Conservation Genetics

, Volume 19, Issue 6, pp 1471–1485 | Cite as

Characterizing range-wide divergence in an alpine-endemic bird: a comparison of genetic and genomic approaches

  • Kathryn M. LanginEmail author
  • Cameron L. Aldridge
  • Jennifer A. Fike
  • R. Scott Cornman
  • Kathy Martin
  • Gregory T. Wann
  • Amy E. Seglund
  • Michael A. Schroeder
  • Clait E. Braun
  • David P. Benson
  • Brad C. Fedy
  • Jessica R. Young
  • Scott Wilson
  • Donald H. Wolfe
  • Sara J. Oyler-McCance
Research Article


The delineation of intraspecific units that are evolutionarily and demographically distinct is an important step in the development of species-specific management plans. Neutral genetic variation has served as the primary data source for delineating “evolutionarily significant units,” but with recent advances in genomic technology, we now have an unprecedented ability to utilize information about neutral and adaptive variation across the entire genome. Here, we use traditional genetic markers (microsatellites) and a newer reduced-representation genomic approach (single nucleotide polymorphisms) to delineate distinct groups of white-tailed ptarmigan (Lagopus leucura), an alpine-obligate species that is distributed in naturally fragmented habitats from Alaska to New Mexico. Five subspecies of white-tailed ptarmigan are currently recognized but their distinctiveness has not been verified with molecular data. Based on analyses of 436 samples at 12 microsatellite loci and 95 samples at 14,866 single nucleotide polymorphism loci, we provide strong support for treating two subspecies as distinct intraspecific units—L. l. altipetens, found in Colorado and neighboring states; and L. l. saxatilis, found on British Columbia’s Vancouver Island—but our findings reveal more moderate patterns of divergence within the remainder of the species’ range. Results based on genetic and genomic datasets generally agreed with one another, indicating that in many cases microsatellite loci may be sufficient for describing major patterns of genetic structure across species’ ranges. This work will inform future conservation and management decisions for the white-tailed ptarmigan, a species that may be vulnerable to future changes in climate.


Conservation Evolutionarily significant unit Population genetics Population genomics White-tailed ptarmigan 



We thank Kathryn Bernier, Sharon Birks, John Bulger, Ray Collingwood, Avery Cook, Sarah Hudson, Doug Jury, Lee Kaiser, Richard Merizon, Jason Robinson, Serena Rocksund, William Taylor, and many others for help obtaining ptarmigan samples. We also thank Daniel Taylor for digitizing the morphology data, Kevin Oh for advice on genomic methods, and Christin Pruett and Patricia Stevens for manuscript comments. The University of Washington Burke Museum provided four samples through their tissue collection program. Colorado Parks and Wildlife, the Utah Division of Wildlife Resources, and the Alaska Department of Fish and Game provided samples collected by hunters. Funding was provided by the U.S. Geological Survey, the U.S. National Park Service, the Natural Sciences and Engineering Research Council of Canada, Forest Renewal British Columbia, Environment and Climate Change Canada, Colorado Parks and Wildlife, the Washington Department of Fish and Wildlife, and the Utah Division of Wildlife Resources. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Supplementary material

10592_2018_1115_MOESM1_ESM.xlsx (243 kb)
Supplementary material 1 (XLSX 243 KB)
10592_2018_1115_MOESM2_ESM.xlsx (46 kb)
Supplementary material 2 (XLSX 45 KB)
10592_2018_1115_MOESM3_ESM.xlsx (14.4 mb)
Supplementary material 3 (XLSX 14740 KB)
10592_2018_1115_MOESM4_ESM.docx (9.8 mb)
Supplementary material 4 (DOCX 10076 KB)


