Most species consist of groups of individuals that are more or
less isolated from one another. Isolating mechanisms may be geographical,
temporal, ecological, or ethological, and the degree of isolation
varies between species and their component populations. Local
populations typically exhibit some degree of genetic differentiation,
and the pattern of the distribution of genetic differences between
populations is commonly referred to as genetic population structure.
Species differ both in their total amount of genetic variation
and in the distribution of this variation between and within populations.
In some species, such as several forest trees and marine fishes,
populations show little differentiation over large geographic
areas; that is, randomly selected individuals from different parts
of the geographical range are genetically similar. In other species,
for example many amphibians, even nearby populations may display
substantial genetic divergence.
For characters that are not strongly influenced by natural selection,
the extent of differentiation among subpopulations depends primarily
on their size, the time since they were separated, and the degree
of isolation between them. Large populations change more slowly
than small ones, and even in the case of complete isolation it
may take considerable periods of time before substantial genetic
differences have accumulated between them. Genetically effective
migration, gene flow, typically retards differentiation and prevents
extensive divergence.
The strategy for conserving genetic variation within species depends on the species' genetic population structure. The extinction of a particular population may have little effect on the overall genetic resources of a species that exhibits only minor or no divergence among the constituent populations. In contrast, a corresponding extirpation in a highly differentiated species implies the loss of a significant portion of the gene pool of that species.
A considerable number of studies on the genetic population structure
of salmonids and other fishes have been published. The population
genetic characteristics of salmonids are reasonably well known
and have been discussed in several contexts. The present summary
on population structure is to a large extent based on material
compiled from previous publications, particularly those by Hindar
et al. (1991), Utter et al. (1989), and Ryman et al. (1995a,b);
the reader is referred to those original papers for a fuller discussion
and for references to other articles.
Most of our current knowledge of genetic population structure
has been obtained within the past two decades with biochemical
genetic techniques, such as protein electrophoresis, and with
the direct analysis of DNA. Genetic variation detected with these
techniques is typically considered to be neutral, or nearly neutral,
to natural selection. The analysis of this kind of genetic variation
is generally preferred in evolutionary genetic studies, because
the dynamics of selectively neutral genes is reasonably well understood
theoretically, whereas variability patterns of genes affected
by selection may be more difficult to interpret in an evolutionary
context. Thus, selectively neutral genes may provide valuable
information on evolutionary relationships among populations, their
historical sizes, and the levels of gene flow between them. In
contrast, such genes are typically less informative about the
adaptive characteristics of a population. We would expect that
loci subjected to directional selection exhibit a greater degree
of genetic differentiation among populations than is observed
at loci detected by various biochemical genetic techniques.
Biologists have long recognized intuitively that salmonid species
are subdivided into more or less genetically distinct subunits.
The reason for this recognition was generally based on the existence
of striking ecological and morphological differences between fish
of different origins. A large fraction of the variation of ecological
and morphological characters is, however, caused by environmental
factors. The extent that observed phenotypic differences reflected
genetic divergence was difficult to determine until quite recently.
Such evaluations could not be performed before techniques became
available which revealed characters that were completely genetically
determined and which varied independently of environmental conditions.
Salmonids are represented by both freshwater resident and anadromous
species, ranging widely in the waters of the northern hemisphere.
A common feature of all salmonids is the existence of genetically
distinct local populations. Salmonid populations appear to maintain
their genetic integrity through a remarkably accurate homing behavior.
In some species, such as brown trout (Salmo trutta) and
cutthroat trout (Oncorhynchus clarki), genetically distinct
populations can be found over short geographical distances. As
an example from my own experience in Scandinavia, we may consider
the brown trout community of Lake Bunnersjîarna in central
Sweden. Lake Bunnersjîarna consists of two small basins
connected by a channel that permits fish to migrate freely between
the two segments of the lake. Physically, the lake is not unusual,
but we found that it harbors two genetically distinct populations
of brown trout that differ at several protein coding loci, and
that are markedly different in adult size. There is no apparent
gene flow between the two populations, which seem to be completely
reproductively isolated from each other, even though fish from
both populations coexist in both lakes and can be caught in the
same gill nets. Several additional, and genetically distinct,
populations of brown trout occur in the same area only a few kilometers
away, and finding similar levels of genetic differentiation over
short geographical distances is not unusual for Scandinavia.
The genetic data that have accumulated for the salmonid species
show that their population structure is more complex than was
previously acknowledged. Several studies have also demonstrated
that earlier concepts of genetic structure and evolutionary relationships
should be modified. As exemplified in Table 1 [below], such modifications
have been justified for several species and have included populations
distributed over a wide range of geographical distances. In particular,
several evolutionary relationships have been revealed that were
previously not recognized, and traditional classifications based
on morphology or behavioral characteristics, such as time of spawning
or residency vs. anadromy, have proven unreliable indicators of
common ancestry.
