Ecology

Affiliations

Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
Devi Veytia, Stuart Corney & Sophie Bestley

Australian Antarctic Division, Department of Agriculture, Water and the Environment, Kingston, Tasmania, Australia
Klaus M. Meiners & So Kawaguchi

Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia
Klaus M. Meiners, So Kawaguchi & Sophie Bestley

British Antarctic Survey, Cambridge, UK
Eugene J. Murphy

Authors
Devi Veytia

Stuart Corney

Klaus M. Meiners

So Kawaguchi

Eugene J. Murphy

Sophie Bestley

Corresponding author
Correspondence to Devi Veytia. More

Deformation measurement accuracy
To evaluate the accuracy of ICT, synthetic deformation fields were added to the R-specimen datasets (Fig. 3a). For this purpose, constant strain was simultaneously introduced in both R and L directions ((varepsilon_{RR}),({ }varepsilon_{LL})). Absolute accuracy (hat{varepsilon }) was measured by adding synthetic deformation to the reference state #1 (#1 + synthetic), and then measuring with ICT strain of #1 + synthetic with respect to #1. Differential accuracy ({Delta }hat{varepsilon }) was measured by adding synthetic deformation to the deformed state #2 (#2 + synthetic), measuring with ICT strain of #2 + synthetic with respect to the reference state #1, and finally subtracting the ICT measured strain between #2 and #1.
Figure 3 shows ICT strain estimates for tracheids and wood rays. The accuracy is highest along the cell-cross section (RT for tracheids, LT for wood rays) and lowest in the cell longitudinal direction (L for tracheids, R for wood rays). Due to the tubular cell geometry (Fig. 2b,c) of both cell types, tracking is more challenging in the longitudinal cell axis, for which symmetry reduces the available landmarks (for instance, wood pits in Fig. 2) and deformation tracking accuracy. Absolute accuracy is limited by both cell segmentation and deformation parametrization. Differential accuracy is further influenced by experimental uncertainties between #1 and #2 acquisitions, such as vibration artifacts and sample relaxation strains. While absolute and differential accuracy are similar in cell-cross sections, differential accuracy is reduced with respect to absolute accuracy along the longitudinal cell axis. The sensitivity limit for strain measurements in tracheids is (varepsilon_{RR})  More

Collecting plant specimens
In the present study, 10 accessions assigned to 4 genera Atalantia, Luvunga, Paramignya, and Severinia were collected from different sites in Khanh Hoa and Lam Dong provinces of Vietnam (Fig. 1). Of these, six accessions of P. trimera (Oliv.) Burkill were collected at different sites in Khanh Hoa provinces including Ninh Van (PT1.NV, PT2.NV), Ninh Hoa (PT1.NH, PT2.NH), Dien Khanh (PT1.DK, PT2.DK); 1 accession of A. buxifolia (Poir.) Oliv. ex Benth collected in Van Ninh (PA.VN); 1 accession of S. monophylla (Lour.) Tanaka collected in Don Duong, Lam Dong province (PC.DD); two accessions of L. scandens (Roxb.), Wight, collected in Di Linh (PR.DL) and Cat Tien (PR.CT) in Lam Dong province (Fig. 1). The list of the collected accessions and information was summarized in Table 1.
Figure 1

Map of the sampling sites. Accessions of species P. trimera (Oliv.) Burkill, A. buxifolia (Poir.) Oliv. ex Benth, S. monophylla (Lour.) Tanaka, and L. scandens (Roxb.), Wight were collected at sites displayed as circles in the map. The map was created by using ArcGIS 10.3 using the color rendering and grouping tools built-in and Paintbrush version 2.5 (20190914) on mac OS Catalina.

