SESSION I : Stock Structure and Population Dynamics
Session Chair: J. D. McIntyre, U.S. Fish and Wildlife Service, Seattle, Washington
STOCK STRUCTURE AND GENE CONSERVATION IN
COLUMBIA RIVER SPRING CHINOOK SALMON
Robin S. Waples
National Marine Fisheries Service
Northwest Fisheries Science Center
2725 Montlake Boulevard East
Seattle, Washington 98112
Three major races of chinook salmon, Oncorhynchus tshawytscha (spring, summer, and fall run fish), are recognized in the Columbia River Basin. These races are fairly distinct with respect to life history features, such as time of entry into fresh water and age and timing of outmigration, but somewhat less so if other characters are used. For example, Utter et al. (1989) found that fish from the same stream with different run times were typically more similar genetically than were fish of the same run time from different streams. They suggested that each race was probably polyphyletic, with life history divergence occurring within some streams after colonization by fish of a single run time.
Regardless of whether this hypothesis is true, considerable geographic structure can be identified among spring chinook salmon populations. Figure 1 shows a dendrogram. of genetic relationships based on electrophoretic data for 15 polymorphic gene loci. Although it is easy to overinterpret such dendrograms, several points emerge from an examination of the figure. First, the major groupings fall out along geographic lines and generally are compatible with those identified by Schreck et al. (1986) using meristic, morphological, and genetic data. There is a major distinction between spring chinook salmon from west of the Cascades (group A, Willamette River and group B, lower Columbia River) and those farther east. Second, mid- to upper Columbia River stocks (group C) cluster very tightly together, as do those from the Snake River system (group D). It is interesting to note that Kooskia Hatchery, which is in Idaho but has been stocked with Carson fish, clusters with the Columbia River stocks rather than the other Snake River stocks. Finally, stocks in group C do not differ substantially from those in group D; this probably reflects the origin of the Carson-Leavenworth hatchery stock, which was started with upriver fish intercepted at Bonneville Dam.
Further insight regarding stock structure in spring chinook salmon is gained by considering average heterozygosity, a measure of overall genetic variability within individuals or populations. Winans (1989) reported much lower levels of heterozygosity in upriver spring chinook salmon than is found in lower river spring chinook or fall chinook from throughout the basin (Table 1). The cause of this pattern is not clear, but one possibility is that upriver spring chinook salmon stocks have experienced severe (or repeated) bottlenecks in population size during which homozygosity increased through inbreeding (matings between individuals of similar genotype). Low heterozygosity does not necessarily imply reduced fitness, as little detectable genetic variation has been found in a number of apparently healthy species. However, data for a variety of organisms provide evidence for a relationship between population bottlenecks, reductions in genetic variability, and increased susceptibility to disease (O'Brien and Evermannn 1988). A plausible explanation for this relationship is suggested by the observation that immune and disease response systems in vertebrates are typically controlled by extremely polymorphic genetic systems; that is, a large number of alleles occur in a population, with few individuals sharing any one genotype. Such a population is much less susceptible to decimation by a newly evolved viral or bacterial pathogen than is a largely monomorphic population. In this regard, it is interesting to note that problems caused by bacterial kidney disease ars, most severe in upriver spring chinook salmon, which have the lowest levels of genetic variability of any spring or fall stocks (Table 1).
This simple relationship by no means represents conclusive evidence for a causal relationship, but it does serve to illustrate the point that the erosion of genetic variability can have serious, and often unforeseen, consequences. In some respects, the extinction of rare alleles may be of more importance than the loss of heterozygosity per se (see Allendorf 1986). As shown in Figure 2, which depicts results from a series of computer simulations modelling the rate of loss of genetic variability from chinook salmon populations (Waples, in press), a substantial percentage of low frequency alleles can be permanently lost before there is an appreciable decline in heterozygosity. In chinook salmon, as in most species, rare alleles are more common than any other class of alleles (Fig. 3), so large numbers of rare alleles are subject to rapid extinction in small populations. It is not clear what immediate adaptive value this large class of rare alleles has, but it is certain that they represent the bulk of the raw material upon which natural selection might act. Permanent loss of a substantial portion of such alleles may compromise the ability of a population to respond to challenges presented by changing environmental conditions.
