Skip to main content
NCBI home page
Journal of Insect Science logoLink to Journal of Insect Science
. 2020 Oct 24;20(5):28. doi: 10.1093/jisesa/ieaa117

Microsatellites for the Neotropical Ant, Odontomachus chelifer (Hymenoptera: Formicidae)

Alessandra S M Lemos 1, Marianne Azevedo-Silva 2, Salatiel Gonçalves-Neto 1, Anete P Souza 3, Paulo S Oliveira 4,✉
Editor: Konrad Fiedler
PMCID: PMC7585319  PMID: 33098430

Abstract

Odontomachus chelifer (Latreille) (Ponerinae) is a ground-dwelling, predominantly carnivorous ant whose colonies may contain multiple egg-laying queens and are potentially susceptible to border effects in the Brazilian savanna known as Cerrado. The ecology and natural history of O. chelifer is well studied, but very little is known about the genetic diversity of O. chelifer colonies. In this study, we developed microsatellite markers for the study of genetic variation in O. chelifer. We created a microsatellite-enriched library that resulted in the development and characterization of 22 markers, of which 18 were found to be polymorphic in the population studied. The mean expected heterozygosity was 0.59, whereas the mean rarified allelic richness was determined as 4.27 alleles per locus. The polymorphism level detected was similar to genetic diversity estimates found in other poneromorph ant species. The microsatellites developed here are likely to be useful for the investigation of colony structure, functional polygyny, breeding system, and population genetics in O. chelifer. Moreover, the description of O. chelifer’s genetic diversity is crucial for its conservation and maintenance of its ecological role in the Cerrado savanna.

Keywords: molecular marker, simple sequence repeat, Ponerinae, Hymenoptera, social insect

Odontomachus chelifer (Latreille) (Formicidae: Ponerinae) (Fig. 1) is a ground-dwelling, nocturnal ant that is widely distributed in the Neotropical region, occurring in forest and savanna habitats from Mexico to Argentina (Kempf 1972). The species is predominantly carnivorous and feeds on litter-dwelling arthropods, mostly termites (Raimundo et al. 2009). Additionally, members of this species frequently harvest lipid- and protein-rich fleshy fruits and seeds, which are fed to larvae in the nest (Passos and Oliveira 2002, 2004; Bottcher and Oliveira 2014). Odontomachus chelifer is facultatively polygynous (i.e., more than one egg-laying queen per colony) and each queen’s reproductive activity is mediated by queen–queen dominance interactions, even in mature colonies, with highly ranked queens laying more eggs and foraging less frequently outside the nest (Medeiros et al. 1992). In the Brazilian Cerrado savanna, this species is vulnerable to border effects (Christianini and Oliveira 2013), which seem to vary through time (see Salles et al. 2018). Even though O. chelifer’s natural history is relatively well studied, little is known about its genetic diversity (but see Larabee et al. 2016, Hoenle et al. 2020).

Fig. 1.

Fig. 1.

Open in a new tab

Odontomachus chelifer worker carrying eggs (photo by P. S. Oliveira).

Microsatellites—or SSR (simple sequence repeats)—are frequently used as a molecular tool to study genetic diversity in ant colonies. These molecular markers are repetitive sequences (one to six nucleotide repeats), distributed in tandem along the DNA and frequently and randomly distributed across the genome in most eukaryote species (Selkoe and Toonen 2006). Microsatellites are polymorphic, segregate as codominant markers, and are usually selectively neutral (Goldstein and Schlötterer 1999). Several characteristics make them ideal studies on population genetic, genome mapping, and marker-assisted breeding (Sun et al. 2011). Previous works have used microsatellites to identify ant species (Goodisman and Hahn 2005, Ronque et al. 2016), describe their breeding systems (Azevedo-Silva et al. 2020), and analyze dispersion strategies (Pérez-Espona et al. 2012, Soare et al. 2014), colony spatial distribution (Debout et al. 2007), and population genetics patterns (Pamilo et al. 2016). Given that genetic diversity is key for the persistence and adaptation of populations to environmental changes (Seppä 2008), the development of molecular tools to portray such diversity is crucial for elucidating species biology and conservation. Here, we developed and characterized more than 20 microsatellite loci in O. chelifer, which will enable future studies on genetic variation and maintenance of O. chelifer populations.

