Founder effects and genetic population

structure of brown trout (Salmo trutta) in a

Danish river system

Michael M. Hansen and Karen-Lise D. Mensberg

Abstract: The influence of founder effects on the genetic population structure of brown trout (Salmo trutta) was studied in asmall Danish river system. Samples of trout from seven locations were analysed by allozyme electrophoresis andmitochondrial DNA restriction fragment length polymorphism analysis. For comparison, allozyme data from other Danishtrout populations and mtDNA data from two hatchery strains were included. Genetic differentiation among populations wasfound to be small but significant. Pairwise tests for homogeneity of allele and haplotype frequencies between samples showedthat significance was predominantly due to a single population. In two of the locations studied, extinction–recolonizationevents had taken place. In one of the populations, founding had not resulted in divergence, while the other founded populationwas the one that had diverged significantly from the remaining populations. Computer simulations of the influence of foundereffects on mitochondrial DNA differentiation and variability showed that the observed divergence could be due either tonatural founder effects or to a genetic contribution by hatchery trout. However, the allozyme results pointed towards naturalfounder effects as the most likely explanation.

Résumé: Les auteurs ont étudié l’influence des effets fondateurs sur la structure génétique de populations de truite brune(Salmo trutta) dans un petit réseau hydrographique du Danemark. Des spécimens de truites provenant de sept localités ont étésoumis à l’électrophorèse des allozymes et à l’analyse RFLP de l’ADN mitochondrial (ADNmt). À des fins de comparaison,les auteurs ont inclus des données relatives aux allozymes obtenues avec d’autres populations danoises de truites ainsi que desdonnées relatives à l’ADNmt de deux souches d’élevage. La différentiation génétique entre populations est réduite, maissignificative. Des tests par paires appariées visant à juger de l’homogénéité, d’un échantillon à l’autre, des fréquences d’allèleset d’haplotypes, ont permis d’établir que le degré de signification mesuré est principalement attribuable à une seulepopulation. Dans deux des localités étudiées, il y a eu disparition puis recolonisation. Dans l’une de ces deux populations,l’effet fondateur n’a pas été à l’origine d’une divergence; cependant, l’autre population recréée divergeait significativementdes populations restantes. Des simulations numériques de l’influence qu’ont les effets fondateurs sur la différentiation et lavariabilité de l’ADNmt ont montré que la divergence observée pouvait être attribuable soit à des effets fondateurs naturels,soit à la contribution génétique des truites d’élevage. Cependant, les résultats des tests sur les allozymes indiquent que leseffets fondateurs naturels constituent l’explication la plus plausible.[Traduit par la Rédaction]

Introduction

Several studies of the genetic population structure of salmonidfishes, particularly brown trout (Salmo trutta) and cutthroattrout (Oncorhynchus clarkii), have documented strong popula-tion subdivision (Allendorf and Leary 1988; Ferguson 1989).On a larger geographical scale, genetic differentiation inbrown trout may reflect postglacial colonization events and insome cases even speciation processes (Hamilton et al. 1989;Bernatchez et al. 1992; Giuffra et al. 1994; Largiadèr andScholl 1996). Considerable population subdivision has inmany instances also been observed on microgeographicalscales (e.g., Crozier and Ferguson 1986). This is probably to alarge extent due to the strong homing of brown trout, resulting

in partial reproductive isolation of populations (Ferguson1989). Geological conditions, such as the presence of impass-able waterfalls resulting in landlocked populations, are clearlyalso important factors promoting genetic differentiation (Hin-dar et al. 1991; Cross et al. 1992). However, a third class ofpotentially important mechanisms shaping the structure of sal-monid populations, metapopulation dynamics, have onlyrarely been considered (e.g., Elo 1993). A metapopulation canbe defined as a subdivided population in which some subpopu-lations may go extinct whereas new ones are established bycolonists from other subpopulations. Therefore, subpopula-tions are linked not only by exchange of migrants but alsothrough recolonization (Gliddon and Goudet 1994).

During the last couple of centuries pollution and other sortsof habitat destruction have resulted in the extirpation of manysalmonid populations; however, under natural circumstancesextinctions are expected to take place also. Brown trout, inparticular, may spawn in small rivers (less than 1 m wide andless than 40 cm deep; cf. Elliott 1994). In such habitats thepopulations are expected to be highly exposed to catastrophicevents, such as droughts and harsh winters. Extinctions andrecolonizations by brown trout are therefore probably frequentin some rivers.

Received October 13, 1995. Accepted April 16, 1996.J13110

M.M. Hansen1 and K.-L.D. Mensberg.Danish Institute forFisheries Research, Department of Inland Fisheries,Vejlsøvej 39, DK–8600 Silkeborg, Denmark.

1 Author to whom all correspondence should be addressed.e-mail: mmh@dfu.min.dk

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Metapopulation dynamics may drastically affect geneticvariation at the level of both population and species (Gilpin1991), as well as genetic differentiation among populations(reviewed by McCauley 1991). However, in this paper we willfocus on the latter. Wade and McCauley (1988) analysed theconsequences of extinction–recolonization events for geneticdifferentiation in the island model of gene flow. They distin-guished between propagule pool and migrant pool models ofrecolonization. In the first of these models a number of indi-viduals from the same source population found a new popula-tion. In this case, founding will normally result in increasedgenetic differentiation if the number of founders is less thanthe equilibrium population size. In the migrant pool model,founders may be derived from all possible source populations,and extinction–recolonization events may either reduce or in-crease genetic differentiation, depending on the relationshipbetween the number of founders and the number of migrantsthat are exchanged among existing populations. However,

even though it is possible to make such general predictionsconcerning the influence of metapopulation dynamics on ge-netic population structures, there is a lack of actual observa-tions of these processes in populations of salmonid fishes.

We studied trout from seven localities within the smallOdder River system in Denmark, using allozyme electropho-resis and restriction fragment length polymorphism (RFLP)analysis of polymerase chain reaction (PCR) amplified mito-chondrial DNA (mtDNA) segments. The purpose of the studywas to determine if genetic differentiation could be observedamong trout from the sampling locations, and to study patternsof recolonization at locations where the original populationshad been extirpated. For comparing the genetic relationshipsamong populations on a larger geographical scale, allozymedata from other Danish trout populations were included (fromHansen et al. 1993).

