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Article

Associations Between the Leaf Microbiome and the Health of Irish Ash Trees Affected by Hymenoscyphus fraxineus

by
Michael Andrew Stevenson
†,
Neil Warnock
†,
Kirsty Elizabeth McLaughlin
,
Johnathan Dalzell
,
Rhonda Swan
,
Thomas Fleming
,
James Trudgett
,
Archie Kelso Murchie
,
Allison Reid
,
Deacem Hamilton
and
Eugene Carmichael
*,†
Agri-Food and Biosciences Institute, Belfast BT9 5PX, UK
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2026, 17(3), 389; https://doi.org/10.3390/f17030389
Submission received: 5 February 2026 / Revised: 13 March 2026 / Accepted: 19 March 2026 / Published: 21 March 2026
(This article belongs to the Section Forest Health)
Browse Figures
Versions Notes

Abstract

Ash dieback, caused by the fungus Hymenoscyphus fraxineus, continues to threaten European ash (Fraxinus excelsior), yet the contribution of the leaf microbiome to disease severity remains poorly understood. We surveyed 133 ash trees across nine sites in Northern Ireland, using canopy cover as a proxy for health, and characterised leaf-associated microbial communities using Pacific Biosciences (PacBio) long-read amplicon sequencing (full-length 16S and ITS) and QIIME2-based workflows. Many trees exhibited partial tolerance to ash dieback, with most maintaining stable canopy cover year-on-year, while fewer trees show a decline and a smaller portion showing improvement. Microbial communities were largely stable irrespective of ash health with little difference in alpha diversity (Shannon) or beta diversity (Bray–Curtis PERMANOVA) for either bacteria or fungi. Differential abundance and correlation analyses showed that H. fraxineus was, as expected, negatively associated with canopy cover. Only one fungal species, Papiliotrema flavescens, demonstrated a strong positive association with healthier trees, consistent with previous findings. These results indicate that Northern Ireland hosts a reservoir of ash trees displaying tolerance to ash dieback. While the leaf microbiome does not appear to drive this tolerance at the community level, one fungus, P. flavescens, was correlated with healthier ash.

Graphical Abstract

1. Introduction

Hymenoscyphus fraxineus is an ascomycete fungus and the causal agent of ash dieback disease, a condition that has caused extensive mortality of European ash (Fraxinus excelsior) since its emergence in Europe. The pathogen, thought to have originated in East Asia, was first detected in Poland in 1992 [1,2] and subsequently spread rapidly throughout much of Europe, with particularly severe impacts on native ash populations [3,4]. Early in the epidemic, the pathogenic fungus was misidentified as the anamorph Chalara fraxinea [2] and later conflated with the morphologically similar but non-pathogenic native species Hymenoscyphus albidus. Only following genetic analyses in 2011 was H. fraxineus definitively identified as the causal agent of ash dieback, nearly two decades after the disease was first observed [5]. This delayed identification contributed to ineffective containment efforts and facilitated widespread establishment of the pathogen across Europe.
Ash dieback was first reported in Ireland in 2012 [6,7] and, despite initial eradication measures, has since spread throughout the island [8,9]. Ireland is generally considered to be an area of low tree diversity, and ash is among its most abundant native species, occurring widely in hedgerows, woodlands, and mixed landscapes [10,11]. Ash trees support a large number of associated organisms, including many species that are obligate or strongly associated with ash [12], and they are also of cultural and economic importance in Ireland, notably in the manufacture of hurls used in Gaelic games. Consequently, the decline of ash due to ash dieback represents a major ecological and socio-economic concern in Ireland [6].
Hymenoscyphus fraxineus infects ash primarily through wind-dispersed ascospores, which colonise leaves and rachises during the growing season [13]. Following germination, the fungus invades the leaves and vascular tissue, causing leaf necrosis [14,15], and progresses into shoots and woody tissues, where it can induce bark lesions and cankers [16]. The pathogen overwinters in fallen leaf litter as pseudosclerotial structures [17], from which apothecia develop in the spring to release new ascospores, thereby sustaining annual cycles of reinfection [18]. While individual infections are often localised, repeated yearly infection can result in progressive canopy loss, reduced vigour, and eventual mortality, with early estimates suggesting death rates exceeding 80% in susceptible populations [4,19]. The loss of ash has important implications for carbon sequestration, as reduced growth and elevated mortality diminish the capacity of these ecosystems to capture and store carbon [20,21].
Attempts to curb the spread and effects of ash dieback have included felling of infected trees, research to understand the genetics of ash trees [22,23,24] and the environmental factors which contribute to disease severity, as well as the breeding of resistant ash [25,26]. Because the initial infection and early pathogen development occur within leaf tissues, the ash leaf and its associated microbiome may influence the establishment and progression of H. fraxineus [27,28,29,30]. Plant-associated microbial communities can affect host health through mechanisms such as niche competition, antibiosis, and immune modulation [31,32]. Several studies have investigated the ash leaf microbiome in tolerant and susceptible trees, with particular interest in identifying the microbial taxa associated with reduced disease severity [29,33,34]. However, results to date suggest that disease tolerance is complex and may reflect interactions between pathogen pressure, host genetics, microbiome composition, and environmental factors [3].
We surveyed 133 ash trees at nine sites in 2023, spanning a gradient of canopy cover used as a proxy for health, and characterised bacterial and fungal communities using PacBio long-read amplicon sequencing and QIIME2-based workflows. By integrating alpha and beta diversity metrics, differential abundance testing, and correlation analyses, we examined whether variation in the microbiome of ash tree leaves is associated with canopy health and tolerance to ash dieback. This work provides new insight into ash–microbe–pathogen interactions in an Irish context and helps clarify the extent to which microbiome variation contributes to observed differences in disease impact.