A convenient method to quantify the amount of genetic differentiation
within a species is the so-called gene diversity analysis. Here,
the total gene diversity (HT) estimates the total genetic variability
within and among the populations sampled. HT is the sum of the
components representing the gene diversity within subpopulations
(HS), and the gene diversity due to differences among subpopulations
(DST), such that HT = HS + DST.
The quantities HS and DST therefore
provide a representation of the amount of differentiation among
the populations sampled, and the coefficient GST, defined as
HT-HS | DST | ||
GST = | = | |
|
HT | HT |
can be used as a measure of the proportion of the total genetic
variation that is due to differences among populations. GST can
take values between zero and unity, GST = 0, indicating that all
populations have identical gene frequencies, and GST = 1, that
the populations are as different as they can be (all variation
is due to differences between populations). Of course, a fairly
large number of loci is necessary to provide an accurate picture
of the average variability pattern among the populations of a
species. It should also be noted that GST in many cases is equivalent
to the quantity FST that was originally defined by Sewall Wright
for describing variation between populations at a single locus.
Table 1. Examples of biochemical genetic studies identifying new
groups or modifying previous assumptions of the genetic population
structure of salmonid fishes (modified and expanded from Allendorf
et al. 1987, Table 1.4).
Issue | Observation | Reference |
New grouping | Major genetic groups of rainbow trout (Oncorhynchus | Allendorf & Utter 1979 |
mykiss) correspond to geographic region (coastal-inland) | ||
rather than to drainage or life-history pattern. | ||
New grouping | Reproductively isolated populations of brown trout | Ryman et al. 1979; |
(Salmo trutta) coexisting in the same lake. | Ferguson & Mason 1981 | |
New grouping | Sharp genetic discontinuity in Europe of Atlantic | Stahl 1987 |
salmon (S. salar) from rivers draining into the Baltic | ||
Sea and the Atlantic Ocean, respectively. | ||
New grouping | Genetic divergence among subspecies of cutthroat trout | Allendorf & Leary 1988 |
(O. clarki spp.) range from that typically observed | ||
among congeneric species to virtual genetic identity. | ||
Results suggest that the cutthroat trout taxonomy needs | ||
to be revised by recognizing westslope cutthroat trout | ||
as a distinct species. | ||
New grouping | Disclosure of nine major genetically defined regions of | Utter et al. 1989 |
chinook salmon (O. tshawytscha) in the Pacific Northwest. | ||
New grouping | Identification of six genetically distinct regional | Kondzela et al. 1994 |
groups of chum salmon (O. keta) in southeastern Alaska | ||
and northern British Columbia. | ||
New grouping | Recognition of three geographic clusters of genetically | Shaklee et al. 1991 |
similar populations of odd-year pink salmon (O. | ||
gorbuscha) from Washington (USA) and British Columbia. | ||
Residency vs. | Conspecificity of anadromous and landlocked forms of | Kornfield et al. 1981 |
anadromy | char (Salvelinus alpinus) of eastern North America. | |
Residency vs. | Lack of genetic divergence between anadromous and | Allendorf & Utter 1979; |
anadromy | resident populations of rainbow trout, Atlantic salmon, | Ryman 1983; Stahl 1987; |
and brown trout. | Hindar et al. 1991 | |
Time of | Major genetic groups of chinook salmon corresponding to | Utter et al. 1989 |
spawning | geographic region rather than time of spawning. | |
Morphology | Little genetic divergence among morphologically | Busack and Gall 1981; |
distinct forms of cutthroat trout. | Loudenslager & Kitchin 1979 | |
Morphology | Lack of apparent genetic divergence between arid | Wishard et al. 1984 |
adapted (redband) and adjacent anadromous | ||
(steelhead) populations of rainbow trout. |
The quantity GST has been estimated from various biochemical genetic
data sets for several species. For example, GST among the three
races of man is about 10%; that is, about 10% of the total genetic
variability is due to differences between races, and 90% of the
total variability is found, on average, within each race. Caution
is necessary when comparing the degree of population differentiation
across species, because the various studies used different numbers
of populations with various degrees of isolation and spatial separation.
Similarly, sample sizes of loci and individuals examined per
population sampled differed among investigations. Nevertheless,
conspicuous differences appear among species in the extent of
differentiation among populations.
Marine fishes, for example Atlantic herring and Atlantic cod,
typically show low levels of differentiation among local populations
(Fig. 1). In contrast, salmonid species are generally characterized
by pronounced genetic heterogeneity between populations. In some
salmonids, such as brown trout or Atlantic salmon (S. salar),
30% or more of the total genetic diversity may be due to differences
among populations. Although most salmonids show considerable
genetic differences among local populations, there are species
such as chum salmon (O. keta) for which the differentiation
appears to be less pronounced. It is not always clear why some
salmonids show less differentiation among populations than others;
some species may have a greater rigidity in homing behavior.
