Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Apr 9;107(4):e70374. doi: 10.1002/ecy.70374

Foliar pathogen epidemic slows decomposition of invasive plant litter

Brett R Lane 1,2,3,✉, Chris Wojan 4, Carlie Meehan 1, Amy E Kendig 5, Robert D Holt 6, Philip F Harmon 1, Keith Clay 4,7, S Luke Flory 5,8, Erica M Goss 1,9
PMCID: PMC13063362  PMID: 41954120

Abstract

Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. The rate of litter decomposition can determine its environmental impact, where accelerated decomposition alters the timing and rate of nutrient release and may promote nutrient leaching, whereas slowed decomposition can result in litter accumulation, which impacts seedling recruitment, fire regimes, perennation of microbial communities, and slows nutrient release. Mutualistic endophytes are known to slow litter decomposition, but less is known about the impact that plant pathogens, present in diseased litter, have on decomposition rates. We compared litter decomposition of the invasive annual grass Microstegium vimineum with Bipolaris leaf spot symptoms, a fungal disease, to litter without symptoms of the disease in a year‐long common garden experiment. We found leaf tissue with disease symptoms decomposed later in the year compared to litter without symptoms. By summer, 54% of leaf tissue from healthy sites remained compared to 80% of leaf material from diseased litter. Fungal infection did not impact the lignin or C:N content of the litter. There were significant differences in fungal community composition between infected and healthy litter at the start of the experiment that persisted until the end of summer. Disease epidemics prior to senescence contributed to the persistence of infected tissue, which could slow the return of nutrients to the environmental pool and promote the survival and dispersal of pathogen inoculum the following season.

Keywords: Bipolaris gigantea, decomposition, lignin, microbial communities, Microstegium vimineum, stiltgrass

INTRODUCTION

Plant litter decomposition is a critical component of ecosystem function, with numerous environmental impacts, including mineralization of organic nutrients, generation of soil organic matter, and return of CO2 fixed during photosynthesis to the atmosphere (Canessa et al., 2021; Vivanco & Austin, 2019). The timing and rate of litter decomposition contribute to its environmental impact, where accelerated decomposition may promote nutrient leaching, while slowed decomposition results in litter accumulation, with impacts on seedling recruitment, fire regimes, and perennation of microbial communities (Dubeux & Sollenberger, 2020; Facelli & Pickett, 1991). Microbes are responsible for the vast majority of the decomposition of terrestrial plant litter (Berg & McClaugherty, 2003), but the rate of litter decomposition can be significantly impacted by the phyllosphere community, including mutualistic endophytes, prior to host senescence (Purahong & Hyde, 2011). Mutualistic endophytes have been shown to drive alterations to the chemical make‐up of the plant, specifically through the production of toxic alkaloids, resulting in slowed decomposition post litter‐fall (Lemons et al., 2005; Omacini et al., 2004; Purahong & Hyde, 2011). In contrast, phytopathological research has focused more on monitoring the survival of pathogen inoculum in litter, rather than the effects of pathogens on the rates or mechanisms of litter decomposition. As climate change promotes the emergence and spread of plant pathogens (Bebber, 2015), it is important to understand the role that pathogens play in decomposition dynamics.

Pathogen colonization of host material prior to senescence may alter the chemical composition through multiple potential mechanisms, including secretion of cell wall degrading enzymes, detoxification of antimicrobial plant secondary metabolites, and induction of host defense responses prior to senescence (Dangl & Jones, 2001; Kubicek et al., 2014). These molecular processes subsequently could speed or slow decomposition. Alternatively, changes to plant‐associated fungal communities during disease epidemics could affect the prevalence of fungi that serve key roles in litter decomposition by breaking down complex macromolecules (Purahong et al., 2016).

Here, we investigate the impact of an emerging foliar pathogen on plant litter decomposition dynamics. The study host is Microstegium vimineum, a C4 annual grass, native to Asia, that has widely invaded the eastern United States. Impacts of M. vimineum on native plant communities are caused, in part, by accumulation of thick layers of slowly decomposing litter that inhibit recruitment and growth of native species (Ehrenfeld et al., 2001; Flory & Clay, 2010) (e.g., Figure 1C). Flory et al. (2011) first reported a leaf spot disease epidemic on M. vimineum at Big Oaks National Wildlife Refuge (BONWR) in southeastern Indiana, USA. Subsequent studies have established that annual disease epidemics occur on M. vimineum at BONWR and throughout the central part of its invaded range, and that they are caused primarily by the fungus Bipolaris gigantea (Lane et al., 2020, 2023; Stricker et al., 2016). The patchy distribution of disease on M. vimineum within BONWR creates an ideal study system to investigate foliar pathogen impacts on litter decomposition. We used the M. vimineum–B. gigantea system as a model to test the hypotheses that (1) senescent litter consisting of diseased plant material colonized by B. gigantea decomposes differently than M. vimineum litter free of Bipolaris leaf spot symptoms, and (2) differences in decomposition between infected and healthy litter are associated with altered litter chemistry and different microbial assemblages during litter decomposition. The experiment was conducted in a common garden to account for the possibility of altered decomposition dynamics following translocation; thus, we also used comparisons between healthy translocated and healthy non‐translocated litter to ask if M. vimineum, like native species, experiences more rapid decomposition at the site of origin, known as “home‐field advantage” (Ayres et al., 2009).

FIGURE 1.

FIGURE 1

Open in a new tab

(A) Location of Big Oaks National Wildlife Refuge in southeastern Indiana, USA. (B) Map of litter collection sites within the refuge (blue dots indicate healthy sites and red dots indicate infected sites). The black dot indicates the experimental site; See also Appendix S1: Table S1. Collection sites were clustered around the borders of the wildlife refuge due to access limitations (i.e., it is a former military testing facility). (C) Naturally senesced M. vimineum litter blanketing infected site no. 3. Photo was taken by Brett R. Lane.

MATERIALS AND METHODS

Site description

BONWR in southeastern Indiana, USA, located 850–900 feet above sea level, is a 50,000‐acre wildlife refuge comprised of approximately 27,000 acres of upland forests. These temperate deciduous forests contain both evergreen and deciduous species. Our experimental common garden site was primarily dominated by deciduous red maple (Acer rubrum) and sweetgum (Liquidambar styraciflua). In the early 1900s, the land was used for farming and grazing before being purchased by the United States Army in 1940 for munitions testing and was allowed to rewild. In 2000, the land was turned over to the United States Fish and Wildlife Service. Public access to the wildlife refuge is limited. The hottest month is July with an average high of 31.1°C, the coldest month is January with an average low of −4.6°C. The mean annual precipitation is 1295 mm (NOAA/NCEI, 2021; USFWS, 2000).

Site selection and litter preparation

Microstegium vimineum litter used in this experiment was collected from discrete sites of Bipolaris leaf spot disease and from sites not known to have symptoms of Bipolaris leaf spot disease (Benitez et al., 2022; Kendig et al., 2022; Lane et al., 2020, 2023). Bipolaris leaf spot symptoms (or lack of) were confirmed through scouting efforts in the summer months prior to litter collection and by inspection for signs of B. gigantea as described below. We henceforth refer to litter collected from sites with signs and symptoms of Bipolaris leaf spot as “infected litter” and litter free of signs and symptoms of Bipolaris leaf spot as “healthy litter.” Following natural senescence, we measured decomposition at a single, independent, healthy site (henceforth common garden site).

On September 23, 2019, prior to senescence, we collected 20 leaves from each site to confirm the presence or absence of B. gigantea. At sites with zonate eyespot lesions (see pictures Lane et al., 2020) on M. vimineum, confirmation that symptoms were caused by Bipolaris leaf spot was achieved by placing the collected symptomatic leaves in a plastic bag with a damp paper towel for 48 h to induce sporulation of B. gigantea conidia which were identified by their unique morphology and size using a stereoscope. This procedure was repeated with randomly selected leaves from healthy sites (as none contained eyespot lesions) to confirm B. gigantea was not observed.