  1. Aldrich JW (1963) Geographic orientation of American Tetraonidae. J Wildl Manag 27:529–545CrossRefGoogle Scholar
  2. Allendorf FW, Luikart GH (2007) Conservation and the genetics of populations. Wiley, HobokenGoogle Scholar
  3. Allendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and the future of conservation genetics. Nature 11:697–709Google Scholar
  4. Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410CrossRefGoogle Scholar
  5. Avise JC, Arnold J, Ball RM et al (2008) Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics. Annu Rev Ecol Syst 18:489–522CrossRefGoogle Scholar
  6. Bergmann C (1847) Ueber die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Gottinger Stud 3:595–708Google Scholar
  7. Bi K, Linderoth T, Vanderpool D et al (2013) Unlocking the vault: next-generation museum population genomics. Mol Ecol 22:6018–6032CrossRefGoogle Scholar
  8. Blackburn TM, Gaston KJ, Loder N, Jul N (1999) Geographic gradients in body size: a clarification of Bergmann’ s rule. Divers Distrib 5:165–174CrossRefGoogle Scholar
  9. Bradbury IR, Hubert S, Higgins B et al (2013) Genomic islands of divergence and their consequences for the resolution of spatial structure in an exploited marine fish. Evol Appl 6:450–461CrossRefGoogle Scholar
  10. Braun CE, Williams SO (2015) History and status of the white-tailed ptarmigan in New Mexico. West Birds 46:233–243Google Scholar
  11. Braun CE, Hoffman RW, Rogers GE (1976) Wintering areas and winter ecology of white-tailed ptarmigan in Colorado. Special report (Division of Wildlife, Colorado); No. 38Google Scholar
  12. Braun CE, Nish DH, Giesen KM (1978) Release and establishment of white-tailed ptarmigan in Utah. Southwest Nat 23:661–667CrossRefGoogle Scholar
  13. Braun CE, Taylor WP, Ebbert SE et al (2011) Protocols for successful translocation of ptarmigan. Gyrfalcons Ptarmigan Chang World 2:339–348Google Scholar
  14. Camacho C, Coulouris G, Avagyan V et al (2009) BLAST plus: architecture and applications. BMC Bioinform 10:1CrossRefGoogle Scholar
  15. Chapman FM (1902) List of birds collected in Alaska by the Andrew J. Stone Expedition of 1901. Bull Am Mus Nat Hist 16:231Google Scholar
  16. Clements JF, Schulenberg TS, Iliff MJ et al (2018) The eBird/Clements checklist of birds of the world: v2016.
  17. Crandall K, Bininda-Emonds O, Mace G, Wayne R (2000) Considering evolutionary processes in conservation biology. Trends Ecol Evol 15:290–295CrossRefGoogle Scholar
  18. Dickinson EC, Remsen JJV (2013) The Howard and Moore complete checklist of the birds of the world, 4th edn, vol 1. Non-passerinesGoogle Scholar
  19. Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361Google Scholar
  20. 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
  21. Fedy BC, Martin K, Ritland C, Young J (2008) Genetic and ecological data provide incongruent interpretations of population structure and dispersal in naturally subdivided populations of white-tailed ptarmigan (Lagopus leucura). Mol Ecol 17:1905–1917CrossRefGoogle Scholar
  22. Fike JA, Oyler-McCance SJ, Zimmerman SJ, Castoe TA (2015) Development of 13 microsatellites for Gunnison Sage-grouse (Centrocercus minimus) using next-generation shotgun sequencing and their utility in Greater Sage-grouse (Centrocercus urophasianus). Conserv Genet Resour 7:211–214CrossRefGoogle Scholar
  23. Frankham R, Ballou JD, Eldridge MDB et al (2011) Predicting the probability of outbreeding depression. Conserv Biol 25:465–475CrossRefGoogle Scholar
  24. Frantz AC, Cellina S, Krier A et al (2009) Using spatial Bayesian methods to determine the genetic structure of a continuously distributed population: clusters or isolation by distance? J Appl Ecol 46:493–505CrossRefGoogle Scholar
  25. Fraser D, Bernatchez L (2001) Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Mol Ecol 10:2741–2752CrossRefGoogle Scholar
  26. Funk WC, McKay JK, Hohenlohe PA, Allendorf FW (2012) Harnessing genomics for delineating conservation units. Trends Ecol Evol 27:489–496CrossRefGoogle Scholar
  27. Gautier M (2015) Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201:1555–1579CrossRefGoogle Scholar
  28. Giesen KM, Braun CE (1993) Natal dispersal and recruitment of juvenile white-tailed ptarmigan in Colorado. J Wildl Manag 57:72–77CrossRefGoogle Scholar
  29. Gill F, Donsker D (eds) (2018) IOC World Bird List (v 8.2).
  30. Günther T, Coop G (2013) Robust identification of local adaptation from allele frequencies. Genetics 195:205–220CrossRefGoogle Scholar
