Conservation Genetics

, Volume 16, Issue 2, pp 431–441 | Cite as

Eco-geographic units, population hierarchy, and a two-level conservation strategy with reference to a critically endangered salmonid, Sakhalin taimen Parahucho perryi

  • Lev A. Zhivotovsky
  • Andrey A. Yurchenko
  • Vitaly D. Nikitin
  • Sergei N. Safronov
  • Marina V. Shitova
  • Sergei F. Zolotukhin
  • Sergei S. Makeev
  • Steven Weiss
  • Peter S. Rand
  • Anatoly Yu. Semenchenko
Research Article


Hierarchical population structure can result from range-wide geographic subdivision under conditions of environmental heterogeneity and weak gene flow. While a lower level of structure can be formed by local populations within eco-geographic regions, an upper level can be characterized by variation between populations from different regions, and thus, be represented by evolutionarily significant units (ESUs) defined by environmental, ecological and genetic variation. Selection of ESUs may depend on the sequence of using these three sources of variation. We propose to determine ESUs by first using non-genetic, ecological and geographical gradients for defining preliminary population groups (eco-geographic units, EGUs) and then testing whether the boundaries of these units are genetically coherent and thus represent ESUs or warrant their further modification. We evaluate this approach using Sakhalin taimen, an East Asian endangered endemic fish. Forty-one samples (473 fish) were drawn from thirty populations across the species range and genotyped at microsatellite DNA markers. We assign the populations into ESUs based on geographic and life history criteria and subsequent application of genetic diversity analyses. The ESUs appeared to be greatly diverged genetically. Within ESUs, local populations are genetically differentiated, have low effective sizes, show signatures of demographic decline and extremely restricted gene flow. Conservation plans aimed to restore or maintain a specific threatened population should take into account such hierarchical structure, and in particular be based on the genetic resources drawn from each population or using ecologically and genetically similar populations from the same ESU as donors for restoration of the population.


Semi-anadromous salmonid fish Genetic divergence Reproductive isolation Restricted gene flow Small population and sample size Landscape genetics 



We are thankful to the Agency ‘Rosprirodnadzor’ for permissions to obtain genetic samples from Sakhalin taimen populations and to S. Didenko for setting up of field expeditions. We are indebted to D. Didenko (travel agency “Ostrov”) for his great help in sampling, as well as other samplers listed in Table A1. Field expeditions and genetic work were supported in part by Grants to L.Zh. from RAS program “Living Nature– Genofonds”, and from “Sakhalin Salmon Initiatives”; all participants were also supported by funds of their institutions, and P.R. by a Grant from National Geographic Society. We are grateful to Dr. Robin Waples and an anonymous reviewer for their valuable comments on the manuscript.

Supplementary material

10592_2014_670_MOESM1_ESM.docx (163 kb)
Supplementary material 1 (DOCX 162 kb)
10592_2014_670_MOESM2_ESM.docx (219 kb)
Supplementary material 2 (DOCX 219 kb)


