THE GENETIC POPULATION STRUCTURE
OF MARINE SPECIES IN RELATION TO
THEIR GENERAL BIOLOGY
J. Mork, TBS
Lecture notes in population genetics at MNK BI 260 spring 2001
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CONTENTS:
INTRODUCTION .................................................
PAGE:
3
A.
I.
II.
III.
IV.
DEFINITIONS AND TOPICS:.......................................
Species.......................................................
Population....................................................
Evolution.....................................................
Genetic population sructure...................................
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4
4
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4
V.
VI.
VII.
VIII.
The four evolutionary forces .................................
The Hardy-Weinberg theorem....................................
Estimating gene frequencies...................................
How to test for H-W genotypic proportions.....................
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5
5
B.
I.
II.
IIa.
IIb.
III.
IV.
V.
VI.
VII.
VIII.
GENETIC DIFFERENTIATION:...................................... 6
How to test for differences in allelic proportions ........... 6
Measures of general differentiation........................... 7
Relative measures (Wright’s Fst, Nei’s Gst)................... 7
Absolute measures (Nei's I and D)............................. 8
Genetically effective population size......................... 8
Genetic drift................................................. 8
Fitness- and selection coefficients........................... 8
Gene flow..................................................... 9
Genetic equilibrium situation................................. 9
Marine, versus anadromous and limnic species’ pop. structure.. 10
C.
SPECIFIC BIOLOGY AND GENETIC STRUCTURE ........................11
D.
TYPES OF MILIEU ADAPTATIONS ...................................14
Referenced articles ...........................................14
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INTRODUCTION
Many aquatic organisms are notoriously difficult study objects. In particular this applies to marine
species in oceanic environment. Their generally low accessability and hence the difficulties
connected with obtaining direct observations of behaviour, interactions and distribution have often
created problems in solving e.g. taxonomic problems. Below the species level, the identification and
delineation of subtle population structures have proved to be a rather hazardeous exercise if attacked
with traditional methodology (general biology, morphology, migrations etc). There are several
characteristic effects related to a marine way of life which lead to these difficulties:
1. The variances in growth patterns and other biological characteristics are larger than for terrestric
organisms. Fish are, for example, phenotypically more variable than terrestric vertebrates (Mayr
1969).
2. The strong influence of milieu regimes may mask genetic differences between groups.
3. In poikiloterms, ocean current transport and different temperature regimes can create temporal
and spatial ecotypes which are easily misclassified as genetic "races".
4. Migrations are often on a large geographic scale and include areas not available for study.
5. Recaptures from tagging experiments often depend on commercial catches and are therefore
restricted to a narrow range of species.
6. Animal communities are still not explored in large areas of the oceans and the seafloor.
7. Large depths, a hostile environment for man, and non-optimal sampling techniques may result in
inferior experimental designs for traditional studies of demographics and dynamics.
Thus both the characteristics traits of marine organisms and the difficulties in obtaining reliable
measurements of those characteristics, are obstacles for achieving good species and population
descriptions with classical tools.
However, modern population genetic techniques offer alternative approaches in taxonomy and
population descriptions. Genetic characteristics of species and population include stable traits which
are not affected by short term changes in the environment. Furthermore, genes are present in all
individuals and at all stages of development. This makes them suited not only for taxonomic
purposes, but also for delineation of the population structure that may exist within species.
This compendium contains a brief introduction to population genetics; its basic theory, a few
statistical tools, and some general patterns resulting from applications in marine biology.
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A. DEFINITIONS AND TOPICS
I. Species: A structure of populations. These populations may in practice be wholly or partially
reproductively isolated from each other, but interbreeding between them would produce fertile
offspring. This structure of populations is totally reproductively isolated from, and would also not
produce fertile offspring if interbred with similar population groups constituting other species.
II. Population: An intraspesific group of individuals which share a common gene pool, and which is
wholly or partially reproductively isolated from other such groups within the species. Populations
are the real evolutionary units. It is the populations which give basis for new species if their gene
frequencies become sufficiently changed by reproductive isolation from each other (i.e no or little
gene flow) over extended evolutionary periods.
III. Evolution: Any change in gene frequency (although not covering all aspects of evolution, this
definition is very useful in population genetics).
