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Mitochondrial COI sequence-based population genetic analysis of the grasshopper, Patanga japonica Bolívar, 1898 (Acrididae: Orthoptera), which is a climate-sensitive indicator species in South Korea

  • Jee-Young Pyo (College of Agriculture & Life Sciences, Chonnam National University) ;
  • Jeong Sun Park (College of Agriculture & Life Sciences, Chonnam National University) ;
  • Seung Hyun Lee (College of Agriculture & Life Sciences, Chonnam National University) ;
  • Sung-Soo Kim (Research Institute for East Asian Environment and Biology) ;
  • Heon Cheon Jeong (Korea Native Animal Resources Utilization Convergence Research Institute, Soonchunhyang University) ;
  • Iksoo Kim (College of Agriculture & Life Sciences, Chonnam National University)
  • Received : 2023.10.18
  • Accepted : 2023.11.25
  • Published : 2023.12.31

Abstract

Patanga japonica Bolívar, 1898 (Orthoptera: Acrididae) is listed as a climate-sensitive indicator species in South Korea and is called southern group of insects in that the main distributional range is southern region of South Korea and Asian continent. In South Korea, thus, the species was distributed mainly in southern region of South Korea including southward a remote Jeju Island, but recently the species has often been detected in mid to northern region of South Korea, implying northward range expansion in response to climate change. Understanding the characteristics of the changes in genetic diversity during range expansion in response to climate change could be a foundation for the understanding of future biodiversity. Thus, in this study, we attempted to understand the changing pattern of the genetic diversity of the P. japonica in newly expanded regions. For the purpose of study, we collected 125 individuals from seven localities throughout South Korea including two newly distributed regions (Pyeongtaek and Yeongwol at ~37° N). These were sequenced for a segment of mitochondrial cytochrome oxidase subunit I (COI) and analyzed for genetic diversity, haplotype frequency, and population genetic structure among populations. Interestingly, northward range expansion accompanied only haplotypes, which are most abundant in the core populations, providing a significant reduction in haplotype diversity, compared to other populations. Moreover, genetic diversity was still lower in the expanded regions, but no genetic isolation was detected. These results suggest that further longer time would take to reach to the comparable genetic diversity of preexisting populations in the expanded regions. Probably, availability of qualified habitats at the newly expanded region could be pivotal for successful northward range expansion in response to climate change.

Keywords

Acknowledgement

This work was supported by a grant from the National Institute of Biological Resources (NIBR), funded by the Ministry of Environment (MOE) of the Republic of Korea [NIBR202206203].

