Stock-Recruit Analyses of Fraser River Sockeye Salmon

Abstract

Three types of stock-recruit models (log-log, Ricker, Beverton-Holt) were applied to 57 years of adult returns (R) and eff ective female spawners (E) data from 17 biologically-based Conservation Units (CUs) of sockeye salmon from the Fraser River in British Columbia, Canada (hereafter "Fraser sockeye"). Log-log regressions of R on E showed little evidence of eff ects of density (within CUs) on survival, implying that habitat capacity does not presently limit Fraser sockeye abundance. Shared survival among CUs, the fi rst principal component of log(R/E), accounted for 46% of the variance, remarkable given the wide variety of freshwater and marine habitats experienced by the CUs. Six low-survival events in six decades accounted for much of the shared survival pattern. The r 2 values for Ricker models were low, indicating that attempts to manage and/or assess Fraser sockeye using Ricker curves fi t to individual CUs will, in general, face low explanatory power. A suite of increasingly complicated Bayesian regressions, based on the Beverton-Holt model, quantifi ed the precision of capacity estimates, but these were always imprecise and the Widely Applicable Information Criterion (WAIC) indicated overfi tting in all but the simplest models. A variance factor for the relative precision of estimates of R, based on the proportion of spawners from each CU in groups of co-migrating CUs (i.e., runs), was eff ective only in models in which WAIC indicated over-fi tting. Improving the precision of capacity estimates for Fraser sockeye salmon, using similar models, will require mobilizing biological knowledge (i.e., historical metadata) about each CU, including estimates of abundance with identifi ed precision and indicators of habitat capacity at multiple life-history stages including the abundance of potential competitors in marine habitats. Researchers analyzing stock recruitment data for salmon populations and other species are strongly encouraged to pay attention to factors aff ecting the various categories of data in their models, i.e., to examine the associated metadata.

Covariability of Fraser River sockeye salmon productivity and phytoplankton biomass in the Gulf of Alaska

“…Unaccounted variation in migratory behavior among different sockeye salmon stocks, including multiple ocean entries for each brood year, and uncertainty in spawner, smolt, and catch abundances (Akenhead et al, 2016; Connors et al, 2020; Grant et al, 2011; Ogden et al, 2015), could contribute to variation in the linear relationship between (Chl‐a) and stock‐specific productivity (Table 1). Channel construction in the Nadina and Weaver Creek nurseries and mine pollution in the Quesnel Lake watershed could have impacted productivity metrics for these individual stocks (Grant et al, 2011, 2017), potentially leading to the positive ∆AIC (i.e., decreased model performance) for these individual stocks (Figure 3).…”