Conservation Genetics

, Volume 15, Issue 6, pp 1417–1431 | Cite as

Historical and contemporary forces shape genetic variation in the Olympic mudminnow (Novumbra hubbsi), an endemic fish from Washington State, USA

  • Patrick W. DeHaanEmail author
  • Brice A. Adams
  • Roger A. Tabor
  • Denise K. Hawkins
  • Brad Thompson
Research Article


Genetic data have become increasingly useful for conservation planning when data regarding population status and long-term viability is limited. The Olympic mudminnow is the only fish species endemic to Washington State, USA. The species is an increasing priority for conservation given its limited distribution and increasing habitat loss. Presently, information important for developing conservation plans including population abundance data, knowledge of population boundaries, and estimates of gene flow among populations are limited. We used microsatellite markers to assess the level of genetic variation within and among Olympic mudminnow collections from 23 sites across the species range. Genetic variation within collections ranged widely and was greatest within the Chehalis River Basin, a former glacial refugium. Analysis of population boundaries showed that each collection site represented a unique population with the exception of collections made within two large wetland and stream complexes. Genetic variation among populations appears to be strongly influenced by glacial history and the species’ life history. Populations originating from the Chehalis River glacial refugium clustered together in multiple analyses and populations from the Olympic Coast, which persisted in separate refugia and have limited capacity for dispersal, showed a high level of differentiation. Competing theories existed regarding the origins of disjunct populations in east Puget Sound and genetic data showed that these populations represent undocumented introductions rather than a glacial remnant or historic colonization from the Chehalis refugium. Data presented in this study will help fill important information gaps and advance conservation planning for this species.


Olympic mudminnow Microsatellites Genetic variation Historic isolation Introduced populations 



Funding for this study was provided by the U.S. Fish and Wildlife Service, Washington Fish and Wildlife Office. We would like to thank the following individuals for assisting with genetic sample collections: Molly Hallock (WDFW), Teal Waterstrat (USFWS), Kira Mazzi (USFWS), Dan Lantz (USFWS), Larry Gilbertson (Quinault Tribe), Pat Trotter (retired), Dan Spencer (USFWS), Hans Berge (King County), Pat Crain (NPS), and John Trobaugh (WDNR). We would also like to thank Teal Waterstrat for producing the study map, Pat Crain for sharing information regarding the history of the James Pond population, Jeanelle Miller and Molly Hallock for sharing information regarding the origins of Olympic mudminnow in east Puget Sound, and Patty Crandell, Christian Smith, and two anonymous reviewers for providing helpful comments on earlier versions of this manuscript. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the U.S. Fish and Wildlife Service.

Supplementary material

10592_2014_627_MOESM1_ESM.pdf (875 kb)
Supplementary material 1 (PDF 875 kb)