We collected naturally senesced M. vimineum litter during October 26–November 1, 2019, from five infected, five healthy, and the healthy common garden site at BONWR, a temperate deciduous forest (Figure 1; Appendix S1: Table S1). At all sites, invasive Microstegium litter was conspicuously abundant (e.g., Figure 1C). Litter samples were shipped to the University of Florida where they were spread in a single layer on a greenhouse bench (25°C) to air dry to constant mass (~1 week). Samples were air‐dried rather than oven‐dried to enhance survival of the resident microbial community.

Litter bags

Litter bags were made with 18 × 16 (openings horizontally × vertically per square inch) weave DocaScreen fiberglass mesh window screening (Docazoo, Memphis, TN, USA). A 30 cm × 25 cm section of mesh was folded in half lengthwise, forming a 15 cm × 25 cm rectangle. We folded over the edges by 5 mm and stapled along the edge at 3‐cm intervals to form the litter bags. The bags were filled with 10 g of the air‐dried litter and stapled shut. Each bag also contained a numbered aluminum tag (Forestry Suppliers, Jackson, MS, USA) for identification.

Because litter samples were not oven‐dried, the absolute moisture content of the greenhouse dried litter from each site was estimated by taking five 10‐g samples per site of the air‐dried litter. Each sample was dried at 70°C to constant mass. The resulting dry masses were used to estimate the starting dry mass of the litter bags. In practice, our first litter collection showed that initial dry mass varied among bags because the dry mass of some litter samples in February 2021 was greater than our estimates from December (Figure 2). The initial proportion of stem and leaf composition of each sample, measured as the proportion of dry mass comprised of either stem or leaf material, was determined by separating the stem and leaf tissue of a 2‐g sample, oven drying, and weighing.

FIGURE 2.

FIGURE 2

Open in a new tab

Percent of (A) overall mass, (B) leaf mass, (C) stem mass, remaining at each timepoint. Data points from litter originating from infected sites are offset to the left of each timepoint while points from healthy litter sites are offset to the right. Shaded region represents 95% CI. Stars indicate significant differences between infected and healthy samples, for full statistical results see Table 1 and Appendix S1: Table S8. Litter quality was estimated by the (D) acid detergent lignin content (as percent or organic mass) and (E) the carbon/nitrogen ratio of litter at each timepoint. The acid detergent lignin content was significantly higher in litter from infected sites. Post hoc comparisons were not conducted for litter quality metrics as there was no significant interaction between month and infection (Table 1).

Experiment at common garden site

On December 10, 2019, five blocks (3 m × 6 m) were established at the healthy common garden site with 10 m between each block. Blocks were selected such that senesced, healthy M. vimineum was relatively uniform across each block. In each block, seven bags of litter from each site (35 bags from five infected sites, 35 bags from five healthy sites, plus 7 bags from the healthy common garden site) were placed into two plots by infection status of the litter bags. One plot contained litter bags from the infected sites while the other contained litter bags from the healthy sites and the common garden site. Within each plot, litter bags were randomized and placed in a grid with 0.5‐m spacing below the naturally fallen M. vimineum litter on the soil surface and held in place by a 10‐cm steel landscape fabric pin (Sta‐Green, Mooresville, NC, USA). Plots containing litter bags from infected and healthy sites were separated by 1.5 m within a block to minimize potential pathogen transmission from infected litter to healthy litter. To track hourly temperature and humidity over the course of the experiment, a HOBO Pro v2 temperature and humidity logger (Cape Cod, MA, USA) was placed in each block above the litter layer (Appendix S1: Figure S1). Rainfall data were obtained from the National Climatic Data Center (Station US1KYTB0005).

Bag collection and analysis

At two‐month intervals (Appendix S1: Table S2), one replicate bag of each site of origin was collected from each block (11 bags per block per collection date, 55 total bags per collection date). The litter bags were transported to Indiana University where approximately 1 g of litter was removed from each bag and shipped on dry ice to the University of Florida where it was stored at −80°C for nucleic acid extraction. The bags with remaining litter were then shipped to the University of Florida and stored at −20°C for biomass analysis.

Each litter bag was gently brushed to remove dirt and debris and rinsed to remove soil from the interior. A representative 1‐g sub‐sample of tissue (wet mass) was removed from the litter bags and separated into leaf and stem material. The separated leaf and stem material, as well as the remaining biomass material, were then dried at 70°C for 1 week to determine dry mass. Stem and leaf material could not be separately identified for the later October and December 2020 samples due to advanced decomposition. Following biomass measurements, samples were passed through a cyclone sample mill (UDY Corporation, Fort Collins, CO) with a 1‐mm screen. We homogenized the ground tissue and collected 0.5‐g subsamples for the determination of litter carbon/nitrogen ratios at the University of Florida Light Stable Isotope Mass Spectrometry Laboratory. The remainder of the sample was sent to the University of Florida Forage Analysis Lab for the determination of lignin content by acid detergent analysis.

Statistical analyses

Decomposition at each collection time was measured as the dry mass of the remaining litter relative to the estimated initial dry mass. Tests of decomposition, C:N ratio, lignin content, and microbial alpha diversity metrics between litter from infected and healthy sites at each collection time were conducted using a linear mixed‐effects model with infection status, month (ordinal categorical), and their interaction as fixed effects. Block and the litter source (site from which litter was collected; Appendix S1: Table S1) were used as random effects: lmer(Response ~ Infection × Month + (1|Block) + (1|Source)). Normality of the residuals was confirmed through inspection of residual plots, and homogeneity of variance across litter sources at each timepoint was confirmed using Levene's test (Kozak & Piepho, 2018). Data with nonnormal residuals were transformed as necessary. Significant effects of infection status indicated that foliar Bipolaris pathogens altered the decomposition of host litter, and significant interactions between infection status and time showed temporal variation in the decomposition of litter from infected and healthy sites. Post hoc comparisons were conducted with the emmeans package in R. Effect sizes (calculated as Cohen's D) were calculated with the cohensD function in the R package lsr (Navarro, 2015). All statistical analyses were conducted in R version 4.0.3 using the packages car, lme4, and lmerTest (Bates et al., 2015; Fox & Weisberg, 2019; Kuznetsova et al., 2017).

Molecular methods and sequence processing

Nucleic acids were extracted from subsamples of litter and amplified for Illumina MiSeq 2 × 300 bp sequencing. In brief, litter samples containing both stem and leaf tissue and negative controls were lyophilized, bead beat, and DNA was extracted using CTAB with a glass milk silica matrix used for DNA purification (Lane et al., 2023). The ITS2 region of fungal DNA was amplified using the primers fITS7 and ITS4. Sequences were quality filtered with DADA2 (Callahan et al., 2016) and exported to R for further analyses. The decontam package (Davis et al., 2018) was used to process negative controls and remove contaminating sequences. The rarefaction curves of all samples were manually inspected to confirm adequate sequencing depth; all samples containing less than 10,000 high‐quality reads were removed. Full details of DNA extraction and quality filtering may be found in Appendix S1.

For analysis of alpha diversity metrics, samples were rarified to the sequencing depth of the smallest sample. We estimated community richness using the Chao1 index and the Shannon's diversity index using the estimate_richness function in the R package phyloseq. The Chao1 index is a nonparametric index used to estimate the total number of species in microbial community data (Kim et al., 2017). Pielou's evenness index was calculated as Shannon's diversity index divided by the natural log of the total number of species (as estimated by the Chao1 index) (Pielou, 1966). Alpha diversity metrics were compared using a linear mixed‐effects model with infection status, month, and their interaction as fixed effects with block and litter source as random effects.

