Conservation Genetics

, Volume 19, Issue 6, pp 1367–1378 | Cite as

Spatial genetic variation and habitat association of Rhinichthys cataractae, the longnose dace, in the Driftless Area of the upper Mississippi River basin

  • Anna C. Wieman
  • Peter B. BerendzenEmail author
Research Article


The Driftless Area of the upper Mississippi River drainage is a unique geographic region because of its complex geological history and the influence of recent, intensive human activities. The longnose dace, Rhinichthys cataractae, is a relatively common, small freshwater fish that is distributed in swift, cool streams within the region. The aim of this study was to determine the spatial genetic differentiation of the longnose dace and define the broad scale environmental variables that shape the distribution of the species in the southwestern portion of the Driftless Area. Genotypic data from seven microsatellite loci were analyzed for 276 individuals from 15 localities representing major drainages within the region in northeast Iowa. Broad scale environmental variables including hydrologic, soil, and climatic factors were evaluated to construct an ecological niche model (ENM) to predict the suitability of habitat for the species within the region. Results of the genetic analyses revealed two distinct, but somewhat admixed genetic clusters of longnose dace in Iowa. The genetic differentiation between localities and between drainages was low to moderate with some evidence of isolation by distance. Most of the variation was observed by differences between individuals within local populations. The ENM generated largely reflected the known distribution of the species in Iowa with a decreasing probability of suitable habitat from northern to southern drainages. Geologic factors played a key role in the model. The distribution and population structure of the longnose dace in the northeast Iowa revealed that isolation by distance, historical processes and the underlying geology are primarily responsible for the observed spatial distribution of genetic variation.


Last glacial maximum Population connectivity Ecological niche model Cyprinidae Indicator species 



We thank Courtney Calhoun, Megan Merner, Haley Rinehart, Erica Scullin, and Janelle Woodin for their assistance with field collection and laboratory work. We also thank John DeGroote and UNI GeoTREE for help with GIS data and mapping. Partial funding for this project was provided through the State Wildlife Grants Program (SWG Grant# T-53-R-1) in cooperation with the U.S. Fish and Wildlife Service, Wildlife and Sport Fish Restoration Program and the Iowa Department of Natural Resources. Funding was also provided by the Environmental Science Graduate Program, University of Northern Iowa.

Supplementary material

10592_2018_1106_MOESM1_ESM.docx (54 kb)
Supplementary material 1 (DOCX 54 KB)


