CSIRO PUBLISHING
www.publish.csiro.au/journals/ajz
Australian Journal of Zoology, 2007, 55, 219–227
Molecular-genetic analyses of dispersal and breeding behaviour
in the Australian termite Coptotermes lacteus: evidence for
non-random mating in a swarm-dispersal mating system
Graham J. ThompsonA,D,E, Michael Lenz B, Ross H. Crozier C and Bernard J. Crespi A
A
Department of Biological Science, Simon Fraser University, Burnaby, BC V5S 1S6, Canada.
CSIRO Entomology, GPO Box 1700, Canberra, ACT 2601, Australia.
C
School of Tropical Biology, James Cook University, Townsville, Qld 4811, Australia.
D
Present address: School of Biological Science, University of Sydney, Macleay Building A12,
Sydney, NSW 2006, Australia.
E
Corresponding author. Email: gthompson@usyd.edu.au
B
Abstract. We used microsatellite DNA markers to infer the dispersal and breeding behaviour of Coptotermes lacteus, a
termite whose large mounds are a conspicuous feature of Australia’s central east coast. We genotyped a subsample of
neuter offspring for each of 38 colonies sampled over two spatially separated populations, one in a natural forest and the
other in an exotic radiata pine plantation. All colonies showed offspring genotype frequencies consistent with a single
reproductive pair. This result confirms that stable monogamy is the normal breeding arrangement for this species and that
multi-reproductive colonies are rare. The two study populations were significantly differentiated and the distance
separating them (~150 km) is therefore an effective constraint on gene flow. The populations themselves, however, were
not noticeably subdivided above the level of colony. This lack of within-population viscosity is unexpected for weakly
dispersing species and suggests that local gamete dispersal is in fact quite effective in C. lacteus. Nonetheless, dispersing
sexuals do not appear to mate randomly. Instead, all four microsatellite loci are deficient in heterozygotes, indicating that
populations are substantially inbred, irrespective of habitat. Evidence from hierarchical F-statistics, spatial genetic
autocorrelation and relatedness calculations suggests that deviations from Hardy–Weinberg equilibrium may result from
either a preference for non-sibling relatives over totally unrelated mates, or from random mating with viscosity – though
evidence for the latter hypothesis was not detected. These findings suggest that swarm-dispersal mating systems, usually
considered to produce outbreeding and panmixia, can instead involve a notable degree of non-random mating.
Additional keywords: Isoptera, microsatellites, population genetics, social insects.
Introduction
Studies on the genetic structure of animal populations can
provide insights into their dispersal and breeding behaviour. In
termite populations, mating typically occurs between dealates
(sexuals that have dropped their wings) following their brief dispersal flight (Nutting 1969). Thus, for these highly social
insects, dispersal and mate choice are tightly linked. As is
typical for subterranean species (Rhinotermitidae), the Australian mound-building termite Coptotermes lacteus (Froggatt,
1898) synchronises its dispersal flights to seasonal cues (Hill
1942). In unison, alates (winged sexuals) take flight from natal
colonies to search for mates and suitable nesting sites. Incipient
colonies are therefore headed by primary reproductives, the
imaginal (adult) king and queen. For many species, however,
primary reproductives can eventually be replaced by secondary
reproductives recruited in situ (Roisin 1993; Shellman-Reeve
1997; Myles 1999). For C. lacteus, the precise frequency of succession by neotenic (immature) reproductives in natural populations is not yet known, but orphaning experiments
demonstrate capacity in this species for neotenic differentiation
© CSIRO 2007
from late-instar nymphs (Lenz and Runko 1993). The resulting
inbred colonies become characteristically polygamous, i.e.
contain more than one pair of reproductives.
Given the opportunity for C. lacteus and other species of
termite to inbreed at the nest via the differentiation of neotenics,
and the presumed vulnerability of such colonies to inbreeding
over time (DeHeer and Vargo 2006; Husseneder et al. 2006),
there is a general expectation for the dispersive phase to be associated with outbreeding. This would presumably reduce
inbreeding depression and promote new genetic combinations
potentially favoured by natural selection (Keller and Waller
2002). In C. lacteus, the extent of outbreeding via dispersal
from primary-led colonies is not known, but secondary-led
colonies do produce all male alates (Lenz and Runko 1993;
Roisin and Lenz 2002), which must promote their mating to
females from a different colony.
