the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jan 2026
Unlocking the air: DNA metabarcoding sheds light on seasonal fungal dynamics in a temperate floodplain forest
Abstract. Airborne fungi play a pivotal role in ecosystem functioning, agriculture, people health and wellbeing, yet their response to increasingly frequent climate extremes remains poorly understood. There is thus a need for long-term studies that can capture both seasonal and annual dynamics of fungal remnants in the air. Here, we applied DNA metabarcoding of the ITS2 region to investigate the composition and responses of fungal aerosols to meteorological variables in a temperate floodplain forest habitat. Passive air samples were collected continuously at three heights above the ground between March 2019 and February 2020 at the Leipzig Canopy Crane (Germany). Fungal aerosol assemblages were found to be dominated by Ascomycota (74.3 %) and Basidiomycota (25.1 %), with the genera Cladosporium, Epicoccum, and Alternaria consistently prevailing across samples. Our results revealed that seasonal changes in air temperature were the primary driver for compositional changes in fungal aerosols, with Ascomycota increasing in abundance during warmer months and Basidiomycota dominating during colder months. Through abundance differential analysis, we identified 66 genera, including allergenic and pathogenic taxa, that shifted significantly in abundance with seasonal temperatures. Interestingly, neither sampling height nor humidity had a significant effect. Our study highlights the importance of conducting long-term monitoring of bioaerosols under changing climate conditions while also creating a benchmark for future comparative studies.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-372', Anonymous Referee #1, 02 Mar 2026
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RC2: 'Comment on egusphere-2026-372', Anonymous Referee #2, 12 Mar 2026
General comment:
The authors investigate fungal diversity in air samples collected at three locations and heights over the course of one year, including both summer and winter sampling, in a forest environment. In the introduction, the authors note that bioaerosol composition depends on the emission source, and therefore seasonal variation is expected. The manuscript places considerable emphasis on correlations with meteorological parameters. At the same time, the sampling design is likely influenced by seasonal changes related to tree phenology, vegetation dynamics, and fungal life cycles. These broader seasonal processes are very likely reflected in the results, with temperature acting as one contributing factor. The authors also acknowledge this in the discussion (lines 352–361), where they state that environmental factors influence the phenology of source species and thereby the spore‑forming cycles of fungi. As a result, the findings appear to primarily reflect seasonal changes, which is also consistent with the manuscript’s proposed title. In this context, a clearer focus on seasonal variation could strengthen the overall narrative of the study.
Many phytopathogens are present in the dataset, and it is therefore expected that these taxa peak when host plants and canopy are present. Similarly, it is reasonable that these taxa occur predominantly during the warmer months, while wood‑ and plant‑debris–decaying fungi peak later in the season during cooler periods. Given the current design, it may be challenging to disentangle the direct effects of meteorological variables from the broader seasonal context. A more robust assessment of direct meteorological influences would likely require multi‑year sampling.
The dataset includes many phytopathogens and parasitic fungi, which understandably show seasonal patterns. However, the manuscript would benefit from deeper reflection on the identities and ecological relevance of the detected species. Some taxa appear to have been previously reported only outside Europe (e.g., Alanphillipsia aloetica from South Africa). Conversely, oak powdery mildew (Eryisphe alphitoides) is described as an invasive species, although to my knowledge it is already widespread across Europe and present on multiple continents. The detection of Zymoseptoria tritici, a wheat pathogen, in some samples (Table S3) likely reflects nearby wheat fields as a source and further supports the interpretation that the dataset captures seasonal dynamics.
In my view, the stated aim of providing a foundation for studying the impact of climate change on ecosystem functioning, agriculture, and human health is not fully achieved in the current version. However, with revisions that align the manuscript more closely with the seasonal patterns clearly present in the data, and with consideration of the points raised above, the study has the potential to make a valuable contribution and would be suitable for publication.
Comments on materials and methods
The authors state that the ITS2 region was used for the study; however, the primers listed in the Materials and Methods section will also amplify ITS1. It is unclear why the authors amplified the entire ITS region and assembled sequences across this region if only ITS2 was used in subsequent analyses. It is also unclear whether all analyses were based on assembled sequence data or whether assembly was used only for taxonomic identification. Please clarify this. The geographical location of the sampling stations is not described in sufficient detail. The methods state that “three distinct tree gaps were identified on site and designated as sampling stations”— How far into the forest were they located and how far apart were these gaps, and were the samples compared individually or pooled? It is also unclear how the weekly samples were selected. Was only one week per month chosen for analysis? According to Table S2, sampling appears to have occurred approximately every fourth week. This needs to be detailed in the materials and methods section, although listed in Table S2
Technical details
Line 145-152: Please list the primer pairs in a table and indicate which ITS region they are amplifying
Line 188: How was the data normalized?
Line 233: To stay consistent “areas” should probably be changed to “sources”
Line 218: it says that data is grouped into 760 ASVs, according to Table S3 1,877 ASVs were identified (Table S3). Please explain this discrepancy.
Table S3: Column (sample?) labeling should be clarified, month and/or dates should be added. Heights and locations should be explained. It is unclear why some entries are highlighted or colored.
In Figure 2, it is unclear why the correlation between wind speed and Shannon diversity is not significant, while the correlation with Gini–Simpson diversity is significant. Shannon and Gini–Simpson indices are highly correlated. This may need some discussion.
Figure S1: what does the different colours of the trajectories indicate? Explanation is needed
Citation: https://doi.org/10.5194/egusphere-2026-372-RC2
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General comments
This study addresses an interesting and relevant topic, contributing to the understanding of how environmental parameters shape bioaerosol composition within a specific ecosystem. Although their findings are generally consistent with previous studies, there remains a scarcity of data from environments such as the one investigated here, particularly studies providing long-term temporal resolution (one year). The scientific rationale motivating this work is comprehensively addressed; however, certain explanations lack clarity, and the organization of ideas could be improved in some sections of the manuscript.
The methodology employed—NGS-based sequencing—is appropriate for this type of study. In addition, the multi-marker metabarcoding strategy is commendable, as it attempts to mitigate primer-related biases. The statistical analyses are properly conducted, and the manuscript is, overall, well written and easy to follow. Nevertheless, several aspects could benefit from further refinement (see detailed comments below).
Globally, I find this work suitable for publication when addressing the following changes.
Specific comments
Materials and methods. The criteria and distance between the tree gaps are not mentioned (line 127). This may be relevant to support or discuss the lack of differences.
Three different sets of primers were employed for NGS libraries. Is a novel approach? If not, please cite previous works. It is not specified whether the sequences were or not pooled. I assume they were, but please clarify. Also, if this is a novel approach, the differences found in richness or diversity between them should be mentioned and a recommendation for the specific set of primers yielding the best resolution.
For the altitudes employed, I doubt about the relevance of the mass air trajectories and the accuracy, especially within a forest. Please, discuss and justify.
Results. A figure or table indicating the taxa of the core mycobiome would be appreciated and relevant for future comparable studies.
Figure 3 can be improved (legend names are cut and units are not necessary).
A statistical test must be added as support for Figure 4 and statement in lines 273-275.
Line 283. …” in several other studies”. Discuss the relevance (similar or different environment?). In addition, this and many lines in page 11 are more suitable for the Discussion section.
Discussion. Paragraph 408-429 can be shorter.
Technical corrections
Lines134-139 are confusing. Please simplify.
Line 96. … “we would expect?”
Lines 334, 335: italics for the cited genera.
Figure S2. A mix of languages in the figures