References

  1. Adhikari P, Jeon JY, Kim HW, Oh HS, Adhikari P, Seo C (2020) Northward range expansion of southern butterflies according to climate change in South Korea. J Clim Chang Res 11, 643-656. http://dx.doi.org/10.15531/KSCCR.2020.11.6.643 
  2. Bandelt HJ, Forster P, Rohl A (1999) Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol 16, 37-48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 
  3. Barton NH (2000) Genetic hitchhiking. Phil Trans R Soc Lond B 355, 1553-1562. https://doi.org/10.1098/rstb.2000.0716 
  4. Beaumont MA, Balding DJ (2004) Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol 13, 969-980. https://doi.org/10.1111/j.1365-294X.2004.02125.x 
  5. Bohonak AJ (2002) IBD (Isolation by Distance): a program for analyses of isolation by distance. J Hered 93, 153-154. https://doi.org/10.1093/jhered/93.2.153 
  6. Canale CI, Henry PY (2010) Adaptive phenotypic plasticity and resilience of vertebrates to increasing climatic unpredictability. Clim Res 43, 135-147. https://doi.org/10.3354/cr00897 
  7. Cigliano MM, Braun H, Eades DC, Otte D (2022) Orthoptera Species File [Internet]. Available from: http://Orthoptera.SpeciesFile.org/ [accessed on 1 October 2022]. 
  8. Corander J, Tang J (2007) Bayesian analysis of population structure based on linked molecular information. Math Biosci 205, 19-31. https://doi.org/10.1016/j.mbs.2006.09.015 
  9. Excoffier L, Lischer HE (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10, 564-567. https://doi.org/10.1111/j.1755-998.2010.02847.x 
  10. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotype: application to human mitochondrial DNA restriction date. Genetics 131, 479-491. https://doi.org/10.1093/genetics/131.2.479 
  11. Frankham R (1995) Conservation genetics. Annu Rev Genet 29, 305-327.  https://doi.org/10.1146/annurev.ge.29.120195.001513
  12. Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conserv Biol 10, 1500-1508. https://doi.org/10.1046/j.1523-1739.1996.10061500.x 
  13. Hoffmann AA, Sgro CM (2011) Climate change and evolutionary adaptation. Nature 470, 479-485. https://doi.org/10.1038/nature09670 
  14. Jo YS, Kim HN, Baccus JT, Jung J (2017) Genetic differentiation of the Korean striped field mouse, Apodemus agrarius (Muridae, Rodentia), based on microsatellite polymorphism. Mammalia 81, 297-307. https://doi.org/10.1515/mammalia-2015-0152 
  15. Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol 16, 111-120. https://doi.org/10.1007/BF01731581 
  16. Lanfear R, Kokko H, Eyre-Walker A (2014) Population size and the rate of evolution. Trends Ecol Evol 29, 33-41. https://doi.org/10.1016/j.tree.2013.09.009 
  17. Lee SC, Bae JS, Kim I, Suzuki H, Kim SR, Kim JG, et al. (2003) Mitochondrial DNA sequence-based population genetic structure of the firefly, Pyrocoelia rufa (Coleoptera: Lampyridae). Biochem Genet 41, 427-452. https://doi.org/10.1023/B:BIGI.0000007777.87407.1b 
  18. Lee YS, Markov N, Argunov A, Voloshina I, Bayarlkhagva D, Kim BJ, et al. (2016) Genetic diversity and phylogeography of Siberian roe deer, Caproulus pygargus, in central and peripheral populations. Ecol Evol 6, 7286-7297. https://doi.org/10.1002/ece3.2458 
  19. Lourenco-de-Moraes R, Lansac-Toha FM, Schwind LTF, Arrieira RL, Rosa RR, Terribile LC, et al. (2019) Climate change will decrease the range size of snake species under negligible protection in the Brazilian Atlantic Forest hotspot. Sci Rep 9, 8523. https://doi.org/10.1038/s41598-019-44732-z 
  20. Maggini R, Lehmann A, Kery M, Schmid H, Beniston M, Jenni L, et al. (2011) Are Swiss birds tracking climate change?: Detecting elevational shifts using response curve shapes. Ecol Model 222, 21-32. https://doi.org/10.1016/j.ecolmodel.2010.09.010 
  21. Mantel N (1967) Detection of disease clustering and generalized regression approach. Cancer Res 27, 209-220. 
  22. McCain CM, Garfinkel CF (2021) Climate change and elevational range shifts in insects. Curr Opin Insect Sci 47, 111-118. https://doi.org/10.1016/j.cois.2021.06.003 
  23. McInerny GJ, Turner JRG, Wong HY, Travis JMJ, Benton TG (2009) How range shifts induced by climate change affect neutral evolution. Proc R Soc B 276, 1527-1534. https://doi.org/10.1098/rspb.2008.1567 
  24. Miller MA, Pfeiffer W, Schwartz T (2010) Creating the CIPRES Science Gateway for Inference of large phylogenetic trees. In Proc Gateway Computing Environments Workshop (GCE), IEEE, New Orleans, pp. 1-8. http://doi.org/10.1109/GCE.2010.5676129 
  25. Nam SH (1996) The insects of Korea. Kyohaksa, Seoul.
  26. National Institute of Biological Resources (2017) List of 100 climatesensitive biological indicator species and 30 candidate species [Internet]. Available from: https://species.nibr.go.kr/home/mainHome.do?cont_link=011Ab&subMenu=011017&contCd=011017 [accessed on 08 October 2023]. 
  27. Nei M (1987) Molecular Evolutionary Genetics. Columbia University Press, New York. 
  28. Neigel JE, Avise JC (1993) Application of a random walk model to geographic distributions of animal mitochondrial DNA variation. Genetics 135, 1209-1220. https://doi.org/10.1093/genetics/135.4.1209 
