, Volume 605, Issue 1, pp 55–63 | Cite as

Strong genetic divergence indicates that congeneric damselflies Coenagrion puella and C. pulchellum (Odonata: Zygoptera: Coenagrionidae) do not hybridise

  • Chris D. Lowe
  • Ian F. Harvey
  • David J. Thompson
  • Phillip C. Watts
Primary research paper


Coenagrionid damselflies are in general decline in the British Isles. Numerous factors have been implicated in the loss of these species including recent speculation that hybridisation between congeners may result in species decline. Here we use a panel of 12 microsatellite loci to examine levels of genetic divergence and the likely occurrence of hybridisation in five populations of Coenagrion puella and C. pulchellum using samples from four sites in south-east England. Coenagrion puella and C. pulchellum were highly genetically divergent, and there was no evidence of hybridisation between any of the populations examined, even where C. puella and C. pulchellum were sympatric. There was some suggestion that C. pulchellum was less genetically diverse than C. puella, though this may have been a result of ascertainment bias associated with cross-species application of microsatellite markers. We conclude that there is no evidence that hybridisation between C. puella/pulchellum could be responsible for the on-going demographic decline in C. pulchellum. Nevertheless, further genetic studies such as this one are likely to provide estimates of diversity, population structure and dispersal capacity that will be invaluable in future conservation management strategies for coenagrionid damselflies.


Introgression Conservation Habitat-loss Population structure 


Like many European species of odonate (see Van Tol & Verdonk, 1988), damselflies (Zygoptera: Odonata) of the genus Coenagrion are becoming increasingly rare in the British Isles. Of the seven British species that were regular breeders in the 1950s two (C. armatum and C. scitulum) are now absent, three have highly restricted distributions (C. hastulatum, C. lunulatum and C. mercuriale) and one other, C. pulchellum, while locally abundant, is in widespread decline. By contrast, the remaining species, C. puella, is abundant and widely distributed both throughout the British Isles and much of continental Europe (Askew, 1988; Dijkstra & Lewington, 2006).

Numerous factors have been implicated in the general decline of coenagrionid damselflies including habitat loss, disturbance, and pollution. More recently, a study by Freeland and Conrad (2002) explored the hypothesis that the current decline in C. pulchellum may be a result of hybridisation with the congener C. puella. In a broad context, the mechanisms of hybridisation, and the extent to which interbreeding influences species persistence and distributions, are highly variable (Jiggins & Mallet, 2000). Examples of hybrid zones between abutting interbreeding species that remain stable for long time periods are relatively common (e.g. Turner, 1971; Barton & Hewitt, 1985, Nielsen et al., 2003). Conversely, hybridisation may also result in species decline when the difference in abundance between interbreeding populations is large (Young et al., 2001). Given their relative abundance and distributions—C. puella’s range entirely overlaps the fragmented distribution of C. pulchellum—such a mechanism is a plausible factor for the present stability of C. puella and the parallel decline of C. pulchellum. Establishing whether hybridisation occurs between C. puella and C. pulchellum is likely to be important in future conservation management strategies.

Hybridisation is well documented in a number of damselfly species; for example, hybrids of the damselflies Calopteryx splendens and C. virgo are common and likely a result of limited pre-reproductive isolation between the two species (Tynnkynen et al., 2004, 2007); amongst the Coenagrionidae, hybridisation between Ishnura elegans and I. graellsii occurs where their distributions overlap in the Iberian peninsula (Sanchez-Guillen et al., 2005). While hybridisation certainly occurs in some species and numerous instances of heterospecific mating have been recorded in odonates, convincing evidence for the production of hybrid progeny is limited to a few cases (Corbet, 1999). This relatively limited description of hybridisation in damselflies undoubtedly reflects general difficulties associated with identifying hybrid individuals in the field rather than the limited occurrence of hybridisation in the taxon per se. In the case of C. puella and C.pulchellum one instance of a putative cross has been reported (Miller & Fincke, 2004), but wider recognition of hybridisation between these two species is hampered by their similar morphology and, in particular, the highly variable form of C. pulchellum (hence the common English name of Variable Damselfly). Quantification of the amount of introgression between sympatric individuals of these species using molecular markers overcomes such identification problems.

