Genetic diversity-fitness correlation revealed by microsatellite analyses in European alpine marmots ( Marmota marmota )

Abstract

The relationship between individual genetic diversity and fitness-related traits are poorly understood in the wild. The availability
of highly polymorphic molecular markers, such as microsatellites, has made research on this subject more feasible. We used
three microsatellite-based measures of genetic diversity, individual heterozygosity H, mean d
2 and mean d
2
outbreeding to test for a relationship between individual genetic diversity and important fitness trait, juvenile survival, in a population
of alpine marmots (Marmota marmota), after controlling for the effects of ecological, social and physiological parameters that potentially influence juvenile
survival in marmots. Analyses were conducted on 158 juveniles, and revealed a positive association between juvenile survival
and genetic diversity measured by mean H. No association was found with mean d
2 and with mean d
2
outbreeding. This suggests a fitness disadvantage to less heterozygous juveniles. The genetic diversity-fitness correlation (GDFC) was
somewhat stronger during years with poor environmental conditions (i.e. wet summers). The stressful environmental conditions
of this high mountain population might enhance inbreeding depression and make this association between genetic diversity and
fitness detectable. Moreover the mating system, allowing extra pair copulation by occasional immigrants, as well as close
inbreeding, favours a wide range of individual genetic diversity (mean H ranges from 0.125 to 1), which also may have facilitated the detection of the GDFC. The results further suggest that the
observed GDFC is likely to be explained by the “local effect” hypothesis rather than by the “general effect” hypothesis.

Full text

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Genetic diversity-fitness correlation revealed by microsatellite analyses
in European alpine marmots (Marmota marmota)
A. Da Silva1,*, G. Luikart2, N. G. Yoccoz3, A. Cohas1& D. Allaine ´1
1Laboratoire de Biome´trie et Biologie Evolutive, UMR CNRS 5558, Universite´ Claude Bernard Lyon I, 43 Bd
du 11 novembre 1918, 69622, Villeurbanne cedex, France;
Genomics and Biodiversity), UMR CNRS 5553, Universite´ J. Fourier, BP53, F-38041, Grenoble, Cedex 09,
France;3Institute of Biology, University of Tromsø, 9037, Tromsø, Norway (*Corresponding author: Fax:
+33-478974920; E-mail: anne_dasilva@hotmail.com)
2Laboratoire d’Ecologie Alpine (Population
Received 24 May 2005; accepted 2 August 2005
Key words: heterozygosity, inbreeding, juvenile survival, Marmota marmota, microsatellites
Abstract
The relationship between individual genetic diversity and fitness-related traits are poorly understood in the
wild. The availability of highly polymorphic molecular markers, such as microsatellites, has made research
on this subject more feasible. We used three microsatellite-based measures of genetic diversity, individual
heterozygosity H, mean d2and mean d2outbreedingto test for a relationship between individual genetic
diversity and important fitness trait, juvenile survival, in a population of alpine marmots (Marmota
marmota), after controlling for the effects of ecological, social and physiological parameters that potentially
influence juvenile survival in marmots. Analyses were conducted on 158 juveniles, and revealed a positive
association between juvenile survival and genetic diversity measured by mean H. No association was found
with mean d2and with mean d2outbreeding. This suggests a fitness disadvantage to less heterozygous juveniles.
The genetic diversity-fitness correlation (GDFC) was somewhat stronger during years with poor envi-
ronmental conditions (i.e. wet summers). The stressful environmental conditions of this high mountain
population might enhance inbreeding depression and make this association between genetic diversity and
fitness detectable. Moreover the mating system, allowing extra pair copulation by occasional immigrants, as
well as close inbreeding, favours a wide range of individual genetic diversity (mean H ranges from 0.125 to
1), which also may have facilitated the detection of the GDFC. The results further suggest that the observed
GDFC is likely to be explained by the ‘‘local effect’’ hypothesis rather than by the ‘‘general effect’’
hypothesis.
Introduction
The relationship between individual genetic vari-
ability and fitness, or more often fitness-related
traits, has long been of interest to evolutionary and
conservation biologists (Allendorf and Leary 1986;
Houle 1989; Lacy 1993; Mitton 1993; David 1998;
Hansson and Westerberg 2002; Coltman and Slate
2003). Because of the difficulty of obtaining
pedigrees in the wild, studies commonly use
molecular markers to measure multilocus hetero-
zygosity, in order to infer individual inbreeding
coefficients. This approach examining the associ-
ation between marker heterozygosity and fitness
traits is sometimes termed genetic diversity–fitness
correlation (GDFC) approach. The strength of
GDFCs in the wild is still debated. Studies
reporting negative results exist (Rowe and Beebee
Conservation Genetics (2005)
DOI 10.1007/s10592-005-9048-y
? Springer 2005

