Microbial communities in the tropical air ecosystem follow a precise diel cycle

Abstract

The atmosphere is vastly underexplored as a habitable ecosystem for microbial organisms. In this study, we investigated 795 time-resolved metagenomes from tropical air, generating 2.27 terabases of data. Despite only 9 to 17% of the generated sequence data currently being assignable to taxa, the air harbored a microbial diversity that rivals the complexity of other planetary ecosystems. The airborne microbial organisms followed a clear diel cycle, possibly driven by environmental factors. Interday taxonomic diversity exceeded day-to-day and month-to-month variation. Environmental time series revealed the existence of a large core of microbial taxa that remained invariable over 13 mo, thereby underlining the long-term robustness of the airborne community structure. Unlike terrestrial or aquatic environments, where prokaryotes are prevalent, the tropical airborne biomass was dominated by DNA from eukaryotic phyla. Specific fungal and bacterial species were strongly correlated with temperature, humidity, and CO₂ concentration, making them suitable biomarkers for studying the bioaerosol dynamics of the atmosphere.

Keywords: air microbiome; bioaerosols; microbial ecology; temperature; tropics.

Fig. 1.

Taxonomic structure of microbial communities. (A) Superkingdom-level taxonomical classification of relative abundances of microbes in samples representing the ecosystems ocean, air, soil, and human gut. (B) Number of species in samples representing the 4 corresponding ecosystems. (C) Relative abundances of the total of metagenomic reads from 5 DVEs, assigned to taxonomic groups. (D) Estimate of bacterial versus fungal cell ratio, based on read count normalized by genome size. (E) Relative abundances of microbes in daytime and nighttime air samples. These 2 samples were randomly selected from the complete DVE1 dataset. (F) Number of assigned species in the corresponding daylight and night air samples.

Fig. 2.

Temporal variation of the airborne microbial community. (A) Venn diagram of species co-occurring in 5 environmental time series (DVE1 to 5). (B–D) Box plots of the number of observed species per sampling time slot. (B) Intraday distribution of species richness within 24 h (2-h sampling time interval). (C) Interday variation of species richness, considering the daily average within 5 d, covering DVE2 to 5. (D) Intermonth distribution of species richness, considering the average of 5-d sampling in intervals of 3 mo, covering a total of 13 mo (DVE2 to 5). Outliers are displayed as unfilled circles.

Fig. 3.

Taxonomical breakdown and temporal resolution of the air microbial communities. (A) Relative abundance of 7 taxonomic groups of organisms in air. Rain events with recorded amounts of precipitation (blue bars) and temperature distribution (red buttons) are denoted for each time series. The timescale is indicated (Bottom), as well as day and night samples denoted by sun and moon symbols. (B) Observed number of species by taxonomical groups in each of the 4 DVEs. The graph depicts the change in species abundance per group, indicating the robustness of the Basidiomycota community versus strong daily variation of the community composition for the other 6 phyla.

Fig. 4.

Diel cycle of the air microbial community. (A) Box plots of total DNA yields (ng) collected during DVE2 to 5. (B) Pairwise Bray–Curtis similarity among consecutive samples (gray) and the periodic oscillation component (red) computed through single-spectrum analysis. (C and D) Structures of the airborne microbial communities in DVE2 to 5. (C) Areas of the 2 groups of samples representing day (7:00 to 19:00, orange circles) and night (19:00 to 7:00, blue triangles) on the first 2 principal coordinate (PCo1 and PCo2) axes. (D) Box plots of the distances to centroid for each group are indicated. P values for the permutation-based test of multivariate homogeneity of group dispersions (PERMDISP2) are shown. Unfilled circles represent outliers.

Fig. 5.

Diel changes of airborne fungi and bacteria in response to environmental factors. (A and B) Plotting species richness (number of observed species or chao1 index) against temperature (A) and CO₂ (B) at a particular time point of sampling. The red line is a regression line (chao1 ∼ temperature). Orange and blue dots correspond to day and night time points, respectively. Pearson’s correlation indices between the chao1 index and temperature/CO₂ are indicated. (C) Temperature. Eight microbial species are observed to vary their abundances, as represented by read counts, in response to temperature changes. The majority of the species responding positively to temperature are bacteria. Nine fungal species of the airborne community respond negatively. (D) Carbon dioxide. Two fungal species respond to diel changes in CO₂. The observed fluctuations caused by both environmental factors are highly correlated (Pearson’s R > 0.75 or <−0.75).

Fig. 6.

Network of probabilistic interactions between taxonomical groups and environmental parameters inferred from the data. Each node represents a variable of the system, and each edge denotes an influence of one over the other. In addition, each node is associated with a probability table indicating the likelihood of a variable being in a particular state in the absence of information about the system. The probability table of each node is updated (recalculated) once information regarding the state of other nodes becomes available. Scenario 1 models a typical midday time point when the temperature reaches its maximum. In response to high temperature, other variables are recalculated making a new prediction (e.g., CO₂ is likely to be low, while bacteria are likely to be in high relative abundance). Low-temperature scenario 2, typical for a nighttime setting, and scenario 3, a rainfall scenario, modeling a local meteorological event typical for the tropics.