Materials and methods

The study areaThe Odder River system is situated in Eastern Jutland (see Fig. 1) andflows into the Kattegat through a small fjord. There are no records ofthe river system being stocked. Within approximately 25 km to thenorth and south along the coastline there are only two rivers with avery modest degree of natural reproduction by trout, but both riversare subject to some stocking with hatchery trout. However, from10 km and farther to the north, in the Bay of Aarhus, hatchery smoltsare stocked directly on the coast. Two hatcheries have supplied themain portion of the stocked smolts. From 1987 to 1991 approximately56 000 smolts from the Spjarup Hatchery and Hårkær Hatcherystrains were stocked.

Trout were sampled from the Odder River system by electrofish-ing in July 1992 and stored at –20°C. Samples were taken from themain river at two locations, the upstream and downstream parts of theOdder River, and from five tributaries, the Fiskbæk, Assedrup, Krag-bæk, Asbæk, and Stampemølle Bæk rivers (see Table 1). One of themajor Odder River tributaries, the Rævs River, is devoid of trout as aresult of organic pollution, but the other tributaries and severalstretches of the main river presently contain excellent spawning andnursery conditions and are inhabited by dense trout populations con-sisting of a mixture of resident and anadromous trout. According toLarsen (1987), the tributary Fiskbæk River and the upstream part ofthe Odder River (FISK and ODR1, respectively; see Fig. 1) werepreviously polluted with waste water year-round. Therefore, thespawning grounds were covered with organic waste, which led to theextinction of the original trout populations. The FISK populationwent extinct in the 1920s, and electrofishing in the early 1960sshowed that the tributary had not yet been recolonized (Larsen 1987).There are no records regarding at what time the ODR1 populationwent extinct, but there was no spawning run in the early 1960s (Larsen1987). The present trout populations at the FISK and ODR1 locationstherefore represent recolonization events that have taken place sincethe 1960s.

The two hatchery strains were sampled in 1992 (Hårkær Hatchery)and 1995 (Spjarup Hatchery). These trout were only subjected tomtDNA analysis, but allozyme data from both strains were available(Simonsen and Rasmussen 1989).

Allozyme electrophoresisStarch gel allozyme electrophoresis followed standard procedures(for more details see Hansen et al. 1993 and references therein). Gelswere stained for the following enzymes (EC numbers and presump-tive loci are in parentheses): aspartate aminotransferase (2.6.1.1)(sAAT-1,2*, AAT-4*), creatine kinase (2.7.3.2) (CK-A1*, CK-A2*),diaphorase (1.6.2.2) (DIA-1*), glucose-6-phosphate isomerase (5.3.1.9)

Fig. 1. Approximate location of the Odder River system in Jutland,Denmark, and a detailed map showing the sampling locations in theriver system. Other locations from which samples included in theanalysis of allozyme data were taken, i.e., the Hårkær and Spjaruphatcheries, the Brandstrup, Klokkedals, and Tirsbæk rivers, andLake Hald (GJBK, KROB, DOLB, KDOL, DMBK, BISB, andKAPL), are also indicated. See Table 1 for a list of abbreviations ofsampling locations.

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(GPI-A1*, GPI-A2*, GPI-B1*), glycerol-3-phosphate dehydro-genase (1.1.1.8) (G3PDH-2*), isocitrate dehydrogenase (sIDHP-1*,sIDHP-2*), lactate dehydrogenase (1.1.1.27) (LDH-A1*, LDH-A2*,LDH-B1*, LDH-B2*, LDH-C1*), malate dehydrogenase (1.1.1.37)(sMDH-A1*, sMDH-A2*, sMDH-B1,2*), malic enzyme (1.1.1.40)(MEP-1*, MEP-2*), mannose—phosphate isomerase (5.3.1.8) (MPI-1*), phosphogluconate dehydrogenase (1.1.1.44) (PGDH-1*), andsuperoxide dismutase (1.15.1.1) (SOD-1*).

Mitochondrial DNA RFLP analysisDNA was extracted from muscle tissue according to the protocol ofTaggart et al. (1992). The mtDNA segments coding for the NADHdehydrogenase-1 and 5/6 subunits (ND-1 and ND-5/6, respectively)were PCR amplified using the primers described by Cronin et al.(1993). The ND-1 segment was analysed using the restriction endonu-cleases AvaII, HaeIII, AluI, HpaII, and HinfI, whereas ND-5/6 wasanalysed with AvaII, HaeIII, HinfI, TaqI, and XbaI. In a previousscreening of Danish trout populations using a larger number of restric-tion enzymes, these enzymes have been shown to detect variability in thetwo analysed mtDNA segments. For a more detailed description ofthe technical procedures see Hansen and Loeschcke (1996a).

Statistical treatmentsDeviations from expected Hardy–Weinberg proportions at the studiedenzyme loci were tested by the Monte Carlo method described by Guoand Thompson (1992). Homogeneity of allelic frequencies among allsamples and between pairs of samples was tested using G tests. Ge-netic differentiation among populations was estimated by the methoddescribed by Weir and Cockerham (1984). These tests and analyseswere performed using the G-STAT program package (Siegismund1994).The genetic relationships among the sampled populations weresummarized by calculating the drift-based genetic distance(coancestry coefficient) of Reynolds et al. (1983), on the basis of thepolymorphic loci, and constructing a dendrogram according to thealgorithm by Fitch and Margoliash (1967), using the program FITCHfrom the PHYLIP package (Felsenstein 1993). In addition to datafrom the Odder River system, allele frequency data from the HårkærHatchery and Spjarup Hatchery strains (data from Simonsen and Ras-mussen 1989) and from the Brandstrup, Tirsbæk, and Klokkedalsrivers, as well as seven tributaries of Lake Hald, were also included(data from Hansen et al. 1993).