IV. Genetic population structure: The distribution of genetic variation within and between
popuoations within a species (i.e. the relative parts of the total genetic variability within a species
that are manifested as genetic differences between populations and between individuals. Strongly
structured species show large genetic differences between populations, while a totally
unstructured species would consist of only one population.
V. The 4 evolutionary forces acting on gene frequencies in populations:
1. Mutations within populations
2. Genetic drift within populations (se B. III og IV)
3. Gene flow between populations
4. Natural selection within populations (se B. V)
Mutations and genetic drift favour evolutionary change (increases the genetic differences between
populations and higher taxa), while gene flow inhibits evolution by counteracting the development of
gene frequency differences between populations, and levelling out those that may exist. Natural
selection can favour or inhibit evolution depending on the actual selection regime being local or
universal.
VI. The Hardy-Weinberg theorem:
"In a panmictic population (random pairing) the expected proportion of genotypes at a polymorphic
two-allel locus is determined by the frequencies (p and q) of the alleles according to the binomial
formula (p+q)2 = (p2 + 2pq + q2). In absence of effects from evolutionary forces (1-4 above) the
gene frequencies and genotype frequencies are constant over generations and can be used as
population characteristics». With more than two alleles the principle is the same, but the expected
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genotype distribution is multinomic: (p+q+r)2, (p+q+r+s)2, etc. In general, the number of possible
genotypes with n alleles is: n*(n+1)/2.
VII. The calculation of gene frequencies from observed genotypic distribution:
Consider a locus with 2 alleles A og B which by sexual reproduction is combined in (segregates to)
the genotypes AA (homozygot)e, AB (heterozygote), og BB (homozygote). In a ramdom sample from
a natural population the following genotype distribution is observed among 100 individuals:
AA
30
AB
60
BB
10
N
100
qA
0.60
qB
0.40
100 diploid individual vil altogether have 200 genes at each locus. Of these 200, 2*30 + 1*60 = 120
is of type A. Hence the frequency of A is: qA = 120/200 = 0.60. The gene frequency of B is
calculated in the same manner, or as (1-qA) = 0.40 since the frequency must sum to 1.0.
VIII. How to test for Hardy-Weinberg distribution of genotypes:
Observed distributions based on classification criterions («Yes-No») variables like sex, genotypes etc
can be tested for deviances from expected values by the chi-square test.
Test statistic (chi-square) for each class = [(observed - expected)2 / (expected)] and is summed over
classes. Degrees of Freedom (DF) in the test is the number of classes which can vary without
«locking» the total. Ordinarily this number will be the total number of classes minus 1, but since in
this «Goodness-of-fit» test we use observed gene frequency for estimating the expected number of the
various genotypes, we subtract one extra DF (see sect. VIII). In the «Goodness-of-fit» test for HardyWeinberg proportions therefore, the DF is generally the number of genotype classes minus the
number of alleles used to calculate them.
In the table above we calculated the gene frequencies qA=0.60 og qB=0.40. According to the HardyWeinberg theorem and the binomial distribution (p2*N + 2pq*N + q2*N) the number of the different
genotypes among 100 individuals (N=100) should ideally be as shown in the row «Calculated» in the
following test for the differences between observed and expected.
Observed
Caculated
Chi-square
AA
30
(36.0)
1.000
AB
60
(48.0)
3.000
BB
10
(16.0)
2.25
N
100
(100.0)
qA
0.60
0.60
qB
0.40
0.40
Sum chi-square = 1.00+3.00+2.25=6.25. DF = No. of genotypes minus no. of alleles = 3-2=1. Hence, P ~ 0.012
The deviance between observed and expected genotypic numbers in the table may be caused by a
stochastic sampling error (kun 100 individ), but how peobable is that? The chi-square «Goodness-offit» test performed indicates that a deviance as large as that observed here is expected to be
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encountered only in about one out of 12000 trials by stochasticity alone if we were re-sampling a
population in Hardy-Weinberg equilibrium. We therefore reject the null hypothesis, which is that the
samples is from one population in H-W equilibrium, and accepts the alternative hypothesis that the
observed deviance reflects reality.
Deviances of this type, i.e. an excess of heterozygotes compared to the H-W expected proportion,
have few other explanations than that there has been a selection which has favoured the survival of
heterozygotes («heterosis», «overdominance», «balanced polymorphism»). The opposite situation, i.e.
a deficit of heterozygotes, would indicate that the sample is taken from a phhysical mixture of
populations with different gene frequencies at the locus under study (The «Wahlund» effect).
