PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of jheredLink to Publisher's site
 
J Hered. 2009 Nov-Dec; 100(6): 732–741.
Published online 2009 September 4. doi:  10.1093/jhered/esp077
PMCID: PMC2877543

Genetic Differentiation among Wild Populations of Tribolium castaneum Estimated Using Microsatellite Markers

Abstract

We report our characterization of the genetic variation within and differentiation among wild-collected populations of the red flour beetle, Tribolium castaneum, using microsatellite loci identified from its genome sequence. We find that global differentiation, estimated as the average FST across all loci and between all population pairs, is 0.180 (95% confidence intervals of 0.142 and 0.218). A majority of our pairwise population comparisons (>70%) were significant even though this species is considered an excellent colonizer by virtue of its pest status. Regional genetic variation between Tribolium populations is 2–3 times that observed in the fruit fly, Drosophila melanogaster. There was a weak positive correlation between genetic distance [FST/(1 − FST)] and geographic distance [ln(km)]; pairs of populations with the highest degree of genetic differentiation (FST > 0.29) have been shown to exhibit significant postzygotic reproductive isolation when crossed in previous studies. We discuss the possibility that local extinction and kin-structured colonization have increased the level of genetic differentiation between Tribolium populations.

Keywords: FST, genetic differentiation, isolation by distance, microsatellite, Tribolium castaneum

The red flour beetle, Tribolium castaneum, is a cosmopolitan human commensal and a common inhabitant of stored products, including cereals, baking flour, and livestock feed (Park 1962). Experimental crosses between 22 pairs of T. castaneum populations showed evidence of nascent reproduction incompatibilities, uncorrelated with geographic distance (Demuth and Wade 2007a, 2007b). In this study, we characterize the extent of the genetic differentiation among wild-collected populations of T. castaneum, some shared with the Demuth and Wade (2007a, 2007b) studies. We use microsatellite loci and investigate to what extent, if any, genetic differentiation is correlated with the observed degree of reproductive isolation. The earlier work (Demuth and Wade 2007a) found a weak, positive correlation with genetic distance using mitochondrial markers. Because our microsatellite markers are nuclear rather than mitochondrial and are much more variable, they allow us a more robust estimate of genetic distance between populations on a shorter time scale and a second, independent test for an association between reproductive isolation and genetic distance.

Genetic differentiation can occur in multiple ways, including both neutral differentiation in the absence of gene flow and adaptive differentiation in response to selection in different environments. It is the latter that is believed to result most often in reproductive incompatibilities between genetically divergent populations (Charlesworth et al. 1987). In the absence of the homogenizing effects of gene flow, 2 populations may take separate evolutionary trajectories in adaptive response to local environments and, if dispersal is geographically restricted, this differentiation manifests itself as isolation by distance. Divergent selection in differing environments may cause genetic differentiation by eliminating an allele from 1 population in 1 environment while fixing it in another population in the other environment. The biology of T. castaneum makes it difficult to predict the amounts of divergent selection and gene flow. Unassisted, Tribolium are notoriously poor migrators with 1 study showing that T. castaneum can only stay air borne for a maximum of 20 s (Good 1933). Although T. castaneum is found wherever grains are stored, it is unable to persist on uncracked grain, relegating its habitat to human stores of processed grain or cohabitation with other, often larger, boring insects. With the advent of agriculture and the subsequent human population expansion, flour beetles were afforded amiable habitats across the globe. Stored processed grain is an abundant yet relatively homogenous medium that is marketed worldwide. The environmental homogeneity of processed grain makes divergence by differential selection relatively unlikely and the likelihood of widespread human dispersal through the grain trade appears to ensure a relatively high level of gene flow. Both factors mitigate genetic differentiation between populations. Nevertheless, evidence from interpopulation hybridization indicates some genetic isolation among local populations with significant measurable effects on hybrid viability and fecundity.

The purpose of this study is to examine the population genetics of T. castaneum using microsatellite markers identified from its genome sequence (Tribolium Genome Sequencing Consortium 2008). We characterize the extent to which globally distributed populations are genetically differentiated and whether the observed differentiation is associated with geographic distance and/or intrinsic reproductive isolation.