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Table 1 List of the collected accessions and information.
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Taxonomic treatment
P. trimera (Oliv.) Burkill distributes in the high land areas in Khanh Hoa, Lam Dong provinces of Vietnam. P. trimera is scrambling shrub or erect, long, and curved spines, non-hairy stem. Leaves simple, typical narrow oblong, lamina 1.0–1.5 cm wide, 5–12 cm long; short petiole 0.5 cm long, leaf sub-vein 8–10 pairs; inflorescences axillary, fasciculate, peduncle 3–4 mm long, separate; calyx 3 lobes, 4 mm long; corolla 3; stamens 5, separate; ovaries 3, only 1 ovule, 2 locules in the ovary; globose fruit, 1.5–2.5 cm in diameter, 2 seeded. flowering time from May-Aug., fruiting Sep-Dec. Roots, leaves and stems were used as traditional medicine to treat liver diseases and cancers (Figs. 2, 4a).
Figure 2

The typical morphology and anatomy of Paramignya trimera (Oliv.) Burkill. Woody shrub 1–4 m or above (a); A flowering tree (b); Typical trimerous flowers (c); Green fruits (d); Ripen fruits (d); Opened ripen fruit with two seeds encapsulated by mucus endocarp (e).

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A. buxifolia (Poir.) Oliv. ex Benth distributed mainly in Van Ninh (Khanh Hoa) with several local names such as “Xao cua ga” or “Quyt gai” are medium climbing shrubs, up to 3 m tall; branches grayish brown, branchlets green; spikes axillary 0.5–1.2 cm or sometimes unarmed, apex yellowish; leaves simple, 2.5–3.5 cm wide, 3.5–4.5 mm long, petiole 4–8 mm, leaf blade ovate, obovate, elliptic, glabrous, coriaceous, midvein slightly ridged, apex rounded to obtuse at tip; inflorescences axillary, 1 to several flowers. Flowers 5 merous, petals white, 3–4 mm, stamens 10, calyx persistent. Fruit bluish black when ripe, globose, slightly oblate, or subellipsoid, 7–10 mm in diam., smooth, 1 or 2 seeded. Flowering from May-Aug., fruiting Sep-Dec. Roots, leaves and stems were used as traditional medicine to treat cough, lung diseases and kidney disorders (Fig. 4b).
S. monophylla (Lour.) Tanaka found in Don Duong (Lam Dong) was thorny shrub or small tree; spikes axillary 1–1.5 cm; leaves simple, ovate, apex round or retuse at tip, coriaceous, glabrous, round at base, short petiole; Inflorescences 4–6-flowered; calyx ca. 3.5–5 mm long; petals 4, petals white, oblong, obtuse, glabrous, stamens 8–10; filaments ca. 12 mm long, glabrous; anthers ca. 5 mm long, linear; ovary ca. 2.5 × 1.5 mm, long-ovoid, glabrous, 3-locular; style ca. 7 mm long, continuous with ovary, cylindric, glandular, glabrous; stigma capitate ca. 2.5 mm broad, glandular. Fruits yellow to orange, globose 1.5–2.0 cm in diameter, 1–2 seeded; flowering time from May-Aug., fruiting Sep-Dec. This species was used effectively for cough, expectorant, fever, anti-inflammatory, sciatica treatment and prevent aging of skin cells, roots and leaves used for skin disease, burning leaves to kill mosquitoes and insects (Fig. 2c).
