Conservation Genetics

, Volume 16, Issue 1, pp 31–42 | Cite as

Fine-scale genetic structure of brook trout in a dendritic stream network

  • Suzanne J. KelsonEmail author
  • Anne R. Kapuscinski
  • Dianne Timmins
  • William R. Ardren
Research Article


Conservation management of threatened species requires identifying the landscape features that shape population structure. Within river ecosystems, the dendritic nature of river networks and physical barriers, such as waterfalls, can strongly shape population structure. We examined population structure of native brook trout in a river network in northern New Hampshire, USA, including above and below waterfalls. We genotyped fish at 12 microsatellite loci including samples from six tributaries, mobile adults from three mainstem rivers, and fish from the hatchery broodstock that had been earlier stocked in the study region. We found that two subpopulations in tributaries above waterfalls were distinguished as unique genetic clusters with high levels of among population genetic diversity (average pairwise FST = 0.20) and low levels of within population genetic diversity (average allelic richness AR = 3.55), including one sub-population above a waterfall. With only one exception, subpopulations below waterfalls exhibited patterns of genetic diversity within and among populations consistent with contemporary gene flow among these subpopulations (average FST = 0.03; AR = 5.83). Most mobile adult fish caught in the mainstem rivers were genetically similar to those found in tributaries without waterfalls, suggesting that mobile individuals are likely connecting below-barrier subpopulations. Despite recent hatchery stocking in this system, we did not observe evidence of hatchery introgression with wild-caught fish. The complex metapopulation of naturally isolated and connected subpopulations of brook trout described in this study highlights the importance of considering fine scale genetic structure in conservation management.


Metapopulation Isolated population Salvelinus fontinalis Hatchery One-way gene flow Landscape genetics 



This project was made possible with funds from the Kaminsky Family Fund Award, Trout Unlimited, the Dartmouth Outing Club Northern Studies Internship, the James O. Freedman Presidential Scholar Assistantship, and the Sherman Fairchild Professorship in Sustainability Science of A.R. Kapuscinski. We thank R. Piampiano and K. Evans for supporting research at the Second College Grant, F.J. Kull for the use of his lab at Dartmouth College, NHFG staff for help with electrofishing, S. Julian with U.S. Fish and Wildlife Service (USFWS) for donating PCR primers, and D. Seiders and D. Boucher with Maine Department of Inland Fisheries and Wildlife and J. Martell from Antioch College for sharing scale samples. We also thank M. Ayres, M. Bogan, S. Carlson, J. C. Garza, A. Sturrock, B. Taylor, and two anonymous reviewers for valuable comments that improved this paper. The findings and conclusions in the article are those of the authors and do not necessarily represent the views of the USFWS.

Supplementary material

10592_2014_637_MOESM1_ESM.pdf (176 kb)
Supplementary material 1 (PDF 175 kb)


