ICES Journal of Marine Science, 54: 1149-l 159. 1997
Minimizing adverse effects of fish culture: understanding the
genetics of populations with overlapping generations
N. Ryman
Ryman, N. 1997. Minimizing adverse effects of fish culture: understanding the genetics
of populations with overlapping generations. - ICES Journal of Marine Science, 54:
1149-1159.
Although an increasing number of natural fish populations are being contaminated by
exogenous immigrants, knowledge is poor regarding the genetic changes expected to
occur in a wild stock once an introgression has taken place. One reason for this lack
of knowledge appears to be that the theory for the genetic dynamics is poorly
developed and complicated for age-structured populations with overlapping generations. Using newly developed theory and results from computer simulations, the
genetic aspects of age-structured populations with overlapping generations are discussed, especially the detection of contamination and the genetic dynamics following
hybridization. When generations overlap, the amount of temporal allele frequency
shift is generally larger than for a population of equal genetically effective size with
discrete generations. This is even more pronounced for the separate cohorts than for
the population as a whole. Therefore, when testing for temporal genetic heterogeneity,
a higher frequency of statistically significant results may be expected than can be
explained by genetic drift caused by a restricted effective population size. During
introgression, a sudden infusion of new genes initiates marked allele frequency
fluctuations, and in salmonids this “genetic instability” may persist for several decades.
In spite of these potentially dramatic fluctuations, even a massive influx of exogenous
genes may be very difficult to detect, particularly in the absence of genetic and
demographic monitoring data from the natural population prior to immigration. A
conservative attitude is recommended when interpreting allele frequency differences
within populations where the history and the demographic characteristics are poorly
known. The risk of incorrect interpretations is particularly apparent for many
salmonid species where only a subset of the existing age classes may be available for
sampling.
0
1997
International Council for
the
Exploration of
the Sea
Key words: Allele frequency, genetics, immigration,
generations, salmonid, Salmo salar.
introgression,
overlapping
N. Ryman: Division of Population Genetics, Stockholm University, S-106 91 Stockholm,
Sweden.
Introduction
There are valid concerns
that animals
and plants
released into natural environments
may constitute
a
threat to the genetic integrity of conspecific wild populations. The risk of genetic deterioration
and reduced
fitness is particularly
obvious
for many salmonids,
because they are released into the wild at a magnitude
that has no parallel among other vertebrate
species.
Because of the high degree of genetic subdivision that is
typically observed among salmonid populations
it is
more than the local stocks that are in danger; there is a
risk of serious depletion
of genetic diversity in the
species as a whole.
For most practical purposes we already know how
to minimize the genetic effects on natural populations
1054-3139/97/061149+ 11 $25,00/0/jm970291
caused by interactions
with fish that are released deliberately or inadvertently.
A series of internally consistent guidelines
and recommendations
for genetically
sound management
have been published, the first ones
almost
two decades
ago (e.g. FAOKJNEP,
1981;
Ryman, 1981, 1991; Meffe, 1986; Hindar et al., 1991;
NASCO,
1991; Waples,
1991; FAO, 1993; Ryman
et al., 1995). All these guidelines suggest a very restrictive attitude towards releases that may result in introgression of exogenous fish, the use of local stocks when
supplementation
is considered
unavoidable,
and the
application
of a series of measures aimed at avoiding
genetic
change
when
founding
and
maintaining
hatchery
populations
for production
of fish for
release. Likewise, it has been repeatedly
pointed out
that stocks used in aquaculture
should be marked
0 1997 International Council for the Exploration of the Sea
1150
N. Ryman
genetically in order to simplify the detection of
introgression
into natural populations
and permit
evaluation
of long-term effects on the receiving
population.
It is highly unfortunate that the implementation of
these recommendations has been so slow that natural
gene pools are being lost at a continuously accelerating
rate due to the rapid growth of the aquaculture industry. This is not a scientific problem, however,
but something that apparently must be addressed
politically in response to a growing common awareness of the increasing need for conservation of biological diversity (cf. Hindar et al., 1991; Ryman et al.,
1995).
