Journal of Ichthyology

, Volume 57, Issue 3, pp 434–444 | Cite as

Genetic characterization of Brycon hilarii (Characiformes) populations within the Pantanal: Aspects of their conservation within a globally important neotropical wetland

  • T. I. Okazaki
  • E. M. Hallerman
  • E. K. de Resende
  • A. W. S. Hilsdorf


Brycon hilarii, a characid species endemic to the Upper Paraguay hydrographic basin, is important to regional artisanal and sports fisheries. To develop effective strategies for conservation of this species in the face of potential environmental changes in the Pantanal region, we characterized genetic structuring within and among six B. hilarii collections based on variation at five microsatellite DNA markers. Within-population genetic variability was high, with 75 different alleles; mean average allelic richness per locus per sample location ranged from 6.06 to 7.99. Nei’s gene diversity (hs) varied among drainages from 0.66 (±0.2) to 0.69 (±0.2), with an average across the four genetically identified populations of 0.68 (±0.02). Analyses of Jost’s D EST and F ST-like indices, AMOVA, and Structure-based clustering analyses indicated that B. hilarii populations exhibit a low level of genetic structure, with some indications that the Taquari River population is somewhat distinct from others. Results of K-means analysis suggested little or no structuring, with weakly differentiated populations above and below the confluence of the Paraguay and Taquari rivers. Because B. hilarii populations in the Pantanal are linked by high levels of gene flow, habitat alterations that would interfere with gene flow may jeopardize the long-term persistance of the species.


conservation evaluation DNA fishery conservation microsatellite floodplain piraputanga 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alho, C.J.R, Biodiversity of the Pantanal: response to seasonal flooding regime and to environmental degradation, Braz. J. Biol., 2008, vol. 68, no. 4, suppl., pp. 957–966.CrossRefPubMedGoogle Scholar
  2. Alho, C.J.R. and Sabino, J.A, Conservation agenda for the Pantanal’s biodiversity, Braz. J. Biol., 2011, vol. 71, no. 1, pp. 327–335.CrossRefPubMedGoogle Scholar
  3. Alho, C.J.R. and Sabino, J, Seasonal Pantanal flood pulse: implications for biodiversity conservation—a review, Oecol. Austral., 2012, vol. 16, no. 4, pp. 958–978.CrossRefGoogle Scholar
  4. Allendorf, F.W. and Phelps, S.R, Loss of genetic variation in a hatchery stock of cutthroat trout, Trans. Am. Fish. Soc., 1980, vol. 109, no. 5, pp. 537–543.CrossRefGoogle Scholar
  5. Assine, M.L, River avulsions on the Taquari megafan, Pantanal wetland, Brazil, Geomorphology, 2005, vol. 70, nos. 3–4, pp. 357–371.CrossRefGoogle Scholar
  6. Barroso, R.M., Hilsdorf, A.W.S., Moreira, H.L., Mello, A.M., Guimarães, S.E., Cabello, P.H., and Traub-Cseko, Y.M, Identification and characterization of microsatellites loci in Brycon opalinus, Mol. Ecol. Notes, 2003, vol. 3, no. 2, pp. 297–298.CrossRefGoogle Scholar
  7. Britski, H.A., de Silimon, K.Z., and Lopes, B.S., Peixes do Pantanal—Manual de Identificação, Corumbá: Embrapa,2007, 2nd ed.Google Scholar
  8. Calcagnotto, D., and DeSalle, R, Population genetic structuring in pacu (Piaractus mesopotamicus) across the Paraná-Paraguay basin: evidence from microsatellites, Neotrop. Ichthyol., 2009, vol. 7, no. 4, pp. 607–616.CrossRefGoogle Scholar