  1. Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell Publishing, MaldenGoogle Scholar
  2. Annett B, Gerlach G, King TL, Whiteley AR (2012) Conservation genetics of remnant coastal brook trout populations at the southern limit of their distribution: population structure and effects of stocking. Trans Am Fish Soc 141:1399–1410CrossRefGoogle Scholar
  3. Araki H, Cooper B, Blouin MS (2007) Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318:100–103PubMedCrossRefGoogle Scholar
  4. Ardren WR, DeHaan PW, Smith CT (2011) Genetic structure, evolutionary history, and conservation units of bull trout in the coterminous United States. Trans Am Fish Soc 140:506–525CrossRefGoogle Scholar
  5. Austin JD, Jelks HL, Tate B, Johnson AR, Jordan F (2011) Population genetic structure and conservation genetics of threatened Okaloosa darters (Etheostoma okaloosae). Conserv Genet 12:981–989CrossRefGoogle Scholar
  6. Baldigo BP, Lawrence G, Simonin H (2007) Persistent mortality of brook trout in episodically acidified streams of the southwestern Adirondack mountains, New York. Trans Am Fish Soc 136:121–134CrossRefGoogle Scholar
  7. Castric V, Bernatchez L (2003) The rise and fall of isolation by distance in the anadromous brook charr (Salvelinus fontinalis Mitchill). Genetics 163:983–996PubMedCentralPubMedGoogle Scholar
  8. Castric V, Bonney F, Bernatchez L (2001) Landscape structure and heirarchical genetic diversity in the brook charr, Salvelinus fontinalis. Evoultion 55:1016–1028CrossRefGoogle Scholar
  9. Curry RA, Sparks D, van De Sande J (2002) Spatial and temporal movements of a riverine brook trout population. Trans Am Fish Soc 131:551–560CrossRefGoogle Scholar
  10. D’Amelio S, Mucha J, Mackereth R, Wilson CC (2008) Tracking coaster brook trout to their sources: combining telemetry and genetic profiles to determine source populations. North Am J Fish Manag 28:1343–1349CrossRefGoogle Scholar
  11. Deiner K, Garza JC, Coey R, Girman DJ (2007) Population structure and genetic diversity of trout (Oncorhynchus mykiss) above and below natural and man-made barriers in the Russian River, California. Conserv Genet 8:437–454CrossRefGoogle Scholar
  12. Dias MS, Cornu J, Oberdorff T, Lasso CA, Tedesco PA (2013) Natural fragmentation in river networks as a driver of speciation for freshwater fishes. Ecography 36:683–689CrossRefGoogle Scholar
  13. Dillane E, McGinnity P, Coughlan JP, Cross C, de Eyto E, Kenchington E, Prodohl P, Cross TF (2008) Demographics and landscape features determine intrariver population structure in Atlantic salmon (Salmo salar L.): the case of the River Moy in Ireland. Mol Ecol 17:4786–4800PubMedCrossRefGoogle Scholar
  14. Drinan DP, Kalinowski ST, Vu NV, Shephard BB, Muhlfied C, Campbell MR (2011) Genetic variation in westslope cutthroat trout Oncorhynchus clarkii lewisi: implications for conservation. Conserv Genet 12:1513–1523CrossRefGoogle Scholar
  15. Earl D, Von Holdt B (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361CrossRefGoogle Scholar
  16. Esselman PC, Infante DM, Wang L, Wu D, Cooper AR, Taylor WW (2011) An index of cumulative disturbance to river fish habitats of the conterminous United States from landscape anthropogenic activities. Ecol Restor 19:1–2Google Scholar
  17. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620PubMedCrossRefGoogle Scholar
  18. Fausch KD, Torgersen CE, Baxter CV, Li HW (2002) Landscapes to riverscapes: bridging the gap between research and conservation of stream fishes. Bioscience 52:483–498CrossRefGoogle Scholar
  19. Franssen J, Lapointe M, Magnan P (2014) Geomorphic controls on fine sediment reinfiltration into salmonid spawning gravels and the implications for spawning habitat rehabilitation. Geomorphology 211:11–21CrossRefGoogle Scholar
