Accounting for Spatial Population Structure in Stock Assessment: Past, Present, and Future

Part of the Fish & Fisheries Series book series (FIFI, volume 31)

Stock identification has been an important prerequisite for stock assessment throughout its history. The earliest evaluations of recruitment variability recognized that understanding the spatial scale of a fishery resource is essential for studying population dynamics. A paradigm of stock structure was based on closed migration circuits and geographic variation of phenotypic traits and formed a premise for fishery modeling conventions in the mid-1900s. As genetic techniques developed in the late 1900s, the “stock concept” was refined to include a degree of reproductive isolation. Realization that there was no single method that addressed the various assumptions of stock assessment and needs of fishery management prompted a more holistic view of population structure that called for multiple sources of demographic and genetic data. Recent applications of advanced techniques challenge the traditional view of populations as geographically distinct units with homogeneous vital rates and isolation from adjacent resources. More complex concepts such as metapopulations and “contingent theory” may be more applicable to many fishery resources with sympatric population structure. These more complex patterns of population structure have been incorporated into some advanced stock assessment techniques and metapopulation models that account for movement among areas and sympatric heterogeneity. Wider application of spatially explicit models in future stock assessments will require clear identification of stock components, evaluating movement rates and determining the degree of reproductive isolation. Because spatial structure affects how populations respond to fisheries, incorporation of heterogeneous patterns and movement in stock assessment models should improve advice for fishery management.


Mixed-stock fisheries migration spatial heterogeneity stock assessment stock identification 


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© Springer Science + Business Media B.V 2009

Authors and Affiliations

  1. 1.NOAA/UMass Cooperative Marine Education and Research ProgramSchool for Marine Science & TechnologyNew BedfordUSA
  2. 2.Chesapeake Biological LaboratoryUniversity of Maryland Center for Environmental ScienceSolomonsUSA

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