Materials and Methods

Sampling Site

Colonies of O. chelifer were sampled in Fazenda Campininha, a Cerrado savanna reserve located in the city of Mogi-Guaçu (22°18′S e 47°11′W), in the state of São Paulo, southeastern Brazil. Via active searching, we collected 15–20 workers from 18 O. chelifer nests. Ant voucher specimens are deposited at the Museu de Zoologia da Universidade Estadual de Campinas (ZUEC, Campinas, Brazil).

DNA Extraction

DNA extraction was performed using methods described by Saghai-Maroof et al. (1984). In brief, ant individuals are solubilized in 2% CTAB solution, followed by DNA purification through extraction with chloroform/isoamyl alcohol (24:1).

Microsatellite Identification

Six workers from the same colony of O. chelifer were used to build a microsatellite-enriched library, based on protocol previously described in Billotte et al. (1999), using a hybridization-based capture with (CT)8 and (GT)8 biotin-linked probes, followed by recovery with streptavidin magnetic-coated beads (Promega, Madison, WI). The selected fragments were cloned into the pGEM-T vector (Promega) and transformed into competent Escherichia coli (XL1-Blue strain). Recombinant colonies were identified by colorimetric white-blue detection using X-gal. Plasmid DNA was then extracted using an alkaline lysis method and inserts were sequenced on a 3500 Genetic Analyzer sequencer (Applied Biosystems, Foster City, CA). We sequenced 103 clones and the electropherograms were edited using CLC Genomics Workbench v. 4.9 software (CLC Bio, Arhus, Denmark). Vector, adapter, and restriction site sequences were removed using Seqman (DNAStarInc, Madison, WI). Additionally, sequences were compared with entries of the public National Center for Biotechnology Information database using Blastn (Altschul et al. 1990), to eliminate possible contaminant sequences. Microsatellites were identified using the web-based SSRIT software (Temnykh et al. 2001) and primer pairs complementary to their flanking sequences were designed using Primer Select (DNAStarInc) and Primer3Plus (Untergasser et al. 2012). Amplified fragments ranging from 100 to 250 bp in size were selected for further identification of putative alleles. For each sequence, primers were designed according to the following guidelines: 1) 18–22 nucleotides in size, 2) G/C content greater than 35%, 3) Tm between 45 and 65°C (maximum of 3°C difference between both primers in the pair), and 4) presence of A or T bases at the 3′ end (to reduce probability of self-pairing). We also avoided designing primers that allow the formation of homo or heterodimers. For automating the genotyping on the 3500 Genetic Analyzer sequencer (Applied Biosystems), an M13 sequence tail (5′-CACGACGTTGTAAAACGAC-3′) was added to the 5′ end of each forward primer (Schuelke 2000). Fluorescent labels 6-FAM, VIC, NED, and PET (Applied Biosystems) were used to increase genotyping efficiency.

Microsatellite Characterization

Five workers from different nests were used to determine ideal amplification conditions for each developed marker. For all markers, we tested two touchdown PCR protocols (Don et al. 1991), with hybridization temperatures between 52 and 57°C or between 55 and 60°C, together with the following steps: 1) 94°C for 4 min; 2) 10 cycles of 94°C for 45 s, 60°C or 57°C (−0.5ºC/cycle) for 1 min, and 72°C for 1 min and 15 s; 3) 25 cycles of 94°C for 45 s, 50°C for 1 min, and 72°C for 1 min and 15 s; and 4) 72°C for 10 min. The amplification products were evaluated on a 3500 Genetic Analyzer sequencer (Applied Biosystems) following by post-sequencing analysis on Geneious prime software (v. 2019.2; Biomatters Limited, New Zealand).

Microsatellite loci with amplification patterns consistent with expected sizes and clear distinguishable peaks were further characterized for polymorphism content. To this end, we used 30 workers from different nests (Hale et al. 2012), and microsatellite loci were evaluated for occurrence of stuttering and reduced amplification of large fragments using Micro-Checker (Van Oosterhout et al. 2004). Polymorphism content (PIC) (Botstein et al. 1980) and observed and expected heterozygosity for each locus were obtained using the Microsatellites Toolkit supplement in Excel (Park 2008). Rarefied allelic richness was estimated by HP-Rare software (Kalinowski 2005). Additionally, we tested for loci adherence to frequencies expected in the Hardy–Weinberg equilibrium (HWE) using Genepop 4.7 (Rousset 2008). Linkage disequilibrium (LD) between each pair of markers was assessed using FSTAT 2.9.4 (Goudet 1995). For both HWE and LD estimates, the significance value (0.05) was corrected for multiple comparisons. The frequency of null alleles was estimated with the FreeNA software (Chapuis and Estoup 2007).