With this in mind, Freeland & Conrad (2002) explored variation at mitochondrial cytochrome oxidase (CoxI) for five populations of C. puella and C. pulchellum, and indicated a single, shared haplotype between sympatric C. puella and C. pulchellum possibly indicative of hybridisation. However, such evidence is equivocal because the extent of intraspecific variability in Cox1 is itself highly inconsistent between insect taxa and has been shown to be low in members of the Coenagrionidae (Chippindale et al., 1999; Turgeon & McPeek, 2002). Thus, it is not clear whether this genetic marker provides sufficient resolution to assess levels of genetic differentiation or hybridisation in these damselflies. Moreover, maternally inherited mitochondrial genes are poor markers to detect hybridisation compared with nuclear genetic markers that assess contribution from both parents. As a consequence, given the limited molecular data available, it remains to be determined whether regular hybridisation takes place between C. puella and C. pulchellum.

In this study we employ a panel of 12 polymorphic, nuclear (microsatellite) genetic markers to quantify the level of genetic differentiation, and the degree of any hybridisation, between sympatric and allopatric populations of C. puella and C. pulchellum. We uncover substantial genetic divergence between species and strong evidence that hybridisation does not occur regularly.

Materials and methods

Specimens of Coenagrion puella and C. pulchellum were collected from four sites in the south of England (Fig. 1). Each site was visited once between May and August 2006 and individuals captured with a sweep net. Species and sex were noted and whole individuals or single legs were placed in 100% ethanol and stored in the dark at 4°C until DNA extractions were performed. Presence and absence of C. puella/C. pulchellum at each location was determined by observation during visits to sites and from historical records in the database of the British Dragonfly Society.
Fig. 1

Sampling locations from where Coenagrion puella and C. pulchellum were collected: Catfield Fen, Norfolk (Grid reference (GR): TG 367 213); Upton Fen, Norfolk (GR: TG 385 136); St Ives, Cambridgeshire (GR: TL 325 703); and Queen Elizabeth Country Park, Hampshire (GR: SU 718 184)

Genotyping and genetic data analysis

DNA was extracted from either thoracic muscle or single legs using a high salt-protocol (Aljanabi & Martinez, 1997). Every individual was genotyped at 12 unlinked microsatellite loci: LIST4-001, LIST4-002, LIST4-030, LIST4-034, LIST4-066, LIST4-067, LIST21-004, LIST21-006, LIST21-007, LIST21-008, LIST21-009, LIST21-010 (see Watts et al., 2004a, b; Lowe et al., 2007 for details of loci). Microsatellite loci described by Watts et al. (loci prefixed ‘LIST4’) were isolated from another coenagrionid, Coenagrion mercuriale, but can be used to PCR-amplify alleles in various members of the Coenagrionidae (Watts et al., 2004a), while loci characterised by Lowe et al. (loci prefixed ‘LIST21’) were isolated from C. puella. Approximately 1–20 ng of DNA was used in a 10-μl PCR containing 75 mm Tris–HCl pH 8.9, 20 mm (NH4)2SO4, 0.01% v/v Tween-20, 0.2 mm each dNTP, 1.5–3.0 mm MgCl2, 2 pmol each primer and 0.25 uTaq polymerase (ABgene). PCRs were performed using a PTC-0221 Dyad thermocycler (MJ research) and the following conditions: 95°C for 3 min, 5 cycles (95°C 30 s, Ta°C 45 s, 72°C 45 s), 35 cycles (92°C 30 s, Ta°C 45 s, 72°C 55 s), 72°C for 10 min (where Ta is the locus specific annealing temperature, see Watts et al., 2004a, b; Lowe et al., 2007). Forward primers were 5′-labelled with either 6-fam, ned, pet or vic fluorophores (Applied Biosystems). PCR products were pooled into one of two genotyping panels along with a genescan-500 liz size standard (Applied Biosystems) and separated by capillary electrophoresis through a denaturing polymer on an ABI3730xl automated sequencer (Applied Biosystems). Allele sizes were determined using the cubic model of analysis in genemapper software (Applied Biosystems).

