Genome-wide analysis of diamondback moth, Plutella xylostella L., from Brassica crops and wild host plants reveals no genetic structure in Australia
1.
Zalucki, M. P. & Furlong, M. J. Forecasting Helicoverpa populations in Australia: a comparison of regression based models and a bioclimatic based modelling approach. Insect Sci. 12, 45–56 (2005).
Article Google Scholar
2.
Mazzi, D. & Dorn, S. Movement of insect pests in agricultural landscapes. Ann. Appl. Biol. 160, 97–113 (2012).
Article Google Scholar
3.
Eigenbrode, S. D. et al. Host-adapted aphid populations differ in their migratory patterns and capacity to colonize crops. J. Appl. Ecol. 53, 1382–1390. https://doi.org/10.1111/1365-2664.12693 (2016).
Article Google Scholar
4.
Furlong, M. J., Wright, D. J. & Dosdall, L. M. Diamondback moth ecology and management: problems, progress, and prospects. Annu. Rev. Entomol. 58, 517–541. https://doi.org/10.1146/annurev-ento-120811-153605 (2013).
CAS Article PubMed Google Scholar
5.
Downes, S. et al. A perspective on management of Helicoverpa armigera: transgenic Bt cotton, IPM, and landscapes. Pest Manag. Sci. 73, 485–492. https://doi.org/10.1002/ps.4461 (2017).
CAS Article PubMed Google Scholar
6.
Endersby, N. M., Ridland, P. M. & Hoffmann, A. A. The effects of local selection versus dispersal on insecticide resistance patterns: longitudinal evidence from diamondback moth (Plutella xylostella (Lepidoptera: Plutellidae)) in Australia evolving resistance to pyrethroids. Bull. Entomol. Res. 98, 145–157. https://doi.org/10.1017/S0007485307005494 (2008).
CAS Article PubMed Google Scholar
7.
Broquet, T. & Petit, E. J. Molecular estimation of dispersal for ecology and population genetics. Annu. Rev. Ecol. Evol. Syst. 40, 193–216. https://doi.org/10.1146/annurev.ecolsys.110308.120324 (2009).
Article Google Scholar
8.
Pelissie, B., Crossley, M. S., Cohen, Z. P. & Schoville, S. D. Rapid evolution in insect pests: the importance of space and time in population genomics studies. Curr. Opin. Insect Sci. 26, 8–16. https://doi.org/10.1016/j.cois.2017.12.008 (2018).
Article PubMed Google Scholar
9.
Parry, H. R. et al. A native with a taste for the exotic: weeds and pasture provide year-round habitat for Nysius vinitor (Hemiptera: Orsillidae) across Australia, with implications for area-wide management. Aust. Entomol. 58, 237–247. https://doi.org/10.1111/aen.12391 (2019).
Article Google Scholar
10.
Wei, S. J. et al. Genetic structure and demographic history reveal migration of the diamondback moth Plutella xylostella (Lepidoptera: Plutellidae) from the southern to northern regions of China. PLoS ONE 8, e59654. https://doi.org/10.1371/journal.pone.0059654 (2013).
ADS CAS Article PubMed PubMed Central Google Scholar
11.
Hereward, J. P., Walter, G. H., DeBarro, P. J., Lowe, A. J. & Riginos, C. Gene flow in the green mirid, Creontiades dilutus (Hemiptera: Miridae), across arid and agricultural environments with different host plant species. Ecol. Evol. 3, 807–821. https://doi.org/10.1002/ece3.510 (2013).
CAS Article PubMed PubMed Central Google Scholar
12.
Zalucki, M. P. et al. Estimating the economic cost of one of the worlds major insect pests, Plutella xylostella (Lepidoptera: Plutellidae): just how long is a piece of string? J. Econ. Entomol. 105, 1115–1129. https://doi.org/10.1603/EC12107 (2012).
Article PubMed Google Scholar
13.
Li, Z., Feng, X., Liu, S.-S., You, M. & Furlong, M. J. Biology, ecology, and management of the diamondback moth in China. Annu. Rev. Entomol. 61, 277–296. https://doi.org/10.1146/annurev-ento-010715-023622 (2016).