Mitochondrial DNA composite genotypes were designated by 10capital letters, each representing individual restriction morphs. Ge-netic variability of the populations at the mtDNA level was estimatedby calculating the nucleon diversity (Nei 1987). Homogeneity ofhaplotype frequencies among the sampled populations was tested ac-cording to Roff and Bentzen (1989), using the MONTE program fromthe REAP package (McElroy et al. 1991). Each test was based on30 000 replicates. Genetic differentiation among populations wasquantified by ΦST (an analogue of FST), which was estimated by theAMOVA procedure, using the program WINAMOVA 1.53 (Excof-fier et al. 1992). On the microgeographical scale considered in thisstudy we assumed that the observed genetic differences among popu-lations would exclusively reflect drift and gene flow and that coloni-zation by different phylogeographical brown trout races could beruled out. Consequently, we ignored the phylogenetic relationshipsamong haplotypes, which were instead treated as equidistant. Thesignificance of the ΦST value was evaluated by a permutation test andwas based on 1000 permutations. The genetic relationships amongpopulations were illustrated by a Fitch and Margoliash (1967) den-drogram, based on Reynolds et al.’s (1983) coancestry coefficient, asdescribed for the treatment of allozyme data. In all cases of multipletests, significance levels were adjusted by the sequential Bonferronitechnique (Rice 1989).

SimulationsThe influence of founder effects and gene flow on mtDNA genetic

variability and divergence of recently founded trout populations wassimulated using a program written in Turbopascal. The mode ofrecolonization considered can be described as a propagule pool modelin a mainland–island system of gene flow. A specified number offemale founders was drawn at random from one wild source popula-tion and in some cases also from a hatchery strain with known haplo-type frequencies. Four recolonization scenarios were considered. Inscenario 1, the number of female founders was equal to the gene flow,i.e., the number of migrant females a population would normallyreceive if extinction had not occurred. In scenario 2, the number offemale founders was larger than the gene flow, which might be thesituation if several females found the unoccupied spawning and nurs-ery grounds more attractive than their densely populated home river.In scenario 3, the number of females spawning in the year of foundingwas equal to the number of females that would have spawned in theriver under normal circumstances. The scenario may not in itself bebiologically realistic, but it was chosen to include the entire span ofpossible numbers of female founders. Scenario 4 resembled scenario1 except that hatchery trout also contributed to gene flow.

Among the resulting offspring, a specified number of femaleswere chosen at random to survive and reproduce for the first time as3-year-old fish. As mortality in fry is highly density dependent (Elliott1994), it was assumed that the carrying capacity, and thus the maxi-mum number of surviving spawners, was reached immediately afterthe founding event regardless of the number of female founders. Onlytrout spawning until the age of 5 years were considered, as it wasassumed, on the basis of sizes of trout spawning in the Odder Riversystem (Larsen 1987), that the number of trout reproducing beyondthat age was negligible. Spawning was repeated each year and recruit-ment of new spawners took place as described above. Unpublisheddata from the Brandstrup River, which is of a similar size to the OdderRiver tributaries, showed that approximately 75–100 resident andanadromous females spawned per year (G. Rasmussen, Danish Insti-tute for Fisheries Research, personal communication). To cover the

Geographic area and location Abbreviation

Odder River systemOdder River

Upstream ODR1Downstream ODR2

Stampemølle River STAMAsbæk River ASBKKragebæk River KRAGAssedrup River ASSEFiskbæk River FISK

Hatchery strainHårkær Hatchery HARKSpjarup Hatchery SPJA

Tributaries to Lake HaldGjelbæk River GJBKKrobæk River KROBDollerup River DOLBKilde v. Dollerup River KDOLDollerup Møllebæk River DMBKBisballe River BISBKapeldal River KAPL

Gudenaa River systemBrandstrup River BRANTirsbæk River TIRSKlokkedals River KLOK

Table 1.Geographic areas in terms of river systemsor hatchery strains, and sampling locations, withabbreviations.

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range in numbers of spawners, the simulations were carried out as-suming spawning populations of 25 and 100 females per year. Assum-ing a survival rate of spawners from one spawning season to the nextof approximately 30% (G. Rasmussen, personal communication),25 spawners translates into 18 females spawning for the first time, 5for the second time, and 2 for the third time. The equivalent numbersfor 100 spawners are 72, 22, and 6. All spawning females had an equalprobability of contributing to the next generation, irrespective of age.

Gene flow was simulated by randomly choosing a specifiednumber of genetically effective migrants from the one or two sourcepopulations. On the short time scale of these simulations the recipro-cal effect of gene flow from the founded population to the naturalsource population was assumed to be insignificant and was not con-sidered. After 5 and 15 years following the original founding, samplesof 20 age 0+, 15 age 1+, and 5 age 2+ trout (which is a reasonablerepresentation of age-classes in the samples subjected to mtDNAanalysis) were taken from the founded population, and a sample of 40individuals was drawn from the wild source population. Differencesin haplotype frequency between the two samples were tested accord-ing to Roff and Bentzen’s (1989) algorithm, and the number of haplo-types and the nucleon diversity of the sample from the foundedpopulation were recorded. Each simulation was based on 1000 runs.Within each run the Roff and Bentzen test was based on 20 000 rep-licates, and significance levels were adjusted by the Bonferroni tech-nique (Rice 1989).

Demographic parameters were primarily based on data from theBrandstrup River (Rasmussen 1986; G. Rasmussen, personal com-munication) and from tributaries to Lake Hald (Larsen 1984), whichwere comparable in size to the Odder River and its tributaries. Theparameters used in the model must be considered simplified, as browntrout generally exhibit considerable variation in several life-history

parameters (Elliott 1994). On average, however, the chosen parame-ters constitute a useful approximation of the typical life history oftrout in a small Danish river system.