Materials and Methods

Sampling

All samples originated from collections of more than 50 adults. Each laboratory stock was established and maintained at a population size of >200 individuals on standard medium (20:1, flour:brewer's yeast, by weight) less than 24 h darkness, 28 °C, and approximately 70% relative humidity. Collection date and geographic location are given in Table 1. The populations, AdMO, BlIN(1), and JzM, were collected at grocery stores from flour or processed products (like pancake mix) intended for human consumption. The DxMO, Bh-I, and Go-IN populations were collected at granaries, where whole grains are processed into finely ground flour. Populations BlIN(2), BaE, DesT, and LiP were collected from markets and BoFL was collected from a pet store. The populations, RcMS and WLIN, were collected from livestock feed. The Chicago Standard Mixture (cSM) population is a laboratory stock created by mass mating the 4r strains of this species that Park used in his classic competition experiments (Park 1962; Park et al. 1964; for a detailed description of cSM, see Wade 1976). The founding stock from which the 4 Park strains were derived originated in Brazil. (It may well be that populations collected from different venues [e.g., granaries vs. livestock feed] experience different degrees of gene flow and may be isolated from one another; but, absent genetic studies, this would be a difficult avenue to pursue and we do not do that here.)

Table 1
Summary statistics of strains of Tribolium castaneum used for genetic analysis

Molecular Biology

Genomic DNA was isolated using a cetyltrimethyl ammonium bromide extraction protocol (Doyle JJ and Doyle JL 1987). Genotypes were determined using 15 nuclear microsatellite loci (Table 2) taken from Demuth et al. (2007). Microsatellite loci were selected based on genetic variability and distribution across the genome (Tribolium Genome Sequencing Consortium 2008). There is at least 1 microsatellite on 8 of the 10 chromosomes, with all but 1 positioned within introns or intergenic regions (see Appendix B). Fragment sizes were scored electronically using an ABI 3730 DNA analyzer and genemapper version 4.0 (Applied Biosystems, Foster City, CA) as well as by electrophoresis through a 4.5% agarose gel (Nuseive) and called independently by Douglas W. Drury and Ashley L. Siniard.

Table 2
Genomic location, primer sequences, number of alleles, locus-specific FST values, and number of populations of 14 in which deviations from HWE were detected

Statistical Analysis

Observed (Hobs) and expected (Hexp) heterozygosity were estimated using Arlequin (Schneider et al. 2000) for each population-by-locus combination and then averaged over all loci to get population estimates. Deviations from Hardy–Weinberg equilibrium (HW) were assessed using the method of Guo and Thompson (1992) for each locus-population combination using a Markov chain of 100 000 steps and 1000 dememorization steps.

Null alleles occur when markers consistently fail to be detected in certain populations when there is 1) inconsistency in template DNA, 2) divergence in primer-binding sites, or 3) an overwhelming size discrepancy between alleles preferentially favoring the smaller allele. The occurrence of undetectable genotypes results in individuals with no genotypic information at a locus (homozygote nulls) and an overabundance of scored homozygotes (heterozygote nulls). The failure to account for null alleles results in an underestimation of within-population genetic diversity and thus an overestimation of differentiation between populations (Avise and Dakin 2004). We estimated the potential frequency of null alleles with the expectation maximization algorithm of Dempster et al. (1977) using FST Refined Estimation by Excluding Null Alleles (FreeNA) (Chapuis and Estoup 2007). We use the mean expected heterozygosity as a measure of genetic variability within-populations because it is robust to the presence of null alleles. To verify that null alleles were not occurring because of the inability of the fluorescently labeled adapter primer to be incorporated into the polymerase chain reactionproduct, we confirmed the electronically scored allele sizes by running product out on a 4.5% agarose gel. Allele calling was performed independently by Ashley L. Siniard and Douglas W. Drury and checked for conformation with electronic annotation.