For analyses of community composition, amplicon sequence variants (ASVs) were scaled to relative abundances within samples without rarefaction. Principal coordinates analysis (PCoA) ordination plots were calculated using the pcoa function in ape using both Bray–Curtis and Jaccard distances (Paradis & Schliep, 2019). We tested the impacts of litter infection at each timepoint and change over time on microbial community composition using Bray–Curtis and Jaccard distances using permutational multivariate analysis of variance (PERMANOVA) with the adonis function using 10,000 permutations. We tested the differential abundance of individual ASVs at each timepoint using ANCOMBC2 (Lin & Peddada, 2024) with a Holm multiple comparison correction as calculated by ANCOMBC2. All analyses using ANCOMBC2 were conducted without scaling to relative abundances (using the count data) as ANCOMBC2 conducts its own data transformations. We also implemented a minimum natural log fold change threshold (by infection status) of two, as calculated by ANCOMBC2. Each differentially abundant ASV was functionally categorized as “Plant‐associated,” “Saprophytic,” “Both,” or “Other” through manual inspection of the FUNGuild database and literature searches (Nguyen et al., 2016). ASVs which were not identified to at least the family level were functionally categorized as “Unknown.” Differential abundance analyses using ANCOMBC2 were conducted between infected and healthy litter and within timepoints. We did not use ANCOMBC2 to quantify changes over time due to potential limitations in the detection of rare organisms (immigration vs. growth of an existing organism), the inability of ITS sequencing to differentiate between dormant and active microbes, and due to the known ability of DNA from dead organisms to persist within the environment which hampers the ability to determine when a fungal taxa has gone extinct from the community (Carini et al., 2016).

RESULTS

Decomposition of total litter from infected and healthy sites

Over the course of 1 year (December 2019 to December 2020), the litter bags lost an average of 48.5% ± 2.7% (mean ± SE) of their total mass (Figure 2A; Appendix S1: Table S3). Most decomposition took place between April and August collection dates, accounting for nearly three‐quarters of the total mass lost. While time was a significant predictor of decomposition, we did not observe a significant difference in the proportion of overall mass remaining between litter from healthy and infected sites nor an interaction between sampling timepoint and infection status for the proportion of overall mass remaining (Table 1).

TABLE 1.

ANOVA examining the impact of infection and month on proportion of litter mass remaining, acid detergent lignin content, and carbon:nitrogen ratio.

Dependent variable Tissue Transformation Factor NumDF DenDF F value p value
Mass remaining Overall None Infection 1 8.20 0.00 0.9971
Month 5 270.64 142.5276 <0.001
Infection:Month 5 270.65 1.4484 0.2072
Leaf Square root Infection 1 7.97 2.0716 0.18817
Month 3 175.13 42.6468 <0.001
Infection:Month 3 175.14 3.1206 0.02741
Stem Squared Infection 1 7.941 1.1348 0.3181
Month 3 175.05 94.9048 <0.001
Infection:Month 3 175.06 1.1493 0.3307
Lignin Overall Cubic root Infection 1 8.00 2.5246 0.1508
Month 6 313.15 24.7818 <0.001
Infection:Month 6 313.17 0.2944 0.9394
C:N ratio Overall Log Infection 1 7.98 0.0519 0.8256
Month 6 313.057 130.2599 <0.001
Infection:Month 6 313.086 2.0301 0.0614
Open in a new tab

Note: Data transformations for each analysis are indicated in the table. Degrees of freedom were calculated with Satterthwaite's method.

Leaf and stem decomposition from infected versus healthy sites

Infected and healthy litter dried mass was initially comprised of 19.0% ± 1.79% and 29.1% ± 2.24% (mean ± SE) leaf material, respectively. The sampling timepoint and the interaction between infection status and sampling timepoint significantly influenced the decomposition of leaf material. Post hoc analysis revealed 53.6% ± 5.30% of leaf material from healthy sites remained undecomposed in June, which was significantly less than leaf material from infected sites (80.3% ± 6.16%) (Tukey post hoc p = 0.0002; Cohen's D = 1.006). There were no significant differences in the decomposition of leaf material by infection status at other collection dates (Figure 2B). The sampling timepoint, but not infection status or their interaction, influenced the decomposition of stems (Figure 2C, Table 1). By August, litter samples had lost 56.9% ± 3.01% of their total leaf mass and 40.9% ± 2.6% of their stem mass in total with no significant differences by infection status.

Litter chemical composition

Lignin composition (as a proportion of organic mass) was impacted by the sampling timepoint, but not by infection status or the interaction of infection status and time. The lignin content of decomposing litter steadily increased at each timepoint from the initial collection until August (Figure 2D; Appendix S1: Table S4). The initial C:N ratio of healthy M. vimineum litter was 67.6 (Figure 2E; Appendix S1: Table S4), consistent with previous reports of naturally senesced M. vimineum (DeMeester & Richter, 2010). The C:N ratio of litter was significantly impacted by the sampling timepoint, continuously decreasing over the course of the experiment. We observed no significant differences in the C:N ratio of litter from infected and healthy sites.

Microbial community analysis

Healthy litter had a significantly higher Shannon's diversity and Pielou's evenness of fungal communities at the December timepoint (Tukey post hoc, p = 0.0161 and p = 0.0048, respectively; Appendix S1: Table S5). All three indices were lowest at the August collection, except for Pielou's evenness, which was lowest at the end of the experiment in December 2020 (Appendix S1: Table S4). Microbial community analysis revealed a gradual shift in community composition during the first 6 months of the experiment. Infection status significantly impacted community composition from the start of the experiment through August with the exception of the June collection (Appendix S1: Table S6). From the August collection onward, there were no significant changes in the community composition of infected or healthy litter between timepoints (Figure 3A; Appendix S1: Table S7). Differences in the microbial communities by litter infection status could be most clearly visualized using PCoA axes 6 and 7 and showed convergence over time (Figure 3B). Analysis of ASVs at each timepoint revealed a total of 72 ASVs which were differentially abundant by infection status and had a minimum natural log fold change of two (range 7–13 for each sampling date) (Figure 4A; Appendix S1: Table S8). When we added the criterion that the ASV must contain at least 0.05% of the reads for that timepoint, there were 44 differentially abundant ASVs (Figure 4B). In our comparison of top 10 orders by infection status/timepoint, we observed a change in the most prominent orders over time (Figure 5). The variation in the relative abundances of prominent orders became more pronounced over the course of the experiment. By August, the Auriculariales and Sordariales comprised a larger proportion of the reads from infected litter while Agaricales and Helotiales comprised a larger proportion of the reads from healthy litter (Figure 5).

FIGURE 3.

FIGURE 3

Open in a new tab

(A) Principal coordinates analysis (PCoA) of microbial populations using Bray–Curtis distances. Ellipses are drawn for each timepoint regardless of infection status. (B) PCoA of microbial populations using Bray–Curtis distances and axes 6 and 7, which best represent the separation of the microbial communities by litter source. Full permutational multivariate analysis of variance results of each comparison can be found in Appendix S1: Table S6.

FIGURE 4.

FIGURE 4

Open in a new tab

(A) Differentially enriched amplicon sequence variants (ASVs) in infected litter (left in the plot) and healthy litter (right in plot). Dot color indicates the estimated function of each ASV, ASVs that could not be identified to at least the family level were labeled as unknown function. Triangular dots indicate ASVs which comprised at least 0.05% of the reads for each timepoint. Gray dots indicate ASVs which were not significant following a Holm correction (dashed horizontal lines) or did not undergo a natural log fold change of at least two (dashed vertical lines) by infection status. (B) Natural log fold change by timepoint of ASVs which contained at least 0.05% of the reads. Bars represent standard error. All statistics for both panels were calculated using ANCOMBC2 and can be found in Appendix S1: Table S8.