  29. Ohshima K (1990) The history of straits around the Japanese Islands in the Late-Quarternary. Quat Res 29, 193-208. https://doi.org/10.4116/jaqua.29.193 
  30. Park YA (1988) Continental shelf sedimentation. Lee DS (ed.), pp. 406-426, Kyohaksa, Seoul. 
  31. Park Y, Nam HY, Baek S, Lee SH, Lee JH (2019) Population genetic structure of Bemisia tabaci MED (Hemiptera: Aleyrodidae) in Korea. PLoS One 14, e0220327. https://doi.org/10.1371/journal.pone.0220327 
  32. Posada D, Crandall KA (1998) MODELTEST: Testing the model of DNA substitution. Bioinformatics 14, 817-818. https://doi.org/10.1093/bioinformatics/14.9.817 
  33. Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA (2018) Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst Biol 67, 901-904. https://doi.org/10.1093/sysbio/syy032 
  34. Rodder D, Schmitt T, Gros P, Ulrich W, Habel JC (2021) Climate change drives mountain butterflies towards the summits. Sci Rep 11, 1-12. https://doi.org/10.1038/s41598-021-93826-0 
  35. Ronquist F, Teslenko M, Mark P, Ayres DL, Darling A, Hohna S, et al. (2012) MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol 61, 539-542. https://doi.org/10.1093/sysbio/sys029 
  36. Shin S, Oh HK, Kang D, Han JE, Lee W, Lee J (2022) Distribution maps of climate-sensitive biological indicator species observed and recorded by the Korea biodiversity observation network. National Institute of Biological Resources, Incheon. 
  37. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7, 539. https://doi.org/10.1038/msb.2011.75 
  38. Song H, Foquet B, Marino-Perez R, Woller DA (2017) Phylogeny of locusts and grasshoppers reveals complex evolution of density-dependent phenotypic plasticity. Sci Rep 7, 6606. https://doi.org/10.1038/s41598-017-07105-y 
  39. Song H, Marino-Perez R, Woller DA, Cigliano MM (2018) Evolution, diversification, and biogeography of grasshoppers (Orthoptera: Acrididae). Insect Syst and Divers 2, 3. https://doi.org/10.1093/isd/ixy008 
  40. Stefanescu C, Torre I, Jubany J, Paramo F (2011) Recent trends in butterfly populations from north-east Spain and Andorra in the light of habitat and climate change. J Insect Conserv 15, 83-93. https://doi.org/10.1007/s10841-010-9325-z 
  41. Swaegers J, Mergeay J, Therry L, Larmuseau MHD, Bonte D, Stoks R (2013) Rapid range expansion increases genetic differentiation while causing limited reduction in genetic diversity in a damselfly. Heredity 111, 422-429. https://doi.org/10.1038/hdy.2013.64 
  42. Swofford DL (2002) PAUP* Phylogenetic analysis using parsimony (*and other method) version 4. 10. Sinauer Associates, Sunderland, Mass. 
  43. Tanaka S, Okuda T (1996) Life cycles, diapause and developmental characteristics in subtropical locusts, Nomadacris succincta and N. japonica (Orthoptera: Acrididae). Jpn J Entomol 64, 189-201.
  44. Templeton AR (1998) Nested clade analyses of phylogeographic data: testing hypotheses about gene flow and population history. Mol Ecol 7, 381-397. https://doi.org/10.1046/j.1365-294x.1998.00308.x 
  45. The Galaxy Community (2022) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic Acids Res 50, W345-351. https://doi.org/10.1093/nar/gkac247 
  46. Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, et al. (2004) Extinction risk from climate change. Nature 427, 145-148. https://doi.org/10.1038/nature02121 
  47. Thomas CD, Franco AM, Hill JK (2006) Range retractions and extinction in the face of climate warming. Trends Ecol Evol 21, 415-416. https://doi.org/10.1016/j.tree.2006.05.012 
  48. Trifinopoulos J, Nguyen LT, von Haeseler A, Minh BQ (2016) W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res 44, W232-W235. https://doi.org/10.1093/nar/gkw256 
  49. Urban MC (2015) Accelerating extinction risk from climate change. Science 348, 571-573. http://doi.org/10.1126/science.aaa4984 
  50. Vandewoestijne S, Van Dyck H (2010) Population genetic differences along a latitudinal cline between original and recently colonized habitat in a butterfly. PLoS One 5, e13810. https://doi.org/10.1371/journal.pone.0013810 
  51. Watterson GA, Guess HA (1977) Is the most frequent allele the oldest? Theor Popul Biol 11, 141-160. https://doi.org/10.1016/0040-5809(77)90023-5 
  52. Wilson RJ, Gutierrez D, Gutierrez J, Monserrat VJ (2007) An elevational shift in butterfly species richness and composition accompanying recent climate change. Glob Change Biol 13, 1873-1887. https://doi.org/10.1111/j.1365-2486.2007.01418.x 
  53. Woolbright SA, Whitham TG, Gehring CA, Allan GJ, Bailey JK (2014) Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol Evol 29, 406-416. https://doi.org/10.1016/j.tree.2014.05.003 
  54. Wright S (1978) Evolution and the genetic of population, variability within and among natural populations. pp. 213-220, University of Chicago Press, Chicago. 
  55. Zografou K, Swartz MT, Adamidis GC, Tilden VP, McKinney EN, Sewall BJ (2021) Species traits affect phenological responses to climate change in a butterfly community. Sci Rep 11, 1-14. https://doi.org/10.1038/s41598-021-82723-1