Artificial intelligence unveils key interactions between soil properties and climate factors on Boletus edulis and B. reticulatus mycelium in chestnut orchards of different ages

Abstract

The main objective of this study was to determine the possible interaction of two important abiotic factors (soil and climate) on the mycelial concentration and frequency of the ectomycorrhizal fungi Boletus edulis and B. reticulatus, using traditional statistics and artificial neural network tools. The frequency and concentration of Boletus mycelium were determined over three months (September, October, and November), and two years (2018 and 2020), in three hybrid chestnuts (Castanea × coudercii) orchards of 40-, 10-, and 3- years-old, using real-time qPCR. Statistical analysis revealed a significant effect of the year on B. edulis mycelium concentration and of the sampling plot (different tree ages) on B. reticulatus frequency. The combination of artificial intelligence networks (ANN) with fuzzy logic, named neurofuzzy logic (NF), allowed the construction of two robust models. In the first, using year, month, and sampling plot as inputs, NF identified hidden interactions between year and month on B. edulis mycelium concentration and between sampling plot and sampling month on B. reticulatus mycelium frequency, thus improving the information obtained from the statistical analysis. In the second model, those three factors were disaggregated into 44 inputs, including 20 soil properties and 24 climatic factors, being NF able to select only 8 as critical factors to explain the variability found in both ectomycorrhizal Boletus species regarding mycelial frequency and concentration. Specifically, NF selected two chemical soil properties (cation exchange capacity and total carbon) and three physical properties (macroaggregates, total porosity, and soil moisture at field capacity), as well as their interactions with three climatic elements (cumulative difference between precipitation and potential evapotranspiration (P-PET-1-2) and water deficit (WD-1-2) in the previous two months and excess water (WE-1) in the month prior to sampling. These results provide a much deeper understanding and new insights into the ecology and the role of abiotic factors which explain the different mycelial development patterns of ectomycorrhizal fungi such as B. edulis and B. reticulatus in chestnut agroecosystems.