  1. Adams B, DeHaan PW, Tabor R, Thompson B, Hawkins DK (2013) Characterization of tetranucleotide microsatellite loci for Olympic mudminnow (Novumbra hubbsi). Conserv Genet Res 5(2):573–575CrossRefGoogle Scholar
  2. Allendorf FW, Luikart GK (2007) Conservation and the genetics of populations, 1st edn. Blackwell Publishing, OxfordGoogle Scholar
  3. Ardren WR, DeHaan PW, Smith CT, Taylor EB, Leary R, Kozfkay CC, Godfrey L, Diggs M, Fredenberg W, Chan J, Kilpatrick CW, Small MP, Hawkins DK (2011) Genetic structure, evolutionary history, and conservation units of bull trout in the coterminous United States. Trans Am Fish Soc 140(2):506–525CrossRefGoogle Scholar
  4. Bernatchez L, Wilson CC (1998) Comparative phylogeography of nearctic and palearctic fishes. Mol Ecol 7(4):431–452CrossRefGoogle Scholar
  5. Brinkman TJ, Person DK, Chapin FS III, Smith W, Hundertmark KJ (2011) Estimating abundance of Sitka black-tailed deer using DNA from fecal pellets. J Wildl Manag 75(1):232–242CrossRefGoogle Scholar
  6. Cavalli-Sforza LL, Edwards AWF (1967) Phylogentic analysis: models and estimation procedures. Evolution 21:550–570CrossRefGoogle Scholar
  7. Charlier J, Laikre L, Ryman N (2012) Genetic monitoring reveals temporal stability over 30 years in a small, lake-resident brown trout population. Heredity 109(4):246–253PubMedCentralPubMedCrossRefGoogle Scholar
  8. Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144(4):2001–2014PubMedCentralPubMedGoogle Scholar
  9. Costello AB, Down TE, Pollard SM, Pacas CJ, Taylor EB (2003) The influence of history and contemporary stream hydrology on the evolution of genetic diversity within species: an examination of microsatellite DNA variation in bull trout, Salvelinus confluentus (Pisces: Salmonidae). Evolution 57(2):328–344PubMedCrossRefGoogle Scholar
  10. Currens KP, Schreck CB, Li HW (2009) Evolutionary ecology of redband trout. Trans Am Fish Soc 138(4):797–817CrossRefGoogle Scholar
  11. Development Core Team R (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  12. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131(2):479–491PubMedCentralPubMedGoogle Scholar
  13. Excoffier L, Laval G, Schneider S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform 1:47–50Google Scholar
  14. Felsenstein J (1993) PHYLIP: phylogeny inference package. (version 3.5c). Accessed June 2009
  15. Fraser DJ, Bernatchez L (2001) Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Mol Ecol 10(12):2741–2752PubMedCrossRefGoogle Scholar
  16. Garza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10(2):305–318PubMedCrossRefGoogle Scholar
  17. George AL, Kuhajda BR, Williams JD, Cantrell MA, Rakes PL, Shute JR (2009) Guidelines for propagation and translocation for freshwater fish conservation. Fisheries 34(11):529–545CrossRefGoogle Scholar
  18. Glaubitz JC (2004) CONVERT: a user-friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Mol Ecol Notes 4(2):309–310CrossRefGoogle Scholar
  19. Gomez-Uchida D, Knight TW, Ruzzante DE (2009) Interaction of landscape and life history attributes on genetic diversity, neutral divergence and gene flow in a pristine community of salmonids. Mol Ecol 18(23):4854–4869PubMedCrossRefGoogle Scholar
  20. Goudet J (2001) FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Accessed June 2009
  21. Hardy OJ, Vekemans X (2002) SPAGEDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2(4):618–620CrossRefGoogle Scholar
  22. Hardy OJ, Charbonnel N, Freville H, Heuertz M (2003) Microsatellite allele sizes: a simple test to assess their significance on genetic differentiation. Genetics 163(4):1467–1482PubMedCentralPubMedGoogle Scholar
  23. Harris CK (1974) The geographical distribution and habitat of the Olympic mudminnow, Novumbra hubbsi. Thesis, University of Washington, SchultzGoogle Scholar
  24. Homola JJ, Scribner KT, Elliott RF, Donofrio MC, Kanefsky J, Smith KM, McNair JN (2012) Genetically derived estimates of contemporary natural straying rates and historical gene flow among Lake Michigan lake sturgeon populations. Trans Am Fish Soc 141(5):1374–1388CrossRefGoogle Scholar
