Re-Envisioning Mixed Stock Analysis from Multiple Data Sources: A Recipe for Stone Soup

Research on migratory animals can generate large amounts of data from a wide variety of sources.  In addition, several projects may be conducted simultaneously on a target species, but the output from those studies is not always integrated and understanding of a particular aspect may be of limited scope.  To gain a broader perspective it is beneficial to utilize several different sources of information and/or to access data-rich sources to inform data-limited situations.  To this end, we are taking advantage of multiple data sources and utilizing data from associated strata to re-envision assessment of the stock composition of salmon fisheries in Alaska, a technique already applied across a diverse set of taxa from fish to turtles to elephants.  Mixed stock analysis traditionally relies primarily on genetic information, but this “gen-plus” MSA method utilizes information from multiple sources to assign individuals to stock of origin. A case study from Southeast Alaska sockeye fisheries is examined, in which mixture analysis was conducted simultaneously across multiple time and area strata using genotypes, ages from matched scales, and hatchery marks from matched otoliths. This approach was developed to allow stock composition estimates to be generated in a situation where individual strata were considered data-poor.