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Extensive spatial genetic structure revealed by AFLP but not SSR molecular markers in the wind-pollinated tree, Fagus sylvatica
Authors:Jump Alistair S  Peñuelas Josep
Institution:Unitat d'Ecofisiologia CSIC-CEAB-CREAF, CREAF (Centre de Recerca Ecològica i Aplicacions Forestals), Universitat Autònoma de Barcelona, 08193, Bellaterra, Catalonia, Spain. aj523@york.ac.uk
Abstract:Studies of fine-scale spatial genetic structure (SGS) in wind-pollinated trees have shown that SGS is generally weak and extends over relatively short distances (less than 30-40 m) from individual trees. However, recent simulations have shown that detection of SGS is heavily dependent on both the choice of molecular markers and the strategy used to sample the studied population. Published studies may not always have used sufficient markers and/or individuals for the accurate estimation of SGS. To assess the extent of SGS within a population of the wind-pollinated tree Fagus sylvatica, we genotyped 200 trees at six microsatellite or simple sequence repeat (SSR) loci and 250 amplified fragment length polymorphisms (AFLP) and conducted spatial analyses of pairwise kinship coefficients. We re-sampled our data set over individuals and over loci to determine the effect of reducing the sample size and number of loci used for SGS estimation. We found that SGS estimated from AFLP markers extended nearly four times further than has been estimated before using other molecular markers in this species, indicating a persistent effect of restricted gene flow at small spatial scales. However, our SSR-based estimate was in agreement with other published studies. Spatial genetic structure in F. sylvatica and similar wind-pollinated trees may therefore be substantially larger than has been estimated previously. Although 100-150 AFLP loci and 150-200 individuals appear sufficient for adequately estimating SGS in our analysis, 150-200 individuals and six SSR loci may still be too few to provide a good estimation of SGS in this species.
Keywords:European beech  gene flow  genetic diversity  population structure  spatial autocorrelation
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