Soil Chemical Properties Barely Perturb the Abundance of Entomopathogenic <i>Fusarium oxysporum</i>: A Case Study Using a Generalized Linear Mixed Model for Microbial Pathogen Occurrence Count Data

• Main Authors: Lav Sharma, Irene Oliveira, Fernando Raimundo, Laura Torres, Guilhermina Marques
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Pathogens
• Subjects: entomopathogenic fungi; soil chemistry; microbial ecology; vineyards; <i>Fusarium oxysporum</i>; generalized linear mixed model
• Online Access: https://www.mdpi.com/2076-0817/7/4/89

<i>Fusarium oxysporum</i> exhibits insect pathogenicity&#8212;however, generalized concerns of releasing phytopathogens within agroecosystems marred its entomopathogenicity-related investigations. In a previous study, soils were sampled from Douro vineyards and adjacent hedgerows. In this study, 80 of those soils were analyzed for their chemical properties and were subsequently co-related with the abundance of entomopathogenic <i>F. oxysporum</i>, after insect baiting of soils with <i>Galleria mellonella</i> and <i>Tenebrio molitor</i> larvae. The soil chemical properties studied were organic matter content; total organic carbon; total nitrogen; available potassium; available phosphorus; exchangeable cations, such as K⁺, Na⁺, Ca²⁺, and Mg²⁺; pH; total acidity; degree of base saturation; and effective cation exchange capacity. Entomopathogenic <i>F. oxysporum</i> was found in 48 soils, i.e., 60% &#177; 5.47%, of the total soil samples. Out of the 1280 insect larvae used, 93, i.e., 7.26% &#177; 0.72%, were found dead by entomopathogenic <i>F. oxysporum</i>. Stepwise deletion of non-significant variables using a generalized linear model was followed by a generalized linear mixed model (GLMM). A higher C:N (logarithmized) (<i>p</i> &lt; 0.001) and lower exchangeable K⁺ (logarithmized) (<i>p</i> = 0.008) were found significant for higher fungal abundance. Overall, this study suggests that entomopathogenic <i>F. oxysporum</i> is robust with regard to agricultural changes, and GLMM is a useful statistical tool for count data in ecology.