Molecular Ecology (2003)

12

, 2399–2407 doi: 1046/j.1365-294X.2003.01925.x

© 2003 Blackwell Publishing Ltd

Blackwell Publishing Ltd.

Aggressiveness is associated with genetic diversity in landlocked salmon (

Salmo salar

)

KATRIINA TI IRA,

*

ANSSI LAURILA,

*‡

NINA PEUHKURI ,

*§

JORMA PI IRONEN,

†

ESA RANTA

*

and CRAIG R. PRIMMER

*

*

Integrative Ecology Unit, Department of Ecology and Systematics, Division of Population Biology, PO Box 65, FIN-00014, University of Helsinki, Finland,

†

Joensuu Game and Fisheries Research, Kauppakatu 18–20, FIN-80100 Joensuu, Finland

Abstract

The amount of intraindividual genetic variation has often been found to have profoundeffects on life history traits. However, studies concerning the relationship between beha-viour and genetic diversity are scarce. Aggressiveness is an important component of compet-itive ability in juvenile salmonids affecting their later performance and survival. In thisstudy, we used an experimental approach to test the prediction that juveniles with low esti-mated genetic diversity should be less aggressive than juveniles with high estimatedgenetic diversity in fry from a highly endangered population of land-locked salmon (

Salmosalar

). This was achieved by using a method enabling the accurate estimation of offspringgenetic diversity based on parental microsatellite genotype data. This allowed us to createtwo groups of offspring expected to have high or low genetic diversity in which aggressivebehaviour could be compared. Salmon fry with low estimated genetic diversity weresignificantly less aggressive than fry with high estimated genetic diversity. Closer analysisof the data suggested that this difference was due to differences in more costly acts ofaggression. Our result may reflect a direct effect of genetic variation on a fitness-relatedtrait; however, we cannot rule out an alternative explanation of allele-specific phenotypematching, where lowered aggression is expressed towards genetically more similarindividuals.

Keywords

: aggression, genetic diversity, inbreeding, mean

d

2

, phenotype matching,

Salmo salar

Received 28 January 2003; revision received 29 April 2003; accepted 1 June 2003

Introduction

The amount of genetic variation can affect the fitness of anindividual depending on the similarity or divergence ofthe parental gametes (Mitton 1993). Matings between closelyrelated parents can result in progeny with lowered fitness,a phenomenon generally known as inbreeding depression(Lynch & Walsh 1998). However, genetic drift in small andisolated populations can also result in increased homozy-gosity and reduced vigour, even in the absence of matings

between close kin (Shields 1993). Heterosis, on the otherhand, is the enhanced fitness of offspring resulting frommatings between genetically differentiated populations orstrains (Mitton 1993). High overall heterozygosity decreasesthe chance of expression of deleterious recessive alleles (thedominance hypothesis) and furthermore, the heterozygotestate itself can enhance fitness relative to homozygotes (theoverdominance hypothesis). With an increase of differen-tiation between the mating individuals there is also anincreasing chance of outbreeding depression, which can beseen as lowered fitness of the offspring. Reasons for this caninvolve the breakdown of local adaptations or coadaptedgene complexes (Emlen 1991).

Exactly which measure of within individual genetic vari-ation is most appropriate for detecting genetic diversity-fitness correlations (if they exist) remains controversial(Hedrick

et al

. 2001; Wang

et al

. 2002). On a number of occa-sions, positive correlations between heterozygosity and

Correspondence: Craig Primmer. Fax: + 358 9191 57694; E-mail:craig.primmer@helsinki.fi‡Present address: Department of Population Biology, EvolutionaryBiology Centre, Uppsala University, Norbyvägen 18d, SE-75236Uppsala, Sweden.§Present address: Finnish Game and Fisheries Research Institute,Kotka Unit, Saponkatu 2, FIN-48100 Kotka, Finland.