CAS Article PubMed Google Scholar
14.
Talekar, N. S. & Shelton, A. Biology, ecology, and management of the diamondback moth. Annu. Rev. Entomol. 38, 275–301. https://doi.org/10.1146/annurev.en.38.010193.001423 (1993).
Article Google Scholar
15.
Mosiane, S. M., Kfir, R. & Villet, M. H. Seasonal phenology of the diamondback moth, Plutella xylostella (L.), (Lepidoptera: Plutellidae), and its parasitoids on canola, Brassica napus (L.), in Gauteng province, South Africa. Afr. Entomol. 11, 277–285 (2003).
Google Scholar
16.
Dosdall, L. M., Mason, P. G., Olfert, O., Kaminski, L. & Keddie, B. A. The origins of infestations of diamondback moth, Plutella xylostella (L.), in canola in western Canada. In The Management of Diamondback Moth and Other Crucifer Pests: Proceedings of the Fourth International Workshop (eds Endersby, N. M. & Ridland, P. M.) 95–100 (The Regional Institute Ltd, Gosford, New South Wales, Australia, 2004).
17.
Dosdall, L. M., Soroka, J. J. & Olfert, O. The diamondback moth in canola and mustard: current pest status and future prospects. Prairie Soils and Crops J. 4, 66–76 (2011).
Google Scholar
18.
Furlong, M. J. et al. Ecology of diamondback moth in Australian canola: landscape perspectives and the implications for management. Aust. J. Exp. Agric. 48, 1494–1505. https://doi.org/10.1071/EA07413 (2008).
Article Google Scholar
19.
Endersby, N. M., McKechnie, S. W., Ridland, P. M. & Weeks, A. R. Microsatellites reveal a lack of structure in Australian populations of the diamondback moth, Plutella xylostella (L.). Mol. Ecol. 15, 107–118. https://doi.org/10.1111/j.1365-294X.2005.02789.x (2006).
CAS Article PubMed Google Scholar
20.
ABARES. Australian commodity production statistics. Australian Bureau of Agricultural and Resource Economics and Sciences cat. no. 7113.0 (2017).
21.
Gu, H., Fitt, G. P. & Baker, G. H. Invertebrate pests of canola and their management in Australia: a review. Aust. J. Entomol. 46, 231–243. https://doi.org/10.1111/j.1440-6055.2007.00594.x (2007).
Article Google Scholar
22.
Baker, G. J. Crucifer vegetable insecticide resistance management strategies and issues in Australia. In The Sixth International Workshop on Management of the Diamondback Moth and Other Crucifer Insect Pests (eds Srinivasan, R., Shelton, A. M. & Collins, H. L.) 21–25 (AVRDC – The World Vegetable Centre, Tainan, Taiwan, 2011).
23.
Mo, J. H., Baker, G., Keller, M. & Roush, R. Local dispersal of the diamondback moth (Plutella xylostella (L.)) (Lepidoptera: Plutellidae). Environ. Entomol. 32, 71–79. https://doi.org/10.1603/0046-225X-32.1.71 (2003).
Article Google Scholar
24.
Baker, G. J. Improving management of diamondback moth in Australian canola: final report for GRDC (DAS00134). Technical Report, South Australian Research and Development Institute (2015).
25.
Pichon, A. et al. Genetic differentiation among various populations of the diamondback moth, Plutella xylostella (Lepidoptera: Yponomeutidae). B. Entomol. Res. 96, 137–144. https://doi.org/10.1079/BER2005409 (2006).
CAS Article Google Scholar
26.
Juric, I., Salzburger, W. & Balmer, O. Spread and global population structure of the diamondback moth Plutella xylostella (Lepidoptera: Plutellidae) and its larval parasitoids Diadegma semiclausum and Diadegma fenestrale (Hymenoptera: Ichneumonidae) based on mtDNA. B. Entomol. Res. 107, 155–164. https://doi.org/10.1017/S0007485316000766 (2017).
CAS Article Google Scholar
27.
