Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-23T00:39:27.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Geographical patterns of gene frequencies in Italian populations of Ornithogalum montanum (Liliaceae)

Published online by Cambridge University Press:  14 April 2009

Massimo Pigliucci  and
Guido Barbujani
Massimo Pigliucci
Affiliation:
Istituto di Agroselvicoltura CNR, Parano (Terni), Italy
Guido Barbujani*
Affiliation:
Dipartimento di Biologia, Università di Padova, via Trieste 75, 1–35121 Padova, Italy
*
† Coresponding author.
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Geographic variation was studied at 15 electrophoretic loci (40 alleles) in Italian populations of Ornithogalum montanum Cyr. ex Ten. (Liliaceae). Homogeneity of allele frequencies was assessed by G tests; gene-frequency patterns were described by spatial autocorrelation statistics; matrices of genetic and environmental distance were compared through a series of Mantel's tests, and the zones of highest overall gene-frequency change per unit distance (steep multi-locus clines, or genetic boundaries) were identified. Nineteen allele frequencies appear heterogeneously distributed, but only 3 of them show significant spatial structure. Only 2 allele frequencies are correlated with 1 environmental parameter. Large genetic differences are observed between spatially close populations. These findings support a model of differentiation in which the genetic relationships between isolates do not depend on their spatial distances, but reflect mainly population subdivision and restricted gene flow.

Type
Research Article
Information
Genetics Research , Volume 58 , Issue 2 , October 1991 , pp. 95 - 104
Copyright
Copyright © Cambridge University Press 1991