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2001; Duarte et al. 2003) and may be underrepre-
sented because of publication bias in favour of
positive results. On the other hand, many recent
studies report a correlation between heterozygosity
at neutral microsatellite markers and individual
fitness-related traits such as survival (Coltman
et al. 1998; Coulson et al. 1998, 1999; Rossiter
et al. 2001; Markert et al. 2004), reproductive
success and recruitment (Slate et al. 2000; Ho ¨ gl-
und et al. 2002; Foerster et al. 2003; Hansson
et al. 2001, 2004; Hoffman et al. 2004; Markert
et al. 2004; Seddon et al. 2004), and disease resis-
tance (Coltman et al. 1999). In a meta-analysis,
Coltman and Slate (2003) found that positive
associations between neutral marker heterozygos-
ity and traits or components of individual fitness
were common but weak.
In addition to the controversy over the strength or
existence of GDFCs, the underlying mechanisms
causing GDFCs are also still debated. Three hypothe-
ses currently prevail (Hansson and Westerberg 2002):
first,underthedirecteffect hypothesis, selection acts
directly on the markers to induce GDFC; because
microsatellites are thought to be selectively neutral
(Queller et al. 1993; Jarne and Lagoda 1996; but
see Kashi and Soller 1999) GDFCs detected by
microsatellites are unlikely caused by direct effects.
Second, in partially inbred populations, multilocus
heterozygosity (MLH) at marker loci might reflect
variation in the individual inbreeding coefficient
(Weir and Cockerham 1973). Under this general
effect hypothesis (Weir and Cockerham 1973;
David 1998), a GDFC is generated as a result of
effects of homozygosity at loci distributed genome-
wide (Hansson and Westerberg 2002; Slate et al.
2004). Third, MLH at marker loci may reflect
heterozygosity at overdominant or dominant fit-
ness trait loci in linkage disequilibrium (LD) with
them (Hansson and Westerberg 2002; Slate et al.
2004). Under this local effect hypothesis (Hill and
Robertson 1968; Otah 1971; David 1998), a
GDFC can arise because heterozygosity will tend
to be the same both at marker and at fitness loci in
the local chromosomal vicinity (Slate et al. 2004).
Until recently, GDFCs were mostly attributed
to inbreeding depression effects (i.e. the general
effect hypothesis). Balloux et al. (2004) and Slate
et al. (2004) revealed that molecular metrics are
often only weakly correlated with individual
inbreeding coefficients (f), suggesting that correla-
tions between MLH and fitness might require a
new interpretation (Pemberton 2004). If multilocus
heterozygosity shows limited power to detect var-
iancein inbreeding, the
explanation for studies reporting GDFC would be
the local effect hypothesis rather than the general
effect hypothesis (Balloux et al. 2004; Slate et al.
2004), unless the population shows particular
patterns (such as strong inbreeding, for example
through selfing or strong population structure
and/or high levels of polygyny) (Balloux et al.
2004). Recently, substantial linkage disequilibrium
has been found in vertebrates (Reich et al. 2001;
McRae et al. 2002), and the local effect hypothesis
has received some support as in the great reed
warbler Acrocephalus arundinaceus (Hansson et al.
2001, 2004). Analyses were conducted with dyads
of full siblings of which only one individual sur-
vived to adult age. This ensured that each member
of a pair had the same f, and allowed to exclude
the general effect hypothesis. Hansson et al. (2001,
2004) thus revealed that recruited individuals
showed significantly higher MLH than their non
recruited siblings.
This study has two aims. First, we investigate
GDFC in the alpine marmot, Marmota marmota,
using the classical measure of heterozygosity (H),
and measures introduced by Coulson et al. (1998,
1999) (d2and d2outbreeding). GDFC remains difficult
to investigate in the wild, and few studies have
considered ecological variables in their analysis.
However, there is some evidence that GDFC may
be related to the harshness of the environment
(Danzmann et al. 1988; Dudash 1990; Borsa et al.
1992; Audo and Diehl 1995; Meagher et al. 1997;
Crnokrak and Roof 1999; Lesbarre ` res et al. 2005).
In this context, the alpine marmot is particularly
interesting because populations occur in high
alpine meadows, and must cope with a harsh cli-
mate and occasionally stressful conditions. More-
over, the mating system of alpine marmots is
socially monogamous, but extra pair paternity
(EPP) occurs frequently (in about 33% of litters,
Goossens et al. 1998). Extra pair males often come
from other groups or populations, which could
increase genetic diversity among individuals, and
hence the power to detect GDFC. Then, we tested
the prediction that a GDFC would be detected in
our population, particularly during stressful years.
For that, we investigated for the relationship be-
tween juvenile survival, a major fitness-related trait
in this species (Farand et al. 2002) and individual
mostparsimonious