Results

The microsatellite enrichment procedure was highly efficient, with 87.85% of the sequenced clones presenting repetitive sequences. Fifty-three clones contained more than one microsatellite sequence, totaling 94 sequences. We were able to design primer pairs for 42 microsatellite loci. Twenty-two loci were successfully amplified using the touchdown PCR protocol with hybridization temperature ranging from 55 to 60°C, and all of them resulted in amplification products consistent with expected sizes (Table 1), without evidence for nonspecific amplification.

Table 1.

Characteristics of 22 microsatellite loci developed for Odontomachus chelifer

Locus Primer sequences (5′–3′) Motif TD (°C) SR Ar HE HO PIC FreqNA GenBank
accession
Och2 F:CACGACGTTGTAAAACGACTGGATGCATGGCTTTAGTCTC
R:TTGCGATTAACTCTCATTGGTC		 (AG)4...(AG)5...(AG)7...(GA)39 60–55 139–239 5.36 0.763 0.455 0.708 0.26735 MT679225
Och3 F:CACGACGTTGTAAAACGACGACAGACTTAATTTTCCTCGTA
R:AAATTCGCCGTTGTGAAT		 (TC)29...(TC)5...(CG)3...(GC)4 60–55 171–231 7.83 0.856 0.9 0.823 0.14942 MT679226
Och6 F:CACGACGTTGTAAAACGACGGCCGTTTCCTGTTCACC
R:TCGAAAAAATATTGCCCTCTTC		 (CA)3...(CA)21 60–55 232–292 10.40 0.891 0.5 0.864 0 MT679227
Och8 F:CACGACGTTGTAAAACGACAGTTTTTACAGTGTAGGGCATC
R:GAGAAAGCGCCCTTAATCTT		 (TC)3...(CA)16 60–55 136–154 6.68 0.805 0.667 0.766 0.00049 MT679228
Och11 F:CACGACGTTGTAAAACGACTCCTAAATGGTGCGTAATAAAC
R:GTATAAAAGGAACGCAGTGAAA		 (CA)9...(TC)3 60–55 190–196 3.65 0.665 0.852 0.584 0 MT679229
Och15 F:CACGACGTTGTAAAACGACAACGCGACGGAAGACGAT
R:CGCGAACGTGATATTGGATTAC		 (TG)3...(AC)3 57–52 190–192 1.69 0.078 0 0.074 0 MT679230
Och16 F:CACGACGTTGTAAAACGACGAGAGCGAGAGGGAGTGC
R:GAGACATCGATACATCAAAGAG		 (GA)5...(GA)5...(AG)5...(AG)6...(AG)10...
(GA)4...(GA)3...(GA)5...(GA)7...(GAGA)3 		60–55 215–265 3.93 0.608 0.167 0.542 0.00100 MT679231
Och27 F:CACGACGTTGTAAAACGACCAATGTGGAGATTACATGGTTA
R:GACTAGCAATCCCGAAAACT		 (TG)10...(TG)4 60–55 228–240 3.38 0.678 0.759 0.597 0.13120 MT679232
Och34 F:CACGACGTTGTAAAACGACTAACGATTCATAGACACGCA
R:CAGGGAGAATTCCGTTACTG		 (AGG)3 60–55 136–142 2.38 0.525 0.31 0.399 0.19195 MT679233
Och47 F:CACGACGTTGTAAAACGACTTACCTCCTCGTCACTGAACA
R:GAAATCTCGTGCAAAATCTG		 (TG)3...(AC)4...(AC)19...(TG)3 60–55 238–258 4.61 0.764 0.724 0.709 0.05239 MT679234
Och54 F:CACGACGTTGTAAAACGACGCGATCGAAGCTCGTATTGT
R:ACAGTCGGGTCGTAGCACTT		 (GGGA)3 60–55 163–172 2.98 0.49 0.633 0.432 0.00100 MT679235
Och60 F:CACGACGTTGTAAAACGACAAGGGAAAACGAAAGTGTAATC
R:TAACCCGAAACAAGAATACC		 (GT)12...(TAT)3 60–55 182–200 4.55 0.682 0.733 0.613 0.11525 MT679236
Och62 F:CACGACGTTGTAAAACGACTCGCAAGTTAATATCCGTTCAT
R:TCTGCTTGGCGAGTGGAC		 (CA)10 60–55 154–162 3.64 0.6 0.556 0.533 0.00100 MT679237
Och63 F:CACGACGTTGTAAAACGACCGTGGCAGCGCGAAGAAAAA
R:GGAAACCCACCCACCGAGACC		 (CG)4 60–55 175 1 0 0 0 0.03469 MT679238
Och69 F:CACGACGTTGTAAAACGACGCTATGCGTCCCGAACAG
R:TCGCCCGACTTGATTGAC		 (GA)3...(CA)8 60–55 216–238 5.28 0.724 0.6 0.673 0.01851 MT679239
Och70 F:CACGACGTTGTAAAACGACGCAATATAAACGCATCTATGTG
R:TGTAAACAAACGTAAAAATGG		 (AC)8 60–55 152–196 3 0.394 0.103 0.344 0.07266 MT679240
Och72 F:CACGACGTTGTAAAACGACCCGTTTATATTGGTATCTTC
R:ATATCGCCCATTCCTAAT		 (CA)3...(AC)7 57–52 156–158 2 0.509 0.96 0.375 0.00100 MT679241
Och76 F:CACGACGTTGTAAAACGACGAGAAACGAGTGTGATAAGGTC
R:AAAGTCGTTGAAGAATACATCC		 (CA)6 60–55 173 1 0 0 0 0 MT679242
Och78 F:CACGACGTTGTAAAACGACCGACGGCTAAACCTTCTGAG
R:TGACATGGTTTCCTTGAACG		 (AT)4 60–55 201 1 0 0 0 0 MT679243
Och83 F:CACGACGTTGTAAAACGACGTGCAATATTTTGGATAGAGAA
R:TTGTTGTCAGTGGAAAGAATAA		 (AT)3...(CA)5...(CA)4...(CACG)3 60–55 175–323 3.62 0.572 0.615 0.501 0.21939 MT679244
Och86 F:CACGACGTTGTAAAACGACCGGAATGAAGAATAAAACAGAT
R:CTAACAACAGTAACGGCTCAAG		 (CT)3...(CA)4...(CA)4...(AC)3 60–55 206 1 0 0 0 0.00002 MT679245
Och88 F:CACGACGTTGTAAAACGACCTTTGATTTTTCCAGTAGCACA
R:TTACGGTCCTCGAAGTGATTTA		 (GC)8...(CG)3 60–55 135–235 1.81 0.073 0.074 0.071 0.13463 MT679246
Open in a new tab

