Drought Tolerance Traits in Neotropical Trees Correlate with the Composition of Phyllosphere Fungal Communities

Abstract

Plant-associated microorganisms have shown to aid plants in coping with drought. However, the underlying mechanisms are poorly understood and there is uncertainty regarding which microbial taxa and functions are mostly involved. We explored these issues in Neotropical rainforests and identified foliar microorganisms that may play a role in drought tolerance of trees. Our objectives were to (1) test the relationship between drought tolerance traits in Neotropical trees and the diversity and composition of their foliar fungal and bacterial communities and (2) identify leaf microbial taxa positively or negatively associated with drought tolerance traits. Our results showed that the composition of leaf fungal communities, but not bacterial communities, was related to drought tolerance. We identified 27 fungal Amplicon Sequence Variants (ASVs) whose relative abundance co-varied with drought tolerance traits. Most variants were assigned to fungal clades often described as plant pathogens and increased in abundance with drought susceptibility. This greater relative abundance of leaf pathogens in the most drought-susceptible trees might increase their vulnerability to climate change. Moreover, we identified the Strelitziana and Ochroconis fungal genera as potential candidates for future culture-dependent studies aimed at understanding and improving drought tolerance in Neotropical forests.

The functional microbiome of grapevine throughout plant evolutionary history and lifetime

“…The statistical model will have the functional trait of interest as the dependent variable and the microbiome properties and environmental factors as explanatory variables (e.g. Asad et al, 2022 for yield;Cambon et al, 2022 for drought tolerance; Pérez-Valera et al, 2020 for an example of paired samples). When the microbiome contributes significantly to the functional trait of interest, identifying which microbial taxa are the main players can be done using the TITAN2 method (Baker et al, 2015) or differential abundance analysis methods (Nearing et al, 2022;Weiss et al, 2017).…”