Behavioral
Ecology
The official journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2015), 26(4), 959–968. doi:10.1093/beheco/arv004
Invited Review
Genetic similarity between mates predicts
extrapair paternity—a meta-analysis of bird
studies
Aneta Arct, Szymon M. Drobniak, and Mariusz Cichoń
1Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7, 30-387 Kraków, Poland
Received 5 August 2014; revised 31 December 2014; accepted 7 January 2015; Advance Access publication 9 February 2015.
Extrapair mating has been recorded in approximately 90% of investigated avian monogamous species. However, factors triggering
female mating decisions and potential fitness benefits from extrapair matings still remain poorly understood. Some studies suggest
that females mate socially with low-quality males but seek extrapair mates offering superior genes for their progeny. This mating
strategy may also help in mitigating the potential negative effects of pairing with a genetically similar mate. Here, we investigate
whether genetic similarity within a social pair may predict the occurrence of extrapair paternity (EPP) in birds. Using a meta-analytical
approach to a number of studies performed on birds, we found a positive relationship between the occurrence of EPP and the relatedness of social mates. Moreover, we found that the type of molecular markers used to estimate relatedness significantly affected the
observed effect size. Specifically, we showed that only microsatellite markers were associated with significantly positive effect sizes.
Thus, failure of some of the previous studies to detect the relationship between occurrence of EPP and the relatedness of social mates
may at least partly arise due to methodological reasons.
Key words: extrapair copulation, genetic compatibility, inbreeding avoidance, indirect benefits, microsatellite markers,
relatedness.
INTRODUCTION
Extrapair matings constitute a relatively common mating strategy
among many socially monogamous species. However, the evolution of this mating strategy is not fully understood and still stimulates strong interest among behavioral ecologists. The reasons
why females mate with extrapair males are particularly not clear
(Griffith et al. 2002; Arnqvist and Kirkpatrick 2005; Akçay and
Roughgarden 2007; Griffith 2007; Eliassen and Kokko 2008). The
evolution of female extrapair matings in socially monogamous species is usually explained in terms of indirect genetic benefits (Petrie
and Kempenaers 1998; Griffith et al. 2002). Such benefits may be
expressed in terms of good paternal genes and/or genetic compatibility of maternal and paternal genomes (Jennions and Petrie
2000; Griffith et al. 2002; Foerster et al. 2003; Mays and Hill 2004;
Neff and Pitcher 2005; Akçay and Roughgarden 2007). Despite of
a substantial research effort, this hypothesis still remains controversial and the debate has even been intensifying; for example, some
comparative studies argued that extrapair copulations may constitute a nonadaptive female behavior (Arnqvist and Kirkpatrick
Address correspondence to S. M. Drobniak. E-mail: szymek.drobniak@
uj.edu.pl.
© The Author 2015. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved. For
permissions, please e-mail: [email protected]
2005, 2007; Akçay and Roughgarden 2007, but see Griffith 2007;
Eliassen and Kokko 2008; Griffith and Immler 2009).
Avian extrapair mating systems serve as excellent models to study
female choice for indirect benefits (Sheldon et al. 1997). First, extrapair paternity (EPP) has been recorded in approximately 90% of
investigated avian species (reviewed in Griffith et al. 2002). Second,
in a majority of these species, potentially confounding nongenetic
benefits of extrapair matings are considered to be of very limited
importance (Griffith et al. 2002). Avian studies usually have concentrated on testing the “good genes” hypothesis, which predicts
that females mate with attractive or healthy extrapair males to
obtain additive genetic benefits. In contrast, the “compatible genes”
hypothesis emphasizes the potential role of nonadditive genetic
effects—combination of maternal and paternal alleles (Jennions and
Petrie 2000; Tregenza and Wedell 2000; Neff and Pitcher 2005).
According to this hypothesis, female genetic benefits may arise due
to the interaction between maternal and paternal genomic contributions. Thus, females are predicted to pursue extrapair copulations
to increase the chances of securing a paternal contribution that is
more compatible with their own genes (Zeh and Zeh 1997).
The avoidance of breeding with close relatives is regarded as a
special case of mate choice for genetic compatibility. Inbreeding is
usually associated with reduced fitness of the offspring probably
Behavioral Ecology
960
because of homozygous loci leading to the expression of recessive
deleterious alleles or loss of a general heterozygote advantage (e.g.,
Keller and Waller 2002). Many studies, including meta-analysis
on 99 avian species, confirmed low reproductive success of parents showing high genetic similarity (reviewed in Keller and Waller
2002; Spottiswoode and Møller 2004). These studies clearly suggest
that natural selection should favor avoidance of genetically similar
mates. Accordingly, females paired socially to closely related males
should be more likely to produce extrapair young (relative to other
females; Griffith et al. 2002). However, to our knowledge, only 13
out of 43 papers studying the relationship between incidence of
EPP and the relatedness of social mates reported the expected
relationship (see Table 1). More importantly, a recent meta-analysis across 23 studies of birds demonstrated an overall nonsignificant effect size, suggesting that relatedness between socially
paired individuals does not explain occurrence of EPP (Akçay and
Roughgarden 2007).