For each locus, the table shows primer sequences (F: forward with M13 tail and R: reverse), microsatellite repetitive motif (Motif), amplification temperature range via touchdown PCR (TD), size range after addition of M13 tail (SR), rarified allelic richness (Ar), expected heterozygosity (HE), observed heterozygosity (HO), polymorphism content (PIC), frequency of null alleles (FreqNA), and GenBank accession number.

Importantly, 18 markers were found to be polymorphic. In these loci, the expected heterozygosity (HE) (mean ± SE) was 0.59 ± 0.05, with the highest values found in the Och6 (0.89), Och3 (0.86), and Och8 (0.80) loci. Very low HE values were identified in Och88 (0.07) and Och15 (0.08) (Table 1). Rarified allelic richness (mean ± SE) was 4.27 ± 0.53 (Table 1). The mean (±SE) PIC value was 0.53 ± 0.05 (Table 1). We found no evidence of allele stuttering or reduced amplification of fragments for any of the markers. Moreover, the frequency of null alleles ranged from 0 to 0.26735. Regarding adherence to frequencies expected at HWE, we found only seven loci at equilibrium (39%) (Table 1). Finally, it should be noted that all microsatellite loci analyzed exhibited independent segregation.

Discussion

A microsatellite-enriched library was developed for O. chelifer, resulting in 22 markers, 18 of which were found to be polymorphic in the population studied. Even though some markers displayed low polymorphic content, most proved to be highly informative (PIC > 0.5) to access genetic diversity in O. chelifer (Table 1). Diversity estimates for our markers were similar to estimates for other ant species in the subfamily Ponerinae. For instance, our results are similar to microsatellite marker allelic richness observed in Pachycondyla inversa (5–12 alleles; Trindl et al. 2004), Hypoponera opacior (9–21 alleles; Rüger et al. 2005) and Pachycondyla luteipes (2–8 alleles; Takahashi et al. 2005), even when rarefied allelic richness is considered in our work.