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genetic diversity after controlling for the effects of
ecological, social and physiological parameters
assumed to potentially influence juvenile survival.
Hence, the conclusions reached here will have
practical use, and particularly for the management
of fragmented population or endangered other
marmot species, such as Marmota vancouverensis
or M. menzbieri, that are relatively more difficult
to study.
Second, we investigate more specifically the
underlying mechanism of GDFC in our popula-
tion. We tested the local effect hypothesis using the
same approach as Hansson et al. (2001, 2004): in
alpine marmots, litters of 3–6 juveniles are com-
mon, it is then possible to compare surviving and
not surviving full-sibs, and to check if survivors
have higher genetic diversity. We also tested the
general effect hypothesis using Balloux et al.’s
analytical approach (2004), based on the principle
that if average heterozygosity reflects f, then the
heterozygosity of loci within an individual should
be correlated.
Materials and methods
Study site and population
The study site is in La Sassie ` re Nature Reserve, in
the eastern part of the Vanoise National Park in
the French Alps. The elevation is about 2300 m
and the climate is typical of high alpine areas.
From 1990 to 2004, alpine marmots were caught
from the beginning of April to the end of July
during a minimum of 45 days a year. Marmots
were trapped using two-door, live-capture traps
baited with dandelion Taraxacum densleonis and
placed near the entrance of the main burrow of
each group in order allowing captured individuals
to be assigned to a family group. Once caught,
individuals were tranquillised with Zole ´ til 100
(0.1 ml kg)1) and individually marked with a
numbered ear tag and a transponder (model
ID100, TrovanTM, Germany) injected under the
skin of the neck for permanent individual recog-
nition. Each individual trapped was sexed, aged,
weighed, and measured for morphological vari-
ables such as body length. From 1992 to 1997,
hairs were collected, and since 1998, tissue biopsies
were used for genetic analyses.
The composition of 20 family groups was
assessed from capture–recapture data on 607 trap-
ped individuals from 1992 to 2002, and from
intensive observation from April to July with 10–
50? binoculars and 20–60? telescopes from a
distance of 80–200 m. Each group was observed
on average 1 h per day for a minimum of 30 h per
year. For each group, the number of yearlings,
2-year-olds and adult individuals of each sex and
their social status were recorded. Individuals were
classified as yearlings, 2-year-olds or adult indi-
viduals from their size. The sex was determined
from ano-genital distance. Each year, we recorded
the date and the litter size at emergence. Virtually
all emerged juveniles were trapped within 3 days
after emergence (Allaine ´ et al. 2000; Allaine ´ 2004).
Despite social monogamy of Marmota marmota,
extra pair copulations (EPC)
approximately 20% of juveniles would be born
from EPC (Goossens et al. 1998). Males impli-
cated in EPC are from other groups or other
populations. Indeed, the population studied is not
isolated, and exchanges of individuals occur
between neighbouring
et al. 2001).
occurred,and
populations(Goossens
Juvenile survival
Juvenile survival (i.e. survival from birth to 1 year)
was determined as the proportion of juveniles still
present as yearlings in the family group, the spring
1 year after birth. Juvenile survival was estimated
from field observations and from capture–recapture
data. The number of yearlings is a reliable indi-
cator of juvenile survival because dispersal does
not occur before 2-years-old, except in very rare
cases (Arnold 1990; Perrin et al. 1993). Trapping
effort was important and the mean recapture rate
of yearlings was 0.92 (Farand et al. 2002). Non-
recaptured yearlings were sometimes identified
from visual observation and from counts of
number of yearling alive in the family group.
Juveniles that were known to have been killed
following territory take-overs by new males and
those of unknown fate were not included in the
analyses.
Non-genetic terms
To examine GDFC, it might be necessary to cor-
rect for potential confounding non-genetic factors.