FIGURE 5.

FIGURE 5

Open in a new tab

Top 10 most abundant fungal orders for each infection status and timepoint. Bars within each panel are retained in the same order as the legend.

Decomposition of litter from experimental site

Healthy litter originally obtained from the common garden site was significantly more decomposed than the translocated healthy litter (Appendix S1: Figure S2, Table S9). By December, translocated healthy litter had 35.58% more biomass than healthy litter obtained from the common garden site (Cohen's D = 0.69; Appendix S1: Table S4). The differences in the decomposition of healthy litter obtained from the common garden site and translocated healthy litter could not be attributed to either the stem or leaf material (Appendix S1: Table S9). The lignin content of healthy litter was influenced by sampling timepoint, but not influenced by the origin of litter (translocated vs. experimental common garden site) or the interaction of litter origin and time. The C:N ratio was 44.08% lower in the litter from the experimental site at the start of the experiment (Cohen's D = 1.24; Appendix S1: Table S4) and was significantly impacted by the sampling timepoint as well as the interaction of litter origin and the sampling timepoint (Appendix S1: Table S9). However, post hoc analyses were not able to identify significant differences in the C:N ratio of litter from the experimental site and translocated healthy litter at any sampling timepoint. The microbial communities of translocated healthy litter and litter obtained from the common garden site were significantly different in composition early in the experiment but had converged by the February sampling based on Jaccard distances and by the April sampling based on Bray–Curtis distances (Appendix S1: Table S6). The significant Bray–Curtis distances, but not Jaccard distances, at the February sampling suggest similarity in membership, but not the differential abundances of prominent members of the community. There were no significant differences in diversity metrics between common garden and translocated healthy litter at any timepoint (Appendix S1: Table S5). We did not conduct direct comparisons between translocated infected litter and healthy litter originating from the experimental common garden site as the effects of infection and litter translocation would be confounded.

DISCUSSION

We hypothesized that pathogens affect litter decomposition by altering the chemistry of host litter. Our results revealed that the establishment of a foliar pathogen on an invasive annual grass shifted the timing of decomposition of infected leaf tissue from spring (April to June) to summer (June to August), but we did not detect differential host lignin nor C:N content. Rather, alterations to decomposition following pathogen colonization may arise from disease‐associated shifts in the microbial community.

Decomposition of leaf litter from infected sites coincided with increased average daily high temperatures (June–August mean daily high 26.0°C). When not limited by moisture availability, early‐stage decomposition of graminicolous litter is driven primarily by temperature, the optimal temperature being approximately 23°C (Coûteaux et al., 2002; Dalias et al., 2001). Prior to the occurrence of these optimal temperatures in mid‐April (Appendix S1: Figure S1), we observed a low rate of leaf decomposition across all samples and no significant differences between infected and healthy samples. Litter quality, which is defined as how rapidly different litter is decomposed under identical conditions (faster decomposition = higher quality), is largely determined by carbon and nitrogen composition (a lower C:N ratio decomposes more rapidly) as well as overall carbon recalcitrancy (recalcitrant carbon is slow to metabolize) (Adair et al., 2008; Parton et al., 1994; Taylor et al., 1989). Fierer et al. (2005) found that higher quality litter is more rapidly decomposed at a wider range of temperatures than lower quality litter and higher daily temperatures are more conducive for the decomposition of substrates with an elevated lignin content. We expected that disease could cause localized lignification resulting from a host defense cascade, potentially reducing the quality of the litter and slowing decomposition at earlier, cooler timepoints (Ayres, 1993; Bhuiyan et al., 2009; Hammerschmidt & Schultz, 1996; Rahman et al., 2013). However, we measured lignin from the whole sample material of leaves and stems, whereas Bipolaris pathogens of M. vimineum do not cause disease on stem tissue (Lane et al., 2020; Manamgoda et al., 2013; Minnis et al., 2012). We suspect alterations to lignin content may have occurred in foliar tissue, where we observed disease‐associated effects on decomposition, but we were unable to detect lignification across the whole sample as it was dominated by stem biomass. The C:N ratio of litter from infected and healthy sites was not significantly different, indicating the C:N ratio did not play a significant role in differential decomposition of litter by infection status.

We found significant differences in fungal communities between litter types at each collection time up to August, except for the June collection (Appendix S1: Table S6). In June, we observed especially high variation in microbial communities among samples (elevated median distance to centroid, Appendix S1: Table S10) and a shift in microbial community composition (Figure 3A). The decomposition of litter mass does not typically occur at a constant rate; rather, initial decomposition occurs rapidly as exponential decay, and the rate of mass loss slows through the early phase of decomposition (Peng et al., 2014; Swift et al., 1979). Early in the litter decomposition process, microbial communities feed on readily available nutrient sources, resulting in an initial rapid loss in dry mass (Berg & McClaugherty, 2003). Following the depletion of readily available carbohydrates, microbial communities must metabolize increasingly complex substrates, subsequently slowing the rate of decomposition (Berg & McClaugherty, 2003; Peng et al., 2014). The utilization of increasingly complex substrates is associated with shifts in the microbial assemblage (Veen et al., 2019) such as those observed in this study (Figure 3A). In our experiment, there were no significant differences in the leaf mass of litter from infected and healthy sites by August, and there was little loss of litter mass after August when temperatures were still optimal for microbial growth and decomposition. By October, the overall degradation of samples had progressed to the point that we were no longer able to differentiate stem and leaf material within the litter bags. Moreover, lignin composition, C:N ratios, and microbial communities of our samples began to stabilize starting with the August samples; all these observations suggest the depletion of labile carbohydrates and the concurrent transition of microbial communities to the metabolism of primarily complex substrates.

While temporal dynamics of litter decomposition play the largest role in microbial community composition (Gołębiewski et al., 2019; Tláskal et al., 2016; Voříšková & Baldrian, 2013), our results suggest that establishment of a foliar pathogen prior to senescence can have a persistent role in the trajectory of microbial decomposer communities. The history of fungal community assembly can be critical in the decomposition of litter material (Cline & Zak, 2015). Early colonizers may modify their habitat and alter later emerging assemblages (Lane et al., 2023; Vannette & Fukami, 2014). In our study system, B. gigantea produces several herbicidal ophiobolins with potential antimicrobial activity (Evidente et al., 2006; Li et al., 1995). The induction of chlorophyll synthesis within B. gigantea lesions, sometimes referred to as the “green island effect”, indicates the potential production of secondary metabolites which may alter microbial communities (Lane et al., 2020). In addition, pathogen colonization may exclude the natural microbial communities that are involved in rapid decomposition of M. vimineum litter through resource competition, priority effects, or even indirectly, such as through holistic shifts in the plant‐associated community resulting from growth‐defense trade‐offs (Adame‐Álvarez et al., 2014; Lane, Kuhs, et al., 2025; Noman et al., 2021). Differences in fungal communities in the spring appeared to have downstream impacts on the most prominent fungal orders by the end of the experiment. Differentially enriched ASVs in the translocated healthy litter were primarily identified as saprophytic taxa by April, while the differentially enriched taxa in infected litter continued to be primarily plant‐associated pathogens and endophytes. These differences in the microbial community may in part explain the differential decomposition of leaf tissue and could have significant potential impacts on ecological functions including alterations to the metabolism of carbon substrates and the profile of greenhouse gasses released (Popp et al., 2017), the availability of nitrogen compounds within the soil (Wang et al., 2021), and germination of seeds.