L. scandens (Roxb.), Wight was discovered in Lam Dong of Vietnam with the local name “Xao leo”. L. scandens is woody climber or scrambling shrub; rough tufted from the ground with strong axillary sharp straight or slightly recurved spines. Leaves compound, digitately trifoliate or bifoliolate or simple; petioles 2–6 cm long, glabrous; lamina ca. 6.0–18.0 × 2.5–4.0 cm, variable, oblong-elliptic or oblanceolate, cuneate at base, shortly acuminate at apex, coriaceous, glabrous; secondary nerves 15 pairs; branches brown puberulent. No information from flowering time has been described. According to traditional experience, this plant is used to treat rheumatism, liver disease and ascites (Fig. 2d).
Phylogenetic relation analysis
The phylogenetic tree from ITS sequences included 3 groups (Fig. 5a). The first monophyletic group was only S. monophylla (PC.DD) as an out group. The second monophyletic group included 2 accessions of L. scandens (PR.DL and PR.CT). The third group was paraphyletic group with 9 accessions clustered in 2 sub-groups. The first sub-group included only P. trimera, whereas the second sub-group included 3 accessions P. trimera nested with P. confertifolia and A. buxifolia. In addition, in the second sub-group, the accessions of P. trimera collected in Dien Khanh, Vietnam (PT1.DK) and P. confertifolia from Mensong, China were in the same monophyletic clade whereas A. buxifolia (PA.VN) was clearly separated from others.
The unrooted tree from matK sequences included 3 groups in which the first monophyletic group were 2 species P. lobata and P. scandens (Australia), the second monophyletic group included only P. confertifolia (China) and the third group (paraphyletic group) included 3 sub-groups (Fig. 5b). The first sub-group included all accessions of P. trimera, the second sub-group included only S. monophylla and the third sub-group included L. scandens and A. buxifolia.
The unrooted tree from rbcL sequences included 2 main groups in which the first group included 3 species P. scandens, P. monophylla and P. lobata (Australia) and the second group (paraphilic group) included 5 species P. trimera, P. confertifolia (China), S. monophylla (Japan), A. buxifolia, and L. scandens (Fig. 5c). In this group, some accessions of P. trimera were nested in the paraphylic sub-groups because they did not share an immediate common ancestor.
The pattern of the phylogenetic tree constructed from the concatenated sequences was similar to that of ITS sequences (Fig. 5d). The tree included one monophyletic group with only L. scandens and one paraphyletic group with the accessions of P. trimera nested within P. confertifolia, A. buxifolia and S. monophylla.
Genetic distance analysis
The overall genetic distances for ITS, matK, rbcL and concatenated sequences were 0.11 ± 0.01, 0.29 ± 0.02, rbcL 0.48 ± 0.05 and 0.05 ± 0.0, respectively (Table 2). An overlap between the maximum intraspecific distances and the minimum interspecific distances were observed in the cases of ITS, rbcL and concatenated sequences (Table 2, Fig. 6a,c,d). In case of matK, a clear barcode gap was found between the maximum intraspecific distance (0.0028) and the minimum interspecific distance (0.0056). The histogram and ranked pairwise (K2P) distances demonstrated a significant difference in the cases of matK and rbcL (Fig. 6b,c).
Table 2 Intraspecific and interspecific distances across all data.
Full size table More

