Plant Ecology

, Volume 215, Issue 12, pp 1385–1397 | Cite as

Asymmetric gene flow and the distribution of genetic diversity in morphologically distinct Abies mariesii populations in contrasting eco-habitats

  • Shenhua Qian
  • Waka Saito
  • Makiko Mimura
  • Shingo Kaneko
  • Yuji Isagi
  • Eri Mizumachi
  • Akira S. Mori


The steep and complex topography in high-elevation mountains usually generates heterogeneous habitats, where reproductive isolation in spatially discrete populations, such as unsynchronized flowering, likely occurs. Therefore, genetically differentiated populations in some cases can be expected. Patterns of genetic variation and directions of gene flow among discrete populations could hold crucial information for a better understanding of population genetic structures in heterogeneous habitats. In this study, we examined the local-scale genetic structure and gene flow patterns among Abies mariesii populations in a heterogeneously snowy subalpine ecosystem. We found that A. mariesii populations, though spatially discrete and undergoing strong habitat heterogeneity, are less likely to be genetically differentiated. Gene flow was biased toward the moorlands, and compared to the forest populations, the genetic diversity in the moorland populations was found to be significantly higher, suggesting that the moorlands could serve as sinks of genetic diversity for A. mariesii. Finally, we quantified the morphological variation of A. mariesii and discovered that being able to adjust tree morphology in contrasting eco-habitats may strengthen the competitiveness of A. mariesii. This may also make a tangible contribution to the maintenance of populations in contrasting habitats.


Abies mariesii Gene flow Genetic diversity Habitat heterogeneity Microsatellite Tree morphology 



We thank Ryo Kitagawa for help with field work, Aino T. Ota for guidance in laboratory work, and Dr. Keiichi Ohno and Dr. Akiko Sakai for valuable comments. We are grateful to two anonymous reviewers for their constructive comments on this paper. We also thank Andrés J. Cortés for his kind assistance with the Linear Mixed Models. This study was supported by a Sasakawa Scientific Research Grant from The Japan Science Society [25-507] to SQ and MM, a Grant from the Yokohama Academic Foundation to ASM, and a Grant from the Fujiwara National History Foundation to EM and ASM.

Supplementary material

11258_2014_395_MOESM1_ESM.jpg (1.3 mb)
Online Resource 1 Panorama photo showing the two kinds of habitats with heterogeneous snow accumulation. Letter “a” indicates snowless forested upper slope habitats; letter “b” indicates snowy moorland habitats nearby. Forest A. mariesii individuals are larger in size, and usually stand above snow cover throughout winter, while moorland A. mariesii are mostly dwarfed individuals and mostly buried under snow cover until snowmelt in late spring. (JPEG 1309 kb)
11258_2014_395_MOESM2_ESM.doc (31 kb)
Online Resource 2 Summary of woody species composition in six study sites. (DOC 31 kb)
11258_2014_395_MOESM3_ESM.doc (54 kb)
Online Resource 3 Description of (a) microsatellite marker development process for A. mariesii, (b) primer characteristics, and (c) PCR amplification mixtures and thermal profile. (DOC 54 kb)
11258_2014_395_MOESM4_ESM.tif (14.3 mb)
Online Resource 4 Observed first flowering in 2013 in a forest habitats and b moorland habitats. Dark-colored bars show A. mariesii individuals found with male flowers within study sites, light-colored bars show A. mariesii individuals found with male flowers within study sites. Observations began in mid-June 2013 to ensure that they were ahead of the initial flowering stage of A. mariesii. Starting from 24 June 2013, observations were made every 4 days by direct counting and recording of the number of individuals within each study site. Observations ended on 6 July 2013, resulting in a total of four counts of flowering individuals within each study site. (TIFF 14625 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Shenhua Qian
    • 1
  • Waka Saito
    • 2
  • Makiko Mimura
    • 3
  • Shingo Kaneko
    • 4
  • Yuji Isagi
    • 5
  • Eri Mizumachi
    • 6
  • Akira S. Mori
    • 1
  1. 1.Laboratory of Forest Ecosystem Management, Graduate School of Environment and Information SciencesYokohama National UniversityHodogaya-kuJapan
  2. 2.Laboratory of Forest Ecology, Graduate School of AgricultureKyoto UniversitySakyo-kuJapan
  3. 3.Graduate School of AgricultureTamagawa UniversityMachidaJapan
  4. 4.Graduate School of Symbiotic Systems Science and TechnologyFukushima UniversityFukushimaJapan
  5. 5.Laboratory of Forest Biology, Graduate School of AgricultureKyoto UniversitySakyo-kuJapan
  6. 6.Science Communication Group, Institute for Integrated Cell-Material Sciences (WPI-iCeMS)Kyoto UniversitySakyo-kuJapan

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