Each population and the entire sample were tested for departure from expected Hardy–Weinberg equilibrium (HWE) conditions using the randomisation procedure (5,000 alleles randomised among individuals) implemented by fstat ver. 2.9.3 (Goudet, 1995). We also calculated the extent of departure from expected HWE conditions for all individuals caught at Upton, i.e. a pooled, sympatric sample of C. pulchellum and C. puella. fstat was used to calculate basic measures of genetic diversity: Wright’s (1951) inbreeding co-efficient (f), expected heterozygosity (He) and allelic richness (AR). AR was standardised to eight individuals to account for the size of the smallest sample due to PCR failure of two C. pulchellum samples at some of the loci. The repetition of the same statistical procedure, e.g. to test each loci for HWE conditions independently, increases the probability of type-I error (rejecting Ho when it is actually true); to adjust for the increased likelihood of this error, sequential Bonferroni corrections for k multiple tests were applied where appropriate (see Rice, 1989 for details).

Principal component analysis (PCA) was used to reduce the variation in the multivariate data set (82 variables [alleles] at 10 loci—see results below) to two linear combinations of the original variables, using pca-gen ver. 1.2.1 (Goudet, 1999) software. The significance of each principal component was assessed from 5,000 randomisations of genotypes. The distribution of each population across the two principal components then provides a quantitative measure of the degree of genetic similarity between sites.

Population structure was assessed using the model-based clustering approach implemented by structure v. 2.0 (see Pritchard et al., 2000 for full background) that simultaneously identifies clusters (populations) and assigns individuals to populations using a Bayesian approach. Briefly, structure models K populations (where K may be unknown) that are characterised by a set of allele frequencies at each locus. Individuals are (probabilistically) assigned to populations (or jointly if their genotypes indicate that they are admixed) on the basis of their multilocus genotypes, assuming unlinked loci and HWE conditions within populations. The actual number of distinct populations (K) may be estimated from the value of K that maximises the posterior probability of the data for a given posterior probability distribution Pr(K|X) calculated from the posterior distribution of Pr(X|K) (where X is the multilocus genotype of sampled individuals); in structure output, this criterion ‘Ln P(D)’ is calculated by computing an average of the log likelihood of the data at each step of the Monte Carlo Markov Chain (MCMC) and then half their variance is subtracted from the mean. structure also calculates the proportion of membership of each individual in each cluster (Q).

In situations where there is distinct genetic structure, the true number of populations is identified by the model of K that returns the maximal value of Ln P(D). However, real and simulated data have demonstrated that choosing an appropriate value of K can be difficult, particularly for large and/or complex population structures, and under such circumstances several recommendations have been proposed to identify the best model. First, Pritchard & Wen (2003) suggest that the value of K at the beginning of a ‘plateau’ of estimates of Ln P(D) be selected, i.e. use the smallest value of K that captures the major structure of the data set. Rather than determine the actual number of populations this approach provides a heuristic guide to the models that are most consistent with the data set. A second method to detect the true number of clusters in an unknown sample is to use the ad hoc measure ΔK, the second-order rate of change of Ln P(D) with respect to K (Evanno et al., 2005). ΔK = mean (|L(+ 1) − 2L(K) + L(− 1)|)/sd [L(K)], where L(K) denotes Ln P(D) for a particular model run (value of K), mean is the average and sd is the standard deviation [of L(K)] across replicate runs of structure for each putative value of K. Computer simulations demonstrate that the modal value of ΔK corresponds to the most pronounced partition of the data set (Evanno et al., 2005).

Ten independent runs of structure were carried out for the total data set for = 1 to = 6 using the admixture model and correlated allele frequencies (Pritchard et al., 2000). All model runs were based on 500,000 iterations after an initial burn-in period of 50,000 iterations, which was sufficient to ensure convergence of the MCMC. The replicate runs made for each value of K permitted an assessment of consistency of the results and also calculation of the second-order rate of change of Ln P(D) described above.


We surveyed allelic variation in 197 individual damselflies at twelve microsatellite loci. Out of the 12 microsatellite loci examined, one (LIST21-009) failed to amplify any of the three samples of C. pulchellum, while a second locus (LIST21-007) showed frequent PCR failures and a consistently large heterozygote deficit (= 0.87–0.92) in the same species, which is typical of a locus with null (non-amplifying) alleles; therefore, these two loci were excluded from all subsequent data analyses.