Despite the expectation for outbreeding on dispersal, few
studies have tested for kin-biased mate choice at the dispersive
phase (Shellman-Reeve 2001), or for evidence of inbreeding or
10.1071/ZO07023
0004-959X/07/040219
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Australian Journal of Zoology
G. J. Thompson et al.
inbreeding avoidance at colony foundation (Fei and Henderson
2003; DeHeer and Vargo 2006; Husseneder et al. 2006). Further,
termite alates are often considered to be weak flyers and, if so,
opportunities for them to outbreed may be ecologically constrained by the ability of the alate to navigate geography, distance,
hazard or wind. Detecting limits in the propensity of different
species to outbreed at the dispersive phase will help clarify the
extent to which genetic versus ecological constraints shape
termite breeding-system diversity (Shellman-Reeve 1997).
In the present study, we use microsatellite DNA markers to
infer the mating and dispersal behaviour of C. lacteus from
Australia’s central east coast. We first genotype a sample of
colony offspring and, based on the frequency distribution of offspring genotype classes, we distinguish monogamous from
polygamous colonies in the field. Second, we use the offspring
genotype data to reconstruct the genotypes of the primary reproductives. We use these data to examine how each of two populations conform to expectations under random mating in the
field. Finally, we use hierarchical F-statistics, spatial genetic
autocorrelation and relatedness calculations to test how deviations from equilibrium, if any, are related to the dispersal and
mating habits of this species.
Materials and methods
Field collections
We collected termites from 38 colonies distributed across six
sampling blocks within two geographically separate populations
(Appendix 1). We refer to the population within the Australian
Capital Territory as ‘ACT’, and the population in coastal New
South Wales as ‘NSW’ (Fig. 1). The distance separating the
mean location of colonies within the ACT and NSW populations
was ~150 km. Moreover, the populations differ in their ecological setting. ACT is established in plantations of exotic radiata
pine (Pinus radiata) and termite mounds are visible within this
setting on a roughly 0.1-km scale. NSW occurs, by contrast, in
native Eucalyptus forest and colonies are visible within this
setting on a roughly 1-km scale (Table 1). Within each of these
habitats we collected upward of 100 individual termites per
colony during November 2001. We used a hand-held GPS
device to record the latitude and longitude coordinates of each
colony.
Molecular analysis
We extracted DNA from the heads of worker and soldier termites using a Chelex resin (Walsh et al. 1991). We then PCRamplified four microsatellite DNA loci: Clac 1, Clac 2, Clac 6
and Clac 8 (Thompson et al. 2000) in 25-µL volumes. An additional two loci (Clac 3 and Clac 4) were initially screened but
dropped from the population-wide survey because they were
difficult to multiplex. Reactions contained between 1.5 and
3.0 mM MgCl2, 2.5 mM dinucleotide triphosphate, 0.1 µM of
forward and reverse primer, 0.1 µM of forward-labelled primer
(Clac1F-HEX, Clac2F-FAM, Clac6F-TAMRA, Clac8F-HEX),
20–50 µg genomic DNA and 0.1 U of Ultra Therm DNA poly-
N
W
E
S
200 km
South Brooman State Forest,
near Batemans Bay (NSW)
A
Stromlo State Forest,
near Canberra (ACT)
B
NSW-01 (n = 7)
ACT-31 (n = 6)
NSW-03 (n = 4)
ACT-49 (n = 9)
NSW-02 (n = 6)
1 km
ACT-57 (n = 6)
10 km
Fig. 1. Location of two study populations in south-eastern Australia. Dark symbols show location and number (n)
of colonies sampled. Field codes (i.e. ACT-31, NSW-01, etc) designate groups of colonies (‘blocks’, see text) used
to detect population structure.
Non-random mating in Coptotermes lacteus
Australian Journal of Zoology
merase (Biocan Scientific, Mississauga, Canada). Water and
PCR buffer made up the final reaction volume.