In contrast to the above, we have a relatively poor
idea of the dynamics of the genetic changes that occur in
a wild population once it has been contaminated with
exogenous genes. One reason for this lack of knowledge
appears to be that the theory typically applied refers to
populations with discrete (non-overlapping) generations
and without an age structure, whereas salmonid
populations
are typically age-structured
and have
overlapping generations.
Although the genetic processes in populations with
overlapping and non-overlapping generations are identical in many respects there are also a number of
differences that are important to consider in a management situation. Among other things, the allele (gene)
frequency dynamics and the temporal aspects of genetic
change are different. Without proper consideration of
those dissimilarities it may be difficult both to predict
how a population will be affected by introgression and
to evaluate observations from a population aimed at
assessing its genetic status.
This paper discusses, on the basis of results from
computer simulations, some of the genetic aspects of
age-structured populations with overlapping generations, focusing on the possibility of detecting whether
contamination has occurred and on the genetic dynamics following a hybridization event. The examples refer
primarily to the Atlantic salmon (Salmo salar), but the
general conclusions are applicable to any species
with similar life history characteristics. The reader is
referred to Jorde and Ryman (1995, 1996) for a more
detailed theoretical treatment of the allele frequency
dynamics of age-structured populations
with overlapping generations, and basic considerations on the
genetically effective size of such populations are found
in Felsenstein (1971) and Hill (1979). Waples (1989)
also discusses problems associated with the interpretation of temporal allele frequency shifts in populations
with non-overlapping generations, and Waples (1990)
and Waples and Tee1 (1990) have examined the
mechanisms behind such shifts with reference to the
unusual life history characteristics of Pacific salmon
(Oncorhynchus spp.).
Basic concepts
When considering changes in populations over time, text
books on population genetics refer almost exclusively to
organisms with non-overlapping generations. In order to
facilitate a comparison between populations with overlapping and non-overlapping generations we will assume
initially that other conditions are “ideal”, i.e. a diploid
organism with an even sex ratio, constant population
size, random mating among all adults available for
breeding, and absence of migration, mutation, and
selective differences
among
genotypes
(selective
neutrality). Under these conditions a population with
non-overlapping
generations displays the following
characteristics:
(1) all individuals
- at any time of inspection or
sampling - can be regarded as being of the same
age;
(2) at reproduction all individuals have the same
probability of contributing gametes to the next
generation;
(3) genetic drift is the only reason for allele frequency
change, and the amount of drift is directly proportional to 1/(2n,), where n, is the effective population size (Fig. 1). By definition, ne is equal to the
actual population size (nT) under perfectly “ideal”
conditions, but deviations such as a variable
population size or a sex ratio that differs from 1:l
may make the two quantities different, typically
resulting in ne being smaller than nT.
In contrast, in an age-structured
generations overlap:
population
where
(1) the total population is made up of individuals that
belong to different age classes (Fig. 2);
(2) only some age classes participate in breeding,
and the contribution to the young (progeny) of
the next year may differ among those age classes.
As a consequence, the parents of a particular
year class (cohort) do not represent a random
sample from the entire population of the previous
year;
(3) as a consequence of 1+2 above, an allele frequency
difference between two consecutive cohorts (the
progeny of year t and t+ 1) does not necessarily
reflect a corresponding frequency change of the
total population (cf. Jorde and Ryman, 1995).
In an age-structured population, collection of individuals for genetic analysis frequently focuses on a
restricted number of age classes, and not all of them may
even be available for sampling. For an anadromous
species such as the Atlantic salmon, for example,
commercial catches at sea will not include the first few
Minimizing adverse efects of fish culture
I
1151
I
20
30
Generation
Figure 1. Simulated allele frequency change due to genetic drift in three populations with non-overlapping generations of different
effective size (n,=50, 200, and infinity). The initial allele frequency is 0.5. ~
n,=50; ~ ~ ~ n,=200;
ne= co.
