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
Population-specific spatial and temporal distribution data are necessary to identify mechanisms regulating abundance and to manage anthropogenic impacts. However the distributions of highly migratory species are often difficult to resolve, particularly when multiple populations' movements overlap. Here we present an integrated model to estimate spatially-stratified, seasonal trends in abundance and population composition, using data from extensive genetic sampling of commercial and recreational Chinook salmon (Oncorhynchus tshawytscha) fisheries in southern British Columbia. We use the model to estimate seasonal changes in population-specific standardized catch per unit effort (a proxy for abundance) across six marine regions, while accounting for annual variability in sampling effort and uncertain genetic stock assignment. We also share this model as an R package stockseasonr for application to other regions and species. Even at the relatively small spatial scales considered here, we found that patterns in seasonal abundance differed among regions and stocks. While certain locations were clearly migratory corridors, regions within the Salish Sea exhibited diverse, and often weak, seasonal patterns in abundance, emphasizing that they are important, year-round foraging habitats. Furthermore, we found evidence that stocks with similar freshwater life histories and adult run timing, as well as relatively proximate spawning locations, exhibited divergent distributions. Our findings highlight subtle, but important differences in how adult Chinook salmon use marine habitats. Down-scaled model outputs could be used to inform ecosystem-based management efforts by resolving the degree to which salmon overlap with other species of concern, as well as specific fisheries. More broadly, variation in stock-specific abundance among regions indicates efforts to identify mechanisms driving changes in size-at-maturity and natural mortality should account for distinct marine distributions.