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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39261551
69514
10.1038/s41598-024-69514-0
Article
Development and validation of an experimental life support system to study coral reef microbial communities
Stuij T. M. tamarastuij@ua.pt

1
Cleary D. F. R. 1
Rocha R. J. M. 1
Polonia A. R. M. 1
Machado e Silva D. A. 1
Frommlet J. C. 1
Louvado A. 1
Huang Y. M. 2
De Voogd N. J. 34
Gomes N. C. M. gomesncm@ua.pt

1
1 https://ror.org/00nt41z93 grid.7311.4 0000 0001 2323 6065 Centre for Environmental and Marine Studies (CESAM) and Department of Biology, University of Aveiro, Campus Universitário Santiago, 3810-193 Aveiro, Portugal
2 https://ror.org/03fxxpd92 grid.440393.9 0000 0004 0639 3714 National Penghu University of Science and Technology, Magong, Taiwan
3 https://ror.org/0566bfb96 grid.425948.6 0000 0001 2159 802X Naturalis Biodiversity Center, Leiden, the Netherlands
4 https://ror.org/027bh9e22 grid.5132.5 0000 0001 2312 1970 Institute of Biology (IBL), Leiden University, Leiden, the Netherlands
11 9 2024
11 9 2024
2024
14 2126026 7 2023
6 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
In the present study, we developed and validated an experimental life support system (ELSS) designed to investigate coral reef associated bacterial communities. The microcosms in the ELSS consisted of coral reef sediment, synthetic seawater, and specimens of five benthic reef species. These included two hard corals Montipora digitata and Montipora capricornis, a soft coral Sarcophyton glaucum, a zoanthid Zoanthus sp., and a sponge Chondrilla sp.. Physicochemical parameters and bacterial communities in the ELSS were similar to those observed at shallow coral reef sites. Sediment bacterial evenness and higher taxonomic composition were more similar to natural-type communities at days 29 and 34 than at day 8 after transfer to the microcosms, suggesting microbial stabilization after an initial recovery period. Biotopes were compositionally distinct but shared a number of ASVs. At day 34, sediment specific ASVs were found in hosts and visa versa. Transplantation significantly altered the bacterial community composition of M. digitata and Chondrilla sp., suggesting microbial adaptation to altered environmental conditions. Altogether, our results support the suitability of the ELSS developed in this study as a model system to investigate coral reef associated bacterial communities using multi-factorial experiments.

Subject terms

Ecosystem ecology
Microbial ecology
Tropical ecology
Marine microbiology
Metagenomics
http://dx.doi.org/10.13039/501100007601 Horizon 2020 813360 Stuij T. M. http://dx.doi.org/10.13039/501100001871 Fundação para a Ciência e a Tecnologia UIDP/50017/2020+UIDB/50017/2020 http://dx.doi.org/10.13039/501100004663 Ministry of Science and Technology, Taiwan MOST 110-2621-B-346-001 Huang Y. M. http://dx.doi.org/10.13039/501100003246 Nederlandse Organisatie voor Wetenschappelijk Onderzoek 16.161.301 De Voogd N. J. issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Coral reefs are among the most diverse and productive of marine ecosystems and are home to an estimated 25% of known marine species1. They also provide important economic services including coastal protection, tourism and fisheries1–3. However, anthropogenic perturbations are increasingly affecting the health and resilience of coral reefs4–6. For example, the frequency of extreme El Niño southern oscillation (ENSO) events has increased over the past decades7–9; this has been linked to larger-scale and more severe coral bleaching events4,10,11. Given the above, it is important to understand how coral reef organisms respond to environmental perturbations, particularly those related to predicted increases in temperature and UVB intensity and their relationship with large-scale processes12.

In coral reef ecosystems, understanding the impact of multiple environmental factors on community dynamics is challenging due to the complex interplay between these factors13. Randomised controlled microcosm experiments offer a valuable approach to distinguish correlation from causation by reducing environmental complexity. Such experiments allow for hypothesis testing and the examination of independent and interactive effects of different environmental factors14–17. This facilitates a more mechanistic understanding of the relationship between specific environmental factors and the response variable or variables of interest14,15,18. Moreover, microcosm-based studies offer a way to study organismal responses to specific and controllable treatments19,20, which may not be possible in a natural setting.

