Flight phenology and landscape predictors of invasive Coleophora deauratella populations in Oregon and New Zealand red clover

• Published in: Journal of pest science 2024-03, Vol.97 (2), p.631-643
• Main Authors: Dorman, Seth J., Kaur, Navneet, Anderson, Nicole P., Sim, Richard E., Tanner, K. Christy, Walenta, Darrin L., Cooper, W. Rodney
• Format: Article
• Language: English
• Subjects: Agricultural practices; Agriculture; Bayesian analysis; Bayesian theory; Biomedical and Life Sciences; Butterflies & moths; Coleophora; Coleophora deauratella; Composition; diet history; Ecology; Entomology; females; flight; Forestry; Grasslands; insect pests; Insects; Integrated pest management; Invasive insects; Invasive species; landscapes; Life Sciences; males; Mathematical models; New Zealand; Oregon; Original Paper; Pest control; Pest outbreaks; Pests; Phenology; Plant Pathology; Plant Sciences; Population dynamics; Populations; prediction; Red clover; regression analysis; Remote sensing; risk; Sex pheromone; sex pheromones; space and time; Statistical models; temperature; Trifolium pratense
• ISSN: 1612-4758; 1612-4766
• DOI: 10.1007/s10340-023-01684-8

Red clover casebearer moth ( Coleophora deauratella ) (Leinig and Zeller) (Lepidoptera: Coleophoridae) is an invasive insect pest in red clover ( Trifolium pratense L.) seed production systems in North America and New Zealand. Recent discoveries of C. deauratella in Oregon and New Zealand prompted research investigating the seasonal phenology and population dynamics of C. deauratella to inform management strategies and develop a risk prediction framework to mitigate outbreak severity. We sampled 76 site-years across three geographic regions, including western (Willamette Valley) and eastern Oregon and New Zealand. An attractant-based trap network was deployed across sampled regions using a female sex pheromone to lure male moths in commercial red clover seed production fields. Remotely sensed temperature and landscape composition data were extracted for phenological and geospatial modeling. Nonlinear logistic regression was used to develop regionally explicit phenology models that predict the unimodal timing of C. deauratella flights. Molecular gut-content analyses revealed the dietary history of early-season captures and informed landscape analysis covariate selection. A spatial Bayesian generalized linear mixed model (GLMM) was developed to test landscape-level effects of landscape composition and configuration predictors on C. deauratella abundance. The spatiotemporal dominance of clover and grassland land area was positively associated with Oregon C. deauratella populations. These results can be used to forecast C. deauratella risk across space and time and advise integrated pest management practices.