Litter is degraded most rapidly at the site of origin in a phenomenon often dubbed, ‘home‐field advantage’ (Ayres et al., 2009), thus necessitating the use of common garden experiments. Here we observed that healthy litter from the experimental common garden site decomposed more rapidly than translocated healthy litter, confirming that using a common garden site to which both infected and healthy litter were translocated was essential for the experiment. Notably, differences between microbial communities of healthy locally sourced litter and translocated healthy litter persisted only through the April collection, while differences between translocated infected and translocated healthy litter persisted through August (Appendix S1: Table S6). Home‐field advantage has been linked to competition between allopatric soil‐inhabiting decomposers, which are colonizing the translocated litter, and native litter‐microbial communities. Non‐native plant species may leave behind their coevolved microbial communities when they establish in a new region (Mitchell & Power, 2003). However, due to the complex dynamics which govern the assembly of native microbes on non‐native plant species, it was unclear whether invasive M. vimineum decomposition would reflect home‐field advantage. Our results suggest M. vimineum (with implications for other non‐native plant species) develops an assemblage of endo‐ and epiphytic fungi (Lane et al., 2023) and forms native litter‐microbial communities which may compete with soil‐inhabiting decomposers following translocation, consistent with the hypothesized mechanisms underlying home‐field advantage in native plant systems. While we cannot exclude the possibility that soil‐microbial communities of infected sites would be better suited for the decomposition of infected litter (and vice‐versa), M. vimineum is known to spread via floodwaters that translocate senesced plant material with their seeds, emphasizing the ecological relevance of the decomposition dynamics of translocated litter of invasive plant species (Barden, 1987). Natural translocation could also bring B. gigantea infected M. vimineum to non‐diseased M. vimineum populations, highlighting the importance of the decomposition dynamics of infected litter in healthy plant populations.

Pathogen impacts on litter dynamics could alter epidemic development, because plant litter contributes to the survival and dispersal of fungal inoculum between seasons (Knudsen et al., 1995; Neate, 1987; Suffert et al., 2011). Optimal temperatures for germination and growth of Bipolaris pathogens occur in the summer months (Almaguer et al., 2013). Positive feedback on disease could arise from slowed decomposition, in which pathogen‐mediated persistence of infected litter promotes the survival and dispersal of primary inoculum the following season. Alternatively, relative increases in the composition of recalcitrant carbon (such as those we hypothesize occurred in the leaf material) may lead to antibiosis‐mediated competition for recalcitrant carbon, potentially reducing the survival of pathogenic inoculum within the litter (Lane, Tran, et al., 2025).

These results have important implications for our growing understanding of relationships between plant disease dynamics and nutrient cycling (Paseka et al., 2020). Pathogen colonization may increase host mortality and thereby alter nutrient dynamics by accelerating the rate at which nutrients are returned to the environmental pool (Borer et al., 2022). Our work demonstrates marked differences in the seasonal phenology of litter decomposition, depending on the infection status of the litter. The effect of the pathogen on decomposition is transient, but the timing of the delay in decomposition comes at a critical juncture for both plant and pathogen. As the growing season progresses, M. vimineum populations become dense and allocate resources to rapid vegetative growth, which may indicate more intense resource competition (De Wit, 1960) and amplify potential impacts of delayed decomposition on nutrient availability. Different temporal patterns in decomposing litter are expected to have different timing of release of nutrient pulses and magnitudes of litter impacts on germination. Indeed, many plants have narrow temporal windows for germination and early development. There is increasing attention being given to seasonality in analyses of ecological systems (White & Hastings, 2020), and our results suggest that infection influences the phenology of decomposition. Together, these findings suggest foliar fungal pathogens may contribute opposing forces to nutrient cycling: speeding senescence but slowing decomposition.

Our results demonstrate for the first time that fungal pathogen colonization alters litter decomposition phenology with potential wide‐ranging ecosystem effects including nutrient cycling and the development of subsequent epidemics. Moreover, the impact of pathogens on litter decomposition could interact with plant competition to alter plant community composition, with possibly surprising feedback effects on litter decomposition (Kortessis et al., 2022). While this study was conducted in a single system, the interplay of disease dynamics and litter decomposition processes is likely to be important in many plant‐pathogen systems. Rising global temperatures and increasing atmospheric CO2 are anticipated to expand pathogen range and disease severity in both natural and agronomic systems (Raza & Bebber, 2022). The expansion of pathogen ranges to include new potential hosts may subsequently have wide‐ranging impacts on ecosystem processes—including decomposition dynamics, nutrient cycling, the flow of energy through food webs, and carbon sequestration—further exacerbating the impact of rising global temperatures on ecosystem health.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Supporting information

Appendix S1.

ECY-107-e70374-s001.pdf (529.1KB, pdf)

ACKNOWLEDGMENTS

We would like to thank Chris Wilson for his input on the study design, Jason Kruse for his assistance in sample processing, and Nicholas Heiss for his assistance in the laboratory. This work was funded by USDA award number 2017‐67013‐26870 as part of the joint USDA‐NSF‐NIH Ecology and Evolution of Infectious Diseases program. Brett R. Lane was supported in part by NSF award number 2305753. Robert D. Holt thanks the University of Florida Foundation for its continued support.

Lane, Brett R. , Wojan Chris, Meehan Carlie, Kendig Amy E., Holt Robert D., Harmon Philip F., Clay Keith, Flory S. Luke, and Goss Erica M.. 2026. “Foliar Pathogen Epidemic Slows Decomposition of Invasive Plant Litter.” Ecology 107(4): e70374. 10.1002/ecy.70374

Handling editor: Hugh Henry

DATA AVAILABILITY STATEMENT

All sequencing data are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under accession number PRJNA978091 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA978091. All other data (Lane, 2023) are available in the Environmental Data Initiative (EDI) at https://doi.org/10.6073/pasta/d148c1ea2ae5bc8a9ec4403c34027d35. Code (Lane, 2026) is available in Zenodo at https://doi.org/10.5281/zenodo.18258613.