References

Barbujani, G. (1987). Autocorrelation of gene frequencies under isolation by distance. Genetics 117, 777–782.Google Scholar
Barbujani, G., Jacquez, G. M. & Ligi, L. (1990). Diversity of some gene frequencies in European and Asian populations. V. Steep multilocus clines. American Journal of Human Genetics 48, 867–875.Google Scholar
Barbujani, G., Oden, N. L. & Sokal, R. R. (1989). Detecting areas of abrupt change in maps of biological variables. Systematic Zoology 38, 376–389.Google Scholar
Barbujani, G. & Pigliucci, M. (1989). Geographical patterns of karyotype polymorphism in Italian populations of Ornithogalum montanum (Liliaceae). Heredity 62, 67–75.Google Scholar
Barton, N. H. & Hewitt, G. M. (1985). Analysis of hybrid zones. Annual Review of Ecology and Systematics 16, 113–148.Google Scholar
Beck, E. & Renner, U. (1990). Net fluxes and pools of nitrogenous compounds during suspension culture of photoautotrophic Chenopodium rubrum cells. Plant, Cell and Environment 13, 111–122.Google Scholar
Brassel, K. E. & Reif, D. (1979). A procedure to generate Thiessen polygons. Geographical Analysis 11, 289–303.CrossRefGoogle Scholar
Capineri, R., D'Amato, G. F., Marchi, P., Marazia, T., Maggini, F. (1979). Band pattern polymorphism in Ornithogalum montanum Cyr. ex Ten. (Liliaceae). Atti Associazione Genetica Italiana 24, 83–85.Google Scholar
Cliff, A. D. & Ord, J. K. (1981). Spatial Processes. Models and Applications. London: Pion.Google Scholar
Cullen, J. & Ratter, J. A. (1967). Taxonomic and cytological notes on turkish Ornithogalum. Notes Royal Botanical Garden 27, 293–339.Google Scholar
Douglas, M. E. & Endler, J. A. (1982). Quantitative matrix comparisons in ecological and evolutionary investigations. Journal of Theoretical Biology 99, 777–795.Google Scholar
Easteal, S. (1981). The history of introductions of Bufo marinus (Amphibia: Anura); a natural experiment in evolution. Biological Journal of the Linnean Society 16, 93–113.Google Scholar
Easteal, S. (1985). The ecological genetics of introduced populations of the giant toad, Bufo marinus, III. Geographical patterns of variation. Evolution 39, 1065–1075.Google Scholar
Endler, J. A. (1977). Geographic Variation, Speciation, and Clines. Princeton, NJ: Princeton University Press.Google ScholarPubMed
Endler, J. A. (1982). Problems in distinguishing historical from ecological factors in biogeography. American Zoologist 22, 441–452.CrossRefGoogle Scholar
Endler, J. A. (1986). Natural Selection in the Wild. Princeton, NJ: Princeton University Press.Google Scholar
Epperson, B. K. (1990). Spatial autocorrelation of genotypes under directional selection. Genetics 124, 757–771.Google Scholar
Felsenstein, J. (1982). How can we infer geography and history from gene frequencies? Journal of Theoretical Biology 96, 9–20.CrossRefGoogle ScholarPubMed
Green, P. J. & Sibson, R. (1977). Computing Dirichlet tessellations in the plane. Computer Journal 21, 168–173.Google Scholar
Gould, S. J. & Johnston, R. F. (1972). Geographic variation. Annual Review of Ecology and Systematics 3, 457–498.Google Scholar
Legendre, P., Oden, N. L., Sokal, R. R., Vaudor, A. & Kim, J. (1990). Approximate analysis of variance of spatially autocorrelated regional data. Journal of Classification 7, 53–75.CrossRefGoogle Scholar
Manly, B. F. J. (1985). The Statistics of Natural Selection. London: Chapman & Hall.Google Scholar
Manly, B. J. F. (1986). Randomization and regression methods for testing for association with geographical, environmental and biological distances between populations. Research in Population Ecology 28, 201–218.Google Scholar
Mantel, N. (1967). The detection of disease clustering and a generalised regression approach. Cancer Research 27, 209–220.Google Scholar
Monmonier, M. (1973). Maximum-difference barriers: An alternative numerical regionalization method. Geographical Analysis 3, 245–261.CrossRefGoogle Scholar
Nagylaki, T. (1988). The influence of spatial inhomogeneities on neutral models of geographic variation. I. Formulation. Theoretical Population Biology 33, 291–310.Google Scholar
Nei, M. (1972). Genetic distance between populations. American Naturalist 106, 283–291.Google Scholar
Oden, N. L. (1984). Assessing the significance of a spatial correlogram. Geographical Analysis 16, 1–16.CrossRefGoogle Scholar
Pigliucci, M., Benedettelli, S. & Villani, F. (1990 a). Spatial patterns of genetic variability in Italian chestnut (Castanea saliva). Canadian Journal of Botany 68, 1962–1967.CrossRefGoogle Scholar
Pigliucci, M., Serafini, M., Bianchi, G. (1990 b). A multi-variate approach to the study of genetic variability in Ornithogalum montanum Cyr. (Liliaceae). Canadian Journal of Botany 68, 1788–1795.Google Scholar
Pigliucci, M., Politi, F., Bellincampi, D. (1991). Implications of phenotypic plasticity for numerical taxonomy of Ornithogalum montanum (Liliaceae). Canadian Journal of Botany 69, 34–38.Google Scholar
Richardson, S. & Hémon, D. (1981). On the variance of the sample correlation between two independent lattice processes. Journal of Applied Probability 18, 943–948.Google Scholar
Slatkin, M. (1985). Gene flow in natural populations. Annual Review of Ecology and Systematics 16, 393–430.Google Scholar
Statkin, M. (1987). Gene flow and the geographic structure of natural populations. Science 236, 787–792.Google Scholar
Slatkin, M. (1989). Population structure and evolutionary progress. Genome 31, 196–202.Google Scholar
Smouse, P. E., Long, J. C. & Sokal, R. R. (1986). Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Systematic Zoology 35, 627–632.Google Scholar
Sneath, P. H. A. & Sokal, R. R. (1973). Numerical Taxonomy. San Francisco: Freeman & Co.Google Scholar
Sokal, R. R. (1979 a). Testing statistical significance of geographical variation patterns. Systematic Zoology 28, 227–232.Google Scholar
Sokal, R. R. (1979 b). Ecological parameters inferred from spatial correlograms. In: Contemporary Quantitative Ecology and Related Ecometrics (ed. Patil, G. P. and Rosenzweig, M. L.), pp. 167–196. Fairland, Maryland: International Co-operative Publishing House.Google Scholar
Sokal, R. R. (1990). Update to Haldane's ‘Blood-group frequencies of European peoples and racial origins’. Human Biology 61, 691–702.Google Scholar
Sokal, R. R., Jacquez, G. M. & Wooten, M. C. (1989 a). Spatial autocorrelation analysis of migration and selection. Genetics 121, 845–855.Google Scholar
Sokal, R. R. & Oden, N. L. (1978 a). Spatial autocorrelation in biology. 1. Methodology. Biological Journal of the Linnean Society 10, 199–228.Google Scholar
Sokal, R. R. – Oden, N. L. (1978 b). Spatial autocorrelation in biology. 2. Some biological implications, and four applications of evolutionary and ecological interest. Biological Journal of the Linnean Society 10, 229–249.CrossRefGoogle Scholar
Sokal, R. R., Oden, N. L. & Barker, J. S. F. (1987). Spatial structure in Drosophila buzzatii populations: simple and directional spatial autocorrelation. American Naturalist 129, 122–142.Google Scholar
Sokal, R. R., Oden, N. L., Legendre, P., Fortin, M. J., Kim, J. & Vaudor, A. (1989 b). Genetic differences among language families in Europe. American Journal of Physical Anthropology 79, 489–502.Google Scholar
Terracciano, N. (1906). L'Ornithogalum montanum Cyr. e sue forme nella flora di Monte Pollino. Rendiconti dell' Accademia di Scienze Fisiche e Matematiche, Serie 3a, 12, 527–531.Google Scholar
Van Raamsdonk, L. W. D. (1985). Pollen fertility and seed formation in the Ornithogalum umbrellatum/angustifolium complex (Liliaceae/Scilloideae). Plant Systematics and Evolution 149, 287–297.Google Scholar
Womble, W. H. (1951). Differential systematics. Science 114, 315–322.Google Scholar
Wright, S. (1931). Evolution in Mendelian populations. Genetics 16, 97–159.CrossRefGoogle ScholarPubMed
Wright, S. (1970). Random drift and the shifting balance theory of evolution. In: Mathematical Topics in Population Genetics (ed. Kojima, K.s), pp. 1–31. Berlin: Springer.Google Scholar