Kim, I. et al. Mitochondrial COI gene sequence-based population genetic structure of the diamondback moth, Plutella xylostella, Korea. Korean J. Genet. 25, 155–170 (2003).
CAS Google Scholar
28.
Yang, J. et al. Insight into the migration routes of Plutella xylostella in China using mtCOI and ISSR markers. PLoS ONE 10, e0130905. https://doi.org/10.1371/journal.pone.0130905 (2015).
CAS Article PubMed PubMed Central Google Scholar
29.
Caprio, M. A. & Tabashnik, B. E. Allozymes used to estimate gene flow among populations of diamondback moth (Lepidoptera, Plutellidae) in Hawaii. Environ. Entomol. 21, 808–816. https://doi.org/10.1093/ee/21.4.808 (1992).
Article Google Scholar
30.
Chang, W. X. Z. et al. Mitochondrial DNA sequence variation among geographic strains of diamondback moth (Lepidoptera: Plutellidae). Ann. Entomol. Soc. Am. 90, 590–595. https://doi.org/10.1093/aesa/90.5.590 (1997).
CAS Article Google Scholar
31.
Saw, J., Endersby, N. M. & McKechnie, S. W. Low mtDNA diversity among widespread Australian diamondback moth Plutella xylostella (L.) suggests isolation and a founder effect. Insect Sci. 13, 365–373 (2006).
CAS Article Google Scholar
32.
Endersby, N. M. Population structure and gene flow in diamondback moth in Australia and around the world: current state of knowledge and directions for the future. In The Management of Diamondback Moth and Other Crucifer Pests: Proceedings of the Fifth International Workshop (eds Shelton A. M. et al.) 132–147 (China Agricultural Science and Technology Press, Beijing, 2008).
33.
Fu, X., Xing, Z., Liu, Z., Ali, A. & Wu, K. Migration of diamondback moth, Plutella xylostella, across the Bohai sea in northern China. Crop Prot. 64, 143–149. https://doi.org/10.1016/j.cropro.2014.06.021 (2014).
Article Google Scholar
34.
Delgado, A. M. & Cook, J. M. Effects of a sex-ratio distorting endosymbiont on mtDNA variation in a global insect pest. BMC Evol. Biol. 9, 49. https://doi.org/10.1186/1471-2148-9-49 (2009).
CAS Article PubMed PubMed Central Google Scholar
35.
Perry, K. D. et al. Cryptic Plutella species show deep divergence despite the capacity to hybridize. BMC Evol. Biol. 18, 77. https://doi.org/10.1186/s12862-018-1183-4 (2018).
CAS Article PubMed PubMed Central Google Scholar
36.
Roux, O. et al. ISSR-PCR: tool for discrimination and genetic structure analysis of Plutella xylostella populations native to different geographical areas. Mol. Phylogenet. Evol. 43, 240–250. https://doi.org/10.1016/j.ympev.2006.09.017 (2007).
CAS Article PubMed Google Scholar
37.
Landry, J. F. & Hebert, P. D. N. Plutella australiana (Lepidoptera, Plutellidae), an overlooked diamondback moth revealed by DNA barcodes. Zookeys 327, 43–63. https://doi.org/10.3897/zookeys.327.5831 (2013).
Article Google Scholar
38.
Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351. https://doi.org/10.1038/nrg.2016.49 (2016).
CAS Article PubMed Google Scholar
39.
Davey, J. W. et al. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat. Rev. Genet. 12, 499–510. https://doi.org/10.1038/nrg3012 (2011).
ADS CAS Article PubMed Google Scholar
40.
Narum, S. R., Buerkle, C. A., Davey, J. W., Miller, M. R. & Hohenlohe, P. A. Genotyping-by-sequencing in ecological and conservation genomics. Mol. Ecol. 22, 2841–2847. https://doi.org/10.1111/mec.12350 (2013).
CAS Article PubMed PubMed Central Google Scholar
41.
Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, e3376. https://doi.org/10.1371/journal.pone.0003376 (2008).
ADS CAS Article PubMed PubMed Central Google Scholar
42.