In recent years, a growing body of studies have highlighted the importance of reef microbial communities for coral reef resistance and resilience to environmental perturbations21–24. These communities play important roles in biogeochemical cycling, pollutant degradation, coral nutrition and defense against pathogens and predators25–30. Both non-host (e.g., in sediment and seawater) and host-associated communities, have been shown to respond to spatial and environmental processes including large and local-scale perturbations15,31–33. However, controlled experiments, which include microbial communities from various reef biotopes are scarce.

In the current study, we developed and validated an experimental life support system (ELSS), designed to perform multi-factorial experiments to investigate coral reef associated bacterial communities of non-host and host associated biotopes. We monitored physical and chemical parameters, assessed coral photosynthetic efficiency and investigated sediment, water, and host-associated bacterial communities. Subsequently, we validated the system by comparing the conditions and bacterial communities in the ELSS with those occurring in natural coral reef environments.

Methods

ELSS design

The design of the ELSS developed in this study was based on a microcosm system previously developed to assess the effects of global climate change and environmental contamination on sediment communities14. In the current study, the ELSS included two frames of 16 microcosms (glass aquaria, 23 cm in height, 16 cm length and 12 cm width). Each individual microcosm was connected to another aquarium (referred to as reservoir, 30 cm in height, 12 cm length and 12 cm width); see Fig. 1. The microcosms and reservoirs contained outflow-holes (two centimetres in diameter) positioned 3 and 5 cm below the top of the glass, respectively. Each reservoir was equipped with a small hydraulic pump (compactON 300, EHEIM) to pump water out of the microcosm into the reservoir, where subsequently the water level would rise and flow out into the accompanied microcosm with a constant flow rate of approximately 8.64 ml/s (s = 0.66 ml/s). Each reservoir-microcosm unit contained a functional water volume of approximately 5 L.Figure 1 Picture (a) and graphical representation (b) of the experimental life support system.

Water changes, temperature, light and aeration control system

Water was renewed daily by replacing 1 L of water with newly prepared synthetic seawater. Synthetic seawater was prepared by mixing coral reef salt (including associated oligo-nutrients) (CORAL PRO SALT, Red Sea) with deionized water (produced in a four-stage reverse osmosis unit—V2Pure 360) to a concentration of 35 ppt. To obtain sufficient mixing, the synthetic seawater was prepared in a large container supplied with a recirculating pump at least 12 h prior to addition to the reservoirs.

Temperature was regulated using water bath tanks, each of which surrounded four microcosms14 (Fig. 1). For 34 days, water temperature was kept at 28 °C using heaters equipped with an internal thermostat (V2Therm 100 Digital heater). This temperature was chosen to resemble water temperatures previously measured at Pacific reefs34,35. The modular design of the ELSS set up enables randomized split-plot design experiments, whereby each plot consists of four microcosms of equal temperature.

Lighting was controlled by four fully programmable luminaire systems (Reef—SET, Rees, Germany), each holding eight fluorescent lamps14. During the current experiment, four UV fluorescent tubes (SolarRaptor, T5/54W) and four full spectra fluorescent tubes (ATI AquaBlue Special, T5/54W) were connected alternately. To simulate photoperiod conditions of tropical latitudes, the lamps were programmed to a 12 h diurnal light cycle with light intensity varying from 2.61*10–3 J cm−2 s−1 in the morning to 8.82*10–3 J cm−2 s−1 at mid-day (measured using a Fibre optic probe positioned at the water surface, Flame spectrometer, Ocean Optics). The total light energy transmitted during the day equalled 256 J cm−2 day−1, of which 97.7% came from the photoactive radiation (PAR) wavelengths (300–700), 1.85% from the UVA wavelengths (315–400) and 0.43% from the UVB wavelengths (280–315). Photosynthetic photon flux density (PPFD, mol photons m−2 s−1) at the sediment–water interface was measured using an underwater PAR meter (MQ-510 PAR/Quantum-Meter Underwater, Apogee, USA). A total of 3.47 ± 0.68 mol photons m−2 d−1 reached the sediment surface. A transparent polyester film (Folanorm SF-AS, Folex coating, Köln, Germany) was used to block UVB light (290–320 nm)36,37. This film absorbed 90% of the UVB irradiance, 31% of UVA and 9% of PAR irradiance. A detailed figure of the light spectrum can be found in Supplementary Fig. 1. When the ELSS is used to investigate the effects of UVB on coral reef organisms, this film can be removed from the microcosms. All microcosms were connected to an air pump (530L/h, Resun) providing equal and constant aeration in each microcosm via a small hose and a diffuser stone (1.5 cm in diameter, 3 cm in length).

