RAS BiologyГенетика Russian Journal of Genetics

  • ISSN (Print) 0016-6758
  • ISSN (Online) 3034-5103

Population Genetic Structure of the Demoiselle Crane L. in the Space of Ecological Factors

PII
S3034510325120071-1
DOI
10.7868/S3034510325120071
Publication type
Article
Status
Published
Authors
Volume/ Edition
Volume 61 / Issue number 12
Pages
72-81
Abstract
The article presents the results of an analysis of the population-genetic structure by microsatellite loci and modeling of ecological niches in a widespread species, of the Demoiselle crane ( L.). It is shown that in the structure of the Demoiselle crane gene pool, three groups were distinguished, confined to different wintering sites of this species: Azov-Black Sea/Chadian; Caspian-Sudanese and Asian-Indian subpopulations, uniting the Trans-Ural, South Siberian, Baikal and Trans-Baikal/ Indian subpopulations. The Demoiselle cranes from the Trans-Ural/Indian subpopulation occupy an intermediate position between the European and the other three Asian subpopulations. An analysis of the ecological differentiation of the Demoiselle crane by temperature, precipitation, and altitude in breeding sites revealed a structure that generally corresponds to that of microsatellite loci, as well as previously obtained data on cytochrome of mitochondrial DNA. The presented results may indicate a possible role of climatic factors in the formation of the intraspecific genetic structure of the Demoiselle crane due to the restriction of the gene flow that occurs in specific environmental conditions.
Keywords
Array журавль-красавка микросателлитные локусы популяционно-генетическая структура моделирование экологических ниш
Date of publication
01.07.2025
Year of publication
2025
Number of purchasers
0
Views
26