We evaluated the genetic differentiation among populations and among loci by calculating locus-specific and population pairwise FST's excluding null alleles ([ENA] algorithm as implemented in FreeNA). Pairwise differentiation was also evaluated using Fisher's Exact tests implemented in GENEPOP (Raymond and Rousset 1995). We investigated isolation by distance with Mantel tests implemented in GENETIX (10 000 permutations), using a semimatrix of ratios, FST/(1 − FST) with ENA corrected FST's, and a semimatrix of ln transformed geographic distance (Belkhir et al. 2004). We used the Bonferroni correction of 0.05 divided by the number of tests to correct for the multiplicity of comparisons (see Table 3 and Appendix A). We used R to plot the regression of FST/(1 − FST) on ln(km) and generate 95% confidence bands and prediction intervals (Ihaka and Gentleman 1996).

Pairwise Cavalli-Sforza and Edwards’ chord distance measures (1967), DC, which are robust to the presence of null alleles, were calculated in POPULATION (Langella 1999) from the genotype data (including null alleles algorithm implemented in FreeNA (Chapuis and Estoup 2007). The resulting distance matrix was used to reconstruct the phylogenic relationships using the neighbor joining method. Node stability was assessed by 10 000 bootstrap replications (over loci). The tree was visualized using TREEVIEW (Page 1996).

Results

Analysis of Microsatellite Variation

A total of 57 alleles were observed at the 15 loci. The number of alleles per locus ranged from 2 alleles for loci 34G3 and 32H3 to 6 alleles for locus LG9F3. Observed heterozygosity ranged from 0 for alleles fixed within a population to 0.833, most individuals were heterozygotes. Of 196 locus-by-population combinations, only 1% (2) show a significant deviation from HW after Bonferroni correction for multiple comparisons (P < [0.05/196] = 0.00025). All populations contained at least 1 individual that failed to amplify at 1 locus. Locus 34E3 failed to amplify from every individual of the cSM population, raising the possibility that it could be fixed for a null allele. Overall, the mean frequency of segregating null alleles in each population ranged from 0.21 to 0.52. Expected heterozygosities varied significantly among populations from a low of 0.21 (standard error of the mean, SEM ± 0.07) to a high of 0.53 (SEM ± 0.09).

Genetic Differentiation

Most pairs of populations differed significantly at most loci with Bonferroni correction for multiple comparisons (P < [0.05/91] = 0.0005; 70 of 91 comparisons, including 6 cases with borderline significance 0.0001 < P < 0.0005). Pairwise FST ranged from 0.0289 to 0.353 (WlIN/BlIN(2) and BhI/cSM comparisons, respectively; Table 3). Global FST, calculated as the average pairwise FST for all loci and population pairs, was 0.180 with a 95% confidence interval of 0.142–0.218. Figure 1 depicts the relationships among populations using the Cavalli-Sforza and Edwards chord distances. Based on this tree, there is no obvious clustering of populations. This suggests that we are not characterizing previously, undescribed cryptic species, although populations from disparate ends of the clade show significant postzygotic reproductive isolation when crossed (see Discussion; Demuth and Wade 2007a, 2007b).

Table 3
Pairwise comparison matrix of FST values with ENA correction (above diagonal) and Fisher's Exact test for genotypic differentiation between populations (below diagonal)
Figure 1
Unrooted neighbor joining tree based on Cavalli-Sforza and Edwards chord distance (DC). Values at the nodes indicate bootstrap support of 10 000 replicates.