  1. Allendorf FW, Luikart GH, Aitken SN (2012) Conservation and the genetics of populations. Wiley & Sons, ChichesterGoogle Scholar
  2. Andrew RL, Bernatchez L, Bonin A, Buerkle CA, Carstens BC, Emerson BC, Garant D, Giraud T, Kane NC, Rogers SM (2013) A road map for molecular ecology. Mol Ecol 22:2605–2626CrossRefPubMedGoogle Scholar
  3. Baillie J, Butcher ER, Commission ISS (2012) Priceless Or worthless?: The world’s most threatened species. Zoological Society of London, LondonGoogle Scholar
  4. Dionne M, Caron F, Dodson JJ, Bernatchez L (2008) Landscape genetics and hierarchical genetic structure in Atlantic salmon: the interaction of gene flow and local adaptation. Mol Ecol 17:2382–2396CrossRefPubMedGoogle Scholar
  5. Do C, Waples RS, Peel D, Macbeth G, Tillett BJ, Ovenden JR (2014) NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Res 14:209–214CrossRefGoogle Scholar
  6. Edo K, Kawaguchi Y, Nunokawa M, Kawamula H, Higashi S (2005) Morphology, stomach contents and growth of the endangered salmonid, Sakhalin taimen Hucho perryi, captured in the Sea of Okhotsk, northern Japan: evidence of an anadromous form. Environ Biol Fishes 74:1–7CrossRefGoogle Scholar
  7. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620CrossRefPubMedGoogle Scholar
  8. Falconer DS (1960) Introduction to quantitative genetics. Ronald Press Co., New YorkGoogle Scholar
  9. Fraser DJ, Weir LK, Bernatchez L, Hansen MM, Taylor EB (2011) Extent and scale of local adaptation in salmonid fishes: review and meta-analysis. Heredity 106:404–420CrossRefPubMedCentralPubMedGoogle Scholar
  10. Fukushima M, Shimazaki H, Rand PS, Kaeriyama M (2011) Reconstructing Sakhalin taimen Parahucho perryi historical distribution and identifying causes for local extinctions. Trans Am Fish Soc 140:1–13Google Scholar
  11. Funk WC, McKay JK, Hohenlohe PA, Allendorf FW (2012) Harnessing genomics for delineating conservation units. Trends Ecol Evol 27:489–496CrossRefPubMedCentralPubMedGoogle Scholar
  12. Gagnaire P-A, Normandeau E, Côté C, Hansen MM, Bernatchez L (2012) The genetic consequences of spatially varying selection in the panmictic American eel (Anguilla rostrata). Genetics 190:725–736CrossRefPubMedCentralPubMedGoogle Scholar
  13. Gritsenko OF (2002) Diadromous Fishes of Sakhalin (Systematics, Ecology, Fisheries). VNIRO Publishing, Moscow, p 247 [in Russian]Google Scholar
  14. Honda K, Arai T, Takahashi N, Miyashita K (2010) Life history and migration of Sakhalin taimen, Hucho perryi, caught from Lake Akkeshi in eastern Hokkaido, Japan, as revealed by Sr: Ca ratios of otoliths. Ichthyol Res 57:416–421CrossRefGoogle Scholar
  15. Honda K, Kagiwada H, Takahashi N, Miyashita K (2012) Seasonal stream habitat of adult Sakhalin taimen, Parahucho perryi, in the Bekanbeushi River system, eastern Hokkaido, Japan. Ecol Fresw Fish 21:640–657CrossRefGoogle Scholar
  16. Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Res 9:1322–1332CrossRefGoogle Scholar
  17. Lewis PO, Zaykin D (2001) Genetic data analysis: computer program for the analysis of allelic data (http//lewis.eeb.uconn.lewishome/software.html)Google Scholar
  18. Manel S, Joost S, Epperson BK, Holderegger R, Storfer A, Rosenberg MS, Scribner KT, Bonin A, Fortin MJ (2010) Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Mol Ecol 19:3760–3772CrossRefPubMedGoogle Scholar
  19. Martynenko AB (2007) Provisory regionalization of the Far East Federal district of the Russian Federation for zoogeographical purposes. In: Kurentsov AI (ed) Annual memorial meetings. The Far East National University, Vladivostok, 18, 29–47. [in Russian]Google Scholar
  20. Moritz C (2002) Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst Biol 51:238–254CrossRefPubMedGoogle Scholar
  21. Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New YorkGoogle Scholar
  22. Nomura T (2008) Estimation of effective number of breeders from molecular coancestry of single cohort sample. Evol Appl 1:462–474CrossRefPubMedCentralPubMedGoogle Scholar
  23. Paetkau D, Slade R, Burden M, Estoup A (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power. Mol Ecol 13:55–65CrossRefPubMedGoogle Scholar
  24. Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539CrossRefPubMedCentralPubMedGoogle Scholar
  25. Pearse DE, Miller MR, Abadía-Cardoso A, Garza JC (2014) Rapid parallel evolution of standing variation in a single, complex, genomic region is associated with life history in steelhead/rainbow trout. Proc Royal Soc B 281(1783):20140012CrossRefGoogle Scholar
