No abstract

We used starch-gel electrophoresis to examine over 13 000 adult chum salmon, Oncorhynchus keta, from 153 collections at 105 locations in Washington, Oregon, and southern British Columbia from 1985–92. We identified 39 variable loci and 36 monomorphic loci. In general, alleles that occurred at a frequency > 10% were found in all locations and were temporally stable within locations. Localized alleles usually occurred at a frequency <6%. Significant allele frequency differences were found among chum salmon populations with different spawning times (summer, fall, and winter), even within the same watershed, and among populations of the same spawning time in different regions. Significant among-population diversity was also found within regions. Multidimensional scaling and UPGMA cluster analysis grouped populations by geography and run-timing, except where past stock transfers have resulted in interbreeding of hatchery and wild fish. These analyses indicate that both geography and spawning time are important isolating mechanisms and that, compared with the other populations, summer-run chum salmon from the Strait of Juan de Fuca and Hood Canal are a distinct evolutionary lineage. This information is currently being used to conserve the genetic diversity of wild chum salmon and manage mixed-stock fisheries.

We analyzed allozymes from 13 collections of lingcod Ophiodon elongatus representing over 1,100 individuals throughout most of the species' range from Alaska to central California. Data were gathered from 41 presumptive gene loci of which 19 loci (46%) were polymorphic (Po.99 criterion, whereby the frequency of the most common allele is 0.99 or less in at least one collection). Average observed heterozygosity was 0.050 (SE, 0.001). Significant differences in genotype frequencies were found between sexes at the AH-2* locus for aconitate hydralase. Although lingcod populations are genetically similar throughout the range, significant allele frequency differences suggest that lingcod in Puget Sound are partially isolated from those along the open coast.

No abstract

Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent-offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop-out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent-offspring pairs and unintentionally recaptured individuals, respectively. By direct-count error estimates, the recapture and known parent-offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent-offspring comparisons. Furthermore, there was no correlation in locus-specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus-specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent-offspring comparisons in parentage studies).