Pest and disease management: why we shouldn't go against the grain.

Given the wide range of scales and mechanisms by which pest or disease agents disperse, it is unclear whether there might exist a general relationship between scale of host heterogeneity and spatial spread that could be exploited by available management options. In this model-based study, we investi...

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Autores principales: Peter Skelsey, Kimberly A With, Karen A Garrett
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2013
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Acceso en línea:https://doaj.org/article/90ad9ee716a64a5dbf10227fdb3b8d7f
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Sumario:Given the wide range of scales and mechanisms by which pest or disease agents disperse, it is unclear whether there might exist a general relationship between scale of host heterogeneity and spatial spread that could be exploited by available management options. In this model-based study, we investigate the interaction between host distributions and the spread of pests and diseases using an array of models that encompass the dispersal and spread of a diverse range of economically important species: a major insect pest of coniferous forests in western North America, the mountain pine beetle (Dendroctonus ponderosae); the bacterium Pseudomonas syringae, one of the most-widespread and best-studied bacterial plant pathogens; the mosquito Culex erraticus, an important vector for many human and animal pathogens, including West Nile Virus; and the oomycete Phytophthora infestans, the causal agent of potato late blight. Our model results reveal an interesting general phenomenon: a unimodal ('humpbacked') relationship in the magnitude of infestation (an index of dispersal or population spread) with increasing grain size (i.e., the finest scale of patchiness) in the host distribution. Pest and disease management strategies targeting different aspects of host pattern (e.g., abundance, aggregation, isolation, quality) modified the shape of this relationship, but not the general unimodal form. This is a previously unreported effect that provides insight into the spatial scale at which management interventions are most likely to be successful, which, notably, do not always match the scale corresponding to maximum infestation. Our findings could provide a new basis for explaining historical outbreak events, and have implications for biosecurity and public health preparedness.