Multiscale interactions between plant part and a steep environmental gradient determine plant microbial composition in a tropical watershed

Plant microbiomes are shaped by forces working at different spatial scales. Environmental factors determine a pool of potential symbionts while host physiochemical factors influence how those microbes associate with distinct plant tissues. Interactions between these scales, however, are seldom considered. Here we analyze epiphytic microbes from nine Hibiscus tiliaceus trees across a steep environmental gradient within a single Hawaiian watershed. At each location we sampled eight microhabitats: leaves, petioles, axils, stems, roots, and litter from the plant, as well as surrounding air and soil. While the composition of microbial communities is driven primarily by microhabitat, this variable predicted more than twice the compositional variance for bacteria compared to fungi. Fungal community compositional dissimilarity increased more rapidly along the gradient than did bacteria. Additionally, the spatial dynamics of fungal communities differed among plant parts, and these differences influenced the distribution patterns and range size of individual taxa. Within plants, microbes were compositionally nested such that aboveground communities contained a subset of the diversity found belowground. Our findings identify potential differences underlying the mechanisms shaping communities of fungi and bacteria associated with plants, and indicate an interaction between assembly mechanisms working simultaneously on different spatial scales.


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Plants harbor communities of microorganisms that influence their biology, including phenology 44 [1], water conductance [2], niche occupancy and range expansion [3][4][5], and competitive ability 45 [6]. Nearly all plant traits are likely affected by microbial partners. Our understanding of 46 symbiotic microbial functions, however, has outpaced our understanding of how plants and their 47 microbes form relationships that persist in nature. This disjuncture stems from the sheer 48 complexity of microbial communities, and is compounded by assembly patterns governed by 49 interacting processes at multiple ecological scales, from the landscape level to variation among 50 plant tissues within an individual plant [7,8]. 51 At large scales, abiotic environmental factors influence plant microbiome composition 52 [9]. Plant-associated microbial communities can change across elevation gradients [10][11][12][13][14] or in 53 response to soil properties (e.g., organic carbon, soil pH, nitrogen content [15] and land-use 54 history [16]). Even in the absence of obvious environmental clines, geographic distance coupled 55 with presumed dispersal limitation can alter microbiome composition [17][18][19]. Host identity [20] 56 and genotype [8,21,22] can also covary with environmental factors, which can influence 57 microbial composition as well [23].

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Site description and sampling 99 We collected microbial samples from, and adjacent to, nine mature healthy Hibiscus tiliaceus L.  To sample the soil microbiome, we dug a 5-cm hole using a sterilized corer ~1 m from the 115 canopy edge to avoid sampling the rhizosphere, and swabbed the sides and bottom of the hole.

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For two weeks prior to collecting the above samples, we also deployed an air sampler modified Supplementary Methods for details). In total, we collected 72 biological samples (9 sites by 8 122 microhabitats). For negative controls, we exposed sterile swabs dipped in lysis buffer to the air 123 for ~20 sec. and processed them in the same way as the biological samples. A single air sampling 124 film that we did not expose to the environment served as an air sampler negative control. 125 Hereafter, we refer to the 5 plant tissue types (leaves, petioles, axils, stems, roots) as well as the 126 air, litter, and soil collectively as "microhabitats," which we treat as categorical variables.  Because of fundamental differences between ITS and 16S loci, we used different processing 149 pipelines. Due to heterogeneity in ITS amplicon lengths, we did not attempt to pair ITS reads. 150 We processed read 1 fungal FASTQ files using ITSxpress [42] to isolate ITS1 regions of 151 variable lengths from adjacent conserved ribosomal subunit genes. We used the FASTX-Toolkit

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[43] to filter sequences by quality scores, and to discard reads that met at least one of the 153 following conditions: 1) 10% or more of their bases contained a q-score lower than 25; 2) they 154 contained an "N" nucleotide; or 3) their length was less than 20 bp. We used VSEARCH [44]  We used DADA2 to process 16S reads. First we truncated reads at position 210 (190 for 161 the reverse read) and discarded these if they contained at least one base below quality 2 or a 162 number of expected errors above 3. We used DADA2's default parameters to denoise the data, 163 and merged reads if they overlapped by at least 20 bases, allowing for one mismatch at most. We

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PERMANOVA tests indicate that communities were significantly differentiated by 209 microhabitat type, with more variance explained for bacteria than fungi (Table 1). Composition  Fig. S2). At the ASV scale, however, compositional differences were more 214 readily apparent among bacteria samples from different microhabitats and between fungal 215 samples from below-and aboveground (Fig. 2).

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There was no evidence for a distance decay relationship among bacterial communities, 217 except for those associated with roots (Table 2) (Table 1).

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At the ASV level, both bacterial and fungal communities are distinguished primarily by whether 264 they are located above-or belowground (Fig. 2). While this distinction is still apparent at the 265 class level for bacteria ( Supplementary Fig. S1), nearly all fungal classes were uniformly 266 distributed across the eight microhabitats ( Supplementary Fig. S2).

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Our study focused on epiphytic microbiomes, which may have important implications for  Interactions between microhabitat and environmental gradient 288 In contrast to communities of bacteria, fungi demonstrated a strong interaction between 289 microhabitat type and compositional dissimilarity along the environmental gradient (Table 2). 290 Our results show that subterranean fungal communities had the steepest dissimilarity slopes, fungal communities in air, which we suspected should be the least constrained by either dispersal 299 or environment, showed a strong distance decay pattern (Table 2). 300 Furthermore, the correlation between an ASV's range size and the number of 301 microhabitats it occupies (Fig. 4)   Alternatively, taxa with broad niches have a higher probability of establishing after dispersal as 308 they are more likely to find a suitable microhabitat. In our study, the most widespread bacterium

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The weak (fungi) or non-significant (bacteria) relationship between ASV abundance and 313 microhabitat occupancy suggests that the correlations with niche breadth at small and large 314 scales are not solely attributable to numerical dominance and ascertainment biases. Despite 315 having wider ranges, generalists are not, on average, more abundant than less generalist taxa, 316 suggesting that neither generalists nor more specialized taxa dominate the communities in our 317 study system (Fig. 4).