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fitness traits have been found, but several studies have alsofailed to detect them (Turelli & Ginzburg 1983; David 1998;Wang

et al

. 2002; Reed & Frankham 2003). Several studieshave suggested alternatively that the microsatellite specificgenetic diversity measure, mean

d

2

, may be a better indica-tor of outbreeding and inbreeding than heterozygosity(Coltman

et al

. 1998; Coulson

et al

. 1998, 1999; Rossiter

et al

. 2001; Heath

et al

. 2002; Höglund

et al

. 2002). Mean

d

2

is the square of the difference in repeat units between thetwo alleles at a microsatellite locus. It is based on theassumption that microsatellites (approximately) evolveaccording to the stepwise mutation model (Estoup &Cornuet 1999), and therefore alleles that are more similarin repeat unit numbers have a shorter time to coales-cence. However, more recent evaluations suggest thatheterozygosity may be a more appropriate estimator ofgenetic diversity in most cases (Hedrick

et al

. 2001; Tsitrone

et al

. 2001; Slate & Pemberton 2002), at least when a suffi-cient number of loci are analysed (Slate & Pemberton2002).

Even though studies on the effect of genetic variation onother fitness-related traits have been common (reviewedin Mitton & Grant 1984; Mitton 1993; David

et al

. 1995;Hansson & Westerberg 2002; Wang

et al

. 2002; Reed &Frankham 2003), the influence of the amount of geneticvariation on behaviour has been given little considerationother than comparing inbred and outbred laboratorystrains of mice (Barnard & Fitzsimons 1989; Eklund 1996;Meagher

et al

. 2000) and

Drosophila

(Latter & Robertson1962; Latter & Sved 1994). There is good reason to believethat aggressiveness and competitive ability are importantfitness-related traits in salmonids. Most salmonid speciesare territorial and defend aggressively a feeding station inthe river (Chapman 1962; Keenleyside & Yamamoto 1962).The ability to win and defend a feeding station is a crucialtrait for the juveniles, as fish without a feeding station aremore likely to die (Elliot 1986). In addition, dominant andmore aggressive Atlantic salmon (

Salmo salar

) with highermetabolic rates are more likely to adopt a faster develop-mental pathway, whereas fish with poorer competitiveability exhibit slower growth, later migration age and possiblyalso later maturation (Metcalfe

et al

. 1989; Metcalfe 1991;Metcalfe 1998; Nicieza & Metcalfe 1999). Consequently,aggressiveness can be seen as a fitness-related behaviouraltrait in salmonids. However, it is important to note that theoptimal level of aggression depends on the situation, andas high aggressiveness also carries high energetic costs (Li& Brocksen 1977; Sneddon

et al

. 1999), the highest possibleaggressiveness is not always the best solution.

In this study, we used an experimental approach to testthe prediction that juveniles with low estimated geneticdiversity should be less aggressive than juveniles withhigh estimated genetic diversity in a highly endangeredpopulation of Finnish land-locked salmon. This was achieved

by using a method enabling the accurate estimation of off-spring genetic diversity based on parental genotype data(Primmer

et al

. 2003). This allowed us to create two groupsof offspring expected to have high or low genetic diversityin which aggressive behaviour could be compared.

Materials and methods

Saimaa Lake salmon is an endangered landlocked salmonpopulation living in Lake Saimaa (southeastern Finland),and its existence relies completely on stocking management(Kaukoranta

et al

. 1998; Pursiainen

et al

. 1998). The experimentwas conducted at the Saimaa Fisheries Research andAquaculture facilities in Enonkoski, eastern Finland. Parentsused in the fertilizations were the first hatchery generationof the landlocked Saimaa Lake salmon (Fig. 1). The parentfish had been marked with PIT-tags for individualrecognition. Females were year class 1990, whereas maleswere year class 1996. A large difference in the age of thefemales and males was chosen to avoid matings betweenfull- and half-siblings.

Fig. 1 The crossing scheme used to produce the test fish. We firstanalysed genetic samples from 51 females and 49 males, and usedcomputer simulations to estimate offspring genetic diversity (asmeasured by mean ) for each possible pair. Among all thepossible combinations between 51 females and 49 males we thenselected 10 pairs estimated to produce offspring with high geneticdiversity, and 10 pairs estimated to produce offspring with lowgenetic diversity. Offspring of these pairs were then used in theexperiment.