Year
L
i
Y
Total population
Figure 2. Schematic representation of an age-structured population with overlapping generations. Circles represent age groups of
ni individuals (i-14), continuous arrows indicate survival, and dotted arrows reproduction from each particular age group.
Columns of age groups constitute the total population (nr) in different years (t), rows the age classes, and diagonals (from left to
right) represent cohorts, i.e. individuals spawned in the same year. Modified from Jorde and Ryman (1995).
age classes, and electrofishing on the nursery grounds
will primarily result in a catch of young fish.
Methods
The patterns for annual change of allele frequency in
age-structured populations with overlapping generations
were examined by means of pseudo-random
number
computer simulations. Genetic sampling, survival, and
reproduction were treated as stochastic processes on the
basis of the population-specific probabilities characterizing a life history table. In particular, the magnitude
of such allele frequency shifts were compared among
populations of identical effective size (n,) that differed
with respect to various demographic and life history
characteristics.
Life history tables for demographically stable populations with overlapping generations exhibiting identical
N. Ryman
1152
Table 1. Life history characteristics of two hypothetical Atlantic salmon populations (A and B) with the same survival rates (l,),
age structure, and effective size (n,). The birth rates (b,) represent the average number of progeny spawned per individual in age
class x averaged over all individuals of that age class, and the proportion of breeders is the fraction actually participating in the
spawning. G and nr are generation interval (years) and total population size, respectively. Each population maintains a constant
total (and effective) population size. l,b, represents the proportion of progeny contributed from age class x. See text for details.
Population A
Age
class x
1
2
3
4
5
6
Nr
N,
G
Age
(years)
1,
Age
structure
b,
0
1
2
3
4
5
1.oooo
0.7893
0.6235
0.1086
0.0245
0.0027
0.392
0.310
0.245
0.043
0.010
0.001
0
0
0
1.4121
25.6097
81.3065
Prop.
breeders
0.008
0.070
0.148
25 025
124
5.1
Population B
l,b,
b,
0
0
0
0.15
0.63
0.22
0
0
0.4009
2.3161
10.2775
91.3059
Prop.
breeders
1
1
1
1
2380
124
4.5
l,b,
0
0
0.25
0.25
0.25
0.25
Population A: the values for survival and spawning rates were obtained from Ackefors et al. (1991); the b,-values were chosen so
as to result in relative fecundities of 1:2:3 among the spawners of age classes 4, 5, and 6 and scaled to provide a constant population
size (Zl,b,= 1).
Population B: as compared with population A, sexual maturity occurs one year earlier, all adults participate in the spawning, and
the birth rates are those obtained when population size is constant and all the adult age classes (3-6) contribute similar numbers
of progeny
age structures and effective sizes were constructed for
simulation of temporal allele frequency changes using
the relationships derived by Felsenstein (1971), Hill
(1979) and Jorde and Ryman (1995). Time is measured
in years, and the population is enumerated in the spring
such that age indicates the number of completed winter
seasons. Thus, the first age class is denoted 1 (one)
corresponding to the age of 0 (zero) years (cf. Murray
and Girding, 1984).
The primary interest is focused on a “typical” Atlantic
salmon population that is denoted “A” in Table 1. This
population has six age classes (x; 1 I x 16) with agespecific survival rates (1,) and an age structure representing an average for rivers draining into the Baltic Sea
(Ackefors et al., 1991). The proportion of adults of
different age participating in breeding (returning to the
spawning grounds) was also adopted from Ackefors
et al. (1991) assuming smolting at two years of age. The
age-specific reproduction rate (b,) is the mean number
of progeny per individual in age class x, averaged over
all individuals in that age class (including those that do
not return for spawning). The b, values were somewhat
arbitrarily chosen so as to result in a relative reproductive success of 1:2:3 among the spawners of ages 3, 4,
and 5 and subsequently scaled to provide a constant
population size (I&b,= 1) when a stable age structure
had been attained. (All spawners are assumed to have
spent at least one year at sea, and the potential effects on
the patterns for allele frequency change arising from
mature parr participating in breeding have not been
considered.)