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Non-genetic factors may include both environ-
mental and individual characteristics. Because the
deleterious effects of homozygosity may be more
apparent under stressful conditions (Danzmann
et al. 1988; Dudash 1990; Borsa et al. 1992; Audo
and Diehl 1995; Meagher et al. 1997; Crnokrak
and Roof 1999; Lesbarre ` res et al. 2005), we con-
sidered two environmental factors: the cohort and
the territory quality. We considered two individual
characteristics: the sex and the body condition,
and a social factor: the presence of helpers.
Cohort (categorical variable, two levels)
Cohorts from 1991 to 2000 were considered.
Farand et al. (2002) showed that the only envi-
ronmental factor to which juvenile survival seemed
to be sensitive was summer rainfall. Wet summers
induce stressful climatic conditions because juve-
niles spend less time feeding and thus gaining
weight. We have used ‘‘Me ´ te ´ o France’’ data
(France weather service), on daily readings of
precipitation at the Tignes station (elevation,
1800 m high), the nearest station to our study site
(7 km to the reserve). Because precipitation
increases with altitude (Ozenda 1985), data from
Tignes station underestimated precipitation at our
study site. However the Tignes station is in the
same valley system as the study site and therefore
accurately reflects annual weather variability. We
separated years into two groups (‘‘good’’ years and
‘‘bad’’ years), based on summer precipitation.
The median (2.8 mm day)1over the period July–
September) was used to determine the two groups:
good=dry years (1990, 1991, 1992, 1993, 1995 and
1997), with a mean summer rainfall lower than the
median, and bad=wet years (1994, 1996, 1998,
1999 and 2000), with a mean summer rainfall
greater than the median. Using cohort as a cate-
gorical covariate helped investigating interactions
with genetic variables.
Territory quality (categorical variable, two levels)
The quality of each territory was estimated
from the date of snowmelt because early snow melt
favoured early access to food resources (Bibikov
1996; Schwartz et al. 1998). During 2 years, pho-
tographs of each territory were regularly taken
during May and June. Territories were then
ranked according to the date at which 20% of the
surface territory was accessible (no more covered
by snow), and also, 50%, 75%, and 100%. For
each territory, the ranks for the four percentages
were summed, and the sum was used as a proxy of
territory quality. Again, to keep large sample size,
the 19 territories were divided into two groups:
‘‘high quality’’ territories (12 territories) and ‘‘poor
quality’’ territories (7 territories). Territories of
high quality had a sum lower than 40, whereas
those of low quality had a sum from 55 to 84.
Body condition (continuous variable)
The juvenile body condition was the residual of
the multiple regression of juvenile body mass on
body size, age, sex and litter size. Body condition
thus corresponded to body mass corrected for (i)
structural size effect, and (ii) the difference in date
of capture among individuals, and (iii) sexual
dimorphism (Allaine ´ et al.1998), and (iv) the
trade-off between size and number of juveniles
(Allaine ´ et al. 1998). Body size was the body length
from head to tail. Age was determined from the
date of emergence from the natal burrow. A
positive residual indicated a juvenile in relatively
good condition whereas a negative residual indi-
cated a juvenile in relatively poor condition.
Sex (categorical variable, two levels)
Becausethe
monogamous (Goossens et al. 1998), the juvenile
survival is expected to be independent of the sex of
individuals (see also analyses of Farand et al.
2002). Indeed, many studies on mammals found a
correlation between sexual dimorphism and sexual
bias in survival. The sex investigating more energy
in development (generally males) would suffer
higher mortality,(Clutton-Brock
Loison 1995; Jorgenson et al. 1997; Van Horne
et al. 1997). Consequently, a sexual bias in survival
should be preferentially found in polygynous spe-
cies in which sexual dimorphism is important
(Promislow 1992).
alpinemarmotisprimarily
et al.1982;
Helpers (categorical variable, two levels)
The presence of helpers (subordinate males) in
the hibernaculum was found to be an important
factor for juvenile winter survival, probably
because they actively warm juveniles (Arnold
1993) and facilitate thermoregulation (Allaine ´
et al. 2000; Allaine ´ and Theuriau 2004). For a gi-
ven year, each group was encoded by 1 (helpers
presence) or by 0 (helpers absence).

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Molecular markers and genetic terms
We typed 187 individuals (101 males and 86
females) at up to 9 microsatellites: SS-Bibl1,
SS-Bibl4, SS-Bibl18, SS-Bibl20, SS-Bibl25, SS-
Bibl31 (Klinkicht 1993) and MS45, MS47, MS53
(Hanslik and Kruckenhauser 2000). PCR amplifi-
cation conditions in Marmota marmota are de-
scribed,for SS-Bibl1,
SS-Bibl20, SS-Bibl25, SS-Bibl31 in Goossens et al.
(1998) and for MS45, MS47, MS53 in Hanslik and
Kruckenhauser (2000). Amplified fragments were
loaded on 5% Long Ranger polyacrylamide gel
and electrophoresis was run for 3 h on an auto-
mated sequencer ABI 377TM(Applied Biosys-
tems). Microsatellite patterns were examined with
Genotyper? 2.0 (Applied Biosystem).
Tests for deviation from Hardy–Weinberg
proportions and linkage disequilibrium between
markers were implemented using GENEPOP v3.3
(Raymond and Rousset 1995). Tests were per-
formed using all dominant adults (to avoid bias
due to family group structure) and on all cohorts
gathered (to ensure adequate sample size). The
analysesof SS-Bibl1,
SS-Bibl20, SS-Bibl25, SS-Bibl31, were conducted
on 69 dominant adults (cohorts from 1992 to
2000); whereas analyses of MS45, MS47 and
MS53 were conducted on 31 dominant adults
(cohorts from 1997 to 2000), because these mark-
ers are only recently used in our population (i.e.
less individuals are typed with them and older
DNAs were no more available).
Three metrics of genome-wide diversity were
used:
H: mean individual heterozygosity. Individual
heterozygosity is classically measured by the
proportion of heterozygous loci.
In addition, we used two measures derived
from mean d2, i.e. the squared difference in num-
ber of repeat units between the two alleles of an
individual at a microsatellite locus, averaged over
all loci at which the individual was scored
(Coulson et al. 1998):
LD=log10(mean d2+1).
LDO=log10(mean d2outbreeding+1) where mean
d2outbreedingis the mean d2score excluding homo-
zygous loci (Coulson et al. 1999).
The distributions of mean d2and mean
d2outbreedingwere right-skewed. Consequently and
following Coltman et al. (1998) the logarithmic
SS-Bibl4, SS-Bibl18,
SS-Bibl4, SS-Bibl18,
transformation was used to improve the spread of
the variables.
Assuming that microsatellites evolve under the
stepwise mutation model (SMM) (Valdes et al.
1993), mean d2and mean d2outbreedingprovide a
measure of the genetic distance between parental
gamete genomes and appear particularly sensitive
to outbreeding (Coulson et al. 1998, 1999). The fit
of each locus distribution to expected distribution
under three different mutation models, the SMM,
the IAM (infinite allele model) and an intermediate
two-phase model (TPM) was tested using the
program BOTTLENECK (Cornuet and Luikart
1996). These analyses provide a test statistic, the
Wilcoxon sign-rank test, for the probability that
an observed allele distribution with a given het-
erozygosity (gene diversity) was generated under
each of the three mutation models (SMM, IAM,
TPM).
Individuals were typed with approximately the
same number of microsatellites (see Results).
Hence, no standardisation transformation was
necessary for measures of genetic diversity.
Statistical modelling of survival
We used generalized linear mixed models with the
penalized quasi-likelihood
(Breslow and Clayton 1993) and a binomial error
to examine the terms that affected juvenile sur-
vival. GLMMs were appropriate for these analyses
because data were not independent (McCullagh
and Nelder 1989). Indeed, mothers may have
several litters of one to seven juveniles, so we used
litters nested within mothers as random terms
(Steele and Hogg 2003).
(PQL) procedure
(i) The significance of the genetic terms was first
tested by fitting each genetic term separately
andwithout non-genetic
the model. Identically the significance of non-
genetic terms was tested by fitting them sepa-
rately and without genetic terms into the
model.
(ii) Then the significance of genetic terms was
assessed after taking into account potential ef-
fectsofnon-geneticfactorsonjuvenilesurvival.
In this approach, we selected a non-genetic
minimal model as described by Coltman et al.
(2001): All of the non-genetic terms were fitted
into the model. The non-genetic full model was
factors, into