Despite the availability of microsatellite markers for other Ponerinae or for other ant species (Butler et al. 2014), interchangeability of such markers between species is challenging. This occurs due to their high specificity, which makes cross-amplification limited even at the genus level (Barbará et al. 2007). Thus, developing specific microsatellite loci for each species is a necessity, especially in the context of studies on genetic diversity, reinforcing the importance of the markers developed here for future studies on O. chelifer genetic variation.

Eleven microsatellite loci exhibited deviations from HWE, indicating violation of one or more assumptions of the Hardy–Weinberg model, namely presence of selection, migration and/or mutation, finite population size, and non-random mating (Hartl and Clark 2010). Additionally, such deviations may arise from overlapping generations and/or the high relatedness between ant workers. Moreover, our sampling location is a fragmented Cerrado area (Christianini and Oliveira 2013), which may have reduced population size due to habitat loss and/or increased probability of inbreeding (Frankham 2010, Banks et al. 2013). Further investigation is needed to evaluate the effect of habitat fragmentation on genetic variation of O. chelifer.

Although the behavior and ecology of O. chelifer have already been studied in Brazilian forests and savannas (Oliveira et al. 2017, and references therein), the microsatellite markers described here will stimulate further investigation on colony structure and breeding system in this ant species, including potential effects of habitat fragmentation, a crucial knowledge in the context of conservation of its populations and maintenance of its ecological role.

Acknowledgments

We are grateful to Gustavo M. Mori and Rebecca C. U. Ferreira for helpful comments on the manuscript, and to Luis Salles for help during field work. The final version of the manuscript was revised by Fabio Papes. The Instituto Florestal de São Paulo and the staff of the Estação Experimental de Itirapina provided logistic support during field work. The study was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Finance Code 001). ASML and SG-N were supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; 116435/2019-4), and the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, 2019/12683-1). MA-S was funded by the CNPq (167161/2017-2) and FAPESP (2017/18291-2). APS was supported by CAPES (Computational Biology Program). PSO was supported by research grants from the CNPq (306115/2013-1, 302219/2017-0), and FAPESP (Biota Program, 2014/23141-1, 2017/16645-1).

Author contributions

Conceptualization: M.A-S., A.P.S. Data collection and formal analysis: A.S.M.L., M.A-S., and S.G-N. Resources: A.P.S. and P.S.O. Writing-original draft: A.S.M.L., M.A-S., P.S.O. Writing-review and editing: M.A-S. and P.S.O.