Isolation by Distance

Although positive, there was no significant correlation between geographic distance ln(km) and genetic distance (Mantel test, R2 = 0.09, P > 0.05). A plot of genetic distance versus geographic distance (Figure 2) reveals that 5 population comparisons (AdMO × BhI, BoFl × BhI, BaE × BhI, GoIN × BhI, and AdMO × DxMO) lie outside the 95% prediction intervals. The Indian population represents the majority (4 of 5) of significant deviations from the expected genetic/geographic distance relationship. Evidence presented elsewhere (Drury DW, in preparation; Demuth and Wade 2007a, 2007b) supports the inference that the Indian beetle populations are undergoing a speciation event relative to the remainder of the species distribution (Thomson and Labonne 1998; Thomson and Beeman 1999). The only comparison not involving BhI to lie beyond the 95% predictive interval is the comparison with the second smallest interpopulation geographic proximity, AdMo × DxMO. Although AdMO and DxMO were collected only 25 miles apart, their deviation could be explained by venue of collection. The DxMO population was collected from a granary where local farmers bring their products to be milled into livestock feed. The AdMO population was found residing among bags of commercial pastry flour. Therefore, the true origin of the AdMO population could reside at or near any of the product distribution centers, unlike the case of the DxMO where transmission is assumed only to be among local farms.

Figure 2
Relationship between genetic distance (FST/[1 − FST]) and the natural logarithm of geographic distance. Dashed lines correspond to 95% confidence bands. Dotted lines correspond to 95% prediction intervals. Filled circles indicate comparisons involving ...

Discussion

The data show that the species T. castaneum exhibits a fair amount of genetic structure from the perspective of essentially neutral microsatellite markers. The value of FST, averaged over markers and populations, lies between the confidence limits of 0.142 and 0.218. Considering only the North American populations, the average pairwise FST among the 4 Indiana populations (BlIN(1), BlIN(2), GoIN, and WLIN) is 0.089 and that between the 2 Missouri populations (DxMO and AdMO) is 0.224; the average genetic differentiation between the 2 states is intermediate, 0.127. If we compare Africa (DesT), Central America (JzM), South America (LiP), and North American (DxMO), we find the average pairwise FST to be 0.090. Thus, there is as much or more regional variation among the Indiana or Missouri populations as there is among populations much more geographically distant from one another. This level of regional genetic variation in the red flour beetle is 2–3 times that observed in the fruit fly, Drosophila melanogaster, where the average FST across Australia is 0.054 and across the United States is 0.036 (Turner et al. 2008). Our global FST estimate of 0.180 is within the range of what has been seen in the Coleoptera; in a survey of genetic differentiation using allozymes, among populations in 7 beetle species, McCauley and Eanes (1987) found FST to range between 0.030 and 0.154. McCauley and Eanes (1987) also surveyed many other non-Coleopteran insects and found FST to range from 0.003 to 0.380. More recent beetle studies have found a range of genetic differentiation among populations of agricultural beetle pests, including the Western corn rootworm (Coleoptera: Chrysomelidae; FST = 0.006; Kim and Sappington 2005) and the Boll weevil (Coleoptera: Curculionidae; FST =0.241; Kim and Sappington 2006). Substantial heterogeneity in the degree of genetic differentiation also observed among species within the same genera, for example, bark beetle species (Dendroctonus ponderosae: FST = 0.030, Mock et al. 2007; Dendroctonus mexicanus: FST = 0.104, Zuniga et al. 2006) and Carabid beetle species (Agonum elongatulum: FST = 0.003, Platynus tenuicollis: FST = 0.27; Liebherr 1988).

We point out that the laboratory strain, cSM, has reduced levels of heterozygosity relative to the majority of the wild populations surveyed, ranking 13th of the total of 14 surveyed (Table 1). This confirms the earlier finding of Wade (1990, 1991) using quantitative genetic methods that natural populations of T. castaneum were more variable phenotypically and genetically for rate of population increase than the laboratory hybrid strain-d, cSM. A majority of our pairwise population comparisons, 70%, indicate significant genetic differentiation even though Tribolium beetles are considered excellent colonizers as per their “pest” status. The processes of local extinction and recolonization can increase the genetic differentiation variation among groups (McCauley and Wade 1980, Wade 1982; Wade and McCauley 1984), but the effect depends critically on the mode of colonization (Slatkin 1985; Wade and McCauley 1988; Whitlock and McCauley 1990). If a majority of the existing populations contribute migrants to newly colonized populations, then colonization represents increased gene flow, and the among-population genetic diversity decreases. However, when colonists are derived from only a single population or from a small group of relatives within a population (kin-structured colonization; Wade and McCauley 1988; Whitlock and McCauley 1990; Wade et al. 1994), migrants founding a new population can share high levels of genetic similarity and an array of new populations, each descended from such related colonizing propagules, will exhibit high levels of genetic differentiation one from another. Our genetic evidence suggests that, despite being good colonizers, Tribolium populations are likely to be founded by genetically related beetles from a single source. A single female can store the sperm of multiple males for 2–3 months and on finding a suitable substrate can produce upwards of 200 offspring. This facet of Tribolium biology, along with extremely poor long-distance flight ability, lends credence to the propagule model of colony foundation (Wade and McCauley 1984).

