RESUMO
Wild Pacific salmon, including Chinook salmon Oncorhynchus tshawytscha, have been supplemented with hatchery propagation for over 50 years in support of increased ocean harvest, mitigation for hydroelectric development, and conservation of threatened populations. In Canada, the Wild Salmon Policy for Pacific salmon was established with the goal of maintaining and restoring healthy and diverse Pacific salmon populations, making conservation of wild salmon and their habitats the highest priority for resource management decision-making. For policy implementation, a new approach to the assessment and management of Chinook salmon and the associated hatchery production and fisheries management are needed. Implementation of genetic stock identification (GSI) and parentage-based tagging (PBT) for marine fisheries assessment may overcome problems associated with coded-wire tag-based (CWT) assessment and management of Chinook salmon fisheries, providing at a minimum information equivalent to that derived from the CWT program. GSI and PBT were used to identify Chinook salmon sampled in 2018 and 2019 marine fisheries (18,819 individuals genotyped) in British Columbia to specific conservation units (CU), populations, and broodyears. Individuals were genotyped at 391 single nucleotide polymorphisms via direct sequencing of amplicons. Very high accuracy of assignment to population and age (>99.5%) via PBT was observed for 1994 Chinook salmon of ages 2-4 years, with a 105,722-individual, 380-population baseline available for assignment. Application of a GSI-PBT system of identification to individuals in 2019 fisheries provided high-resolution estimates of stock composition, catch, and exploitation rate by CU or population, with fishery exploitation rates directly comparable to those provided by CWTs for 13 populations. GSI and PBT provide an alternate, cheaper, and more effective method in the assessment and management of Canadian-origin Chinook salmon relative to CWTs, and an opportunity for a genetics-based system to replace the current CWT system for salmon assessment.
RESUMO
We describe a distance-based clustering method using a proximity matrix of genetic distances to partition populations into genetically similar groupings. The optimization heuristic mean-field annealing (MFA) was used to find locally optimal solutions where exhaustive search was not possible. To illustrate this method, we analysed both simulated and real data sets. Simulated data indicated that MFA successfully differentiated population groups, even with small F(ST) values, as long as there was separation of within and between group distances. Reanalysis of microsatellite data from various human populations using mean-fields found similar ethnic groups corresponding to major geographic regions reported by Rosenberg et al. (2002) who used the model-based computer program Structure. However, with MFA, the Kalash population was found to group with other Central/South Asian populations instead of being the only member of its own genetic cluster. Europe/Middle East populations formed a separate group from Central/South Asian populations instead of being a single group in the Structure analysis. The MFA analysis determined the greatest genetic distances (largest mean intracluster distance) occurred in native American populations, identifying three groups instead of only one found with Structure. For conservation purposes, it is not only important to identify genetically similar groupings but also to determine the relative level of genetic differentiation captured within these groups. To illustrate this, we compare two separate MFA analyses of Chinook salmon (Oncorhynchus tshawytscha) populations from British Columbia, Canada. The software called PORGS-MFA used in this article can be downloaded from http://www.pac.dfo-mpo.gc.ca/science/facilities-installations/pbs-sbp/mgl-lgm/apps/porgs/index-eng.htm.