Molecular Ecology (2003) 12, 2399 –2407
doi: 1046/j.1365-294X.2003.01925.x
Aggressiveness is associated with genetic diversity in landlocked salmon (Salmo salar)
Blackwell Publishing Ltd.
K A T R I I N A T I I R A ,* A N S S I L A U R I L A ,*‡ N I N A P E U H K U R I ,*§ J O R M A P I I R O N E N ,† E S A R A N T A * 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 profound effects on life history traits. However, studies concerning the relationship between behaviour and genetic diversity are scarce. Aggressiveness is an important component of competitive ability in juvenile salmonids affecting their later performance and survival. In this study, we used an experimental approach to test the prediction that juveniles with low estimated genetic diversity should be less aggressive than juveniles with high estimated genetic diversity in fry from a highly endangered population of land-locked salmon (Salmo salar). This was achieved by using a method enabling the accurate estimation of offspring genetic diversity based on parental microsatellite genotype data. This allowed us to create two groups of offspring expected to have high or low genetic diversity in which aggressive behaviour could be compared. Salmon fry with low estimated genetic diversity were significantly less aggressive than fry with high estimated genetic diversity. Closer analysis of the data suggested that this difference was due to differences in more costly acts of aggression. Our result may reflect a direct effect of genetic variation on a fitness-related trait; however, we cannot rule out an alternative explanation of allele-specific phenotype matching, where lowered aggression is expressed towards genetically more similar individuals. Keywords: aggression, genetic diversity, inbreeding, mean d2, 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 an individual depending on the similarity or divergence of the parental gametes (Mitton 1993). Matings between closely related parents can result in progeny with lowered fitness, a phenomenon generally known as inbreeding depression (Lynch & Walsh 1998). However, genetic drift in small and isolated populations can also result in increased homozygosity and reduced vigour, even in the absence of matings
Correspondence: Craig Primmer. Fax: + 358 9191 57694; E-mail:
[email protected] ‡Present address: Department of Population Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18d, SE-75236 Uppsala, Sweden. §Present address: Finnish Game and Fisheries Research Institute, Kotka Unit, Saponkatu 2, FIN-48100 Kotka, Finland. © 2003 Blackwell Publishing Ltd
between close kin (Shields 1993). Heterosis, on the other hand, is the enhanced fitness of offspring resulting from matings between genetically differentiated populations or strains (Mitton 1993). High overall heterozygosity decreases the chance of expression of deleterious recessive alleles (the dominance hypothesis) and furthermore, the heterozygote state itself can enhance fitness relative to homozygotes (the overdominance hypothesis). With an increase of differentiation between the mating individuals there is also an increasing chance of outbreeding depression, which can be seen as lowered fitness of the offspring. Reasons for this can involve the breakdown of local adaptations or coadapted gene complexes (Emlen 1991). Exactly which measure of within individual genetic variation is most appropriate for detecting genetic diversityfitness correlations (if they exist) remains controversial (Hedrick et al. 2001; Wang et al. 2002). On a number of occasions, positive correlations between heterozygosity and
2400 K . T I I R A E T A L . fitness traits have been found, but several studies have also failed to detect them (Turelli & Ginzburg 1983; David 1998; Wang et al. 2002; Reed & Frankham 2003). Several studies have suggested alternatively that the microsatellite specific genetic diversity measure, mean d2, may be a better indicator 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 d2 is the square of the difference in repeat units between the two alleles at a microsatellite locus. It is based on the assumption that microsatellites (approximately) evolve according to the stepwise mutation model (Estoup & Cornuet 1999), and therefore alleles that are more similar in repeat unit numbers have a shorter time to coalescence. However, more recent evaluations suggest that heterozygosity may be a more appropriate estimator of genetic diversity in most cases (Hedrick et al. 2001; Tsitrone et al. 2001; Slate & Pemberton 2002), at least when a sufficient number of loci are analysed (Slate & Pemberton 2002). Even though studies on the effect of genetic variation on other fitness-related traits have been common (reviewed in 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 genetic variation on behaviour has been given little consideration other than comparing inbred and outbred laboratory strains of mice (Barnard & Fitzsimons 1989; Eklund 1996; Meagher et al. 2000) and Drosophila (Latter & Robertson 1962; Latter & Sved 1994). There is good reason to believe that aggressiveness and competitive ability are important fitness-related traits in salmonids. Most salmonid species are territorial and defend aggressively a feeding station in the river (Chapman 1962; Keenleyside & Yamamoto 1962). The ability to win and defend a feeding station is a crucial trait for the juveniles, as fish without a feeding station are more likely to die (Elliot 1986). In addition, dominant and more aggressive Atlantic salmon (Salmo salar) with higher metabolic rates are more likely to adopt a faster developmental pathway, whereas fish with poorer competitive ability exhibit slower growth, later migration age and possibly also later maturation (Metcalfe et al. 1989; Metcalfe 1991; Metcalfe 1998; Nicieza & Metcalfe 1999). Consequently, aggressiveness can be seen as a fitness-related behavioural trait in salmonids. However, it is important to note that the optimal level of aggression depends on the situation, and as high aggressiveness also carries high energetic costs (Li & Brocksen 1977; Sneddon et al. 1999), the highest possible aggressiveness is not always the best solution. In this study, we used an experimental approach to test the prediction that juveniles with low estimated genetic diversity should be less aggressive than juveniles with high estimated genetic diversity in a highly endangered population of Finnish land-locked salmon. This was achieved
by using a method enabling the accurate estimation of offspring genetic diversity based on parental genotype data (Primmer et al. 2003). This allowed us to create two groups of offspring expected to have high or low genetic diversity in which aggressive behaviour could be compared.