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Study area
Mount Cameroon (South-Western Province, Cameroon) is the highest mountain in West/Central Africa. This active volcano rises from the Gulf of Guinea seashore up to 4095 m asl. Its southwestern slope represents the only complete altitudinal gradient of primary forests from lowland up to the timberline (~ 2200 m asl.) in the Afrotropics. Belonging to the biodiversity hotspot, Mount Cameroon harbour numerous endemics45,46,47. With  > 12,000 mm of yearly precipitation, foothills of Mount Cameroon belong among the globally wettest places42. Most precipitation occur during the wet season (June–September;  > 2000 mm monthly), whilst the dry season (late December–February) usually lacks any strong rains42. Since 2009, most of its forests have become protected by the Mount Cameroon National Park.
Volcanism is the strongest natural disturbance on Mount Cameroon with the frequency of eruptions every ten to thirty years. Remarkably, on the studied southwestern slope, two eruptions in 1982 and 1999 created a continuous strip of bare lava rocks (in this study referred as ‘the lava flow’) interrupting the forests on the southwestern slope from above the timberline down to the seashore (Fig. 1a).
A small population of forest elephants (Loxodonta cyclotis) strongly affects forests above ca. 800 m asl. on the southwestern slope28,45. It is highly isolated from the nearest populations of the Korup NP and the Banyang-Mbo Wildlife Sanctuary, as well as from much larger metapopulations in the Congo Basin48. It has been estimated to ~ 130 individuals with a patchy local distribution28. On the southwestern slope, they concentrate around three crater lakes representing the only available water sources during the high dry season, although their local elevational range covers the gradient from lowlands to montane grasslands just above the timberline28. They rarely (if ever) cross the old lava flows, representing natural obstacles dividing forests of the southwestern slope to two blocks with different dynamics. As a result, forests on the western side of the longest lava flow have an open structure, with numerous extensive clearings and ‘elephant pastures’, whereas eastern forests are characteristic by undisturbed dense canopy (Fig. 1). To our knowledge, the two forest blocks are not influenced by any extensive human activities, nor differ in any significant environmental conditions28,45. Hereafter, we refer the forests west and east from the lava flow as disturbed and undisturbed, respectively. Effects of forest elephant disturbances on communities of trees and insects were investigated at four localities, two in an upland forest (1100 m asl.), and two in a montane forest (1850 m asl.).
Tree diversity and forest structure
At each of four sampling sites, eight circular plots (20 m radius, ~ 150 m from each other) were established in high canopy forests (although sparse in the undisturbed sites), any larger clearings were avoided. In the disturbed forest sites, the plots were previously used for a study of elevational diversity patterns40,42. In the undisturbed forest sites, plots were established specifically for this study.
To assess the tree diversity in both disturbed and undisturbed forest plots, all living and dead trees with diameter at breast height (DBH, 1.3 m) ≥ 10 cm were identified to (morpho)species (see40 for details). To study impact of elephant disturbances on forest structure, each plot was characterized by twelve descriptors. Besides tree species richness, living and dead trees with DBH ≥ 10 cm were counted. Consequently, DBH and basal area of each tree were measured and averaged per plot (mean DBH and mean basal area). Height of each tree was estimated and averaged per plot (mean height), together with the tallest tree height (maximum height) per plot. From these measurements, two additional indices were computed for each tree: stem slenderness index (SSI) was calculated as a ratio between tree height and DBH, and tree volume was estimated from the tree height and basal area49. Both measurements were then averaged per plot (mean SSI and mean tree volume). Finally, following Grote50, proxies of shrub, lower canopy, and higher canopy coverages per plot were estimated by summing the DBH of three tree height categories: 0–8 m (shrubs), 8–16 m (lower canopy), > 16 m (higher canopy).
Insect sampling
Butterflies and moths (Lepidoptera) were selected as the focal insect groups because they belong into one of the species richest insect orders, with relatively well-known ecology and taxonomy, and with well-standardized quantitative sampling methods. Moreover, they strongly differ in their habitat use29. In conclusion, butterflies51 and moths52 are often used as efficient bioindicators of changes in tropical forest ecosystems, especially useful if both groups are combined in a single study. Within each sampling plot, fruit-feeding lepidopterans were sampled by five bait traps (four in understory and one in canopy per sampling, i.e. 40 traps per sampling site, and 160 traps in total) baited by fermented bananas (see Maicher et al.42 for details). All fruit-feeding butterflies and moths (hereinafter referred as butterflies and fruit-feeding moths) were killed (this is necessary to avoid repetitive counting of the same individuals53) daily for ten consecutive days and identified to (morpho)species.