Here, we aim at testing whether the occurrence of EPP positively
correlates with the relatedness of the social pair using meta-analytical approach. Since the meta-analysis of Akçay and Roughgarden
(2007), a significant improvement in genetic analysis and genotyping
methods has been achieved, giving a better resolution in calculating
relatedness. Here, we aim at repeating this meta-analysis using an
extended set of studies. Because different genetic tools may give different resolution in estimating relatedness, in our meta-analysis, we
also test whether genetic tool used may affect mean effect size.
in the previous meta-analysis of Akçay and Roughgarden (2007).
We excluded the study of Otter et al. (2001) because it lacks information on genetic similarity between social mates (only heterozygosity of social mates was compared).
Statistical analysis
All models were run as random-effects meta-analyses, which is in
fact a weighted linear regression approach. The basic model can
be defined as:
For systematic literature search, we followed the PRISMA
(Preferred Reporting Items for Systematic reviews and MetaAnalyses) statement as much as possible (http://www.prismastatement.org/). We searched the Scopus database using various
combinations of the following keywords: extrapair copulation,
genetic similarity, birds, genetic compatibility, genetic complementarity, inbreeding avoidance, relatedness. We also solicited
unpublished results by EvolDir mailing list but failed to receive any
additional data. We considered only studies that reported: 1) exclusion and/or assignment of paternity based on molecular genetic
techniques; 2) estimation of relatedness between social mates, hereafter referred to as genetic similarity; (3) relationship between the
presence of extrapair young in the brood and relatedness of the
social mate; and 4) adequate statistics allowing to determine the
effect size and its direction. There are a number of studies, which
we had to exclude (Figure 1), although they potentially had relevant information (Otter et al. 2001; Foerster et al. 2003; Masters et
al. 2003; Kleven et al. 2005; Dowling and Mulder 2006; Oh and
Badyaev 2006; Stewart et al. 2006; Rubenstein 2007; Kudernatsch
et al. 2010; Townsend et al. 2010; Brouwer et al. 2011; Casey et
al. 2011). If a publication lacked some of the required information, or information in the publication indicated that the data of
interest were collected, but not presented, the corresponding author
or coauthors were contacted. For each of the studies, the following
data were extracted: species name, sample size, effect size, type of
the statistical procedure applied, type of molecular markers used to
estimate relatedness, and (if applicable) the number of microsatellite loci used and the type of relatedness estimator used (Table 1).
Our database includes 43 effect sizes from 39 studies, performed on
33 different species. Twenty-two of these studies were already used
where y is the response variable, µ is the intercept, that is, the overall mean effect size, a is the respective individual phylogenetic effect
of the species from the ith study (and is included in phylogenetically
controlled meta-analysis), s is the individual species effect not associated with phylogeny (included in the model if multiple effect sizes
per species are available), e is the residual (error) variance that is
present on top of the sampling variance of effect sizes, and m is the
sampling error of the ith effect size. All random effects are expected
to be drawn from appropriate normal distributions:
( )
s ~ N (0, σ I )
.
e ~ N (0, σ I )
m ~ N (0, σ M ) a ~ N 0, σ2a S
2
s
2
e
METHODS
Data collection and preparation
yi = µ + s i + ai + ei + mi ,
2
m
Relevant matrices are defined as follows: S is the expanded (by
including internal tree nodes) (co)variance matrix of phylogenetic
effects, and each Sij element is the sum of branch lengths from
root of the tree to the most recent common ancestor of nodes ij (if
the tree is scaled to unit length the matrix becomes a correlation
matrix); I is an identity matrix (i.e., Iij = 1 for i = j and Iij = 0 otherwise); M is a diagonal matrix with its diagonal terms containing
sampling variances of effect sizes. In our model, σ2m is fixed at unity
(i.e., the sampling variance is provided based on published studies
and not estimated). The phylogenetic (a) effect was included only in
some of the analyzed models (see below).
Pearson’s correlation coefficient was used as a measure of effect
size. If the original sources did not provide a correlation coefficient,
we transformed the provided statistics into a correlation coefficient
(Coltman and Slate 2003; Nakagawa and Cuthill 2007). Pearson’s
r was transformed to Fisher’s Z before analysis to improve normality of effect size distribution. Meta-analyses were performed using
restricted maximum likelihood linear mixed models in ASReml-R in R
(Butler et al. 2007; R Development Core Team 2011). This approach
is highly flexible and allows for the inclusion of random effects
together with the information on sampling variance for each estimate
and phylogenetic information (Hadfield and Nakagawa 2010).