We amplified DNA using a GeneAmp 9700 (Applied
Biosystems, Foster City, CA) and used a three-step thermocycle, repeated 30 times: 30 s at 94°C, 30 s at 50–56°C and 30 s
at 72°C. We preceded this profile with an initial denaturation of
3 min at 94°C and added a final extension of 5 min at 72°C. We
visualised fluorescently labelled PCR products on an ABI Prism
373 (Applied Biosystems) and scored alleles against a molecular size standard (GeneScan-400 HD, PE Applied Biosystems) using GeneScan version 2.0.1 software. Initially,
alleles were scored by two people to guard against scoring bias.
We deemed gel scoring bias or genotyping errors to be minimal
and, accordingly, the senior author scored the bulk of samples
with reference only to the standard. We genotyped between 12
and 48 individuals per colony per locus; the number depending
on sample size required to accurately assess the genotype ratio
of each colony. In monogamous colonies, loci with more than
one segregating allele should fit a stereotypic Mendelian ratio
(Thompson and Hebert 1998b). Polygamous colonies, by contrast, can deviate from these genotypic ratios and may also
contain more than four alleles per locus.
Genetic data analysis
To identify polygamous colonies, we used contingency table
analyses to compare colony offspring genotype ratios with
Mendelian expectations under a monogamous cross. We
assessed significance of potential deviations in genotype frequency for each colony at each locus using χ2 tests, applying a
classic Bonferroni adjustment to maintain the family-wise error
rate at α = 0.05. For each monogamous colony, we reconstructed
the parental genotypes using a Punnett square and used this
information to perform exact tests (Guo and Thompson 1992)
for Hardy–Weinberg equilibrium (HWE) for each population.
Throughout the present study, the sexual parents, rather than
their neuter offspring, were the focus of analyses because it is
the mating and dispersal behaviour of sexuals that determines
colony and population genetic structure. The neuter offspring,
by contrast, do not mate and do not disperse.
We tested the extent of population structure using two complementary approaches. First, we used an ANOVA estimation
procedure (Yang 1998) to calculate analogues to Wright’s
(1978) F-statistics across s = 4 levels of biological organisation.
Table 1. Mean and maximum geographic distances separating
colonies within populations, and within blocks within populations
Distance in kilometres
Mean (s.d.)
Maximum
Within populations
Within blocks
ACT
ACT-31
ACT-49
ACT-57
NSW
NSW-01
NSW-02
NSW-03
1.326 (0.934)
0.354 (0.166)
0.256 (0.110)
0.199 (0.115)
5.987 (3.869)
1.246 (0.977)
1.362 (0.680)
2.990 (1.541)
3.201
0.652
0.450
0.404
11.508
2.240
2.205
4.577
221
The levels were: population, sampling block, colony, and individual (Appendix 1). For this part of the analysis, we tested the
significance of inter-individual correlations (FST) using a permutation test (Goudet 2005). We tested the significance of intraindividual correlation (FIT) by first assuming the gametic phase
is known, then using permutation (Excoffier et al. 2005).
Second, we used a multilocus autocorrelation method to detect
spatial patterns in parental genotype data (Smouse and Peakall
1999). Specifically, we ask: are parental genotypes in geographic proximity any more similar than those with greater
physical separation? For this part of the analysis we derived
inter-individual genetic distance matrices using the multivariate
metric of Peakall et al. (1995) and likewise derived interindividual geographic distance matrices from latitude–longitude coordinates. Using the computer program GENALEX 6
(Peakall and Smouse 2006) we calculated the two-population
average spatial genetic autocorrelation coefficient rc (combined
r; Peakall et al. 2003) at variable-distance classes (D ≈ 0 km,
then D = 1 km, 2 km, 3 km, ... 12 km). We chose distance classes
that span the full range of geographic distances among our
sampled colonies (Table 1). This allowed us to detect the spatial
scale at which genetic differentiation, if any, occurs. In the
present study, a distance class of D ≈0 simply corresponds to
sets of paired individuals located within the same colony and
positive D-values correspond to sets of paired individuals
located in spatially separated colonies. We tested the significance of rc-values at each of the 13 distance classes by comparing the observed value of rc with the pseudo-distribution
obtained by permutation (Peakall and Smouse 2006).