Additionally, moths were attracted by light at three ‘mothing plots’ per sampling site, established out of the sampling plots described above. These plots were selected to characterize the local heterogeneity of forest habitats and separated by a few hundred meters from each other. To keep the necessary standardisation, all mothing plots at both types of forest were established in semi-open patches, avoiding both dense forest and larger openings. Moths were attracted by a single light (see Maicher et al.42 for details) during each of six complete nights per elevation (i.e., two nights per plot). Six target moth groups (Lymantriinae, Notodontidae, Lasiocampidae, Sphingidae, Saturniidae, and Eupterotidae; hereafter referred as light-attracted moths) were collected manually, killed, and later identified into (morpho)species. The three lepidopteran datasets (butterflies, and fruit-feeding and light-attracted moths) were extracted from Maicher et al.42 for the disturbed forest plots, whilst the described sampling was performed in the undisturbed forest plots specifically for this study. Voucher specimens were deposited in the Nature Education Centre, Jagiellonian University, Kraków, Poland.
To partially cover the seasonality54, the insect sampling was repeated during transition from wet to dry season (November/December), and transition from dry to wet season (April/May) in all disturbed and undisturbed forest plots.
Diversity analyses
To check sampling completeness of all focal groups, the sampling coverages were computed to evaluate our data quality using the iNEXT package55 in R 3.5.156. For all focal groups in all seasons and at all elevations, the sampling coverages were always ≥ 0.84 (mostly even ≥ 0.90), indicating a sufficient coverage of the sampled communities (Supplementary Table S1). Therefore, observed species richness was used in all analyses57.
Effects of disturbance on species richness were analysed separately for each focal group by Generalized Estimated Equations (GEE) using the geepack package58. For trees, species richness from individual plots were used as a ‘sample’ with an independent covariance structure, with disturbance, elevation, and their interaction treated as explanatory variables. For lepidopterans, because of the temporal pseudo-replicative sampling design, species richness from a sampling day (butterflies and fruit-feeding moths) or night (light-attracted moths) at individual plot was used as a ‘sample’ with the first-order autoregressive relationship AR(1) covariance structure (i.e. repeated measurements design). Disturbance, season, elevation, disturbance × season, and disturbance × elevation were treated as explanatory variables. All models were conducted with Poisson distribution and log-link function. Pairwise post-hoc comparisons of the estimated marginal means were compared by Wald χ2 tests. Additionally, species richness of individual families of trees, butterflies, and light-attracted moths were analysed by Redundancy Analyses (RDA), a multivariate analogue of regression, based on the length of gradients in the data59. All families with  > 5 species were included in three RDA models, separately for the studied groups (the subfamily name Lymantriinae is used, because they are the only group of the hyperdiverse Erebidae family of the light-attracted moths). Fruit-feeding moth families were not analyzed because 83% of their specimens belonged to Erebidae and all other families were therefore minor in the sampled data. Species richness of individual families per plot were used as response variables, whilst interaction of disturbance and elevation were applied as factorial explanatory variable (for butterflies and light-attracted moths, the temporal variation was treated by adding season as a covariate).
Differences in composition of communities between the disturbed and undisturbed forests were analysed by multivariate ordination methods59, separately for each focal group. Firstly, the main patterns in species composition of individual plots were visualized by Non-Metric Multidimensional Scaling (NMDS) in Primer-E v660. NMDSs were generated using Bray–Curtis similarity, computed from square-root transformed species abundances per plot. Subsequently, influence of disturbance on community composition of each focal group was tested by constrained partial Canonical Correspondence Analyses (CCA) with log‐transformed species’ abundances as response variables and elevation as covariate59. Significance of all partial CCAs were tested by Monte Carlo permutation tests with 9999 permutations.
Finally, differences in the forest structure descriptors between the disturbed and undisturbed forests were analysed by partial Redundancy Analysis (RDA). Prior to the analysis, preliminary checking of the multicollinearity table among the structure descriptors was investigated. Only forest structure descriptors with pairwise collinearity More

