Sockeye salmon ( Oncorhynchus nerka ) is a commercially and culturally important species to the people that live along the northern Pacific Ocean coast. There are two main sockeye salmon ecotypes—the ocean-going (anadromous) ecotype and the fresh-water ecotype known as kokanee. The goal of this study was to better understand the population structure of sockeye salmon and identify possible genomic differences among populations and between the two ecotypes. In pursuit of this goal, we generated the first reference sockeye salmon genome assembly and an RNA-seq transcriptome data set to better annotate features of the assembly. Resequenced whole-genomes of 140 sockeye salmon and kokanee were analyzed to understand population structure and identify genomic differences between ecotypes. Three distinct geographic and genetic groups were identified from analyses of the resequencing data. Nucleotide variants in an immunoglobulin heavy chain variable gene cluster on chromosome 26 were found to differentiate the northwestern group from the southern and upper Columbia River groups. Several candidate genes were found to be associated with the kokanee ecotype. Many of these genes were related to ammonia tolerance or vision. Finally, the sex chromosomes of this species were better characterized, and an alternative sex-determination mechanism was identified in a subset of upper Columbia River kokanee.

Estimating angling effort on more than a few lakes can be prohibitively expensive using creel surveys and often requires finer‐scale angler distribution data than aerial surveys can provide. An alternate method uses remote cameras to capture images of lakes at hourly intervals over long time periods. Technicians then visually analyze the thousands of generated images for features of interest (e.g., angler counts and environmental conditions) and use those data to estimate angling effort. The problem is that the visual analysis step is time‐consuming, expensive, and difficult to validate. Consequently, we elicited the strategies and best practices technicians used when analyzing images and identified bottlenecks. We then designed software, called Timelapse to better support image analysis. In use for several years, Timelapse has proven a cost‐effective method of estimating angling effort in British Columbia's small lakes fisheries; it significantly eases a technician's workflow and doubles the number of images one can process per hour.

Understanding how environmental productivity and resource competition influence somatic growth rates and plasticity in life-history traits is a critical component of population ecology. However, evolutionary effects often confound the relationship between plasticity in life-history characteristics and environmental productivity. We used a unique set of experimentally stocked populations of rainbow trout (Oncorhynchus mykiss) to empirically test predictions from life-history theory relating to patterns in immature growth rates, age- and size-at-maturity, and the energy allocated into reproduction across climatic and fish density gradients. Our results support theoretical predictions that plasticity in life-history characteristics is a function of environmental variables. In particular, we demonstrate that immature growth rates are best explained by climatic and density-dependent competition effects and that age-at-maturity and the energy allocated to reproduction depends on juvenile growth conditions. Empirical evidence of these relationships helps to improve our understanding of optimal life-history strategies of fish populations.