2. Materials and Methods

2.1. Sampling and Surveys

Sampling attempted to select trees with a broad range of leaf canopy cover from a range of different forests sites across Northern Ireland (Figure 1). Sites were selected to cover a broad area of Northern Ireland to even out the effect of site-specific microbiomes. Tree canopy cover was used as a proxy for the health of ash and was used to estimate the sensitivity of ash trees to H. fraxineus. Once a suitable ash tree was located, the GPS coordinates were recorded, and the percentage canopy cover was estimated. This was scored as a percentage of healthy foliage, with 100% indicating no sign of dieback disease and 0% indicating no sign of healthy foliage due to dieback disease. Trees that were difficult to score or which showed significant damage (i.e., from wind) were not sampled. For consistency, all ash trees, across both years, were scored by the same two observers. Ash trees were scored by examining the tree from multiple distances and angles. The two observers reached a consensus score for canopy coverage rounded to the nearest 5%. From every tree, three leaves which did not show signs of dieback but were in proximity to those which did, were collected, bagged and labelled, and stored at –40 °C until DNA extraction. Leaf samples were all taken in August 2023 and all the samples were collected within a 2-week window. A total of 133 ash trees across 9 sites throughout Northern Ireland were assessed. A follow-up survey of ash trees which were sampled was conducted a year later in August 2024.

2.2. Sample Preparation for DNA Extraction

For each sample, a sub-samples of five leaflets, including petioles, were ground to a fine powder using liquid nitrogen and a mortar and pestle. The ground samples were then stored at –40 °C until further processing. Between samples, equipment was washed with ‘DNA decontamination reagent’ (Sigma Aldrich) and sterilised via autoclaving at 121 °C for 15 min to ensure no cross-contamination. Samples were prepared for lysing using a Precellys evolution grinder. Approximately 100 mg of ground leaf material was added to a CK28 Precellys tube containing ceramic beads and homogenised twice at 5800 rpm for 20 s. DNA was extracted from samples using a HF 16 MagCore nucleic acid extraction system and a MagCore genomic DNA plant kit (301) following the manufacturer’s instructions. The DNA was eluted in 100 µL of elution buffer; this was divided into two 50 µL aliquots and stored at –40 °C.