  1. Aadland LP (1993) Stream habitat types: their fish assemblages and relationships to flow. N Am J Fish Manag 13:790–806CrossRefGoogle Scholar
  2. Anderson WI (1998) Iowa’s geological past: three billion years of change. University of Iowa Press, Iowa CityCrossRefGoogle Scholar
  3. Ardren WR, Miller LM, Kime JA, Kvitrud MA (2002) Microsatellite loci for fathead minnow (Pimephales promelas). Mol Ecol Resour 2:226–227CrossRefGoogle Scholar
  4. Bartnik VG (1970) Reproductive isolation between two sympatric dace, Rhinichthys atratulus and R. cataractae, in Manitoba. J Fish Res Board Can 27:2125–2141CrossRefGoogle Scholar
  5. Bartnik VG (1972) Comparison of the breeding habits of two subspecies of longnose dace (Rhinichthys cataractae). Can J Zool 50:83–86CrossRefGoogle Scholar
  6. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188CrossRefGoogle Scholar
  7. Bernatchez L, Wilson CC (1998) Comparative phylogeography of Nearctic and Palearctic fishes. Mol Ecol 7:431–452CrossRefGoogle Scholar
  8. Bohonak AJ (2002) IBD (isolation by distance): a program for analyses of isolation by distance. J Heredity 93:153–154CrossRefGoogle Scholar
  9. Boutin-Ganache I, Raposo M, Raymond M, Deschepper CF (2001) M13-tailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods. Biotechniques 31:24–26CrossRefGoogle Scholar
  10. Butchart SHM, Walpole M, Collen B, van Strien A, Scharlemann JPW, Almond REA et al (2010) Global biodiversity: indicators of recent declines. Science 328:1164–1168CrossRefGoogle Scholar
  11. Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014PubMedPubMedCentralGoogle Scholar
  12. Davis DJ, Wieman AC, Berendzen PB (2014) The influence of historical and contemporary landscape variables on the spatial genetic structure of the rainbow darter (Etheostoma caeruleum) in tributaries of the upper Mississippi River. Conserv Genet 16:167–179CrossRefGoogle Scholar
  13. Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB (1994) Mutational processes of simple-sequence repeat loci in human populations. Proc Nat Acad Sci USA 91:3166–3170CrossRefGoogle Scholar
  14. Dimsoski P, Toth GP, Bagley MJ (2000) Microsatellite characterization in central stoneroller Campostoma anomalum (Pisces: Cyprinidae). Mol Ecol 9:2187–2189CrossRefGoogle Scholar
  15. Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ et al (2006) Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev 81:163–182CrossRefGoogle Scholar
  16. Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361CrossRefGoogle Scholar
  17. Edwards EA, Li H, Schreck CB (1983) Habitat suitability index models: longnose dace (FWS/OBS – 82/10.33). Fish and Wildlife Service Fort Collins Co Western Energy and Land Use TeamGoogle Scholar
  18. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620CrossRefGoogle Scholar
  19. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetic analyses under Linux and Windows. Mol Ecol Resour 10:564–567CrossRefGoogle Scholar
  20. 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 131:479–491PubMedPubMedCentralGoogle Scholar
  21. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedPubMedCentralGoogle Scholar
  22. François O, Durand E (2010) Spatially explicit Bayesian clustering models in population genetics. Mol Ecol Resour 10:773–784CrossRefGoogle Scholar
  23. Garza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10:305–318CrossRefGoogle Scholar
  24. Girard P, Angers B (2006) Characterization of microsatellite loci in longnose dace (Rhinichthys cataractae) and interspecific amplification in five other Leuciscinae species. Mol Ecol Notes 6:69–71CrossRefGoogle Scholar
  25. Gleick PH (1996) Water resource. In: Schneider SH (ed) Encyclopedia of climate and weather. Oxford University Press, New York, pp 817–823Google Scholar
  26. Hallberg GR, Bettis EA, Prior JC (1984) Geologic overview of the Paleozoic Plateau region of northeastern Iowa. Proc Iowa Acad Sci 91:5–11Google Scholar
  27. Heitke JD, Pierce CL, Gelwicks GT, Simmons GA, Siegwarth GL (2006) Habitat, land use, and fish assemblage relationships in Iowa streams: preliminary assessment in an agricultural landscape. Am Fish Soc Symp 48:287–303Google Scholar
  28. Hill J, Grossman GD (1987) Home range estimates for three North American stream fishes. Copeia 1987:376–380CrossRefGoogle Scholar
  29. Hobbs H (1999) Origin of the driftless area by subglacial drainage—a new hypothesis. Geol Soc Am Spec Pap 337:93–102Google Scholar
  30. Howe RW (1984) Zoogeography of Iowa’s Paleozoic Plateau. Proc Iowa Acad Sci 91:32–36Google Scholar
  31. Hughes JM, Schmidt DJ, Finn DS (2009) Genes in streams: using DNA to understand the movement of freshwater fauna and their riverine habitat. BioScience 59:573–583CrossRefGoogle Scholar
  32. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806CrossRefGoogle Scholar
  33. Jensen JL, Bohanak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genet 6:13CrossRefGoogle Scholar
  34. Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026CrossRefGoogle Scholar
  35. Jost L (2009) D vs. GST: response to and. Mol Ecol 18:2088–2091CrossRefGoogle Scholar
  36. Kalinowski ST (2004) Counting alleles with rarefaction: private alleles and hierarchical sampling design. Conserv Genet 5:539–543CrossRefGoogle Scholar
  37. Kalinowski ST (2005) HP-Rare 1.0: a computer program for performing rarfaction on measures of alleleic richness. Mol Ecol Resour 5:187–189CrossRefGoogle Scholar
  38. Kim D, Conway KW (2014) Phylogeography of Rhinichthys cataractae (Teleostei: Cyprinidae): pre-glacial colonization across the continental divide and Pleistocene diversification within the Rio Grande drainage. Biol J Linnean Soc 111:317–333CrossRefGoogle Scholar