In all analyses, the vector of Fisher-transformed effect sizes (Zr
values) was treated as the dependent variable and each Zr value was
weighted by the inverse of its sampling variance. As in most cases,
direct estimates of sampling variance of effect sizes were not available, the weights were approximated as ni − 3 + 1/τ2, where ni is
the sample size of the respective study and τ2 is the overall betweenstudies variance (estimated prior to the actual analysis; Nakagawa
and Cuthill 2007). Two random factors were specified: 1) species
ID, to account for variation in the effect sizes resulting from differences between species, and 2) residual variance (accounting for any
unexplained variability above that included as estimates’ sampling
Arct et al. • Genetic similarity between mates is associated with EPP
961
Table 1
Effect sizes for the relationship between occurrence of EPP and the relatedness of social mates
Estimation
of pairwise
relatedness
References
Species
Type of marker
Augustin et al. (2007)
Riparia riparia
Barber et al. (2005)
Tachycineta bicolor
Blackmore and
Heinsohn (2007)
Blomqvist et al. (2002)
Blomqvist et al. (2002)
Pomatostomus
temporalis
Charadrius
alexandrinus
Actitis hypoleuca
Multilocus DNA Band sharing
fingerprints
Multilocus DNA Band sharing
fingerprints
Microsatellite
Lynch
Blomqvist et al. (2002)
Calidris mauri
Bollmer et al. (2012)
Geothlypis trichas
Bouwman et al. (2006)
Charmantier et al.
(2004)
Cramer et al. (2011)
Dreiss et al. (2008)
Dreiss et al. (2008)
Durrant and Hughes
(2005)
Edly-Wright et al.
(2007)
Eimes et al. (2005)
Ferretti et al. (2011)
Foerster et al. (2006)
Fossøy et al. (2008)
Freeman-Gallant et al.
(2003)
Freeman-Gallant et al.
(2006)
Hansson et al. (2004)
Huyvaert and Parker
(2010)
Jones et al. (2012)
Jouventin et al. (2007)
Juola and Dearborn
(2012)
Kempenaers et al.
(1996)
Kleven and Lifjeld
(2005)
Kraaijeveld et al. (2004)
No
Yes
7
72 −0.2274 Noncooperative
42
Yes
Queller and
Goodnight
Emberiza
Microsatellite
Queller and
schoeniclus
Goodnight
Cyanistes caeruleus Microsatellite
Queller and
Goodnight
Thryothorus
Microsatellite/
Queller and
pleurostictus
SNP
Goodnight
Cyanistes caeruleus Microsatellite
Queller and
Goodnight
Cyanistes caeruleus Microsatellite
Queller and
Goodnight
Gymnorhina tibicen Microsatellite
Queller and
tyrannica
Goodnight
Passer domesticus
Multilocus DNA Band sharing
fingerprints
Aphelocoma
Multilocus DNA Band sharing
ultramarina
fingerprints
Tachycineta
Microsatellite
Queller and
leucorrhoa
Goodnight
Cyanistes caeruleus Microsatellite
Queller and
Goodnight
Luscina s. svecica
Microsatellite
Queller and
Goodnight
Passerculus
MHC
Band sharing
sandwichensis
Passerculus
Microsatellite
Li
sandwichensis
Acrocephalus
Microsatellite
Queller and
arundinaceus
Goodnight
Phoebastria
Multilocus DNA Band sharing
irrorata
fingerprints
Diomedea exulans Microsatellite
Li
Phoebastria
Microsatellite
Wang
irrorata
Fregata minor
MHC
Band sharing
No
10
No
6
t-test
No
6
t-test
No
7
No
5
Logistic
Reg.
GLM
28 −0.1138 Noncooperative
Yes
5
GLM
60
0.5635 Noncooperative
No
8
Correlation
21
0.0380 Cooperative
98
0.1134 Noncooperative
31
0.6792 Cooperative
65
0.0645 Noncooperative
202 −0.0134 Noncooperative
182
Emberiza
schoeniclus
Cygnus atratus
Paradoxornis
webbianus
Carpodacus
mexicanus
Carpodacus
erythrinus
Fideluca hypoleuca
Richardson et al. (2005) Acrocephalus
sechellensis
130
0.3531 Cooperative
Band sharing
No
Logistic
Reg.
Logistic
Reg.
Logistic
Reg.
t-test
0.1897 Noncooperative
Yes
Lee (2012)
Rätti et al. (1995)
41
Mann–
Whitney U
Logistic
Reg.
GLMM
Breeding
systems
Band sharing
Parus montanus
Promerova et al. (2011)
No
Z
Yes
Lampila et al. (2011)
Lindstedt et al. (2007)
N
Band sharing
Cyanistes caeruleus
Multilocus DNA
fingerprints
Multilocus DNA
fingerprints
Multilocus DNA
fingerprints
Microsatellite
Result confirms
positive
Number Statistical
relationship
of loci
test
0.2532 Noncooperative
30
0.483
Noncooperative
50
0.5871 Noncooperative
41 −0.3089 Noncooperative
61
0.0546 Noncooperative
177 −0.0144 Noncooperative
37 −0.0147 Noncooperative
No
8
No
5
Logistic
Reg.