Finally, as an intuitive test of whether sexuals of C. lacteus
mate randomly with respect to genotype we compared the mean
relatedness coefficient b for reproductives occurring in the same
nest with the null value for totally unrelated mates (b = 0) and
sibling–sibling or parent–sibling (b = 0.5) mates. We calculated
b using the program Relatedness 5.0.8 (Goodnight 2000), which
implements the methods described by Queller and Goodnight
(1989). For this part of the analysis, we weighted individuals
equally and calculated standard errors by jackknifing over
colonies (n = 38). To account for differences in the allele frequencies of ACT and NSW populations, we calculated b from
the respective background allele frequencies using the ‘deme’
function of the Relatedness program. We used t-tests to determine if estimates of b differed statistically from null expectation
or from each other.
Results
Genetic marker variation and test for HWE
We genotyped between 456 and 480 neuter offspring per locus
for a total of 1863 genotypic scores. All 38 colonies from which
individuals were sampled showed offspring genotype frequencies at each locus consistent with Mendelian expectations for a
single reproductive pair (adjusted χ2 tests not significant).
Moreover, no colony contained more than four alleles per locus,
which likewise is consistent with monogamy. We therefore
found no evidence for the presence of polygamous colonies in
this survey. Out of hundreds of mounds of C. lacteus collected
by CSIRO Entomology, only three natural cases of colonies
headed by multiple secondary reproductives have been found
222
Australian Journal of Zoology
G. J. Thompson et al.
These findings indicate that there is a significant degree of nonrandom mating by genotype in these populations.
Table 2. Exact tests for Hardy–Weinberg equilibrium
HO and HE, observed and expected heterozygosities
Population
Locus
HO
HE
P-value
s.d.
ACT
Clac 1
Clac 2
Clac 6
Clac 8
Clac 1
Clac 2
Clac 6
Clac 8
0.57143
0.16667
0.23810
0.26190
0.50000
0.29412
0.47059
0.58824
0.67613
0.41165
0.77625
0.65663
0.65540
0.46356
0.88718
0.73573
0.00512
0.00000
0.00000
0.00000
0.03058
0.00024
0.00000
0.01864
0.00021
0.00000
0.00000
0.00000
0.00052
0.00024
0.00000
0.00037
NSW
Combined spatial structure analysis
We detected significant positive spatial autocorrelation at the
smallest spatial scale examined in this study, namely, among
sexuals located within the same colony (rc = 0.086, n = 38,
P = 0.020). By contrast, we did not detect any significant spatial
autocorrelation at any positive distance class (P > 0.08 in all
clases; Fig. 2).
Genetic relatedness
The mean (± s.e.) relatedness of reproductives inhabiting the
same colony was b = 0.087 ± 0.059. This estimate is positive,
but may only be marginally greater than b = 0 (t = 1.46, onesided P = 0.076, d.f. = 37). The estimate is significantly less
than b = 0.5 (t = 6.92, one-sided P < 0.0001, d.f. = 37).
Moreover, the distribution of pairwise king–queen b-values is
normal (Shapiro–Wilk test statistic = 0.968, d.f. = 38, P = 0.33)
and there was no difference in mean relatedness between ACT
and NSW populations (b–b’ = 0.025 ± 0.122; t = 0.20, two-sided
P = 0.841, d.f. = 36).
(Gay 1955; Lenz and Runko 1993; M. Lenz, unpubl. data). For
each colony, we were therefore able to unambiguously infer the
parental genotypes: one corresponding to the resident king, and
the other to the resident queen. In total, we inferred 76 parental
genotypes, 42 from ACT and 34 from NSW. We use these
parental data as the focus of all subsequent analyses.
Despite differences in ecological setting, the ACT and NSW
populations had similar indices of genetic diversity. By comparison, each population had a similar number of alleles per locus
(mean ± s.e.: ACT = 6.75 ± 0.63, NSW = 5.75 ± 1.37; t6 = 0.661,
P = 0.533), a similar mean expected heterozygosity (ACT =
0.607 ± 0.08, NSW = 0.667 ± 0.083; t6 = –0.528, P = 0.617), and
the two populations did not differ in the extent to which parents
were inbred (standard one-level inbreeding coefficient FIT, ACT
= 0.491 ± 0.12, NSW = 0.301 ± 0.06; t6 = 1.37, P = 0.220). We
supply additional summary information on microsatellite diversity for the two populations in Appendix 2.