Baxter, S. W. et al. Linkage mapping and comparative genomics using next-generation RAD sequencing of a non-model organism. PLoS ONE 6, e19315. https://doi.org/10.1371/journal.pone.0019315 (2011).
ADS CAS Article PubMed PubMed Central Google Scholar
43.
Andrews, K. R., Good, J. M., Miller, M. R., Luikart, G. & Hohenlohe, P. A. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 17, 81–92. https://doi.org/10.1038/nrg.2015.28 (2016).
CAS Article PubMed PubMed Central Google Scholar
44.
Davey, J. L. & Blaxter, M. W. RADSeq: next-generation population genetics. Brief. Funct. Genomics 9, 416–423. https://doi.org/10.1093/bfgp/elq031 (2010).
CAS Article PubMed Google Scholar
45.
Putman, A. I. & Carbone, I. Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecol. Evol. 4, 4399–4428. https://doi.org/10.1002/ece3.1305 (2014).
Article PubMed PubMed Central Google Scholar
46.
Haasl, R. J. & Payseur, B. A. Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites. Heredity 106, 158–171. https://doi.org/10.1038/hdy.2010.21 (2011).
CAS Article PubMed Google Scholar
47.
Vendrami, D. L. J. et al. RAD sequencing resolves fine-scale population structure in a benthic invertebrate: implications for understanding phenotypic plasticity. R. Soc. Open Sci. 4, 160548. https://doi.org/10.1098/rsos.160548 (2017).
ADS Article PubMed PubMed Central Google Scholar
48.
Rasic, G. et al. Aedes aegypti has spatially structured and seasonally stable populations in Yogyakarta, Indonesia. Parasit. Vectors 8, 610. https://doi.org/10.1186/s13071-015-1230-6 (2015).
CAS Article PubMed PubMed Central Google Scholar
49.
Rasic, G. et al. Contrasting genetic structure between mitochondrial and nuclear markers in the dengue fever mosquito from Rio de Janeiro: implications for vector control. Evol. Appl. 8, 901–915. https://doi.org/10.1111/eva.12301 (2015).
CAS Article PubMed PubMed Central Google Scholar
50.
Perry, K. D., Pederson, S. M. & Baxter, S. W. Genome-wide SNP discovery in field and laboratory colonies of Australian Plutella species. In Proceedings of the Seventh International Workshop on Management of the Diamondback Moth and Other Crucifer Insect Pests, Mysore J. Agric. Sci., 51A, 18–31 (2017).
51.
Fountain, E. D., Pauli, J. N., Reid, B. N., Palsboll, P. J. & Peery, M. Z. Finding the right coverage: the impact of coverage and sequence quality on single nucleotide polymorphism genotyping error rates. Mol. Ecol. Resour. 16, 966–978. https://doi.org/10.1111/1755-0998.12519 (2016).
CAS Article PubMed Google Scholar
52.
Wright, S. Isolation by distance. Genetics 28, 114–138 (1943).
CAS PubMed PubMed Central Google Scholar
53.
Grapputo, A., Boman, S., Lindstrom, L., Lyytinen, A. & Mappes, J. The voyage of an invasive species across continents: genetic diversity of North American and European Colorado potato beetle populations. Mol. Ecol. 14, 4207–4219. https://doi.org/10.1111/j.1365-294X.2005.02740.x (2005).
CAS Article PubMed Google Scholar
54.
Janes, J. K. et al. The (K=2) conundrum. Mol. Ecol. 26, 3594–3602. https://doi.org/10.1111/mec.14187 (2017).
Article PubMed Google Scholar
55.
Pritchard, J., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS PubMed PubMed Central Google Scholar
56.
Kalinowski, S. T. The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure. Heredity 106, 625–632. https://doi.org/10.1038/hdy.2010.95 (2011).
CAS Article PubMed Google Scholar
57.
Rodriguez-Ramilo, S. T. & Wang, J. The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis. Mol. Ecol. Resour. 12, 873–884. https://doi.org/10.1111/j.1755-0998.2012.03156.x (2012).
Article PubMed Google Scholar
58.
