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Tree Genetics & Genomes

, Volume 7, Issue 4, pp 707–723 | Cite as

Long-term human impacts on genetic structure of Italian walnut inferred by SSR markers

  • Paola Pollegioni
  • Keith Woeste
  • Irene Olimpieri
  • Danilo Marandola
  • Francesco Cannata
  • Maria Emilia Malvolti
Original Paper

Abstract

Life history traits, historic factors, and human activities can all shape the genetic diversity of a species. In Italy, walnut (Juglans regia L.) has a long history of cultivation both for wood and edible nuts. To better understand the genetic variability of current Italian walnut resources, we analyzed the relationships among the genetic structure of local walnut populations (inferred by SSR markers) and human migrations along ancient routes, using the territory of Royal Tratturo Candela-Pescasseroli (RT) as a case study. Sixteen J. regia provenances were collected along RT and compared with 13 Italian provenances and the landrace Sorrento. Although the level of SSR polymorphism we observed was moderately high, AMOVA revealed that most of the diversity was located within individuals (92.58%), and geographical differentiation was low (D est = 0.076). Evidence for human-mediated domestication bottleneck events was detected in about 95% of walnut provenances. A Bayesian approach divided 456 walnut samples into three clusters: (1) Sorrento genotypes, (2) trees from the island of Sicily, and (3) the remaining germplasm. The UPGMA tree based on Nei's distances distinguished northeastern provenances and weakly grouped 12 of 16 provenances of RT. The observed genetic differences derived mainly from gradations in allele frequencies. Separation of the Sicilian provenance from the mainland may be explained in terms of founder effects and prolonged geographic isolation. Two contrasting forces, selection, and frequent inter-regional transfer of propagules, appear to drive the patterns of genetic variability for J. regia.

Keywords

J. regia SSR genetic structure Royal Tratturo 

Notes

Acknowledgments

The study was developed in the framework of the Italian Project “FIMONT” (Metodi e sistemi per aumentare il valore aggiunto degli alimenti tradizionali ed a vocazione territoriale nelle zone montane) supported by Italian Ministry of Research, scientific coordinator Professor Giacomo Elias (University of Milan), financial coordinator Dr. Rosanna Farina (Italian Mountain Institution, Rome). The authors thank Giovanni De Simoni, Marcello Cherubini, Daniela Taurchini, and Daniele Canestrelli for their support in statistical and laboratory analysis and Dr. Claudia Mattioni (CNR-IBAF, Porano) and Dr. Isacco Beritognolo (CNR-ISAFOM, Perugia) for their critical review of the manuscript. A warm thanks to Francesco Pallotta for logistic support in Molise and Abruzzo. The use of trade names is for the information and convenience of the reader and does not imply official endorsement or approval by the United States Department of Agriculture or the Forest Service of any product to the exclusion of others that may be suitable.

