Tree Genetics & Genomes

, Volume 10, Issue 4, pp 839–851 | Cite as

New insights into the genetic structure of Araucaria araucana forests based on molecular and historic evidences

  • M. A. Martín
  • C. Mattioni
  • I. Lusini
  • J. R. Molina
  • M. Cherubini
  • F. Drake
  • M. A. Herrera
  • F. Villani
  • L. M. Martín
Original Paper


The conservation of genetic resources is a prerequisite for the maintenance of long-lived forest species. Araucaria araucana (Mol.) K. Koch is one of the oldest conifers in South America and a representative symbol of Chilean forest due to its endemicity and longevity. The aim of this study was to evaluate the genetic structure of the current A. araucana populations in Chile, to verify the possible genetic divergence between Coastal and Andean populations and to assess whether bottleneck events have influenced habitat fragmentation and threaten the genetic resources and evolutionary potential of the species. Twelve natural populations, nine from the Andes Cordillera and three from the Coast Cordillera were analysed by means of eight genomic microsatellite markers developed in A. araucana. Results of analysis of molecular variance (AMOVA) highlighted significant differentiation between Coastal and Andean populations (16 %; P = 0.004), detecting one significant barrier that separated populations from both Cordilleras as maximally differentiated areas. At local scale, both ranges revealed significant inter-population differentiation, with higher values for Coastal populations compared with Andean populations. These results suggested the presence of four gene pools (three in the Andes and one in the Coast Cordilleras) and one population (VIL) in the Coast Cordillera that differed to the rest. The differentiation between the Andean and Coastal populations may provide important baseline data that should allow further studies of landscape genetics in the species and that can contribute to develop conservation strategies for its genetic resources.


Araucaria araucana Genetic resources Nuclear microsatellites Population structure 



This research was partially supported by grants (No. 207-141-018-1.0) from the Research Service of Concepción University (Chile), A/023099/09 and A/030789/10, of the Spanish Agency of International Cooperation for the Development from the Spanish Ministry for Foreign Affairs and Cooperation. The first author is grateful to Agrifood Campus of International Excellence (ceiA3) from the Spanish Ministry of Education and the Ministry of Science and Innovation for the financial support.

Data archiving statement

Following the standard Tree Genetics and Genomes policy, we declare that the samples and populations SSR data will be uploaded in the TreeGenes Database (

Supplementary material

11295_2014_725_MOESM1_ESM.doc (28 kb)
ESM 1 Matrix of correlation between geographical variables and diversity parameters. (DOC 28 kb)
11295_2014_725_MOESM2_ESM.doc (33 kb)
ESM 2 Percentage of membership (admixture proportion-Q) of each population in each cluster inferred by STRUCTURE software. A Q-value greater than 0.75 indicates that the population belongs to the cluster. (DOC 33 kb)
11295_2014_725_MOESM3_ESM.doc (46 kb)
ESM 3 Inference of K, the most probable number of clusters, using Structure software, with locprior. A) Log-likelihood value of data L(K) as a function of K averaged over six replicates; B) Log-likelihood of the data (ΔK) as a function of K, calculated over six replicates. Plots made using Structure Harvester (Earl and von Holdt 2011). (DOC 45 kb)
11295_2014_725_MOESM4_ESM.doc (34 kb)
ESM 4 Genetic diversity indices for the four groups of populations identified by Samova software analysis. (DOC 34 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • M. A. Martín
    • 1
  • C. Mattioni
    • 2
  • I. Lusini
    • 2
  • J. R. Molina
    • 3
  • M. Cherubini
    • 2
  • F. Drake
    • 4
  • M. A. Herrera
    • 3
  • F. Villani
    • 2
  • L. M. Martín
    • 1
  1. 1.Departamento de Genética, E.T.S.I.A.M., Campus de Excelencia Internacional Agroalimentario (ceiA3)Universidad de CórdobaCordobaSpain
  2. 2.Istituto di Biologia Agroambientale e Forestale (IBAF)Consiglio Nazionale delle Ricerche (CNR)PoranoItaly
  3. 3.Departamento de Ingeniería Forestal, E.T.S.I.A.M., Campus de Excelencia Internacional Agroalimentario (ceiA3)Universidad de CórdobaCordobaSpain
  4. 4.Departamento de Manejo de Bosques y Medioambiente, Facultad de Ciencias ForestalesUniversidad de ConcepciónConcepciónChile

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