No sample contained a significant excess of heterozygotes and just one significant (< 0.05, k  = 8–10) excess of homozyotes was observed within samples—C. pulchellum at Upton (Table 1). By contrast, 5 loci (50 %) had significant (< 0.05, = 10) heterozygote deficits when genotype data for all individuals collected at Upton were pooled to generate a sympatric C. puella and C. pulchellum ‘population’, implying that this sample did not meet expected HWE conditions. Full details of variation in genetic diversity are provided in Table 1. Briefly, genetic diversity was moderate in these samples, with AR varying between approximately 1 and 7, and He varying from 0.04 up to 0.85. Genetic diversity was greater in samples of C. puella than samples of C. pulchellum (Table 1).
Table 1

Basic measures of genetic diversity for 10 microsatellite loci in populations of two species of damselfly (Coenagrion pulchellum andC. puella) and for a sympatric population comprising both species (Pul–Pue)


Coenagrion pulchellum

Coenagrion puella




St Ives
















































































All loci











































































All loci














































































All loci







n, sample size; f, Wright’s (1951) inbreeding coefficient; He, expected heterozygosity; AR, allelic richness; *indicates significant (< 0.05) deficit of heterozygotes, with values remaining significant after sequential Bonferroni correction (α = 0.05) highlighted bold. QECP—Queen Elizabeth Country Park, Hampshire

Only the first principal component axis contained a significant amount of the genetic variation among samples (FST = 0.133, P  = 0.0002, 80.3 % of the total variation between samples). Although not significant, the second axis nonetheless accounted for a substantial amount of the allelic variation among samples (FST = 0.0286, P  = 0.972, 17.3% of the total variation). A scatterplot (not shown) of the sample scores (eigenvectors) of the first two principal components thus summarises almost all (∼98% of the global FST = 0.1656) variation in the sample data. Differences at the first principal component scores revealed a clear separation of samples of C. puella and C. pulchellum (Fig. 2). Interestingly, the sample of C. pulchellum from Upton did not cluster with C. pulchellum from Catfield and St Ives; however, the major differences between these samples occurred at the second (not significant) axis. Hence, the major variation between samples correlated with differences between species, rather than geographic separation.
Fig. 2

Variation in the first and second principal component scores (based on differences in allele frequencies) for samples of the damselflies C. puella (white circles) and C. pulchellum (black circles) from four sites in the UK. Only the first principal component axis accounts for statistically significant (P  = 0.0002) variation in genetic differences between samples, but both axes represent ∼98 % of the total genetic variation

Independent runs of structure generated similar clustering solutions. Thus, while the variance in values of L(K) increased with greater values of K, this effect is not particularly evident from Fig. 3a. Overall, values of L(K) produced by structure analysis were similar for all values of K selected and were highest at = 3. However, the mean values of L(K) for = 2, 3 and 4 (Fig. 3a) were similar, providing support for partition of the data set into two genetic clusters. The mean difference between successive likelihood values of K, corresponding to the rate of change of the likelihood function with respect to K, showed a clear drop between = 2 and = 3 and remained low thereafter (data not shown). Values of ΔK produced a strong modal value at = 2 (Fig. 3b); since the height of the modal value of ΔK indicates the strength of the population structure (Evanno et al. 2005), these data provide unequivocal evidence for subdivision of these samples into two distinct genetic units. Individual proportion of membership into either of the two model clusters revealed a clear partition between C. puella and C. pulchellum; moreover, none of the individuals examined is inferred to have a reasonable proportion of mixed ancestry (Fig. 4). Indeed, the average membership coefficients for all samples of C. pulchellum and C. puella in their respective hypothetical clusters were all ≥0.99. Thus, there is no evidence for any gene flow between damselfly species, even at the sympatric site.
Fig. 3

Graphical detection of the true number of groups (K) in a genetic sample using structure (Pritchard et al. 2000) software. (a) Mean (±standard deviation) L(K) (a measure of the posterior probability of data for a value of K) over 10 independent runs for each value of K. (b) Statistic ΔK (Evanno et al. 2005), a measure of the second-order rate of change of L(K) (see methods for details); the modal value of ΔK represents either the true value of K or the uppermost level of genetic structure

Fig. 4

Probabilities of individual membership to cluster in a two-cluster simulation using structure (Pritchard et al. 2000). Each bar represents a single damselfly (either C. puella or C. pulchellum), and the proportion of the bar that is black or white represents the proportion of assignment to cluster one or cluster two, respectively


We have provided an extensive analysis of a panel of twelve microsatellite loci for sympatric and allopatric populations of C. puella and C. pulchellum and indicate that the occurrence of regular hybridisation between these two species is unlikely.

