Please use this identifier to cite or link to this item:
http://acervodigital.unesp.br/handle/11449/116331
- Title:
- A mathematical approach to simulate spatio-temporal patterns of an insect-pest, the corn rootworm Diabrotica speciosa (Coleoptera: Chrysomelidae) in intercropping systems
- Universidade Estadual Paulista (UNESP)
- Universidade de São Paulo (USP)
- 0921-2973
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
- Fundação para o Desenvolvimento da UNESP (FUNDUNESP)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
- Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
- FUNDUNESP: 2275/002/14-PROPe/CDC
- CNPq: 483567/2012-4
- FAPESP: 13/24140-6
- FAPESP: 12/00254-0
- We report on the use of a spatially explicit model and clustering analysis in order to investigate habitat manipulation as a strategy to regulate natural population densities of the insect-pest Diabrotica speciosa. Habitat manipulation involved four major agricultural plants used as hosts by this herbivore to compose intercropping landscapes. Available biological parameters for D. speciosa on bean, soybean, potato and corn obtained under laboratory conditions were used to group the homogeneous landscapes, composed by each host plant, by a similarity measure of host suitability either for larval survival and development, and adult survival and fecundity. The results pointed corn as the most dissimilar culture. Therefore, intercropping corn with any other crop system tested could reduce insect spread through landscape. This was proved using a cellular automata model which simulate the physiological and behavioural traits of this insect, and also different spatial configurations of the intercropping. Spatio-temporal patterns obtained by simulations demonstrated that the availability of corn bordering the field edge, which are areas more likely to invasion, is key for insect population control.
- 1-Nov-2014
- Landscape Ecology. Dordrecht: Springer, v. 29, n. 9, p. 1531-1540, 2014.
- 1531-1540
- Springer
- Cellular automata
- Clustering algorithm
- Nutritional ecology
- Agricultural landscapes
- http://dx.doi.org/10.1007/s10980-014-0073-4
- Acesso restrito
- outro
- http://repositorio.unesp.br/handle/11449/116331
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