References

  1. 1. Ilyashenko E.I. Demoiselle Crane (Anthropoides virgo) // Crane Conservation Strategy. Baraboo, Wisconsin, USA: Int. Crane Foundation, 2019. P. 383–396.
  2. 2. Ильяшенко Е.И. Журавль-красавка Anthropoides virgo (Linnaeus, 1758) // Красная книга Российской Федерации. Том “Животные”. М.: ФГБУ ВНИИ Экология, 2021. С. 689–691.
  3. 3. BirdLife International. Anthropoides virgo (Europe assessment). The IUCN Red List of Threatened Species 2021: e.T22692081A166235355. https://dx.doi.org/10.2305/IUCN.UK.2021-3.RLTS.T22692081A166235355.en
  4. 4. Meine C.D., Archibald G.W. The Cranes: Status Survey and Conservation Action Plan. Gland, Switzerland: IUCN, 1996. 294 p.
  5. 5. Абушин А.А., Музаев В.М., Эрдненов Г.И. Динамика численности красавки в Калмыкии в первой четверти XXI века // Журавли Евразии (распространение, охрана). М.: Товарищество научных изданий КМК, 2024. С. 46–66.
  6. 6. Мудрик Е.А., Ильяшенко Е.И., Казимиров П.А. и др. Данные митохондриальной ДНК позволяют выделить субпопуляции широкоареального вида журавлей красавки (Anthropoides virgo) // Вавиловский журнал генетики и селекции. 2025. Т. 29. № 4. С. 568–577. https://doi.org/10.18699/vjgb-25-60
  7. 7. Kanai Y., Minton J., Nagendran M. et al. Migration of Demoiselle Cranes in Asia based on satellite tracking and fieldwork // Glob. Environ. Res. 2000. V. 4. P. 143–153.
  8. 8. Guo Y., He F. Preliminary results of satellite tracking on Ordos Demoiselle Cranes // Chinese J. Wildlife. 2017. V. 38. № 1. P. 141–143.
  9. 9. Ильяшенко Е.И., Мудрик Е.А., Андрющенко Ю.А. и др. Миграции красавки (Anthropoides virgo, Gruiformes): дистанционное слежение на путях пролета и зимовках // Зоол. журн. 2021. Т. 100. № 9. С. 1028–1054. https://doi.org/10.31857/S0044513421070059
  10. 10. Ильяшенко Е.И., Кондракова К.Д., Доржиев Ц.З. и др. Новые сведения о миграции красавки // Мат. XVI Междунар. орнитологической конф. Северной Евразии. Казань, 2025. С. 108.
  11. 11. Mudrik Е.А., Ilyashenko Е.I., Goroshko O.A. et al. The Demoiselle crane (Anthropoides virgo) population genetic structure in Russia // Vavilov J. Genet. Breed. 2018. V. 22. № 5. P. 586–592. https://doi.org/10.18699/VJ18.398
  12. 12. Milanesi P., Caniglia R., Fabbri E. et al. Combining Bayesian genetic clustering and ecological niche modeling: Insights into wolf intraspecific genetic structure // Ecol. Evol. 2018. V. 8. № 22. P. 11224–11234. https://doi.org/10.1002/ece3.4594
  13. 13. Gotelli N.J., Stanton‐Geddes J. Climate change, genetic markers and species distribution modelling // J. Biogeography. 2015. V. 42. № 9. P. 1577–1585. https://doi.org/10.1111/jbi.12562
  14. 14. Perez-Martinez A., Eguiarte L., Mercer K. et al. Genetic diversity, gene flow and differentiation among wild, semiwild and landrace chile pepper (Capsicum annuum) populations in Oaxaca, Mexico // Am. J. Bot. 2022. V. 109. № 7. P. 1–20. https://doi.org/10.1002/ajb2.16019
  15. 15. Fameli A., Pereira J., Gomez Fernandez M., Gomez J. Genetic structure and climate niche differentiation among populations of Leopardus geoffroyi // Ecol. Evol. 2024. V. 14. https://doi.org/10.1002/ece3.70223
  16. 16. Meares K., Dawson D., Horsburgh G. et al. Charac-terisation of 14 blue crane Grus paradisea (Gruidae, AVES) microsatellite loci for use in detecting illegal trade // Conserv. Genet. 2008. V. 9. P. 1363–1367. https://doi.org/10.1007/s10592-007-9490-0
  17. 17. Van Oosterhout C., Hutchinson W.F., Wills D.P., Ship-ley P. Micro-Checker: Software for identifying and correcting genotyping errors in microsatellite data // Mol. Ecol. Notes. 2004. V. 4. № 3. P. 535–538. http://doi.org/10.1111/J.1471-8286.2004.00684.X
  18. 18. Peakall R., Smouse P.E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research – an update // Bioinformatics. 2012. V. 28. № 19. P. 2537–2539. http://doi.org/10.1093/bioinformatics/bts460
  19. 19. Pritchard J.K., Stephens M., Donnelly P. Inference of population structure using multilocus genotype data // Genetics. 2000. V. 155. № 2. P. 945–959. http://dx.doi.org/10.3410/f.1015548.197423
  20. 20. Puechmaille S.J. The program structure does not reliably recover the correct population structure when sampling is uneven: Subsampling and new estimators alleviate the problem // Mol. Ecol. Res. 2016. V. 16. № 3. P. 608–627. https://doi.org/10.1111/1755-0998.12512
  21. 21. Li Y.L., Liu J.X. StructureSelector: A web-based software to select and visualize the optimal number of clusters using multiple methods // Mol. Ecol. Res. 2018. V. 18. № 1. P. 176–177. https://doi.org/10.1111/1755-0998.12719
  22. 22. Kopelman N.M., Mayzel J., Jakobsson M. et al. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K // Mol. Ecol. Res. 2015. V. 15. № 5. P. 1179–1191. http://doi.org/10.1111/1755-0998.12387
  23. 23. Hijmans R.J., Barbosa M., Ghosh A., Mandel A. geodata: Download Geographic Data. 2024.
  24. 24. R Core Team: A Language and Environment for Statistical Computing. 2022.
  25. 25. Hijmans R.J. raster: Geographic Data Analysis and Modeling. 2025.
  26. 26. Blonder B., Morrow C.B., Brown S. et al. hypervolume: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls. 2025.
  27. 27. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
  28. 28. Hvitfeldt E. paletteer: Comprehensive Collection of Color Palettes. 2021.
  29. 29. Mudrik E.A., Ilyashenko E.I., Ilyashenko V.Y. et al. Genetic diversity and differentiation of the widespread migratory Demoiselle Crane, Grus virgo, on the northern edge of the species’ distribution // J. Ornithol. 2022. V. 163. № 1. P. 291–299. https://doi.org/10.1007/s10336-021-01919-4
  30. 30. Мудрик Е.А., Политов Д.В. Молекулярно-генетические подходы в изучении и сохранении популяционных генофондов журавлей (Gruidae, Aves) // Успехи соврем. биол. 2022. Т. 142. № 5. С. 477–486. https://doi.org/10.31857/S004213242205009X
  31. 31. Parau L.G, Wink M. Common patterns in the molecular phylogeography of western palearctic birds: a comprehensive review // J. Ornithol. 2021. V. 162. P. 937–959. https://doi.org/10.1007/s10336-021-01893-x
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