Coral reef sediment and organisms

Approximately 3 cm of sediment, equivalent to 700 g wet weight per microcosm, was added to the bottom of each microcosm. This sediment consisted of a mixture of commercially available (Reef Pink dry aragonite sand, Red Sea) and natural coral reef sediment. Natural sediment was collected from a coral reef south of Fongguei, Penghu Islands, Taiwan (22° 19′ 50.5′′ N 120° 22′ 19.8′′ E). Within 5 days, the sediment was transported to our laboratory in Portugal in a coolbox and stored at 4 °C until use. The commercial sediment was washed and sterilized. Sterilization was performed by 3 times autoclavation at 121 °C for 20 min38. 20 kg of sterilized commercial sediment was spiked with 4 kgs of natural coral reef sediment, which equates to a 1 to 6 ratio of live to sterilized sediment. The sediments were thoroughly mixed and added to the microcosms.

The ELSS was left to stabilize with spiked reef sediment and synthetic seawater for the first eight days of the experiment. Subsequently, five reef animal species were added, after which the system ran for an additional 26 days. Overall, the system ran continuously for 34 days. The species used in the ELSS included two hard corals, Montipora digitata (Dana, 1846) and Montipora capricornis (Veron 1985), one soft coral, Sarcophyton glaucum (Quoy & Gaimard, 1833), one zoanthid, Zoanthus sp., and one sponge, Chondrilla sp.. All animals used in this study were previously grown in aquaria at ECOMARE (CESAM, University of Aveiro, Portugal)39. A detailed explanation of the fragmentation process of the organisms can be found in the Supplementary material.

Physical and chemical analysis

Water quality was monitored daily by measuring temperature, pH, dissolved oxygen and salinity (Multi 3420 multimeter, WTW GmbH, Weilheim, Germany). Samples to determine dissolved inorganic nutrient concentrations (nitrate NO3−; nitrite NO2− ammonium NH4+, and phosphate PO43−) in the water column were taken every 7 days, using disposable syringes (50 ml), and were measured immediately, according to the Sali test kit protocol (colorimetric method test kit, salifert, Aquarium Masters). Additionally, we analysed dissolved inorganic nutrients (nitrate NO3−; nitrite NO2−; ammonium NH4+; phosphate PO43−; and sulphate SO42−) and total organic carbon (TOC) concentrations from samples of ELSS sediment porewater after eight, 28 and 34 days. A detailed description on the methodology of these measurements can be found in the Supplementary material.

In vivo chlorophyll fluorescence analysis

Chlorophyll fluorescence of the corals and zoanthid was measured at days 0 and 34 in vivo using a pulse amplitude modulation (PAM) fluorometer (Walz™). Fluorescence was measured in dark-adapted samples (for 20 min), with Junior PAM and WinControl3 software (Walz™). Saturating light pulses (450 nm) were performed perpendicularly to the sample surface, with a 1.5 mm fibre optic. The maximum quantum yield (Fv/Fm) of photosystem II was calculated as Fv/Fm = Fm-F0Fm. Fv/Fm ratios reflect the efficiency of PSII (photosynthetic efficiency) of coral-algal symbionts. Reductions in Fv/Fm ratios are an indicator of increased stress experienced by algae and their coral hosts40,41.

Bacterial community analysis

Sampling and DNA extraction

Sediment, water and porewater samples were collected after 8, 29 and 34 days of four independent microcosms. Reef animals were sampled at the end of the experiment (after 34 days) and rinsed with filtered (0.22-μm pore size) synthetic seawater to remove loosely attached organisms. Additionally, samples from the natural sediment collected in Taiwan and from benthic reef animals prior to addition to the microcosms (hereafter referred to as “Pre”) were preserved for further analysis (see Fig. 2). During sediment collection in Taiwan, three sediment samples of approximately 20 g were collected in situ and immediately preserved in 96% alcohol. From the microcosms, a composite sediment sample consisting of four smaller subsamples (~ 3 g each) was obtained using a sterilized scoop. The subsamples were taken haphazardly from each microcosm (1 cm of surface sediment with a ~ 2 cm diameter). One sample from the sterilized commercial sediment was obtained as control for ELSS contamination with environmental DNA42 and sample collection43.Figure 2 Graphical summary of the experimental set-up.