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DNA methods

DNA was chelex extracted from approximately 1 mm

3

ofadipose/dorsal fin according to the method of Estoup

et al

.(1996). The general protocol for 10

µ

L polymerase chainreactions (PCR) was as follows: 1% of the chelex extractedDNA solution, 3–6 pmol of each primer 200

µ

m

dNTP, 10 m

m

Tris-HCl (pH 8.3), 50 m

m

KCl, 1.5 m

m

MgCl

2

and 0.25/0.5U Ampli

Taq

/Ampli

Taq

Gold DNA polymerase (PerkinElmer). All reactions were carried out on either PTC100,PTC200 (MJ Research) or Mastercycler gradient (Eppendorf)thermal cyclers. For most markers, a touchdown PCRprotocol was used which consisted of 94

°

C for 10 min (min)x

°

C (x indicates marker-specific annealing temperature,see Table 1) for 60 s (s), 72

°

C for 60 s, followed by 20 cyclesof 94

°

C for 30 s, x

°

C for 30 s, 72

°

C for 30 s, with the an-nealing temperature decreasing 0.5

°

C per cycle, followedby 15 cycles of 94

°

C for 30 s (x-10)

°

C for 30 s and 72

°

C for30 s and a final 72

°

C extension of 5 min. The general PCRprofile for other loci was as follows: 94

°

C for 3 min, fol-lowed by 30–35 cycles of 94

°

C for 30 s, x

°

C (see Table 1)for 30 s, 72

°

C for 30 s, with a final 72

°

C extension of 5 min.All loci were PCR amplified separately. Following PCR,the 11 loci were electrophoresed in two ‘panels’ on anABI377 sequencer (see Primmer

et al

. (1999) for electropho-resis details). Analysis of 162 wild caught individuals fromthe same population revealed that all markers were inHardy–Weinberg equilibrium and there was only one pairof loci of 45 comparisons in significant linkage disequi-librium following correction for multiple tests, which is notmore than expected due to Type I error.

Genetic diversity estimation

Predictive offspring genetic diversity was estimated usinga method similar to that described by Primmer

et al

. (2003).Briefly, for each possible male–female pair (49 males and51 females) for which microsatellite data were available(see above), multilocus microsatellite genotypes were simu-lated for 5000 offspring. The simulation assumed Mendelianinheritance, no linkage between loci and no mutation.Pairs that were genotyped with less than nine loci were notconsidered. The mean

d

2

,

-

values and heterozygositywere then calculated for each simulated individual asoutlined in Primmer

et al

. (2003). These values wereaveraged over the 5000 offspring resulting in one value permale–female pair (2136 possible pairs) for each estimator.Genetic diversity values estimated from parental data arereferred to hereafter as mean , mean and

H

EST.

Twenty pairs of parents predicted to produce, onaverage, offspring with high or low genetic diversity (10high and 10 low families) were then chosen for artificialfertilizations, which were conducted in November 1998(Fig. 1). The choice was made initially based on values, as this measure has been suggested to reduce thebias of any single locus on the estimate (mean is mean

d

2

standardized with the locus-specific standard deviation)(Coltman

et al

. 1998). This measure was chosen becausetwo of the 11 loci used in this study exhibited consider-ably larger allele size ranges in potential parents than thenine remaining loci [

FGT1

: 24 repeat unit (r.u.);

Ssa197

: 19r.u. vs. 2–10 r.u. for the remaining loci], which could havepotentially biased estimates based on

d

2

. It is important to

Table 1 Microsatellite loci used in this study, their analyses conditions and diversity indices. A is the total number of alleles observed atthe locus and H is observed heterozygosity

This studyc Totald

Microsatellite locusa Source species PCR annealing temp. (°C) A H A H

Ssa 1711 Salmo salar 55° 4 0.61 5 0.51Ssa 1971 Salmo salar 58° 4 0.46 6 0.43Ssa 2021 Salmo salar 55° to 45°b 4 0.90 5 0.73Ssa2892 Salmo salar 55° to 45°b 2 0.45 2 0.49SSOSL4173 Salmo salar 60° to 50°b 2 0.41 4 0.60SSOSL4384 Salmo salar 50° to 40°b 3 0.45 7 0.56MST155 Salmo trutta 50° to 40°b 2 0.31 6 0.25543AE6 Salmo trutta 60° to 50°b 2 0.15 2 0.40FGT17 Oncorhynchus mykiss 60° to 50°b 4 0.28 5 0.44Omy278 Oncorhynchus mykiss 50° to 40°b 3 0.60 4 0.58Sfo89 Salvelinus fontinalis 60° 3 0.54 5 0.34

aMicrosatellite references: 1O’Reilly et al. (1996), McConnell et al. (1995a), 3Slettan et al. (1995), 4Slettan et al. (1996), 5Estoup et al. (1993), 6Estoup et al. (1998), 7Sakamoto et al. (1994), 8McConnell et al. (1995b), 9Angers et al. (1995). bTouchdown PCR protocol as in Primmer et al. (1999). cFigures based on the 40 adult fish used in this study. dMicrosatellite diversity observed in 162 wild caught individuals (Primmer et al. 2003).

