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Status of Stocks & Multispecies Assessment

11th National Stock Assessment Workshop  (abstracts)
 

  • Estimating Scientific Uncertainty in Allowable Biological Catch (ABC) Control Rules for Bering Sea and Aleutian Islands (BSAI) Crab Stocks
    Jack Turnock1, Robert Foy2, Anne Hollowed1, André E. Punt3, Lou Rugolo1, and Diana L. Stram41AFSC, Seattle, WA; 2AFSC, Kodiak, AK; 3University of Washington, School of Aquatic and Fishery Sciences, Seattle, WA; 4North Pacific Fishery Management Council, Anchorage, AK

    A shared management scheme exists for the BSAI crab stocks, between the Federal government and the State of Alaska. Annual catch limit (ACL) provisions of the Magnuson-Stevens Fishery Conservation and Management Act require that ACL control rules be devised that establish a buffer between the overfishing limit (OFL) and an ABC to account for scientific uncertainty in the OFL. Scientific uncertainty arises from several sources but can be divided into two main categories for computing the ABC:  1) uncertainty within a stock assessment that can be quantified using standard methods of variance estimation, and  2) sources of uncertainty which cannot be captured in this way. Examples of the latter include: a) errors in proxy definitions for FMSY and BMSY ;  b) errors associated with the values for prespecified parameters of population models (e.g., natural mortality, M, and catchability, q);  c) methodology (e.g., how survey area swept estimates are computed); and d) the choice of which data sources are included in assessments. For stocks with functional assessment models, within-assessment uncertainty is a standard output while additional uncertainty can be estimated using other methods (retrospective analyses, between-year variability in assessment outcomes). In these cases, the relationship between P* (the probability that the ABC exceeds the true OFL) and the buffer between the OFL and ACL, can be estimated by stock. For stocks without assessment models, the scientific uncertainty associated with OFL can be computed using Monte Carlo simulation. For stocks with insufficient biomass data, the OFL is based on historical catch data, and a default buffer must be assumed based on informed judgement.

     
  • Deciphering Environmental Patterns and Effects From Messy Data
    Sandra A. Lowe; AFSC, Seattle, WA

    Alaska Atka mackerel (P. monopterygius) is an important component of the Aleutian Islands (AI) ecosystem and supports a large commercial fishery. Sustainability of this population has been dependent on highly variable recruitment and the consistent appearance of strong year classes. Interestingly, strong year classes of AI Atka mackerel have occurred in years of hypothesized climate regime shifts 1977, 1988, and 1999, as indicated by indices such as the Pacific Decadal Oscillation. El Niño Southern Oscillation (ENSO) events are another source of climate forcing that influences the North Pacific. Preliminary analyses have not indicated a relationship between strong year classes of Aleutian Atka mackerel and ENSO events. We reexamine this relationship in light of significant recent recruitment events. Quantitative observations about the ENSO effects on fishes can be difficult, and as such we also examine anomalies of weight-at-age tracked by cohort to decipher potential patterns that may reflect environmental influences. We suggest ways that environmental indicators of growth patterns may be incorporated into the stock assessment.
     

By Sandra Lowe
 

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