2.3. Library Preparation and PacBio Sequencing

Library preparation and PacBio sequencing was performed by Novogene. DNA quality was assessed by gel electrophoresis. Samples which passed quality control were used to amplify the full-length 16S ribosomal RNA gene (16S) and the full-length internal transcribed spacer (ITS) region of the nuclear ribosomal RNA region. Full-length marker genes/regions were used to maximise taxonomic resolution. The 16S amplicon was generated using the primers 27F (AGAGTTTGATCCTGGCTCAG) and 1514R (GNTACCTTGTTACGACTT) which span the V1–V9 regions. The ITS amplicon was generated using the primers ITS9MUNngs (TACACACCGCCCGTCG) and ITS4ngsUni (CCTSCSCTTANTDATATGC). Amplified DNA fragments were quality checked by electrophoresis, and those which passed were end-repaired and A-tailed. Barcoded SMRTbell adapters (Pacific Biosciences, Menlo Park, CA, USA) were ligated to the DNA fragments during library preparation using the SMRTbell® Prep Kit 3.0 (PacBio). The DNA fragments were purified with AMPure PB magnetic beads (Beckman Coulter, Brea, CA, USA) to construct barcoded SMRTbell libraries. Sequencing primers were annealed to the SMRTbell templates, followed by binding of the sequencing polymerase using the Sequel II Binding Kit 3.2 (PacBio). Library concentration was determined using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Quantified libraries were pooled and sequenced on the PacBio Sequel IIe system to generate High Fidelity (HiFi) reads at a depth of 100,000. Sequencing and metadata files were submitted to the National Center for Biotechnology Information (NCBI) (PRJNA1412523).

2.4. Taxonomic Assignment

Following sequencing, lima software was used to distinguish the data from each sample based on barcode sequences. Circular Consensus Sequencing (CCS) (SMRT Link v7.0) was used to correct the sequence, with a correction parameter of CCS = 3 and a minimum accuracy of 0.99. Sequences were saved in FASTQ and fasta format. Subsequently, SSR filtration was performed, and the primers were removed using ‘cutadapt’ to filter out sequences containing more than 8 consecutive identical base numbers. The ‘Quantitative Insights Into Microbial Ecology’ (QIIME2 Amplicon 2025.4 distribution) was used for pre-processing and subsequent denoising/dereplication of raw reads received from our 3rd-party sequencing provider, Novogene. Raw FASTQ files provided by Novogene were imported into QIIME2 using a manifest file in Phred33 format using the ‘qiime tools import’ command with parameter type ‘SampleData[SequencesWithQuality]’. Denoising and dereplication was performed using the ‘Divisive Amplicon Denoising Algorithm’ (‘DADA2’) to obtain initial Amplicon Sequence Variants (ASVs); this was done using the ‘qiime dada2 denoise-ccs’ command. This produced a feature table, representative sequences, and denoising statistics. All identical sequences were subsequently collapsed into unique features. Representative sequences were classified using Naive Bayes classifiers trained in QIIME2, using the ‘qiime feature-classifier fit-classifier-naive-bayes’ command. For bacterial 16S data, taxonomy was assigned using a classifier which had been trained on the Greengenes2 2024.09 reference database. For fungal ITS data, taxonomy was assigned using a classifier which had been trained on the UNITE reference database (v10.0—dynamic). Classifications were subsequently collapsed to species level (Level 7) using the ‘qiime taxa collapse’ command, for downstream analyses.

2.5. Statistical Analysis

To compare the health and microbiome of ash trees, trees were split into five health groups based on their percentage canopy cover: 1%–20%, 21%–40%, 41%–60%, 61%–80%, and 81%−100%. Creating distinct groups in this way, rather than having a continuous variable, made it easier to compare the most and least healthiest trees, and helped uncover correlations, with just 133 samples. For ITS and the 16S data, features assigned to non-fungal and non-bacterial taxa were removed, this included host reads which accounted for up to 95% of total reads in some libraries. The high prevalence of host reads across all samples could potentially bias taxonomic identification and mask biological or statistically important relationships. Carrying out this filtering step ensured that downstream diversity analyses were carried out on true fungal and bacterial communities. Alpha diversity (Shannon index) and beta diversity (Bray–Curtis distances) were computed in R Studio using the phyloseq and vegan packages and visualised using ggplot2. Statistical differences between canopy cover were assessed using Kruskal–Wallis and PERMANOVA Type III tests implemented in the R package, vegan on normalised counts. Differential abundance analysis was carried out using ANCOM-BC (Analysis of Compositions of Microbiomes with Bias Correction) in R. Site was added as a co-variate to the PERMANOVA and ANCOM-BC to control for site-related microbiome variation. Table 1 shows the tools and versions used in this analysis.