References

  1. Avram M (2009) The legacy of transhumance in national park of Abruzzo, Lazio and Molise (PNALM): rediscovery and exploitation. Geo J Tour Geosites 4:153–159Google Scholar
  2. Bagnoli F, Vendramin GG, Buonamici A, Doulis G, Gonzàlez-Martìnez C, La Porta N, Magri D, Raddi P, Sebastiani F, Fineschi S (2009) Is Cupressus sempervirens native in Italy? An answer from genetic and paleobotanical data. Mol Ecol 18:2276–2286PubMedCrossRefGoogle Scholar
  3. Bai WN, Zeng YF, Zhang DY (2007) Mating patterns and pollen dispersal in a heterodichogamous tree, Juglans mandshurica (Juglandaceae). New Phytol 176:699–707PubMedCrossRefGoogle Scholar
  4. Balloux F, Lugon-Moulin N (2002) The estimation of population differentiation with microsatellite markers. Mol Ecol 11:155–165PubMedCrossRefGoogle Scholar
  5. Beaumont MA, Balding DJ (2004) Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol 13:969–980PubMedCrossRefGoogle Scholar
  6. Beaumont MA, Nichols RA (1996) Evaluating loci for use in the genetic analysis of population structure. Proc Roy Soc Lond B Biol Sci 263:1619–1626CrossRefGoogle Scholar
  7. Belaj A, Munoz-Diez C, Baldoni L, Porceddu A, Barranco D, Satovic Z (2007) Genetic diversity and population structure of wild olives from the North-Western Mediterranean assessed by SSR markers. Ann Bot 100:449–458PubMedCrossRefGoogle Scholar
  8. Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014PubMedGoogle Scholar
  9. Cournet JM, Piry S, Luikart G, Estoup A, Soligna M (1999) New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153:1989–2000Google Scholar
  10. Crawford NG (2009) SMOGD: software for the measurement of genetic diversity. Mol Ecol Resources, doi: 10.1111/j.1755-0998.2009.02801.x
  11. Dangl GS, Woeste K, Ardhya MK, Koehmstedt A, Simon C (2005) Characterization of 14 microsatellite markers for genetic analysis and cultivar identification of walnut. J Am Soc Hortic Sci 130:348–354Google Scholar
  12. Di Vaio C, Minotta G (2005) Indagine sulla coltivazione del noce da legno in Campania. Forest@ 2:185-197Google Scholar
  13. Dupanloup I, Schneider S, Excoffier L (2002) A simulated annealing approach to define the genetic structure of populations. Mol Ecol 11:2571–2581PubMedCrossRefGoogle Scholar
  14. Dutech C, Joly HI, Jarne P (2004) Gene flow, historical population dynamics and genetic diversity within French Guianan populations of a rainforest tree species, Vouacapoua americana. Heredity 92:69–77PubMedCrossRefGoogle Scholar
  15. El Mousadik A, Petit RJ (1996) High level of genetic differentiation for allelic richness among populations of the argan tree (Argania spinosa (L.) Skeels) endemic to Morocco. Theor Appl Genet 92:832–839CrossRefGoogle Scholar
  16. 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
  17. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491PubMedGoogle Scholar
  18. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: an integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1:47–50Google Scholar
  19. Falush D, Stephens M, Pritchard JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes 7:574–578PubMedCrossRefGoogle Scholar
  20. Ferrazzini D, Monteleone I, Lecce F, Belletti P (2007) Variabilità genetica del noce comune (Juglans regia) in Piemonte. Forest 4:386–394CrossRefGoogle Scholar
  21. Fineschi S, Taurchini D, Grossoni P, Petit RJ, Vendramin G (2002) Chloroplastic DNA variation of white oaks in Italy. Forest Ecol Manag 156:103–114CrossRefGoogle Scholar
  22. Fornari B, Cannata F, Spada M, Malvolti ME (1999) Allozyme analysis of genetic diversity and differentiation in European and Asiatic walnut (Juglans regia L.) populations. Forest Genet 6:115–127Google Scholar
  23. Fornari B, Malvolti ME, Taurchini D, Fineschi S, Beritognolo I, Maccaglia E, Cannata F (2001) Isozyme ad organellar DNA analysis of genetic diversity in natural/naturalised European and Asiatic walnut (Juglans regia L.) populations. Acta Hortic 544:167–178Google Scholar
  24. Foroni I, Woeste K, Monti LM, Rao R (2007) Identification of “Sorrento” walnut using simple sequence repeats (SSRs). Genet Resour Crop Evol 54:1081–1094CrossRefGoogle Scholar
  25. Gaza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10:305–318CrossRefGoogle Scholar
  26. Gunn BF, Aradhya M, Salick JM, Miller AJ, Yongping Y, Lin L, Xian H (2010) Genetic variation in walnuts (Juglans regia and J. sigillata; Juglandaceae): Species distinctions, human impacts, and the conservation of agrobiodiversity in Yunnan, China. Am J Bot. doi: 10.3732/ajb.0900114 Google Scholar