Materials and methods Saimaa Lake salmon is an endangered landlocked salmon population living in Lake Saimaa (southeastern Finland), and its existence relies completely on stocking management (Kaukoranta et al. 1998; Pursiainen et al. 1998). The experiment was conducted at the Saimaa Fisheries Research and Aquaculture facilities in Enonkoski, eastern Finland. Parents used in the fertilizations were the first hatchery generation of the landlocked Saimaa Lake salmon (Fig. 1). The parent fish had been marked with PIT-tags for individual recognition. Females were year class 1990, whereas males were year class 1996. A large difference in the age of the females and males was chosen to avoid matings between full- and half-siblings.
Fig. 1 The crossing scheme used to produce the test fish. We first analysed genetic samples from 51 females and 49 males, and used computer simulations to estimate offspring genetic diversity (as 2 measured by mean d scaled ) for each possible pair. Among all the possible combinations between 51 females and 49 males we then selected 10 pairs estimated to produce offspring with high genetic diversity, and 10 pairs estimated to produce offspring with low genetic diversity. Offspring of these pairs were then used in the experiment. © 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399 –2407
A G G R E S S I O N A N D G E N E T I C D I V E R S I T Y I N S A L M O N 2401 Table 1 Microsatellite loci used in this study, their analyses conditions and diversity indices. A is the total number of alleles observed at the locus and H is observed heterozygosity This studyc
Totald
Microsatellite locusa
Source species
PCR annealing temp. (°C)
A
H
A
H
Ssa 1711 Ssa 1971 Ssa 2021 Ssa2892 SSOSL4173 SSOSL4384 MST155 543AE6 FGT17 Omy278 Sfo89
Salmo salar Salmo salar Salmo salar Salmo salar Salmo salar Salmo salar Salmo trutta Salmo trutta Oncorhynchus mykiss Oncorhynchus mykiss Salvelinus fontinalis
55° 58° 55° to 45°b 55° to 45°b 60° to 50°b 50° to 40°b 50° to 40°b 60° to 50°b 60° to 50°b 50° to 40°b 60°
4 4 4 2 2 3 2 2 4 3 3
0.61 0.46 0.90 0.45 0.41 0.45 0.31 0.15 0.28 0.60 0.54
5 6 5 2 4 7 6 2 5 4 5
0.51 0.43 0.73 0.49 0.60 0.56 0.25 0.40 0.44 0.58 0.34
references: 1O’Reilly et al. (1996), McConnell et al. (1995a), 3Slettan et al. (1995), 4Slettan et al. (1996), 5Estoup et al. (1993), 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).
aMicrosatellite 6Estoup
DNA methods
Genetic diversity estimation
DNA was chelex extracted from approximately 1 mm3 of adipose/dorsal fin according to the method of Estoup et al. (1996). The general protocol for 10 µL polymerase chain reactions (PCR) was as follows: 1% of the chelex extracted DNA solution, 3 – 6 pmol of each primer 200 µm dNTP, 10 mm Tris-HCl (pH 8.3), 50 mm KCl, 1.5 mm MgCl2 and 0.25/0.5 U AmpliTaq/AmpliTaq Gold DNA polymerase (Perkin Elmer). All reactions were carried out on either PTC100, PTC200 (MJ Research) or Mastercycler gradient (Eppendorf) thermal cyclers. For most markers, a touchdown PCR protocol 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 cycles of 94 °C for 30 s, x °C for 30 s, 72 °C for 30 s, with the annealing temperature decreasing 0.5 °C per cycle, followed by 15 cycles of 94 °C for 30 s (x-10) °C for 30 s and 72 °C for 30 s and a final 72 °C extension of 5 min. The general PCR profile for other loci was as follows: 94 °C for 3 min, followed 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 an ABI377 sequencer (see Primmer et al. (1999) for electrophoresis details). Analysis of 162 wild caught individuals from the same population revealed that all markers were in Hardy–Weinberg equilibrium and there was only one pair of loci of 45 comparisons in significant linkage disequilibrium following correction for multiple tests, which is not more than expected due to Type I error.