GLM/
χ2
Logistic
Reg.
GLM
No
8
GLM
Yes
5
No
21
Logistic
41
0.5412 Noncooperative
Reg.
Logistic
144
0.1599 Noncooperative
Reg.
Correlation 246 −0.0139 Cooperative
Yes
Yes
No
0.1398 Noncooperative
129
74
0.2385 Noncooperative
0.2250 Noncooperative
No
Logistic
Reg.
t-test
Logistic
Reg.
t-test
46
0.1631 Noncooperative
No
t-test
103
0.0914 Noncooperative
53
0.1542 Noncooperative
No
Yes
Multilocus DNA Band sharing
fingerprints
Microsatellite
Queller and
Goodnight
Microsatellite
Queller and
Goodnight
Microsatellite
Queller and
Goodnight
Microsatellite
Queller and
Goodnight
Microsatellite
Wang
MHC
Band sharing
No
Multilocus DNA Band sharing
fingerprints
MHC
Band sharing
No
12
10
118 −0.2910 Noncooperative
No
9
No
8
Logistic.
Reg.
t-test
No
8
t-test
No
14
GLM
50
0.1197 Noncooperative
Yes
16
Logistic
Reg.
Spearman
45
0.3953 Noncooperative
No
Logistic
Reg.
Logistic
Reg.
65 −0.0843 Noncooperative
117 −0.0041 Noncooperative
104 −0.25
Noncooperative
44 −0.7285 Noncooperative
82
0.0691 Cooperative
Behavioral Ecology
962
Table 1 Continued
Estimation
of pairwise
relatedness
References
Species
Type of marker
Schmoll et al. (2005)
Periparus ater
Schmoll et al. (2005)
Periparus ater
Smith et al. (2005)
Dendroica
caerulescens
Stapleton et al. (2007)
Tachycineta bicolor
Suter et al. (2007)
Tarvin et al. (2005)
Emberiza
schoeniclus
Malurus splendes
Multilocus DNA Band sharing
fingerprints
Multilocus DNA Band sharing
fingerprints
Microsatellite
Index of mate
compatibility
C
Microsatellite
Queller and
Goodnight
Microsatellite
—
Thuman and Griffith
(2005)
Varian-Ramos and
Webster (2012)
Wang and Lu (2011)
Philomachus
pugnax
Malurus
melanocephalus
Parus humilis
Microsatellite
Microsatellite
Microsatellite
Microsatellite
Queller and
Goodnight
Wang
Queller and
Goodnight
Queller and
Goodnight
Result confirms
positive
Number Statistical
relationship
of loci
test
N
No
Correlation
44 −0.0100 Noncooperative
No
Correlation 202 −0.0701 Noncooperative
No
5
No
10
No
6
Yes
6
Yes
5
Yes
12
No
16
Z
Breeding
systems
Regression
92
0.1700 Noncooperative
GLM
98
0.08
Logistic
Reg.
One-way
Anova
t-test
Noncooperative
145 −0.1027 Noncooperative
114
0.2461 Cooperative
13
0.8433 Noncooperative
GLM
64
0.5674 Cooperative
t-test
72
0.0845 Cooperative
Information on references, taxonomy, type of molecular markers used to estimate social mate relatedness, type of relatedness estimator used, type of and the
number of microsatellite loci used, type of the statistical procedure applied, sample size and effect size (Fisher’s Z), and the type of breeding systems (cooperative
vs. noncooperative species). Logistic Reg.: logistic regression; Correlation: Pearson’s correlation coefficient; Spearman: Spearman’s rank correlation coefficient;
GLM: generalized linear model; GLMM: generalized linear mixed model; SNP: single nucleotide polymorphism.
variance). In 2 cases (Schmoll et al. 2005; Dreiss et al. 2008), the
data of a single study were reported in separate papers. However,
we treated the effect size published in these papers as independent
(hence duplicate values in Figure 2, assigned to the same species).
To assess the influence of such decision on our estimates, we performed separate analyses after removing these papers. This procedure did not significantly affect our conclusions, and so we consider
our result to be robust to this negligible violation of independence.
We calculated an overall weighted mean effect size by considering
all available studies without any additional fixed effects. Afterward,
we used the information on molecular markers as a moderator fixed
variable running the model containing the type of the molecular
marker (3 levels: multilocus minisatellite markers [12 studies, henceforth termed fingerprinting], microsatellite markers [27 studies],
major histocompatibility complex [MHC] genes markers [4 studies]).
This model allowed us to conclude which types of molecular markers
were associated with significant effect sizes in our data set.
Apart from molecular markers, a number of other factors might
influence the magnitude of analyzed correlations. For example, we
might expect a higher correlation between genetic relatedness and
EPP among cooperative breeding species (e.g., Townsend et al. 2009;
Brouwer et al. 2011). Thus, we have rerun the basic overall model
including a variable accounting for cooperative behavior (cooperative
vs. noncooperative species). This factor did significantly influence the
magnitude of observed effect sizes (see Supplementary Material).