Page 6

then reduced by removing each non-significant
terms.Followingreduction,theresultingmodel
consisted of only significant term (the non-ge-
netic minimal model). In order to test for an
association between genetic diversity and juve-
nile survival after having corrected for non-ge-
netic variables, genetic terms were fitted
separately in the non-genetic minimal model.
Interactions in the non-genetic model and be-
tween genetic and non-genetic terms (that re-
mainedinthenon-geneticminimalmodel)were
also assessed.
The significance of explanatory fixed (non-
genetic and genetic) terms was assessed by their
Waldstatisticswhicharedistributedasav2.WeusedR
1.8.0 (Ihaka and Gentleman 1996) for all analyses.
Underlying mechanisms of GDFC
To test for the local effect hypothesis we used the
approach described by Hansson et al. (2001, 2004)
applied to great reed warblers. Dyads of siblings
marmots of which only one individual survived to
one year were selected. Siblings within dyads were
confirmed to have the same genetic parents (all
litters with EPP – presence of mismatch with the
social father – were excluded). We tested the dif-
ferences in H, LD and LDO between siblings
within dyads with two-tailed (non-parametric)
Wilcoxon signed-rank test because the number of
dyads was small.
To test for the general effect hypothesis, we
randomly divided our sample of loci into two
groups and asked whether the heterozygosity (H)
of the first group across individuals was correlated
with the heterozygosity (H) of the second group, as
recommended by Balloux et al. (2004). Then by
resampling the data we repeated the procedure
many times to obtain a mean and a standard error
for the correlation. All of the possible combina-
tions with the eight microsatellites used were rea-
lised.
Results
Preliminary analyses
Analyses were performed on 158 juveniles (85
males and 73 females), because survival or identity
of mother was undetermined for 29 juveniles in the
initial sample of 187 individuals. About 54 litters
bred by 27 mothers were considered. The sample
size for each variable was 158, except for body
condition (151 juveniles) and for the ‘‘helpers
presence’’ (140 juveniles). Mean juvenile survival
rate was 0.705 and the range across years was 0.55
to 0.95.
Microsatellite analyses revealed that one locus,
SS-Bibl25, deviated significantly from Hardy–
Weinberg proportions (deficit of heterozygotes,
P=0.0015, see Table 1); perhaps due to non
amplifying (null) allele. Further analyses, con-
ducted with all typed juveniles, revealed that at
this locus, mean heterozygosity was only 0.094
(SE=0.023,n=101) for
(SE=0.404, n=86) for females. This locus was
therefore excluded from all analyses. Hence anal-
males,and 0.622
Table 1. Polymorphism characteristics of microsatellite loci used to type juvenile marmots
Locus name NaSize range (bp) Ho HeHWEMean d2
SS-Bibl11
SS-Bibl14
SS-Bibl18
SS-Bibl20
SS-Bibl25
SS-Bibl131
MS45
MS47
MS53
6
6
6
5
5
4
3
7
3
100–114
175–193
138–150
206–220
145–155
167–173
109–113
178–190
135–145
0.61
0.51
0.68
0.29
0.41
0.25
0.52
0.68
0.52
0.65
0.52
0.71
0.29
0.62
0.32
0.59
0.74
0.61
0.10
0.67
0.06
0.99
0.0015
0.14
0.54
0.07
0.09
26.12
45.75
11.46
28.21
1.82
5.29
2.25
11.81
15.28
PCR amplification conditions in Marmota marmota are described, for SS-Bibl1, SS-Bibl4, SS-Bibl18, SS-Bibl20, SS-Bibl25, SS-Bibl31
in Goossens et al. (1998) and for MS45, MS47, MS53 in Hanslik and Kruckenhauser (2000).
Na: number of alleles; Ho: observed Heterozygosity; He: unbiased expected heterozygosity (Nei 1978); HWE: significance level of
deviation in expected genotype frequencies from Hardy–Weinberg expectation calculated by Fisher’s exact test (Raymond and Rousset
1995).