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point out, however, that the and HEST values of thesame two groups also differed significantly (Table 2) andthe two groups used in this study can be considered toreflect different levels of general genetic diversity, ratherthan based only on a particular genetic diversity estimator.In no case was an individual used more than once. Thesegroups formed the basis of the experiment and are referredto hereafter as high and low genetic diversity groups. Anestimate of the level of relatedness of individuals withinthe two groups was obtained using the pairwise relatednessestimator, rxy (Queller & Goodnight 1989) as calculated inthe program kinship 1.3 (Goodnight & Queller 1999).

Rearing conditions

Fertilizations were carried out on the same day when allthe females were ripe, thus eliminating the possibility thatthe differences in aggressiveness between the low andhigh groups would relate to the spawning time of thefemales. Fertilized eggs of each family were reared in family-wise hatching compartments in a common hatcherytrough with constant through-flow of lake-water of naturaltemperature. After swim-up in June, and one week beforethe behavioural observations started, the fish were trans-ferred to family-wise tanks (bottom area 0.385 m2, waterdepth 13 cm), where the density was 150 juveniles/tank.Mortality in these tanks was minimal. The fish were fedwith commercial food (nutraG EWOS, diameter 0.6 mm)ad libitum. The study fish were kept in similar conditions,and they were subjected to the same hatchery practices.Our study was conducted in common garden conditions toassure that the possible observed differences were due toeither genetic differences or environmental parental (mainlymaternal) effects (Bernardo 1996).

Aggression trials

The aggression trials were conducted from 15 to 30 June1999, with 10 families estimated to have relatively lowestimated genetic diversity values and 10 families havinghigher estimated values (Table 2). Aggressive behaviour

was monitored for groups of six fish originating from thesame family. Three replicated groups from each familywere used, except for one family from which we had onlytwo replicated groups. The same fish were not used morethan once in the experiment. Six similarly sized individualsfrom the same family were placed into an aquarium in theafternoon, and the observations were started in the follow-ing morning. To prevent handling stress caused by anaes-thetizing and measurements, the size of the fish wasdetermined visually. Eighteen aquaria (40 cm × 25 cm, waterdepth 30 cm) were used simultaneously in the experiment.These were covered with plastic from three sides and thetop to avoid unnecessary disturbance and to prevent thefish from jumping from the aquarium. Water velocity inthe aquarium was adjusted to 3 L per minute and photo-period was 12 L:12D.

The fish were observed once a day (8.00 a.m.−12.30 p.m.)for a 3-day period, one observation session lasting for30 min. The observations were conducted blindly; that is,the observer was not aware which treatment (high/low) wasunder observation. Because food has been observed tostimulate aggressive activity (Newman 1956), the fish werefed with similar food pellets as in their holding tanks at thebeginning of each observation period. Food pellets wereintroduced into a floating plastic frame (diameter 8 cm),which prevented the pellets floating through the outletand assured that food was always provided at the samelocation.

We recorded the following aggressive behaviours:charge, chase, lateral display, nip (Keenleyside & Yamamoto1962), approach (Symons 1968) and circle ( Johnsson &Åkerman 1998). In order to assess further the importanceof different forms of aggressive acts, these behaviours werebroken down into two groups. Approach and charge, whichwere the least costly and risky behaviours, were classifiedas mild aggressions. Nip, chase, circle and lateral displaywere regarded as overt aggressions as they were morecostly behaviours (chase), required physical contact (nip)or took place in an actual fighting situation where both fishwere motivated to fight (circle, lateral display). Althoughlateral displays are not categorized conventionally as overt

d EST2

Table 2 Descriptive genetic diversity indices for the high and low genetic diversity experimental groups as estimated from the parents’genotypes (see text for details)

high group low group

Mean Mean HEST Mean Mean HEST

Max. 1.368 31.178 0.752 0.550 6.645 0.499Min. 0.706 7.1734 0.365 0.222 0.758 0.203Median 1.145 10.579 0.637 0.439 3.688 0.298Mean 1.115 15.766 0.615 0.424 3.345 0.325Variance 0.026 82.177 0.013 0.010 2.736 0.017

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aggressions, it was the best option in this case as they wereseen only in intense fighting situations. The family-wise meanweight of the fish was measured after the behaviouraltrials were completed. This was performed by weighing150 fish from each family in a bucket with standardizedwater level. The fish were measured without anaesthesia. Wedid not take individual measures in order to avoid risksincluded in anaesthesia during warm weather, especiallyin the case of an endangered species. The number of allaggressive behaviours was summed into one variable, andthe mean aggression rate over the three 30-min observa-tion sessions per aquarium was calculated and used laterin the statistical analyses. Similarly, we calculated meanovert and mean mild aggression rates for the 30-minobservation periods.