The relative proportions of heterozygotes at a locus is called the heterozygosity at the locus. We
distinguish between observed (actually counted) and expected (proportions of genotypes based on
gene frequency calculations assuming H-W) heterozygosity.
B. GENETIC DIFERENTIATION
Given this genotypic distribution at one polymorpic locus (alleles A and B) in two populations:
AA
AB
BB
N
qA
qB
Pop1
36
48
16
100
.6
.4
Pop2
16
48
36
100
.4
.6
Total
52(50)
96(100)
52(50)
200
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I. How to test for differences in allelic proportions (=frequencies) between samples:
The null hypothesis is (as in most tests) that there are no differences between samples, i.e. that they
can be two samples drawn from the same population. Under this assumption, the best estimate we can
have of the real allelic proportion in that population is the allele proportions in the pooled samples
(because increasing the sample size will always improve the accuracy). The first step in the test is to
calculate the observed numbers (NB! not proportions) of alleles in the two samples and in the pooled
samples. These numbers are easily obtained by counting two A-alleles in the AA homozygote and
one A-allele in the AB heterozygote, and so on. Thereafter, we calculate the expected numbers under
the null hypothesis for each sample using the allele numbers in the pooled samples as model (the
relative allelic proportions in the separate samples A and B are expected to be the same as in n the
pooled samples). The following table can be set up:
# allele A (and exp # )
# allele B (and exp # )
Total
Sample A
72+48=120 (100)
32+48= 80 (100)
200
Sample B
32+48= 80 (100)
72+48=120 (100)
200
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Pooled sample A & B
200
200
400
We then calculate the partial chi-square value for each cell, and sum them to a total chi-square. For
example, the chi-square for allele A in sample A is calculated as: (120-100)2/100=4. In this particular
case, since the sample sizes are equal and the allelic proportions in the pooled samples are also
equal, the chi-square will actually be the same (=4) in all the four cells, giving a total chi-square=16.
Before looking up this value in a chi-square table, we need the DF (degrees of freedom). In RxC
(rows x column) tables like this the DF is (R-1) x (C-1), here (2-1) x (2-1) = 1. Thus, the chi-square
value is 16 with one degree of freedom, which corresponds to a probability of P<0.0001. We can
safely conclude that the allelic proportions in the two samples are too differerent to have been caused
by chance in the sampling, and that they therefore very probably reflects real differences in allelic
frequencies. Often, we can from such results also infer that the populations from which the two
samples were drawn must be reproductively isolated (i.e., too low gene flow to ‘homogenize’ the
populations).
II. Measures of general differentiation:
Relative measures: Fst (S.Wright), Gst (M. Nei)
Absolute measures: Genetic Identity (I) og Genetic distance (D) (M. Nei).
IIa. Wright's Fst = 1 - (Hs/Ht),
where Hs is the mean observed heterozygosity over populations, while Ht is the expected
heterozygosity in the total material (i.e. basert on "overall" gene frequencies). Wright's Fst is
calculated for single loci with two alleles. From the tabulated data both Pop1 and Pop2 will have an
observed heterozygosity of 0.48, giving a mean (Hs) of 0.48. The expected number of heterozygotes
in the total material (Hardy-Weinberg expectation) is (2*0.5*0.5*200)=100, i.e. Ht=0.5. Therefore
Fst = 1 - (0.48/0.50) = 0.04 in this case.
Nei's Gst expands Fst to include multiple alleles at multiple loci in one single measure. While
Wright’s Fst is based on the actual genotypic distribution, Nei’s Gst is calculated from the observed
gene frequencies (assuming H-W equilibria in each of the single populations). Mathematically, the
two measures are not principally different, and Gst can bee viewed as an average Fst over loci.
IIb. Nei's (Genetic Identity) I = (xiyi) / [ ( (xi2)( (yi2)],
where xi, yi is the frequency of the i-th allele in population X and Y, respectively.
I = (0.6*0.4 + 0.4*0.6) / [ ((0.62 + 0.42)(0.42 + 0.62))]
I = 0.48/0.52 = 0.9231
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Nei's (Genetic Distance) D = - ln(I) = - ln(0.9231) = 0.08 in this case.