Publication bias can be a potential caveat in meta-analysis given
that the tendency not to publish nonsignificant relationships can
inflate average effect sizes (Begg and Berlin 1988). In order to
determine possible sources of bias in meta-analyses, we employed
funnel graphs by plotting (residual) effect size (residuals from
models fitted to Fisher’s Z effect size) against the studied sample
sizes. Rank correlation coefficient was used to assess the degree of
funnel-plot asymmetry, an indication of possible publication bias
(Begg and Mazumdar 1994). Moreover, we have employed a trimand-fill method (Duval and Tweedie 2000), as implemented in the
metaform package (Viechtbauer 2010). This implementation of
trim-and-fill method has the advantage of using a hypothesis-testing approach to establish whether there are gaps in the funnel plot
that might indicate publication bias.
Meta-analysis has been increasingly used in ecology and evolutionary biology. However, this technique has an important
limitation when it does not account for the phylogenetic history
(Chamberlain et al. 2012). For this reason, we also conducted
the phylogenetic meta-analysis (phylogenetic comparative mixed
model, PCMM). The inverse of the relationship matrix used in
the phylogenetic meta-analyses was generated based on the maximum clade credibility tree created by matrix representation parsimony method (Baum 1992) from trees published by Jetz et al.
(2012) (see Supplementary Material for details). Finally, we have
also performed a randomization-based analysis to further confirm
low support for a significant phylogenetic signal in our paper (see
Supplementary Material; Blomberg et al. 2003).
RESULTS
We found that the overall effect size (expressed as Fisher’s Z)
was significant: b = 0.09 ± 0.03 (P = 0.02) (Figure 2). The type
of molecular marker used had a significant effect, but only
microsatellite markers significantly explained the effect size
(bmicrosatellite = 0.11 ± 0.04, P = 0.02). Effect sizes for both MHC and
fingerprinting markers were nonsignificant (bMHC = 0.05 ± 0.13,
P = 0.58; bfingerprint = 0.05 ± 0.07, P = 0.45). The resulting model
containing only microsatellite studies (b = 0.13 ± 0.08) indicated
that there was no association between the number of microsatellite loci and effect size (slope for the number of microsatellites loci
against effect size: bno.loci = −0.002 ± 0.008, P = 0.82). We also performed a similar analysis on a subset of 12 studies based on microsatellite loci that were included in the meta-analysis of Akçay and
Roughgarden (2007). The estimated overall effect size was significant (b = 0.17 ± 0.06, P = 0.02).
Phylogeny explained a marginally low proportion of variance (0.003 ± 0.005). The inclusion of phylogenetic effect in
Identification
Arct et al. • Genetic similarity between mates is associated with EPP
963
Additional records identified through
other sources
(n = 23, the previous meta-analysis of
Akçay and Roughgarder, 2007)
Records identified through
database searching
(nstudy = 898)
Eligibility
Screening
Records after duplicates removed
(nstudy = 875)
Records screened
(nstudy = 875)
Records excluded
(nstudy = 824)
Full-text articles assessed
for eligibility
(nstudy = 51)
Full-text articles excluded,
with reasons
(n=12) These studies had
no information to
calculate effect size
and/or its direction (Otter
et al. 2001; Foerster et al.
2003; Masters et al. 2003;
Kleven et al. 2005;
Dowling and Mulder 2006;
Oh and Badyaev 2006;
Stewart et al. 2006;
Townsend et al. 2010;
Brouwer et al. 2011;
Kudernatsch et al. 2010;
Casey et al. 2011;
Rubenstein 2007).
Included
Studies included in
qualitative synthesis
(nstudy = 39)
Studies included in quantitative
synthesis (meta-analysis)
(nstudy=39)
(neffect size=43)
Figure 1
PRISMA diagram describing subsequent steps of selecting and validating data included in the meta-analysis.
the model led to an increase in the uncertainty of the main
effect size estimate; however, direction of the effect size was
the same and its magnitude larger (see Supplementary Table
S1). Comparison of conditional Akaike information criteria
(cAIC = −2ℓ + 2(ρ + 1) where ρ is the trace of the model’s hat
matrix; Vaida and Blanchard 2005) indicates that including phylogenetic effect provides no additional improvement in the fit of
the model (cAICnon-PCMM − cAICPCMM = 0.34 < 2; Burnham and
Andersson 2002). This is further supported by lack of statistical
significance of the phylogenetic effect in the likelihood-ratio test
2
( χdf =1 = 0.56, P = 0.45). See the Supplementary Material for more
in-depth data regarding the phylogenetic signal.