Classical genetic studies with flour beetles have revealed heterogeneous distributions of cryptic phenotypes. One of these phenotypes is associated with maternally acting selfish genes, termed MEDEA, which spreads by killing embryos that fail to inherit the element (Beeman et al. 1992). MEDEA elements are distributed heterogenously across the globe but are not found in India or Australia (Beeman and Friesen 1999). In a survey of the southern United States, 29 of 50 populations collected and tested contained MEDEA but the factor was less frequent in beetles collected below 33°N latitude (Beeman 2003). However, MEDEA is completely absent from Indian populations. MEDEA containing beetles are completely reproductively incompatible when females are M+ and males are from Indian populations (Thomson and Labonne 1998; Thomson and Beeman 1999). This incipient reproductive isolation is the first stage in the conversion of genetic variation within a species into permanent variation between 2 separate species.

We detected a positive but nonsignificant correlation between genetic distance [FST/(1 − FST)] and geographic distance (ln(km); Figure 2). Interpopulation differentiation in these beetles, on a global scale, is likely dictated by human traffic. Unlike most nonpest organisms, flour beetles can traverse an ocean by occupying a vessel's food stores. Therefore, we would expect population differentiation on a geographic scale to be determined by the frequency of human transport between locations, the number of populations contributing to new colonies, and the extent to which gene flow is prevented by intrinsic barriers, that is, reproductive isolation.

Among these population pair comparisons, 4 are particularly noteworthy. The 4 comparisons identified statistically as outliers (see shaded and numbered points in Figure 2), correspond to comparisons between the Indian population and 3 North American and 1 Ecuador population. Using the regression line in Figure 2, populations genetically differentiated to this degree should be more than 65 × 106 km apart. Demuth and Wade (2007a) and Thomson and Beeman (1999) found high levels of postzygotic reproductive isolation between the Indian and Ecuadorian and Indian and North American strains. Thus, the high degree of genetic differentiation (FST > 0.29) is coincident with significant postzygotic isolation.

Funding

National Institutes of Health (5R01GM65414-4 to M.J.W.).

Acknowledgments

We thank J. P. Demuth, T. E. Cruickshank, A. N. Brothers, and Y. Brandvain for their helpful comments on the manuscript and B. T. Furomoto and M. E. Whitesell for assistance in the laboratory.

Appendix A

Summary of genetic data per sample and locus; number of scored individuals (N) number of alleles, observed (Hobs) and expected (Hexp) heterozygosities, P values for deviation from HW(significance level Bonferroni corrected for multiple comparisons [0.05/196])