3. Results

3.1. Survey Results

A total of 133 ash trees (F. excelsior) were surveyed from nine forests in Northern Ireland in 2023, and 128 of the same trees were resurveyed in the following year. Approximately 22% of trees showed a high degree of tolerance to ash dieback (80%–100% canopy cover); one particular tree in Roe Valley, had little signs of ash dieback with 99% canopy cover. Several sites, such as Drum Manor Forest, had few trees with a high degree of tolerance to ash dieback (Table 2).
Between 2023, and the resurvey in 2024, 63% of trees showed no noticeable difference in canopy cover, while 30% of trees showed a decline compared to the previous year, with only 8% showing a noticeable increase in canopy cover, mainly due to epicormic growth (Figure 2). Trees showing a decline in canopy cover had an average decrease of 12.4%, while those showing an increase had an average gain of 10.4%. The average net change in canopy cover of all the trees surveyed was −2.8%. The only site that showed an overall net gain in canopy cover within the site was Roe Valley, all other sites showed either a small decline in tree health overall, or no change over 12 months (Supplementary S1).

3.2. Microbiome Compared with Percentage Canopy Cover

Within-sample species richness and evenness (alpha diversity/Shannon Index) significantly differed across canopy coverage for both bacteria (DF = 4, Statistic = 9.83, p = 0.043) and fungi (DF = 4, Statistic = 9.95, p = 0.041) as assessed by Kruskal–Wallis. Post hoc Dunn’s tests revealed no significant pairwise comparisons for bacteria after p adjustment. However, for fungi, the pairwise comparison of canopy coverage categories 61–80 and 81–100 remained significant after adjustment (Z = 2.82, p.unadj = 0.0004, p. adj = 0.047) (Figure 3A,B). Between-sample species richness and evenness (beta diversity) significantly differed across canopy coverage for both bacteria (DF = 4, Sum Of Squares = 1.31, R2 = 0.041, F = 1.66, Pr (>F) = 0.021) and fungi (DF = 4, Sum Of Squares = 1.78, R2 = 0.040, F = 1.56, Pr (>F) = 0.001) as assessed by Type III PERMANOVA. Site was included in the PERMANOVA as a co-variate to control for site-related microbiome variation (Bacteria (DF = 7, Sum Of Squares = 6.34, R2 = 0.20, F = 4.57, Pr (>F) = 0.001), Fungi (DF = 7, Sum Of Squares = 8.19, R2 = 0.19, F = 4.11, Pr (>F) = 0.001)). Assumptions of PERMANOVA were met, as homogeneity of multivariate dispersion was confirmed for both datasets (PERMDISP 16S: F = 0.71, p = 0.58; PERMDISP ITS: F = 0.80, p = 0.54). This indicates that differences detected by PERMANOVA are unlikely to be driven by unequal group variances. The residuals from the Type III PERMANOVA indicated that 74% (Residual R2 = 0.74) of the variation in the bacterial microbiome and 76% (Residual = 0.76) of variation in the fungal microbiome remains unexplained. Ordination plots were used to visualise any canopy coverage clustering in fungal and bacterial communities and were created using NMDS (Non-metric Multidimensional Scaling) (Figure 3C,D).
Differential abundance analysis, assessed via ANCOM-BC, with site as a co-variate, revealed no bacterial or fungal species that were differentially expressed between canopy cover groups. However, after examining the relative abundance of species across the canopy cover groups, several species showed strong correlations. H. fraxineus was strongly negatively correlated with canopy cover (R2 = 0.66) while Papiliotrema flavescens was strongly positively correlated with canopy cover (R2 = 0.94) (Figure 4).