Page 7

yses were conducted with eight microsatellites. A
total of 66% of juveniles were successfully typed
on 8 microsatellites and 34% on 7 microsatellites.
There was no evidence for allelic disequilibrium
between any loci (P>0.05 for all pairs of loci).
Mean H was 0.54±0.035, and ranged from 0.125
to 1; mean d2was 1.1026±0.133 and ranged from
0.3 to 1.9, and mean d2outbreedingwas 1.365±0.112
and ranged from 0.7 to 2.1.
Tests of mutation model (BOTTLENECK
analyses) showed that the distribution of the 8
microsatellites included into the analyses did not
depart from the distribution expected under the
SMM model (P=0.25, n=158). On the contrary it
departed from distribution expected under the two
other models (IAM: P=0.0039, n=158, TPM:
P=0.004, n=158).
Modelling juvenile survival
When
P=0.27), nor territory quality (v2=0.11, P=0.73),
nor juvenile body condition (v2=0.44, P=0.50)
affected juvenile survival. The cohort significantly
affected juvenile survival (v2=7.97, P=0.0086). In
particular, juvenile survival was higher during dry
years (‘‘good’’ years, mean survival=0.88) than
duringwet years (‘‘bad’’
vival=0.62). The presence of helpers affected
juvenile survival (v2=5.45, P=0.02); mean juve-
nile survival appeared to be higher when helpers
were present (0.76 vs. 0.59). However, when all the
considered separately,sex(v2=1.22,
years,mean sur-
non-genetic terms were considered together, the
‘‘cohort’’ was the only non-genetic term signifi-
cantly associated with juvenile survival (Table 2).
The ‘‘cohort’’ was consequently the unique factor
included into the minimal non-genetic model.
Genetic factors were also considered separately
and alone. In this case, H showed a positive
associationwith juvenile
P=0.026), whereas no significant association was
found with LD (v2=2.57, P=0.11), and with
LDO (v2=1.32, P=0.25).
To assess the influence of genetic factors after
taking into account the effect of non-genetic terms,
we built three models by adding each of the three
genetic terms (H, LD and LDO) separately into the
non-genetic minimal model (i.e. with the factor
‘‘cohort’’). H affected juvenile survival significantly
(v2=5.74, P=0.018). This disadvantage to homo-
zygous individuals was stronger during bad ‘‘years’’,
with an interaction between the ‘‘cohort’’ and H
(v2=3.31, P=0.036, one-tailed test). No association
appeared between LD and juvenile survival or
between LDO and juvenile survival (Table 2).
survival(v2=5.04,
Underlying mechanisms of GDFC
We tested if one particular locus significantly
influenced our results. We fitted, separately, d2and
H values based on each individual locus, and mean
d2and H scores calculated by omitting a single
locus, in the selected non-genetic model (Table 3).
Significantassociations werefound between
Table 2. Generalised linear mixed model of juvenile survival
Model terms Wald statistic (v2) Coefficient (SE) (with numbers of
degrees of freedom associated
to each parameter)
P-value Direction of associations
Non-genetic terms
Cohort7.97 2.29 (±0.814) df=270.0086Better survival
during ‘‘good’’ years
Sex
Territory quality
Birth weight
helpers
Genetic terms
H
1.40
0.006
0.60
2.11
)0.44 (±0.373) df=99
0.07 (±0.970) df=27
0.0026 (±0.003) df=96
0.93 (±0.64) df=23
0.23
0.93
0.44
0.15
5.742.90 (±1.212) df=990.018Disadvantage to
homozygotes
LD
LDO
2.55
1.04
0.909 (±0.585) df=99
0.657 (±0.642) df=99
0.10
0.31
Sample Size=158. The significance of each genetic and non-genetic term is assessed taking into account for the factor ‘‘cohort’’.

