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Article Dans Une Revue Landscape Ecology Année : 2021

Modelling Monochamus galloprovincialis dispersal trajectories across a heterogeneous landscape to optimize monitoring by trapping networks

Résumé

Context The pine wood nematode (PWN) is an invasive species which was introduced into Europe in 1999. It represents a major economic and ecological threat to European forests. In Europe, the maritime pine is the main host and Monochamus galloprovinciallis is its only vector. Objectives Our goal was to analyze the effect of landscape heterogeneity on the vector’s dispersal. We further aimed at developing a new method to locate the origin of insects captured in a systematic network of pheromone traps. Methods A mark-release-recapture experiment was carried out in a heterogeneous landscape combining maritime pine plantations, clear-cuts and isolated patches of broadleaved and mixed forests in the southwest of France. Least-cost path analysis was used to model dispersal trajectories and assign friction values to each land-use type in the landscape. We used the trap’s geographical coordinates, capture levels and mean friction values of neighbouring patches to calculate a weighed barycentre and the position of the release of marked beetles. Results Least Cost Path modelling revealed the vector’s tendency to avoid habitat patches such as mixed or deciduous forests and not avoid clear-cuts. The weighted barycentre method was greatly improved when the friction values of the trap’s surrounding land-uses were used. Conclusions Our study demonstrates the value of applying landscape ecology concepts and methods to improve our understanding and prediction of pest invasion processes. A practical application is the design of systematic grids of pheromone traps to locate the infection focus from which PWN vectors originate in a newly colonized area.

Dates et versions

hal-03610281 , version 1 (16-03-2022)

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Citer

Pedro Nunes, Manuela Branco, Inge van Halder, Hervé Jactel. Modelling Monochamus galloprovincialis dispersal trajectories across a heterogeneous landscape to optimize monitoring by trapping networks. Landscape Ecology, 2021, 36 (3), pp.931-941. ⟨10.1007/s10980-020-01188-1⟩. ⟨hal-03610281⟩
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