  9. Carvalho-Costa, L.F., Hatanaka, T., and Galetti, P.M, Jr., Evidence of lack of population substructuring in the Brazilian freshwater fish Prochilodus costatus, Genet. Mol. Biol., 2008, vol. 31, no. 1, pp. 377–380.Google Scholar
  10. Catella, A.C., A Pesca no Pantanal Sul: Situação Atual e Perspectivas. Embrapa Pantanal, Documentos 48, Corumbá, 2003. DOC48.pdf. Accessed February 10, 2014.Google Scholar
  11. Catella, A.C., Albuquerque, S.P., Campos, F.LR., and Santos, D.C., Sistema de Controle da Pesca de Mato Grosso do Sul—SCPESCA/MS 18–2011, Corumbá: Embrapa Pantanal, 2013, 2013. online/BP123.pdf. Accessed February 10, 2014.Google Scholar
  12. Catella, A.C., Albuquerque, S.P., Campos, F.L.R., and Santos, D.C, Sistema de Controle da Pesca de Mato Grosso do Sul SCPESCA/MS 20–2013, Corumbá: Embrapa Pantanal, 2014. online/BP127.pdf. Accessed August 10, 2015.Google Scholar
  13. Cunha, C.N. and Junk, W.J., A preliminary classification of habitats of the Pantanal of Mato Grosso and Mato Grosso do Sul, and its relation to national and international wetland classification systems, in The Pantanal: Ecology, Biodiversity, and Sustainable Management of a Large Neotropical Seasonal Wetland, Junk, W.J., da Silva, C.J., Cunha, N.C., and Wantzen, K.M., Eds., Moscow: Pensoft, 2009, pp. 127–141.Google Scholar
  14. Dewoody, J.A., and Avise, J, Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals, J. Fish. Biol., 2000, vol. 56, no. 3, pp. 461–473.Google Scholar
  15. Esguícero, A.L.H., and Arcifa, M.S, Fragmentation of a Neotropical migratory fish population by a century-old dam, Hydrobiologia, 2010, vol. 638, no. 1, pp. 41–53.CrossRefGoogle Scholar
  16. Evanno, G., Regnaut, S., and Goudet, J, Detecting the number of clusters of individuals using the software structure: a simulation study, Mol. Ecol., 2005, vol. 14, no. 8, pp. 2611–2620.CrossRefPubMedGoogle Scholar
  17. Excoffier, L. and Lischer, H.E.L, Arlequin suite, ver. 3.5: a new series of programs to perform population genetics analyses under Linux and Windows, Mol. Ecol. Resour., 2010, vol. 10, no. 3, pp. 564–567.PubMedGoogle Scholar
  18. Excoffier. L., Smouse, P.E., and Quattro, J.M, Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data, Genetics, 1992, vol. 131, no. 2, pp. 479–491.PubMedPubMedCentralGoogle Scholar
  19. Gaggiotti, O.E., Lange, O., Rassmann, K., and Gliddon, C., A comparison of two indirect methods for estimating average levels of gene flow using microsatellite data, Mol. Ecol., 1999, vol. 8, no. 9, pp. 1513–1520.CrossRefPubMedGoogle Scholar
  20. Gerlach, G., Jueterbock, A., Kraemer, P., Deppermann, J., and Harmand, P., Calculations of population differentiation based on GST and D: forget GST but not all of statistics! Mol. Ecol., 2010, vol. 19, no. 18, pp. 3845–3852.CrossRefPubMedGoogle Scholar
  21. Goudet, J., FSTAT: a computer program to calculate F-statistics (version, J. Hered., 2002, vol. 86, no. 6, pp. 485–486.CrossRefGoogle Scholar
  22. Guo, S.W. and Thompson, E.A, Performing the exact test of Hardy–Weinberg proportions for multiple alleles, Biometrics, 1992, vol. 48, no. 2, pp. 361–372.CrossRefPubMedGoogle Scholar
  23. Iervolino, F., de Resende, E.K., and Hilsdorf, A.W.S, The lack of genetic differentiation of pacu (Piaractus mesopotamicus) populations in the Upper-Paraguay Basin revealed by the mitochondrial DNA D-loop region: implications for fishery management, Fish. Res., 2010, vol. 101, nos. 1–2, pp. 27–31.CrossRefGoogle Scholar
  24. Jost, L., GST and its relatives do not measure differentiation, Mol. Ecol., 2008, vol. 17, no. 18, pp. 4015–4026.CrossRefPubMedGoogle Scholar