Results

AllozymesVariation was detected in 11 of the enzyme loci studied. Thefollowing variant alleles (i.e., alleles differing in mobility fromthe presumed 100 alleles) were observed (Table 2): sAAT-1,2*140, sAAT-4*74, DIA-1*90, G3PDH-2*50, GPI-B1*110,sIDHP-1*160, LDH-A1*n (null allele), sMDH-A2*152, andMPI-1*105. We assumed that the observed alleles were iden-tical to others described in the literature (Ferguson 1989). Vari-ation was also detected at loci CK-A1* and sMDH-B1,2*, butit was not possible in all instances to make a reliable distinctionbetween heterozygotes and one of the homozygotes. Conse-quently, these loci were omitted from the analyses. No vari-ation was detected in locus LDH-C1*, and in a comparisonwith samples from Atlantic salmon (Salmo salar) the fixedallele appeared to be identical to LDH-C1*90.

No significant deviations from expected Hardy–Weinbergproportions were observed (data are not included). The test forhomogeneity of allele frequencies among all samples, basedon loci sAAT-1,2*, sAAT-4*, G3PDH-2*, IDHP-1*, sMDH-A2*, and MPI-1*, yielded a significant result (G2 = 73.4, 36 df,p < 0.001). However, only two of the pairwise tests betweensamples, both involving ODR1, resulted in a significant out-come (Table 3). Though allele frequencies were not homoge-neous among samples, gene diversity analysis resulted in anFST value of 0.008 (SD = 0.005), indicating a small degree ofgenetic differentiation. The Fitch and Margoliash (1967) den-drogram based on allozyme data (Fig. 2) supported the closegenetic relationships among the sampled populations from theOdder River system, while the relationships with populationsfrom other regions were more distant.

Mitochondrial DNAIn total, six different mtDNA haplotypes were observed, andthe number of haplotypes in samples from the Odder Riversystem was five or six, while the hatchery strains, in particular,appeared to be less variable (Table 4). We used the restrictionmorph and haplotype nomenclature of Hansen and Loeschcke

Allele ODR1 ODR2 STAM ASBK KRAG FISK ASSE

sAAT–1,2*140 0.121 0.071 0.132 0.108 0.097 0.152 0.090

sAAT–4*74 0.205 0.107 0.090 0.155 0.090 0.123 0.104DIA–1*90 0.023 0.007 0 0 0.007 0 0G3PDH–2*50 0.070 0.007 0.028 0.007 0.014 0.007 0.049GPI-B1*110 0 0.014 0 0 0 0.007 0

sIDHP–1*160 0.424 0.286 0.347 0.243 0.340 0.384 0.403LDH-A1*n 0.023 0 0 0 0.014 0 0MDH-A2*152 0.167 0.143 0.118 0.176 0.167 0.109 0.194MPI–1*105 0.348 0.407 0.364 0.541 0.431 0.435 0.444

He 0.086 0.066 0.068 0.071 0.071 0.072 0.075Ho 0.082 0.073 0.063 0.068 0.072 0.076 0.076N 66 70 72 74 72 69 72

Note: He denotes expected heterozygosity, Ho denotes observed heterozygosity, and N denotes sample size.

Table 2.Allozyme allele frequencies for the rarer of two alleles of the studied samples from the Odder River system.

KRAG ASBK STAM ASSE ODR1 ODR2

FISK 5.4(5) 14.3(5) 3.0(5) 11.9(6) 16.5(6) 8.8(5)

KRAG 9.9(5) 4.3(6) 4.9(6) 17.7(6) 2.3(5)

ASBK 18.1(5) 18.6(6) 30.8(6)*** 9.1(5)

STAM 8.4(6) 13.3(6) 5.3(5)

ASSE 9.8(6) 11.4(6)

ODR1 22.3(6)*

Note: Numbers in parentheses are degrees of freedom. Tests were basedon the loci sAAT–1,2*, sAAT–4*, IDHP–1*, sMDH-A2*, MPI–1*, and, forthe tests with six degrees of freedom, G3PDH–2*. ***, p < 0.001; *, p <0.05.

Table 3.Pairwise contingency tests (G tests) for homogeneity ofallele frequencies between pairs of samples (degrees of freedom inparentheses).

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(1996a). All haplotypes have been observed in other Danishtrout populations (Hansen and Loeschcke 1996a). The differ-ences in haplotype frequency among samples from the OdderRiver system were highly significant (χ2 = 115.7, p < 0.001).The pairwise homogeneity tests (Table 5) showed that thehaplotype frequencies in the ODR1 sample were significantlydifferent from those in all other samples, while only two of theremaining tests yielded significant outcomes. The genetic dis-tinctness of the ODR1 sample was also supported by the Fitchand Margoliash (1967) dendrogram based on mtDNA data(Fig. 3). The haplotype frequencies of the two hatchery strainsdid not differ significantly (χ2 = 6.1, ns) and were similar over-all (Table 4). This was not surprising, as both strains had beenfounded by fish from the same hatchery strain (Høllund Hatch-ery) (Simonsen and Rasmussen 1989). Genetic differentiation,estimated by the AMOVA procedure, was modest but highlysignificant (ΦST = 0.059, p < 0.001).

SimulationsGenetic differentiation among the five samples from STAM,ODR2, KRAG, ASSE, and ASBK was minimal (for an expla-nation of abbreviations see Table 1). In the simulations offounder events these samples were therefore pooled to createone large source population. The haplotype frequencies of thesource population were calculated as the mean haplotype fre-quencies of these five samples. Founder females migrating tolocations ODR1 and FISK were drawn at random from thissource population. Haplotype frequencies in the two hatcherystrains were very similar, and as the Hårkær Hatchery strainhas most recently been used for stocking, the haplotype fre-quencies of this strain were chosen as representative of thealternative source population contributing to founding andgene flow. The simulation results demonstrated that there maybe a wide variety of outcomes in terms of genetic differentia-tion and variability, even under the same recolonization sce-narios. Generally, however, and as expected, a large number offemale founders and (or) considerable migration from thenatural source population resulted in few cases of genetic di-vergence but high levels of variation, while the opposite wastrue for a small number of female founders and migrants(Table 6). The time elapsed since the founding event was im-portant, as gene flow tended to swamp initial founder effects(e.g., scenarios 1e vs. 1f and 2a vs. 2b in Table 6). Largespawning population sizes (100 females) rendered populationsless susceptible to swamping (e.g., scenarios 1e and 1f vs. 1gand 1h). This affected not only genetic divergence but alsolevels of variability. In particular, when the number of femalefounders was small (two) but at the same time the geneflow was strong (two), levels of variability tended to remainlower in large populations than in small populations (this be-comes evident when the numbers of cases in which six haplo-types were sampled, scenarios 1b vs. 1d and 1f vs. 1h, forexample, are compared). An additional genetic contributionfrom hatchery trout resulted in high levels of variation and, atthe same time, increased divergence (scenarios 4a and 4b).