Predictive offspring genetic diversity was estimated using a method similar to that described by Primmer et al. (2003). Briefly, for each possible male–female pair (49 males and 51 females) for which microsatellite data were available (see above), multilocus microsatellite genotypes were simulated for 5000 offspring. The simulation assumed Mendelian inheritance, no linkage between loci and no mutation. Pairs that were genotyped with less than nine loci were not considered. The mean d2, d 2scaled-values and heterozygosity were then calculated for each simulated individual as outlined in Primmer et al. (2003). These values were averaged over the 5000 offspring resulting in one value per male–female pair (2136 possible pairs) for each estimator. Genetic diversity values estimated from parental data are referred to hereafter as mean d 2EST, mean d 2scaled-EST and HEST. Twenty pairs of parents predicted to produce, on average, offspring with high or low genetic diversity (10 high and 10 low families) were then chosen for artificial fertilizations, which were conducted in November 1998 (Fig. 1). The choice was made initially based on d 2scaled-EST values, as this measure has been suggested to reduce the bias of any single locus on the estimate (mean d 2scaled is mean d2 standardized with the locus-specific standard deviation) (Coltman et al. 1998). This measure was chosen because two of the 11 loci used in this study exhibited considerably larger allele size ranges in potential parents than the nine remaining loci [FGT1: 24 repeat unit (r.u.); Ssa197: 19 r.u. vs. 2–10 r.u. for the remaining loci], which could have potentially biased estimates based on d2. It is important to
© 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399–2407
2402 K . T I I R A E T A L . 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
Max. Min. Median Mean Variance
low group
2 Mean d scaled-EST
Mean d 2EST
H EST
2 Mean d scaled-EST
Mean d 2EST
HEST
1.368 0.706 1.145 1.115 0.026
31.178 7.1734 10.579 15.766 82.177
0.752 0.365 0.637 0.615 0.013
0.550 0.222 0.439 0.424 0.010
6.645 0.758 3.688 3.345 2.736
0.499 0.203 0.298 0.325 0.017
point out, however, that the d 2EST and HEST values of the same two groups also differed significantly (Table 2) and the two groups used in this study can be considered to reflect different levels of general genetic diversity, rather than based only on a particular genetic diversity estimator. In no case was an individual used more than once. These groups formed the basis of the experiment and are referred to hereafter as high and low genetic diversity groups. An estimate of the level of relatedness of individuals within the two groups was obtained using the pairwise relatedness estimator, rxy (Queller & Goodnight 1989) as calculated in the program kinship 1.3 (Goodnight & Queller 1999).
Rearing conditions Fertilizations were carried out on the same day when all the females were ripe, thus eliminating the possibility that the differences in aggressiveness between the low and high groups would relate to the spawning time of the females. Fertilized eggs of each family were reared in familywise hatching compartments in a common hatchery trough with constant through-flow of lake-water of natural temperature. After swim-up in June, and one week before the behavioural observations started, the fish were transferred to family-wise tanks (bottom area 0.385 m2, water depth 13 cm), where the density was 150 juveniles/tank. Mortality in these tanks was minimal. The fish were fed with 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 to assure that the possible observed differences were due to either genetic differences or environmental parental (mainly maternal) effects (Bernardo 1996).
Aggression trials The aggression trials were conducted from 15 to 30 June 1999, with 10 families estimated to have relatively low estimated genetic diversity values and 10 families having higher estimated values (Table 2). Aggressive behaviour
was monitored for groups of six fish originating from the same family. Three replicated groups from each family were used, except for one family from which we had only two replicated groups. The same fish were not used more than once in the experiment. Six similarly sized individuals from the same family were placed into an aquarium in the afternoon, and the observations were started in the following morning. To prevent handling stress caused by anaesthetizing and measurements, the size of the fish was determined visually. Eighteen aquaria (40 cm × 25 cm, water depth 30 cm) were used simultaneously in the experiment. These were covered with plastic from three sides and the top to avoid unnecessary disturbance and to prevent the fish from jumping from the aquarium. Water velocity in the aquarium was adjusted to 3 L per minute and photoperiod 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 for 30 min. The observations were conducted blindly; that is, the observer was not aware which treatment (high/low) was under observation. Because food has been observed to stimulate aggressive activity (Newman 1956), the fish were fed with similar food pellets as in their holding tanks at the beginning of each observation period. Food pellets were introduced into a floating plastic frame (diameter 8 cm), which prevented the pellets floating through the outlet and assured that food was always provided at the same location. We recorded the following aggressive behaviours: charge, chase, lateral display, nip (Keenleyside & Yamamoto 1962), approach (Symons 1968) and circle (Johnsson & Åkerman 1998). In order to assess further the importance of different forms of aggressive acts, these behaviours were broken down into two groups. Approach and charge, which were the least costly and risky behaviours, were classified as mild aggressions. Nip, chase, circle and lateral display were regarded as overt aggressions as they were more costly behaviours (chase), required physical contact (nip) or took place in an actual fighting situation where both fish were motivated to fight (circle, lateral display). Although lateral displays are not categorized conventionally as overt © 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399 –2407
A G G R E S S I O N A N D G E N E T I C D I V E R S I T Y I N S A L M O N 2403 aggressions, it was the best option in this case as they were seen only in intense fighting situations. The family-wise mean weight of the fish was measured after the behavioural trials were completed. This was performed by weighing 150 fish from each family in a bucket with standardized water level. The fish were measured without anaesthesia. We did not take individual measures in order to avoid risks included in anaesthesia during warm weather, especially in the case of an endangered species. The number of all aggressive behaviours was summed into one variable, and the mean aggression rate over the three 30-min observation sessions per aquarium was calculated and used later in the statistical analyses. Similarly, we calculated mean overt and mean mild aggression rates for the 30-min observation periods.