REFERENCES

  1. Adair, E. C. , Parton W. J., Del Grosso S. J., Silver W. L., Harmon M. E., Hall S. A., Burke I. C., and Hart S. C.. 2008. “Simple Three‐Pool Model Accurately Describes Patterns of Long‐Term Litter Decomposition in Diverse Climates.” Global Change Biology 14: 2636–2660. 10.1111/j.1365-2486.2008.01674.x. [DOI] [Google Scholar]
  2. Adame‐Álvarez, R.‐M. , Mendiola‐Soto J., and Heil M.. 2014. “Order of Arrival Shifts Endophyte‐Pathogen Interactions in Bean from Resistance Induction to Disease Facilitation.” FEMS Microbiology Letters 355: 100–107. 10.1111/1574-6968.12454. [DOI] [PubMed] [Google Scholar]
  3. Almaguer, M. , Rojas T. I., Dobal V., Batista A., and Aira M. J.. 2013. “Effect of Temperature on Growth and Germination of Conidia in Curvularia and Bipolaris Species Isolated from the Air.” Aerobiologia 29: 13–20. 10.1007/s10453-012-9257-z. [DOI] [Google Scholar]
  4. Ayres, E. , Steltzer H., Simmons B. L., Simpson R. T., Steinweg J. M., Wallenstein M. D., Mellor N., Parton W. J., Moore J. C., and Wall D. H.. 2009. “Home‐Field Advantage Accelerates Leaf Litter Decomposition in Forests.” Soil Biology and Biochemistry 41: 606–610. 10.1016/j.soilbio.2008.12.022. [DOI] [Google Scholar]
  5. Ayres, M. P. 1993. “Plant Defense, Herbivory, and Climate Change.” In Biotic Interactions and Global Change 75–94. Sunderland: Sinauer Associates Inc. [Google Scholar]
  6. Barden, L. S. 1987. “Invasion of Microstegium Vimineum (Poaceae), an Exotic, Annual, Shade‐Tolerant, C 4 Grass, into a North Carolina Floodplain.” American Midland Naturalist 118: 40. 10.2307/2425626. [DOI] [Google Scholar]
  7. Bates, D. , Mächler M., Bolker B., and Walker S.. 2015. “Fitting Linear Mixed‐Effects Models Using lme4.” Journal of Statistical Software 67: 1–48. 10.18637/jss.v067.i01. [DOI] [Google Scholar]
  8. Bebber, D. P. 2015. “Range‐Expanding Pests and Pathogens in a Warming World.” Annual Review of Phytopathology 53: 335–356. 10.1146/annurev-phyto-080614-120207. [DOI] [PubMed] [Google Scholar]
  9. Benitez, L. , Kendig A. E., Adhikari A., Clay K., Harmon P. F., Holt R. D., Goss E. M., and Flory S. L.. 2022. “Invasive Grass Litter Suppresses a Native Grass Species and Promotes Disease.” Ecosphere 13: e3907. 10.1002/ecs2.3907. [DOI] [Google Scholar]
  10. Berg, B. , and McClaugherty C.. 2003. Plant Litter: Decomposition, Humus Formation, Carbon Sequestration. Cham: Springer International Publishing. 10.1007/978-3-030-59631-6. [DOI] [Google Scholar]
  11. Bhuiyan, N. H. , Selvaraj G., Wei Y., and King J.. 2009. “Role of Lignification in Plant Defense.” Plant Signaling & Behavior 4: 158–159. 10.4161/psb.4.2.7688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Borer, E. T. , Paseka R. E., Peace A., Asik L., Everett R., Frenken T., González A. L., et al. 2022. “Disease‐Mediated Nutrient Dynamics: Coupling Host–Pathogen Interactions with Ecosystem Elements and Energy.” Ecological Monographs 92: e1510. 10.1002/ecm.1510. [DOI] [Google Scholar]
  13. Callahan, B. J. , McMurdie P. J., Rosen M. J., Han A. W., Johnson A. J. A., and Holmes S. P.. 2016. “DADA2: High‐Resolution Sample Inference from Illumina Amplicon Data.” Nature Methods 13: 581–583. 10.1038/nmeth.3869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Canessa, R. , Van Den Brink L., Saldaña A., Rios R. S., Hättenschwiler S., Mueller C. W., Prater I., Tielbörger K., and Bader M. Y.. 2021. “Relative Effects of Climate and Litter Traits on Decomposition Change with Time, Climate and Trait Variability Ed. Frida Piper.” Journal of Ecology 109: 447–458. 10.1111/1365-2745.13516. [DOI] [Google Scholar]
  15. Carini, P. , Marsden P. J., Leff J. W., Morgan E. E., Strickland M. S., and Fierer N.. 2016. “Relic DNA Is Abundant in Soil and Obscures Estimates of Soil Microbial Diversity.” Nature Microbiology 2: 16242. 10.1038/nmicrobiol.2016.242. [DOI] [PubMed] [Google Scholar]
  16. Cline, L. C. , and Zak D. R.. 2015. “Initial Colonization, Community Assembly and Ecosystem Function: Fungal Colonist Traits and Litter Biochemistry Mediate Decay Rate.” Molecular Ecology 24: 5045–5058. 10.1111/mec.13361. [DOI] [PubMed] [Google Scholar]
  17. Coûteaux, M.‐M. , Aloui A., and Kurz‐Besson C.. 2002. “Pinus Halepensis Litter Decomposition in Laboratory Microcosms as Influenced by Temperature and a Millipede, Glomeris Marginata.” Applied Soil Ecology 20: 85–96. 10.1016/S0929-1393(02)00013-6. [DOI] [Google Scholar]
  18. Dalias, P. , Anderson J. M., Bottner P., and Coûteaux M.. 2001. “Temperature Responses of Carbon Mineralization in Conifer Forest Soils from Different Regional Climates Incubated under Standard Laboratory Conditions.” Global Change Biology 7: 181–192. 10.1046/j.1365-2486.2001.00386.x. [DOI] [Google Scholar]
  19. Dangl, J. L. , and Jones J. D. G.. 2001. “Plant Pathogens and Integrated Defence Responses to Infection.” Nature 411: 826–833. 10.1038/35081161. [DOI] [PubMed] [Google Scholar]
  20. Davis, N. M. , Proctor D. M., Holmes S. P., Relman D. A., and Callahan B. J.. 2018. “Simple Statistical Identification and Removal of Contaminant Sequences in Marker‐Gene and Metagenomics Data.” Microbiome 6: 226. 10.1186/s40168-018-0605-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. De Wit, C. T. 1960. “On Competition.” Verslagen van Landbouwkundige Onderzoekingen 66: 1–82. [Google Scholar]
  22. DeMeester, J. E. , and Richter D.. 2010. “Differences in Wetland Nitrogen Cycling between the Invasive Grass Microstegium Vimineum and a Diverse Plant Community.” Ecological Applications 20: 609–619. 10.1890/09-0283.1. [DOI] [PubMed] [Google Scholar]
  23. Dubeux, J. C. B. , and Sollenberger L. E.. 2020. “Nutrient Cycling in Grazed Pastures.” In Management Strategies for Sustainable Cattle Production in Southern Pastures 59–75. London: Elsevier. 10.1016/B978-0-12-814474-9.00004-9. [DOI] [Google Scholar]
  24. Ehrenfeld, J. G. , Kourtev P., and Huang W.. 2001. “Changes in Soil Functions Following Invasions of Exotic Understory Plants in Deciduous Forests.” Ecological Applications 11: 1287–1300. 10.1890/1051-0761(2001)011[1287:CISFFI]2.0.CO;2. [DOI] [Google Scholar]
  25. Evidente, A. , Andolfi A., Cimmino A., Vurro M., Fracchiolla M., and Charudattan R.. 2006. “Herbicidal Potential of Ophiobolins Produced by Drechslera gigantea .” Journal of Agricultural and Food Chemistry 54: 1779–1783. 10.1021/jf052843l. [DOI] [PubMed] [Google Scholar]
  26. Facelli, J. M. , and Pickett S. T. A.. 1991. “Plant Litter: Its Dynamics and Effects on Plant Community Structure.” The Botanical Review 57: 1–32. 10.1007/BF02858763. [DOI] [Google Scholar]
  27. Fierer, N. , Craine J. M., McLauchlan K., and Schimel J. P.. 2005. “Litter Quality and the Temperature Sensitivity of Decomposition.” Ecology 86: 320–326. 10.1890/04-1254. [DOI] [Google Scholar]