This D-value (genetic distance) is an absolute measure of genetic differences, and an estimate of the
mean number of amino acid substitutions («opposite fixations») per locus. Usually, D-values are
estimated as an average over many loci (>10), monomorphic as well as polymorphic.
III. Ne - genetically effective population size:
Definition: "The size of the ideal (H-W) population which looses genetic variability (by genetic drift)
at the same rate as the one under study». Ne is strongly affected by the relative proportion of males
and females (1), the degree ov generation overlapping (2), and the historical variation in population
size (3).
(1)
Ne = (4*Nm*Nf) / (Nm + Nf)
(2) Ne = N0*t*l
(3) Ne = n / [(Ni-1)]
Symbols in I-III: m=males, f=females, N0=number born, t=mean reproductive age, l=probability for
surviving to reproductive age, n=no. of generations, Ni = N in the i-th generation.
IV. Genetic drift:
In populations of limited size, the transfer of a gene frequency (p) from parents to offspring will
usually not be completely accurate. Therefore, the expected value of p in the offspring will have a
variance which magnitude depends on frequency itself and on the effective size of the parental
generation:
Var(p) = p(1-p)/(2Ne), og SE = [Var(p)].
V. Fitness- and selection coefficients:
Consider a locus with two alleles A and B with three possible genotypes AA, AB and BB. Also
consider a population of 100 individuals where the frequency of both alleles initially were 0.5, and
the genotype distribution therefore 25AA, 50AB, and 25 BB. Towards reproductive age, the
genotypes show differential survival, i.e. a selection is taking place:
AA
AB
BB
Ne
qA
Before selection
25
50
25
100
0.500
After selection
15
45
10
70
0.536
Survival
15/25=0.6
45/50=0.9
10/25=0.4
w (fitness)
0.6/0.9=0.67
0.9/0.9=1.00
0.4/0.9=0.44
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s (=1-w)
0.33
0.00
0.56
Genotypic fitness coeffisient (w) is the relative survival compared to the «best» genotype (AB in this
example). The selection coefficent is defined as 1-w.
VI. Gene flow:
If population A (gene frequency q) receives a proportion m of immigrants each generation from
population B (gene frequency p), there will be a change q in gene frequency at each locus
according to the formula:
q = m(p-q)
The magnitude of the impact in each generation thus depends on the proportion of immigrants and on
the actual difference in gene frequency between donor and recipient population.
VII. Genetic equilibrium situation:
When an evolutionary regime has been stable for evolutionary significant periods of time (order of
4N generations where N=population size), an equilibrium situation is expected where the various
evolutionary forces cancel out each other and the change in gene frequency is small between
generations.
How genetically different the populations will be at this stage depends on the relative magnitude of
the evolutionary forces involved. Thus the equilibrium population structure will, besides the effect of
the age of the system, be affected by the population’s general biology and habitats through their
effective population sizes (genetic drift), stationarity/migration habits (effective gene flow), and
degree of genetic adaptation to local environmental factors. Knowledge of biological traits may
therefore form basis for general considerations concerning genetic differentiation in various species.
One problem, however, is that the evolutionary factors population size, gene flow and selection are
not probably constant over time, and that dramatic events like severe population bottlenecks (see pt
B.III.3), large immigrations fluxes, and milieu catastrophies may leave their impact on the genetic
population structure for long evolutionary periods even if their occurrences are generally rare.
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VIII. Marine versus anadromous and limnic species’ population structures
Some characteristics of marine organisms and their environment have a clear bearring to the
important evolutionary forces genetic drift, gene flow and selection.
First, since space and geography usually pose no restriction on the size of marine populations, they
are often more abundant (e.g. cod, herring, capelin) than anadromous (e.g salmon, trout) and limnic
(e.g. whitefish, garpike, charr, trout) populations. Since the number of individuals often is so large in
marine populations, the evolutionary factors gene flow and local adaptation are probably much more
significant than genetic drift in moulding the genetic population structures (see C and Table I and II).
This argument holds for invertebrates as well as for fish, and has been supported by studies in both
taxa.
