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We have investigated the fine-scale spatial genetic structure in a managed Scots pine forest. For this purpose, we perform a Bayesian genetic-cluster analysis of 96 geographically mapped individual seed trees of Swedish Scots pine based on 14 microsatellite loci. The analysis was carried out with the recently developed program GENECLUST (François et al., 2006), which provides the facility to jointly incorporate both spatial information from a geographical neighborhood structure through a Potts-Dirichlet model and account for variable degrees of inbreeding within the clusters. To evaluate whether inbreeding and spatial interaction should be included in the best-fitting statistical model for our data, we used the deviance information criterion (DIC), a weighted measure of model fit that accounts for an effective number of free parameters in a model. Analysis shows that a model with a single estimated cluster, with high levels of inbreeding (0.25) and with a moderate amount of spatial dependency within the unique cluster (Psi=0.2-0.4), best explains the data. We also carried out Bayesian parentage analysis, which enabled us to exclude the possibility that the sample constitutes one single full-sib family.