  20. Fraser J (1981) Comparative survival and growth of planted wild, hybrid, and domestic strains of brook trout (Salvelinus fontinalis) in Ontario. Can J Fish Aquat Sci 38:1672–1684CrossRefGoogle Scholar
  21. Fullerton AH, Burnett KM, Steel EA, Flitcroft RL, Pess GR, Feist BE, Torgersen CE, Miller DJ, Sanderson BL (2010) Hydrological connectivity for riverine fish: measurement challenges and research opportunities. Freshw Biol 55:2215–2237Google Scholar
  22. Garza JC, Gilbert-Horvath EA, Spence BC, Williams TH, Fish H, Gough SA, Anderson JH, Hamm D, Anderson EC (2014) Population structure of steelhead in coastal California. Trans Am Fish Soc 143:134–152CrossRefGoogle Scholar
  23. Gharrett AJ, Joyce J, Smoker WW (2013) Fine-scale temporal adaptation within a salmonid population: mechanism and consequences. Mol Ecol 22:4457–4469PubMedCrossRefGoogle Scholar
  24. 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 18:4854–4869PubMedCrossRefGoogle Scholar
  25. Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 86:485–486Google Scholar
  26. Guo S, Thompson E (1992) Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics 48:361–372PubMedCrossRefGoogle Scholar
  27. Haak AL, Williams JE (2012) Spreading the risk: native trout management in a warmer and less-certain future. North Am J Fish Manag 32:387–401CrossRefGoogle Scholar
  28. Hudman SP, Gido KB (2013) Multi-scale effects of impoundments on genetic structure of creek chub (Semotilus atromaculatus) in the Kansas River basin. Freshw Biol 58:441–453CrossRefGoogle Scholar
  29. Hudy M, Thieling TM, Gillespie N, Smith EP (2008) Distribution, status, and land use characteristics of subwatersheds within the native range of brook trout in the eastern United States. North Am J Fish Manag 28:1069–1085CrossRefGoogle Scholar
  30. Humston R, Bezold KA, Adkins ND, Bisey RJ, Huss J, Meekins BA, Cabe PR, King TL (2012) Consequences of stocking headwater impoundments on native populations of brook trout in tributaries. North Am J Fish Manag 32:100–108CrossRefGoogle Scholar
  31. 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 23:1801–1806PubMedCrossRefGoogle Scholar
  32. Jensen J, Bohonak A, Kelley S (2005) Isolation by distance, Web service.
  33. Kalinowski ST (2005) Do polymorphic loci require large sample sizes to estimate genetic distances? Heredity 94:33–36PubMedCrossRefGoogle Scholar
  34. Kalinowski ST (2008) Oncor: software for genetic stock identification. Department of Ecology, Montana State University.
  35. Kalinowski ST, Meeuwig MH, Narum SR, Taper ML (2008) Stream trees: a statistical method for mapping genetic differences between populations of freshwater organisms to the sections of streams that connect them. Can J Fish Aquat Sci 65:2752–2760CrossRefGoogle Scholar
  36. Kanno Y, Vokoun JC, Letcher BH (2011a) Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks. Mol Ecol 20:3711–3729PubMedCrossRefGoogle Scholar
  37. Kanno Y, Vokoun JC, Letcher BH (2011b) Sibship reconstruction for inferring mating systems, dispersal and effective population size in headwater brook trout (Salvelinus fontinalis) populations. Conserv Genet 12:619–628CrossRefGoogle Scholar
  38. Kanno Y, Letcher BH, Coombs JA, Nislow KH, Whiteley AR (2014) Linking movement and reproductive history of brook trout to assess habitat connectivity in a heterogeneous stream network. Freshw Biol 59:142–154CrossRefGoogle Scholar
  39. King TL, Lubinski BA, Burnham-Curtis MK, Stott W, Morgan RP II (2012) Tools for the management and conservation of genetic diversity in brook trout (Salvelinus fontinalis): tri- and tetranucleotide microsatellite markers for the assessment of genetic diversity, phylogeography, and historical demographics. Conserv Genet Resour 4:539–543CrossRefGoogle Scholar
