Logistic regression of proportion by return year of Chinook salmon by stock, age class, and year.

<p>Closed circles and solid line = 4-ocean; open triangle and dotted line = 3-ocean, open square and dashed line = 2-ocean. Red lines indicate slopes are significantly different from zero (<i>P</i> <0.01).</p

Changes in Size and Age of Chinook Salmon <i>Oncorhynchus tshawytscha</i> Returning to Alaska

<div><p>The average sizes of Pacific salmon have declined in some areas in the Northeast Pacific over the past few decades, but the extent and geographic distribution of these declines in Alaska is uncertain. Here, we used regression analyses to quantify decadal trends in length and age at maturity in ten datasets from commercial harvests, weirs, and spawner abundance surveys of Chinook salmon <i>Oncorhynchus tshawytscha</i> throughout Alaska. We found that on average these fish have become smaller over the past 30 years (~6 generations), because of a decline in the predominant age at maturity and because of a decrease in age-specific length. The proportion of older and larger 4-ocean age fish in the population declined significantly (<i>P</i> < 0.05) in all stocks examined by return year or brood year. Our analyses also indicated that the age-specific lengths of 4-ocean fish (9 of 10 stocks) and of 3-ocean fish (5 of 10 stocks) have declined significantly (<i>P</i> < 0.05). Size-selective harvest may be driving earlier maturation and declines in size, but the evidence is not conclusive, and additional factors, such as ocean conditions or competitive interactions with other species of salmon, may also be responsible. Regardless of the cause, these wide-spread phenotypic shifts influence fecundity and population abundance, and ultimately may put populations and associated fisheries at risk of decline.</p></div

Beta regression of the proportions of 4-, 3-, and 2-ocean Chinook salmon by brood year returning to spawning areas from 1983 to 2012.

<p>Bold indicates slope (log(odds)year^-1) is significantly (<i>P</i> < 0.03) different from 0.</p><p>Beta regression of the proportions of 4-, 3-, and 2-ocean Chinook salmon by brood year returning to spawning areas from 1983 to 2012.</p

Linear regression of mean annual length (mm) Chinook salmon by stock, age class, and year.

<p>Closed circles and solid line = 4-ocean; triangles and dotted line = 3-ocean, open square and dashed line = 2-ocean. Red lines indicate slopes significantly different from zero (<i>P</i> <0.05).</p

Beta regression of proportion by brood year of Chinook salmon stock, stock, age class and year.

<p>Closed circles and solid line = 4-ocean; open triangle and dotted line = 3-ocean, open square and dashed line = 2-ocean. Red lines indicate slopes significantly different from zero (<i>P</i> <0.05).</p

Linear regression of Chinook salmon mean annual length (mm) by stock and year.

<p>Linear regression of Chinook salmon mean annual length (mm) by stock and year.</p

Logistic regression of the proportions of 4-, 3- and 2-ocean aged Chinook salmon returning to spawning areas from 1983 to 2012.

<p>Bold indicates slope (log(odds)year^-1) is significantly (<i>P</i> < 0.05) different from 0.</p><p>Logistic regression of the proportions of 4-, 3- and 2-ocean aged Chinook salmon returning to spawning areas from 1983 to 2012.</p

Linear regression of mean annual length (mm) of mature Chinook salmon in Alaska by return year from 1983 to 2012.

<p>Bold indicates slope (mm/year) is significantly (<i>P</i> < 0.05) different from 0.</p><p>Linear regression of mean annual length (mm) of mature Chinook salmon in Alaska by return year from 1983 to 2012.</p

Source-Sink Estimates of Genetic Introgression Show Influence of Hatchery Strays on Wild Chum Salmon Populations in Prince William Sound, Alaska

<div><p>The extent to which stray, hatchery-reared salmon affect wild populations is much debated. Although experiments show that artificial breeding and culture influence the genetics of hatchery salmon, little is known about the interaction between hatchery and wild salmon in a natural setting. Here, we estimated historical and contemporary genetic population structures of chum salmon (<i>Oncorhynchus keta</i>) in Prince William Sound (PWS), Alaska, with 135 single nucleotide polymorphism (SNP) markers. Historical population structure was inferred from the analysis of DNA from fish scales, which had been archived since the late 1960’s for several populations in PWS. Parallel analyses with microsatellites and a test based on Hardy-Weinberg proportions showed that about 50% of the fish-scale DNA was cross-contaminated with DNA from other fish. These samples were removed from the analysis. We used a novel application of the classical source-sink model to compare SNP allele frequencies in these archived fish-scales (1964–1982) with frequencies in contemporary samples (2008–2010) and found a temporal shift toward hatchery allele frequencies in some wild populations. Other populations showed markedly less introgression, despite moderate amounts of hatchery straying. The extent of introgression may reflect similarities in spawning time and life-history traits between hatchery and wild fish, or the degree that hybrids return to a natal spawning area. The source-sink model is a powerful means of detecting low levels of introgression over several generations.</p></div

Diagram of a model of genetic introgression based on the classic source-sink model of migration.

<p>Explanation of variables: <i>q_l</i> is the allele frequency at a locus in a source population and is assumed to be unchanging over <i>n</i> generations of introgression. <i>q_n,i,l</i> is the allele frequency at locus, <i>l,</i> in a wild sink population, <i>i</i> after <i>n</i> generations.</p