Oceanic, riverine, and genetic influences on spring chinook salmon migration timing

Abstract

Migrating salmonids often return to their spawning habitats in overlapping timing patterns of multiple stocks (populations) collectively called a run that varies in its genetic makeup across and within years. Managers, tasked with developing harvest strategies on these runs, may have preseason estimates of total run size but little information on run timing. Without both it is difficult to assess a run's status in real time. Consequently, to avoid overharvest, managers tend to control the timing of harvest. However, this strategy may inadvertently affect the component stocks disproportionately and therefore the run's diversity. Thus, accurate estimates of run timing are needed to improve management. We developed a model that includes genetic and environmental factors to predict the mean run timing of chinook salmon (Oncorhynchus tshawytscha) at Bonneville Dam on the Columbia River, Oregon, USA. The model predicted mean runtiming (P < 0.00001, r2 = 0.78) by characterizing genetic run timing components from the arrival timing of precocious males returning one year prior to the remainder of the adults and environmental influences of oceanic and riverine flows that impede or advance the run timing. Variations in the relative abundances of the populations in the run explain 62% of the interannual variation in mean run timing while the oceanic and riverine factors combined account for 15.5%. We suggest that when genetic run timing characteristics are preserved in species with multiple maturation strategies the information can be used to improve run time predictions and maintain genetic diversity of harvested species.

Integration of Random Forest with population‐based outlier analyses provides insight on the genomic basis and evolution of run timing in Chinook salmon (Oncorhynchus tshawytscha)

“…This outcome was a result of using samples that do not have associated individual phenotypic measures, and points towards the importance of matching phenotypes with DNA samples in future field studies. Average peak run timing also varies by year (Anderson & Beer ), but in this study, these values were largely measured in the same years that the samples were obtained. Future analyses would be strengthened using covariates that might explain environmental influences on the phenotype (Anderson & Beer ).…”

“…Average peak run timing also varies by year (Anderson & Beer ), but in this study, these values were largely measured in the same years that the samples were obtained. Future analyses would be strengthened using covariates that might explain environmental influences on the phenotype (Anderson & Beer ). Finally, the adjustment of the genotypes and phenotypes for population structure has the consequence of reducing the effects of individual markers, and hence explanatory power, but this step was necessary to reduce false‐positive results (Zhao et al .…”

Use of Radiotelemetry and Direct Observations to Evaluate Sea Lion Predation on Adult Pacific Salmonids at Bonneville Dam

“…At a broad scale, we think the multiyear data set captured many of the among-population migration timing differences because individual stocks consistently migrated at the same time each year relative to the overall run (Keefer et al 2004b). Therefore, while the initiation of the Columbia River spring Chinook salmon run varies by several weeks as a function of environmental and run composition effects (Keefer et al 2008b;Anderson and Beer 2009), the populations present early in each migration are similar across years. This conclusion is broadly supported by studies showing population-specific heritability of migration timing in salmonids (e.g., Quinn et al 2000Quinn et al , 2011Waples et al 2004) and suggests that sea lion predation will probably continue to disproportionately affect early timed Columbia River populations.…”

Multivariate Models of Adult Pacific Salmon Returns

“…The multivariate techniques we used resulted in two important products: a pre-season forecast of adult salmon returns, primarily for management of the fisheries, and a measure of indicator importance, which can improve understanding of ocean ecology and guide future marine research. Moreover, the pre-season estimates obtained through these analyses can be used as a starting point for more detailed in-season management adjustments [30], [31].…”