Discussion

Genetic variation and differentiationGenetic variability among the sampled Odder River populations

was very similar (Tables 2 and 4). Variability at the allozymelevel fell well within the range of what had previously beenobserved in Danish trout populations (Hansen et al. 1993).Compared with data based on the same technique as formtDNA analysis, the five or six haplotypes observed in thesamples also seemed typical of other Danish nonstocked troutpopulations accessible to gene flow (Hansen and Loeschcke1996a; M.M. Hansen and K.-L.D. Mensberg, unpublished re-sults). The observed genetic differentiation was slight andcould for the most part be ascribed to the ODR1 sample diverg-ing from the remaining samples (Tables 3 and 5). The possiblereasons for its genetic divergence are discussed below.

The observed ΦST value, 0.059, corresponded to an ex-change of approximately eight mitochondria/females amongpopulations per generation, as estimated from the relationshipbetween female gene flow and genetic differentiation at themtDNA level (Takahata and Palumbi 1985), which must beconsidered strong gene flow. Assuming a generation length of4 years, this would correspond to an average exchange of twofemales per year. It should be kept in mind that because of the

Fig. 2. Dendrogram constructed according to the algorithm of Fitchand Margoliash (1967) to summarize the genetic relationshipsamong the sampled populations from the Odder River system andother Danish trout populations, the locations of which are indicatedin Fig. 1. The dendrogram was based on Reynolds et al.’s (1983)genetic distance, calculated from allozyme allele frequencies, andwas constructed with the program FITCH from Felsenstein’s(1993) PHYLIP package. Abbreviations in boldface type denotelocalities in the Odder River system; locations that are underlinedare those where the population was recently founded.Abbreviations in italics are hatchery strains. See Table 1 for a list ofabbreviations of sampling locations.

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recent recolonization events, drift and gene flow are unlikelyto have reached equilibrium. However, this is most likely tohave resulted in an overestimation of ΦST at equilibrium and,consequently, an underestimation of gene flow.

We did not study temporal variation in allele and mtDNAhaplotype frequencies. Haplotype frequencies, in particular,may in some cases exhibit temporal instability (e.g., DeSalleet al. 1987; Hansen and Loeschcke 1996b). However, theminimal differentiation among the populations that had notbeen founded recently strongly suggested that drastic temporalchanges in allele and haplotype frequencies were unlikely tooccur, at least on a short time scale.

Genetic differentiation among the Odder River populationswas much less than that among trout from another geographicarea of similar size, the Lake Hald area. Studies of the troutpopulations inhabiting the tributaries of this lake revealed con-siderably stronger differentiation (FST = 0.047, ΦST = 0.209;Hansen et al. 1993; Hansen and Loeschcke 1996a). One ex-planation of these differences is that suitable spawninggrounds are available throughout large parts of the OdderRiver system, which means that there are no well-definedboundaries between populations. In contrast, the spawninggrounds of the Lake Hald tributaries are physically separatedby the lake itself, resulting in much more pronounced fragmen-tation of spawning grounds.

Extinction and recolonizationIn our simulations of founder events we chose to focus on thematernally inherited mtDNA, as there were reasonably good

records of the annual number of female spawners in smallrivers. The situation is considerably more complicated for nu-clear loci, as it is difficult to estimate the number of males

HaplotypeCompositegenotype ODR1 ODR2 STAM ASBK KRAG ASSE FISK SPJA HARK

Type 2 BAACBCABAA 0.13 0.33 0.10 0.41 0.33 0.26 0.31 0.38 0.32Type 4 AABCABCABA 0.08 0.17 0.10 0 0.03 0.02 0.07 0.07 0.04Type 5 AAABABCABA 0.48 0 0.05 0.08 0.10 0.05 0.07 0.50 0.64Type 7 AAAAABAAAA 0.13 0.31 0.28 0.15 0.38 0.21 0.36 0.02 0Type 8 AAACABCABA 0.03 0.05 0.23 0.05 0 0.24 0.10 0.03 0Type 10 ABABBBCABA 0.18 0.14 0.25 0.31 0.15 0.21 0.10 0 0

N 40 42 40 39 39 42 42 58 76Nucleon

diversity 0.72 0.76 0.81 0.72 0.72 0.80 0.77 0.61 0.49

Note: Composite genotypes are denoted by capital letters for each restriction morph in the following order: ND–1: AvaII, HinfI, AluI, HaeIII, HpaII; ND–5/6:AvaII, HinfI, HaeIII, TaqI, and XbaI. Haplotype and restriction morph nomenclature is according to Hansen and Loeschcke (1996a).

Table 4.Composite mtDNA genotypes (haplotypes), haplotype frequencies, unbiased nucleon diversity, and sample sizes (N) of thepopulations sampled.

KRAG ASBK STAM ASSE ODR1 ODR2

FISK 5.4 11.7 10.2 7.4 22.8** 5.9KRAG 9.3 17.8* 12.8 20.4** 10.6ASBK 17.5* 8.4 22.1** 14.6STAM 5.3 23.2*** 14.0ASSE 25.7** 13.5ODR1 28.8***

Note: The significances of the χ2 statistics were estimated by theMonte–Carlo test described by Roff and Bentzen (1989), using the programMONTE from the REAP package (McElroy et al. 1991). ***, p < 0.001; **,p < 0.01; *, p < 0.05.

Table 5.Pairwise contingency tests for homogeneity of mtDNAhaplotype frequencies between pairs of samples.