For comparison, Table 1 also depicts a second hypothetical population (B) which differs from population A
such that smolting occurs one year earlier and all adults
are assumed to participate in the breeding. The b, values
provide a constant population size when age structure is
stable, but here approximately equal proportions of the
offspring (l,b,) are produced from each of the adult age
classes.
As shown by Felsenstein (1971), the effective size (n,)
of a population with overlapping generations that has
reached a stable age distribution is determined exclusively by the values of 1, and b, and the absolute
population size (nT). Thus, in a final step the absolute
sizes (nT) of populations A and B were scaled so as to
provide similar effective sizes. The absolute size of
population A (n,=25 025) was chosen to mimic a
population yielding an annual catch of about 1000
adults (cf. Ackefors et al., 1991), which results in ne= 124
(Table 1). For population B an effective size of 124
corresponds to a much smaller total size (n,=2380); this
difference is due to the earlier age for smolting that
results in both a higher rate of reproduction and a
somewhat shorter generation interval (G=4.5) in this
population.
Computer simulations of the allele frequency change
at a locus with two alleles were conducted through
sampling of gametes from the various age classes as
described by Jorde and Ryman (1995). At the start of
each simulation (t=O), allele frequencies were assigned
to each age class, and survival, reproduction, and allelic
state of each gamete were subsequently determined
Minimizing adverse efsects of jish culture
through generation of random numbers. All simulations
started with the population at a stable age distribution,
and for the following years the necessary number of
gametes was sampled to each age class to maintain the
population at a constant size with a stable age distribution. Thus, the model is stochastic with respect to genetic
change but not with respect to demographic processes.
Starting with an allele frequency of 0.5 in all age classes,
100 000 replicates of each of populations A and B were
simulated for 150 years to check that the observed
amount of drift corresponded to that expected for
ne= 124 (cf. Jorde and Ryman, 1995).
Temporal allele frequency shifts
The results from typical simulations of the change of a
selectively neutral allele over 200 years in populations A
and B with an initial allele frequency of 0.5 in all age
classes are depicted at the top of Figure 3. For comparison, the outcome from a corresponding simulation of an
“ideal” population with non-overlapping
generations
and the same effective size as that of populations A and
B (n,=124) is also shown, and for this “ideal” population the generation interval was scaled to coincide
with that of population A (5.1 years). The interval
of 200 yrs was chosen in order to provide a
reasonably representative picture of the dynamics that is
not overly affected by random events during a short
period of time.
The most important observation from Figure 3 is that
both populations with overlapping generations (A and
B) display considerably larger allele frequency shifts
than the population with discrete generations of the
same effective size (ne= 124). Moreover, there is a tendency towards a pattern in populations A and B, i.e. the
allele frequencies tend to fluctuate in a regular way that
is most obvious in population A.
Clearly, effective size is not the only determinant of
the amount of temporal allele frequency change when
generations overlap. In organisms with overlapping
generations the total population does not represent a
single genetically homogeneous unit. Rather, it consists
of several age classes (cohorts) produced, partly or
completely, from different sets of parents (Fig. 2), and
the different age classes may therefore exhibit different
allele frequencies. The amount of allele frequency shift
of the total populations
thus depends on the age
structure, i.e. on the relative occurrence of individuals
belonging to different age classes. Likewise, a regularity
is introduced because each cohort of progeny tends to be
most like the cohort dominating the breeders of the
previous year. The strength of this periodicity is therefore a function of the number of cohorts present among
those breeders and their relative contribution to reproduction. Thus, in addition to effective population size
1153
the amount of temporal allele frequency shift depends
on the age-specific birth (b,) and survival (1,) rates of
each particular population (Jorde and Ryman, 1995).
Population B, for example, exhibits less variation and
periodicity than population A, because the b, and 1,
rates result in the different age classes contributing
more evenly to each new year class of offspring (Table 1,
Fig. 3).