The rate of loss of genetic variability is a function of effective population size (Ne). If sex ratio of spawners is unequal or if the variance among families in reproductive success is large, Ne is less than the census number, and perhaps a great deal less. Given that spawning escapement to many upriver stocks is already seriously low (e.g., Williams 1989), reduced Ne is a very real concern for spring chinook salmon. Evaluating effective population size, however, is particularly difficult with Pacific salmon. Even if spawning is carefully monitored, smolt mortality after outmigration may be 99% or more, making it difficult to estimate the variance among families in number of progeny returning to spawn. Furthermore, because of the complex life history features of chinook salmon, it is not apparent what relationship the effective number of breeders each year (Nb) has to the more familiar concept of effective population size per generation (Ne).
To address these questions, a computer simulation model was developed that incorporates the pattern of overlapping year classes and one-time reproduction typical of chinook salmon. Results (Waples, in press) indicate that generation length in chinook salmon can be defined as the average age at reproduction and that effective population size per generation is equivalent to the generation length times the effective number of breeders per year. Thus, for a chinook salmon population with average age at spawning of 4 years, Ne » 4Nb. This relationship allows us to apply the large body of theory developed for population and conservation genetics directly to problems of concern to spring chinook salmon. A method has also been developed to estimate Nb from year-to-year changes in allele frequency, and this approach can be used to identify hatcheries (or wild populations) in which Nb is lower than expected; remedial actions can be taken before serious problems associated with inbreeding develop.
Allendorf, F. W. 1986. Genetic drift and the loss of alleles versus heterozygosity, Zoo Biol. 5:181-190.
Nei, M. 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583-590.
O'Brien, S. J., and J. F. Evermann. 1988. Interactive influence of infectious disease and genetic diversity in natural populations. Trends Ecol. Evol. 3:254-259.
Schreck, C. B., H. W. Li, R. C. Hjort, and C. S. Sharpe. 1986. Stock identification of Columbia River chinook salmon and steelhead trout. Final Report of Research to Bonneville Power Administration, Portland, Oregon. (Available from Oregon State University, Corvallis, OR 97331.)
Utter, F., G. Milner, G. Stahl, and D. Teel. 1989. Genetic population structure of chinook salmon in the Pacific northwest. Fish. Bull., U.S. 87:239-263.
Waples, R. S. In press. Conservation genetics of Pacific salmon. II. Effective population size and the rate of loss of genetic variability. J. Heredity 81(4):267-276.
Williams, J. G. 1989. Snake River spring and summer chinook salmon: can they be saved? Regul. Rivers 4:17-26.
Winans, G. 1989. Genetic variability in chinook salmon stocks from the Columbia River Basin. N. Am. J. Fish. Manage. 9:47-52.
Figure 1. Dendrogram of genetic relationships among 25 populations of spring chinook salmon. Dendrogram was constructed using electrophoretic data for 15 gene loci gathered by National Marine Fisheries Service, Seattle, and Washington Department of Fisheries, Olympia; clustering strategy was unweighted pain-group method analysis using Nei's (1978) genetic distance. A number of loci known or suspected to be monomorphic were not included in the analysis, so values shown are relative rather than unbiased estimates of true genetic distance. Major geographic groupings: A: Willamette River; B: Lower Columbia River (plus Klickitat); C: Mid- to upper Columbia River (Washington); D: Snake River (plus Yakima); E: Mid-Columbia (Oregon). Samples from wild/naturally spawning populations are indicated by an asterisk (*); others are hatchery samples.
Figure 2. Proportion of initial genetic variability remaining in a simulated chinook salmon population after periods of time up to 100 years. Large numbers of alleles with low initial frequency (P0) can be lost before an appreciable cange in heterozygosity is detected. Nb is the effective number of breeders per year.
Figure 3. Distribution of alleles at various frequencies found in samples from 177 chinook salmon populations in the Pacific Northwest.
Table 1. Average heterozygosity valuesa for spring and fall chinook salmon from the Columbia River Basin. Regional values are means based on the number of stocks indicated in parentheses.
|Region||Spring chinook||Fall chinook|
|Lower Columbia Basin||0.082 (3)||0.081 (5)|
|Willamette River||0.080 (4)||0.084 (l)|
|Mid-Columbia Basin||0.060(5)||0.075 (3)|
|Upper Columbia Basin||0.061 (1)||0.088 (2)|
|Snake River Basin||0.045 (4)||0.077 (1)|
|Snake River Basinb||0.040 (3)b|
aBased on data for 33 gene loci reported by Winans (1989). A number of loci known or suspected to be monomorphic were not included in the analysis, so values shown are relative rather than unbiased estimates of total heterozygosity.
bExcluding Kooskia Hatchery.
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