Fig. 3. Dendrogram constructed according to the algorithm of Fitchand Margoliash (1967) to summarize the genetic relationshipsamong the sampled populations from the Odder River system andthe two sampled hatchery strains. The dendrogram was based onReynolds et al.’s (1983) genetic distance, calculated from mtDNAhaplotype frequencies, and was constructed with the programFITCH from Felsenstein’s (1993) PHYLIP package. Abbreviationsin boldface type denote locations in the Odder River system;locations that are underlined are those where the population wasrecently founded. Abbreviations in italics denote hatchery strains.See Table 1 for a list of abbreviations of sampling locations.

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participating in the spawning, particularly as mature male parrmay make a substantial genetic contribution (Jordan andYoungson 1992). A potential problem, regardless of the classof genetic marker being considered, relates to the variance inreproductive success among individuals, which may undernatural circumstances turn out to be larger than assumed in thesimulations (cf. Gall 1987; Hedrick et al. 1995). Unfortu-nately, no data concerning such variance in natural populationsof salmonid fishes are at present available.

The maximum likelihood tree based on allozymes (Fig. 2)clearly showed that the newly founded populations FISK andODR1 were more closely related to the remaining Odder Riverpopulations than to the other populations included in the tree.Trout from Odder River tributaries have therefore made atleast a very substantial contribution to the founding. Thismeans that the pattern of recolonization bears most resem-blance to the propagule pool model as described in Wade andMcCauley (1988).

In the case of FISK neither genetic divergence nor lowerlevels of variability than in the other populations could bedetected. Given the estimated gene flow of (at least) two fe-males per year, the simulations showed that this outcome wasnot unexpected, regardless of the numbers of spawning fe-males and numbers of female founders (1e–1h, 2e–2h, and3e–3h in Table 6).

Considerable genetic variation, in terms of both nucleondiversity and number of haplotypes, was also observed inODR1 (Table 4). At the same time, however, the population

diverged significantly from all other populations; in particular,the high frequency of haplotype 5 (0.48), which was relativelyrare in the other populations, was not consistent with the simu-lations that assumed strong gene flow, i.e., two females peryear (1e–1h, 2e–2h, and 3e–3h). Limited gene flow (one fe-male per year) to this population would be likely to result insignificant genetic divergence and could in some instancesalso lead to a high frequency of haplotype 5 (1a–1d, 2a–2d,and 3a–3d), but assuming the parameters of scenarios 1a, 1c,and 1d, observation of six haplotypes would be unlikely. Con-sequently, the genetic composition that was observed in ODR1was most consistent with the simulations that assumed limitedgene flow (one female per year) and a relatively large numberof female founders. If, indeed, less gene flow takes place toODR1 than to FISK and the other locations sampled, it maybe due to the geographical distribution of locations. FISK issituated near the outlet of the Odder River, while ODR1 issituated in the most upstream part of the river system (Fig. 1).Thus, during the spawning run, FISK is passed by nearly allascending spawners, while fewer trout would go as far up-stream as ODR1.

As an alternative hypothesis, one should consider whetherthe hatchery trout stocked directly on the coast entered theriver and made a genetic contribution to the newly foundedpopulation. The simulations showed that this could easily ex-plain the high frequency of haplotype 5 (which is common inboth hatchery strains), the strong divergence, and the high lev-els of variability observed in ODR1 (scenarios 4a and 4b in

ScenarioNo. of

spawnersNo. of

founders

Gene flow Yearafter

foundingNucleondiversity

No. ofhaplotypes

No. ofsignificant

testsa

Sixhaplotypesb

Frequency oftype 5 > 0.48cNatural Hatchery

1a 25 1 1 0 5 0.54 (0.15) 3.0 (0.8) 606 0 91b 25 1 1 0 15 0.57 (0.14) 3.7 (0.9) 509 16 131c 100 1 1 0 5 0.53 (0.17) 2.6 (0.7) 699 0 91d 100 1 1 0 15 0.54 (0.15) 3.0 (0.8) 635 0 111e 25 2 2 0 5 0.66 (0.10) 4.1 (0.9) 221 51 21f 25 2 2 0 15 0.67 (0.10) 4.6 (0.8) 162 125 31g 100 2 2 0 5 0.65 (0.12) 3.8 (0.9) 281 15 11h 100 2 2 0 15 0.65 (0.11) 4.2 (0.8) 221 30 22a 25 6 1 0 5 0.57 (0.13) 3.9 (0.8) 433 26 122b 25 6 1 0 15 0.62 (0.12) 4.2 (0.9) 304 56 92c 100 25 1 0 5 0.57 (0.14) 4.4 (0.9) 420 108 82d 100 25 1 0 15 0.62 (0.11) 4.6 (0.9) 245 150 32e 25 6 2 0 5 0.67 (0.10) 4.5 (0.9) 137 127 22f 25 6 2 0 15 0.69 (0.09) 4.8 (0.8) 95 201 52g 100 25 2 0 5 0.67 (0.10) 4.7 (0.8) 154 151 32h 100 25 2 0 15 0.70 (0.08) 4.9 (0.8) 81 253 03a 25 25 1 0 5 0.58 (0.12) 4.3 (0.8) 412 63 43b 25 25 1 0 15 0.63 (0.12) 4.4 (0.9) 271 89 103c

3d 100 100 1 0 15 0.62 (0.12) 4.8 (0.8) 240 181 73e

3g 100 100 2 0 5 0.68 (0.09) 4.8 (0.8) 137 196 33h

4a 100 2 2 1 5 0.71 (0.08) 4.6 (0.8) 256 126 384b 100 2 2 2 5 0.70 (0.08) 4.7 (0.8) 375 140 118

Note: Each simulation scenario is based on 1000 runs. Values in parentheses are standard deviations. For more details on the model see the text.aNumber of tests of homogeneity of haplotype frequencies between the source and founded populations that were significant at the 5% level or less.bNumber of outcomes where six haplotypes (the number of haplotypes in ODR1) were observed.cNumber of times the frequency of haplotype 5 was higher than 0.48 (the frequency of this haplotype in ODR1) in the simulated samples.

