Advertisement

Genetic diversity and genotypic stability in Prunus avium L. at the northern parts of species distribution range

  • Albin Lobo
  • Erik Dahl Kjær
  • Ditte Christina Olrik
  • Lars-Göran Stener
  • Jon Kehlet Hansen
Research Paper

Abstract

Key message

Large genetic variation was found in Prunus avium L. populations from the northern parts of the species distribution range. The ranking of genotypes in terms of growth was stable when tested at three trial sites within the northern parts of the species distribution range.

Context

Peripheral populations especially those in the leading edge are isolated from rest of the areas in the species distribution range. This can make them less genetically diverse yet genetically distinct from the rest of the populations in the species distribution range. Evaluation of their genetic diversity is thus crucial in understanding the local adaptation potential of a species.

Aims

We investigated the genetic diversity and genotype by environment interaction at the northern parts of the distribution range of P. avium.

Methods

Quantitative genetic variation of growth, stem form, and spring phenology were assessed in progenies from 93 plus trees of P. avium selected from 43 locations at the north of the species distribution range in Sweden and tested at two Swedish sites and one Danish site.

Results

We find large quantitative genetic variation in growth and phenology at the northern part of the distribution range of P. avium. Only a limited genotype by environment interaction was observed with no clear indication of local adaptation at the northern parts of the species distribution.

Conclusion

We conclude that P. avium harbors a high level of genetic diversity at the north of its distribution range. Present patterns therefore reflect more likely the recent introduction of the species and dispersal dynamics rather than a long-term loss of diversity along South-North ecological clines during the Holocene. With no indications of genetic depletion in growth or phenology, the gene pool in the breeding program is considered suitable for the future propagation of the species in the tested area.

Keywords

Marginal populations Forest genetics Climate change Local adaptation Wild cherry 

Notes

Acknowledgements

The authors would like to thanks Morten Alban Knudsen and Johan Malm for helping in field data collection from the study sites.

Author contribution

Albin Lobo is responsible for data collection in Danish field trial, data analysis, and writing of manuscript.

Erik Dahl Kjær is the responsible for supervising the project and writing of manuscript.

Ditte Christina Olrik is responsible for the field trial in Denmark, data collection, and writing of the manuscript.

Lars-Göran Stener is responsible for the field trials in Sweden, data collection, and writing of manuscript.

Jon Kehlet Hansen is responsible for supervising the study, data analysis, and writing the manuscript.

Funding

The Villum Foundation provided financial support for the data collection and the analysis as part of the Trees for Future Forests Project (VKR-023063).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

13595_2018_740_MOESM1_ESM.docx (29 kb)
ESM 1 (DOCX 29 kb)