By all methods of analysis, populations of these two species were highly genetically divergent. In the first instance, two of the markers developed in C. puella failed to amplify during PCR (LIST21-009) or produced a high degree of PCR failures/allele dropouts (LIST21-007) in C. pulchellum. These patterns are typical of cross-species application of microsatellite markers, where species-specific mutations in flanking sequence prevent amplification in the non-target organism (e.g. Watts et al., 2004a). A further locus, LIST21-006, possessed one allele that completely segregated between species, i.e. was present in all C. pulchellum individuals and absent in C. puella (data not shown). In addition to locus-specific patterns, genetic diversity across all loci (measured as allelic richness) was consistently lower in C. pulchellum. Although whether this is a genuine signal of lower diversity or a result of ascertainment bias is difficult to determine. Commonly, microsatellite loci isolated from one species and applied to another show reduced diversity in the non-target species (Amos, 1999; Schlötterer, 2004). Most microsatellite development protocols positively select for longer repeat regions, which are also typically more variable; however, the most diverse loci in one species may not be the most diverse in closely related species and so an artificial bias in diversity estimates can result from cross-species comparisons. Regardless of whether the diversity signal is real in this case or not it again suggests divergence between C. puella/C. pulchellum. Moreover, PCA of allele frequencies indicated clustering of populations into species groups between which extensive differentiation occurred (Fig. 3). Finally, individual-based cluster analysis produced a clear delineation of species, with no evidence for mixed ancestry. In combination, the failure of some markers to amplify in C. pulchellum, variation in genetic diversity, complete segregation of some alleles, large estimates of genetic divergence and a lack of introgression unequivocally support an absence of hybridisation between C. puella/C. pulchellum.

For the sympatric C. puella/C. pulchellum populations, where presumably the highest chance of identifying hybridisation exists, the same strong signal of genetic divergence remained. For instance, ostensibly treating the Upton C. puella/C. pulchellum as a single random-mating population resulted in heterozygote deficiencies in 5 of the 10 microsatellite loci, an indication that the nominal population is subdivided (Hare et al., 1996); conversely, when sympatric C. puella and C. pulchellum were treated as two separate populations all loci conformed to HWE expectations. Independently, assignment and clustering analysis (Fig. 2) provides an unequivocal indication that the Upton C. puella and C. pulchellum are two distinct populations with no signal of gene flow. Despite these clear indications of differentiation we must necessarily add a word of caution. We have assessed differentiation for a small number of populations and only assessed one sympatric site. Although we would speculate, based on this study, that hybridisation is unlikely an entirely conclusive result would require the assessment of more locations where the two species co-occur.

It is clear that hybridisation between C. puella/C. pulchellum is unlikely to be responsible for the decline in C. pulchellum numbers. Whether or not other interactions between these two species are responsible for changes in species distributions has not been assessed. Competitive interactions between odonate species have been documented, but where potential conflicts have been studied there has been no evidence of the exclusion of a species from a habitat by a competitor (Corbet, 1999). For example, differential responses to environmental pressures, such as predation, can account for species co-existence: McPeek et al. (2001) demonstrated that differences in physiological stress responses to fish predation mediated co-existence between Enallagma spp. and Ischnura spp. The most likely cause for the decline in C. pulchellum numbers is undoubtedly linked with habitat loss, change or degradation, although no studies have explicitly assessed decline in this species. Coenagrion puella and C. pulchellum certainly have differing, if substantially overlapping, habitat requirements (Schindler et al., 2003; Chovanec & Waringer, 2001), and Coenagrion pulchellum is almost certainly more sensitive to the effects of agricultural intensification (e.g. eutrophication, drainage) than C. puella (Smallshire & Swash, 2004; Smallshire, pers. comm.). It is notable that C. pulchellum is absent from numerous apparently suitable habitats in England, but is still abundant across most of Ireland. Whether differences in agricultural practices and the subsequent effects on habitat quality do influence C. pulchellum distributions remains to be examined. Regardless, given the decreasing distribution of C. pulchellum and the decline of coenagrionid damselflies as a whole, conservation measures are required to preserve these species; the provision of genetic data to estimate population structure and dispersal capacity will be an important component in the design of future conservation strategies (Watts et al., 2006).