d.f. = 18, P = 0.979) between the two groups, indicating that the differences in aggressiveness were not due to eggsize-related maternal effects. There was no significant correlation between the family specific body weight and aggressiveness (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 d 2scaled-EST-value (r = 0.167, P = 0.48), or HEST (r = 0.168, P = 0.48, n = 20 in each case). Therefore, weight was not included as a covariate in the nested analysis of variance. Considering associations of different genetic diversity estimators at the family level, aggressiveness was correlated positively with family wise estimated d 2scaled-EST (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 d 2scaled-EST and HEST values
Results We used nested anova to analyse differences in aggression between the two groups of low and high genetic diversity families, where the term ‘family’ was nested under group. Salmon fry in families with low estimated genetic diversity were significantly less aggressive than salmon fry estimated to have higher estimated genetic diversity (Fig. 2, Table 3). Family effects were not significant in each case (data not shown), suggesting that estimated family-specific genetic diversity was a more significant determinant of aggression than random family effects. Breakdown of aggressions into overt and mild categories revealed that the two groups differed especially in the amount of overt aggressive acts, the most costly forms of aggression, with the low group showing significantly less overt aggressive behaviour (Table 3). No difference was observed in the egg size (t = 0.026,
Fig. 2 Mean aggression (log-transformed + SE) for 30 min observation period in two groups having either low or high estimated genetic diversity. The number of replicates in both groups was 10. © 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399–2407
Table 3 Nested anova tables for the effects of genetic diversity group and family (nested within group) on total aggressiveness and the number of overt and mild aggressions. Our approach was hierarchical, the prime interest being in the variation of total aggression between groups and families, rather than all variables being of equal importance. Therefore we have not conducted corrections for multiple comparisons Source
MS
d.f.
F
P
Total aggression Group Family (group) Error
0.585 0.131 0.135
1 18 39
4.466 0.970
0.048 0.510
Overt aggressions Group Family (group) Error
1.246 0.258 0.244
1 18 39
4.829 1.056
0.041 0.427
Mild aggressions Group Family (group) Error
0.027 0.077 0.076
1 18 39
0.351 1.006
0.561 0.474
Fig. 3 Mean aggression (log-transformed) of offspring in the high (black circles) and low (white circles) genetic diversity families 2 plotted against family wise (a) d scaled-EST values (b) HEST values.
2404 K . T I I R A E T A L . behaving more aggressively. Further analysis of the associations within the low and high groups revealed a significant relationship between genetic diversity measured with d 2scaled-EST and aggression in the high group ( d 2scaled-EST : r = 0.69, P = 0.029, HEST: r = 0.56, P = 0.09), and a similar, although nonsignificant trend in the low group ( d 2scaled-EST : r = 0.58, P = 0.08, HEST: (r = 0.34, P = 0.34, n = 10 in each case). Overt aggressions were correlated positively with d 2scaled-EST (r = 0.63, P = 0.003) and HEST (r = 0.59, P = 0.006), whereas no significant association between mild aggressions and d 2scaled-EST value (r = 0.15, P = 0.52), or Hest (r = 0.14, P = 0.55) was found. The mean d 2EST-value (nonscaled) did not correlate significantly with either aggressiveness (r = −0.024, P = 0.92), overt aggressions (r = −0.004, P = 0.99), mild aggressions (r = −0.156, P = 0.51) or weight (r = 0.169, P = 0.48, n = 20 in each case)(log(x + 1) transforming the d 2EST it did not affect the results). Mean d 2EST -value was associated relatively weakly with its standardized value mean d 2scaled-EST (r = 0.503, P = 0.02) and not with HEST (r = 0.359, P = 0.12). However, there was a strong positive correlation between mean d 2scaled-EST and HEST (r = 0.942, P < 0.001, n = 20 in each case), indicating that these two values are more or less equivalent in this population. The average relatedness of the 10 parent pairs in the high group was –0.35 (SD 0.33), indicating that these parents share fewer alleles with each other than was expected under random matings. In the low group, the average relatedness of the 10 parent pairs was +0.52 (SD 0.25), the parents being as related as full sibs. The relatedness values of the two groups were significantly different (t-test; t18 = −6.55, P < 0.001). In order to evaluate how accurate our relatedness 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 the pairs and then checked their position in the obtained frequency distribution. The probability of nonrelated pairs obtaining as high relatedness values as observed in this study is rather low (Fig. 4). There was a highly significant negative correlation between relatedness and the estimated mean d 2scaled-EST-value (r = −0.936, P < 0.001) and between relatedness and the estimated heterozygosity (r = −0.978, P < 0.001). The mean d2 value also correlated negatively with relatedness (r = −0.58, P = 0.007). Aggressiveness of the offspring was correlated negatively with the relatedness of the parents (r = −0.566, P = 0.009, n = 20, Fig. 5), indicating that the parents which were more related had less aggressive offspring. Similarly, overt aggressions were associated negatively with the relatedness of the parents (r = −0.549, P = 0.012). There was no significant correlation between relatedness of the parents and family-specific mean weight (r = −0.268, P = 0.253) or mild aggressions (r = −0.160, P = 0.502, n = 20 in all cases).