  25. Jarne P, Lagoda PJL (1996) Microsatellites, from molecules to populations and back. Trends Ecol Evol 11(10):424–429PubMedCrossRefGoogle Scholar
  26. Jombart T (2008) Adegenet: a R package for the multivariate analysis of genetic markers. Bioinform 24(11):1403–1405CrossRefGoogle Scholar
  27. Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics 11Google Scholar
  28. Kalinowski ST (2005) HP-RARE 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5(1):187–189CrossRefGoogle Scholar
  29. Kendall KC, Stetz JB, Roon DA, Waits LP, Boulanger JB, Paetkau D (2008) Grizzly bear density in Glacier National Park. Montana. J Wildl Manag 72(8):1693–1705CrossRefGoogle Scholar
  30. Lesica P, Allendorf FW (1995) When are peripheral populations valuable for conservation? Conserv Biol 9(4):753–760CrossRefGoogle Scholar
  31. Lewis PO, Zaykin D (2001) Genetic Data Analysis: Computer program for the analysis of allelic data (version 1.0). (accessed June 2009)
  32. McPhail JD (1967) Distribution of freshwater fishes in western Washington. Northwest Sci 41(1):1–11Google Scholar
  33. McPhail JD, Lindsey CC (1986) Zoogeography of the freshwater fishes of Cascadia (the Columbia system and rivers north to the Stikine). In: Hocutt CH, Wiley EO (eds) The zoogeogrpahy of North American freshwater fishes. Wiley, New York, pp 615–638Google Scholar
  34. Meldgaard T, Nielsen EE, Loeschcke V (2003) Fragmentation by weirs in a riverine system: a study of genetic variation in time and space among populations of European grayling (Thymallus thymallus) in a Danish river system. Conserv Genet 4(6):735–747CrossRefGoogle Scholar
  35. Meldrim JW (1968) The ecological zoogeography of the Olympic mudminnow (Novumbra hubbsi, Schultz 1929). Dissertation, University of WashingtonGoogle Scholar
  36. Metcalf JL, Love Stowell S, Kennedy CM, Rogers KB, McDonald D, Epp J, Keepers K, Cooper A, Austin JJ, Martin AP (2012) Historical stocking data and 19th century DNA reveal human-induced changes to native diversity and distribution of cutthroat trout. Mol Ecol 21(21):5194–5207PubMedCrossRefGoogle Scholar
  37. Mock KE, Latch EK, Rhodes OE (2004) Assessing losses of genetic diversity due to translocation: long-term case histories in Merriam’s turkey (Meleagris gallopavo merriami). Conserv Genet 5(5):631–645CrossRefGoogle Scholar
  38. Mongillo P, Hallock M (1999) Washington state status report for the Olympic mudminnow. Washington Department of Fish and Wildlife, OlympiaGoogle Scholar
  39. Nielsen JL (ed) (1995) Evolution and the aquatic ecosystem: defining unique units in population conservation. American Fisheries Society Symposium 17, Bethesda, MarylandGoogle Scholar
  40. Osborne MJ, Davenport SR, Hoagstrom CW, Turner TF (2010) Genetic effective size, N-e, tracks density in a small freshwater cyprinid, Pecos bluntnose shiner (Notropis simus pecosensis). Mol Ecol 19(14):2832–2844PubMedCrossRefGoogle Scholar
  41. Osborne MJ, Carson EW, Turner TF (2012a) Genetic monitoring and complex population dynamics: insights from a 12-year study of the Rio Grande silvery minnow. Evol Appl 5(6):553–574PubMedCentralPubMedCrossRefGoogle Scholar
  42. Osborne M, Sharp A, Monzingo J, Propst DL, Turner TF (2012b) Genetic analysis suggests high conservation value of peripheral populations of Chihuahau chub (Gila nigrescens). Conserv Genet 13(5):1317–1328CrossRefGoogle Scholar
  43. Peterson DP, Ardren WR (2009) Ancestry, population structure, and conservation genetics of Arctic grayling (Thymallus arcticus) in the upper Missouri River, USA. Can J Fish Aquat Sci 66(10):1758–1774CrossRefGoogle Scholar
  44. Piry S, Luikart G, Cornuet JM (1999) BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J Hered 90(4):502–503CrossRefGoogle Scholar
  45. Quattro JM, Vrijenhoek RC (1989) Fitness differences among remnant populations of the endangered Sonoran topminnow. Science 245(4921):976–978PubMedCrossRefGoogle Scholar