  26. Perrier C, Bourret V, Kent MP, Bernatchez L (2013) Parallel and nonparallel genome-wide divergence among replicate population pairs of freshwater and anadromous Atlantic salmon. Mol Ecol 22:5577–5593CrossRefPubMedGoogle Scholar
  27. Piry S, Alapetite A, Cornuet J-M, Paetkau D, Baudouin L, Estoup A (2004) GENECLASS2: a software for genetic assignment and first-generation migrant detection. J Hereid 95:536–539CrossRefGoogle Scholar
  28. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedCentralPubMedGoogle Scholar
  29. Pudovkin A, Zhdanova O, Hedgecock D (2010) Sampling properties of the heterozygote-excess estimator of the effective number of breeders. Cons Gen 11:759–771CrossRefGoogle Scholar
  30. Rand PS (2006) Hucho perryi. In: IUCN red list of threatened species. Version 2010.4.
  31. Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci 94:9197–9201CrossRefPubMedCentralPubMedGoogle Scholar
  32. Safronov SN and Sukhonos PS (2006) Morphological features and the condition of populations of Sakhalin taimen (Parahucho perryi) in Dagi River (The Nyi Bay, Sakhalin Island). In: Economic, social, and ecological problems at the Okhotsk Sea and the ways of their solution. Petropavlovsk-Kamchatskiy State University, Petropavlovsk-Kamchatskiy, [in Russian]Google Scholar
  33. Safronov SN, Nikitin VD, Sukhonos PS (2006) Current condition and conservation of populations of Sakhalin taimen Parahucho perryi in Sakhakin Island rivers. In: Economic enviromental sustainability for Sakhakin’s people and salmon. ANO “Sakhalin Salmon Initiative”, Yuzno-Sakhalinsk. [in Russian]Google Scholar
  34. Semenchenko AYu, Zolotukhin SF (2011) Sakhalin taimen Parahucho perryi reproduction efficiency in Sakhalin rivers, and a strategy of its protection. Levanidov VYa Bienn Meml Meet 5:472–482 [in Russian]Google Scholar
  35. Smouse PE, Peakall R (1999) Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82:561–573CrossRefPubMedGoogle Scholar
  36. Sork VL, Waits L (2010) Contributions of landscape genetics–approaches, insights, and future potential. Mol Ecol 19:3489–3495CrossRefPubMedGoogle Scholar
  37. Waples RS (1991) Pacific salmon, Oncorhynchus spp., and the definition of “species” under the endangered species act. Mar Fish Rev 53:11–22Google Scholar
  38. Waples RS (2006) Distinct Population Segments. In: Scott JM, Goble DD, Davis FW (eds) The endangered species act at thirty: conserving biodiversity in human-dominated landscapes. Island Press, Washington DC, pp 127–149Google Scholar
  39. Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol Appl 3:244–262CrossRefPubMedCentralPubMedGoogle Scholar
  40. Waples RS, Gustafson RG, Weitkamp LA, Myers JM, Johnson OW, Busby PJ, Hard JJ, Baker B (2001) Characterizing diversity in Pacific salmon. J Fish Biol 59(Suppl A):1–41Google Scholar
  41. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CrossRefGoogle Scholar
  42. Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMedCentralPubMedGoogle Scholar
  43. Zaykin D, Zhivotovsky L, Weir B (1995) Exact tests for association between alleles at arbitrary numbers of loci. Genetica 96:169–178CrossRefPubMedGoogle Scholar
  44. Zimmerman CE, Rand PS, Fukushima M, Zolotukhin SF (2012) Migration of Sakhalin taimen (Parahucho perryi): evidence of freshwater resident life history types. Environ Biol Fishes 93:223–232CrossRefGoogle Scholar
  45. Zolotukhin SF, Semenchenko AYu (2008) Growth and distribution of Sakhalin taimen Parahucho perryi (Brevoort) in watersheds. Levanidov VYa Bienn Meml Meet 4:317–338 [in Russian]Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Lev A. Zhivotovsky
    • 1
  • Andrey A. Yurchenko
    • 1
  • Vitaly D. Nikitin
    • 2
  • Sergei N. Safronov
    • 2
  • Marina V. Shitova
    • 1
  • Sergei F. Zolotukhin
    • 3
  • Sergei S. Makeev
    • 4
  • Steven Weiss
    • 5
  • Peter S. Rand
    • 6
  • Anatoly Yu. Semenchenko
    • 7
    • 8
  1. 1.Institute of General GeneticsRussian Academy of SciencesMoscowRussia
  2. 2.Sakhalin Research Institute of Fisheries and OceanographyYuzhno-SakhalinskRussia
  3. 3.Khabarovsk Branch of Pacific Research Fisheries CentreKhabarovskRussia
  4. 4.FGUP “Sakhalinrybvod”Yuzhno-SakhalinskRussia
  5. 5.Institute of ZoologyGrazAustria
  6. 6.Wild Salmon CenterPortlandUSA
  7. 7.ANO “Sakhalin Salmon Initiatives”Yuzhno-SakhalinskRussia
  8. 8.Scientific and Educational Complex “Primorsky Aquarium”Far Eastern Branch of Russian Academy of SciencesVladivostokRussia

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