  27. Hamrick JL, Godt MJW, Sherman-Broyles SL (1992) Factors influencing levels of genetic diversity in woody plant species. New Forest 6:95–124CrossRefGoogle Scholar
  28. Heller R, Siegismund HR (2009) Relationship between three measures of genetic differentiation GST, DEST and GSR how wrong have we been? Mol Ecol 18:2080–2083PubMedCrossRefGoogle Scholar
  29. Heuertz M, Carnevale S, Fineschi S, Sebastiani F, Hausman JF, Paule L, Vendramin GG (2006) Chloroplast DNA phylogeography of European ashes, Fraxinus sp. (Oleaceae): roles of hybridization and life history traits. Mol Ecol 15:2131–2140PubMedCrossRefGoogle Scholar
  30. Hu LJ, Uchiyama K, Shen HL, Saito Y, Tsuda Y, Ide Y (2008) Nuclear DNA microsatellites reveal genetic variation but a lack of phylogeographical structure in an endangered species, Fraxinus mandshurica, across North-East China. Ann Bot 102:195–205PubMedCrossRefGoogle Scholar
  31. Huntley B, Birks HJB (1983) An atlas of past and present pollen maps for Europe: 0-13000 Years Ago. Cambridge University Press, New YorkGoogle Scholar
  32. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodaly in analysis of population structure. Bioinformatics 14:1801–1806CrossRefGoogle Scholar
  33. Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026PubMedCrossRefGoogle Scholar
  34. Kalinowski ST (2004) Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conserv Genet 5:539–543CrossRefGoogle Scholar
  35. Kimura M, Otha T (1978) Stepwise mutation model and distribution of allelic frequencies in finite populations. PNAS 75:2868–2872PubMedCrossRefGoogle Scholar
  36. Krutovsky KV, Clair JBS, Saich R, Hipkins VD, Neale DB (2009) Estimation of population structure in coastal Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] using allozyme and microsatellite markers. Tree Genet Genomes 5:641–658CrossRefGoogle Scholar
  37. Lewontin RC, Krakauer J (1973) Distribution of gene frequency as a test of the theory of the selective neutrality of polymphisms. Genetics 74:175–195PubMedGoogle Scholar
  38. Luikart G, England PR, Tallmon D, Jordan S, Taberlet P (2009) The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet 4:981–994CrossRefGoogle Scholar
  39. Malvolti ME, Paciucci M, Cannata F, Fineschi S (1993) Genetic variation in Italian populations of Juglans regia. Acta Hortic 311:86–91Google Scholar
  40. Malvolti ME, Beritognolo I, Spada M, Cannata F (1997) Ricerche sulle risorse genetiche e sulla biologia riproduttiva in Juglans regia L. in Italia mediante marcatori molecolari. Ann. Ist. Sper. Selv. XXV-XXVI: 9–34.Google Scholar
  41. Malvolti ME, Pollegioni P, Mapelli S, Cannata F (2010) Research of Juglans regia provenances by molecular, morphological and biochemical markers: a case study in Italy. Bioremediation, Biodiversity and Bioavailability 4:84–92Google Scholar
  42. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220PubMedGoogle Scholar
  43. Marandola D, Malvolti ME, Farina ME, Tognetti R (2008) Biodiversity and rural development: the case-study of the “Shepherd’s walnut” or “Tratturo’s walnut”. An action model for sustainable rural development shaped on the peculiar feature of a rural area. EFI 2008 Annual Conference week scientific seminar, 15–20 September, Orvieto, Terni, Italy.Google Scholar
  44. Marshall TC, Slate J, Kruuk LEB, Pemberton JM (1998) Statistical confidence for likelihhod-based paternity inference in natural populations. Mol Ecol 7:639–655PubMedCrossRefGoogle Scholar
  45. Martin MA, Mattioni C, Cherubini M, Turchini D, Villani F (2010) Genetic diversità in European chestnut populations by means of genomic and genic microsatellite markers. Tree Genet Genomes 6:735–744CrossRefGoogle Scholar
  46. McGranahan G, Leslie C (2009) Breeding walnuts (Juglans regia). In: Jain SM, Pryadarshan PM (eds) Breeding Plantation Tree Crop: Temperate Species. Springer Science + Business Media, LLC, pp 249–273CrossRefGoogle Scholar
  47. Meyer FG (1980) Carbonized food plants of Pompeii, Herculaneum, and the Villa at Torre Annunziata. Econ Bot 34:401–437CrossRefGoogle Scholar
  48. Müller-Starck G, Baradat P, Bergmann F (1992) Genetic variation within European tree species. New Forest 6:23–47CrossRefGoogle Scholar
  49. Nei M (1972) Genetic distance between populations. Am Nat 106:283–292CrossRefGoogle Scholar