Table 6.Results of simulations of the influence of founder effects on mtDNA variability and genetic divergence between the founder andsource populations.

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© 1996 NRC Canada

Table 6). However, the allozyme data were not consistent withthis hypothesis. If ODR1 is indeed an admixture population,allele frequencies in the sample should be intermediate be-tween those of the other Odder River populations and those ofthe hatchery strains. This was only the case for the weaklypolymorphic loci DIA-1* and GPI-B1* (data not shown). Fur-thermore, no cases of significant gametic phase disequilibriawere observed (using the method described in Weir 1979; datanot shown), though large sample sizes as well as substantialdifferences in allele frequencies between the hatchery and wildpopulations may be required to detect this, particularly forunlinked loci. Finally, in another Danish river system theHårkær Hatchery strain was unable to make a persistent ge-netic contribution to the present trout population despite in-tense stocking activity (Hansen et al. 1995). A foundingscenario not involving hatchery trout therefore provides themost likely explanation.

A third hypothesis should also be considered, namely thepossibility that the FISK and ODR1 populations were nevercompletely extinct. Though this scenario is rather unlikely, wecannot, in principle, rule out the possibility that the spawninggrounds in one or a few places were suitable for reproduction,despite the organic pollution. The result of this would be thatthe populations survived but also experienced bottlenecks. Thepossible outcomes would be difficult to distinguish from thoseof recolonization scenarios involving founding by individualsfrom neighbouring and genetically very similar populations,and they would resemble the founding scenarios with few fe-male founders (1a–1h and 2a–2h). As discussed above, thehigh levels of variability and lack of genetic divergence of theFISK population could occur even with a small number offemale founders if, at the same time, strong gene flow to thispopulation took place. The same conclusion therefore also ap-plies if a bottleneck rather than a founding event occurred.Similarly, the observation of genetic divergence, but at thesame time high levels of variability in the ODR1 population,was found to be most consistent with a relatively large numberof female founders and limited gene flow. Given a bottleneckscenario, this would translate into a relatively mild reductionof the effective population size accompanied by limited geneflow.

Our results illustrate the importance of being aware of thehistory of the studied localities and populations, as extinc-tion–recolonization events may influence genetic populationstructure and lead to exaggerated conclusions concerning mi-crogeographical differentiation. Also, further studies of themetapopulation dynamics of salmonid fishes could lead to abetter understanding of the possible existence of local adapta-tions, as this depends not only on selective regimes, effectivepopulation sizes, and levels of gene flow, but also on thenumber of generations the population has persisted (cf. Endler1986; Chakraborty and Leimar 1987).

Acknowledgements

We thank E.E. Nielsen, V. Loeschcke, P.W. Hedrick, G. Ras-mussen, and two anonymous reviewers for useful commentson this or previous versions of the manuscript, and C. Håkans-son, H. Nuhanovic, and K. Jørgensen for technical assistance.

References

Allendorf, F.W., and Leary, R.F. 1988. Conservation and distributionof genetic variation in a polytypic species, the cutthroat trout.Conserv. Biol. 2: 170–184.

Bernatchez, L., Guyomard, R., and Bonhomme, F. 1992. DNA se-quence variation of the mitochondrial control region among geo-graphically and morphologically remote European brown troutSalmo trutta populations. Mol. Ecol. 1: 161–173.

Chakraborty, R., and Leimar, O. 1987. Genetic variation within asubdivided population. In Population genetics and fishery man-agement. Edited by N. Ryman and F.M. Utter. University ofWashington Press, Seattle, Wash. pp. 89–120.

Cronin, M.A., Spearman, W.J., Wilmot, R.L., Patton, J.C., and Bick-ham, J.W. 1993. Mitochondrial DNA variation in chinook (On-corhynchus tshawytscha) and chum salmon (O. keta) detected byrestriction enzyme analysis of polymerase chain reaction (PCR)products. Can. J. Fish. Aquat. Sci. 50: 708–715.

Cross, T.F., Mills, C.P.R., and Courcy Williams, M. de. 1992. Anintensive study of allozyme variation in freshwater resident andanadromous trout, Salmo trutta L., in Western Ireland. J. Fish Biol.40: 25–32.

Crozier, W.W., and Ferguson, A. 1986. Electrophoretic examinationof the population structure of brown trout (Salmo trutta) from theLough Neagh catchment, Northern Ireland. J. Fish Biol.28: 459–477.

DeSalle, R., Templeton, A., Mori, I., Pletscher, S., and Johnston, J.S.1987. Temporal and spatial heterogeneity of mtDNA polymor-phisms in natural populations of Drosophila mercatorum. Genetics,116: 215–223.

Elliott, J.M. 1994. Quantitative ecology and the brown trout. OxfordUniversity Press, Oxford, U.K.

Elo, K. 1993. Gene flow and conservation of genetic variation inanadromous Atlantic salmon (Salmo salar). Hereditas (Lund),119: 149–159.

Endler, J.A. 1986. Natural selection in the wild. Princeton UniversityPress, Princeton, N.J.

Excoffier, L., Smouse, P.E., and Quattro, J.M. 1992. Analysis of mo-lecular variance inferred from metric distances among DNAhaplotypes: application to human mitochondrial DNA restrictiondata. Genetics, 131: 479–491.

Felsenstein, J. 1993. PHYLIP (Phylogeny Inference Package, version3.5c). Department of Genetics, SK-50, University of Washington,Seattle, Wash.

Ferguson, A. 1989. Genetic differences among brown trout (Salmotrutta) stocks and their importance for the conservation and man-agement of the species. Freshwater Biol. 21: 35–46.

Fitch, W.M., and Margoliash, E. 1967. Construction of phylogenetictrees. Science (Washington, D.C.), 155: 279–284.

Gall, G.A.E. 1987. Inbreeding. In Population genetics and fisherymanagement. Edited by N. Ryman and F.M. Utter. University ofWashington Press, Seattle, Wash. pp. 47–87.

Gilpin, M. 1991. The genetic effective size of a metapopulation. Biol.J. Linn. Soc. 42: 165–175.