MarkerVariableAdMOBaEBhIBLBlIN(1)cSMDesTDxMOJzMLiPGoINWLINBlIN(2)RcMS
32A3N101212111110106111098109
No of alleles32333334332333
Hobs0.300.083330.272730.181820.10.30.333330.181820.10.111110.1250.10.44444
Hexp0.747370.54340.543480.744590.718610.631580.652630.878790.714290.726320.633990.80.726320.77124
HWNSNSNSNSNSNSNSNSNSNSNSNS<0.01NS
32C3N9109812121191129675
No of alleles31231332322231
Hobs0.33333M0.111110.25M0.083330.090910.111110.0909100.1111100M
Hexp0.660130.398690.6750.528990.580090.633990.2597410.424840.454550.78022
HWNSNSNSNSNSNSNSNSNSNSNS
34D3N101012111111119111110101212
No of alleles32133233341332
Hobs0.30.5M0.545450.636360.181820.727270.444440.454550.18182M0.70.833330.16667
Hexp0.363160.57360.627710.696970.502160.6710.74510.606060.714290.726320.561590.23551
HWNSNSNSNSNSNSNSNSNSNSNSNS
34E3N121091011012116111291110
No of alleles31123NA24222324
Hobs0MM0.10.090910.083330.363640.50.090910.250.222220.272730.2
Hexp0.713770.489470.623380.423910.744590.53030.593070.510870.679740.506490.74737
HW0NSNSNSNSNSNSNSNSNSNS
34G3N129997117911856108
No of alleles33133242342333
Hobs0.50.3333M0.444440.142860.636360.285710.555560.1818200.20.50.60.5
Hexp0.688410.67970.647060.593410.506490.835160.424840.753250.866670.644440.742420.60.65
HWNSNSNSNSNSNSNSNSNSNSNSNSNS
32C7N1212111112121112111111101212
No of alleles13111121111111
HobsM0.4166MMMM0.09091MMMMMMM
Hexp0.43220.17749
HWNSNS
32D7N121298911651176574
No of alleles22222222231222
Hobs0.166670.500.1250.222220.0909100.20.363640.42857M0.40.285710.25
Hexp0.159420.46370.313730.241670.385620.324680.636360.442110.311690.648350.355560.527470.46429
HWNSNSNSNSNSNSNSNSNSNSNSNSNS
34H3N111191011101281010910910
No of alleles22211121121111
Hobs0.090910.454550.11111MMM0.16667MM0.2MMMM
Hexp0.177490.541130.398690.235510.27895
HWNSNSNSNSNS
32H7N111181010712101010106910
No of alleles22131122211223
Hobs0.272730.09091M0.7MM0.50.20.2MM0.166670.555560.4
Hexp0.558440.177490.510530.45290.278950.415790.318180.424840.54211
HWNSNSNSNSNSNSNSNSNS
32E7N1111111012111210121111111212
No of alleles2213233122222
Hobs0.090910.81818M0.10.3333300.333330.2M000.181820.166670.25
Hexp0.177490.506490.621050.373190.259740.304350.442110.259740.502160.173160.235510.4529
HWNSNSNSNSNSNSNSNSNSNSNSNS
32F3N12121110121072910118910
No of alleles22222121122222
Hobs0.250.333330.0909130.166670.42857MM0.40.272730.3750.222220.3
Hexp0.300720.289860.177490.50.235510.362640.415790.246750.4250.307190.26842
HWNSNSNSNSNSNSNSNSNSNSNS
LG9B7N1011119810777510789
No of alleles21223223322212
Hobs0.4M0.090910.222220.1250.30.285710.142860.2857100.30M0.33333
Hexp0.542110.333330.450980.80.521050.571430.70330.39560.533330.510530.571430.29412
HWNSNSNSNSNSNSNSNSNSNSNSNS
32H3N12107912129371099102
No of alleles22222122231222
Hobs0.50.1000M0.11111000.1M0.222220.20
Hexp0.521740.194740.659340.313730.239130.215690.80.39560.757890.575160.189471
HWNSNSNSNSNSNSNSNSNSNSNSNS
LG9F3N1010121011111011910119105
No of alleles12122222222222
HobsM0.3M0.30.272730.636360.10.181820.222220.60.545450.333330.40
Hexp0.542110.468420.324680.506490.352630.606060.450980.442110.484850.294120.542110.71111
HWNSNSNSNSNSNSNSNSNSNSNSNS
32F7N1212101111127546981011
No of alleles11211121231111
HobsMM0.5MMM0M00.16667MMMM
Hexp0.394740.659340.642860.72727
HWNSNSNSNS

M, populations monomorphic for allele type.

NA, no. of alleles amplified.

NS, not significantly different from Hardy–Weinberg expectation with Bonferroni correction for multiplicity of comparisons (0.00025 = [0.05/196]).