Fig. 4 The frequency distribution of pairwise relatedness values simulated for 1000 offspring calculated using data from 162 wildcaught Saimaa salmon for 11 microsatellite loci (Primmer et al. 2003). , Relatedness frequency distribution assuming unrelatedness; , frequency distribution expected for 0.5 pairwise relatedness. Black bars indicate the proportion of pairwise parental relatedness values of the high families used in this study, whereas white bars show the proportion of pairwise parental relatedness values in the low group.
Fig. 5 Mean aggression (log-transformed) of offspring in the high (black circles) and low (white circles) genetic diversity families plotted against the relatedness of the parents of each family.
Discussion Salmon fry with low estimated genetic diversity were less aggressive than fry with higher estimated genetic diversity, indicating that the amount of genetic variation influences a fitness-related behavioural trait in Saimaa Lake salmon. Instead of using observed values of genetic diversity in this study we used indices estimated from parental genotype data. In an earlier study, the estimated genetic diversity © 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399 –2407
A G G R E S S I O N A N D G E N E T I C D I V E R S I T Y I N S A L M O N 2405 indices predicted accurately the observed mean genetic diversity values in Saimaa salmon (r s = 0.666 – 0.898, Primmer et al. 2003). Therefore, it is reasonable to assume that the estimated genetic diversity indices are a reliable estimation of the observed mean genetic diversity values, even though the observed values of the individuals in the experiment were not assessed. As the ability to obtain and defend a feeding territory is of utmost importance for the later success of an individual, competitive ability is a crucial trait in young salmonids. During the stream period juvenile salmonids experience resource competition where only a relatively small proportion of fish may survive, and by autumn the density may have been reduced by up to 80% (Elliott 1986). Poor competitive ability may also result in slow growth rate and late migration age and may affect the later reproductive parameters (Metcalfe et al. 1989; Metcalfe & Thorpe 1990; Metcalfe 1991, 1998). The results of the present study suggest that the variation observed in individual salmon competitive ability might be explained partly by the amount of genetic variation; fish with less genetic diversity behaved less aggressively. The fact that the positive association between genetic diversity and aggressiveness was also seen within the high group, and not just between the groups, gives further support for this hypothesis. Closer examination of aggressiveness revealed that particularly the overt aggression forms differed between the high and low groups. Overt aggressive behaviours are likely to be energetically more costly, and probably more risky than mild aggressions, and may therefore not be performed as intensively by individuals of lower genetic quality. The observed differences in aggressiveness between the high and low groups can be a direct effect of the amount of genetic variation on an individual’s ability to show aggressive behaviour. However, some caution should be taken when generalizing from the present results, as all the mothers of the study fish were either full- or half-siblings (Fig. 1). We avoided intentionally fertilizations between known relatives, and used the same parents that are used in normal hatchery practises to produce the fish for stocking purposes. Despite this, the pairwise relatedness estimates suggest that parents in the low group were as related as full-siblings. Microsatellite-based estimates of relatedness have been suggested to predict 29–79% of the true pairwise relatedness (van de Casteele et al. 2001). In order to evaluate the accuracy of the relatedness values, we simulated 1000 relatedness values for 0 and 0.5 relatedness. Figure 4 shows the frequency distribution of these simulated relatedness values, together with the values obtained for the families in this study. As the probability to obtain as high relatedness values, as observed in this study, is rather low for nonrelated pairs, it is likely that offspring in the low group were considerably more inbred than offspring in the high group. The genetic diversity in this population, © 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399–2407
measured both with allozymes and microsatellites, is very low (Vuorinen 1982; Primmer et al. 2000). Low genetic diversity and the small number of parents used to produce the hatchery strains are most probably the main reasons for the high relatedness of parents in the low group. This could have also resulted in a situation where part of the mutational load has been purged, and the homozygotes do not suffer as severe inbreeding depression as do the ancestral members of a population (Waller 1993). Nevertheless, the results suggest that the low aggressiveness in the low group may be a consequence of inbreeding depression. Inbreeding has been found earlier to decrease aggressiveness in mice, as male mice from inbred lines show less aggressive behaviour and are poorer competitors compared with males from outbred lines (Barnard & Fitzsimons 1989; Eklund 1996; Meagher et al. 2000). Alternatively, high aggressiveness shown by fish in the high group can be an indicator of better performance due to heterosis or a combination of both factors. Due to the design of the experiment used in this study, there is one additional possible explanation for the result obtained in this study. Salmonids generally behave less aggressively towards relatives than towards unrelated fish (Olsén 1999 and references therein; Griffiths & Armstrong 2002; but see Griffiths & Armstrong 2001). Phenotype matching is one of the suggested mechanisms behind kin discrimination (Slater 1994). According to this hypothesis, individuals either have a learned or genetically dictated recognition template against which individuals compare other conspecifics (Porter et al. 1983). The genetically ‘more similar’ siblings in the low group, which shared a higher number of alleles with each other, might also have a more similar recognition template, resulting in enhanced kin recognition, or interpreted in another way, a lowered efficiency of recognizing potential competitors. Both phenomena should lead to lowered aggression. This interpretation is supported by studies where reduced aggressiveness and inability to recognize related competitors were found to result from insufficient genetic differentiation due to inbreeding (Nevison et al. 2000; Tsutsui et al. 2000). In the future, the inbreeding-depression and phenotype matching hypotheses could be tested by including one additional treatment in the experimental setup, whereby the aggression levels of fish from the low group would be observed together with fish from the high group. In such a scenario, the inbreeding depression hypothesis would predict that the aggression levels of fish from the low group would remain low, while the phenotype-matching hypothesis would predict that the aggression levels of fish from the low group would increase, as their phenotype would no longer be similar to their opponents’. Regardless of which hypothesis is correct, this study provides some interesting options for future studies of the relationship between genetic variation and behavioural traits.