Water was sampled by filtering 250 ml of water through a Millipore® Isopore polycarbonate membrane filter (0.22-μm pore size; Millipore®) using a vacuum filtration system. All sediment samples, whole membrane filters and reef organisms were frozen at − 80 °C until DNA extraction.

DNA extraction and sequence analysis

Details on DNA extraction, library preparation and sequencing can be found in the Supplementary material. Briefly, PCR-ready genomic DNA was isolated from all samples using the FastDNA® SPIN soil Kit (MPbiomedicals) following the manufacturer’s instructions. From the extracted DNA, the V3/V4 variable region of the 16S rRNA gene was amplified with Illumina Nextera XT overhang adapters in a dual-barcoding PCR library preparation approach. The prepared DNA was subsequently sequenced at a commercial company (Baseclear, Leiden, The Netherlands) on the Illumina MiSeq platform using 2 × 300 bp paired-end sequencing (Illumina MiSeq PE300). Three negative control samples were included to detect possible contamination during library preparation and sequencing. Sequences from each end were paired following Q25 quality trimming and removal of short reads (< 150 bp). The DNA sequences generated in this study can be downloaded from NCBI BioProject Id PRJNA904682.

The generated 16S rRNA gene amplicon libraries were imported into QIIME244. Within the QIIME2 environment, forward and reverse sequences were trimmed to a length of 245 and 200 nt, respectively, using the DADA2 plugin45. The DADA2 analysis produced a quality filtered table of all amplicon sequence variants (ASVs), a fasta file of representative sequences, and a table summarising the denoising statistics. Following this, the QIIME2 feature-classifier plugin with the extract-reads option was used to extract reads from the Silva database with the silva-138-99-seqs.qza file as input and the forward and reverse PCR primers as parameters. This produced a file of reference sequence reads, which was used as input for the feature-classifier plugin with the fit-classifier-naive-bayes option. The plugin trained a Naive Bayes classifier and produced a classifier file (classifier.qza) as output. The feature-classifier plugin was then used with the classify-sklearn alogrithm using the representative sequences file generated by the DADA2 analysis as input, which produced a table with taxonomic assignments for all ASVs. Mitochondria, chloroplasts, and ASVs classified as Eukaryota were filtered out using the QIIME2 taxa plugin with the filter-table method. The ASV and taxonomy tables were later merged in R. After quality control and removal of singletons, chloroplasts and mitochondria, the total dataset of sediment, water and host bacterial samples consisted of 1,549,657 sequences and 13,711 ASVs. The ASV count table and a fasta file containing all sequences are presented in Supplementary Tables 1 and 2, respectively.

Subsequently, we removed all ASVs classified as Archaea and ASVs that were unassigned at the phylum level. We also removed ASVs, which occurred in the triple-autoclaved commercial sediment (control for sampling and eDNA contamination) and negative controls used for sequencing (removed ASVs are listed in Supplementary Table 3). Overall, the removed ASVs were assigned to known contaminants, for example, the genera Ralstonia, Burkholderia-Caballeronia-Paraburkholderia, Reyranella, Bacillus and Bradyrhizobium46–48. After these filtering steps, we removed two samples with low read counts before subsequent analysis; E2G4 (339 sequences) and E2Z1 (1004 sequences). The resulting dataset consisted of 62 samples, 1,414,326 sequence reads and 13,225 ASVs.

The 50 most abundant ASVs were referenced against the NCBI nucleotide database using NCBI Basic Local Alignment Search Tool (BLAST)50. BLAST identifies locally similar regions between sequences, compares sequences to extant databases and assesses the significance of matches. Each BLAST run produces a list of results between the sequence in question (the query sequence), obtained from selected ASVs in the present study, and target sequences in the NCBI nucleotide database.