Appendix B

Marker information

LocusLinkage groupGenomic contextMotifNumber of repeatsChromosomal positiona
32A31 (x)Intron 1 of “Dual oxidase”tat12805 638
32C32Intergenicaat23229 980
32C73Intergenicttat526 284
32D73Intron 2 of “Glutamine:fructose-6-phosphate aminotransferase 1”taa17302 244
32E74Intron 17 of “CG1516 Isoform E”ttta5121 805
32F34Intron 4 of a conserved hypothetical proteina1333 750
32F74Intergenictaa7722 262
32H75Intron 8 of “rho-associated protein kinase 1”a141 445 763
32H35Intron 7 of “neural-cadherin precursor”aat8713 852
34D38Intergeneicggc8216 850
34E38Intron 5 of “CG1486 isoform A”tta6698 301
LG9B79Intron 1 of “Teashirt”t1466 579
LG9F39Intron 8 of “vacuolar protein sorting 13D”a13765 219
34G310Intergenictat18844 615
34H310Second exon of “CG10936”Tct7416 449

Information based on Tribolium castaneum genome NCBI Build 2.1.

aPosition information based on forward primer position.

References

  • Avise JC, Dakin EE. Microsatellite null alleles in parentage analysis. Heredity. 2004;93:504–509. [PubMed]
  • Beeman RW. Distribution of the Medea factor M4 in populations of Tribolium castaneum (Herbst) in the United States. J Stored Prod Res. 2003;39:45–51.
  • Beeman RW, Friesen KS. Properties and natural occurrence of maternal-effect selfish genes (‘Medea’ factors) in the red flour beetle, Tribolium castaneum. Heredity. 1999;82:529–534. [PubMed]
  • Beeman RW, Friesen KS, Denell RE. Maternal-effect selfish genes in flour beetles. Science. 1992;256:89–92. [PubMed]
  • Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F. GENETIX, logiciel sous WindowsTM pour la génétique des populations. Montpellier (France): Université de Montpellier II; 2004.
  • Cavalli-Sforza LL, Edwards AWF. Phylogenetic analysis. Models and estimation procedures. Am J Hum Genet. 1967;19:233–257. [PubMed]
  • Chapuis M-P, Estoup A. Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol. 2007;24:621–631. [PubMed]
  • Charlesworth B, Coyne JA, Barton NH. The relative rates of evolution of sex chromosomes and autosomes. Am Nat. 1987;130:113–146.
  • Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol) 1977;39:1–38.
  • Demuth JP, Drury DW, Peters ML, Van Dyken JD, Priest NK, Wade MJ. Genome-wide survey of Tribolium castaneum microsatellites and description of 509 polymorphic markers. Mol Ecol Notes. 2007;7:1189–1195.
  • Demuth JP, Wade MJ. Population differentiation in the beetle Tribolium castaneum. I. Genetic architecture. Evolution. 2007a;61:494–509. [PubMed]
  • Demuth JP, Wade MJ. Population differentiation in the beetle Tribolium castaneum. II. Haldane's rule and incipient speciation. Evolution. 2007b;61:694–699. [PubMed]
  • Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987;19:11–15.
  • Good N. Biology of the flour beetles, Tribolium confusum Duv. and T. ferrugineum Fab. J Agric Res. 1933;46:327–334.
  • Guo SW, Thompson EA. Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics. 1992;48:361–372. [PubMed]
  • Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat. 1996;5:299–314.
  • Kim K, Sappington T. Molecular genetic variation of boll weevil populations in North America estimated with microsatellites: implications for patterns of dispersal. Genetica. 2006;127:143–161. [PubMed]
  • Kim KS, Sappington TW. Genetic structuring of western corn rootworm (Coleoptera: Chrysomelidae) populations in the United States based on microsatellite loci analysis. Environ Entomol. 2005;34:494–503.
  • Langella O. 1999. Populations, Ver. 1.2.30. a population genetic software. CNRS UPR9034. Available from: http://bioinformatics.org/~tryphon/populations/