2406 K . T I I R A E T A L .
Acknowledgements Saimaa Fisheries Research and Aquaculture provided excellent working facilities, allowing us to use their Saimaa Lake salmon stock in this study. We thank Teija Aho and Tiina Berg for assisting with the microsatellite analyses and Teija Aho for helping to conduct the artificial fertilizations. Outi Ovaskainen helped with the behavioural observations, and Tuuli Mäkinen took care of the fish. J. Merilä and N. Metcalfe gave constructive comments on earlier versions of this manuscript. Our research was funded by the Finnish ministry of education (to K.T.) and the Academy of Finland [A.L. (project no. 164206), N.P. (project no. 169049), C.R.P. (project no. 17296) and E.R. (project no. 162961)].
References Angers BL, Bernatchez A, Angers & Desgroseillers L (1995) Specific microsatellite loci for brook char reveal strong population subdivision on a microgeographic scale. Journal of Fish Biology, 47 (Suppl. A), 177–185. Barnard CJ, Fitzsimons J (1989) Kin recognition and mate choice in mice: fitness consequences of mating with kin. Animal Behaviour, 38, 35–40. Bernardo J (1996) Maternal effects in animal ecology. American Zoologist, 36, 83–105. van de Casteele T, Galbusera P, Matthysen E (2001) A comparison of microsatellite-based pairwise relatedness estimators. Molecular Ecology, 10, 1539–1549. Chapman DW (1962) Aggressive behaviour in juvenile coho salmon as a cause of emigration. Journal of the Fisheries Research Board of Canada, 19, 1047–1079. Coltman DW, Bowen WD, Wright JM (1998) Birth weight and neonatal survival of harbour seal pups are positively correlated with genetic variation measured by microsatellites. Proceedings of the Royal Society of London B, 265, 803 – 809. Coulson T, Albon S, Slate J, Pemberton J (1999) Microsatellites loci reveal sex-dependent responses to inbreeding and outbreeding in red deer calves. Evolution, 53, 1951–1960. Coulson TN, Pemberton JM, Albon SD et al. (1998) Microsatellites reveal heterosis in red deer. Proceedings of the Royal Society of London B, 265, 489– 495. David P, Delay B, Berthou P, Jarne P (1995) Alternative models for allozyme-associated heterosis in the marine bivalve Spisula ovalis. Genetics, 139, 1719 –1726. David P (1998) Heterozygosity–fitness correlations: new perspectives on an old problem. Heredity, 80, 531– 537. Eklund A (1996) The effects of inbreeding on aggression in wild male house mice (Mus domesticus). Behaviour, 133, 883 –901. Elliott JM (1986) Spatial distribution and behavioural movements of migratory trout, Salmo trutta, in a Lake District stream. Journal of Animal Ecology, 55, 907– 922. Emlen JM (1991) Heterosis and outbreeding depression: a multilocus model and an application to salmon production. Fisheries Research, 12, 187–212. Estoup A, Cornuet J-M (1999) Microsatellite evolution: inferences from population data. In: Microsatellites. Evolution and Applications (eds Goldstein DB, Schlötterer C), pp. 49– 65. Oxford: Oxford University Press. Estoup A, Largiader CR, Perrot E, Chourrout D (1996) Rapid onetube DNA extraction for reliable PCR detection of fish polymorphic markers and transgenes. Molecular Marine Biology and Biotechnology, 5, 295 –298.