Figure 2 shows the percentage of between-population variability
(GST: shaded bars) and the total variability (HT: open bars)
for several species of salmonids. The first observation is that
different species have different overall levels of genetic variability
(HT), and these differences are often difficult to explain. Sometimes
genetic variability is associated with the extent of a species'
geographic range or with the number of populations examined.
This tendency is illustrated in three studies of Atlantic salmon
(Fig. 2), where HT (as well as GST) increased as the sampling
range increased from northern Sweden to northern Europe to Europe
and North America. In other cases, however, as when comparing
the four subspecies of cutthroat trout (O. clarki spp.),
no obvious relation appears between the amount of genetic variation
and the geographic range covered. Second, there is no direct
relationship between the amount of genetic variation (HT) and
the extent of heterogeneity among populations (GST). As for HT,
GST generally increases when more distantly located populations
are included in the estimate (e.g., Atlantic salmon), except for
cutthroat trout.
Species of Pacific salmon tend to be similar to other salmonids
in their overall levels of genetic variability, but they tend
to show less variability between populations than other salmonids.
For example, chinook salmon (O. tshawytscha) populations
of the Pacific Northwest show several population genetic groupings,
even though only about 10% of the total variability is due to
population differences. Utter et al. (1989) found evidence for
nine genetically distinct groups of populations in chinook salmon
(Fig. 3). The populations within each of these groups apparently
share a common ancestral background that produces the genetic
similarity between them. A closer look at the populations of
chinook salmon in the south fork of the Salmon River, however,
shows that populations with similar run timings (e.g., spring,
summer) do not always share a common ancestral background
(Fig. 4; Waples et al. 1993). These life history characteristics
have apparently arisen independently several times (Table 1 [above]).
In summary, several factors affect the amount and distribution
of genetic variation among populations. In general, the larger
the geographic distance between populations, the more genetic
differentiation they tend to show, most likely because geographic
distance enhances reproductive isolation when migration is limited.
The presence of physical or geographic barriers to migration
may also lead to genetic differentiation between populations because
of absent or reduced levels of gene flow. Life history patterns
can also influence the degree of genetic differentiation among
populations. Anadromous populations of a species tend to have
more genetic variation than landlocked or freshwater resident
populations, but anadromous populations tend to show less genetic
differentiation than do resident populations. However, even if
these general tendencies exist, there are many exceptions.
These exceptions generally make it impossible to predict with
reasonable precision the reproductive relationships and distribution
of genetic variation among a set of salmonid populations. Therefore,
direct assessment of the amount and distribution of genetic variation
is necessary in any situation where information on the genetic
population structure is needed for a management decision.
Allele-frequency differences that are taken to reflect population
differentiation are difficult to interpret unless the variability
patterns are stable over time. To date, little has been done
to describe how quickly population structure can change. The
data that do exist for salmonids, however, indicate that observed
variability patterns are temporally stable. For example, consider
the results of Waples et al. (1993) on chinook salmon from the
Snake River that includes samples from 2 consecutive years (1989
and 1990) for all localities studied (Fig. 4). Clearly, samples
from different years from the same locality tend to cluster together,
as they should if the dendrogram depicts the relationship among
samples from a set of local populations that are genetically stable
over time. It should be noted, though, that the time span considered
is fairly short (1 year), considerably less than a generation
for chinook and other species of Pacific salmon.
The largest set of data on temporal variability in a salmonid
is apparently for brown trout in central Sweden (Jorde and Ryman
1996). The populations studied were selected to represent a set
of natural populations with different degrees of reproductive
isolation and are as unaffected as possible by human activities
(stocking, pollution, excessive harvest, and so on). Allele-frequency
shifts at 14 polymorphic protein loci have been monitored for
15 years, with sample sizes of about 100 fish annually from each
of four lakes (Fig. 5).
For populations in these four lakes, about 95% of the total variation
was contained, on average, within lakes, and 5% was due to variation
between lakes and between years (GST = 0.05; Fig. 5).
These relative proportions of within and between locality variability
are about the same as those frequently observed for populations
of Pacific salmon, such as the Snake River chinook populations
depicted in Figure 4. The "explained" 5% component
of variability among the four brown trout populations can be broken
down into two sources of variation: between lakes and between
years within lakes. The component corresponding to temporal variability
is small and represents only about 0.5% of the total variation
observed over the 15 years. This result strongly indicates that
the genetic structures of the populations are quite stable over
time. In turn, the biological characteristics common to salmonids
in general suggest that biochemical genetic data collected at
a single time for natural salmonid populations reflect geographical
structures that are temporally stable.