The difference in allele frequency dynamics of populations with discrete and overlapping generations is even
more pronounced for the separate cohorts than for the
population as a whole. Using population A as an
example, the bottom part of Figure 3 depicts the allele
frequencies of the total population and of the first age
class for the same simulation as in the top part of Figure
3. Clearly, both the amplitude and the regularity of the
shifts are much more conspicuous for age class 1 than
for the total population.
Focusing on population A, an Atlantic salmon population with similar demographic characteristics will consist of six distinguishable age classes with different allele
frequencies, as illustrated in Figure 4 for the years
100-120 of the same simulation as discussed previously
(Fig. 3). Fish of different age collected in the same year
are expected to show some degree of allele frequency
differentiation, as will fish of a particular age sampled in
separate years. The magnitude of those differences will
be larger than expected for an “ideal” population of the
same effective size, and the probability of actually
observing an allele frequency difference that is statistically significant will depend on the cohorts examined,
the number of years included in the study, and the
sample sizes. As illustrated for the two youngest age
classes, there are considerable differences in some years
whereas the allele frequencies are virtually identical in
others (Fig. 4, bottom).
Obviously, for a population of the “A” type it is not
appropriate to expect allele frequency homogeneity
among age classes at a selectively neutral locus. Conversely, the observation of statistically significant differences should not necessarily be considered an indication
of introgression from another population, the operation
of selective forces at the locus under study, non-random
sampling of, e.g. family groups, or an alarmingly small
effective population size.
As an illustration of the fairly high probability of
obtaining a statistically significant allele frequency
difference, a loo-year cycle for population A was
repeated 1000 times (runs) with a starting allele frequency of 0.5. At the end of each run a random sample
of 100 fish was drawn from each of the two youngest
age classes (1 and 2) to mimic the collection of small fish
for genotyping and allele frequency estimation, and
the samples were compared for allele frequency
homogeneity by means of conventional contingency
chi-square tests.
1154
N. Ryman
A- total
----B-total
MXWYXW
Non-overlapping
0.8
50
100
Year
-
150
2’00
A- total
---------- A- age class 1
II::MXWS<.~:~
Non_overlappi,,g
0.8
0
50
100
150
2:0
Figure 3. Simulated temporal allele frequency shifts of a selectively neutral allele in populations of identical effective size (ne= 124)
with overlapping (A and B) and non-overlapping generations. The demographic characteristics of populations A and B are
described in Table 1; for the “non-overlapping” population the generation length was set to be identical to that of “A”. The initial
allele frequency is 0.5. Each population was simulated for a single suite of 200 years, and the graphs for “A-total” and
“non-overlapping” are the same in the top and bottom plates. See text for details. Top: Total populations (A and B pooled over
all age classes). Bottom: Population A (total and age class 1) and non-overlapping.
In almost half of the runs (466 of 1000) there was a
significant (~~0.05) allele frequency difference between
the two age classes in year 100. Likewise, a comparison
among fish of age class 1 sampled in the last three years
(98-100) yielded a significant temporal allele frequency
heterogeneity
in about two-thirds
of the runs (669 of
1000). A similar simulation of an “ideal” population
with non-overlapping
generations
and an effective size
identical to that of population A (ne= 124) yielded only
101 (10.1%) significances between consecutive
generations. Here, the relatively small excess of significant
results - over the 5% expected by pure chance - reflects
the low statistical power for detection of the comparatively small frequency differences between consecutive
generations
at the present effective size. Clearly, when
testing for temporal allele frequency heterogeneities
in a
-
Minimizing adverse efsects of fish culture
Age class 1
~~~_*~ Age class 2
- Age class 3
-.-n
- **----
Age class 4
Age class 5
Ageclass
B
1155
Total
I’
I’\
: !
0.4
100
105
110
Year
.._.__
o.3,
100
105
Age class 1
Age class 2
110
115
120
Year
Figure 4. Allele frequencies in separate age classes of population A for part of the period (years lOC120) of the 200 year simulation
depicted in Figure 3. Top: All age classes and the total population. Bottom: Same as above for the two youngest age classes only.
population
with overlapping
generations,
considerably
more significant results may be expected than can be
explained by changes due to genetic drift generated by a
restricted effective size.