Estoup A, Presa P, Krieg F, Vaiman D, Guyomard R (1993) (CT)n and (GT)n microsatellites: a new class of genetic markers for Salmo trutta L. (brown trout). Heredity, 71, 488–496. Estoup A, Rousset F, Michalakis Y et al. (1998) Comparative analysis of microsatellite and allozyme markers: a case study investigating microgeographic differentiation in brown trout (Salmo trutta). Molecular Ecology, 7, 339–353. Goodnight KF, Queller DC (1999) Computer software for performing likelihood tests of pedigree relationship using genetic markers. Molecular Ecology, 8, 1231–1234. Griffiths SW, Armstrong JD (2001) The benefits of genetic diversity outweigh those of kin association in a territorial animal. Proceedings of the Royal Society of London B, 268, 1293–1296. Griffiths SW, Armstrong JD (2002) Kin-biased territory overlap and food sharing among Atlantic salmon juveniles. Journal of Animal Ecology, 71, 480–486. Hansson B, Westerberg L (2002) On the correlation between heterozygosity and fitness in natural populations. Molecular Ecology, 11, 2467–2474. Heath DD, Bryden CA, Shrimpton JM et al. (2002) Relationships between heterozygosity, allelic distance (d2), and reproductive traits in chinook salmon, Onchorhynchus tshawytscha. Canadian Journal of Fisheries and Aquatic Science, 59, 77–84. Hedrick P, Fredrickson R, Ellegren H (2001) Evaluation of d2, a microsatellite measure of inbreeding and outbreeding, in wolves with a known pedigree. Evolution, 55, 1256–1260. Höglund J, Piertney SB, Alatalo RV et al. (2002) Inbreeding depression and male fitness in black grouse. Proceedings of the Royal Society of London B, 269, 711–715. Johnsson J, Åkerman A (1998) Watch and learn: preview of the fighting ability of opponents alters contest behaviour in rainbow trout. Animal Behaviour, 56, 771–776. Kaukoranta M, Koljonen M-L, Koskiniemi J, Pennanen J (1998) Kala-atlas. Kalatutkimuksia nro 150 [in Finnish]. Finnish Game and Fisheries Research. Keenleyside MH, Yamamoto FT (1962) Territorial behaviour of juvenile Atlantic salmon (Salmo salar L.). Behaviour, 19, 139–169. Latter BDH, Robertson A (1962) The effects of inbreeding and artificial selection on reproductive fitness. Genetical Research, 3, 110–138. Latter BDH, Sved JA (1994) A re-evaluation of data from competitive tests shows high levels of heterosis in Drosophila melanogaster. Genetics, 137, 509–511. Li HW, Brocksen RW (1977) Approaches to the analysis of energetic costs of intraspecific competition for space by rainbow trout (Salmo gairdneri). Journal of Fish Biology, 11, 329–341. Lynch M, Walsh B (1998) Genetics and Analysis of Quantitative Traits. Sinauer Associates, Inc., USA. McConnell S, Hamilton L, Morris D et al. (1995b) Isolation of salmonid microsatellite loci and their application to the population genetics of Canadian east coast stocks of Atlantic salmon. Aquaculture, 137, 19–30. McConnell SK, O’Reilly P, Hamilton L, Wright JM, Bentzen P (1995a) Polymorphic microsatellite loci from Atlantic salmon (Salmo salar): genetic differentiation of North American and European populations. Canadian Journal of Fisheries and Aquatic Sciences, 52, 1863–1872. Meagher S, Penn DJ, Potts WK (2000) Male–male competition magnifies inbreeding depression in wild house mice. Proceedings of the National Academy of Sciences USA, 97, 3324–3329. Metcalfe NB (1991) Competitive ability influences seaward migration age in Atlantic salmon. Canadian Journal of Zoology, 69, 815 – 817. © 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399 –2407
A G G R E S S I O N A N D G E N E T I C D I V E R S I T Y I N S A L M O N 2407 Metcalfe NB (1998) The interaction between behaviour and physiology in determining life history patterns in Atlantic salmon (Salmo salar). Canadian Journal of Fisheries and Aquatic Sciences, 55 (Suppl. 1), 93–103. Metcalfe NB, Huntingford FA, Graham WD, Thorpe JE (1989) Early social status and the development of life-history strategies in Atlantic salmon. Proceedings of the Royal Society of London B, 236, 7–19. Metcalfe NB, Thorpe JE (1990) Determinants of geographical variation in the age of seaward-migrating salmon, Salmo salar. Journal of Animal Ecology, 59, 135 – 145. Mitton JB (1993) Theory and data pertinent to the relationship between heterozygosity and fitness. In: The Natural History of Inbreeding and Outbreeding: Theoretical and Empirical Perspectives (ed. Thornhill NW), pp. 17–41. University of Chicago Press, Chicago. Mitton JB, Grant MC (1984) Associations among protein heterozygosity, growth rate, and developmental homeostasis. Annual Reviews of Ecology and Systematics, 15, 479 – 499. Nevison CM, Barnard CJ, Beynon RJ, Hurst JL (2000) The consequences of inbreeding for recognizing competitors. Proceedings of the Royal Society