We thank the Norfolk Wildlife Trust for permission to sample from Upton and Catfield Fens and Queen Elizabeth Country Park, Hampshire, for access to their site. The work was supported by NERC grant NE/C511205/1.


  1. Aljanabi, S. & I. Martinez, 1997. Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Research 25: 4692–4693.PubMedCrossRefGoogle Scholar
  2. Amos, W., 1999. A comparative approach to the study of microsatellite evolution. In Goldstein, D. B. & C. Schlotterer (eds), Microsatellites: evolution and application. Oxford University Press, USA.Google Scholar
  3. Askew, R. R., 1988. The dragonflies of Europe. Harley Books, Essex, England.Google Scholar
  4. Barton, N. H. & G. M. Hewitt, 1985. Analysis of hybrid zones. Annual Review of Ecology and Systematics 16: 113–148.CrossRefGoogle Scholar
  5. Chippindale, P. T., V. K. Davé, D. H. Whitmore & J. V. Robinson, 1999. Phylogenetic relationships of North American damselflies of the genus Ischnura (Odonata: Zygoptera: Coenagrionidae) based on sequences of three mitochondrial genes. Molecular Phylogenetics and Evolution 11: 854–860.CrossRefGoogle Scholar
  6. Chovanec, A. & J. Waringer, 2001. Ecological integrity of river-flood plain systems—assessment by dragonfly surveys (Insecta: Odonata). Regulated Rivers: Research & Management 17:493–507.CrossRefGoogle Scholar
  7. Corbet, P. S., 1999. Dragonflies: behaviour and ecology of Odonata, revised edition. Harley Books, Essex, England.Google Scholar
  8. Dijkstra, K. D. B. & R. Lewington, 2006. Field guide to the dragonflies of Britain and Europe including western Turkey and north-western Africa, British Wildlife Publishing, Milton on Stour.Google Scholar
  9. Evanno, G., S. Regnaut & J. Goudet, 2005. Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology 14: 2611–2620.PubMedCrossRefGoogle Scholar
  10. Freeland, J. R. & K.F. Conrad, 2002. Genetic similarity within and among populations of the Variable and Azure damselflies (Coenagrion pulchellum and C. puella). Hydrobiologia 479: 69–73.CrossRefGoogle Scholar
  11. Goudet, J., 1995. fstat (vers. 1.2): a computer program to calculate F-statistics. Journal of Heredity 86:485–486.Google Scholar
  12. Goudet, J., 1999. pca-general for Windows, ver. 1.2. http://www2.unil.ch/izea/softwares/pcagen.html.
  13. Hare, M. P., S. A. Karl & J. C. Avise, 1996. Anonymous nuclear DNA markers in the American Oyster and their implications for the heterozygote deficiency phenomenon in marine bivalves. Molecular Biology and Evolution 13: 334–345.PubMedGoogle Scholar
  14. Jiggins, C. D. & J. Mallet, 2000. Bimodal hybrid zones and speciation. Trends in Evolution and Ecology 15: 250–255.CrossRefGoogle Scholar
  15. Lowe, C. D., S. J. Kemp, I. F. Harvey, D. J. Thompson & P. C. Watts, 2007. Variable microsatellite loci isolated from the azure damselfly, Coenagrion puella (L.) (Zygoptera; Coenagrionidae). Molecular Ecology Notes 7: 880–882.CrossRefGoogle Scholar
  16. McPeek, M. A., M. Grace & J. M. L. Richardson, 2001. Physiological and behavioural responses to predators shape the growth/predation risk trade-off in damselflies. Ecology 82: 1535–1545.CrossRefGoogle Scholar
  17. Miller, M. N. & O. M. Fincke, 2004. Mistakes in sexual recognition among sympatric Zygoptera vary with time of day and colour polymorphism (Odonata: Coenagrionidae). International Journal of Odonatology 7: 471–491.Google Scholar
  18. Nielsen, E., M. M. Hansen, D. E. Ruzzante, D. Meldrup & P. Grønkjær, 2003. Evidence of a hybrid-zone in Atlantic Cod (Gadhus morhua) in the Baltic and Danish Belt Sea revealed by individual admixture analysis. Molecular Ecology 12: 1497–1508.PubMedCrossRefGoogle Scholar