Predicted metagenomic analysis

To analyse the putative functional profile of the bacterial communities, we used the Tax4Fun2 library49 in R. Tax4Fun2 predicts the metagenomic content of the samples using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. Output of Tax4Fun2 consists of a table of functional counts for individual pathways and KEGG orthologs (KOs). Note that because of functional overlap, some KEGG orthologs (KOs) can be represented in multiple pathways. Tax4Fun2 also produces output on the amount of ASVs and sequences used in the prediction for each sample. This can vary among samples depending on the availability of closely-related, sequenced genomes available in the Tax4Fun2-supplied (Ref100NR) database. Note that the Tax4Fun2 results as presented are predictive and thus provide information on potential enrichment and putative function as opposed to measuring actual gene presence/expression and function.

Statistical analysis

We tested for significant differences among sampling events in SO42− and TOC concentrations, and Fv/Fm ratios. Histograms revealed significant deviations from normality. The distributions continued to deviate significantly following logarithmic and square root transformations. We, therefore, tested for significant differences in SO42− and TOC concentrations, and Fv/Fm ratios among sampling events using a repeated measures permutational analysis of variance with the adonis2() function (Vegan package) in R. In the function, the method was set to “euclidean” and permutations to “999”.

To analyse the bacterial community data, a table containing the ASV counts was imported into R. The ASV table was used to analyse the impact of the microcosm system on bacterial diversity, composition and higher taxon abundance. Diversity indices were obtained using the rarefy() and diversity() functions from the vegan package51 package in R. Evenness was calculated by dividing Shannon’s H’ by the log of the number of ASVs in each sample. Differences in diversity indices, higher taxon abundances and selected KEGG pathways were investigated using an analysis of deviance (glm() function of the R package stats). In the function, we set the family argument to “quasipoisson” (richness) or “quasibinomial” (evenness, higher taxa and KEGG pathways), which is appropriate when analysing proportional data and allowed us to model overdispersion. Using the glm model, we tested for significant variation using the anova() function in R with the F test, which is most appropriate when dispersion is estimated by moments as is the case with quasibinomial fits. We subsequently used the emmeans() function in the emmeans library52 to perform multiple comparisons of mean abundance among sample events using the false discovery rate (fdr) method in the adjust argument.

Variation in bacterial composition among groups (time points and biotopes) was visualized with Principal Coordinates Analysis (PCO). For the PCO, the ASV table was rarefied to the minimum sample size using the rrarefy() function of the R package vegan (4818 sequences in the present study). For compositional analyses, the ASV table was transformed using the decostand() function in vegan with the method argument set to ‘Hellinger’. With this transformation, the ASV table is adjusted such that subsequent analyses preserve the chosen distance among objects (samples in this case). The ASV table was transformed because of the inherent problems with the Euclidean-based distance metric, which is frequently used in cluster analyses53. A distance matrix was subsequently created with the vegdist() function in vegan using the Hellinger-transformed ASV table as input and the method argument set to “euclidean”. Subsequently, we used the cmdscale() function of the R package stats with the Hellinger transformed distance matrix as input. We quantified the degree of similarity in bacterial communities between Pre (environmental) sediments and sediments sampled at days 8, 29 and 34 by calculating their mean dissimilarity using the meandist() function in the R package vegan with the dist argument set to the Hellinger transformed distance matrix. A permutational anova was performed using the adonis2() function of the R package vegan to test for significant differences in composition among sampling events per biotope (999 permutations). Additionally, we analysed the variance of host-associated bacterial communities within Pre and day 34 samples by first computing a Bray–Curtis transformed dissimilarity matrix using the decostand() and vegdist() functions (method set to “Bray”), which was subsequently used to compute distances to centroids using the betadisper() function in the R package vegan. Detailed descriptions of the functions used here can be found in R (e.g., ?cmdscale) and online in reference manuals (http://cran.rproject.org/web/packages/vegan/index.html). To acquire information on the connectivity between sediment and host-associated bacterial communities, we analysed the number of ASVs shared among host and sediment biotopes at Pre versus day 34 conditions using the function upset() in the R package UpSetR. Lists of ASVs detected in each sample group were used as input. Lists were computed after rarefaction of the ASV table and only ASVs with ≥ 0.1% were included.