of London B, 267, 687 – 694. Newman MA (1956) Social behaviour and interspesific competition in two trout species. Physiological Zoology, 29, 64 – 81. Nicieza AG, Metcalfe NB (1999) Cost of rapid growth: risk of aggression is high for fast-growing salmon. Functional Ecology, 13, 793–800. O’Reilly PT, Hamilton LC, McConnell SK, Wright JM (1996) Rapid detection of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotide and tetranucleotide microsatellites. Canadian Journal of Fisheries and Aquatic Sciences, 53, 2292–2298. Olsén H (1999) Present knowledge of kin discrimination in salmonids. Genetica, 104, 295– 299. Porter RH, Matochik JA, Makin JW (1983) Evidence for phenotype matching in spiny mice (Acomys cahirinus). Animal Behaviour, 31, 978–984. Primmer CR, Aho T, Piironen J et al. (1999) Microsatellite analysis of hatchery stocks and natural populations of Arctic charr, Salvelinus alpinus, from the Nordic region: implications for conservation. Hereditas, 130, 277 – 289. Primmer CR, Koskinen MT, Piironen J (2000) The one that did not get away: individual assignment using microsatellite data detects a case of fishing competition fraud. Proceedings of the Royal Society of London B, 267, 1699 – 1704. Primmer CR, Landry P-A, Ranta E et al. (2003) Prediction of offspring fitness based on parental genetic diversity in endangered salmonid populations. Journal of Fish Biology, in press. Pursiainen M, Makkonen J, Piironen J (1998) Maintenance and exploitation of landlocked salmon, Salmo salar m. sebago, in the Vuoksi watercourse. In: Stocking and Introduction of Fish. Fishing News Book (ed. Cowx IG), pp. 46 – 58. Blackwell Science Ltd, Oxford. Queller DC, Goodnight KF (1989) Estimation of genetic relatedness using allozyme data. Evolution, 43, 258 – 275.
© 2003 Blackwell Publishing Ltd, Molecular Ecology, 12, 2399–2407
Reed DC, Frankham R (2003) Correlation between fitness and genetic diversity. Conservation Biology, 17, 230–237. Rossiter SJ, Jones G, Ransome RD, Barrath EM (2001) Outbreeding increases offspring survival in wild greater horseshoe bats (Rhinolophus ferrumequinum). Proceedings of the Royal Society of London B, 268, 1055–1061. Sakamoto T, Okamoto N, Ikeda Y, Nakamura Y, Sato T (1994) Dinucleotide-repeat polymorphism in DNA of rainbow trout and its application in fisheries science. Journal of Fish Biology, 44, 1093–1096. Shields WM (1993) The natural and unnatural history of inbreeding and outbreeding. In: The Natural History of Inbreeding and Outbreeding: Theoretical and Empirical Perspectives (ed. Thornhill NW), pp. 143–169. University of Chicago Press, Chicago. Slate J, Pemberton JM (2002) Comparing molecular measures for detecting inbreeding depression. Journal of Evolutionary Biology, 15, 20–31. Slater PJB (1994) Kinship and altruism. In: Behaviour and Evolution (eds Slater PJB, Halliday TR), pp. 193–222. Cambridge University Press, Cambridge. Slettan A, Olsaker I, Lie Ø (1995) Atlantic salmon, Salmo salar, microsatellites at the SSOSL25, SSOSL85, SSOSL311, SSOSL417 loci. Animal Genetics, 26, 281–282. Slettan A, Olsaker I, Lie Ø (1996) Polymorphic Atlantic salmon, Salmo salar L., microsatellites at the SSOSL438, SSOSL439 and SSOSL444 loci. Animal Genetics, 27, 57–58. Sneddon LU, Taylor AC, Huntingford FA (1999) Metabolic consequences of agonistic behaviour: crab fights in declining oxygen tension. Animal Behaviour, 67, 353–363. Symons PEK (1968) Increase in aggression and in social hierarchy among juvenile Atlantic salmon deprived of food. Journal of the Fisheries Research Board of Canada, 25, 2387–2401. Tsitrone A, Rousset F, David P (2001) Heterosis, marker mutational processes and population inbreeding history. Genetics, 159, 1845–1859. Tsutsui N, Suarez AV, Holway DA, Case TJ (2000) Reduced genetic variation and the success of an invasive species. Proceedings of the National Academy of Sciences USA, 97, 5948–5953. Turelli M, Ginzburg LR (1983) Should individual fitness increase with heterozygosity? Genetics, 104, 191–209. Vuorinen J (1982) Little genetic variation in the Finnish Lake salmon, Salmo salar sebago (Girard). Hereditas, 97, 189–192. Waller DM (1993) The statistics and dynamics of mating system evolution. In: The Natural History of Inbreeding and Outbreeding: Theoretical and Empirical Perspectives (ed. Thornhill NW), pp. 97 – 117. University of Chicago Press, Chicago. Wang S, Hard JJ, Utter F (2002) Genetic variation and fitness in salmonids. Conservation Genetics, 3, 321–333.
This study was a part of KT’s PhD thesis. The authors share a common interest in combining genetic and ecological research methods for studying the effects of hatchery practices on individual fitness, with a view to conserving the biological diversity of exploited species.
