Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/38063
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Type: Journal article
Title: Optimal sampling strategy for estimation of spatial genetic structure in tree populations
Author: Cavers, S.
Degen, B.
Caron, H.
Lemes, M.
Margis, R.
Salgueiro, F.
Lowe, A.
Citation: Heredity, 2005; 95(4):281-289
Publisher: Nature Publishing Group
Issue Date: 2005
ISSN: 0018-067X
1365-2540
Statement of
Responsibility: 
S Cavers, B Degen, H Caron, MR Lemes, R Margis, F Salgueiro and AJ Lowe
Abstract: Fine-scale spatial genetic structure (SGS) in natural tree populations is largely a result of restricted pollen and seed dispersal. Understanding the link between limitations to dispersal in gene vectors and SGS is of key interest to biologists and the availability of highly variable molecular markers has facilitated fine-scale analysis of populations. However, estimation of SGS may depend strongly on the type of genetic marker and sampling strategy (of both loci and individuals). To explore sampling limits, we created a model population with simulated distributions of dominant and codominant alleles, resulting from natural regeneration with restricted gene flow. SGS estimates from subsamples (simulating collection and analysis with amplified fragment length polymorphism (AFLP) and microsatellite markers) were correlated with the 'real' estimate (from the full model population). For both marker types, sampling ranges were evident, with lower limits below which estimation was poorly correlated and upper limits above which sampling became inefficient. Lower limits (correlation of 0.9) were 100 individuals, 10 loci for microsatellites and 150 individuals, 100 loci for AFLPs. Upper limits were 200 individuals, five loci for microsatellites and 200 individuals, 100 loci for AFLPs. The limits indicated by simulation were compared with data sets from real species. Instances where sampling effort had been either insufficient or inefficient were identified. The model results should form practical boundaries for studies aiming to detect SGS. However, greater sample sizes will be required in cases where SGS is weaker than for our simulated population, for example, in species with effective pollen/seed dispersal mechanisms.
Keywords: Spatial genetic structure; trees; sampling; spatial autocorrelation; dominant; codominant
Description: © 2005 Nature Publishing Group
RMID: 0020071401
DOI: 10.1038/sj.hdy.6800709
Appears in Collections:Earth and Environmental Sciences publications
Environment Institute Leaders publications

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