Integrating models of atmospheric dispersion and crop-pest dynamics: Linking detection of local aphid infestations to forecasts of region-wide invasion of cereal crops

Authors: KORALEWSKI, TOMASZ - Texas A&M University; WANG, HSIAO-HSUAN - Texas A&M University; GRANT, WILLIAM - Texas A&M University; BREWER, MICHAEL - Texas Agrilife Research; Elliott, Norman - Norm; Westbrook, John

Submitted to: Annals of the Entomological Society of America
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/30/2019
Publication Date: 2/11/2020
Citation: Koralewski, T.E., Wang, H., Grant, W.E., Brewer, M.J., Elliott, N.C., Westbrook, J.K. 2020. Integrating models of atmospheric dispersion and crop-pest dynamics: Linking detection of local aphid infestations to forecasts of region-wide invasion of cereal crops. Annals of the Entomological Society of America. 113(2):79-87. https://doi:10.1093/aesa/saz047.
DOI: https://doi.org/10.1093/aesa/saz047

Interpretive Summary: Invasive insect pests pose major challenges to agriculture production. Field pest monitoring and insecticides are important tools to prevent and control infestations but require additional information to be useful in an areawide pest management context. Mathematical modeling techniques have been effective in both simulating local population dynamics and capturing regional-scale invasive species spread. Advances in the field include model integration approaches, in which independent models are integrated in a common framework. We use an integrated ecological model to simulate local and regional infestation dynamics of sorghum fields by sugarcane aphids in the southern to central Great Plains of the USA. Local population dynamics of sugarcane aphids and of their host plant sorghum are simulated by a spatially-explicit, individual-based model developed in NetLogo, whereas the regional aphid migration is simulated by an atmospheric model HYSPLIT that computes inert air particle transport, dispersion, and deposition. Our results indicate that the timing of the initial infestation on the geographic patterns of infestation throughout the region varies. The probability of a given field to be infested by sugarcane aphids reflects both percentage the geographic area occupied by sorghum fields and atmospheric processes, such as prevailing winds. The presented approach could be applied in region-wide management systems as a forecasting tool, to trigger field sampling in any particular geographic area.

Technical Abstract: Invasive airborne species pose major challenges in natural resource and agriculture management as they can rapidly spread over large distances and cross physical boundaries. Field monitoring and local management are important tools to prevent and control infestations but require additional coordination to be operative region-wide. Computational modeling techniques have been effective in both simulating local population dynamics and capturing regional-scale invasive species spread. Advances in the field include model integration approaches, in which independent models are integrated in a common framework. In this work we utilize an integrated ecological model to simulate local and regional infestation dynamics of sorghum, Sorghum bicolor (L.), fields by sugarcane aphids, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae), in the southern to central Great Plains of the USA. Local population dynamics of sugarcane aphids and of their host plant sorghum are simulated by a spatially-explicit, individual-based model developed in NetLogo, whereas the regional aphid migration is simulated by an atmospheric model HYSPLIT that computes inert air particle transport, dispersion, and deposition. Our results indicate that the impact of timing of the initial infestation in the southern Great Plains of the USA on the spatio-temporal patterns of infestation throughout the region varies. The probability of a given landscape cell infestation reflects both percentage of the cell occupied by sorghum and the atmospheric processes, such as prevailing winds. The presented approach could be applied in region-wide management systems as a forecasting tool, especially in coordination with field monitoring techniques.