The existence of genetically distinct local populations, which
are typical for salmonids, indicates that the amount of genetically
effective migration (gene flow) between populations is fairly
restricted. In the context of straying, it is of considerable
interest to have at least a rough idea of the amount of gene flow
that is compatible with the level of divergence observed among
local salmonid populations (see Ryman et al. 1995a).
A major difficulty in estimating natural levels of gene flow is
that we are interested in genetically effective migration, which
cannot be estimated by observing the movement of marked individuals.
Direct observations of movement from one locality to another
may lead to inaccurate estimates of gene flow, because migrants
may be reproductively less successful than residents.
A convenient attribute of the quantity GST is that it can
be related to the amount of gene flow. By assuming 1) that migration
roughly follows the so-called island model of migration (see Felsenstein
this report), 2) that mutation is not an important force changing
allelic frequencies, and 3) that the populations are at pseudo-equilibrium
relative to random drift and migration, we can estimate the number
of migrants from the approximation
Here, Ne is the genetically effective population size,
m the migration rate, and the product Nem is the
effective number of migrants per generation. Although some care
must be taken in converting GST values into estimates of
migration, this approach in most cases is expected to provide
a reasonably accurate estimate of gene flow, especially if a large
number of protein coding loci are used and averaged (Ward et al.
1994).
The relationship between GST and the number of migrants
per generation (Nem) is plotted in Figure 6. Clearly,
only a few migrants are needed to prevent major differentiation
among populations. For example, GST values of the order
of 0.05 and 0.10, which are commonly observed among conspecific
salmonid populations, correspond to an average number of migrants
per generation of no more than about 5 and 2, respectively. Thus,
values of GST observed for salmonids suggest that the amount
of genetically effective migration or "straying" between
natural populations is quite small, especially relative to the
levels of straying that occur from hatchery or supplementation
populations such as those of the Columbia River Basin (Waples
1991).
Results from our temporal study of brown trout populations indicate
that the amount of differentiation observed may to some extent
underestimate the direct or indirect gene flow among populations
that are geographically distant. This impression stems from,
among other things, the observation that the amount of genetic
variation found within local populations is larger than would
be expected from estimates of their effective sizes (Ne).
This finding may be explained if larger groups of populations
are connected through some gene flow that is both irregular and
restricted. Such an explanation implies that low levels of gene
flow between larger groups of local conspecific populations constitute
part of an evolutionary strategy; gene flow between neighboring
populations is small enough to permit local differentiation, but
the flow within the metapopulation is large enough to maintain
adequate levels of genetic variability.
The implication for management is that efforts to protect individual
natural populations represent only a first necessary step in the
conservation of genetic resources of salmonids. In the more ambitious
program with the goal of creating opportunities for maintaining
reasonable levels of genetic diversity within local populations,
conservation efforts should be aimed at preserving systems of
populations with the potential for a restricted gene flow between
them.
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Question: Robin Waples: The FST value for the Snake River
chinook populations in the dendrogram of Figure 3 is about 0.035,
which is fairly typical of Pacific salmon depending on species
and geographic area sampled. If you sampled a larger geographic
area that included divergent populations, the value of FST
would be larger. The point is that, at the values of FST
we find in natural populations, we are at the end of the scale
in Figure 6 where a small difference in FST makes a large
difference in the estimate of Nem, the number of migrants.
So considering the errors associated with estimating FST,
the accuracy in estimating Nem is very low. A point estimate
of 10 or 20 migrants could well be 100 in nature. We generally
lack the ability to draw inferences about the levels of gene flow
that concern managers. Do you have any comments about this limitation
on inferences from genetic data?
Answer: Nils Ryman: As you point out, we are limited in our
ability to use genetic data, but this approach is the best we
have for the moment.
Question: Robin Waples: Another way of expressing this concern
is that if you sample a restricted geographic area, you may see
fairly modest genetic differences, which are often statistically
significant but which are not large. Nevertheless, when you plot
that degree of differentiation against geographic distance, you
see a strong correlation between genetic differences between populations
and the geographic distances between them. Populations located
close together tend to be more similar to one another than populations
located farther apart. The result is that you have a combination
of genetic differences between salmon populations that are typically
not large when you compare them to a larger geographic scale.
Answer: Nils Ryman: But then we are back to the point of determining
the number of populations that should be targeted for conservation
in a critical geographic area. Our data indicate that gene flow
occurs between natural populations, and that it is important to
conserve the entire grouping rather than a particular population.
In this way you escape the problem of estimating gene flow.
Comment: Joe Felsenstein: One reason you may be interested in estimating Nem that are rather large is if you were worried about fitness effects of migration on selected loci, because it may make a great deal of difference whether Nem is 40 or 80. Such values of Nem would not make much of a difference to the amount of geographic differentiation for neutral alleles, but may still be important in how much impact migration from other areas would have on local selected differentiation.