Cooperative Fish and Wildlife Research Units Program: Oregon
Education, Research and Technical Assistance for Managing Our Natural Resources

Rapid phenotypic stock identification in mixed-stock fisheries can provide a useful alternative to more time-intensive methods (e.g., genetics) in assessing harvest and informing management decisions. We leveraged anecdotal knowledge and understanding of life history differences to develop rapid stock identification tools for fall-run Chinook salmon (Oncorhynchus tshawytscha) encountered in the Buoy 10 recreational fishery at the mouth of the Columbia River. Specifically, we sought to differentiate between the fishery’s two dominant genetic lineages: lower river tules and upriver brights. We sampled recreationally landed Chinook salmon in 2017, 2018, and 2019, assigned sampled individuals to genetic group using a probabilistic approach and single nucleotide polymorphisms, and collected measurements on phenotypic traits. Using traits including pigmentation patterns (e.g., spotting), fin morphology, characters indicative of sexual maturity, and muscle lipid content, random forest classification models provided consistently high classification success across and within genetic groups (i.e., up to 90% in certain cases). Classification success remained consistent over time within fishery seasons and between years, but showed meaningful bias between sexes. Based on observed classification success, we developed and evaluated a categorical visual identification guide capable of facilitating more rapid trait observations and on-site stock identification. The resulting classification key, built using classification trees and visual guide observations from 2019, achieved slightly lower classification success across and within genetic groups and highly variably success among samplers. Trade-offs among stock identification methods offer unique management applications for each alternative, including their complementary use in harvest assessment alongside standard data collection methods. Rapid phenotypic stock identification methods offer the potential for increased precision and timeliness in harvest assessments.