Also, there are relatively fewer physical barriers to migration and gene flow in the marine milieu, and
many marine species have pelagic stages in their lifespan. Furthermore, «homing» to the place of
birth seems less common among marine than among anadromous species. All these factors have a
limiting effect on genetic differentiation. In addition, important physical factors in the marine
environment (e.g. temperature, salinity) are characterized by homogeneity over vast geographical
ranges. This reduces the necessity of local genetic adaptation to milieu factors and leeds to a lower
overall degree of genetic differentiation. In accordance with this, comparative studies have shown
that marine species in general are less genetically structured and differensiated than anadromous and
limnic species.
There is, nevertheless, some genetic substructuring among marine fishes (e.g. in herring, cod, and
blue whiting), at least on a large geographic scale (e.g. East and West Atlantic). In many cases
however, geographic variability in morphologic and meristic traits as well as in single-locus
molecular markers has been shown to be adaptive (i.e., results of selection) rather than being the
effect of isolation and genetic drift. To the degree that the term “reproductive isolation” applies to
marine fish species it appears to often be «Isolation by distance».
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C. SPECIFIC BIOLOGY AND GENETIC STRUCTURE
Table I. Biological traits relevant for the de 4 evolutionary main forces and therefore for the
level of genetic structuring in marine species (No of plus signs denotes relative potensials).
Mutations
Genetic drift
Selection
Gene flow
+++
+++
++
Stationarity
++
+++
Milieu tolerance
+++
+++
Fecundity
+++
++++
Shoaling behav.
+
+++
Homing
+
+++
Pelagic/benthic
eggs/larvae
Weaning
++
++++
+
++
Pop. size
Multiple spawn
++
Migrations
Marine/anadrom.
+
+
+++
+++
+++
This table indicates that the biology of the species has greatest potensial for affecting the
evolutionary forces SELECTION and GENE FLOW. These two forces will often have opposite
effects on genetic differentiation, and their relative magnitudes will therefore determine the actual
level of genetic structuring of the populations within a species.
Hence, species with populations spread on many types of habitats with large milieu fluctuations will
be expected to show genetic substructuring. The actual level of structuring will depend on the
effective gene flow between populations. Biological traits that increase the tendency towards
structuring would thus be high stationarity, solitarity, strong homing, benthic rather than pelagic eggs
and larvae, low migration tendency, and anadromous/katadromous rather than a pure marine life
history.
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ATLANTIC COD. Distributed on the
shelves on both sides of the North
Atlantic. Very large populations
which experience relatively similar
milieu conditions. Can undertake
extensive migrations, but do not show
a very accurate homing. The pelagic
egg- and larvae stage lasts for several
months. Thrives in cold and
temperate climatic condtitions.
Extensive genetic studies have
revealed only limited genetic
differentiation thoughout the range.
ATLANTIC SALMON. Distributed all
over the North Atlantic. Anadromous
(spawning in fresh water). Relatively
small river populations often with
substantial differences in local
environment between rivers. Benthic
egg, larvae and young stages.
Thrives in cold and temperate climatic
conditions. Can undertake extensive
migrations and shows an extremely
accurate homing. Extensive studies
of genetic structure have indicated a
moderate level of genetic
differentiation.
DOG WHELK Nucella lamellosa is
distributed on the American West
coast. Relatively small spawning
groups. Lives in the tidal zone, where
microclimate can vary extensively.
Limited migration capability, but
shows tendency of homing. Benthic
egg capsules in which the larvae
develop to small whelks before
hatching. No pelagic stadia. Thrives in
cold and temperate climatic
conditions. Genetic studies have
revealed a considerable genetic
differentiation on both small and large
geographic scales.
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Table II. Two marine and one anadromous species; biological traits relevant for the relative
strength of the two evolutionary forces gene flow and selection (local adaptation).
Atlantic cod
Atlantic salmon
Dog whelk
Pop. size
Large
Small
Small
Stationary
No
Yes
Yes
Milieu tolerance
High
High
High
Fecundity
High
Medium
Low/medium
No (?)
Yes
Yes
Both
Both
Benthic
Pelagic
Benthic
Benthic
Migrations
Large
Large
Small
Marine/anadrom.
Marine
Anadromous
Marine
Homing
Pelagic/benthic life
Pelagic eggs or larvae
Adult cod may undertake extensive migrations in connection with annual spawning. Cod population
sizes are usually very large, and the spawning products (eggs and larvae) drift pelagically for
extended periods of time (several months).