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Calculation of solar elevation and azimuth angles versus time
For our numerical calculations, the solar elevation angle θs(t) from the horizon and the solar azimuth angle αs(t) from south (axis y, Fig. 7A) were calculated as a function of time t with an algorithm based on a semi-analytical approximation (analytical Kepler’s orbits modified with astronomical perturbations) and the planetary theory VSOP 87 (Variations Séculaires des Orbites Planètaires) of Bretagnon and Francou30. This method is valid for the 1950–2050 period with an accuracy of 0.01°. Using this algorithm, we calculated the geocentric ecliptical, then the geocentric equatorial, and finally the geocentric horizontal coordinates of the Sun, resulting in the values of θs(t) and αs(t).
Diurnal cloudiness
Total cloud cover (TCC) time series of high temporal resolution (1 h) were evaluated for the period 01.01.2009–31.12.2018 from the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts31. The geographic coverage is global with a native spatial resolution of 0.25° × 0.25° ≈ 27 km × 27 km. Climatological mean values of TCC were determined by averaging for each hour of each calendar day of every year in the vegetative period of sunflowers. Since TCC is a dimensionless relative parameter in the range 0–1 (0 is clear sky, 1 is overcast), the hourly climatological means are equivalent to the time-dependent probability 0 ≤ σ(t) ≤ 1 of cloudy situation. We determined the diurnal cloud probability function σ(t) in July, August and September in Boone County (Kentucky, USA, 39° N, − 84.75° E, Fig. 2A), central Italy (41.0° N, 15.0° E, Fig. 2B), central Hungary (47.0° N, 19.0° E, Fig. 2C), and south Sweden (58.0° North, 13.0° East, Fig. 2D). The cloudiness data used in our calculations correspond to the decade between 2009 and 2018. Because similar data are not readily available for the period when sunflowers were domesticated, we assume in this work that the data obtained in the last decade is historically representative. The validity of this assumption can be evaluated when paleo-climatological cloudiness data become available.
Measurement of the elevation angle of mature sunflower heads versus time
In a sunflower plantation at Budaörs (near Budapest), we measured the elevation angle θn of the normal vector of the mature head of the same 100 sunflowers as a function of time t, approximately weakly from 6 July to 11 September 2020. The studied sunflowers were individuals in a given row of the plantation.
Measurement of the absorption spectra of mature sunflower heads
The absorption spectra A(λ) of young (2 weeks after anthesis) and old (4 weeks after anthesis) inflorescence and back of mature sunflower heads were measured in the field with an Ocean Optics STS-VIS spectrometer (Ocean Insight, Largo, USA) in July 2020. Measurements were performed under total overcast conditions to ensure isotropic diffuse skylight illumination. At first, the reflection spectrum of the inflorescence/back was determined as follows: a spectrum was measured by directing the spectrometer’s head on the target at a distance of 5 cm, then another spectrum was registered by pointing the spectrometer to the overcast sky. In the laboratory these two spectra were divided by each other. Finally, assuming that all non-reflected light was absorbed, the absorption spectrum A(λ) = 1 − R(λ) was obtained by subtracting the reflection spectrum R(λ) from 1. Absorption spectra were measured for 3 sunflowers and then averaged.
Calculation of sky irradiance absorbed by a sunflower inflorescence
In the x–y-z reference frame of Fig. 7A, let the normal vector of a mature sunflower inflorescence be