Data used in our meta-analysis exhibited moderate to high levels of heterogeneity (Rothstein et al. 2014), which can be defined
as lack of complete consistency of individual effect sizes, resulting
in their variance around the estimated mean effect size (Nakagawa
and Santos 2012). The overall data set exhibited high levels of
heterogeneity (I2 = 98%). Most of this heterogeneity was present
at the residual error level: other random effects explained negligibly low levels of heterogeneity: species level I s2 < 0.001%; phylogenetic level I 2p = 0.3%) (Nakagawa and Santos 2012). Attempts
to explain this heterogeneity were unsuccessful: inclusion of additional explanatory variables such as social system (cooperative vs.
noncooperative, Supplementary Table S2), average body mass,
and type of relatedness estimator (Wang estimator (Wang 2002),
Queller and Goodnight estimator (Queller and Goodnight 1989),
Lynch estimator (Lynch 1988), Li estimator (Li et al. 1993), bandsharing coefficient) did not reduce heterogeneity. Data exhibited
no signs of publication bias, although estimated asymmetry statistics were close to being significant (see also funnel plot, Figure 3):
for the whole data set, the rank correlation test returned the
Kendall’s tau τ = 0.19, P = 0.07; for the restricted data set
(microsatellite)—τ = 0.15, P = 0.26. The implementation of the
trim-and-fill method from the metaform package did not suggest
the need of updating the funnel plot with additional observations
(test of H0 that there are no missing studies on left/right side of
Behavioral Ecology
964
Reference
Estimate and 95% Cl
0.19 [ –0.07 , 0.45 ]
–0.23 [ –0.44 , –0.02 ]
0.35 [ 0.10 , 0.61 ]
0.48 [ 0.20 , 0.77 ]
0.59 [ 0.35 , 0.83 ]
0.25 [ 0.09 , 0.42 ]
–0.31 [ –0.57 , –0.05 ]
0.05 [ –0.17 , 0.28 ]
–0.01 [ –0.16 , 0.13 ]
–0.01 [ –0.28 , 0.25 ]
–0.11 [ –0.41 , 0.18 ]
0.56 [ 0.34 , 0.79 ]
0.04 [ –0.28 , 0.36 ]
0.11 [ –0.07 , 0.30 ]
0.68 [ 0.39 , 0.96 ]
0.06 [ –0.15 , 0.28 ]
–0.01 [ –0.15 , 0.12 ]
0.14 [ 0.00 , 0.28 ]
0.54 [ 0.28 , 0.80 ]
0.16 [ 0.01 , 0.31 ]
–0.01 [ –0.13 , 0.11 ]
–0.29 [ –0.46 , –0.12 ]
0.24 [ 0.08 , 0.40 ]
0.22 [ 0.02 , 0.43 ]
0.16 [ –0.08 , 0.41 ]
0.09 [ –0.09 , 0.27 ]
0.15 [ –0.08 , 0.39 ]
–0.08 [ –0.30 , 0.13 ]
0.00 [ –0.17 , 0.17 ]
0.12 [ –0.12 , 0.36 ]
0.40 [ 0.15 , 0.65 ]
–0.25 [ –0.43 , –0.07 ]
–0.73 [ –0.98 , –0.48 ]
0.07 [ –0.13 , 0.27 ]
–0.01 [ –0.26 , 0.24 ]
–0.07 [ –0.20 , 0.06 ]
0.17 [ –0.02 , 0.36 ]
0.08 [ –0.10 , 0.26 ]
–0.10 [ –0.26 , 0.05 ]
0.25 [ 0.07 , 0.42 ]
0.84 [ 0.48 , 1.20 ]
0.57 [ 0.35 , 0.79 ]
0.08 [ –0.12 , 0.29 ]
Augustin et al. 2007 (Riparia riparia)
Barber et al. 2005 (Tachycineta bicolor)
Blackmore & Heinsohn 2007 (Pomatostomus temporalis)
Blomqvist et al. 2002 (Actitis hypoleuca)
Blomqvist et al. 2002 (Calidris mauri)
Blomqvist et al. 2002 (Charadrius alexandrinus)
Bollmer et al. 2012 (Geothlypis trichas)
Bouwman et al. 2006 (Emberiza schoeniclus)
Charmantier et al. 2004 (Cyanistes caeruleus)
Cramer et al. 2001 (Thryothorus pleurostictus)
Dreiss et al. 2008 (Cyanistes caeruleus)
Dreiss et al. 2008 (Cyanistes caeruleus)
Durrant & Hughes 2005 (Gymnorhina tybicen)
Edly–Wright et al. 2007 (Passer domesticus)
Eimes et al. 2005 (Aphelocoma ultramarina)
Ferretti et al. 2011 (Tachycineta leucorrhoa)
Foerster et al. 2006 (Cyanistes caeruleus)
Fossøy et al. 2007 (Luscinia svecica)
Freeman–Gallant et al. 2003 (Passerculus sandwichensis)
Freeman–Gallant et al. 2006 (Passerculus sandwichensis)
Hansson et al. 2004 (Acrocephalus arundinaceus)
Huyvaert & Parker 2010 (Phoebestria irrorata)
Jones et al. 2012 (Diomedea exulans)
Jouventin et al. 2007 (Phoebestria irrorata)
Juola and Dearborn 2012 (Fregata minor)
Kempenaers et al. 1996 (Cyanistes caeruleus)
Kleven & Lifjeld 2005 (Emberiza schoeniclus)
Kraaijeveld et al. 2004 (Cygnus atratus)
Lampila et al.. 2011 (Parus montanus)
Lee 2012 (Paradoxornis webbianus)
Lindstedt et al. 2007 (Carpodacus mexicanus)
Promerova et al. 2011 (Carpodacus erythrinus)
Rätti et al. 1995 (Ficedula hypoleuca)
Richardson et al. 2005 (Acrocephalus sechellensis)
Schmoll et al. 2005 (Periparus ater)
Schmoll et al. 2005 (Periparus ater)
Smith et al. 2005 (Dendroica caerulescens)
Stapleton et al. 2007 (Tachycineta bicolor)
Suter et al. 2007 (Emberiza schoeniclus)
