The relative influence of temperature and size-structure on fish distribution shifts: A case-study on Walleye pollock in the Bering Sea

2017 ◽  
Vol 18 (6) ◽  
pp. 1073-1084 ◽  
Author(s):  
James T Thorson ◽  
James N Ianelli ◽  
Stan Kotwicki
Keyword(s):  
Bering Sea ◽  
Size Structure ◽  
Walleye Pollock ◽  
Fish Distribution ◽  
Relative Influence ◽  
Influence Of Temperature ◽  
Distribution Shifts ◽  
The Bering Sea

scholarly journals Comprehensive incentives for reducing Chinook salmon bycatch in the Bering Sea walleye Pollock fishery: Individual tradable encounter credits

2018 ◽  
Vol 22 ◽  
pp. 70-81 ◽  
Author(s):  
George Sugihara ◽  
Keith R. Criddle ◽  
Mac McQuown ◽  
Alfredo Giron-Nava ◽  
Ethan Deyle ◽  
...  
Keyword(s):  
Chinook Salmon ◽  
Bering Sea ◽  
Walleye Pollock ◽  
The Bering Sea

scholarly journals Modeling connectivity of walleye pollock in the Gulf of Alaska: Are there any linkages to the Bering Sea and Aleutian Islands?

2016 ◽  
Vol 132 ◽  
pp. 227-239 ◽  
Author(s):  
Carolina Parada ◽  
Sarah Hinckley ◽  
John Horne ◽  
Michael Mazur ◽  
Albert Hermann ◽  
...  
Keyword(s):  
Bering Sea ◽  
Gulf Of Alaska ◽  
Walleye Pollock ◽  
Aleutian Islands ◽  
The Bering Sea

Relating trends in walleye pollock (Theragra chalcogramma) abundance in the Bering Sea to environmental factors

1995 ◽  
Vol 52 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Gordon Swartzman ◽  
Emily Silverman ◽  
Neal Williamson
Keyword(s):  
Water Column ◽  
Bering Sea ◽  
Walleye Pollock ◽  
Additive Models ◽  
Theragra Chalcogramma ◽  
Thermocline Depth ◽  
Depth Range ◽  
Walleye Pollock Theragra Chalcogramma ◽  
Shelf Slope ◽  
The Bering Sea

Generalized additive models (GAM), a nonparametric regression method with less restrictive statistical assumptions than traditional regression methods, were used to model the trend in mean abundance of Bering Sea walleye pollock (Theragra chalcogramma) as a function of ocean environmental conditions including water column depth, temperature at 50 m, and depth of the thermocline. Acoustic survey data collected in the summers of 1988 and 1991 were used to test these relationships. In both surveys, mean walleye pollock abundance was highest in areas having a 70–130 m depth range and where the 50-m temperature was close to 2.5 °C. Thermocline depth, while not itself significant, had a significant effect on walleye pollock abundance through interactions with both bottom depth and temperature at 50 m. Walleye pollock in the top 50 m of the water column (mostly juveniles) were influenced differently by temperature and thermocline depth than the adult walleye pollock, which were generally deeper in the water column. The depth, temperature, and thermocline preferences of walleye pollock are hypothesized to be linked to food availability which is, in turn, related to temperature regimes or fronts along the Bering Sea shelf slope.

Spatial modeling of Bering Sea walleye pollock with integrated age-structured assessment models in a changing environment

2013 ◽  
Vol 70 (9) ◽  
pp. 1402-1416 ◽  
Author(s):  
Peter-John F. Hulson ◽  
Terrance J. Quinn ◽  
Dana H. Hanselman ◽  
James N. Ianelli
Keyword(s):  
Climate Change ◽  
Bering Sea ◽  
Walleye Pollock ◽  
Spatially Explicit ◽  
Parameter Estimates ◽  
Effective Sample Size ◽  
Climate Change Effects ◽  
Spatially Explicit Models ◽  
Distribution Shifts ◽  
Age Structured

Climate change may affect the spatial distribution of fish populations in ways that would affect the accuracy of spatially aggregated age-structured assessment models. To evaluate such scenarios, spatially aggregated models were compared with spatially explicit models using simulations. These scenarios were based on hypothetical climate-driven distribution shifts and reductions in mean recruitment of walleye pollock (Gadus chalcogrammus) in the eastern Bering Sea. Results indicate that biomass estimates were reasonably accurate for both types of estimation models and precision improved with the inclusion of tagging data. Bias in some aggregated model scenarios could be attributed to unaccounted-for process errors in annual fishing mortality rates and was reduced when estimating effective sample size or time-varying selectivity. Spatially explicit models that allow estimation of variability in movement and ontogenetic parameters (specified as a random walk process) were shown to be feasible, whereas models that misspecified ontogenetic movement and climate change effects resulted in biased biomass and movement parameter estimates. These results illustrate that more complex models may characterize processes better but may be less robust for management advice.

Climatic and economic drivers of the Bering Sea walleye pollock (Theragra chalcogramma) fishery: implications for the future

2013 ◽  
Vol 70 (6) ◽  
pp. 841-853 ◽  
Author(s):  
Alan C. Haynie ◽  
Lisa Pfeiffer
Keyword(s):  
Climate Change ◽  
Bering Sea ◽  
Climate Models ◽  
Walleye Pollock ◽  
Spatial And Temporal Variation ◽  
Theragra Chalcogramma ◽  
Climate Change Scenarios ◽  
Walleye Pollock Theragra Chalcogramma ◽  
The Impact ◽  
The Bering Sea

This paper illustrates how climate, management, and economic drivers of a fishery interact to affect fishing. Retrospective data from the Bering Sea walleye pollock (Theragra chalcogramma) catcher–processer fishery were used to model the impact of climate on spatial and temporal variation in catch and fishing locations and make inferences about harvester behavior in a warmer climate. Models based on Intergovernmental Panel on Climate Change scenarios predict a 40% decrease in sea ice by 2050, resulting in warmer Bering Sea temperatures. We find that differences in the value of catch result in disparate behavior between winter and summer seasons. In winter, warm temperatures and high abundances drive intensive effort early in the season to harvest earlier-maturing roe. In summer, warmer ocean temperatures were associated with lower catch rates and approximately 4% less fishing in the northern fishing grounds, contrary to expectations derived from climate-envelope-type models that suggest fisheries will follow fish poleward. Production-related spatial price differences affected the effort distribution by a similar magnitude. However, warm, low-abundance years have not been historically observed, increasing uncertainty about future fishing conditions. Overall, annual variation in ocean temperatures and economic factors has thus far been more significant than long-term climate change-related shifts in the fishery's distribution of effort.

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