$$underline {text{n}} = , left( {{text{cos}}theta_{{text{n}}} cdot {text{sin}}alpha_{{text{n}}} ,{text{ cos}}theta_{{text{n}}} cdot {text{cos}}alpha_{{text{n}}} ,{text{ sin}}theta_{{text{n}}} } right),$$
(2)

where axes x and y point to west and south, axis z points vertically upward, the elevation angle − 90° ≤ θn ≤  + 90° is measured from the horizontal (θn  > 0°: above the horizon, θn  More

Time-series sampling
Air samples were collected in Yurga (55.711 N, 84.937 E), where the average temperatures range from 6 to 24 °C in summer (June–August), and from − 21 to − 6 °C in winter (November–March) (the open source service https://weatherspark.com/). Meteorological characteristics (temperature, relative humidity, and wind direction) during the time series are represented in Fig. S1 and S2. Specifically, air samplers were positioned at an open-air balcony (~ 4 m above the ground level under a concrete canopy) of a five-storey residential setting. Samples were collected in duplicates (i.e., two technical replicates) with two high flowrate and filter-based air samplers (SASS3100, Research International, USA). The first set of samples were collected during three time periods (1:00–3:00, 9:00–11:00, and 15:00–17:00) on 26 and 28 July 2017; the second set was also collected during three time periods (9:00–11:00, 15:00–17:00, and 21:00–23:00) within consecutive days from 2 to 5 December 2017, the third set was collected during four time periods (1:00–3:00, 9:00–11:00, 15:00–17:00, and 21:00–23:00) within consecutive days from 27 August to 2 September, 2018. In total, 78 samples in 39 time intervals were collected and used for preparation of 62 sequencing libraries (Table S1).
High volumetric, filter-based air samplers (SASS3100, Research International, USA) were used in this study, with SASS bioaerosol electret filters (6 cm diameter, expected 50% efficiency for 0.5 µm particle size, Research International, USA) as the filter medium. Sampling was performed at 300 L/min air flowrate for 2 h. After sampling, the SASS filters were stored at − 20 °C. During transport from Siberia to Singapore, the samples were hand-carried with cooling.
Sample blanks
In each sampling set, blanks were also collected as controls. The blanks consisted of 12 filter blank samples (FB) and three reagent blank samples (RB). The filter blank samples were collected by installing a new filter on the air sampler at the sampling location for about 5 s. The filter was then collected and analysed with the same protocol as the time-series samples. Reagent blank samples involved extractions performed with extraction reagents without any filter.
Details on metagenomic analysis for blanks are provided in the Supplementary section (Fig. S11).
DNA extraction
Technical replicates were isolated separately. For processing, the SASS filter was first transferred into a sterile 5 mL tube. Phosphate buffered saline (pH 7.2) with 0.1% (v/v) Triton X-100 (2 mL, PBS-T) was added to the 5 mL tube as the wash buffer. Using tweezers, the SASS filter in the tube was moved up and down a few times to let the PBS-T penetrate the filter. The tube was then sonicated for 1 min in a sonication bath without heating to dislodge the biomass from the filter. After sonication, the filter was squeezed with tweezers and the PBS-T with suspended particles was transferred into a sterile 50 mL conical tube to complete the first washing step. This washing step was repeated three times for each filter sample, using fresh 2 mL PBS-T for each repeat. At the end of the second and third repeats, the filter was transferred into the barrel of a 10 mL syringe, placed in the same 50 mL conical tube containing the wash liquid. The 50 mL tube with the syringe and SASS filter was then centrifuged at 5000×g for 2 min to remove any leftover PBS-T. The expected total recovered supernatant volume from the three washes for each sample was 6 mL, which contained the captured airborne particles.
Upon completion of the wash steps, the supernatant was subsequently filtered through a 0.02 µm Anodisc filter (Whatman, UK) using a vacuum manifold (DHI, Denmark). The Anodisc was finally transferred into a 5 mL bead tube provided in the DNeasy PowerWater Kit (Qiagen, Germany) for DNA extraction.
DNA extraction from the Anodisc was mostly performed following the standard protocol of the DNeasy Power Water Kit with the following modifications to increase DNA yield. Briefly, 0.1 mg/mL (final) Proteinase K was added to the lysis buffer (solution PW1) prior to the initial 55 °C incubation. The initial incubation time at 55 °C was also prolonged from the recommended 10 min to overnight incubation. After initial incubation, the sample tubes were vortexed for 3 min and subsequently placed into an ultrasonic bath (Elmasonic, USA) for sonication at 65 °C for 30 min29, followed by another 5 min vortex. The remaining extraction steps were completed as instructed in the manufacturer’s protocol.
In the first and second time series (SUMMER 2017 and WINTER 2017), the DNA isolated from the technical replicates was pooled to provide sufficient material for sequencing.
Metagenomic sequencing
For the metagenomic sequencing and NGS data processing, we used standardised procedures and pipelines described in detail elsewhere1. Extracted air DNA samples were quantitated on a Qubit 2.0 fluorometer, using the Qubit dsDNA HS (High Sensitivity) Assay Kit (Invitrogen). Immediately prior to library preparation, sample quantitation was repeated on a Promega QuantiFluor fluorometer, using Invitrogen’s Picogreen assay.
Next-generation sequencing libraries were prepared with Swift Biosciences’ Accel-NGS 2S Plus DNA Library Kit, following the instructions provided in the kit. With the exception of samples that had a concentration of  More