  50. Paetkau D, Calvert W, Stirling W, Strobeck C (1995) Microsatellite analysis of population structure in Canadian polar bears. Mol Ecol 4:347–354PubMedCrossRefGoogle Scholar
  51. 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 13:55–65PubMedCrossRefGoogle Scholar
  52. Palasciano I (1999) The long grass routes (Tratturi and pastors of the South) Capone (Ed), pp 1–88.Google Scholar
  53. Peakall R, Smouse PE (2005) GenAlEx V6: Genetic Analysis in Excel. Population genetic software for teaching and research. The Australian National University, Canberra, Australia. http://www.anu.edu.au/BoZo/genAlEx/.
  54. Petit RJ, Duminil J, Fineschi S, Hampe A, Salvini D, Vendramin GG (2005) Comparative organization of chloroplast, mitochondrial and nuclear diversity in plant populations. Mol Ecol 14:689–701PubMedCrossRefGoogle Scholar
  55. Piry S, Luikart G, Cornuet JM (1999) BOTTLENECK: a computer program for detecting recent reductions in effective population size from allele frequency data. J Hered 90:502–503CrossRefGoogle Scholar
  56. Pollegioni P, Woeste K, Major A, Scarascia Mugnozza G, Malvolti ME (2009a) Characterization of Juglans nigra (L.), Juglans regia (L.) and Juglans x intermedia (Carr.) by SSR markers: a case study in Italy. Silvae Genet 58:68–78Google Scholar
  57. Pollegioni P, Woeste K, Scarascia Mugnozza G, Malvolti ME (2009b) Retrospective identification of hybrodogenic walnut plants by SSR fingerprinting and parentage analysis. Mol Breed 24:321–335CrossRefGoogle Scholar
  58. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedGoogle Scholar
  59. Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. PNAS 94:9197–9201PubMedCrossRefGoogle Scholar
  60. Robichaud RL, Glaubitz JC, Rhodes OE Jr, Woeste K (2006) A robust set of black walnut microsatellites for parentage and clonal identification. New Forest 32:179–196CrossRefGoogle Scholar
  61. Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138CrossRefGoogle Scholar
  62. Rousset F (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145:1219–1228PubMedGoogle Scholar
  63. Slatkin M (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics 139:457–462PubMedGoogle Scholar
  64. Soulsbury CD, Iossa G, Edwards KJ, Baker PJ, Harris S (2007) Allelic dropout from a high-quality DNA source. Conservat Genet 8:733–738CrossRefGoogle Scholar
  65. Storz JF (2005) Using genome scans of DNA polymorphism to infer adaptative population divergence. Mol Ecol 14:671–688PubMedCrossRefGoogle Scholar
  66. Takezaki N, Nei M, Tamura K (2010) POPTREE2: Software for constructing population trees from allele frequency data and computing other population statistics with Windows interface. Mol Biol Evol 27:747–752PubMedCrossRefGoogle Scholar
  67. Taylor JS, Durkin JMH, Breden F (1999) The death of a microsatellite: a phylogenetic perspective on microsatellite interruptions. Mol Biol Evol 16:567–572PubMedGoogle Scholar
  68. Van der Schoot J, Pospíšková M, Vosman B, Smulders MJM (2000) Development and characterization of microsatellite markers in black poplar (Populus nigra L.). Theor Appl Genet 101:317–322CrossRefGoogle Scholar
  69. Victory E, Glaubitz JC, Rhodes OE Jr, Woeste K (2006) Genetic homogeneity in Juglans nigra (Juglandaceae) at nuclear microsatellites. Am J Bot 93:118–126CrossRefGoogle Scholar
  70. Wang H, Pei D (2008) Genetic diversity and structure of walnut populations in Central and Southwestern China revealed by microsatellite markers. J Am Soc Hortic Sci 133:197–203Google Scholar
  71. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CrossRefGoogle Scholar
  72. Woeste K, Burns R, Rhodes O, Michler C (2002) Thirty polymorphic nuclear microsatellite loci from black walnut. J Hered 93:58–60PubMedCrossRefGoogle Scholar
  73. Wright S (1943) Isolation by distance. Genetics 28:114–138PubMedGoogle Scholar
  74. Yeh FC, Yang RC, Boyle TJB, Ye ZH, Mao JX (1997) POPGENE, the user-friendly shareware for population genetic analysis. Edmonton, Molecular Biology and Biotechnology Centre, University of Alberta, CanadaGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Paola Pollegioni
    • 1
  • Keith Woeste
    • 2
  • Irene Olimpieri
    • 1
  • Danilo Marandola
    • 1
  • Francesco Cannata
    • 1
  • Maria Emilia Malvolti
    • 1
  1. 1.C.N.R. Institute of Agro-environmental and Forest BiologyPoranoItaly
  2. 2.Department of Forestry and Natural Resources, U.S.D.A. Forest Service, Hardwood Tree Improvement and Regeneration CenterPurdue UniversityWest LafayetteUSA

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