Tarvin et al. 2005 (Malurus splendens)
Thuman and Griffith 2005 (Philomachus pugnax)
Varian–Ramos & Webster 2012 (Malurus melanocephalus)
Wang & Lu 2011 (Parus humilis)
0.05 [ –0.08 , 0.18 ]
0.11 [ 0.07 , 0.15 ]
0.05 [ –0.02 , 0.12 ]
MHC
Microsatellites
DNA fingerprinting
0.09 [ 0.03 , 0.15 ]
Overall effect size
–1.00
–0.50
0.00
0.50
Effect size (Fisher Z)
1.00
1.50
Figure 2
Forest plot for all studies included in meta-analysis (squares) and mean effect sizes (diamonds) with 95% confidence intervals (CIs). Dot size reflects sample size.
the funnel plot: P = 0.5 [using R0 estimator]; for microsatelliterestricted data: P = 0.5).
DISCUSSION
Our meta-analysis of 43 effect sizes derived from studies of 33 bird
species, showed a significant positive relationship between incidence
of EPP and relatedness of social mates. This contrasts with earlier
meta-analyses that found no evidence for genetic benefits from
engaging in extrapair copulations (Arnqvist and Kirkpatrick 2005;
Akçay and Roughgarden 2007). Importantly, our study indicates
that failure of some of the previous studies to detect a relationship
between occurrence of EPP and the relatedness of social mates
may at least partly arise due to methodological reasons. Specifically,
we showed that only microsatellite markers were associated with
significantly positive effect sizes. Observed effect size falls between
Marker type:
MHC
Microstatellites
Fingerprinting
0.184
0.138
Standard Error
0.092
0.046
0.000
Arct et al. • Genetic similarity between mates is associated with EPP
−0.50
0.00
0.50
Residual effect size
1.00
Figure 3
Funnel plot for all studies included in meta-analysis. The points represent
meta-analytical residuals plotted against the standard error of each effect size.
small and medium magnitude of effect sizes according to Cohen
(1988)—however, those guidelines were developed for social sciences and may not fully reflect biologically relevant magnitudes of
effect sizes (Rothstein et al. 2014). To our knowledge, this is the first
meta-analysis supporting theoretical predictions that EPP might be
an important mechanism to avoid negative effects of pairing with a
genetically similar mate.
Weak effects have also been reported by other meta-analysis testing genetic benefit of extrapair copulation and polyandry (Arnqvist
and Kirkpatrick 2005; Slatyer et al. 2012). This may arise if genetic
effects interact with other variables. The studies considered in these
meta-analyses usually do not employ experimental manipulations,
so many confounding factors such as territory quality (see Eliassen
and Kokko 2008 for more discussion), female breeding experience
(Whittingham and Dunn 2010), and ecological and social determinants of mate availability (Oh and Badyaev 2006) might affect
the likelihood of EPP resulting in generally low observed effect
sizes. The level of outbred in the study population may constitute
one of the most important confounding factors in the studies of
the relationship between genetic relatedness and EPP (Chapman
et al. 2009). In highly outbreed populations, negative relationship
between incidence of EPP and the relatedness between social mates
may be expected, whereas in inbreed populations, the opposite relationship should be observed. So, variation in the level of outbreeding may constitute an important confounding factor leading to low
global estimates of the weighted mean effect size. Unfortunately,
the studies considered in this meta-analysis do not provide information on the level of inbreeding, so such effect cannot be tested. In
our data set, negative effects were less common than positive effects
(65% of positive effects). This may indicate that the populations
considered in our meta-analysis were more likely to suffer inbreeding depression favoring the avoidance of matings with genetically
similar individuals (Szulkin et al. 2013). This may be particularly
relevant to cooperatively breeding species with limited postnatal dispersal (e.g., Townsend et al. 2009; Brouwer et al. 2011). However,
we were not able to confirm this supposition—the breeding system
(i.e., cooperative; noncooperative) did not significantly explain the
variation in the effect sizes (see Supplementary Material).
