Integrated modelling of the life cycle and aeroecology of wind-borne pests in temporally-variable spatially-heterogeneous environment

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

Submitted to: Ecological Modelling
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/28/2019
Publication Date: 3/8/2019
Citation: Wang, H., Grant, W.E., Elliott, N.C., Brewer, M.J., Koralewski, T.E., Westbrook, J.K., Alves, T.M., Sword, G.A. 2019. Integrated modelling of the life cycle and aeroecology of wind-borne pests in temporally-variable spatially-heterogeneous environment. Ecological Modelling. 399:23-38. https://doi.org/10.1016/j.ecolmodel.2019.02.014.
DOI: https://doi.org/10.1016/j.ecolmodel.2019.02.014

Interpretive Summary: Sugarcane aphid is an economic pest of sorghum in Asia, Africa, Australia, and South America and recently have invaded North America, threatening over 90% of the continent's sorghum production. Long-distance, wind-aided dispersal together with the ability to overwinter in the southernmost sorghum-producing areas appear responsible for their geographic spread in North America. We describe a spatially-explicit, individual-based, stochastic model that integrates the life cycle and aeroecology of sugarcane aphids to forecast regional infestations of sorghum fields. We parameterized and calibrated the model to represent environmental conditions and sorghum phenologies representative of the central Great Plains of the USA. The geographical pattern of infestation probabilities approximately mirrored the proportion of land cover in sorghum production, with a prominent infestation "hot spot" covering west-central Kansas and the panhandles of Texas and Oklahoma, and a smaller "hot spot" in the Rio Grande Valley of south Texas. Models capable of simulating both local population dynamics as well as long-range, multi-generational dispersal can aide in understanding the population dynamics of the aphid and contribute to developing an effective area-wide pest management program for the aphid.

Technical Abstract: Sugarcane aphids (Melanaphis sacchari) are an economic pest of sorghum in Asia, Africa, Australia, and South America and recently have invaded North America, threatening over 90% of the continent's sorghum production. Long-distance, wind-aided dispersal together with the ability to overwinter in the southernmost sorghum-producing areas appear responsible for their geographic spread in North America. We describe a spatially-explicit, individual-based, stochastic model that integrates the life cycle and aeroecology of sugarcane aphids to forecast regional infestations of sorghum fields. We parameterized and calibrated the model to represent environmental conditions and sorghum phenologies representative of the central Great Plains of the USA. We validated the model by comparing simulated spatial-temporal patterns of aphid infestations to georeferenced field data on infestations collected as part of an extensive USDA-ARS field project. We assessed potential usefulness of the model by simulating regional patterns of aphid infestations on sorghum under climatic conditions recorded in the central Great Plains during recent years, assuming different dates of initial infestations along the Texas-Mexico border. Our results indicated the stochastic nature of aphid infestations resulted in wide variation in the time lag between appearance of sorghum and initial infestation, which led to wide variation in infestation levels among landscape cells within latitudinally-defined regions and obscured the underlying northward seasonal migration pattern. The geographical pattern of infestation probabilities approximately mirrored the proportion of land cover in sorghum production, with a prominent infestation "hot spot" covering west-central Kansas and the panhandles of Texas and Oklahoma, and a smaller "hot spot" in the Rio Grande Valley of south Texas. Models capable of simulating both local population dynamics as well as long-range, multi-generational dispersal of wind-borne pests show great promise as integral parts of adaptive integrated pest management programs.