Fire alters plant microbiome assembly patterns: integrating the plant and soil microbial response to disturbance

Abstract

It is increasingly evident that the plant microbiome is a strong determinant of plant health. While the ability to manipulate the microbiome in plants and ecosystems recovering from disturbance may be useful, our understanding of the plant microbiome in regenerating plant communities is currently limited. Using 16S ribosomal RNA (rRNA) gene and internal transcribed spacer (ITS) region amplicon sequencing, we characterized the leaf, stem, fine root, rhizome, and rhizosphere microbiome of < 1-yr-old aspen saplings and the associated bulk soil after a recent high-intensity prescribed fire across a burn severity gradient. Consistent with previous studies, we found that soil microbiomes are responsive to fire. We extend these findings by showing that certain plant tissue microbiomes also change in response to fire. Differences in soil microbiome compositions could be attributed to soil chemical characteristics, but, generally, plant tissue microbiomes were not related to plant tissue elemental concentrations. Using source tracking modeling, we also show that fire influences the relative dominance of microbial inoculum and the vertical inheritance of the sapling microbiome from the parent tree. Overall, our results demonstrate how fire impacts plant microbiome assembly, diversity, and composition and highlights potential for further research towards increasing plant fitness and ecosystem recovery after fire events.

Keywords: 16S rRNA; internal transcribed spacer (ITS); microbial community assembly; microbial source tracking; microbiomes; phyllosphere; plant-microbe interactions; rhizosphere.

Fig. 1

Mean (and standard error) of Hill numbers (q = 0, analogous to richness) of Archaea and Bacteria and Fungi across habitats and levels of burn severity. Letters represent significant differences among burn severity levels within a habitat (P < 0.05). Note different axis scales.

Fig. 2

Principal coordinate analysis (PCoA) ordinations of Archaea and Bacteria community composition (using Bray–Curtis dissimilarity) across habitats and levels of burn severity. The percentage in parentheses represents the variance explained by each axis. The asterisk (*) next to each habitat title indicates significant differences among burn severities (PERMANOVA: P < 0.05; see Supporting Information Table S7 for full statistics). Note different axis scales.

Fig. 3

Principal coordinate analysis (PCoA) ordinations of fungal community composition (using Bray–Curtis dissimilarity) across habitats and levels of burn severity. The percentage in parentheses represents the variance explained by each axis. The asterisk (*) next to each habitat title indicates significant differences among burn severities (PERMANOVA: P < 0.05; see Supporting Information Table S7 for full statistics). Note different axis scales.

Fig. 4

Distance‐based redundancy analysis (dbRDA) ordinations of archaeal and bacterial bulk soil (a), fungal bulk soil (b), archaeal and bacterial rhizosphere (c), fungal rhizosphere (d) community composition across burn severities (using Bray–Curtis dissimilarity). Arrows represent the direction and magnitude (arrow length) of the relationship between the microbiome composition and soil chemistry. The percentage in parentheses represents the variance explained by each axis. Note: different axis scales. Key: carbon = C (%), nitrogen = N (%), ammonium = NH₄ (mg kg⁻¹), nitrate = NO₃ (mg kg⁻¹).

Fig. 5

Mean relative abundance of potential fungal pathogen reads as a proportion of all fungal reads in the leaf across levels of burn severity. Different letters represent significantly different total pathogen relative abundances among levels of burn severity (α = 0.05). Error bars represent standard error of the mean of total pathogen relative abundance. The three fungal pathogen genera with the highest relative abundances are highlighted in color.

Fig. 6

Percentage of microbial taxa sourced from the rhizosphere, bulk soil, or rhizome across endospheric habitats and levels of burn severity for Archaea and Bacteria and Fungi. Error bars represent the standard error of the mean. Different letters represent significantly different groups (α = 0.05). Note: y‐axis varies, and percentage does not add to 100% because some microbial taxa were attributed to ‘unknown’ sources. Unknown sources represented, on average, 80% of source taxa for both Archaea and Bacteria and Fungi.