References Cited

  1. Altschul S F, Gish W, Miller W, Myers E W, and Lipman D J. . 1990. Basic Local Alignment Search Tool. J. Mol. Biol. 215: 403–410. [DOI] [PubMed] [Google Scholar]
  2. Azevedo-Silva M, Mori G M, Carvalho C S, Côrtes M C, Souza A P, and Oliveira P S. . 2020. Breeding systems and genetic diversity in tropical carpenter ant colonies: different strategies for similar outcomes in Brazilian Cerrado savanna. Zool. J. Linn. Soc. 190. [Google Scholar]
  3. Banks S C, Cary G J, Smith A L, Davies I D, Driscoll D A, Gill A M, Lindenmayer D B, and Peakall R. . 2013. How does ecological disturbance influence genetic diversity?  Trends Ecol. Evol. 28: 670–679. [DOI] [PubMed] [Google Scholar]
  4. Barbará T, Palma-Silva C, Paggi G M, Bered F, Fay M F, and Lexer C. . 2007. Cross-species transfer of nuclear microsatellite markers: potential and limitations. Mol. Ecol. 16: 3759–3767. [DOI] [PubMed] [Google Scholar]
  5. Billotte N, Lagoda P J L, Risterucci A-M, and Baurens F-C. . 1999. Microsatellite-enriched libraries: applied methodology for the development of SSR markers in tropical crops. Fruits. 54: 277–288. [Google Scholar]
  6. Botstein D, White R L, Skolnick M, and Davis R W. . 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32: 314–331. [PMC free article] [PubMed] [Google Scholar]
  7. Bottcher C, and Oliveira P S. . 2014. Consumption of lipid-rich seed arils improves larval development in a neotropical primarily carnivorous ant, Odontomachus chelifer (Ponerinae). J. Trop. Ecol. 30: 621–624. [Google Scholar]
  8. Butler I A, Siletti K, Oxley P R, and Kronauer D J. . 2014. Conserved microsatellites in ants enable population genetic and colony pedigree studies across a wide range of species. PLoS ONE  9: e107334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chapuis M P, and Estoup A. . 2007. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24: 621–631. [DOI] [PubMed] [Google Scholar]
  10. Christianini A V, and Oliveira P S. . 2013. Edge effects decrease ant-derived benefits to seedlings in a neotropical savanna. Arthropod Plant Interact. 7: 191–199. [Google Scholar]
  11. Debout G, Schatz B, Elias M, and Mckey D. . 2007. Polydomy in ants: what we know, what we think we know and what remains to be done. Biol. J. Linn. Soc. 90: 319–348. [Google Scholar]
  12. Don R H, Cox P T, Wainwright B J, Baker K, and Mattick J S. . 1991. ‘Touchdown’ PCR to circumvent spurious priming during gene amplification. Nucleic Acids Res. 19: 4008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Frankham R. 2010. Inbreeding in the wild really does matter. Heredity (Edinb). 104: 124. [DOI] [PubMed] [Google Scholar]
  14. Goldstein D B, and Schloètterer C. . 1999. Microsatellites: evolution and applications. Oxford University Press, Oxford, United Kingdom. [Google Scholar]
  15. Goodisman M A, and Hahn D A. . 2005. Breeding system, colony structure, and genetic differentiation in the Camponotus festinatus species complex of carpenter ants. Evolution  59: 2185–2199. [PubMed] [Google Scholar]
  16. Goudet J. 1995. FSTAT (v.2): a computer program to calculate F-statistics. J. Hered. 86: 485–486. [Google Scholar]
  17. Hale M L, Burg T M, and Steeves T E. . 2012. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE  7: e45170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hartl D L, and Clark A G. . 2010. Princípios de genética de populações, 4ª edição. Artmed, Porto Alegre, RS, Brasil. [Google Scholar]
  19. Hoenle P O, Lattke J E, Donoso D A, von Beeren C, Heethoff M, Schmelzle S, Argoti A, Camacho L, Ströbel B, and Blüthgen N. . 2020. Odontomachus davidsoni sp. nov. (Hymenoptera, Formicidae), a new conspicuous trap-jaw ant from Ecuador. Zookeys  948: 75–105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kalinowski S T. 2005. HP-Rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes. 5: 187–189. [Google Scholar]
  21. Kempf W W. 1972. Catálogo abreviado das formigas da região Neotropical. Stud. Entomol. 15: 1–344. [Google Scholar]
  22. Larabee F J, Fisher B L, Schmidt C A, Matos-Maraví P, Janda M, and Suarez A V. . 2016. Molecular phylogenetics and diversification of trap-jaw ants in the genera Anochetus and Odontomachus (Hymenoptera: Formicidae). Mol. Phylogenet. Evol. 103: 143–154. [DOI] [PubMed] [Google Scholar]
  23. Medeiros F N S, Lopes L E, Moutinho P R S, Oliveira P S, and Hölldobler B. . 1992. Functional polygyny, agonistic interactions and reproductive dominance in the neotropical ant Odontomachus chelifer (Hymenoptera, Formicidae, Ponerinae). Ethology  91: 134–146. [Google Scholar]