  28. Flory, S. L. , and Clay K.. 2010. “Non‐native Grass Invasion Alters Native Plant Composition in Experimental Communities.” Biological Invasions 12: 1285–1294. 10.1007/s10530-009-9546-9. [DOI] [Google Scholar]
  29. Flory, S. L. , Kleczewski N., and Clay K.. 2011. “Ecological Consequences of Pathogen Accumulation on an Invasive Grass.” Ecosphere 2: art120. 10.1890/ES11-00191.1. [DOI] [Google Scholar]
  30. Fox, J. , and Weisberg S.. 2019. An R Companion to Applied Regression, Third ed. Los Angeles London New Delhi Singapore Washington, DC Melbourne: SAGE. [Google Scholar]
  31. Gołębiewski, M. , Tarasek A., Sikora M., Deja‐Sikora E., Tretyn A., and Niklińska M.. 2019. “Rapid Microbial Community Changes during Initial Stages of Pine Litter Decomposition.” Microbial Ecology 77: 56–75. 10.1007/s00248-018-1209-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hammerschmidt, R. , and Schultz J. C.. 1996. “Multiple Defenses and Signals in Plant Defense against Pathogens and Herbivores.” In Phytochemical Diversity and Redundancy in Ecological Interactions, edited by Romeo J. T., Saunders J. A., and Barbosa P., 121–154. Boston, MA: Springer US. 10.1007/978-1-4899-1754-6_5. [DOI] [Google Scholar]
  33. Kendig, A. E. , Adhikari A., Lane B. R., Wojan C. M., Kortessis N., Simon M. W., Barfield M., et al. 2022. “Effects of Disease Emergence on Invasive Grass Impacts.” 10.1101/2022.03.09.483680. [DOI]
  34. Kim, B.‐R. , Shin J., Guevarra R. B., Lee J. H., Kim D. W., Seol K.‐H., Lee J.‐H., Kim H. B., and Isaacson R. E.. 2017. “Deciphering Diversity Indices for a Better Understanding of Microbial Communities.” Journal of Microbiology and Biotechnology 27: 2089–2093. 10.4014/jmb.1709.09027. [DOI] [PubMed] [Google Scholar]
  35. Knudsen, I. M. B. , Hockenhull J., and Jensen D. F.. 1995. “Biocontrol of Seedling Diseases of Barley and Wheat Caused by Fusarium culmorum and Bipolaris sorokiniana : Effects of Selected Fungal Antagonists on Growth and Yield Components.” Plant Pathology 44: 467–477. 10.1111/j.1365-3059.1995.tb01669.x. [DOI] [Google Scholar]
  36. Kortessis, N. , Kendig A. E., Barfield M., Flory S. L., Simon M. W., and Holt R. D.. 2022. “Litter, Plant Competition, and Ecosystem Dynamics: A Theoretical Perspective.” The American Naturalist 200: 739–754. 10.1086/721438. [DOI] [PubMed] [Google Scholar]
  37. Kozak, M. , and Piepho H.‐P.. 2018. “What's Normal Anyway? Residual Plots Are More Telling than Significance Tests when Checking anova Assumptions.” Journal of Agronomy and Crop Science 204: 86–98. 10.1111/jac.12220. [DOI] [Google Scholar]
  38. Kubicek, C. P. , Starr T. L., and Glass N. L.. 2014. “Plant Cell Wall–Degrading Enzymes and their Secretion in Plant‐Pathogenic Fungi.” Annual Review of Phytopathology 52: 427–451. 10.1146/annurev-phyto-102313-045831. [DOI] [PubMed] [Google Scholar]
  39. Kuznetsova, A. , Brockhoff P. B., and Christensen R. H. B.. 2017. “lmerTest Package: Tests in Linear Mixed Effects Models.” Journal of Statistical Software 82: 1–26. 10.18637/jss.v082.i13. [DOI] [Google Scholar]
  40. Lane, B. 2026. “laneb14/Foliar‐Pathogen‐Epidemic‐Slows‐Decomposition‐of‐Invasive‐Plant‐Litter: 15 January 2026 Decomposition Experiment (Peer_reviewed).” Zenodo. 10.5281/zenodo.18258613 [DOI]
  41. Lane, B. 2023. “Decomposition of Microstegium vimineum litter, plants grew through the Big Oaks National Wildlife Refuge in 2019. Litter used in this experiment naturally senesced in the fall 2019, decomposition data collected through 2020.” Plants were infected or not‐infected with the foliar fungal pathogen Bipolaris gigantea during the 2019 growing season. ver 1. Environmental Data Initiative. 10.6073/pasta/d148c1ea2ae5bc8a9ec4403c34027d35. [DOI]
  42. Lane, B. R. , Kendig A. E., Wojan C. M., Adhikari A., Jusino M. A., Kortessis N., Simon M. W., et al. 2023. “Fungicide‐Mediated Shifts in the Foliar Fungal Community of an Invasive Grass.” Phytobiomes Journal 7: 198–207. 10.1094/PBIOMES-03-22-0018-R. [DOI] [Google Scholar]
  43. Lane, B. R. , Kuhs M. A., Zaret M. M., Song Z., Borer E. T., Seabloom E. W., Schlatter D. C., and Kinkel L. L.. 2025. “Foliar Fungi‐Imposed Costs to Plant Productivity Moderate Shifts in Composition of the Rhizosphere Microbiome.” Frontiers in Plant Science 16: 1558191. 10.3389/fpls.2025.1558191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lane, B. R. , Tran A. K., Gieske M. F., and Kinkel L. L.. 2025. “Incorporation of Plant Residue in Corn Monoculture Selectively Enriches Antagonistic Phenotypes among Lignin‐Degrading Vs Non‐degrading Streptomyces.” 10.1101/2025.07.14.664736. [DOI]
  45. Lane, B. , Stricker K. B., Adhikari A., Ascunce M. S., Clay K., Flory S. L., Smith M. E., Goss E. M., and Harmon P. F.. 2020. “Large‐Spored Drechslera gigantea Is a Bipolaris Species Causing Disease on the Invasive Grass Microstegium vimineum .” Mycologia 112: 921–931. 10.1080/00275514.2020.1781495. [DOI] [PubMed] [Google Scholar]
  46. Lemons, A. , Clay K., and Rudgers J. A.. 2005. “Connecting Plant–Microbial Interactions above and Belowground: A Fungal Endophyte Affects Decomposition.” Oecologia 145: 595–604. 10.1007/s00442-005-0163-8. [DOI] [PubMed] [Google Scholar]
  47. Li, E. , Clark A. M., Rotella D. P., and Hufford C. D.. 1995. “Microbial Metabolites of Ophiobolin A and Antimicrobial Evaluation of Ophiobolins.” Journal of Natural Products 58: 74–81. 10.1021/np50115a009. [DOI] [PubMed] [Google Scholar]
  48. Lin, H. , and Peddada S. D.. 2024. “Multigroup Analysis of Compositions of Microbiomes with Covariate Adjustments and Repeated Measures.” Nature Methods 21: 83–91. 10.1038/s41592-023-02092-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Manamgoda, D. S. , Minnis A. M., Kleczewski N. M., Flory S. L., Castlebury L. A., Clay K., and Hyde K. D.. 2013. “Bipolaris drechsleri, sp. nov.” Fungal Planet 31: 293. [Google Scholar]
  50. Minnis, A. M. , Rossman A. Y., Kleczewski N. M., and Flory S. L.. 2012. “Bipolaris microstegii, sp. nov.” Fungal Planet 29: 151. [Google Scholar]
  51. Mitchell, C. E. , and Power A. G.. 2003. “Release of Invasive Plants from Fungal and Viral Pathogens.” Nature 421: 625–627. 10.1038/nature01317. [DOI] [PubMed] [Google Scholar]
  52. Navarro, D. 2015. Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners (Version 0.6). University of New South Wales. [Google Scholar]
  53. Neate, S. M. 1987. “Plant Debris in Soil as a Source of Inoculum of Rhizoctonia in Wheat.” Transactions of the British Mycological Society 88: 157–162. 10.1016/S0007-1536(87)80210-3. [DOI] [Google Scholar]
  54. Nguyen, N. H. , Song Z., Bates S. T., Branco S., Tedersoo L., Menke J., Schilling J. S., and Kennedy P. G.. 2016. “FUNGuild: An Open Annotation Tool for Parsing Fungal Community Datasets by Ecological Guild.” Fungal Ecology 20: 241–248. 10.1016/j.funeco.2015.06.006. [DOI] [Google Scholar]