965
In our meta-analysis, we showed that only microsatellite markers
were associated with a significantly positive effect size. Band-sharing
coefficients based on multilocus DNA fingerprints have already
been questioned to reliably reflect genetic similarity between individuals because coefficients for different pairs of individuals of the
same (known) relatedness can vary considerably by chance (e.g.,
Reeve et al. 1992; Griffith and Montgomerie 2003). The microsatellite markers are also often criticized as a useful tool to estimate
relatedness (e.g., Szulkin et al. 2013); however, to date, they seem
to be more reliable than the multilocus fingerprint markers (e.g.,
Reeve et al. 1992; Van De Casteele et al. 2001). The microsatellite markers have been applied only recently to estimate relatedness
between mates, whereas earlier studies were usually based on multilocus DNA fingerprinting (Table 1). This may explain why the previous meta-analysis (Akçay and Roughgarden 2007) failed to detect
a relationship between incidence of EPP and relatedness of social
mates (35% studies considered in the earlier meta-analysis used
band-sharing coefficients to determine the degree of genetic similarity between individuals, calculated by comparing bands on DNA
fingerprints). Interestingly, when considering only 12 studies based
on microsatellite markers that were included in previous metaanalysis of Akçay and Roughgarden (2007), we also find a positive
significant relationship between incidence of EPP and relatedness
of social mates.
It has been suggested that the small number of microsatellite loci
used to estimate genetic similarity between partners may provide a
limited power to detect patterns at higher significance levels (e.g.,
Smith et al. 2005). For example, some studies have shown that the
correlation between marker heterozygosity and the inbreeding coefficient is rather weak when few markers are used (e.g., Blouin 2003;
Pemberton 2008; Wetzel and Westneat 2009). However, in our
study, the number of microsatellite loci was not related to the effect
size. Moreover, in many of the studies with negative results, the sample size and number of genetic marker loci used were greater than
for those with positive results. Clearly, number of loci, loci variability, and sample size all will affect the ability of a particular study to
detect any effect of genetic similarity on the occurrence of EPP, but
given that many studies with large sample sizes and using a number
of highly variable loci have failed to detect such effect, statistical limitations are not the only contributing factor to the variation observed
across studies (Mays et al. 2008). It might be more important to consider the characteristics of the markers used to measure the relatedness of individuals (e.g., frequency and location in the genome,
i.e., whether a marker is located in expressed functional regions of
the genome; Smith et al. 2005; Olano-Marin et al. 2011). In our
data set, only 4 studies assessed individual relatedness using MHC
markers, but mean effect size was nearly as big as for microsatellites, albeit not significant. This presumably stems from the critically
small number of studies using these markers. Because the role of the
MHC genes in the vertebrate immune response is well established
(Doherty and Zinkernagel 1975), studies using variation in functional genes, such as MHC genes, should also offer a satisfactory
resolution for assessing genetic relatedness. However, at the moment
we have too little information to assess whether or not MHC markers are better or worse than microsatellites for this purpose.
In conclusion, our results indicate that the occurrence of EPP
may be explained by the degree of relatedness of the social mate;
however, this effect seems to be relatively weak. The only factors we found to explain variation in the strength of the relationship between relatedness of social mates and EPP are related to
methodological issues. Hence, our understanding of the biological
966
reasons for variation in extrapair matings remains poor, leaving this
question open for further exciting research. It is possible that the
potential effects of genetic similarity between social partners are
complex, for example, dependent on spatial and temporal variation
in inbreeding observed at the population level. Thus, additional
work is needed to determine whether animals avoid, tolerate, or
prefer inbreeding across a range of biologically relevant conditions (Szulkin et al. 2013). Future studies examining patterns of
EPP will also benefit from focusing on the evolutionary sexual conflict over costs and benefits from extrapair matings (Westneat and
Stewart 2003; Griffith 2007; Eliassen and Kokko 2008), as well as
the potential interactions between selection for direct and indirect
benefits (Onealt et al. 2007; Rubenstein 2007). It is also important
that further analyses—possibly including more studies published in
the future—should be aimed at explaining excessive heterogeneity
in estimated effect sizes that was apparent in our meta-analysis.
SUPPLEMENTARY MATERIAL
Supplementary material can be found at http://www.beheco.
oxfordjournals.org/
FUNDING
This research was funded by the National Science Centre (Poland)
allocated on the basis of the decision number DEC-2013/09/B/
NZ8/03322. This publication was also supported by funding from
the Jagiellonian University within the SET project (to A.A. and
S.M.D.). The project is cofinanced by the European Union.
We especially thank S. Nakagawa and J. Chapman for comments on the
manuscript and J. Kubacka for her editorial help. We also thank 3 anonymous reviewers for their valuable comments on earlier versions of the paper.
Forum editor: Shinichi Nakagawa
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