4. Discussion

Over the past decade, perceptions of the long-term outlook for European ash have started to shift. Early predictions that ash trees would become functionally extinct across much of Europe [35] have been tempered by increasing evidence that some trees exhibit partial tolerance to ash dieback, allowing survival despite continued pathogen pressure [36]. This trend is supported by the present study (Table 2) and by earlier surveys of Northern Irish ash conducted by the Agri-Food and Biosciences Institute (AFBI) (Supplementary S2), which together indicate that many ash trees in Northern Ireland display a tolerance to ash dieback. The survey in this study shows that most of the ash trees maintained stable canopy cover year-on-year, with approximately 30% showing a decline in condition, and 8% of ash showing an improvement in tree canopy cover (Figure 2). These observations are consistent with reports from other European regions showing that ash populations are not uniformly collapsing but instead displaying considerable variation in disease severity and progression [37].
Scientists believe there are a range of factors that may affect the tolerance of European ash trees to ash dieback; these include host genetics, the age and previous health of the tree, microbiome composition, as well as environmental and climatic factors [38]. Previous unpublished work by AFBI examined a candidate genetic marker identified by Harper et al. [22] in Irish ash trees but found no strong association between the tolerant allele and canopy cover. The aim of this study was to examine whether differences in the leaf-associated microbiome might be linked to variation in ash health and tolerance to ash dieback in Northern Ireland. We define tolerance as the ability of ash trees to live with H. fraxineus, although the mechanisms underlying this tolerance remain unknown.
The analyses showed that overall leaf diversity and community composition were remarkably stable across trees spanning a wide range of canopy cover. Although alpha diversity (Shannon index) and beta diversity (Bray–Curtis) differed significantly between canopy groups for both bacteria and fungi, the effect size was small. Beta diversity analysis indicated that approximately 4% of the observed differences in both the bacterial and fungal communities were related to the canopy cover, and that tolerant and susceptible ash trees harboured broadly similar bacterial and fungal communities (Figure 3). These findings suggest that tolerance to ash dieback is not associated with wholesale shifts in microbiome richness or composition, at least for the trees analysed in this study. Site, as a co-variable, was found to be responsible for approximately 20% of the observed variation in the bacterial and fungal communities, leaving approximately 75% of the observed variation unaccounted for.
Analysis of species abundance, and how that correlated with tree health, revealed several fungal species which strongly trended with tree health, but no bacterial species was found to correlate with tree health. The relative abundance of H. fraxineus was strongly negatively correlated with canopy cover (Figure 4). On average, trees with poor canopy cover consistently carried a higher pathogen load, irrespective of overall microbiome diversity or composition. This finding indicates that pathogen burden, rather than community-wide microbiome change, is the primary microbial driver of disease severity. A second species, belonging to the genus Calloria of the order Helotiales, was also negatively associated with canopy cover. The ecology of most Helotiales species is unknown; however, some species are known to be saprotrophs whilst others are plant pathogens [39].
Only P. flavescens was positively correlated with healthier trees (Figure 4). P. flavescens has previously been identified as a beneficial phyllosphere inhabitant and was shown to promote growth and enhance resistance to fungal pathogens in Arabidopsis thaliana [40]. A previous study on the microbiome of ash trees by Becker et al. [27] also reported a positive association between P. flavescens and ash health and further demonstrated inhibitory effects on H. fraxineus in vitro. The recurrence of P. flavescens as a health-associated taxon across independent studies and geographic regions suggests that its association with ash tolerance is reproducible. Further research is required to explore what role, if any, P. flavescens has in the tolerance of ash trees to dieback.
Taken together, our results indicate that the ash phyllosphere microbiome is largely conserved across trees with differing disease outcomes, and that differences in tolerance are associated with variation in pathogen load rather than broad microbial community restructuring. Nevertheless, the repeated association of specific fungal taxa (P. flavescens) with healthier trees across multiple studies, points to potentially conserved microbe-mediated mechanisms contributing to tolerance. These mechanisms are unlikely to act in isolation, but instead form part of a complex, multifactorial system involving host genetics, microbial interactions, pathogen pressure, and environmental conditions.