Wang, J. Effects of sampling close relatives on some elementary population genetics analyses. Mol. Ecol. Resour. 18, 41–54. https://doi.org/10.1111/1755-0998.12708 (2018).
Article PubMed Google Scholar
59.
Ward, C. M. & Baxter, S. W. Assessing genomic admixture between cryptic Plutella moth species following secondary contact. Genome Biol. Evol. 10, 2973–2985. https://doi.org/10.1093/gbe/evy224 (2018).
CAS Article PubMed PubMed Central Google Scholar
60.
Epps, C. W. & Keyghobadi, N. Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change. Mol. Ecol. 24, 6021–6040. https://doi.org/10.1111/mec.13454 (2015).
Article PubMed Google Scholar
61.
Donnelly, M., Licht, M. & Lehmann, T. Evidence for recent population expansion in the evolutionary history of the malaria vectors Anopheles arabiensis and Anopheles gambiae. Mol. Biol. Evol. 18, 1353–1364. https://doi.org/10.1093/oxfordjournals.molbev.a003919 (2001).
CAS Article PubMed Google Scholar
62.
Niu, Y. Q., Nansen, C., Li, X. W. & Liu, T. X. Geographical variation of Plutella xylostella (Lepidoptera: Plutellidae) populations revealed by mitochondrial COI gene in China. J. Appl. Entomol. 138, 692–700. https://doi.org/10.1111/jen.12130 (2014).
CAS Article Google Scholar
63.
Hurst, G. D. D. & Jiggins, F. M. Problems with mitochondrial DNA as a marker in population, phylogeographic and phylogenetic studies: the effects of inherited symbionts. Proc. R. Soc. B Biol. Sci. 272, 1525–1534. https://doi.org/10.1098/rspb.2005.3056 (2005).
CAS Article Google Scholar
64.
You, M. et al. Variation among 532 genomes unveils the origin and evolutionary history of a global insect herbivore. Nat. Commun. 11, 2321. https://doi.org/10.1038/s41467-020-16178-9 (2020).
ADS CAS Article PubMed PubMed Central Google Scholar
65.
Slatkin, M. Gene flow in natural populations. Annu. Rev. Ecol. Syst. 16, 393–430. https://doi.org/10.1146/annurev.ecolsys.16.1.393 (1985).
Article Google Scholar
66.
Mills, L. S. & Allendorf, F. W. The one-migrant-per-generation rule in conservation and management. Cons. Biol. 10, 1509–1518. https://doi.org/10.1046/j.1523-1739.1996.10061509.x (1996).
Article Google Scholar
67.
Perry, K. D. The colonisation of canola crops by the diamondback moth, Plutella xylostella L., in southern Australia. Ph.D. thesis, The University of Adelaide (2019).
68.
Hatami, B. Seasonal occurrence and abundance of diamondback moth, Plutella xylostella (L.), and its major parasitoids on brassicaceous plants in South Australia. Ph.D. thesis, The University of Adelaide (1996).
69.
Ridland, P. M. & Endersby, N. M. Seasonal phenology of diamondback moth populations in southern Australia. In The Management of Diamondback Moth and Other Crucifer Pests: Proceedings of the Fifth International Workshop (eds Shelton A. M. et al.) 90–101 (China Agricultural Science and Technology Press, Beijing, 2008).
70.
Roush, R. T. & McKenzie, J. A. Ecological genetics of insecticide and acaricide resistance. Annu. Rev. Entomol. 32, 361–380. https://doi.org/10.1146/annurev.en.32.010187.002045 (1987).
CAS Article PubMed Google Scholar
71.
Zalucki, M. P. & Furlong, M. J. Predicting outbreaks of a migratory pest: an analysis of DBM distribution and abundance revisited. In The Sixth International Workshop on Management of the Diamondback Moth and Other Crucifer Insect Pests (eds Srinivasan, R., Shelton, A. M. & Collins, H. L.) 8–14 (AVRDC – The World Vegetable Centre, Tainan, Taiwan, 2011).
72.
