/>abundance"]
  TEMP["Seasonal<br/>temperature"] --> DEV["Development rate<br/>and hatch timing"]
  TEMP --> SPAWN["Spawning timing<br/>and location"]
  ICE["Sea ice extent<br/>and retreat timing"] --> BLOOM["Spring bloom timing<br/>and location"]
  WIND["Wind, storms,<br/>and circulation"] --> TRANS["Egg and larval<br/>transport"]
  WIND --> MIX["Summer mixing<br/>and nutrient flux"]
  SPAWN --> ELDIST["Egg and larval<br/>distribution"]
  EG --> ELDIST
  DEV --> LARV["Larval duration<br/>and growth"]
  TRANS --> ELDIST
  BLOOM --> ZOO["Copepods and<br/>euphausiids"]
  MIX --> PP["Summer primary<br/>production"]
  PP --> ZOO
  ELDIST --> PREYMATCH["Spatial match<br/>with prey"]
  ZOO --> PREYMATCH
  PREYMATCH --> LARV
  LARV --> A0SIZE["Age-0<br/>size"]
  ZOO --> A0DIET["Age-0 diet<br/>lipid"]
  A0DIET --> A0ENERGY["Age-0 energy density<br/>and total energy"]
  A0SIZE --> WINTER["Overwinter survival<br/>to age 1"]
  A0ENERGY --> WINTER
  SST["Late-summer SST<br/>and metabolic demand"] --> A0ENERGY
  SST --> WINTER
  ADULT["Adult pollock<br/>biomass"] --> CANN["Cannibalism<br/>pressure"]
  COLD["Cold pool<br/>area"] --> PREDIST["Predator<br/>distribution"]
  ARROW["Arrowtooth and other<br/>predator biomass"] --> PRED["Predation<br/>pressure"]
  ELDIST --> JUVLOC["Age-0 and age-1<br/>distribution"]
  JUVLOC --> OVERLAP["Juvenile-predator<br/>spatial overlap"]
  PREDIST --> OVERLAP
  CANN --> OVERLAP
  OVERLAP --> A12SURV["Age-1 and age-2<br/>survival"]
  PRED --> A12SURV
  WINTER --> A12SURV
  A12SURV --> AGE3["Age-3<br/>recruitment"]

  classDef spawner fill:#e8f2f7,stroke:#26739b,stroke-width:2.4px,color:#1f2933;
  classDef physical fill:#e8f2f7,stroke:#26739b,stroke-width:2.4px,color:#1f2933;
  classDef prey fill:#ecf5eb,stroke:#6b8f24,stroke-width:2.4px,color:#1f2933;
  classDef early fill:#fff3df,stroke:#c68519,stroke-width:2.4px,color:#1f2933;
  classDef predator fill:#fdf2e8,stroke:#c8523f,stroke-width:2.4px,color:#1f2933;
  classDef survival fill:#e8f6f2,stroke:#13856f,stroke-width:2.4px,color:#1f2933;
  class SSB,EG spawner;
  class TEMP,ICE,WIND,SST,COLD,DEV,SPAWN,TRANS,MIX physical;
  class BLOOM,PP,ZOO,PREYMATCH,A0DIET prey;
  class ELDIST,LARV,A0SIZE,A0ENERGY,JUVLOC early;
  class ADULT,CANN,ARROW,PRED,PREDIST,OVERLAP predator;
  class WINTER,A12SURV,AGE3 survival;

Site Contents

  • Evidence: stage-specific synthesis and candidate indicators.
  • DAGs: alternative directed acyclic graphs for competing hypotheses.
  • PCMCI+: implementation plan for time-series causal discovery.
  • Infographics: compact visual summaries for a science audience.
  • References: cited references with DOI and source links.

Practical Models

A practical model does not need to include every process in the causal map. It should preserve the main stage structure: spawner output sets the starting cohort size, age-0 condition filters overwinter survival, and age-1 to age-2 predator overlap filters survival to age 3.

The models below are useful as a progression from a minimum assessment-linked model to a fuller causal model.

Model Form Best use
1. Baseline stock-recruit model Age3_recruitment = f(spawner output, cohort density, assessment uncertainty) Establishes the baseline recruitment expectation and residual pattern before adding environmental or ecological covariates.
2. Age-0 condition model Age3_recruitment = f(spawner output, age0 total energy or energy density, cohort density, uncertainty) Tests whether fall juvenile size, lipid, or total energy explains later recruitment after accounting for spawner output. This is the most direct practical expression of the age-0 energy gate.
3. Predator-overlap model Age3_recruitment = f(spawner output, juvenile-predator overlap, adult pollock biomass, predator biomass, uncertainty) Tests whether survival from age 1 to age 3 is limited by cannibalism or other predators. Adult pollock biomass and arrowtooth flounder distribution are treated as drivers of spatially explicit predation risk.
4. Transport and nursery-delivery model Age3_recruitment = f(spawner output, spawning location, larval transport, prey match, early survival, uncertainty) Tests whether cohorts succeed because eggs and larvae are delivered to prey-rich and predator-safe nursery habitat. This model is most useful when particle tracking, egg/larval surveys, or spawning-location products are available.
5. Integrated gate model Age3_recruitment = f(spawner output, age0 condition, age1-age2 predator overlap, cohort density, uncertainty) Combines the two best-supported survival gates. Age-0 condition mediates ice timing, bloom timing, summer mixing, prey production, and SST. Predator overlap mediates cold pool area, predator biomass, juvenile distribution, and adult pollock biomass.