  25. Junk, W.J, The flood pulse concept of large rivers: learning from the tropics, Arch. Hydrobiol., Suppl., Large Rivers, 1999, vol. 11, no. 3, pp. 261–280.Google Scholar
  26. Junk, W.J. and Cunha, C.N, Pantanal: a large South American wetland at a crossroads, Ecol. Eng., 2005, vol. 24, no. pp. 391–401.CrossRefGoogle Scholar
  27. Kalinowski, S.T., HW-QUICKCHECK: an easy-to-use computer program for checking genotypes for agreement with Hardy-Weinberg expectations, Mol. Ecol. Notes, 2006, vol. 6, no. 4, pp. 974–979.CrossRefGoogle Scholar
  28. Loverde-Oliveira, S.M., Huszar, V.L., and Fantin-Cruz, I, Implications of the flood pulse on morphometry of a Pantanal lake (Mato Grosso state, Central Brazil), Acta Limnol. Bras., 2007, vol. 19, no. 4, pp. 453–461.Google Scholar
  29. Mantel, N, The detection of disease clustering and generalized regression approach, Cancer Res., 1967, vol. 27, no. 2, pp. 209–220.PubMedGoogle Scholar
  30. Maruyama, T. and Kimura, M, Some methods for treating continuous stochastic processes in population genetics, Jpn. J. Genet., 1971, vol. 46, no. 6, pp. 407–410.CrossRefGoogle Scholar
  31. Mateus, L.A. and Estupiñan, G.M, Fish stock assessment of piraputanga Brycon microlepis in the Cuiabá River basin, Pantanal of Mato Grosso, Brazil, Braz. J. Biol., 2002, vol. 62, no. 1, pp. 165–170.CrossRefGoogle Scholar
  32. Meirman, P.G., AMOVA-based clustering of population genetic data, J. Hered., 2012, vol. 103, no. 5, pp. 744–750.CrossRefGoogle Scholar
  33. Mills, L.S. and Allendorf, F.W, The one-migrant–per–generation rule in conservation and management, Conserv. Biol., 1996, vol. 10, no. 6, pp. 1509–1518.CrossRefGoogle Scholar
  34. Mitton, J.B. and Lewis, W.M, Relationships between genetic variability and life-history features of bony fishes, Evolution, 1989, vol. 43, no. 8, pp. 1712–1723.CrossRefPubMedGoogle Scholar
  35. Moritz, C, Defining ‘Evolutionarily Significant Units’ for conservation, Trends Ecol. Evol., 1994, vol. 9, no. 10, pp. 373–375.CrossRefPubMedGoogle Scholar
  36. Nei, M., Molecular Evolutionary Genetics, New York: Columbia Univ. Press, 1987.Google Scholar
  37. Pereira, L.H., Foresti, F., and Oliveira, C, Genetic structure of the migratory catfish Pseudoplatystoma corruscans (Siluriformes: Pimelodidae) suggests homing behavior, Ecol. Freshwater Fish, 2009 vol. 18, no. 2, pp. 215–225.CrossRefGoogle Scholar
  38. Pritchard, J.K., Stephens, M., and Donnelly, P, Inference of population structure using multilocus genotype data, Genetics, 2000, vol. 155, no. 2, pp. 945–959.PubMedPubMedCentralGoogle Scholar
  39. Porsani, J.L., Assine, M.L., and Moutinho, L, Application of GPR in the study of a modern alluvial megafan: the case of the Taquari River in Pantanal Wetland, west-central Brazil, Subsurf. Sens. Technol. Appl., 2005, vol. 6, no. 2, pp. 219–233.CrossRefGoogle Scholar
  40. Resende, E.K., Paraguay-Paraná Basin: excluding the Upper Paraná Basin, in Migratory Fishes of South America: Biology, Fisheries, and Conservation Status, Carolsfeld, J., Harvey, B., Ross, C., and Baer, A., Eds., Canada: World Fish. Trust, 2003, pp. 103–151.Google Scholar
  41. Reys, P., Sabino, J., and Galetti, M, Frugivory by the fish Brycon hilarii (Characidae) in western Brazil, Acta Oecol., 2009, vol. 35, no. 1, pp. 136–141.CrossRefGoogle Scholar
  42. Rice, W.R, Analyzing tables of statistical tests, Evolution, 1989, vol. 43, no. 1, pp. 223–225.CrossRefPubMedGoogle Scholar