Page 8

survival and H values of MS45 (P=0.017,
v2=5.838), SS-Bibl20 (P=0.034, v2=4.615) and
SS-Bibl131 (P=0.019, v2=5.666). However, when
MS45, SS-Bibl20 and SS-Bibl131, were excluded
(each one separately), P-values were less than 0.10
(withH scores respectively:
P=0.078, v2=3.158; without SS-Bibl20 P=0.002,
v2=9.826; and without SS-Bibl131, P=0.089,
v2=2.933). Moreover, when MS45, SS-Bibl20 and
SS-Bibl131 were all excluded at the same time, the
P-value for H score was still less than 0.1
(P=0.075, v2=3.12). These results indicated that
these three loci (and particularly MS45 and SS-
Bibl131) have certainly a predominant influence on
our results but, globally most or all the loci seemed
to play a role in the final result. When omitting the
other loci (one at a time), association between H
and juvenile survival was always significant (Ta-
ble 3). Analyses with d2revealed that when the d2
value was based on each individual locus, a sig-
nificant associationappeared
(P=0.003, v2=9.264), and when we excluded loci
one by one no association was found significant.
We were able to define 17 dyads of juveniles
after having excluded litters with EPP and litters
where all juveniles died or survived. Surviving
withoutMS45,
with MS45
individuals showed not significantly higher H
(P=0.57, n=17) nor LD (P=0.85, n=17) nor
LDO (P=0.89, n=17) than their non surviving
siblings.
The correlation between H based on one half of
the genetic markers and H based on the other half
on the 8 microsatellites used was calculated 35
times by resampling the data (all combinations
were considered). The mean correlation coefficient
was 0.081 (95% CI=0.070–0.091).
Discussion
We tested for a relationship between individual
genetic diversity and fitness in alpine marmots,
when non-genetic (ecological, social and physio-
logical) confounding effects were taken into
account. Fitness was measured by juvenile sur-
vival, an important component of fitness in this
species (Farand et al. 2002).
Non-genetic factors
The survival of juveniles was not affected by their
sex. This result was expected for a primarily
Table 3. An evaluation of the influence of individual loci on the model of the relationship between heterozygosity, mean d2and
juvenile survival
Locus nameH: loci considered one by oned2: loci considered one by one
P-valueWald Statistic (v2)P-value Wald Statistic (v2)
MS45
MS47
MS53
SS-Bibl11
SS-Bibl14
SS-Bibl18
SS-Bibl20
SS-Bibl131
0.0179
0.311
0.201
0.098
0.117
0.484
0.034
0.019
5.838
1.038
1.659
2.777
2.495
0.493
4.615
5.666
0.003
0.742
0.306
0.889
0.164
0.405
0.957
0.613
9.264
0.108
1.055
0.019
1.960
0.699
0.003
0.257
MS45
MS47
MS53
SS-Bibl11
SS-Bibl14
SS-Bibl18
SS-Bibl20
SS-Bibl131
0.078
0.034
0.028
0.038
0.036
0.003
0.002
0.089
3.158
4.574
4.951
4.383
4.488
9.150
9.826
2.933
0.556
0.521
0.367
0.446
0.501
0.488
0.516
0.563
0.348
0.415
0.819
0.584
0.455
0.483
0.423
0.335
In the first part of the table we fitted, separately, d2and H values based on each individual locus. In the second part, mean d2and H
scores were calculated by omitting a single locus. The significance of each genetic term is assessed taking into account for the factor
‘‘cohort’’ (i.e. fitted in the non-genetic minimal model). Sample Size=158.