For annual prediction, model 5 is the minimum useful causal model. For diagnosis, models 2 through 4 should also be fit separately so that a strong condition signal is not hidden by a predation signal, or vice versa. The transport model is especially useful as a bridge between climate forcing and the two survival gates because it determines where larvae and juveniles encounter prey and predators.

References

Gann, Jeanette C., Lisa B. Eisner, Steve Porter, et al. 2016. “Possible Mechanism Linking Ocean Conditions to Low Body Weight and Poor Recruitment of Age-0 Walleye Pollock (Gadus chalcogrammus) in the Southeast Bering Sea During 2007.” Deep-Sea Research Part II: Topical Studies in Oceanography 134: 115–27. https://doi.org/10.1016/j.dsr2.2015.07.010.
Heintz, Ron A., Elizabeth C. Siddon, Edward V. Farley, and Jeffrey M. Napp. 2013. “Correlation Between Recruitment and Fall Condition of Age-0 Pollock (Theragra chalcogramma) from the Eastern Bering Sea Under Varying Climate Conditions.” Deep-Sea Research Part II: Topical Studies in Oceanography 94: 150–56. https://doi.org/10.1016/j.dsr2.2013.04.006.
Mueter, Franz J., Carol Ladd, Michael C. Palmer, and Brenda L. Norcross. 2006. “Bottom-up and Top-down Controls of Walleye Pollock (Theragra chalcogramma) on the Eastern Bering Sea Shelf.” Progress in Oceanography 68 (2–4): 152–83. https://doi.org/10.1016/j.pocean.2006.02.012.
Petrik, Colleen M., Janet T. Duffy-Anderson, Franz J. Mueter, Katherine Hedstrom, and Enrique N. Curchitser. 2015. “Biophysical Transport Model Suggests Climate Variability Determines Distribution of Walleye Pollock Early Life Stages in the Eastern Bering Sea Through Effects on Spawning.” Progress in Oceanography 138: 459–74. https://doi.org/10.1016/j.pocean.2014.06.004.
Siddon, Elizabeth C., Ron A. Heintz, and Franz J. Mueter. 2013. “Conceptual Model of Energy Allocation in Walleye Pollock (Theragra chalcogramma) from Age-0 to Age-1 in the Southeastern Bering Sea.” Deep-Sea Research Part II: Topical Studies in Oceanography 94: 140–49. https://doi.org/10.1016/j.dsr2.2012.12.007.
Sigler, Michael F., Jeffrey M. Napp, Phyllis J. Stabeno, Ronald A. Heintz, Michael W. Lomas, and George L. Hunt. 2016. “Variation in Annual Production of Copepods, Euphausiids, and Juvenile Walleye Pollock in the Southeastern Bering Sea.” Deep-Sea Research Part II: Topical Studies in Oceanography 134: 223–34. https://doi.org/10.1016/j.dsr2.2016.01.003.
Smart, Tracey I., Janet T. Duffy-Anderson, John K. Horne, Edward V. Farley, Christopher D. Wilson, and Jeffrey M. Napp. 2012. “Influence of Environment on Walleye Pollock Eggs, Larvae, and Juveniles in the Southeastern Bering Sea.” Deep-Sea Research Part II: Topical Studies in Oceanography 65–70: 196–207. https://doi.org/10.1016/j.dsr2.2012.02.018.
Sogard, Susan M., and Bori L. Olla. 2000. “Endurance of Simulated Winter Conditions by Age-0 Walleye Pollock: Effects of Body Size, Water Temperature and Energy Stores.” Journal of Fish Biology 56: 1–21. https://doi.org/10.1111/j.1095-8649.2000.tb02083.x.
Spencer, Paul D., Kirstin K. Holsman, Stephani Zador, et al. 2016. “Modelling Spatially Dependent Predation Mortality of Eastern Bering Sea Walleye Pollock, and Its Implications for Stock Dynamics Under Future Climate Scenarios.” ICES Journal of Marine Science 73 (5): 1330–42. https://doi.org/10.1093/icesjms/fsw040.