Benestan, L. et al. Sex matters in massive parallel sequencing: evidence for biases in genetic parameter estimation and investigation of sex determination systems. Mol. Ecol. 26, 6767–6783. https://doi.org/10.1111/mec.14217 (2017).
CAS Article PubMed Google Scholar
73.
Robertson, P. L. Diamondback moth investigation in New Zealand. N. Z. J. Sci. Technol. 20, 330–340 (1939).
Google Scholar
74.
Zraket, C., Barth, J., Heckel, D. & Abbott, A. Genetic linkage mapping with restriction fragment length polymorphisms in the tobacco budworm, Heliothis virescens. In Molecular Insect Science (eds Hagedorn, H. H., Hildebrand, J. G., Kidwell M. G. & Law, J. H.) 13–20 (Springer, Boston, MA, 1990). https://doi.org/10.1007/978-1-4899-3668-4_2
75.
Andrews, S. FASTQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010). Accessed August 2016.
76.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).
CAS Article PubMed PubMed Central Google Scholar
77.
Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939. https://doi.org/10.1101/gr.111120.110 (2011).
CAS Article PubMed PubMed Central Google Scholar
78.
Broad Institute. http://broadinstitute.github.io/picard/. Accessed 10 December 2017.
79.
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303. https://doi.org/10.1101/gr.107524.110 (2010).
CAS Article PubMed PubMed Central Google Scholar
80.
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491. https://doi.org/10.1038/ng.806 (2011).
CAS Article PubMed PubMed Central Google Scholar
81.
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).
CAS Article PubMed PubMed Central Google Scholar
82.
Lischer, H. E. L. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299. https://doi.org/10.1093/bioinformatics/btr642 (2012).
CAS Article PubMed PubMed Central Google Scholar
83.
Perry, K. D. https://github.com/kymperry01/PlutellaCanola. Accessed November 2018.
84.
Goudet, J. & Jombart, T. hierfstat: Estimation and tests of hierarchical F-statistics (2015). R package version 0.04-22.
85.
Nei, M. Molecular Evolutionary Genetics (Columbia University Press, New York, 1987).
Google Scholar
86.
Weir, B. & Cockerham, C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370. https://doi.org/10.2307/2408641 (1984).
CAS Article PubMed PubMed Central Google Scholar
87.
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodoehl, P. A. diveRsity: an R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788. https://doi.org/10.1111/2041-210X.12067 (2013).
Article Google Scholar
88.
Rousset, F. GENEPOP ‘007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).
Article PubMed Google Scholar
89.
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
MathSciNet MATH Google Scholar
90.
Armstrong, R. A. When to use the Bonferroni correction. Ophthal. Physiol. Opt. 34, 502–508. https://doi.org/10.1111/opo.12131 (2014).
Article Google Scholar
91.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2017).
92.
Slatkin, M. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139, 457–462 (1995).
CAS PubMed PubMed Central Google Scholar
93.
Dray, S. & Dufour, A. B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).
Article Google Scholar
94.
Hijmans, R. J. geosphere: Spherical Trigonometry. R package version 1.5-7 (2017).
95.
Kamvar, Z. N., Tabima, J. F. & Gruenwald, N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281. https://doi.org/10.7717/peerj.281 (2014).
Article PubMed PubMed Central Google Scholar
96.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).
CAS Article PubMed Google Scholar
97.
Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).
Article Google Scholar
98.
Jakobsson, M. & Rosenberg, N. A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806. https://doi.org/10.1093/bioinformatics/btm233 (2007).
CAS Article PubMed PubMed Central Google Scholar
99.
Rosenberg, N. A. DISTRUCT: a program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138. https://doi.org/10.1046/j.1471-8286.2003.00566.x (2004).
Article Google Scholar
100.
Jombart, T. adegenet: an R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405. https://doi.org/10.1093/bioinformatics/btn129 (2008).
CAS Article PubMed PubMed Central Google Scholar
101.
Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).
CAS Article PubMed PubMed Central Google Scholar
102.
Ryman, N. & Palm, S. POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol. Ecol. Notes 6, 600–602. https://doi.org/10.1111/j.1365-294X.2006.01378.x (2006).
Article Google Scholar More