Page 9

monogamous species without sexual dimorphism
in size (Promislow 1992).
Surprisingly, the ‘‘body condition’’ did not
affect juvenile survival. One possible explanation
is that body condition measured a few days after
weaning reflected more maternal condition than
the critical body condition just before hiberna-
tion (Armitage et al. 1976). Another possibility is
that body condition is weakly related to survival
(Allaine ´ et al. 1998; Wood and Armitage 2002)
probably because of the importance of social
thermoregulation (Arnold 1993; Allaine ´
2000). The best way to address this question
would be to weigh juveniles just before hiber-
nation, when they are unfortunately particularly
difficult to trap.
Territory quality was evaluated by the date of
snow melt and, consequently, of access to food
which influences fat reserves (Bibikov 1996;
Schwartz et al. 1998). Contrary to our expectation,
territory quality did not affect juvenile survival. It
is possible that food quality (polyunsaturated fatty
acids, Geiser and Kenagy 1987; Florant et al.
1993) rather than access to food should be used to
measure territory quality of alpine marmots
(Mysterud et al. 2001).
The presence of helpers in family groups was
shown to affect juvenile survival during winter
(Allaine ´ et al. 2000; Allaine ´ and Theuriau 2004).
In the present study, survival was considered
over the first year of life (i.e. including survival
duringwinterand summer).
‘‘helpers’ presence’’ was significant when consid-
ered alone but no longer when considered with
the ‘‘cohort’’ effect. Likely, our dataset was not
large enough to highlight an interaction between
‘‘helpers presence’’ and ‘‘cohort’’ effects. Indeed,
the effect of the presence of helpers was decisive
during stressful environmental conditions: during
‘‘bad’’ years (mean survival was 0.52 in absence
of helpers and 0.71 in presence of helpers), but
not during good years (mean survival was 0.87
in absence of helpers and 0.90 in presence of
helpers).
As expected (Farand et al. 2002), juvenile sur-
vivalwassignificantly
‘‘cohort’’, whichincludes
(rainfalls). Juvenile survival was lower during wet
summers, probably because of reduced efficiency
in thermoregulation and access to food (juveniles
stay within burrows during rainfalls).
et al.
The effectof
associated
climatic
with
conditions
the
Occurrence of a GDFC
Recent studies have suggested that ideal condi-
tions to reveal GDFC require hundred or thou-
sands individuals and more than 10 loci (Slate and
Pemberton 2002; Coltman and Slate 2003). In spite
of a relatively small sample size (n=158), and after
controlling for the effect of non-genetic terms, our
results revealed a GDFC when genetic diversity
was measured by H. Hence our results suggest a
disadvantage to homozygous individuals, particu-
larly during stressful conditions (Danzmann et al.
1988; Dudash 1990; Borsa et al. 1992; Audo and
Diehl 1995; Meagher et al. 1997; Crnokrak and
Roof 1999; Lesbarre ` res et al. 2005). Several pro-
cesses may enhance the range of individual genetic
variability (H ranges from 0.125 to 1) making
possible the detection of a GDFC in our popula-
tion. First, virtually subordinates can disperse over
long distances (greater than 13 km, Arnold 1993;
Rassman et al. 1994). Consequently, mates may be
from genetically differentiated populations. Sec-
ond, some subordinates are philopatric and get
dominance in their natal territory. Thus, some
mates may be closely related (e.g., a dominant
male can be replaced by his son, who will conse-
quently mate with his mother). Finally, immigrant
males implicated in EPC could have genotypes
fairly distinct from local genotypes (Goossens
et al. 1998).
No association was found with LD and LDO.
In recent studies, d2measures appeared less cor-
related with fitness than H measures (Hedrick
et al. 2001; Slate and Pemberton 2002), and theo-
retical results (Tsitrone et al. 2001; Goudet and
Keller 2002) have postulated that conditions where
fitness should be more closely related with d2than
with H are limited and remain to be clearly iden-
tified (admixture for example). Such conditions
seem not to be met in our study where only H was
associated with juvenile survival.
Underlying mechanisms of GDFC
Analyses of the individual contribution of each
locus revealed that MS45 and SS-Bibl131 had a
strong influence on the results. We can hypothesize
that these loci are located near genes and reflect
mainly the heterozygosity of these genes through
linkage disequilibrium (Hill and Robertson 1968;
Otah 1971; David 1998), which remains to be

Page 10

confirmed. However the GDFC we found cannot
be fully explained by these two microsatellites, and
seems to be affected by most or all microsatellites.
To better test the local hypothesis, we considered
dyads of siblings (and hence showing the same f),
as suggested by Hansson et al. (2001; 2004). Sur-
viving juveniles were not more heterozygous than
their dead counterparts. Thus, the local hypothesis
is poorly supported. However, we were able to
consider only 17 dyads, and our results may have
suffered from a lack of statistical power. Finally to
test the general hypothesis we investigated whether
heterozygosity (H) at half of the loci was corre-
lated with heterozygosity at the other half, as
recommended by Balloux et al. (2004). The het-
erozygosity-heterozygosity correlation found in
the data was significantly different from zero,
indicating a possible genome wide effect (Pem-
berton 2004), but remained low (mean =0.081).
We then conclude that the general effect, if it oc-
curred in our population, is limited. It seems likely
that the underlying mechanism of the observed
GDFC is linkage disequilibrium as suggested by
recent studies (Balloux et al. 2004; Hansson et al.
2004; Pemberton 2004; Slate et al. 2004), rather
than inbreeding depression. However, further
analyses with more dyads are required to clearly
test the two hypotheses.
Conclusion
This study provides evidence for a positive influ-
ence of genetic diversity on juvenile survival in the
European Alpine marmot, Marmota marmota.
These results have been obtained after controlling
genetic analyses for ecological variables such as
results could have very practical management
implications and could be generalised to other
marmot species. We suggest that genetic factors
should be considered, along with non-genetic fac-
tors, in the management of fragmented or endan-
gered species such as Marmota vancouverensis or
M. menzbieri, two species that are relatively diffi-
cult to study. Moreover, considering our species,
EPC with males from adjacent populations could
have major impact on the population by increasing
offspring heterozygosity and fitness and then by
reducing potential effects of inbreeding (mating
between relatives).Managementof alpine
marmots should consider monitoring and main-
taining connectivity among populations.
Acknowledgments
We thank the Vanoise National Park for the access
to the park, the region Rho ˆ ne-Alpes, and the
CNRS for their financial support. We are grateful
to C. Miquel, M. Gaudeul for their help and
support, to B. Goossens for his help in typing
individuals, and to D. Coltman for helpful advice.
We also thank Me ´ te ´ o France for kindly providing
us with weather data.
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