  • Liebherr JK. Gene flow in ground beetles (Coleoptera: Carabidae) of differing habitat preference and flight-wing development. Evolution. 1988;42:129–137.
  • McCauley DE, Eanes WF. Hierarchical population structure analysis of the milkweed beetle, Tetraopes tetraophthalmus (Forster) Heredity. 1987;58:193–201.
  • McCauley DE, Wade MJ. Group selection: The genetic and demographic basis for the phenotypic differentiation of small populations of tribolium castaneum. Evolution. 1980;34:813–821.
  • Mock KE, Bentz BJ, O'Neill EM, Chong JP, Orwin J, Pfrender ME. Landscape-scale genetic variation in a forest outbreak species, the mountain pine beetle (Dendroctonus ponderosae) Mol Ecol. 2007;16:553–568. [PubMed]
  • Page RDC. Tree view: an application to display phylogenetic trees on personal computers. Bioinformatics. 1996;12:357–358. [PubMed]
  • Park T. Beetles, competition, and populations: an intricate ecological phenomenon is brought into the laboratory and studied as an experimental model. Science. 1962;138:1369–1375. [PubMed]
  • Park T, Leslie PH, Mertz DB. Genetic strains and competition in populations of Tribolium. Physiol Zool. 1964;37:97–162.
  • Raymond M, Rousset F. GENEPOP (Version 1.2): population genetics software for exact tests and ecumenicism. J Hered. 1995;86:248–249.
  • Schneider S, Roessli D, Excoffier L. Arlequin ver. 2.000: a software for genetic data analysis. Geneva (Switzerland): Genetics and Biometry Laboratory. University of Geneva; 2000.
  • Slatkin M. Gene flow in natural populations. Ann Rev Ecol Syst. 1985;16:393–430.
  • Thomson MMS, Labonne AAM. Maternal effect of a hybrid inviability gene in Tribolium castaneum. Genetica. 1998;104:155–159. [PubMed]
  • Thomson MS, Beeman RW. Assisted suicide of a selfish gene. J Hered. 1999;90:191–194. [PubMed]
  • Tribolium Genome Sequencing Consortium. The genome of the model beetle and pest Tribolium castaneum. Nature. 2008;452:949–955. [PubMed]
  • Turner TL, Levine MT, Eckert ML, Begun DJ. Genomic analysis of adaptive differentiation in Drosophila melanogaster. Genetics. 2008;179:455–473. [PubMed]
  • Wade MJ. Group selections among laboratory populations of Tribolium. Proc Natl Acad Sci USA. 1976;73:4604–4607. [PubMed]
  • Wade MJ. Group selection: migration and the differentiation of small populations. Evolution. 1982;36:949–961.
  • Wade MJ. Genotype environment interaction for climate and competition in a natural population of Tribolium castaneum. Evolution. 1990;44:2004–2011.
  • Wade MJ. Genetic variance for rate of population increase in natural populations of flour beetles, Tribolium spp. Evolution. 1991;45:1574–1584.
  • Wade MJ, McCauley DE. Group selection: the interaction of local deme size and migration on the differentiation of small populations. Evolution. 1984;38:1047–1058.
  • Wade MJ, McCauley DE. The effects of extinction and colonization on the genetic differentiation of populations. Evolution. 1988;42:995–1005.
  • Wade MJ, McKnight ML, Shaffer HB. The effects of kin-structured colonization on nuclear and cytoplasmic genetic diversity. Evolution. 1994;48:1114–1120.
  • Whitlock MC, McCauley DE. Some population genetic consequences of colony formation and extinction: genetic correlations within founding groups. Evolution. 1990;44:1717–1724.
  • Zuniga G, Cisneros R, Salinas-moreno Y, Hayes JL, Rinehart JE. Genetic structure of Dendroctonus mexicanus (Coleoptera: Curculionidae: Scolytinae) in the trans-Mexican volcanic belt. Ann Entomol Soc Am. 2006;99:945–958.

Articles from Journal of Heredity are provided here courtesy of Oxford University Press