Bering Sea sea ice during winter 2017–2018 was the lowest ever recorded. Ecosystem effects of low ice have been observed in the southeastern Bering Sea, but never in the northern Bering Sea. Observations in both systems included weakened water column stratification, delayed spring bloom, and low abundances of large crustacean zooplankton. Summer Cold Pool presence was extremely limited. Young walleye pollock production and condition were similar to prior warm years, though catches of other pelagic forage fishes were low. Summer seabird die‐offs were observed in the northern Bering Sea, and to lesser extent in the southeastern Bering Sea, and reproductive success was poor at monitored colonies. Selected bottom‐up responses to lack of sea ice in the north were similar to those in the south, potentially providing environmental indicators to project ecosystem effects in a lesser studied system. Results offer a potential glimpse of the broader Bering Sea pelagic ecosystem under future low‐ice projections.

It is widely recognized that genetic diversity within species is shaped by dynamic habitats. The quantitative and molecular genetic patterns observed are the result of demographics, mutation, migration, and adaptation. The populations of rainbow trout Oncorhynchus mykiss in the Columbia River basin (including both resident and anadromous forms and various subspecies) present a special challenge to understanding the relative roles of those factors. Standardized microsatellite data were compiled for 226 collections (15,658 individuals) from throughout the Columbia and Snake River basins to evaluate the genetic patterns of structure and adaptation. The data were primarily from fish of the anadromous life history form, and we used a population grouping procedure based on principal components and hierarchical k‐means clustering to cluster populations into eight aggregates or groups with similar allele frequencies. These aggregates approximated geographic regions, and the two largest principal components corresponded to ancestral lineages of Sacramento redband trout O. m. stonei, coastal rainbow trout O. m. irideus, and interior Columbia River redband trout O. m. gairdneri. Genetic data were partitioned among primary aggregates (lower Columbia, middle–upper Columbia, and Snake rivers), and the magnitude of genetic divergence relative to genetic diversity was analyzed (per locus) to test for evidence of selection and subsequent signals of adaptation. Two loci showed higher divergence than expected by chance (i.e., positive selection); however, both of these loci were on the fringe of the 99% confidence level and are potential false positives. Genetic patterns were also significantly correlated with certain environmental and habitat parameters (e.g., precipitation), but the extent to which those correlations are causal as opposed to effectual remains unclear. Despite the remaining questions, these data provide a foundation for more detailed investigations of harvest, admixture, and introgression between hatchery‐ and natural‐origin fish and differences in reproductive success among individuals as well as monitoring trends in productivity.

Protein genetic markers (allozymes) have been used during the last decade in a genetic stock identification (GSI) program by state and federal management agencies to monitor stocks of steelhead Oncorhynchus mykiss in the Columbia River basin. In this paper we report new data for five microsatellite and three intron loci from 32 steelhead populations in the three upriver evolutionarily significant units (ESUs) and compare the performance of allozyme, microsatellite, and intron markers for use in GSI mixture analyses. As expected, microsatellites and introns had high total heterozygosity (HT) values; but there was little difference among marker classes in the magnitude of population differentiation as estimated by Wright's fixation index (FST), which ranged from 0.041 (microsatellite loci) to 0.047 (allozyme loci) and 0.050 (intron loci). For allozyme and microsatellite loci, the relationships among populations followed the patterns of geographic proximity. In computer‐simulated mixture analyses, GSI estimates were more than 85% correct to the reporting group, the exact percentage depending on the marker data set and target group. Microsatellite loci provided the most accurate estimate (83%) in the 100% upper Columbia River ESU simulation, whereas simulation estimates for the 32‐locus allozyme baseline were 93–94% for the 100% middle Columbia River ESU and two Snake River management groups. The simulations also showed that the estimates improved substantially up to a sample size of 50 fish per population. Technical advances will concomitantly increase the number of useful microsatellite loci and the rate of laboratory throughput, making this class of molecular marker more valuable for GSI mixture analyses in the near future. In the meantime, we recommend that steelhead management in the Columbia River rely on both allozyme and microsatellite data for GSI procedures.