Results

Physical and (bio)chemical analysis

Water temperature varied from a minimum of 26.4 ± 0.65 °C in the morning to a maximum of 28.3 ± 0.83 °C in the afternoon. Dissolved oxygen levels varied over the day from a minimum of 7.83 ± 0.50 mg L−1 in the morning to a maximum of 8.60 ± 0.45 mg L−1 in the afternoon. In line with variation in oxygen concentrations, pH varied over the day from 8.06 ± 0.04 to 8.17 ± 0.08 (Supplementary Fig. 2). During the course of the experiment, NO3−, NO2−, NH4+ and PO43− concentrations in the water column and pore water remained below detection limits (< 0.2, < 0.01, < 0.15 and < 0.05 mg L−1, respectively). There was a significant reduction in the concentration of pore water SO42− from 2940.0 ± 16.33 mg L−1 at the first measurement to 2520.0 ± 40.82 mg L−1 at the end of the experiment (repeated measures PERMANOVA, F2,9 = 18.89, R2 = 0.81, P = 0.035, Supplementary Fig. 2). Although not significant, TOC increased from 1.77 ± 0.18 to 4.97 ± 2.59 mg C L−1 at the end of the experiment (repeated measures PERMANOVA, F2,9 = 3.28, R2 = 0.42, P = 0.15, Supplementary Fig. 2).

Coral photosynthetic efficiency

Fv/Fm ratios significantly increased from 0.46 ± 0.01 at day 0 to 0.53 ± 0.01 at day 34 in M. digitata (PERMANOVA: F1, 6 = 148.56; R2 = 0.96; P = 0.031). Fv/Fm ratios increased non-significantly from 0.52 ± 0.01 to 0.54 ± 0.02 in M. capricornis (PERMANOVA: F1, 6 = 3.50; R2 = 0.37; P = 0.157) and non-significantly from 0.52 ± 0.04 to 0.59 ± 0.03 in S. glaucum (PERMANOVA: F1, 6 = 10.41; R2 = 0.63; P = 0.062); in Zoanthus sp. Fv/Fm ratios decreased non-significantly from 0.51 ± 0.02 to 0.44 ± 0.07 (PERMANOVA: F1, 6 = 3.46; R2 = 0.37; P = 0.174).

Bacterial community analysis

Diversity

Richness and evenness were highest within sediment bacterial communities, but varied among sampling events (Table 1 and Supplementary Table 4). In sediment, richness first increased non-significantly from day 8 to day 29 and subsequently declined significantly from day 29 to day 34. Evenness was significantly lower at day 8 than Pre, day 29 and day 34 samples. In water, richness and evenness both significantly increased from day 8 to day 29 and remained stable from day 29 to 34. Richness and evenness did not vary significantly among sampling events in host biotopes, with the exception of M. digitata, in which we observed a significantly lower evenness in Pre compared to day 34 samples. Table 1 Mean and standard deviations of rarefied richness & evenness.

Richness	Pre	D8	D29	D34	
Sediment	1065 ± 87	1023 ± 120	1469 ± 126	621 ± 447	
Water	–	126 ± 16	321 ± 44	298 ± 69	
M. digitata	259 ± 144	–	–	296 ± 147	
M. capricornis	470 ± 48	–	–	377 ± 82	
S. glaucum	289 ± 204	–	–	340 ± 118	
Zoanthus sp.	120 ± 16	–	–	191 ± 152	
Chondrilla sp.	58 ± 13	–	–	56 ± 5	
Evenness	
Sediment	0.95 ± 0.00	0.85 ± 0.04	0.94 ± 0.01	0.91 ± 0.01	
Water	–	0.50 ± 0.04	0.71 ± 0.02	0.66 ± 0.06	
M. digitata	0.67 ± 0.11	–	–	0.85 ± 0.05	
M. capricornis	0.83 ± 0.05	–	–	0.77 ± 0.03	
S. glaucum	0.66 ± 0.05	–	–	0.64 ± 0.09	
Zoanthus sp.	0.88 ± 0.01	–	–	0.79 ± 0.13	
Chondrilla sp.	0.65 ± 0.02	–	–	0.67 ± 0.04	
Values which significantly varied from the previous sampling event are indicated in bold (P < 0.004, GLM with emmeans, see supplementary Table 4).