In ACT and NSW populations, departures from HWE were
common and in the direction of heterozygote deficit (Table 2).
Discussion
Using multiple co-dominant nuclear markers we assessed the
dispersal and breeding behaviour of the Australian moundbuilding termite Coptotermes lacteus. Focussing on two geographically separate and ecologically distinct populations, we
report that all 38 colonies showed offspring genotype frequencies consistent with having a single pair of reproductives. That
is, colony genotype frequencies were predictable for a monogamous cross, and no colony contained more than four alleles at
any one locus. In C. lacteus, primary headed colonies are
monogamous, whereas secondary headed colonies are polygamous (Lenz and Runko 1993; M. Lenz, pers. obs.). Thus we
assume that all colonies sampled in the present study are led by
the founding pair, the imaginal king and queen.
Our genetic survey across native and exotic habitats reveals
that C. lacteus is robust in its social structure. This constancy in
reproductive arrangement despite different ecological settings
differs from the social plasticity noted for some invasive subterranean species. Similar surveys of C. formosanus (Vargo
et al. 2006) and Reticulitermes (Vargo and Carlson 2006) in the
USA typically reveal a mix of primary monogamous (‘simple’)
and secondarily polygamous (‘extended’) colonies. For
C. lacteus – which is non-invasive and not an urban pest – secondarily polygamous colonies do exist in natural populations as
Wright’s F-statistics
The nested ANOVA revealed significant correlations of genes
among individuals within populations, relative to the metapopulation (FPOP/Total = FST = 0.0434, d.f. = 1, P = 0.047;
Table 3). The ANOVA did not detect any significant correlation
of genes among individuals within blocks, relative to populations (FBLOCKS/POP = FST = 0.0128, d.f. = 4, P = 0.104), but did
detect significant correlation among individuals within
colonies, relative to block (FCOLONY/BLOCK = FST = 0.0533,
d.f. = 32, P = 0.028) and relative to population (FCOLONY/POP =
FST = 0.0655, d.f. = 36, P = 0.015). Finally, there was a very significant correlation of genes within individuals, relative to the
metapopulation (FInd/Total = FIT = 0.4428, d.f. = 76, P < 10−5).
Table 3. Analysis of variance for the s = 4 level hierarchical population structure (s1 = population, s2 = block,
s3 = colony, s4 = individual
Level of biological
organisation
Source of variation
d.f.
Wright’s
F-indices
1
2
3
3
4
Among populations
Among blocks in populations
Among colonies in blocks
Among colonies in populations
Among alleles in individuals
1
4
32
36
75
FST
FST
FST
FST
FIT
F-statistic
P-value
FPOP/Total = 0.0434
FBLOCKS/POP = 0.0128
FCOLONY/BLOCK = 0.0533
FCOLONY/POP = 0.0655
FInd/Total = 0.4428
0.047
0.104
0.028
0.015
< 10–5
Non-random mating in Coptotermes lacteus
Australian Journal of Zoology
scape (FBLOCKS/POP near zero) and thus our sampling scheme
revealed no apparent within-population subdivision above the
level of the colony – which was unexpected. This negative result
suggests that related colonies are not patchily distributed from
poor dispersal of the founding alates, and that the reduced heterozygosity we observe within populations is not a result of drift
between locally isolated groups of colonies.
Despite the longstanding assumption for poor dispersal, tests
for isolation by distance in subterranean termite populations are
often negative – and this, despite substantial analytical power
(Vargo et al. 2003, 2006; DeHeer and Vargo 2004; DeHeer et al.
2005; Husseneder et al. 2005; Husseneder et al. 2006; Vargo and
Carlson 2006). These studies, together with the present study that
shows a lack of detectable within-population structure above the
level of colony, suggests that local gamete dispersal in subterranean species can be effective. The discrepancy between the
perception for poor dispersal and the observation of open gene
flow suggests that for certain species, either alates fly far enough
to minimise detectable population structure, or passive dispersal
plays a compensatory role (Brandl et al. 2005). An exception
from Rhinotermitidae appears to be R. hageni, which does show
genetic evidence for locally restricted dispersal (Vargo and
Carlson 2006). Yet even here, it is possible that the strong differentiation between subpopulations is a sampling artefact. The
understudied ‘R. hageni’ might contain cryptic species inadvertently sampled as one (Vargo and Carlson 2006). As information
on dispersal and gene flow becomes available for this and other
species, we can begin to distinguish the norm from the exception.