  19. Pritchard, J. K. & W. Wen, 2003. Documentation for structure software: version 2. http://pritch.bsd.uchicago.edu.
  20. Pritchard, J. K., M. Stephens & P. Donnelly, 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–959.PubMedGoogle Scholar
  21. Rice, W. R., 1989. Analyzing tables of statistical tests. Evolution 43: 223–225.CrossRefGoogle Scholar
  22. Sanchez-Guillen, R. A., H. Van Gossum & A. Cordero Rivera, 2005. Hybridization and the inheritance of female colour polymorphism in two ischnurid damselflies (Odonata: Coenagrionidae). Biological Journal of the Linnean Society 85: 471–481.CrossRefGoogle Scholar
  23. Schindler, M., C. Fesl & A. Chovanec, 2003. Dragonfly associations (Insecta: Odonata) in relation to habitat variables: a multivariate approach. Hydrobiologia 497: 169–180.CrossRefGoogle Scholar
  24. Schlötterer, C., 2004. The evolution of molecular markers—just a matter of fashion? Nature Reviews Genetics 5:63–68.PubMedCrossRefGoogle Scholar
  25. Smallshire, D. & A. Swash, 2004. Britain’s dragonflies. Wild Guides Ltd, Hampshire, UK.Google Scholar
  26. Turgeon, J. & M. A. McPeek, 2002. Phylogeographic analysis of a recent radiation of Enallagma damselflies (Odonata: Coenagrionidae). Molecular Ecology 11: 1989–2002.PubMedCrossRefGoogle Scholar
  27. Turner, J. R. G., 1971. Two thousand generations of hybridisation in a Heliconius butterfly. Evolution 25: 471–482.CrossRefGoogle Scholar
  28. Tynkkynen, K., M. J. Rantala & J. Suhonen, 2004. Interspecific aggression and character displacement in the damselfly Calopteryx splendens. Journal of Evolutionary Biology 17: 759–767.PubMedCrossRefGoogle Scholar
  29. Tynkkynen, K., A. Grapputo, J. S. Kotiaho, M. J. Rantala, S. Vaananen & J. Suhonen, 2007. Hybridization in Calopteryx damselflies: the role of males. Animal Behaviour. doi: 10.1016/j.anbehav.2007.09.017.
  30. Van Tol, J. & M. Verdonk, 1988. Protection des libellules (Odonates) et de leurs biotopes. Conseil de l’Europe, comité européen pour la sauvegarde de la nature et des ressources naturelles, Strasbourg. Collection Sauvegarde de la Nature 38: 188.Google Scholar
  31. Watts, P. C., D. J. Thompson & S. J. Kemp, 2004a. Cross-species amplification of microsatellite loci in some European zygopteran species (Odonata: Coenagrionidae). International Journal of Odonatology 7: 87–96.Google Scholar
  32. Watts, P. C., J. H. Wu, C. Westgarth, D. J. Thompson & S. J. Kemp, 2004b. A panel of microsatellite loci for the southern damselfly, Coenagrion mercuriale (Odonata: Coenagrionidae). Conservation Genetics 5: 117–119.CrossRefGoogle Scholar
  33. Watts, P. C., I. J. Saccheri, S. J. Kemp & D. J. Thompson, 2006. Population structure and the impact of regional and local habitat isolation upon genetic diversity of the endangered damselfly Coenagrion mercuriale (Odonata: Zygoptera). Freshwater Biology 51: 193–295.CrossRefGoogle Scholar
  34. Wright, S., 1951. The genetical structure of populations. Annals of Eugenics 15: 323–354.Google Scholar
  35. Young, W. P., C. O. Ostberg, P. Kiem & G. H. Thorgaard, 2001. Genetic characterisation of hybridisation and introgression between andromous rainbow trout (Oncorhynchus mykiss irideus) and costal cutthroat trout (O. clarki clarki). Molecular Ecology 10: 921–930.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Chris D. Lowe
    • 1
  • Ian F. Harvey
    • 1
  • David J. Thompson
    • 1
  • Phillip C. Watts
    • 1
  1. 1.School of Biological SciencesLiverpool UniversityLiverpoolUK

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