Results

We used nested anova to analyse differences in aggressionbetween the two groups of low and high genetic diversityfamilies, where the term ‘family’ was nested under group.Salmon fry in families with low estimated genetic diversitywere significantly less aggressive than salmon fry estimatedto have higher estimated genetic diversity (Fig. 2, Table 3).Family effects were not significant in each case (data notshown), suggesting that estimated family-specific geneticdiversity was a more significant determinant of aggressionthan random family effects. Breakdown of aggressions intoovert and mild categories revealed that the two groupsdiffered especially in the amount of overt aggressive acts,the most costly forms of aggression, with the low groupshowing significantly less overt aggressive behaviour(Table 3). No difference was observed in the egg size (t = 0.026,

d.f. = 18, P = 0.979) between the two groups, indicatingthat the differences in aggressiveness were not due to egg-size-related maternal effects. There was no significant cor-relation between the family specific body weight andaggressiveness (r = −0.15, P = 0.52), overt aggressions (r =−0.092, P = 0.70) or mild aggressions (r = −0.013, P = 0.96),nor was any association observed between weight and

-value (r = 0.167, P = 0.48), or HEST (r = 0.168,P = 0.48, n = 20 in each case). Therefore, weight was notincluded as a covariate in the nested analysis of variance.

Considering associations of different genetic diversityestimators at the family level, aggressiveness was cor-related positively with family wise estimated (Pearson product–moment correlation: r = 0.63, P = 0.003,n = 20, Fig. 3a) and with HEST (r = 0.62, P = 0.004, n = 20,Fig. 3b), individuals with high and HEST values

Fig. 2 Mean aggression (log-transformed + SE) for 30 min obser-vation period in two groups having either low or high estimatedgenetic diversity. The number of replicates in both groups was 10.

Table 3 Nested anova tables for the effects of genetic diversitygroup and family (nested within group) on total aggressivenessand the number of overt and mild aggressions. Our approach washierarchical, the prime interest being in the variation of totalaggression between groups and families, rather than all variablesbeing of equal importance. Therefore we have not conductedcorrections for multiple comparisons

Source MS d.f. F P

Total aggressionGroup 0.585 1 4.466 0.048Family (group) 0.131 18 0.970 0.510Error 0.135 39

Overt aggressionsGroup 1.246 1 4.829 0.041Family (group) 0.258 18 1.056 0.427Error 0.244 39

Mild aggressionsGroup 0.027 1 0.351 0.561Family (group) 0.077 18 1.006 0.474Error 0.076 39

Fig. 3 Mean aggression (log-transformed) of offspring in the high(black circles) and low (white circles) genetic diversity familiesplotted against family wise (a) values (b) HEST values.d scaled-EST

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behaving more aggressively. Further analysis of theassociations within the low and high groups revealed asignificant relationship between genetic diversity meas-ured with and aggression in the high group( : r = 0.69, P = 0.029, HEST: r = 0.56, P = 0.09), anda similar, although nonsignificant trend in the low group( : r = 0.58, P = 0.08, HEST: (r = 0.34, P = 0.34, n = 10in each case). Overt aggressions were correlated posi-tively with (r = 0.63, P = 0.003) and HEST (r = 0.59,P = 0.006), whereas no significant association betweenmild aggressions and value (r = 0.15, P = 0.52), orHest (r = 0.14, P = 0.55) was found. The mean -value(nonscaled) did not correlate significantly with eitheraggressiveness (r = −0.024, P = 0.92), overt aggressions (r =−0.004, P = 0.99), mild aggressions (r = −0.156, P = 0.51) orweight (r = 0.169, P = 0.48, n = 20 in each case)(log(x + 1)transforming the it did not affect the results). Mean

-value was associated relatively weakly with itsstandardized value mean (r = 0.503, P = 0.02) andnot with HEST (r = 0.359, P = 0.12). However, there was astrong positive correlation between mean andHEST (r = 0.942, P < 0.001, n = 20 in each case), indicatingthat these two values are more or less equivalent in thispopulation.