Given that ACT and NSW populations of C. lacteus are not
obviously structured above the level of colony, the remaining
variance must be explained by differentiation at or below the
level of colony. Indeed, we observed significant differentiation
between colonies (FCOLONY/POP ≠ 0). This differentiation indicates a close genetic affinity between sexuals located within the
same colony, relative to sexuals in different colonies. Nestmate
king and queen are therefore more closely related than would be
expected under random mating, despite their seemingly effective dispersal.
indicated earlier, but they are very rare. We estimate from the
present study that the frequency of secondarily polygamous
colonies is less than ~3% (i.e. less than 1 in 38) and probably far
lower. Reproductive flexibility through secondarily derived
neotenics may therefore be a characteristic that predisposes
some species to be successful as urban pests. By contrast, a
species that is relatively conservative in its use of neoteny as a
reproductive strategy, such as our focal species, may be less
adaptive to urban habitats.
The parental generation of our study populations is deficient
in heterozygotes (Table 2; FInd/Total ≠ 0) and therefore is effectively
inbred. This appears to be due, in part, to genetic drift between
populations. Our estimates of F-statistics indicate that NSW and
ACT are differentiated from each other (FPOP/Total ≠ 0), confirming that dispersal is, as expected, restricted across this large distance and that drift is a factor in shaping metapopulation structure.
This between-population differentiation is, however, a relatively
minor source of the total variation observed (i.e. FPOP/Total <
FInd/Total). The majority of variation in allelic frequency therefore
stems from lower levels of biological organisation.
One possibility is that dispersal is restricted within our
sampled populations, giving rise to subpopulations that are
themselves affected by drift. To test for within-population limits
to dispersal, we subdivided our populations into arbitrary sized
‘blocks’. We then tested for differentiation between blocks. Our
inclusion of a fourth level (block) in hierarchical sampling is
atypical for studies on termites. Studies on the population genetics of rhinotermitids, for example, have tended to allow for s = 3
levels (individual, colony, population) in nested ANOVAs. This
analytical precedent was set by Thorne et al. (1999) and has
since been adopted as a standard for inferring the reproductive
composition of colonies of other subterranean species (Vargo
2003; Vargo et al. 2003, 2006; DeHeer and Vargo 2004; Dronnet
et al. 2005;DeHeer et al. 2005; Husseneder et al. 2005; Vargo
and Carlson 2006). The inclusion of additional levels in hierarchical sampling above colony can, however, be useful for
assessing the spatial scale over which dispersal and mating are
regulated by natural selection (Goodisman and Crozier 2002;
Vargo 2003). In the present study we found no effect of land**
n = 38
ns
ns
ns
ns
ns
ns
ns
ns
ns
380
376
208
72
52
16
32
64
16
1
2
3
4
5
6
7
8
ns
ns
0
120
48
10
11
12
0.300
0.200
rC
0.100
0.000
–0.100
–0.200
~0
223
9
Distance (km)
Fig. 2. Autocorrelogram showing the autocorrelation coefficient rc as a function of distance
class. The genotypes of parents located within the same colony, at distance class ~0, are more
similar than those located in physically separate colonies (as determined via a one-tailed permutation test). ** indicates P = 0.020, ns = not significant and n = number of pair-wise comparisons
within that distance class. For graphical purposes we also superimpose the 95% confidence intervals (error bars) about rc, as estimated by bootstrap resampling (Peakall and Smouse 2006). The
bootstrapping gives a qualitatively identical result to random permutation.