5. Conclusions

Although the mechanisms underlying ash tolerance remain unresolved, our findings and recent research show that resistance seems to be developing naturally [41], and this has practical implications for ash conservation and management. Continued monitoring of ash through regular surveys remains important to track disease progression and to identify tolerant ash trees. We identified many ash trees with high canopy cover and follow-up surveys revealed that many trees were stable year-on-year, with some showing a slight improvement in canopy cover. Simple steps can be taken by policy makers and scientists to leverage these survey findings, such as protecting the most tolerant trees and incorporating their seed into replanting and restoration programmes. This may help promote the persistence and spread of tolerant ash and improve the resilience of European ash trees.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f17030389/s1, Supplementary S1: Northern Ireland Ash Dieback disease survey 2020 to 2021; Supplementary S2: Microbiome analysis metadata.

Author Contributions

Conceptualisation, J.D. and N.W.; methodology, R.S., A.R., D.H., T.F., J.T., M.A.S., K.E.M., N.W. and E.C.; software, M.A.S., K.E.M. and N.W.; validation, M.A.S., K.E.M., N.W. and E.C.; formal analysis, M.A.S., N.W. and E.C.; investigation, M.A.S., K.E.M., N.W. and E.C.; resources, M.A.S., N.W., A.K.M. and E.C.; data curation, M.A.S., N.W. and E.C.; writing—original draft preparation, M.A.S., K.E.M., N.W. and E.C.; writing—review and editing, M.A.S., K.E.M., A.K.M., T.F., J.T., N.W. and E.C.; visualisation, M.A.S., N.W. and E.C.; supervision, N.W. and E.C.; project administration, N.W. and E.C.; funding acquisition, J.D. and N.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Northern Ireland Department of Agriculture, Environment and Rural Affairs, proposal reference 21 3 02.

Data Availability Statement

Sequencing data are submitted to the NCBI SRA database with the accession BioProject: PRJNA1412523. The supplementary files contain data survey data from 2023 and 2024, and from 2020 and 2021.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AFBIAgri-Food and Biosciences Institute

