McRoberts, Neil

Citrus huanglongbing (HLB), associated with the bacterium ‘Candidatus Liberibacter asiaticus’ and spread by the Asian citrus psyllid (Diaphorina citri; ACP), poses a significant threat to California’s citrus industry. First identified in Los Angeles in 2012, HLB has since spread through residential areas across Southern California. A risk-based survey (RBS) model has been developed to improve HLB surveillance and intervention. Within this framework, model components change as HLB dynamics shift, requiring regular updates to maintain data accuracy and model reliability. Disease spread is influenced by natural factors, such as ACP establishment and confirmed HLB locations, as well as human-mediated factors like global mobility (travel introduction from HLB-infected countries), transportation of citrus materials, nurseries, packinghouses, farmers’ markets, and proximity to private or otherwise inaccessible lands. Human-mediated risk factors account for approximately 26.3% (18.4 to 38.4%) of HLB incidence across different years, and natural causes predominantly explain the remaining 73.7% (61.6 to 81.7%). Notably, global mobility was crucial for early HLB detection in new areas, whereas ACP density strongly correlated with disease spread once established. A retrospective analysis from 2015 to 2022 evaluated the RBS model’s performance, showing a predictive power of 88 to 97%, which confirms its validity for developing targeted interventions and early detection strategies in California.

Huanglongbing (citrus greening disease) is caused by the bacterium ‘Candidatus Liberibacter asiaticus’ (CLas) (Alphaproteobacteria) and is one of the most destructive bacterial-vector diseases affecting the citrus industry. The bacterium is transmitted by the Asian citrus psyllid (ACP; Diaphorina citri). Early detection in citrus trees is challenging due to uneven distribution of CLas throughout the tree and a long pre-symptomatic phase of the disease. Due to these limitations, ACP sampling has been suggested as a more reliable early detection strategy. The objective of this study was to develop and optimize approaches for detecting CLas in ACP adults and nymphs collected in citrus groves in California using real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR). The goal was to establish the optimal number of ACP adults and nymphal instar life stages (stages 1–2, 3, or 4–5) that yielded the most reliable detection of CLas (Cq values ≤ 38). Results indicated that CLas detection correlated with psyllid developmental stage, with the 4th–5th instar nymphs (sample size of five to ten per tube) or adult ACP (sample size of three to ten per tube) providing the most consistent qPCR detection. While CLas detection rates increased with adult ACP age, nymphs were preferred for field sampling as adult ACP might have dispersed from non-infected trees, potentially misrepresenting the grove’s CLas status. Detection by droplet digital PCR confirmed the presence and genome copies of CLas in a subset of ACP across life stages. In field populations, detection rates in nymphs were consistent or stable throughout the year, whereas CLas detection in adults exhibited seasonal variation, with CLas detection and genome load peaking in January. These targeted ACP sampling strategies and optimized laboratory processing methods will facilitate CLas detection in psyllids for streamlining citrus greening disease management.

Early detection and prompt response are key factors in the eradication of ‘huanglongbing’ (HLB) in California. Currently, qPCR testing of leaf tissue guides the removal of infected trees. However, because of the uneven distribution of ‘Candidatus Liberibacter asiaticus’ (CLas) in an infected tree and asymptomatic infection, selecting the best leaves to sample, from a mature tree with more than 200,000 estimated leaves, is a major hurdle for timely detection. The goal of this study was to address this issue by testing alternative tissues that might improve the CLas detection rate. Using two years of field data, old and young leaves, peduncle bark of fruit, and feeder roots were evaluated for the presence of CLas. Quadrant-peduncle (Q-P) tissue sampling consistently resulted in better CLas detection than any other tissue type. Q-P samples had a 30% higher qPCR positivity rate than quadrant-leaf (Q-L) samples. No significant seasonal patterns were observed. Roots and single peduncles had similar detection rates; both were higher than single leaves or Q-L samples. If symptoms were used to guide sampling, 30% of infected trees would have been missed. Taken together, these results suggest that Q-P tissue sampling is the optimal choice for improved CLas detection under California growing conditions.