  43. Rousset, F., Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux, Mol. Ecol. Resour., 2008, vol. 8, no. 1, pp. 103–106.CrossRefPubMedGoogle Scholar
  44. Sabino, J. and Andrade, L.P, Uso e conservação da ictiofauna no ecoturismo da região de bonito, Mato Grosso do Sul: o mito da sustentabilidade ecológica no rio baía bonita (aquário natural de bonito), Biota Neotrop., 2003, vol. 3, no. 2, pp. 1–9.Google Scholar
  45. Sanches, A. and Galetti, P.M, Microsatellites loci isolated in the freshwater fish Brycon hilarii, Mol. Ecol Notes, 2006, vol. 6, no. 4, pp. 1045–1046.CrossRefGoogle Scholar
  46. Sanches, A. and Galetti, P.M, Population genetic structure revealed by a school of the freshwater migratory fish, Brycon hilarii, Lat. Am. J. Aquat. Res., 2012, vol. 40, no. 2, pp. 408–417.CrossRefGoogle Scholar
  47. Santos, M.C., Ruffino, M.L., and Farias, I.P, High levels of genetic variability and panmixia of the tambaqui Colossoma macropomum (Cuvier, 1816) in the main channel of the Amazon River, J. Fish Biol., 2007, vol. 71, suppl., pp. 33–44.Google Scholar
  48. Schuelke, M, An economic method for the fluorescent labeling of PCR fragments, Nat. Biotechnol., 2000, vol. 18, no. 2, pp. 233–234.CrossRefPubMedGoogle Scholar
  49. Schwartz, C.E, Estimating the dimension of a model, Ann. Stat., 1978, vol. 6, no. 2, pp. 461–464.CrossRefGoogle Scholar
  50. Slatkin, M, Gene flow in natural populations, Ann. Rev. Ecol. Syst., 1985, vol. 16, pp. 393–430.CrossRefGoogle Scholar
  51. van Oosterhout, C., Hutchinson, W.F., Wills, D.P., and Shipley, P., MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data, Mol. Ecol. Notes, 2004, vol. 4, no. 3, pp. 535–538.Google Scholar
  52. Walsh, M.R., Munch, S.B., Chiba, S., and Conover, D.O, Maladaptive changes in multiple traits caused by fishing: impediments to population recovery, Ecol. Lett., 2006, vol. 9, no. 2, pp. 142–148.CrossRefPubMedGoogle Scholar
  53. Waples, R.S, Separating the wheat from the chaff: patterns of genetic differentiation in high gene flow species, J. Hered., 1998, vol. 89, no. 5, pp. 438–450.CrossRefGoogle Scholar
  54. Waples, R.S, Genetic approaches to the management of Pacific salmon, Fisheries, 1990, vol. 15, no. 5, pp. 19–25.CrossRefGoogle Scholar
  55. Weir, B.S. and Cockerham, C.C, Estimating F-statistics for the analysis of population structure, Evolution, 1984, vol. 38, no. 6, pp. 1358–1370.PubMedGoogle Scholar
  56. Wilkinson, M.J., Marshall, L.G., and Lundberg, J.G, River behavior on megafans and potential influences on diversification and distribution of aquatic organisms, J. S. Am. Earth Sci., 2006, vol. 21, nos. 1–2, pp. 151–172.CrossRefGoogle Scholar
  57. Wright, S., Evolution and the Genetics of Population, Vol. 4: Variability Within and Among Natural Populations, Chicago: Univ. of Chicago Press, 1978.Google Scholar
  58. Yamamoto, S., Morita, K., Koizumi, I., and Maekawa, K, Genetic differentiation of white-spotted charr (Salvelinus leucomaenis) populations after habitat fragmentation: spatial-temporal changes in gene frequencies, Conserv. Genet., 2004, vol 5, no. 4, pp. 529–538.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • T. I. Okazaki
    • 1
  • E. M. Hallerman
    • 2
  • E. K. de Resende
    • 3
  • A. W. S. Hilsdorf
    • 1
  1. 1.University of Mogi das CruzesUnit of BiotechnologyMogi das Cruzes, SPBrasil
  2. 2.Department of Fish and Wildlife ConservationVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  3. 3.EMBRAPA PantanalCaixa Postal 109Corumbá, MSBrazil

Personalised recommendations