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Figure 1. Sampling locations of ash trees across 9 forest sites in Northern Ireland included in the 2023–2024 microbiome survey.
Figure 1. Sampling locations of ash trees across 9 forest sites in Northern Ireland included in the 2023–2024 microbiome survey.
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Figure 2. Change in the canopy cover of surveyed ash trees from 2023 to 2024. The arrows indicate trees with improving (green arrow), declining (orange arrow), or no change (no arrow) in canopy cover year-on-year.
Figure 2. Change in the canopy cover of surveyed ash trees from 2023 to 2024. The arrows indicate trees with improving (green arrow), declining (orange arrow), or no change (no arrow) in canopy cover year-on-year.
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Figure 3. Alpha diversity (Shannon Index) and beta diversity (PCoA (Bray–Curtis)) of the bacterial (16S) and fungal (ITS) communities relative to canopy cover. (A) Alpha diversity of bacteria relative to canopy cover (%). (B) Alpha diversity of fungi relative to canopy cover (%). (C) Beta diversity of bacteria relative to canopy cover (%). (D) Beta diversity of fungi relative to canopy cover (%).
Figure 3. Alpha diversity (Shannon Index) and beta diversity (PCoA (Bray–Curtis)) of the bacterial (16S) and fungal (ITS) communities relative to canopy cover. (A) Alpha diversity of bacteria relative to canopy cover (%). (B) Alpha diversity of fungi relative to canopy cover (%). (C) Beta diversity of bacteria relative to canopy cover (%). (D) Beta diversity of fungi relative to canopy cover (%).
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Figure 4. Relative abundance of fungi strongly correlated with tree canopy cover. The red line is the pathogen (H. fraxineus) which had a strong negative correlation with ash health (R2 = 0.66). The green line shows an endophyte (P. flavescens) which had a strong positive correlation with ash health (R2 = 0.94).
Figure 4. Relative abundance of fungi strongly correlated with tree canopy cover. The red line is the pathogen (H. fraxineus) which had a strong negative correlation with ash health (R2 = 0.66). The green line shows an endophyte (P. flavescens) which had a strong positive correlation with ash health (R2 = 0.94).
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Table 1. Bioinformatics workflow for taxonomic assignment and diversity analysis of 16S and ITS datasets.
Table 1. Bioinformatics workflow for taxonomic assignment and diversity analysis of 16S and ITS datasets.
StepPurposeTools/DatabasesVersionOutput
Taxonomic AssignmentAssign taxonomy to ASVs for bacteria using Greengenes2. and fungi using UNITE.QIIME2
GreenGenes2
UNITE 		Amplicon.2025.4 (q2)
2024.09 (GG2)
v10.0 Dynamic seqs and taxa—All Eukaryotes—19/02/25 (UNITE)		Taxonomy tables (genus/species level)
Diversity AnalysisAssess microbial diversity within and between samples and community structure.R
R Studio
R packages: phyloseq, vegan)		R (4.5.2)
R Studio (2025.09.2)
phyloseq (v.1.44.0)
vegan (v2.6.4)		Diversity indices (Shannon, Bray–Curtis).
Statistical TestingDetermine significance of observed patterns.R functions: Stats
FSA		Stats (v4.3.1)
FSA (v0.10.1)		p-values, ANOVA,
Kruskal–Wallis, Dunns, Bray–Curtis, PERMANOVA (type III)
Differential Abundance AnalysisAssess if any species were significantly differentially abundant.R package: ANCOM-BCANCOM-BC (v2.12.0)DA analysis
VisualisationGenerated ordination plots (PCoA).R packages:
readxl
tidyverse
ggnewscale
scales
ggplot2
cowplot
patchwork		readxl (1.4.5)
tidyverse (2.0.0)
ggnewscale (0.5.2)
scales (1.4.0)
ggplot2 (v3.5.1)
cowplot (v1.1.3)
patchwork (v1.2.0)		Figure 2.
Figure 3.
Figure 4.
Table 2. Distribution of ash trees by canopy cover across 9 forest sites in Northern Ireland in 2023.
Table 2. Distribution of ash trees by canopy cover across 9 forest sites in Northern Ireland in 2023.
Forest1%–20%21%–40%41%–60%61%–80%81%–100%Total Trees
Ballymenoch013318
Castle Archdale6254219
Cladagh Glen002103
Drum Manor1339016
Hillsborough3045517
Loughgall4274926
Oxford Island2537320
Redburn002349
Roe Valley2323515
Total Trees1816313929133
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MDPI and ACS Style

Stevenson, M.A.; Warnock, N.; McLaughlin, K.E.; Dalzell, J.; Swan, R.; Fleming, T.; Trudgett, J.; Murchie, A.K.; Reid, A.; Hamilton, D.; et al. Associations Between the Leaf Microbiome and the Health of Irish Ash Trees Affected by Hymenoscyphus fraxineus. Forests 2026, 17, 389. https://doi.org/10.3390/f17030389

AMA Style

Stevenson MA, Warnock N, McLaughlin KE, Dalzell J, Swan R, Fleming T, Trudgett J, Murchie AK, Reid A, Hamilton D, et al. Associations Between the Leaf Microbiome and the Health of Irish Ash Trees Affected by Hymenoscyphus fraxineus. Forests. 2026; 17(3):389. https://doi.org/10.3390/f17030389

Chicago/Turabian Style

Stevenson, Michael Andrew, Neil Warnock, Kirsty Elizabeth McLaughlin, Johnathan Dalzell, Rhonda Swan, Thomas Fleming, James Trudgett, Archie Kelso Murchie, Allison Reid, Deacem Hamilton, and et al. 2026. "Associations Between the Leaf Microbiome and the Health of Irish Ash Trees Affected by Hymenoscyphus fraxineus" Forests 17, no. 3: 389. https://doi.org/10.3390/f17030389

APA Style

Stevenson, M. A., Warnock, N., McLaughlin, K. E., Dalzell, J., Swan, R., Fleming, T., Trudgett, J., Murchie, A. K., Reid, A., Hamilton, D., & Carmichael, E. (2026). Associations Between the Leaf Microbiome and the Health of Irish Ash Trees Affected by Hymenoscyphus fraxineus. Forests, 17(3), 389. https://doi.org/10.3390/f17030389

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