  24. Oliveira P S, Christianini A V, Bieber A G D, and Pizo M A. . 2017. Anthropogenic disturbances affect the interactions between ants and fleshy fruits in two neotropical biodiversity hotspots, pp. 133–156. In  Oliveira P S and Koptur S (eds.), Ant-plant interactions: impacts of humans on terrestrial ecosystems. Cambridge University Press, Cambridge, United Kingdom. [Google Scholar]
  25. Pamilo P, Seppä P, and Helanterä H. . 2016. Population genetics of wood ants, pp. 51–80. In  Stockan J A and Robinson E J H (eds.), Wood ant.ecology and conservation. Cambridge University Press, Cambridge, United Kingdom. [Google Scholar]
  26. Park S. 2008. MStools v 3.1.1: Excel Spreadsheet Toolkit for Data Conversion. Animal Genomics Lab, University College, Dublin, Ireland. [Google Scholar]
  27. Passos L, and Oliveira P S. . 2002. Ants affect the distribution and performance of Clusia criuva seedlings, a primarily bird-dispersed rainforest tree. J. Ecol. 90: 517–528. [Google Scholar]
  28. Passos L, and Oliveira P S. . 2004. Interaction between ants and fruits of Guapira opposita (Nyctaginaceae) in a Brazilian sandy plain rainforest: ant effects on seeds and seedlings. Oecologia  139: 376–382. [DOI] [PubMed] [Google Scholar]
  29. Pérez-Espona S, McLeod J E, and Franks N R. . 2012. Landscape genetics of a top neotropical predator. Mol. Ecol. 21: 5969–5985. [DOI] [PubMed] [Google Scholar]
  30. Raimundo R L G, Freitas A V L, and Oliveira P S. . 2009. Seasonal patterns in activity rhythm and foraging ecology in the neotropical forest-dwelling ant, Odontomachus chelifer (Formicidae: Ponerinae). Ann. Entomol. Soc. Am. 102:1151–1157. [Google Scholar]
  31. Ronque M U V, Azevedo-Silva M, Mori G M, Souza A P, and Oliveira P S. . 2016. Three ways to distinguish species: using behavioural, ecological, and molecular data to tell apart two closely related ants,  Camponotus renggeri and Camponotus rufipes (Hymenoptera: Formicidae). Zool. J. Linn. Soc.  176: 170–181. [Google Scholar]
  32. Rousset F. 2008. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8: 103–106. [DOI] [PubMed] [Google Scholar]
  33. Rüger M H, Heinze J, and Foitzik S. . 2005. Polymorphic microsatellite loci in the ponerine ant, Hypoponera opacior (Hymenoptera, Formicidae). Mol. Ecol. Notes  5: 236–238. [Google Scholar]
  34. Saghai-Maroof M A, Soliman K M, Jorgensen R A, and Allard R W. . 1984. Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location, and population dynamics. Proc. Natl Acad. Sci. USA  81: 8014–8018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Salles L F P, Christianini A V, and Oliveira P S. . 2018. Dirt roads and fire breaks produce no edge effects on litter-dwelling arthropods in a tropical dry-forest: a case study. J. Insect Conserv. 22: 647–657. [Google Scholar]
  36. Schuelke M. 2000. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18: 233–234. [DOI] [PubMed] [Google Scholar]
  37. Selkoe K A, and Toonen R J. . 2006. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9: 615–629. [DOI] [PubMed] [Google Scholar]
  38. Seppä P. 2008. Do ants (Hymenoptera: Formicidae) need conservation and does ant conservation need genetics?  Myrmecol. News  11: 161–172. [Google Scholar]
  39. Soare T W, Kumar A, Naish K A, and O’Donnell S. . 2014. Genetic evidence for landscape effects on dispersal in the army ant Eciton burchellii. Mol. Ecol. 23: 96–109. [DOI] [PubMed] [Google Scholar]
  40. Sun J-T, Zhang Y-K, Ge C, and Hong X-Y. . 2011. Mining and characterization of sequence tagged microsatellites from the brown planthopper Nilaparvata lugens. J. Insect Sci. 11: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Takahashi J, Kikuchi T, Ohnishi H, and Tsuji K. . 2005. Isolation and characterization of 10 microsatellite loci in the Ponerinae ant Pachycondyla luteipes (Hymenoptera; Formicidae). Mol. Ecol. Notes  5: 749–751. [Google Scholar]
  42. Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, and McCouch S. . 2001. Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res. 11: 1441–1452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Trindl A, Heinze J, and D’Ettorre P. . 2004. Isolation and characterization of five microsatellite loci in the ponerine ant Pachycondyla inversa (Hymenoptera, Formicidae). Mol. Ecol. Notes  4: 583–585. [Google Scholar]
  44. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth B C, Remm M, and Rozen S G. . 2012. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40: e115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Van Oosterhout C, Hutchinson W F, Wills D P M, and Shipley P. . 2004. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes  4: 535–538. [Google Scholar]

Articles from Journal of Insect Science are provided here courtesy of University of Wisconsin Libraries

RESOURCES