Molecular Identification and Virulence of Four Strains of Entomopathogenic Fungi Against the Whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae).
Qian LuPeng WangAsad AliLian-Sheng ZangPublished in: Journal of economic entomology (2022)
The whitefly, Bemisia tabaci (Gennadius), is a key pest of many economically important crops grown in the field and in greenhouses throughout the world. Because entomopathogenic fungi (EPF) are potential biological control agents for B. tabaci, however, minimal research has been conducted on using fungal strains to control B. tabaci. In this study, four EPF strains were isolated and identified as Lecanicillium attenuatum (Zare & Gams) JL-003, Beauveria bassiana Balsamo (Vuillemin) JL-005, Lecanicillium longisporum (Petch) JL-006, and Akanthomyces lecanii (Zimmerman) JL-007, based on rDNA-ITS sequence analysis. In comparing the virulence of the four fungi against the different life stages (i.e., eggs, 1st-, 2nd-, 3rd-, 4th-instar nymphs, and adults) of B. tabaci the mortality of B. tabaci decreased and LT50 values increased as the conidia concentration decreased in a series of conidia concentrations (1 × 105, 106, 107, and 108 conidia/mL). The fungal strains L. attenuatum JL-003 (LC50: 1.31 × 106) and B. bassiana JL-005 (LC50: 0.92 × 106) were found to be more effective than L. longisporum JL-006 (LC50: 4.97 × 107) and A. lecanii JL-007 (LC50: 6.46 × 106). Fourth-instar nymphs, eggs, and adult stages of B. tabaci were less susceptible to all fungal strains compared to 1st-, 2nd-, and 3rd-instar nymphs. The virulence of L. attenuatum, which was tested for the first time on B. tabaci, was found to be more toxic to early-stage nymphs. Our data will be useful in biological control programs that are considering using EPF against B. tabaci.
Keyphrases
- escherichia coli
- early stage
- pseudomonas aeruginosa
- staphylococcus aureus
- simultaneous determination
- biofilm formation
- antimicrobial resistance
- public health
- type diabetes
- squamous cell carcinoma
- electronic health record
- machine learning
- risk assessment
- cardiovascular events
- cystic fibrosis
- radiation therapy
- high resolution
- childhood cancer
- deep learning
- cell wall
- locally advanced