Fungal communities living within leaves of native Hawaiian dicots are structured by landscape-scale variables as well as by host plants

Abstract

AIM: A phylogenetically diverse array of fungi live within healthy leaf tissue of dicotyledonous plants. Many studies have examined these endophytes within a single plant species and/or at small spatial scales, but landscape-scale variables that determine their community composition are not well understood, either across geographic space, across climatic conditions, or in the context of host plant phylogeny. Here, we evaluate the contributions of these variables to endophyte community composition using our survey of foliar endophytic fungi in native Hawaiian dicots sampled across the Hawaiian archipelago. LOCATION: Hawaiʻi.METHODS: The Hawaiian archipelago offers a uniquely tractable system to study biogeography of foliar endophytic fungi, because the islands harbor a wide array of climatic conditions, and native plant species are often found across wide elevational and climactic ranges. We used Illumina technology to sequence fungal ITS1 amplicons in order to characterize foliar endophyte communities in the leaves of 896 plants across 5 islands and 80 host plant genera. Using Generalized Dissimilarity Modeling (GDM) we tested the effect of landscape-scale variables on observed differences in foliar endophyte communities. Bipartite network analysis was used to examine the extent to which each island harbored specialized or cosmopolitan foliar endophytes.RESULTS: Communities of foliar endophytic fungi in the Hawaiian archipelago are structured most strongly by evapotranspiration, elevation, vegetation/habitat type, and by the phylogeny of host plants. The five islands we sampled each harbored significantly specialized endophyte communities as well.MAIN CONCLUSIONS: Factors that structure foliar endophyte communities at small geographic and narrow host phylogenetic scales are broadly generalizable to the larger scales we studied here, although not universally. Evapotranspiration, a variable with resolution 250 m 2 , was the most robust predictor of endophyte community dissimilarity in our study, although it had not previously been considered an important determinant of FEF communities.

Mosquito Microbiome Diversity Varies Along a Landscape-Scale Moisture Gradient

“…We use a cross-sectional study design on O 'ahu to test for an association between relatively stable landscape variables (local climate, land use) and the composition of the mosquito microbiome at fine spatial scales. Several studies have used cross-sectional data to gain insights into distinct drivers of variation in host-associated microbiomes, including links between microbiome composition and local climate and land use [9,18,[25][26][27]. Specifically, Aedes albopictus females were captured from 50 sites across the island of O 'ahu (Fig.…”

“…Here, we explore the β-diversity of the microbiome of a medically important human disease vector, Aedes albopictus, across two major contributors to environmental heterogeneity across the landscape: local climate and land use. Climate variables are well-known modulators of the ecological community structure, including microbiomes [17][18][19]. In addition, land-use patterns such as urbanization drastically alter habitat structure and increase pollution, which may, in turn, promote changes in microbiome structure and composition [8,[20][21][22].…”

specificity: an R package for analysis of feature specificity to environmental and higher dimensional variables, applied to microbiome species data

“…However, the aggregate strength of specificity as visualized in Figure 2 is still a community-level overview, albeit unweighted by species abundance. Here we see that many species have strong and statistically significant specificity to geographic location, which makes sense given the discrete spatial structure of the Hawaiian islands (Tipton et al, 2021), and that these FEF communities only are spatially structured up to distances of 36 kilometers (Darcy et al, 2020). But geographic specificity may be an artifact of specificity to other variables with strong geographic autocorrelation.…”

“…Data from Hawaiian foliar endophytic fungi (Darcy et al, 2020) were downloaded from FigShare. These are illumina MiSeq data of the Internal Transcribed Spacer (ITS) region of fungal ribosomal RNA, from 760 samples collected from the leaves of native Hawaiian plants across five islands in the Hawaiian archipelago.…”

“…In our analysis, we asked which metabolomic classes had the most microbial species specific to them (Figure 4). The results of this type of analysis are intended to mirror common beta-diversity analyses used in microbial ecology, which ask to what extent variables explain differences in microbiome community structure (Sommers et al, 2018;Darcy et al, 2020;Shaw et al) However, more complex questions can be asked of these data, using the results of specificity as a starting point for feature set reduction or variable selection. For example, given an individual bacterial species of interest, the variables to which it is specific may be used in a random forests model to predict its presence.…”