Gene flow from exogenous populations
In an age-structured
population where generations overlap, the consequences
of a sudden change of allele
frequency, for example, as a result of immigration
of
genetically alien individuals, may also be quite different
from those in a population with discrete generations. As
an example we will consider the fluctuations
following
the introduction
of a new gene (allele) that did not exist
in the population prior to the invasion of non-local fish.
As before, we follow the frequency
changes of a
selectively
neutral
allele that is introduced
into a
1156
IV. Ryman
Total population
11
-
Age class 1
- - Age class 2
0.25
0
25
50
Year
75
J
100
0.25 --
0
246
8
10
Year
Total population
1
-
Age class 1
----Age
class 2
0.25
0.25
0
25
50
Year
75
100
0
2
4
6
Year
8
10
Figure 5. Frequency change of a selectively neutral allele introduced into a population where it did not exist previously (a single
event of immigration in year 0). The receiving population is infinitely large, but otherwise it has the life history characteristics of
population A (Table 1). Top: All adults are immigrants and homozygous for the new allele (initial allele frequency in the total
population=0.054). Bottom: All individuals of age class 3 are immigrants homozygous for the new allele (initial allele frequency
in the total population=0.245).
population
characterized
by the 1, and b, values of
population A (Table 1). In order to focus exclusively on
phenomena
related to age-structuring
and overlapping
generations,
however, we assume that the population is
infinitely large. The modelled process is therefore deterministic, and in a real situation with a finite population
the effects of random processes would result in larger
and less regular shifts of the allele frequencies.
Two simple scenarios were used for the introduction
of non-local fish homozygous
for the new allele. In the
first case, intended
to mimic a massive invasion of
spawners originating from, for example, a major searanching project or a damaged net-pen rearing facility,
all adults are replaced with alien fish homozygous
for a
new allele. Thus, immediately after the invasion (year 0)
the allele frequency is unity (1) among the adults (age
classes 46), zero (0) in age classes 1-3, and 0.054 in the
population
as a whole (cf. Table 1). Under the other
scenario, meant to represent an intense stocking operation, all the smolts (age class 3) are replaced with
exogenous fish. Here, the starting allele frequency is
unity in age class 3, zero in the others, and 0.245 in the
total population.
Figure 5 depicts the allele frequency
change for the total population during 100 years following the introduction
and for the first 10 years for each of
the two youngest age classes.
Minimizing adverse eJ&ectsof fish culture
As seen in Figure 5, the genetic dynamics following
the introduction
are quite different from what they
would have been in an “ideal” population with nonoverlapping generations. First, the sudden infusion of
new genes causes a striking “genetic instability” that
persists for a considerable period of time. The allele
frequencies fluctuate dramatically for several years and
an equilibrium is not approached until some 50-100
years after the introgression. Had generations been
discrete, the allele frequency would have stabilized at its
new value in a single generation. Furthermore, the
ultimate allele frequency may be quite different from its
initial value, in spite of the absence of selection, mutation, genetic drift, and additional immigration following
the single initial event. In the case of adult immigrants
the final frequency is just below 0.5 although the starting
value was only 0.054 (Fig. 5, top left). If generations had
been non-overlapping the allele frequency would have
remained at its initial value. In the present case, however, the difference among age classes with respect to the
values of 1, and b, implies that the potential of an
individual of a particular age class for impacting the
population genetically is determined by the expected
reproductive success during the remaining lifetime.
Obviously, an individual that survives until sexual maturity is expected to produce more progeny than an
immature fish that has a high probability of dying before
adulthood.
The allele frequency fluctuations are even more spectacular for the separate age classes than for the population as a whole (Fig. 5, right side). Furthermore, the
magnitude of the frequency differences among age
classes varies dramatically from one year to the next,
particularly in the first few decades following the introgression event. In the case of adult immigrants, for
example, the allele frequencies are very similar in year
7, but they are strikingly different in both the
preceding and the following years (years 6 and 8; Fig. 5,
top right). The same general pattern is observed in the
smolt immigration
situation, although the absolute
amplitude of the shifts is somewhat smaller (Fig. 5,
bottom right).