  31. Hoffman RW (2006) White-tailed ptarmigan (Lagopus leucura): a technical conservation assessment. Report prepared for the USDA Forest Service, Rocky Mountain Region, Species Conservation Project. p 72Google Scholar
  32. Hoffman RW, Giesen KM (1983) Demography of an introduced population of white-tailed ptarmigan. Can J Zool 61:1758–1764CrossRefGoogle Scholar
  33. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70Google Scholar
  34. Holycross AT, Douglas ME (2007) Geographic isolation, genetic divergence, and ecological non-exchangeability define ESUs in a threatened sky-island rattlesnake. Biol Conserv 134:142–154CrossRefGoogle Scholar
  35. Jackson MM, Gergel SE, Martin K (2015) Effects of climate change on habitat availability and configuration for an endemic coastal alpine bird. PLoS ONE 10:e0142110CrossRefGoogle Scholar
  36. 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
  37. Jombart T (2008) adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405PubMedPubMedCentralGoogle Scholar
  38. Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94CrossRefGoogle Scholar
  39. Jones OR, Wang J (2010) COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol Ecol Resour 10:551–555CrossRefGoogle Scholar
  40. Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026CrossRefGoogle Scholar
  41. Kanthaswamy S, Kurushima JD, Smith DG (2006) Inferring Pongo conservation units: a perspective based on microsatellite and mitochondrial DNA analyses. Primates 47:310–321CrossRefGoogle Scholar
  42. Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. Ecol Lett 7:1225–1241CrossRefGoogle Scholar
  43. Kivioja T, Vähärautio A, Karlsson K et al (2011) Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 9:72–74CrossRefGoogle Scholar
  44. Kopelman NM, Mayzel J, Jakobsson M et al (2015) Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour 15:1179–1191CrossRefGoogle Scholar
  45. Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874CrossRefGoogle Scholar
  46. Langmead B, Salzberg SL (2013) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefGoogle Scholar
  47. Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659CrossRefGoogle Scholar
  48. Li H, Handsaker B, Wysoker A et al (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079CrossRefGoogle Scholar
  49. Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197CrossRefGoogle Scholar
  50. Martin K (2001) Wildlife in alpine and sub-alpine habitats. In: Johnson DH, O’Neil TA (eds) Wildlife-habitat relationships in Oregon and Washington. Oregon State University Press, Corvallis, pp 285–310Google Scholar
  51. Martin K, Brown GA, Young JR (2004) The historic and current distribution of the Vancouver Island White-tailed Ptarmigan (Lagopus leucurus saxatilis). J Field Ornithol 75:239–256CrossRefGoogle Scholar
  52. Martin K, Robb LA, Wilson S, Braun CE (2015) White-tailed Ptarmigan (Lagopus leucura). In: Rodewald PG (ed) The birds of North America. Cornell Lab of Ornithology, Ithaca; Retrieved from the Birds of North America:
  53. McTaggart Cowan I (1938) The white-tailed ptarmigan of Vancouver Island. Condor 41:82–83Google Scholar
  54. Moritz C (1994) Defining “evolutionarily significant units” for conservation. Trends Ecol Evol 9:373–375CrossRefGoogle Scholar
  55. Nei M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583–590PubMedPubMedCentralGoogle Scholar
  56. Nei M, Genetics P (1973) Analysis of gene diversity in subdivided populations. Proc Nat Acad Sci USA 70:3321–3323CrossRefGoogle Scholar
  57. Nei M, Kumar S (2000) Molecular evolution and phylogenetics. Oxford University Press, New YorkGoogle Scholar
  58. Osgood WH (1901) New subspecies of North American birds. Auk 18:179–185CrossRefGoogle Scholar
  59. Oyler-McCance SJ, Oh KP, Langin KM, Aldridge CL (2016) A field ornithologist’s guide to genomics: Practical considerations for ecology and conservation. Auk Ornithol Adv 133:626–648Google Scholar
  60. Prince DJ, O’Rourke SM, Thompson TQ et al (2017) The evolutionary basis of premature migration in Pacific salmon highlights the utility of genomics for informing conservation. Sci Adv 3:e1603198CrossRefGoogle Scholar
  61. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  62. Puechmaille SJ (2016) The program structure does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem. Mol Ecol Resour 16:608–627CrossRefGoogle Scholar