224
Australian Journal of Zoology
Further evidence for primary inbreeding with dispersal
comes from the spatial genetic autocorrelation analysis. We
detected positive autocorrelation between sexuals located
within the same colony, but not between sexuals located in different colonies, regardless of how close (or far) their colonies
were to each other. Autocorrelation within colonies suggests
that nestmate king and queen are related (consistent with
FCOLONY/POP ≠ 0, above); yet the absence of autocorrelation
between colonies suggests that this is not a result of limited dispersal (consistent with FBLOCKS/POP = 0, above). Limited dispersal predicts positive rc-values between colonies up to the
limiting distance class, at which point rc would decline through
zero and become negative (Smouse and Peakall 1999). Instead,
rc was never (significantly) positive for any between-colony distance, suggesting that dispersal is effective. We performed an
alternative autocorrelation analysis using smaller distance
classes (D ≈0, then every 500 m, up to 4 km). The same pattern
was observed: positive autocorrelation among nestmate sexuals,
but no autocorrelation among non-nestmates (analysis not
shown). Our hierarchical F-statistics and spatial autocorrelation
analysis therefore suggest that C. lacteus can disperse effectively within its ACT and NSW populations – it shows no spatial
structure. Colonies nonetheless contain two primary reproductives that are sometimes related.
The mean relatedness of colony reproductives is greater than
expected for totally unrelated mates (b – 0 > 1), but the significance of this difference is marginal. Thus, although F-statistics
and spatial autocorrelation analyses reveal a degree of inbreeding among established sexuals, the relatedness calculation indicates that the intensity of inbreeding is mild. At face value, our
estimate of b = 0.087 suggests a level of inbreeding equivalent
to second cousin matings (where b = 0.083) and the coefficient
itself is similar in value to the autocorrelation coefficient (rc =
0.086) and relevant F-statistic (FCOLONY/POP = 0.0655), all of
which estimate the probability that reproductives located within
the same colony share genes identical by descent. The latter two
estimates are significant against null expectation, and their
overall congruence suggests that sexuals established within
colonies are distant relatives, not fully outbred panmictic pairs.
Inbreeding on dispersal, however mild, is not often predicted
for termites; though there is a stochastic expectation for it
(Husseneder et al. 2006), especially if dispersal flights are
sometimes asynchronous (Grassé and Noirot 1951; Nutting
1969). Indeed, many studies report that primaries from several
different species do effectively outbreed, as evidenced by populations remaining in HWE (Luykx et al. 1986; Thompson and
Hebert 1998a; Shellman-Reeve 2001; Vargo 2003; Vargo et al.
2003; Husseneder et al. 2005; Vargo and Carlson 2006;). There
is, however, a growing amount of evidence, from subterranean
species in particular, that primary inbreeding may be more
common than once thought. Vargo and Carlson (2006) found
that primary-led colonies of R. hageni were often (~60%)
founded by sibling pairs, resulting in a high mean level of
king–queen relatedness (b = 0.28). Primary-led colonies of
R. grassei may also include a proportion in which reproductives
are closely related (DeHeer et al. 2005), and so too may colonies
of C. formosanus (Vargo et al. 2003; Vargo et al. 2006). Finally,
sampling of recently dispersed alates from R. virginicus in the
field reveal that ~5% are putative sibling pairs and in R. flavipes
G. J. Thompson et al.
the estimate is as high as 26% (DeHeer and Vargo 2006).
Similarly, by mass trapping dispersed alates of C. formosanus
Husseneder et al. (2006) estimated that these aggregates contained up to 20% full siblings (range 6–20). Thus, the potential
for primary inbreeding at dispersal seems high; presumably
these sibling pairs occasionally found new colonies as inbred
king and queen. The level of primary inbreeding detected in the
current study does not, by contrast, involve sibling–sibling
matings to any significant proportion (i.e. b much less than 0.5;
also, distibution of pairwise b is not bimodal). Instead, the
observed pattern is more consistent with descriptions of
‘optimal outbreeding’ (Bateson 1983), whereby dispersing
sexuals prefer distant relatives within the mating population
over totally unrelated mates.
It is not known what might drive such a mating bias for
C. lacteus. Random mating with viscosity (i.e. occasional,
stochastic sibling–sibling matings) might explain the low
heterozygosity in our populations but, as noted above and contrary to this hypothesis, neither our F-statistics nor our spatial
autocorrelation analyses detected any significant within-population viscosity above the level of colony. The predicted viscosity may be there, but our sampling scheme failed to detect it,
which we deem unlikely given our ability to detect it above
(metapopulation) and below (colony) the population level.