Conclusions and management
implications
The primary issue of this paper does not relate to the
question of whether or not the life history characteristics
of the hypothetical populations A and B are typical for
one or more authentic Atlantic salmon populations.
Rather, the concern is focused on the different genetic
dynamics of age-structured populations with overlapping generations compared with the discrete generations
situation that is typically used for modelling. Addressing
the genetic processes of a population with overlapping
generations from the perspective of discrete generation
1157
theory may hamper the correct interpretation of empirical observations and impede proper management. The
risks are particularly apparent for many salmonid
species where only a subset of the existing age classes
may be conveniently available for sampling.
For organisms with discrete generations it has been
noted previously that sample allele frequencies are
expected to change from one generation to the next
when the population is not infinitely large (Waples,
1989). Similarly, in a series of papers, Waples (1990, and
references therein) has examined, by means of computer
simulations, the effects of overlapping generations on
the allele frequency differences to be expected among
year classes of Pacific salmon. Those species are characterized by an unusual life history, i.e. all the breeders die
at the end of the spawning season (semelparity). As
discussed in the present paper, the same phenomenon of
larger allele frequency differences than can be explained
by genetic drift is also expected to occur among temporally spaced samples from species such as the European
salmonids (Salmo spp.) with a potential for breeding
in multiple seasons (iteroparity).
The simulation
results derived to explain the amount of temporal allele
frequency shifts under semelparity, however, cannot be
applied directly to an iteroparous organism. With
respect to the latter group of species, Jorde and Ryman
(1995) have derived the analytical relationships between
the age-specific survival and birth rates (1, and b,) and
the expected amount of temporal allele frequency fluctuations. Using their formulae it can be shown, for
example, that in populations A and B (Table 1) the
amount of allele frequency shift anticipated between
consecutive year classes is 47 and 29.6 times larger,
respectively, than the amount of genetic drift expected
for the population as a whole from one generation to the
next.
Interpreting
observed
allele frequency
shifts
An increasing number of reports are being published
comparing multiple samples from the same population
for allele frequency heterogeneity at nuclear or mitochondrial loci. Rejecting the null hypothesis of allele
frequency homogeneity, i.e. obtaining a statistically significant heterogeneity, frequently results in the conclusion that something is “wrong” with the population or
with the samples. Examples of hypothesized explanations include a very small effective population size
resulting in large amounts of genetic drift, the operation
of selective forces on the loci examined, introgression
from other populations, and non-random sampling of
individuals being more closely related than expected by
chance (family sampling). Obviously, such conclusions
are not necessarily appropriate without due consideration to the amount of genetic heterogeneity expected in
the population under study.
N. Ryman
1158
In the case of discrete generations, Waples (1989)
discussed the need to adjust the statistical test procedures to account for differences between generations
caused by genetic drift. When generations overlap, however, it is often difficult, or impossible, to collect samples
that are exactly one or more generations apart. Rather,
the temporal heterogeneity must be assessed through
comparisons of particular age groups sampled in the
same or in different years. In such situations the magnitude of the expected differences may be much larger than
anticipated from drift alone, and appraisal of the magnitude expected must be based on at least a rough idea of
the demographic characteristics of the population.
A correct assessment of the genetic status of a population may also require repeated sampling during a series
of years. At any single locus the true amount of allele
frequency heterogeneity among age classes may vary
considerably from one year to another (Figs 3 and 4)
and data from multiple years or loci must be considered
to permit reliable conclusions regarding the temporal
variability pattern.
In general, a conservative attitude is recommended
when interpreting allele frequency differences within
populations where the history and the demographic
characteristics are poorly known. It should also be noted
that the phenomena discussed here are not automatically
accounted for by statistical procedures such as the
Bonferroni approach correcting for multiple tests of the
same null hypothesis (e.g. Rice, 1989). Rather, the issue
at hand is to define the appropriate null hypothesis, i.e.
to estimate the amount of heterogeneity expected among
the groups being compared.