  39. Knox JC (2001) Agricultural influence on landscape sensitivity in the Upper Mississippi River Valley. Catena 42:193–224CrossRefGoogle Scholar
  40. Lee DS, Gilbert C, Hocutt C, Jenkins R, McAllister DE, Stauffer JJR (1980) Atlas of North American freshwater fishes. North Carolina State Museum of Natural History, RaleighGoogle Scholar
  41. Lyons J, Stewart JS, Mitro M (2010) Predicted effects of climate warming on the distribution of 50 stream fishes in Wisconsin, U.S.A. J Fish Biol 77:1867–1898CrossRefGoogle Scholar
  42. May RM, Lawton JH, Stork NE (1995) Assessing extinction rates. In: Lawton JE, May RM (eds) Extinction rates. Oxford University Press, New York, pp 1–24Google Scholar
  43. Meador MR, Goldstein RM (2003) Assessing water quality at large geographic scales” relations among land use, water physicochemistry, riparian conditions, and fish community structure. Environ Manag 31:504–517CrossRefGoogle Scholar
  44. Menzel BW (1983) Agricultural management practices and the integrity of in-stream biological habitat. In: Schaller FW, Bailey GW (eds) Agricultural management and water quality. Iowa State University Press, Ames, pp 305–329Google Scholar
  45. Morin PA, Leduc RG, Archer FI, Martien KK, Huebinger R, Bickham JW, Taylor BL (2009) Significant deviations from Hardy-Weinberg equilibrium caused by low levels of microsatellite genotyping errors. Mol Ecol Resour 9:498–504CrossRefGoogle Scholar
  46. Narum SR (2006) Beyond Bonferroni: less conservative analyses for conservation genetics. Conserv Genet 7:783–787CrossRefGoogle Scholar
  47. Nerbonne BA, Vondracek B (2001) Effects of local land use on physical habitat, benthic macroinvertebrate, and fish in the Whitewater River, Minnesota, USA. Environ Manag 28:87–99CrossRefGoogle Scholar
  48. Nunziata SO, Lance SL, Jones KL, Nerkowski SA, Metcalf AE (2013) Development and characterization of twenty-three microsatellite markers for the freshwater minnow Santa Ana speckled dace (Rhinichthys osculus spp., Cyprinidae) using paired-end Illumina shotgun sequencing. Conserv Genet Resour 5:145–148CrossRefGoogle Scholar
  49. Peakall R, Smouse PE (2006) GenAlEx 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Resour 6:288–295CrossRefGoogle Scholar
  50. Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28:2537–2539CrossRefGoogle Scholar
  51. Phillips SJ, Dudik M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175CrossRefGoogle Scholar
  52. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  53. Pimm SL, Russell GJ, Gittleman JL, Brooks TM (1995) The future of biodiversity. Science 269:347–350CrossRefGoogle Scholar
  54. Prior JC (1991) Landform regions of Iowa. University of Iowa Press, Iowa CityGoogle Scholar
  55. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  56. Rambaut A, Suchard MA, Xie D, Drummond AJ (2014) Tracer v1.6.
  57. Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure. Mol Ecol Resour 4:137–138CrossRefGoogle Scholar
  58. Rousset F (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145:1219–1228PubMedPubMedCentralGoogle Scholar
  59. Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8:103–106CrossRefGoogle Scholar
  60. Rowe DC, Pierce CL, Wilton TF (2009) Fish assemblage relationships with physical habitat in wadeable Iowa streams. N Am J Fish Manag 29:1314–1332CrossRefGoogle Scholar
  61. Sala OE, Chapin ES, Armesto JJ, Berlow E, Bloomfield J, Dirzo R et al (2000) Global biodiversity scenarios for the year 2100. Science 287:1770–1774CrossRefGoogle Scholar
  62. Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18:233–234CrossRefGoogle Scholar
  63. Scott WB, Crossman EJ (1973) Freshwater fishes of Canada. Fisheries Research Board of Canada, OttawaGoogle Scholar
  64. Sindt AR, Pierce CL, Quist MC (2012) Fish species of greatest conservation need in wadeable Iowa streams: current status and effectiveness of Aquatic Gap Program distribution models. N Am J Fish Manag 32:135–146CrossRefGoogle Scholar
  65. Skalski GT, Grose MJ (2006) Characterization of microsatellite loci in the creek chub (Semotilus atromaculatus). Mol Ecol Resour 6:1240–1242CrossRefGoogle Scholar
  66. Turner TF, Dowling TE, Broughton RE, Gold JR (2004) Variable microsatellite markers amplify across divergent lineages of cyprinid fishes (subfamily Leusicinae). Conserv Genet 5:279–281CrossRefGoogle Scholar
  67. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Resour 4:535–538CrossRefGoogle Scholar
  68. Vörösmarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, Glidden S, Bunn SE, Sullivan CA, Liermann CR, Davies PM (2010) Global threats to human water security and river biodiversity. Nature 467:555–561CrossRefGoogle Scholar
  69. Wang L, Lyons J, Kanehl P, Gatti R (1997) Influences of watershed land use on habitat quality and biotic integrity in Wisconsin streams. Fisheries 22:6–12CrossRefGoogle Scholar
  70. Wang LZ, Lyons J, Rasmussen P, Seelbach P, Simon T, Wiley M et al (2003) Watershed, reach, and riparian influences on stream fish assemblages in the Northern Lakes and Forest Ecoregion, USA. Can J Fish Aquat Sci 60:491–505CrossRefGoogle Scholar
  71. Warren DL, Glor RE, Turelli M (2010) ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33:607–611CrossRefGoogle Scholar
  72. Waters TF (1995) Sediment in streams: sources, biological effects and control. Am Fish Soc Mono 7:169–180Google Scholar
  73. Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMedPubMedCentralGoogle Scholar
  74. Zimmerman JKH, Vondracek B, Westra J (2003) Agricultural land use effects on sediment loading and fish assemblages in two Minnesota (USA) watersheds. Environ Manag 32:93–105CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of BiologyUniversity of Northern IowaCedar FallsUSA

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