  55. NOAA/NCEI . 2021. “U.S. Climate Normals. National Centers for Environmental Information (NCEI).” https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals (accessed 25 Jun 2025).
  56. Noman, M. , Ahmed T., Ijaz U., Shahid M., Azizullah, Li D., Manzoor I., and Song F.. 2021. “Plant–Microbiome Crosstalk: Dawning from Composition and Assembly of Microbial Community to Improvement of Disease Resilience in Plants.” International Journal of Molecular Sciences 22: 6852. 10.3390/ijms22136852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Omacini, M. , J. Chaneton, EM. Ghersa, C. , and Otero, P. 2004. Do Foliar Endophytes Affect Grass Litter Decomposition? A Microcosm Approach Using Lolium multiflorum . Oikos 104:581–590. 10.1111/j.0030-1299.2004.12915.x. [DOI] [Google Scholar]
  58. Paradis, E. , and Schliep K.. 2019. “Ape 5.0: An Environment for Modern Phylogenetics and Evolutionary Analyses in R Ed. Russell Schwartz.” Bioinformatics 35: 526–528. 10.1093/bioinformatics/bty633. [DOI] [PubMed] [Google Scholar]
  59. Parton, W. J. , Ojima D. S., Cole C. V., and Schimel D. S.. 1994. “A General Model for Soil Organic Matter Dynamics: Sensitivity to Litter Chemistry, Texture and Management.” In SSSA Special Publications, edited by Bryant R. B. and Arnold R. W., 147–167. Madison, WI, USA: Soil Science Society of America. 10.2136/sssaspecpub39.c9. [DOI] [Google Scholar]
  60. Paseka, R. E. , White L. A., Van De Waal D. B., Strauss A. T., González A. L., Everett R. A., Peace A., Seabloom E. W., Frenken T., and Borer E. T.. 2020. “Disease‐Mediated Ecosystem Services: Pathogens, Plants, and People.” Trends in Ecology & Evolution 35: 731–743. 10.1016/j.tree.2020.04.003. [DOI] [PubMed] [Google Scholar]
  61. Peng, Q. , Qi Y., Dong Y., He Y., Xiao S., Liu X., Sun L., Jia J., Guo S., and Cao C.. 2014. “Decomposing Litter and the C and N Dynamics as Affected by N Additions in a Semi‐Arid Temperate Steppe, Inner Mongolia of China.” Journal of Arid Land 6: 432–444. 10.1007/s40333-014-0002-z. [DOI] [Google Scholar]
  62. Pielou, E. C. 1966. “The Measurement of Diversity in Different Types of Biological Collections.” Journal of Theoretical Biology 13: 131–144. 10.1016/0022-5193(66)90013-0. [DOI] [Google Scholar]
  63. Popp, D. , Von Gillhaussen P., Weidlich E. W. A., Sträuber H., Harms H., and Temperton V. M.. 2017. “Methane Yield of Biomass from Extensive Grassland Is Affected by Compositional Changes Induced by Order of Arrival.” GCB Bioenergy 9: 1555–1562. 10.1111/gcbb.12441. [DOI] [Google Scholar]
  64. Purahong, W. , and Hyde K. D.. 2011. “Effects of Fungal Endophytes on Grass and Non‐grass Litter Decomposition Rates.” Fungal Diversity 47: 1–7. 10.1007/s13225-010-0083-8. [DOI] [Google Scholar]
  65. Purahong, W. , Wubet T., Lentendu G., Schloter M., Pecyna M. J., Kapturska D., Hofrichter M., Krüger D., and Buscot F.. 2016. “Life in Leaf Litter: Novel Insights into Community Dynamics of Bacteria and Fungi during Litter Decomposition.” Molecular Ecology 25: 4059–4074. 10.1111/mec.13739. [DOI] [PubMed] [Google Scholar]
  66. Rahman, M. M. , Tsukamoto J., Rahman M. M., Yoneyama A., and Mostafa K. M.. 2013. “Lignin and its Effects on Litter Decomposition in Forest Ecosystems.” Chemistry and Ecology 29: 540–553. 10.1080/02757540.2013.790380. [DOI] [Google Scholar]
  67. Raza, M. M. , and Bebber D. P.. 2022. “Climate Change and Plant Pathogens.” Current Opinion in Microbiology 70: 102233. 10.1016/j.mib.2022.102233. [DOI] [PubMed] [Google Scholar]
  68. Stricker, K. B. , Harmon P. F., Goss E. M., Clay K., and Luke Flory S.. 2016. “Emergence and Accumulation of Novel Pathogens Suppress an Invasive Species.” Ecology Letters 19: 469–477. 10.1111/ele.12583. [DOI] [PubMed] [Google Scholar]
  69. Suffert, F. , Sache I., and Lannou C.. 2011. “Early Stages of Septoria Tritici Blotch Epidemics of Winter Wheat: Build‐Up, Overseasoning, and Release of Primary Inoculum.” Plant Pathology 60: 166–177. 10.1111/j.1365-3059.2010.02369.x. [DOI] [Google Scholar]
  70. Swift, M. J. , Heal O. W., and Anderson J. M.. 1979. Decomposition in Terrestrial Ecosystems. Berkeley, CA: University of California Press. 10.1525/9780520407114. [DOI] [Google Scholar]
  71. Taylor, B. R. , Parkinson D., and Parsons W. F. J.. 1989. “Nitrogen and Lignin Content as Predictors of Litter Decay Rates: A Microcosm Test.” Ecology 70: 97–104. 10.2307/1938416. [DOI] [Google Scholar]
  72. Tláskal, V. , Voříšková J., and Baldrian P.. 2016. “Bacterial Succession on Decomposing Leaf Litter Exhibits a Specific Occurrence Pattern of Cellulolytic Taxa and Potential Decomposers of Fungal Mycelia.” FEMS Microbiology Ecology 92: fiw177. 10.1093/femsec/fiw177. [DOI] [PubMed] [Google Scholar]
  73. USFWS . 2000. “Interim Plans Big Oaks National WIldlife Refuge.” https://www.nrc.gov/docs/ML1604/ML16048A567.pdf
  74. Vannette, R. L. , and Fukami T.. 2014. “Historical Contingency in Species Interactions: Towards Niche‐Based Predictions.” Ecology Letters 17: 115–124. 10.1111/ele.12204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Veen, G. F. , Snoek B. L., Bakx‐Schotman T., Wardle D. A., and Van Der Putten W. H.. 2019. “Relationships between Fungal Community Composition in Decomposing Leaf Litter and Home‐Field Advantage Effects.” Functional Ecology 33: 1524–1535. 10.1111/1365-2435.13351. [DOI] [Google Scholar]
  76. Vivanco, L. , and Austin A. T.. 2019. “The Importance of Macro‐ and Micro‐Nutrients over Climate for Leaf Litter Decomposition and Nutrient Release in Patagonian Temperate Forests.” Forest Ecology and Management 441: 144–154. 10.1016/j.foreco.2019.03.019. [DOI] [Google Scholar]
  77. Voříšková, J. , and Baldrian P.. 2013. “Fungal Community on Decomposing Leaf Litter Undergoes Rapid Successional Changes.” The ISME Journal 7: 477–486. 10.1038/ismej.2012.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Wang, X. , Bai J., Xie T., Wang W., Zhang G., Yin S., and Wang D.. 2021. “Effects of Biological Nitrification Inhibitors on Nitrogen Use Efficiency and Greenhouse Gas Emissions in Agricultural Soils: A Review.” Ecotoxicology and Environmental Safety 220: 112338. 10.1016/j.ecoenv.2021.112338. [DOI] [PubMed] [Google Scholar]
  79. White, E. R. , and Hastings A.. 2020. “Seasonality in Ecology: Progress and Prospects in Theory.” Ecological Complexity 44: 100867. 10.1016/j.ecocom.2020.100867.4. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1.

ECY-107-e70374-s001.pdf (529.1KB, pdf)

Data Availability Statement

All sequencing data are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under accession number PRJNA978091 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA978091. All other data (Lane, 2023) are available in the Environmental Data Initiative (EDI) at https://doi.org/10.6073/pasta/d148c1ea2ae5bc8a9ec4403c34027d35. Code (Lane, 2026) is available in Zenodo at https://doi.org/10.5281/zenodo.18258613.

Articles from Ecology are provided here courtesy of Wiley

RESOURCES