  40. Krosch MN, Baker AM, Mather PB, Cranston PS (2011) Spatial population genetic structure reveals strong natal site fidelity in Echinocladius martini (Diptera: Chironomidae) in northeast Queensland, Australia. Freshw Biol 56:1328–1341CrossRefGoogle Scholar
  41. Langella O (2002) POPULATIONS 1.2.31. Scholar
  42. Leonard JBK, Stott W, Loope DM, Kusnierz PC, Sreenivasan A (2013) Biological consequences of the coaster brook trout restoration stocking program in Lake Superior tributaries within Pictured Rocks National Lakeshore. North Am J Fish Manag 33:359–372CrossRefGoogle Scholar
  43. Lesica P, Allendorf FW (1995) When are peripheral populations valuable for conservation? Conserv Biol 9:753–760CrossRefGoogle Scholar
  44. Letcher BH, Nislow KH, Coombs JA, O’Donnell MJ, Dubreuil TL (2007) Population response to habitat fragmentation in a stream-dwelling brook trout population. PLoS One 2:e1139PubMedCentralPubMedCrossRefGoogle Scholar
  45. Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197CrossRefGoogle Scholar
  46. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220PubMedGoogle Scholar
  47. Marie AD, Bernatchez L, Garant D (2010) Loss of genetic integrity correlates with stocking intensity in brook charr (Salvelinus fontinalis). Mol Ecol 19:2025–2037PubMedCrossRefGoogle Scholar
  48. Marie AD, Bernatchez L, Garant D (2012) Environmental factors correlate with hybridization in stocked brook charr (Salvelinus fontinalis). Can J Fish Aquat Sci 69:884–893CrossRefGoogle Scholar
  49. Marschall EE, Crowder LB (1996) Assessing population responses to multiple anthropogenic effects: a case study with brook trout. Ecol Appl 6:152–167CrossRefGoogle Scholar
  50. McCracken GR, Perry R, Keefe D, Ruzzante DE (2013) Hierarchical population structure and genetic diversity of lake trout (Salvelinus namaycush) in a dendritic system in Northern Labrador. Freshw Biol 58:1903–1917CrossRefGoogle Scholar
  51. Miyazono S, Taylor CM (2013) Effects of habitat size and isolation on species immigration–extinction dynamics and community nestedness in a desert river system. Freshw Biol 58:1303–1312CrossRefGoogle Scholar
  52. Mollenhauer R, Wagner T, Kepler MV, Sweka JA (2013) Fall and early winter movement and habitat use of wild brook trout. Trans Am Fish Soc 142:1167–1178CrossRefGoogle Scholar
  53. Nislow KH, Lowe WH (2003) Influences of logging history and stream pH on brook trout abundance in first-order streams in New Hampshire. Trans Am Fish Soc 132:166–171CrossRefGoogle Scholar
  54. Nislow KH, Hudy M, Letcher BH, Smith EP (2011) Variation in local abundance and species richness of stream fishes in relation to dispersal barriers: implications for management and conservation. Freshw Biol 56:2135–2144CrossRefGoogle Scholar
  55. O’Connor J, Power G (1973) Homing of brook trout (Salvelinus fontinalis) in Matamek lake, Quebec. J Fish Res Board Canada 30:1012–1014Google Scholar
  56. Page RDM (1996) TreeView: an application to display phylogenetic trees on personal computers. Cabios Appl Note 12:357–358CrossRefGoogle Scholar
  57. Pépino M, Rodríguez MA, Magnan P (2012) Impacts of highway crossings on density of brook charr in streams. J Appl Ecol 49:395–403CrossRefGoogle Scholar
  58. Perkin JS, Gido KB (2012) Fragmentation alters stream fish community structure in dendritic ecological networks. Ecol Appl 22:2176–2187PubMedCrossRefGoogle Scholar
  59. Peterson DP, Wenger SJ, Rieman BE, Isaak DJ (2013) Linking climate change and fish conservation efforts using spatially explicit decision support tools. Fisheries 38:112–127CrossRefGoogle Scholar
  60. Petty JT, Hansbarger JL, Huntsman BM, Mazik PM (2012) Brook trout movement in response to temperature, flow, and thermal refugia within a complex Appalachian riverscape. Trans Am Fish Soc 141:1060–1073CrossRefGoogle Scholar