Giuffra, E., Bernatchez, L., and Guyomard, R. 1994. Mitochondrialcontrol region and protein coding genes sequence variation amongphenotypic forms of brown trout Salmo trutta from Northern Italy.Mol. Ecol. 3: 161–172.

Gliddon, C., and Goudet, J. 1994. The genetic structure of metapopu-lations and conservation biology. In Conservation genetics. Editedby V. Loeschcke, J. Tomiuk, and S.K. Jain. Birkhäuser Verlag,Basel, Switzerland. pp. 107–114.

Guo, S.W., and Thompson, E.A. 1992. Performing the exact test forHardy–Weinberg proportion for multiple alleles. Biometrics,48: 361–372.

Hamilton, K.E., Ferguson, A., Taggart, J.B., Tomasson, T.,Walker, A., and Fahy, E. 1989. Post-glacial colonization of brown

Can. J. Fish. Aquat. Sci. Vol. 53, 19962236

© 1996 NRC Canada

trout, Salmo trutta L.: Ldh-5 as a phylogeographical marker locus.J. Fish Biol. 35: 651–664.

Hansen, M.M., and Loeschcke, V. 1996a. Genetic differentiationamong Danish brown trout (Salmo trutta L.) populations, as de-tected by RFLP analysis of PCR amplified mitochondrial DNAsegments. J. Fish Biol. 148: 422–436.

Hansen, M.M., and Loeschcke, V. 1996b. Temporal variation in mi-tochondrial DNA haplotype frequencies in a brown trout (Salmotrutta L.) population that shows stability in nuclear allele frequen-cies. Evolution, 50: 454–457.

Hansen, M.M., Loeschcke, V., Rasmussen, G., and Simonsen, V.1993. Genetic differentiation among Danish brown trout (Salmotrutta) populations. Hereditas, 118: 177–185.

Hansen, M.M., Hynes, R.A., Loeschcke, V., and Rasmussen, G.1995. Assessment of the stocked or wild origin of anadromousbrown trout (Salmo trutta L.) in a Danish river system, using mi-tochondrial DNA RFLP analysis. Mol. Ecol. 4: 189–198.

Hedrick, P.W., Hedgecock, D., and Hamelberg, S. 1995. Effectivepopulation size in winter-run chinook salmon. Conserv. Biol.9: 615–624.

Hindar, K., Jonsson, B., Ryman, N., and Ståhl, G. 1991. Genetic re-lationships among landlocked, resident, and anadromous browntrout, Salmo trutta L. Heredity, 66: 83–91.

Jordan, W.C., and Youngson, A.F. 1992. The use of genetic markingto assess the reproductive success of mature male Atlantic salmonparr (Salmo salar L.) under natural spawning conditions. J. FishBiol. 41: 613–618.

Largiadèr, C.R., and Scholl, A. 1996. Genetic introgression betweennative and introduced brown trout (Salmo trutta L.) populations inthe Rhône River Basin. Mol. Ecol. 5: 417–426.

Larsen, L.K. 1984. Populationsdynamiske undersøgelser over ørred(Salmo trutta) og regnbueørred (Salmo gairdnerii) i tilløb til HaldSø. (In Danish.) M.Sc. thesis, University of Aarhus, Aarhus, Den-mark.

Larsen, K. 1987. The sea trout spawning run into Danish streams1900–1960. II. Funen, and Eastern Jutland from the German bor-der up to and including the Randers Fjord. (In Danish.) Reportsfrom the Inland Fisheries Laboratory 1/87, Silkeborg, Denmark.

McCauley, D.E. 1991. Genetic consequences of local population ex-tinction and recolonisation. Trends Ecol. Evol. 6: 5–8.

McElroy, D., Moran, P., Bermingham, E., and Kornfield, I. 1991.REAP: the restriction enzyme analysis package. Version 4.0. De-partment of Zoology, Migratory Fish Research Institute and Cen-ter for Marine Studies, University of Maine, Orono.

Nei, M. 1987. Molecular evolutionary genetics. Columbia UniversityPress, New York.

Rasmussen, G. 1986. The population dynamics of brown trout (Salmotrutta L.) in relation to year-class size. Pol. Arch. Hydrobiol.33: 489–508.

Reynolds, J.B., Weir, B.S., and Cockerham, C.C. 1983. Estimation ofthe coancestry coefficient: basis for a short-term genetic distance.Genetics, 105: 767–779.

Rice, W.R. 1989. Analyzing tables of statistical tests. Evolution,43: 223–225.

Roff, D.A., and Bentzen, P. 1989. The statistical analysis of mito-chondrial DNA polymorphisms: χ2 and the problem of small sam-ples. Mol. Biol. Evol. 6: 539–545.

Siegismund, H.R. 1994. G-STAT, version 3. The Arboretum, theRoyal Veterinary and Agricultural University, Kirkegaardsvej 3A,DK–2970 Hørsholm, Denmark.

Simonsen, V., and Rasmussen, G. 1989. Undersøgelse af genetiskvariation hos ørred (Salmo trutta) som funktion af tid og dambrug.DFH-Rapport 367. Report from the Danish Institute for Fisheriesand Marine Research, Charlottenlund, Denmark.

Taggart, J.B., Hynes, R.A,. Prodöhl, P.A., and Ferguson, A. 1992. Asimplified protocol for routine total DNA isolation from salmonidfishes. J. Fish Biol. 40: 963–965.

Takahata, N., and Palumbi, S.R. 1985. Extranuclear differentiationand gene flow in the finite island model. Genetics, 109: 441–457.

Wade, M.J., and McCauley, D.E. 1988. Extinction and recolonisa-tion: their effects on the genetic differentiation of local popula-tions. Evolution, 42: 995–1005.

Weir, B.S. 1979. Inferences about linkage disequilibrium in randomlymating populations. Heredity, 42: 105–111.

Weir, B.S., and Cockerham, C.C. 1984. Estimating F-statistics for theanalysis of population structure. Evolution, 38: 1358–1370.

Hansen and Mensberg 2237

© 1996 NRC Canada