The average relatedness of the 10 parent pairs in the highgroup was –0.35 (SD 0.33), indicating that these parentsshare fewer alleles with each other than was expected underrandom matings. In the low group, the average related-ness of the 10 parent pairs was +0.52 (SD 0.25), the parentsbeing as related as full sibs. The relatedness values of thetwo groups were significantly different (t-test; t18 = −6.55,P < 0.001). In order to evaluate how accurate our related-ness estimates are, we simulated 1000 relatedness values(using allele frequencies from 162 wild-caught salmon)expected for relatedness levels of 0 and 0.5 between thepairs and then checked their position in the obtained fre-quency distribution. The probability of nonrelated pairsobtaining as high relatedness values as observed in thisstudy is rather low (Fig. 4).

There was a highly significant negative correlation be-tween relatedness and the estimated mean -value(r = −0.936, P < 0.001) and between relatedness and theestimated heterozygosity (r = −0.978, P < 0.001). The meand2 value also correlated negatively with relatedness (r =−0.58, P = 0.007). Aggressiveness of the offspring was cor-related negatively with the relatedness of the parents (r =−0.566, P = 0.009, n = 20, Fig. 5), indicating that the parentswhich were more related had less aggressive offspring.Similarly, overt aggressions were associated negativelywith the relatedness of the parents (r = −0.549, P = 0.012).There was no significant correlation between relatednessof the parents and family-specific mean weight (r = −0.268,P = 0.253) or mild aggressions (r = −0.160, P = 0.502, n = 20in all cases).

Discussion

Salmon fry with low estimated genetic diversity were lessaggressive than fry with higher estimated genetic diversity,indicating that the amount of genetic variation influencesa fitness-related behavioural trait in Saimaa Lake salmon.Instead of using observed values of genetic diversity in thisstudy we used indices estimated from parental genotypedata. In an earlier study, the estimated genetic diversity

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Fig. 4 The frequency distribution of pairwise relatedness valuessimulated for 1000 offspring calculated using data from 162 wild-caught Saimaa salmon for 11 microsatellite loci (Primmer et al.2003). �, Relatedness frequency distribution assuming unrela-tedness; �, frequency distribution expected for 0.5 pairwiserelatedness. Black bars indicate the proportion of pairwiseparental relatedness values of the high families used in this study,whereas white bars show the proportion of pairwise parentalrelatedness values in the low group.

Fig. 5 Mean aggression (log-transformed) of offspring in the high(black circles) and low (white circles) genetic diversity familiesplotted against the relatedness of the parents of each family.

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indices predicted accurately the observed mean geneticdiversity values in Saimaa salmon (rs = 0.666–0.898,Primmer et al. 2003). Therefore, it is reasonable to assumethat the estimated genetic diversity indices are a reliableestimation of the observed mean genetic diversity values,even though the observed values of the individuals in theexperiment were not assessed.

As the ability to obtain and defend a feeding territory isof utmost importance for the later success of an individual,competitive ability is a crucial trait in young salmonids.During the stream period juvenile salmonids experienceresource competition where only a relatively small propor-tion of fish may survive, and by autumn the density mayhave been reduced by up to 80% (Elliott 1986). Poor com-petitive ability may also result in slow growth rate and latemigration age and may affect the later reproductiveparameters (Metcalfe et al. 1989; Metcalfe & Thorpe 1990;Metcalfe 1991, 1998). The results of the present study sug-gest that the variation observed in individual salmon com-petitive ability might be explained partly by the amount ofgenetic variation; fish with less genetic diversity behavedless aggressively. The fact that the positive associationbetween genetic diversity and aggressiveness was alsoseen within the high group, and not just between thegroups, gives further support for this hypothesis. Closerexamination of aggressiveness revealed that particularlythe overt aggression forms differed between the high andlow groups. Overt aggressive behaviours are likely to beenergetically more costly, and probably more risky thanmild aggressions, and may therefore not be performed asintensively by individuals of lower genetic quality.