Alternatively, if too much outbreeding were maladaptive for
C. lacteus in some way, then excessive outbreeding may be
selected against, which would lead to mating bias and low
heterozygosity over time. Excessive outbreeding could be maladaptive in termites if, for example, it reduced immunocompetence, as has been demonstrated for Zootermopsis navadensis
(Rosengaus and Traniello 1993; Calleri et al. 2005) and implied
for C. formosanus (Fei and Henderson 2003). Another explanation for optimal outbreeding is kin selection for dispersal genes.
That is, a preference for distant relatives can co-evolve with dispersal if this preference helps the relative to spread (dispersal)
genes identical by descent to the ones of the disperser (Kokko
and Ots 2006).
The cumulative effect of mating with distant relatives on dispersal, by whatever mechanism, will be towards an inbred population, as observed here. We speculate that selective shifts
towards homozygosity may make it progressively harder for
alates to find mates that are sufficiently dissimilar to themselves, which in turn could promote further dispersal, and so on.
In termites, there is therefore ample potential for mate choice
preference to co-evolve with dispersal behaviour. This would
repay further study.
Though data on the genetics of dispersal in termites has been
slow to accumulate, these and other hypotheses on its adaptive
significance can be tested by relating measures of genetic
heterogeneity to dispersal and mate choice behaviour. We
predict that future studies will uncover a continuum of inbreeding on dispersal, ranging from absent to the equivalent of full
sibling–sibling matings. Further, we predict that estimates of F
will vary with the nesting and feeding ecology of different
species. The balance of dispersal versus inbreeding in termites
may therefore be more finely tuned than previously thought.
Predicting how F should vary as a function of ecology and
genetics will require careful modelling.
Non-random mating in Coptotermes lacteus
Acknowledgements
We thank Patrick V. Gleeson and Theo Evans for help and advice collecting
termites and Michael A. D. Goodisman, Nathan Lo, Silke Nebel, Edward L
Vargo and two anonymous reviewers for constructive comments. This work
was funded by an NSERC Discovery Grant to BJC and an NSERC
Postdoctoral Research Fellowship to GJT.
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Non-random mating in Coptotermes lacteus
Australian Journal of Zoology
Appendix 1. Numbers of putative populations (POP), subpopulations
(BLOCK), colonies (COLONY), and parental individuals (n) ‘sampled’
(their genotypes inferred from offspring genotypes) in this study
‘Block’ is defined as a group of nearby colonies (see Fig. 1). Number of
subdivisions at the four levels of biological organisation are: r1 = 2, r2 = 6,
r3 = 38, r4 = 76
POP
BLOCK
COLONY
n3
ACT
ACT-57
1
2
3
4
5
6
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
1
2
3
4
5
6
7
1
2
3
4
5
6
1
2
3
4
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
ACT-49
ACT-31
NSW
NSW-01
NSW-02
NSW-03
n2
n1
227
Appendix 2. Allele frequencies and expected heterozygosities (HE) by
population for the four microsatellite loci
The observed (NA) and effective (NE) number of alleles are also shown for
each marker within each population
Locus
Allele
Clac 1
175
177
183
185
187
189
197
201
207
8, 2.901
0.012
0.024
0.226
0.524
0.119
–
0.060
0.012
0.024
4, 2.823
–
–
0.235
0.515
0.162
0.088
–
–
–
Clac 2
206
210
212
220
222
224
5, 1.627
–
0.060
0.774
0.071
0.083
0.012
3, 1.841
0.118
0.176
0.706
–
–
–
Clac 6
181
183
185
187
189
191
193
195
197
199
203
7, 4.112
–
–
0.024
0.060
0.119
0.131
0.393
0.226
–
0.048
–
9, 6.901
0.118
0.059
0.088
0.235
0.029
–
0.147
0.044
0.118
–
0.162
Clac 8
249
251
253
255
257
259
261
265
275
7, 2.703
0.024
0.012
0.119
0.190
0.560
0.024
0.071
–
–
7, 3.451
–
0.029
0.294
0.044
0.426
0.103
–
0.088
0.015
n4
12
Population
ACT
NSW
NA, NE Frequency NA, NE Frequency
HE
0.655
18
0.428
12
42
14
0.854
12
8
34
76
0.688
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