References

  1. Aitken SN, Whitlock MC (2013) Assisted gene flow to facilitate local adaptation to climate change. Annu Rev Ecol Evol Syst 44:367–388CrossRefGoogle Scholar
  2. Arnaud-Haond S, Teixeira S, Massa SI, Billot C, Saenger P, Coupland G, Duarte CM, Serrão EA (2006) Genetic structure at range edge: low diversity and high inbreeding in southeast Asian mangrove (Avicennia Marina) populations. Mol Ecol 15:3515–3525CrossRefPubMedGoogle Scholar
  3. Augspurger CK (2013) Reconstructing patterns of temperature, phenology, and frost damage over 124 years: spring damage risk is increasing. Ecology 94:41–50CrossRefPubMedGoogle Scholar
  4. Breitbach N, Laube I, Steffan-Dewenter I, Böhning-Gaese K (2010) Bird diversity and seed dispersal along a human land-use gradient: high seed removal in structurally simple farmland. Oecologia 162:965–976CrossRefPubMedGoogle Scholar
  5. Burdon RD (1977) Genetic correlation as a concept for studying genotype–environment interaction in forest tree breeding. Silvae Genet. 26:168–175Google Scholar
  6. Cachi AM, Wünsch A, Vilanova A, Guárdia M, Ciordia M, Aletá N (2017) S-locus diversity and cross-compatibility of wild Prunus avium for timber breeding. Plant Breed 136:126–131CrossRefGoogle Scholar
  7. Channell R, Lomolino MV (2000) Dynamic biogeography and conservation of endangered species. Nature 403:84–86CrossRefPubMedGoogle Scholar
  8. Curnel Y, Jacques D, Nanson A (2003) First multisite clonal test of wild cherry (Prunus avium L.) in Belgium. Silvae Genet 52:45–51Google Scholar
  9. Ding MM, Tier B, Dutkowski GW, Wu HX, McRae TA (2008) Multi-environment trial analysis on Pinus radiata: step by step to search “real” G×E interaction. N.Z. J For Sci 38:143–159Google Scholar
  10. Ducci F, De Cuyper B, De Rogatis A, DufourJ SF (2013) Wild cherry breeding (Prunus avium L.). In: Pâques LE (ed) Forest tree breeding in Europe: current state-of-the-art and perspectives. Springer Netherlands, Dordrecht, pp 463–511CrossRefGoogle Scholar
  11. Dutkowski GW, Silva JC, Gilmour AR, Lopez AG (2002) Spatial analysis methods for forest genetic trials. Can J For Res 32(12):2201–2214CrossRefGoogle Scholar
  12. Eckert CG, Samis KE, Lougheed SC (2008) Genetic variation across species' geographical ranges: the central-marginal hypothesis and beyond. Mol Ecol 17:1170–1188CrossRefPubMedGoogle Scholar
  13. Fady B, Aravanopoulos FA, Alizoti P, Mátyás C, Wühlisch G, Westergren M, Belletti P, Cvjetkovic B, Ducci F, Huber G, Kelleher CT, Khaldi A, Kharrat MBD, Kraigher H, Kramer K, Mühlethaler U, Peric S, Perry A, Rousi M, Sbay H, Stojnic S, Tijardovic M, Tsvetkov I, Varela MC, Vendramin GG, Zlatanov T (2016) Evolution-based approach needed for the conservation and silviculture of peripheral forest tree populations. For Ecol Manag 375:66–75CrossRefGoogle Scholar
  14. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, Ed 4 edn. Longmans Green, HarlowGoogle Scholar
  15. Ganopoulos I, Aravanopoulos FA, Argiriou A, Kalivas A, Tsaftaris A (2011) Is the genetic diversity of small scattered forest tree populations at the southern limits of their range more prone to stochastic events? A wild cherry case study by microsatellite-based markers. Tree Genet Genomes 7:1299–1313CrossRefGoogle Scholar
  16. Gapare W, Aitken S, Ritland C (2005) Genetic diversity of core and peripheral Sitka Spruce ((Bong.) Carr) populations: implications for conservation of widespread species. Biol Conserv 123(1):113–123CrossRefGoogle Scholar
  17. Gibson SY, Van Der Marel RC, Starzomski BM (2009) Climate change and conservation of leading-edge peripheral populations. Conserv Biol 23:1369–1373CrossRefPubMedGoogle Scholar
  18. Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml user guide release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. www.vsni.co.uk
  19. Hamann A, Wang T, Spittlehouse DL, Murdock TQ (2013) A comprehensive, high-resolution database of historical and projected climate surfaces for western North America. Bull Am Meteorol Soc 94(9):1307–1309CrossRefGoogle Scholar
  20. Hampe A, Petit RJ (2005) Conserving biodiversity under climate change: the rear edge matters. Ecol Lett 8(5):461–467CrossRefPubMedGoogle Scholar
  21. Jankowska-Wroblewska S, Meyza K, Sztupecka E, Kubera L, Burczyk J (2016) Clonal structure and high genetic diversity at peripheral populations of Sorbus torminalis (L.) Crantz. iForest 9:892–900CrossRefGoogle Scholar
  22. Jolivet C, Höltken AM, Liesebach H, Steiner W, Degen B (2012) Mating patterns and pollen dispersal in four contrasting wild cherry populations (Prunus avium L.). Eur J For Res 131:1055–1069CrossRefGoogle Scholar
  23. Kendall MG, Stuart A (1979) The advantage theory of statistics. Vol. 2. Inference and relationship. Griffin and Co. LondonGoogle Scholar
  24. Kremer A, Ronce O, Robledo-Arnuncio JJ, Guillaume F, Bohrer G, Nathan R, Bridle JR, Gomulkiewicz R, Klein EK, Ritland K, Kuparinen A, Gerber S, Schueler S (2012) Long-distance gene flow and adaptation of forest trees to rapid climate change. Ecol Lett 15:378–392CrossRefPubMedPubMedCentralGoogle Scholar
  25. Kremer A, Potts BM, Delzon S (2014) Genetic divergence in forest trees: understanding the consequences of climate change. Funct Ecol 28:22–36CrossRefGoogle Scholar
