Geospatial risk-based survey model for ‘Candidatus Liberibacter asiaticus’ detection in residential citrus populations in California

Authors: LUO, WEIQI - North Carolina State University; Gottwald, Timothy; POSNY, DREW - US Department Of Agriculture (USDA); MCROBERTS, NEIL - University Of California, Davis; SITLER, LEIGH - North Carolina State University; DUAN, KEVIN - North Carolina State University; Bock, Clive

Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/18/2025
Publication Date: 4/14/2026
Citation: Luo, W., Gottwald, T.R., Posny, D., Mcroberts, N., Sitler, L., Duan, K., Bock, C.H. Geospatial risk-based survey model for ‘Candidatus Liberibacter asiaticus’ detection in residential citrus populations in California. Plant Disease. 110: 1608-1621. 2026. https://doi.org/10.1094/PDIS-01-25-0075-RE.
DOI: https://doi.org/10.1094/PDIS-01-25-0075-RE

Interpretive Summary: Citrus huanglongbing (HLB) is a major disease threat to citrus-growing regions in California. It was initially identified in the Los Angeles residential basin in 2012, and has spread rapidly in urban neighborhoods across Orange, Riverside, San Bernardino, and San Diego Counties. Timely detection of HLB is crucial to prevent its establishment and safeguard California's citrus industry. A risk-based survey (RBS) model was designed to enhance decision-making for HLB surveillance. A retrospective rank analysis of HLB occurrences from 2015 to 2022 was used to determine predictive capabilities of individual risk factors. The factors include introduction risk from census travel patterns, previous ACP dispersal, previous HLB-confirmed locations, citrus transportation corridors, plant nursery, packinghouses, farmers' markets, proximity to military installations and Native American land. Census travel risk emerged as a pivotal factor in the early detection of HLB during the epidemic's onset. Conversely, historical ACP density was associated with HLB spread following its establishment. Adjusting the weight assigned to each component within the model is critical to enhance predictive accuracy as the HLB epidemic evolves. The predictive power of the RBS ranged from 88% to 97%. This indicates its utility for the developing targeted intervention or early detection strategies. Real time refining of the RBS allows regulatory agencies to proactively allocate resources and surveillance strategies to effectively manage HLB outbreaks.

Technical Abstract: Citrus huanglongbing (HLB), transmitted by the Asian Citrus Psyllid (ACP), has emerged as a devastating threat to citrus-growing regions in California. The presence of HLB was initially identified in the Los Angeles residential basin in 2012, and subsequently, manifested in urban clusters and residential neighborhoods and began spreading across Southern California. Timely detection of HLB is crucial to prevent its establishment and safeguard California's citrus industry. This study introduces a risk-based survey (RBS) model designed to enhance evidence-driven decision-making for HLB surveillance, disease intervention and mitigation. Within this framework, various model components evolve as the HLB dynamic emerges in different landscapes, necessitating continuous updates to ensure data accuracy and model reliability on an annual basis. Disease spread is influenced by natural factors, such as ACP establishment and locations confirmed with HLB, as well as human-mediated factors including risks from introduction through global mobility (international travel from HLB infected countries), transportation corridors for movement of citrus materials, nurseries, packinghouses, farmers' markets, and proximity to private or otherwise inaccessible lands for survey (e.g., military installations and Native American lands). Human-mediated risk factors account for approximately 26.3% (18.4 - 38.4%) of HLB propagation across different years, while natural causes predominantly explain the remaining 73.7% (61.6 -to 81.7%). Notably, the risk from introduction through global mobility emerged as a pivotal factor in the early detection of HLB in new areas across Southern California. Conversely, historical ACP density displayed a strong correlation with disease spread following its establishment. Consequently, it becomes imperative to promptly adjust the weight assigned to each component within the model to enhance predictive accuracy as the HLB epidemic evolves. A retrospective rank analysis was conducted to assess the individual performance of each risk factor from 2015 to 2022, with predictive power ranging from 88% to 97%, indicating its validity for the development of targeted interventions or early detection strategies in California. By refining the risk-based model through real-time integration of dynamic factors, regulatory agencies can proactively allocate resources and implement adaptable surveillance strategies to effectively manage HLB outbreaks.