  63. Raymond M, Rousset F (1995) GENEPOP (Version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249CrossRefGoogle Scholar
  64. Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138CrossRefGoogle Scholar
  65. Ryder OA (1986) Species conservation and systematics: the dilemma of subspecies. Trends Ecol Evol 1:9–10CrossRefGoogle Scholar
  66. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425Google Scholar
  67. Schwartz MK, McKelvey KS (2009) Why sampling scheme matters: The effect of sampling scheme on landscape genetic results. Conserv Genet 10:441–452CrossRefGoogle Scholar
  68. Segelbacher G, Paxton R, Steinbruck G et al (2000) Characterization of microsatellites in capercaillie Tetrao urogallus (AVES). Mol Ecol 9:1934–1935CrossRefGoogle Scholar
  69. Segelbacher G, Cushman SA, Epperson BK et al (2010) Applications of landscape genetics in conservation biology: concepts and challenges. Conserv Genet 11:375–385CrossRefGoogle Scholar
  70. Seglund AE (2012) White-tailed ptarmigan summary report 2011 and project proposal 2012. Colorado Parks and WildlifeGoogle Scholar
  71. Slatkin M (1987) Gene flow and the geographic structure of natural populations. Science 236:787–792CrossRefGoogle Scholar
  72. Taylor WP (1920) A new ptarmigan from Mount Rainier. Condor 22:146–152CrossRefGoogle Scholar
  73. Toews DPL, Campagna L, Taylor SA et al (2016) Genomic approaches to understanding the early stages of population divergence and speciation in birds. Auk 133:13–30CrossRefGoogle Scholar
  74. USFWS NMFS (1996) Policy regarding recognition of distinct vertebrate population segments under the Endangered Species Act. Fed Regist 61:4721–4725Google Scholar
  75. USFWS (2012) Endangered and threatened wildlife and plants; 90-day finding on a petition to list the southern White-Tailed Ptarmigan and the Mt. Rainier White-Tailed Ptarmigan as Threatened With Critical Habitat. Fed Regist 77:33143–33155Google Scholar
  76. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538CrossRefGoogle Scholar
  77. Wann GT, Aldridge CL, Braun CE (2014) Estimates of annual survival, growth, and recruitment of a white-tailed ptarmigan population in Colorado over 43 years. Popul Ecol 56:555–567CrossRefGoogle Scholar
  78. Waples RS (1991) Pacific salmon, Oncorynchus spp., and the definition of “species” under the Endangered Species Act. Mar Fish Rev 53:11–22Google Scholar
  79. Waples R (1995) Evolutionarily significant units and the conservation of biological diversity under the Endangered Species Act. Am Fish Soc Symp 8–27Google Scholar
  80. Waples RS, Anderson EC (2017) Purging putative siblings from population genetic datasets: a cautionary view. Mol Ecol 26:1211–1224CrossRefGoogle Scholar
  81. West-Eberhard MJ (1989) Phenotypic plasticity and the origins of diversity. Annu Rev Ecol Syst 20:249–278CrossRefGoogle Scholar
  82. Wilson A, Bonaparte CL (1831) American ornithology, vol 4 (Jameson R (ed)). Constable, EdinburghGoogle Scholar
  83. Wilson S, Martin K (2011) Life-history and demographic variation in an alpine specialist at the latitudinal extremes of the range. Popul Ecol 53:459–471CrossRefGoogle Scholar
  84. Winter DJ (2012) MMOD: An R library for the calculation of population differentiation statistics. Mol Ecol Resour 12:1158–1160CrossRefGoogle Scholar
  85. Zwickel F, Bendell J (1967) A snare for capturing blue grouse. J Wildl Manag 31:202–204CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  1. 1.Fort Collins Science CenterU.S. Geological SurveyFort CollinsUSA
  2. 2.Natural Resource Ecology Laboratory, Department of Ecosystem Science and Sustainability, In Cooperation with the U.S. Geological SurveyColorado State UniversityFort CollinsUSA
  3. 3.Department of Forest and Conservation SciencesUniversity of British ColumbiaVancouverCanada
  4. 4.Wildlife Research DivisionEnvironment and Climate Change CanadaDeltaCanada
  5. 5.Colorado Parks and WildlifeMontroseUSA
  6. 6.Washington Department of Fish and WildlifeBridgeportUSA
  7. 7.Grouse Inc.TucsonUSA
  8. 8.Department of BiologyMarian UniversityIndianapolisUSA
  9. 9.School of Environment, Resources and SustainabilityUniversity of WaterlooWaterlooCanada
  10. 10.Environment and Sustainability DepartmentWestern State Colorado UniversityGunnisonUSA
  11. 11.Wildlife Research Division, National Wildlife Research CentreEnvironment and Climate Change CanadaOttawaCanada
  12. 12.G. M. Sutton Avian Research CenterBartlesvilleUSA

Personalised recommendations