Also the salmon performs extensive migration, but its anadromous way of life includes a very precise
homing behaviour to home rivers which may have very different environmental conditions. The eggs
are buried in the river-bottom gravel, and the young individuals are stationary until smoltification.
The dog whelk has limited migration capacity. Its population sizes are typically small, and the
offspring (from 40-60 egg capsules) are benthic at all stages. Its intertidal habitat is often
characterized by highly variable environmental conditions.
Population genetic studies using electrophoretic (presumably neutral) markers in these three species
have shown that with respect to level of genetic substructuring they can be ranked as follows (from
lower to higher genetic distance):
Cod ---> salmon ---> dog whelk
For neutral characters, (which indicates the balance between genetic drift and gene flow) this ranking
appears reasonable in light of the biological characterization in Table II.
With respect to local adaptation (selection), very small population size is a disadvantage, whereas
medium size populations may achieve considerable local genetic adaptation if the gene flow between
populations is low. The homing tendency of both salmon and whelk, and the low migration capacity
of the dog whelk are factors that will act to reduce the gene flow and thus facilitate adaptation.
There is evidence that salmon has developed much more local genetic adapations than cod, but little
is known about the dog whelk in this respect.
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D. TYPES OF MILIEU ADAPTATIONS
1. Biochemical
2. Morphological
3. Behavioural
4. Resistence (disease/parasites)
Ad. 1: LDH-genotypes in Fundulus heteroclitus,haemoglobin genotypes in cod (Ref. II and III).
Ad. 2. Pronounced differences in body form and size in salmon populations is believed to reflect
genetic adaptations to the size, depth and water flow of the rivers. The relevance of body form may
be difficult to assess, but body size clearly has effects on the salmon’s ability to ascend rivers, rapids
and waterfalls. In general, small rivers have small salmon. The regulation mechanism appears, at least
partly, to be the number of years at sea before the return spawning migration (Ref. IV).
Ad. 3. Time for return from feeding areas at sea differs greatly between salmon stocks (e.g., the .
Figga-salmon at Steinkjær, which return from the Faroe feeding areas is several weeks ahead of other
rivers stocks in that region. This early return appears neccessary in order to be able to ascend the
Figga river during the spring flood, and is probably a strongly selected trait.(Ref. V).
Ad. 4. The parasite Gyrodactylus salaris L.occurs naturally in the Baltic. The Baltic salmon (e.g.,.
Neva-salmon) is resistent. This parasite/host relationship has probably developed through thousands
of generations.
This parasite was accidentally imported to Norway together with salmon for production plants. It was
soon discovered that Norwegian salmon stocks were not resistant, and that the parasite could totally
wipe out salmon river stocks when introduced during stock reinforcement programs. The status in
1992 was that the parasite had wiped out the salmon stocks in 35 Norwegian rivers (Ref. VI).
REFERENCED ARTICLES [AVAILABLE ON REQUEST AT TBS]
I. Mayr, E. 1970. Chapter 11 ("Geographic variation") in: Populations, Species, and Evolution. Harvard University Press,
Cambridge, Massachusetts. SBN: 674-69010-9. 453 p.
II. Place, A.R. & D.A. Powers 1979. Genetic variation and relative catalytic efficiencies: Lactate dehydrogenase B allozymes
of Fundulus heteroclitus. Proc. Natl. Acad. Sci. USA 76(5): 2354-2358.
III. Mork, J., Giskeødegård, R. & G. Sundnes, 1983. Haemoglobin polymorphism in Gadus morhua: genotypic differences
in maturing age and within-season gonad maturation. - Helgolander Meeresunters. 36: 313-322.
IV. Jonsson, N., Hansen, L.P. & B. Jonsson 1991. Variation in age, size and repeat spawning of adult Atlantic salmon in
relation to river discharge. Journal of Animal Ecology 60: 937-947.
V. Hansen, L.P & B. Jonsson 1991. Evidence of a genetic component in the seasonal return pattern of Atlantic salmon,
Salmo salar L. J. Fish. Biol. 38: 251-258.
VI. Bakke, T.A., Jansen, P.A. & L.P. Hansen 1990. Differences in the host resistance of Atlantic salmon, Salmo salar L.,
stocks to the monogenean Gyrodactylus salaris Malmberg, 1957. J. Fish. Biol. 37: 577-587.
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