  46. Raymond M, Rousset F (1995) GENEPOP (Version-1.2)—population-genetics software for exact tests and ecumenicism. J Hered 86(3):248–249Google Scholar
  47. Reed DH, Frankham R (2003) Correlation between fitness and genetic diversity. Cons Biol 17(1):230–237CrossRefGoogle Scholar
  48. Rice WR (1989) Analyzing tables of statistical tests. Evol 43(1):223–225CrossRefGoogle Scholar
  49. Rosenfeld MJ (1983) Geographic variation in Novumbra hubbsi Schultz 1929 (Pisces: Umbridae): external meristic characters, chromosomal state and nuclear DNA content. University of British Columbia, ThesisGoogle Scholar
  50. Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33PubMedCrossRefGoogle Scholar
  51. Slatkin M (1985) Rare alleles as indicators of gene flow. Evol 39(1):53–65CrossRefGoogle Scholar
  52. Small MP, Frye AE, Von Bargen JF, Young SF (2006) Genetic structure of chum salmon (Oncorhynchus keta) populations in the lower Columbia River: are chum salmon in Cascade tributaries remnant populations? Conserv Genet 7(1):65–78CrossRefGoogle Scholar
  53. Small MP, Burgess D, Dean C, Warheit KI (2011) Does lower crab creek in the eastern Washington desert have a native population of Chinook salmon? Trans Am Fish Soc 140(3):808–821CrossRefGoogle Scholar
  54. Stamford MD, Taylor EB (2004) Phylogeographical lineages of Arctic grayling (Thymallus arcticus) in North America: divergence, origins and affinities with Eurasian Thymallus. Mol Ecol 13(6):1533–1549PubMedCrossRefGoogle Scholar
  55. Stephen CL, Whittaker DG, Gillis D, Cox LL, Rhodes OE (2005) Genetic consequences of reintroductions: an example from Oregon prong horn antelope (Antilocapra americana). J Wildl Manag 69(4):1463–1474CrossRefGoogle Scholar
  56. Strugnell JM, Watts PC, Smith PJ, Allcock AL (2012) Persistent genetic signatures of historic climatic events in an Antarctic octopus. Mol Ecol 21(11):2775–2787PubMedCrossRefGoogle Scholar
  57. Tabor RW (1975) Guide to the geology of Olympic National Park. University of Washington Press, SeattleGoogle Scholar
  58. Taylor EB, Gow JL, Witt J, Zemlak R (2011) Connectivity among populations of pygmy whitefish (Prosopium coulterii) in northwestern North America inferred from microsatellite DNA analyses. Can J Zool 89(4):255–266CrossRefGoogle Scholar
  59. Tilston Smith B, Escalante P, Hernandez Banos B, Navarro-Siguenza A, Rohwer S, Klicka J (2011) The role of historical and contemporary processes on phylogeographic structure and genetic diversity in the Northern Cardinal, Cardinalis cardinalis. BMC Evol Biol 11(1):136CrossRefGoogle Scholar
  60. Trotter PC, McMillan B, Kappes D (2000) Occurrence of the Olympic mudminnow on the east side of the Puget Trough. Northwestern Nat 81(2):59–63CrossRefGoogle Scholar
  61. Waples RS (1995) Evolutionary significant units and the conservation of biological diversity under the endangered species act. In: Nielsen JL (ed) Evolution and the aquatic ecosystem: defining unique units in population conservation. American Fisheries Society Symposium 17, Bethesda, Maryland, pp 8–27Google Scholar
  62. Whiteley AR, Spruell P, Allendorf FW (2004) Ecological and life history characteristics predict population genetic divergence of two salmonids in the same landscape. Mol Ecol 13(12):3675–3688PubMedCrossRefGoogle Scholar
  63. Williamson-Natesan EG (2005) Comparison of methods for detecting bottlenecks from microsatellite loci. Conserv Genet 6(4):551–562CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2014

Authors and Affiliations

  • Patrick W. DeHaan
    • 1
    Email author
  • Brice A. Adams
    • 1
  • Roger A. Tabor
    • 2
  • Denise K. Hawkins
    • 1
    • 3
  • Brad Thompson
    • 2
  1. 1.U.S. Fish and Wildlife ServiceAbernathy Fish Technology CenterLongviewUSA
  2. 2.U.S. Fish and Wildlife ServiceWashington Fish and Wildlife OfficeLaceyUSA
  3. 3.U.S. Fish and Wildlife ServiceWashington Fish and Wildlife OfficeLaceyUSA

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