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Incorporating multiple mixed stocks in mixed stock analysis: ‘many-to-many’ analyses

Authors

  • BENJAMIN M. BOLKER,

    1. Department of Zoology & Archie Carr Center for Sea Turtle Research, University of Florida, PO Box 118525, Gainesville, FL 32611-8525, USA
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  • TOSHINORI OKUYAMA,

    1. Department of Zoology & Archie Carr Center for Sea Turtle Research, University of Florida, PO Box 118525, Gainesville, FL 32611-8525, USA
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  • KAREN A. BJORNDAL,

    1. Department of Zoology & Archie Carr Center for Sea Turtle Research, University of Florida, PO Box 118525, Gainesville, FL 32611-8525, USA
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  • ALAN B. BOLTEN

    1. Department of Zoology & Archie Carr Center for Sea Turtle Research, University of Florida, PO Box 118525, Gainesville, FL 32611-8525, USA
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Ben Bolker, Fax: +1352-392-3704; E-mail: bolker@zoo.ufl.edu

Abstract

Traditional mixed stock analyses use morphological, chemical, or genetic markers measured in several source populations and in a single mixed population to estimate the proportional contribution of each source to the mixed population. In many systems, however, different individuals from a particular source population may go to a variety of mixed populations. Now that data are becoming available from (meta)populations with multiple mixed stocks, the need arises to estimate contributions in this ‘many-to-many’ scenario. We suggest a Bayesian hierarchical approach, an extension of previous Bayesian mixed stock analysis algorithms, that can estimate contributions in this case. Applying the method to mitochondrial DNA data from green turtles (Chelonia mydas) in the Atlantic gives results that are largely consistent with previous results but makes some novel points, e.g. that the Florida, Bahamas and Corisco Bay foraging grounds have greater contributions than previously thought from distant foraging grounds. More generally, the ‘many-to-many’ approach gives a more complete understanding of the spatial ecology of organisms, which is especially important in species such as the green turtle that exhibit weak migratory connectivity (several distinct subpopulations at one end of the migration that mix in unknown ways at the other end).

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