The observed differences in aggressiveness between thehigh and low groups can be a direct effect of the amountof genetic variation on an individual’s ability to showaggressive behaviour. However, some caution should betaken when generalizing from the present results, as all themothers of the study fish were either full- or half-siblings(Fig. 1). We avoided intentionally fertilizations betweenknown relatives, and used the same parents that are usedin normal hatchery practises to produce the fish for stock-ing purposes. Despite this, the pairwise relatedness esti-mates suggest that parents in the low group were asrelated as full-siblings. Microsatellite-based estimates ofrelatedness have been suggested to predict 29–79% of thetrue pairwise relatedness (van de Casteele et al. 2001). Inorder to evaluate the accuracy of the relatedness values, wesimulated 1000 relatedness values for 0 and 0.5 relatedness.Figure 4 shows the frequency distribution of these simu-lated relatedness values, together with the values obtainedfor the families in this study. As the probability to obtain ashigh relatedness values, as observed in this study, is ratherlow for nonrelated pairs, it is likely that offspring in thelow group were considerably more inbred than offspringin the high group. The genetic diversity in this population,

measured both with allozymes and microsatellites, is verylow (Vuorinen 1982; Primmer et al. 2000). Low geneticdiversity and the small number of parents used to producethe hatchery strains are most probably the main reasons forthe high relatedness of parents in the low group. Thiscould have also resulted in a situation where part of themutational load has been purged, and the homozygotes donot suffer as severe inbreeding depression as do the ances-tral members of a population (Waller 1993). Nevertheless,the results suggest that the low aggressiveness in the lowgroup may be a consequence of inbreeding depression.Inbreeding has been found earlier to decrease aggressive-ness in mice, as male mice from inbred lines show lessaggressive behaviour and are poorer competitors com-pared with males from outbred lines (Barnard & Fitzsimons1989; Eklund 1996; Meagher et al. 2000). Alternatively, highaggressiveness shown by fish in the high group can be anindicator of better performance due to heterosis or a com-bination of both factors.

Due to the design of the experiment used in this study,there is one additional possible explanation for the resultobtained in this study. Salmonids generally behave lessaggressively towards relatives than towards unrelated fish(Olsén 1999 and references therein; Griffiths & Armstrong2002; but see Griffiths & Armstrong 2001). Phenotypematching is one of the suggested mechanisms behind kindiscrimination (Slater 1994). According to this hypothesis,individuals either have a learned or genetically dictatedrecognition template against which individuals compareother conspecifics (Porter et al. 1983). The genetically ‘moresimilar’ siblings in the low group, which shared a highernumber of alleles with each other, might also have a moresimilar recognition template, resulting in enhanced kinrecognition, or interpreted in another way, a lowered effi-ciency of recognizing potential competitors. Both phenom-ena should lead to lowered aggression. This interpretationis supported by studies where reduced aggressivenessand inability to recognize related competitors were foundto result from insufficient genetic differentiation due toinbreeding (Nevison et al. 2000; Tsutsui et al. 2000).

In the future, the inbreeding-depression and phenotypematching hypotheses could be tested by including oneadditional treatment in the experimental setup, wherebythe aggression levels of fish from the low group would beobserved together with fish from the high group. In sucha scenario, the inbreeding depression hypothesis wouldpredict that the aggression levels of fish from the lowgroup would remain low, while the phenotype-matchinghypothesis would predict that the aggression levels of fishfrom the low group would increase, as their phenotypewould no longer be similar to their opponents’. Regardlessof which hypothesis is correct, this study provides someinteresting options for future studies of the relationshipbetween genetic variation and behavioural traits.

2406 K . T I I R A E T A L .

© 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399–2407

Acknowledgements

Saimaa Fisheries Research and Aquaculture provided excellentworking facilities, allowing us to use their Saimaa Lake salmonstock in this study. We thank Teija Aho and Tiina Berg for assistingwith the microsatellite analyses and Teija Aho for helping to con-duct the artificial fertilizations. Outi Ovaskainen helped with thebehavioural observations, and Tuuli Mäkinen took care of the fish.J. Merilä and N. Metcalfe gave constructive comments on earlierversions of this manuscript. Our research was funded by the Fin-nish ministry of education (to K.T.) and the Academy of Finland[A.L. (project no. 164206), N.P. (project no. 169049), C.R.P. (projectno. 17296) and E.R. (project no. 162961)].

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This study was a part of KT’s PhD thesis. The authors share acommon interest in combining genetic and ecological researchmethods for studying the effects of hatchery practices on indi-vidual fitness, with a view to conserving the biological diversityof exploited species.