Detecting
introgression
It follows from the argument set out above that it may
be difficult to detect that immigration has occurred into
populations where conspicuous allele frequency shifts
are expected to appear naturally. The temporal variability patterns in undisturbed and introgressed populations may be very similar (Figs 4 and 5), particularly
when considering that the allele freqency changes following an authentic immigration may be less regular
than those depicted in Figure 5. In a real situation entire
age classes may not be replaced with immigrants, and a
less than infinite population size will result in additional
variability blurring the overall picture.
Clearly, the best indication of immigration is the
segregation, at frequencies that cannot be explained by
mutation, of alleles that did not previously occur in the
population. Such an observation requires, however, that
genetic information is available from a year pre-dating
the introgression. Typically, natural populations are not
being monitored for allele frequency variation, and for
many, or most, an adequate genetic characterization has
never been performed. Thus, the observations of the
present study reinforce the need for genetic descriptions,
and sometimes monitoring, of natural populations that
are considered genetically valuable and at risk of being
contaminated with immigrants of non-local origin.
Unfortunately,
with respect to genetic marker loci
detected by means of various biochemical techniques,
most local populations of a species do not contain
unique alleles that do not occur in other populations. In
particular, this is true for many artificially created
salmonid populations of hybrid origin used for sea
ranching and net-pen rearing. Thus, when trying to
detect introgression through analysis of allele frequency
shifts, there is frequently a lower probability of ascertaining immigration from aquaculture populations than
from exogenous natural ones. The present observations
stress the need for a systematic genetic marking of stocks
used in aquaculture. Without such a marking system it
seems unlikely that we will ever obtain a reasonably
accurate understanding of the overall biological consequences of introgression into local populations
of
salmonid fishes (cf. Hindar et al., 1991).
Predicting
the effects of immigration
The allele frequency fluctuations initiated by a sudden
change resulting from immigration imply that it may
take considerable time for the effects of introgression to
become overt. The pattern depicted in Figure 5 refers to
a selectively neutral allele, but the fluctuations imposed
by the population’s life history characteristics will
largely be similar for an allele under the influence of
selection, particularly in the early stages of the process.
We know little or nothing about the selective mechanisms operating in natural populations of salmonids or
other organisms, and any attempt to evaluate the ecological or evolutionary outcome of systematic allele
frequency fluctuations must be highly speculative. Some
possible outcomes may nevertheless be worthwhile mentioning. For example, the effect of selection at a locus
may vary among life history stages, and it may therefore
take several years for an introduced allele to “reach”, at
a substantial frequency, the age class at which selection
becomes manifest. Similarly, if frequency-dependent
selection is operating, i.e. if the particular genotypes are
at a disadvantage above or below particular allele frequencies, the average fitness of the population may
be reduced in some years but not in others. Clearly,
when trying to assess the ecological effects of a known
introgression, the potential for erroneous conclusions is
obvious if information is lacking on the dynamics of the
introduced alleles.
It should finally be noted that we have, here, only
considered a single locus, whereas immigration of
exogenous fish is likely to result in allele frequency
changes at multiple loci. In a real situation the fluctuations will therefore refer to a larger part of the genome
Minimizing adverse eflects of fish culture
than to a particular allele, and knowledge of the pattern
for those changes appears a prerequisite for proper
evaluation of an introgression.
Acknowledgements
P. E. Jorde provided helpful suggestions throughout the
study, and J. A. Beardmore, L. Laikre, S. Palm, R.
Waples, and two anonymous reviewers commented on
early drafts of the manuscript. The work was supported
by the Swedish Natural Science Research Council, and
part of the study was conducted within the framework of
the Swedish research programme on Sustainable Coastal
Zone Management,
SUCOZOMA,
funded by the
Foundation
for Strategic Environmental
Research,
MISTRA.
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