A Metabarcoding Analysis of the Mycobiome of Wheat Ears Across a Topographically Heterogeneous Field

Abstract

Plant associated microbial communities have recently received a lot of attention because thought to play a fundamental role in plant health and development. Focusing on cultivated crops, optimized farming practices must consider the role of these communities when aiming at reducing the impact of pathogens and increasing yields. Typical inhabitants of plant’s phyllosphere are bacteria and microscopic fungi, some of them pathogenic for the plant and dangerous for the consumers, due to the production of toxins. In order to efficiently manage the microbiome, the natural drivers regulating community assembly must be clearly understood. In our study we investigated the within field variation of the phyllosphere mycobiome of wheat ears by metabarcoding of the fungal internal transcribed sequence 1 (ITS1). We selected a field characterized by a high topographic heterogeneity, which is reflected in differences in plant productivity and fitness across it. Samples were taken from 30 sampling points laid across the field where data-loggers were placed, measuring the productivity driven under canopy microclimate. The microclimatic conditions were tested as a source of potential environmental variance. Further independent spatial structures were tested using spatial eigenvector maps (MEMs). Results show considerable differences in the phyllosphere composition across the field. The local under canopy environmental conditions at each point were strong predictors of the community composition. Independent spatial effects given by the geographical position of the sampling points showed also a weaker but significant effect. Moreover we observed different spatial responses from different fungal phyla, with results resembling those described in studies done at a regional scale. This study is the first one to investigate the spatial variation of the phyllosphere mycobiome of a commercial crop within the same field. It contributes to the study of the epidemiology and community assembly dynamics of wheat phyllosphere fungi, showing how in-field community variations are the results of different environmental and spatial processes acting simultaneously. It also shows how heterogeneous fields are a smart and useful system to investigate the ecological mechanisms regulating plant microbiome composition.

Future Climate Significantly Alters Fungal Plant Pathogen Dynamics during the Early Phase of Wheat Litter Decomposition

“…Among them, Zymoseptoria tritci , Microdochium nivalis , Gibellulopsis piscis , Mycosphaerella tassinia , Stemphylium vesicarium , Fusarium poae , F. graminearum , F. tricinctum , and F. langsethiae were detected. We confirmed that this zymogenic community has been inherited from the wheat plant, as reported previously [ 8 , 49 ]. Although wheat residues were buried in the soil after tillage (before 30- and 60-day sampling), the colonizing pathogens continued to be detected in large numbers throughout the first 60 days of straw decomposition, in both ambient and future climate treatments.…”

Blowin’ in the Wind: Wind Dispersal Ability of Phytopathogenic Fusarium in a Wind Tunnel Experiment

“…Fusarium species are generally distributed across fields and often infest large regions when conditions are suitable [4][5][6][7]. Within large wheat fields, Fusarium species concentrate in areas with a more humid and, thus, usually cooler microclimate [8,9]. Therefore, higher Fusarium species abundances are observed mainly in spots with higher plant productivity and higher canopy cover [8,9].…”

Comparative analyses of sooty mould communities from Brazil and Central Europe

“…The colour scale illustrates the dominance (%) within the sites. The dendrogram on top illustrates the similarity of the sites; the one on the left represents the similarity in the pattern of occurrence of the individual fungal OTUs and Corlett 2019; Egidi et al 2019;Epstein et al 2019;Li et al 2019;Lynikiene et al 2020;Morales-Rodriguez et al 2019;Nerva et al 2019;Qian et al 2020;Ricks and Koide 2019;Saravesi et al 2019;Wilkinson et al 2019;Würth et al 2019;Yao et al 2019;Zhang et al 2019), others strongly recommended the use of ITS1 (Mbareche et al 2020), as it had already been implemented in many studies (Beule et al 2019;Checinska Sielaff et al 2019;Del Frari et al 2019;Schiro et al 2019). Few studies are based on both ITS regions (Janakiev et al 2019).…”