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Since 1999, "emerging" (EM) strains of Watermelon mosaic virus (WMV) have been detected in cucurbit crops of southeastern France, probably as a result of recent introductions. Population genetic approaches were used to study the structure of EM isolates in southeastern France and to identify factors involved in their spatial distribution. A population clustering method (SAMOVA) and a maximum-difference algorithm (Monmonier's algorithm) were combined to visualize and quantify barriers to gene flow between populations. Both methods yielded similar results and two main barriers were identified. A North/South oriented barrier may be related to physical obstacles to gene flow (Rhône River, presence of an area with few cucurbit crops). Although the barrier was very strong, some "crossing" events were detected. A second barrier, oriented Northwest to Southeast, was not correlated with obvious geographical features. The two methods used here are complementary and confirm the limited spread of WMV-EM isolates. This approach can be useful in epidemiology studies to characterize the structure of viral populations and identify barriers to gene flow.

Neutral landscape models are not frequently used in the agronomical domain, whereas they would be very useful for studying given agro-ecological or physical processes. Contrary to ecological neutral landscape models, agricultural models have to represent and manage geometrical patches and thus should rely on tessellation methods. We present a three steps approach that aimed at simulating such landscapes. Firstly, we characterized the geometry of three real field patterns; secondly, we generated simulated field patterns with two tessellation methods attempting to control the value of some of the observed characteristics and, thirdly, we evaluated the simulated field patterns. For this evaluation, we considered that good simulated field patterns should capture characteristics of real landscapes that are important for the targeted agro-ecological process. Real landscapes and landscapes simulated using either a Voronoi or a rectangular tessellation were thus compared when used as input data within a gene flow model. The results showed that neither tessellation method captured field shapes correctly, thus leading to over or (small) under estimation of gene flow. The Voronoi tessellation, though, performed better than the rectangular tessellation. Possible research directions are proposed to improve the simulated patterns, including the use of post-processing, the control of cell orientation or the implementation of other tessellation techniques. [Copyright &y& Elsevier]