  26. Kreyling J, Constanze B, Sabrina B, Martin H, Gerhard H, Lukas H, Anke J (2014) Local adaptations to frost in marginal and central populations of the dominant forest tree Fagus Sylvatica L. as affected by temperature and extreme drought in common garden experiments. Ecol Evol 4:594–605CrossRefPubMedPubMedCentralGoogle Scholar
  27. Lamy JB, Bouffier L, Burlett R, Plomion C, Cochard H, Delzon S (2011) Uniform selection as a primary force reducing population genetic differentiation of cavitation resistance across a species range. PLoS One 6:e23476CrossRefPubMedPubMedCentralGoogle Scholar
  28. Lenormand T (2002) Gene flow and the limits to natural selection. Trends Ecol. Evol 17:183–189CrossRefGoogle Scholar
  29. Lesica P, Allendorf FW (1995) When are peripheral populations valuable for conservation? Conserv Biol 9:753–760CrossRefGoogle Scholar
  30. Lira-Noriega A, Manthey JD (2014) Relationship of genetic diversity and niche centrality: a survey and analysis. Evolution 68:1082–1093CrossRefPubMedGoogle Scholar
  31. Loveless MD, Hamrick JL (1984) Ecological determinants of genetic structure in plant populations. Annu Rev Ecol Syst 15:65–95CrossRefGoogle Scholar
  32. Martinsson O (2001) Wild cherry (Prunus avium L.) for timber production: consequences for early growth from selection of open-pollinated single-tree progenies in Sweden. Scand J For Res 16:117–126CrossRefGoogle Scholar
  33. Mohanty A, Martin JP, Aguinagalde I (2001) A population genetic analysis of chloroplast DNA in wild populations of Prunus avium L. in Europe. Heredity 87:421–427CrossRefPubMedGoogle Scholar
  34. Muir G, Lowe AJ, Colin CF, Claus V (2004) High nuclear genetic diversity, high levels of outcrossing and low differentiation among remnant populations of Quercus Petraea at the margin of its range in Ireland. Ann Bot 93:691–697CrossRefPubMedPubMedCentralGoogle Scholar
  35. Nadeau S, Meirmans PG, Aitken SN, Ritland K, Isabel N (2016) The challenge of separating signatures of local adaptation from those of isolation by distance and colonization history: the case of two white pines. Ecol Evol. 00:1–16.  https://doi.org/10.1002/ece3.2550 8664CrossRefGoogle Scholar
  36. Petit RJ, Aguinagalde I, de Beaulieu JL, Bittkau C, Brewer S, Cheddadi R, Ennos R, Fineschi S, Grivet D, Lascoux M, Mohanty A, Müller-Starck G, Demesure-Musch B, Palmé A, Martín JP, Rendell S, Vendramin GG (2003) Glacial refugia: hotspots but not melting pots of genetic diversity. Science 300:1563–1565CrossRefPubMedGoogle Scholar
  37. Ramos G, Kirkpatrick M (1997) Genetic models of adaptation and gene flow in peripheral populations. Evolution 51:21–28CrossRefGoogle Scholar
  38. Rasmussen KK, Kollmann J (2007) Low genetic diversity in small peripheral populations of a rare European tree (Sorbus torminalis) dominated by clonal reproduction. Conserv Genet 9:1533–1539CrossRefGoogle Scholar
  39. Russell K (2003) In: EUFORGEN (ed) Technical Guidelines for genetic conservation and use for wild cherry (Prunus avium). International Plant Genetic Resources Institute, Rome, Italy, p 6Google Scholar
  40. Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biometrics 2(6):110–114CrossRefPubMedGoogle Scholar
  41. Savolainen O, Pyhäjärvi T, Knürr T (2007) Gene flow and local adaptation in trees. Annu Rev Ecol Evol Syst 38:595–619CrossRefGoogle Scholar
  42. Savolainen O, Kujala ST, Soko C, Pyhäjärvi T, Komlan A, Knürr T, Kärkkäinen K, Hicks S (2011) Adaptive potential of northernmost tree populations to climate change, with emphasis on Scots pine (Pinus sylvestris L.). J Hered 102:526–536CrossRefPubMedGoogle Scholar
  43. Sexton JP, Hangartner SB, Hoffmann AA (2014) Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution 68:1–15CrossRefPubMedGoogle Scholar
  44. Spitze K (1993) Population structure in Daphnia obtusa: quantitative genetic and allozymic variation. Genetics 135:367–374PubMedPubMedCentralGoogle Scholar
  45. Tukey JW (1958) Bias and confidence in not-quite large samples. Ann Math Stat 29(2):614–614CrossRefGoogle Scholar
  46. Vucetich JA, Waite TA (2003) Spatial patterns of demography and genetic processes across the species’ range: null hypotheses for landscape conservation genetics. Conserv Genet 4:639–645CrossRefGoogle Scholar
  47. Wang T, Hamann A, Yanchuk A, O’Neill GA, Aitken SN (2006) Use of response functions in selecting lodgepole pine populations for future climates. Glob Change Biol 12(12):2404–2416CrossRefGoogle Scholar
  48. Welk E, de Rigo D, Caudullo G (2016) Prunus avium in Europe: distribution, habitat, usage and threats. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European atlas of forest tree species. Publ. Off. EU, Luxembourg, pp.e01491dGoogle Scholar
  49. Yakimowski SB, Eckert CG (2008) Populations do not become less genetically diverse or more differentiated towards the northern limit of the geographical range in clonal Vaccinium stamineum (Ericaceae). New Phytol 180:534–544CrossRefPubMedGoogle Scholar
  50. Yeh FC, Layton C (1979) The organization of genetic variability in central and marginal populations of lodgepole pine Pinus contorta spp.latifolia. Canad J Genet Cytol. 21:487–503CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Geosciences and Natural Resource Management (IGN)University of CopenhagenFrederiksberg CDenmark
  2. 2.NaturstyrelsenMiljø- og FødevareministerietGræstedDenmark
  3. 3.SkogforskSvalövSweden

Personalised recommendations