  61. Poissant J, Knight TW, Fergurson MM (2005) Nonequilibrium conditions following landscape rearrangement: the relative contribution of past and current hydrological landscapes on the genetic structure of a stream-dwelling fish. Mol Ecol 14:1321–1331CrossRefGoogle Scholar
  62. Poplar-Jeffers IO, Petty JT, Anderson JT, Kite SJ, Strager MP, Fortney RH (2009) Culvert replacement and stream habitat restoration: implications from brook trout management in an Appalachian watershed, U.S.A. Restor Ecol 17:404–413CrossRefGoogle Scholar
  63. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedCentralPubMedGoogle Scholar
  64. Reilly JR, Paszkowski CA, Coltman DW (2014) Population genetics of arctic grayling distributed across large, unobstructed river systems. Trans Am Fish Soc 143:802–816CrossRefGoogle Scholar
  65. Rice W (1989) Analyzing tables of statistical tests. Evolution 43:223–225CrossRefGoogle Scholar
  66. Rieman BE, Dunham JB (2000) Metapopulations and salmonids: a synthesis of life history patterns and empirical observations. Ecol Freshw Fish 9:51–64CrossRefGoogle Scholar
  67. Rodríguez-Ramilo ST, Wang J (2012) The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis. Mol Ecol Resour 12:873–884PubMedCrossRefGoogle Scholar
  68. Rosenberg NA (2004) Distruct: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138CrossRefGoogle Scholar
  69. Rousset F (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145:1219–1228PubMedCentralPubMedGoogle Scholar
  70. Scribner K, Huckins C, Baker E, Kanefsky J (2012) Genetic relationships and gene flow between resident and migratory brook trout in the Salmon Trout River. J Great Lakes Res 38:152–158CrossRefGoogle Scholar
  71. Sterling KA, Reed DH, Noonan BP, Warren MLJ (2012) Genetic effects of habitat fragmentation and population isolation on Etheostoma raneyi (Percidae). Conserv Genet 13:859–872CrossRefGoogle Scholar
  72. Stolarski JT, Hartman KJ (2008) An evaluation of the precision of fin ray, otolith, and scale age determinations for brook trout. North Am J Fish Manag 28:1790–1795CrossRefGoogle Scholar
  73. Timmins D (2005) Migration patterns of wild adult brook trout in northern New Hampshire, F50R Project Segment Report. pp 1–3Google Scholar
  74. Timmins D (2006) Migration patterns of wild adult brook trout in northern New Hampshire, F50R Project Segment Report. pp 1–6Google Scholar
  75. Timmins D (2007) Migration patterns of wild adult brook trout in northern New Hampshire, F50R Project Segment Report. pp 1–9Google Scholar
  76. Vincent RE (1960) Some influences of domestication upon three stocks of brook trout (Salvelinus fontinalis Mitchill). Trans Am Fish Soc 89:35–52CrossRefGoogle Scholar
  77. Weir B, Cockerham C (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CrossRefGoogle Scholar
  78. Wenger SJ, Isaak DJ, Luce CH, Neville HM, Fausch KD, Dunham JB, Dauwalter DC, Young MK, Elsner MM, Rieman BE, Hamlet AF, Williams JE (2011) Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change. Proc Natl Acad Sci 108:14175–14180PubMedCentralPubMedCrossRefGoogle Scholar
  79. 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 13:3675–3688PubMedCrossRefGoogle Scholar
  80. Whiteley AR, Coombs JA, Hudy M, Robinson Z, Colton AR, Nislow KH, Letcher BH (2013) Fragmentation and patch size shape genetic structure of brook trout populations. Can J Fish Aquat Sci 70:678–688CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Suzanne J. Kelson
    • 1
    Email author
  • Anne R. Kapuscinski
    • 2
  • Dianne Timmins
    • 3
  • William R. Ardren
    • 4
  1. 1.Dartmouth CollegeUniversity of California, BerkeleyBerkeleyUSA
  2. 2.Environmental Studies ProgramDartmouth CollegeHanoverUSA
  3. 3.New Hampshire Fish and GameLancasterUSA
  4. 4.U.S. Fish and Wildlife ServiceEssex JunctionUSA

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