Ordination analysis (Fig. 3) showed that sampling event was a significant predictor of variation in the bacterial community composition of the sediment, water, and the host organisms M. digitata and Chondrilla sp. (PERMANOVA: P < 0.05, Supplementary Table 5). When grouped together, variance among samples of host-organisms was similar in Pre compared to day 34 conditions, as evidenced by similar distances to centroids (Pre: 0.625 ± 0.035; day 34: 0.632 ± 0.045, Supplementary Fig. 3). Samples clustered according to their host-species (PERMANOVA, F4,30 = 6.93; R2 = 0.48; P = 0.001). In water and sediment, the main axis of variation separated samples according to biotope whereas the second axis separated water and sediment samples collected at day 8 from Pre, day 29 and day 34 samples. However, the mean bacterial community dissimilarity was marginally higher between Pre and day 34 (89%) compared to Pre and day 8 and 29 samples (85 and 86%, respectively).Figure 3 Ordinations showing the first two axes of the principal coordinates analysis (PCO) of prokaryote ASV composition for sediment and water, and each host species separate. Light grey symbols represent operational taxonomic unit (ASV) scores with the symbol size representing their abundance (number of sequence reads). Supplementary Table 5 lists eigenvalues, the total variation explained and results of the PERMANOVA for each ordination.

Higher taxonomic composition

The most abundant phyla across the complete dataset were Proteobacteria (4156 ASVs, 751,894 sequences), Planctomycetota (2214 ASVs, 138,421 sequences), Bacteriodota (1634 ASVs, 84,631 sequences), Cyanobacteria (221 ASVs, 78,311 sequences), Actinobacteriota (485 ASVs, 59,621 sequences), Firmicutes (215 ASVs, 54,368 sequences), Verrumicrobiota (1112 ASVs, 52,766 sequences) and Patescibacteria (490 ASVs, 45,003 sequences) (Supplementary Table 6). Of these, Cyanobacteria were particularly abundant in Chondrilla sp., whereas Patescibacteria were most prevalent in water communities (Fig. 4).Figure 4 Mean relative abundance of the most abundant proteobacterial classes and phyla of biotopes at the different sample events. Error bars represent standard deviations of the mean. Sd: sediment, Wt: water, Md: M. digitata, Mc: M. capricornis, Sg: S. glaucum, Zs: Zoanthus sp., Cs: Chondrilla sp.

The relative abundances of the most abundant phyla and proteobacterial classes varied significantly among sampling events in water and sediment communities (Fig. 4), whereas no significant differences were observed between sampling events in host organisms (Pre and day 34) (emmeans, Supplementary Table 7). In sediment, Desulfobactereota, Firmicutes and Spirochaetota abundances were significantly (P < 0.0004) lower at days 8, 29 and 34 compared to Pre samples, whereas the abundance of Dadabacteria was significantly higher at days 29 and 34 compared to Pre and day 8 (emmeans, Supplementary Table 7). Chloroflexi abundance first declined significantly from Pre to day 8, increased significantly from day 8 to 29, and did not vary significantly from day 29 to 34, whereas the inverse held for Gammaproteobacteria (emmeans, Supplementary Table 7). In water, the abundance of Verrucomicrobiota and Planctomycetota significantly increased from day 8 to 34 (emmeans, Supplementary Table 7).

Shared ASVs among sediment and host-organisms in Pre and ELSS conditions

All biotopes consisted of both biotope-specific ASVs alongside ASVs which were shared among two or more biotopes (Supplementary Fig. 4–6). Among host organisms, there were a total of 183 shared ASVs in Pre and 277 shared ASVs in day 34 samples. A total of 168, 137 and 157 ASVs were detected in both Pre sediments and sediments at days 8, 29 and 34, respectively. At day 34, 169 ASVs were shared between sediment and host-biotopes. Of these, 18 were also detected in both Pre sediments and hosts, whereas 37 ASVs were only detected in Pre host samples and 36 ASVs in Pre sediments.

Most abundant ASVs and their closest relatives

Of the 50 most abundant ASVs, five were recorded in sediment across all sample events (Fig. 5). These ASVs were assigned to the genera Ruegeria (ASVs 5, 25), Methyloceanibacter (ASV-36), Filomicrobium (ASV-37) and the family Geminicoccaceae (ASV-67). A more in-depth analysis showed that ASVs 5 and 25 were related to R. lacuscaerulensis isolates (100% sequence similarity), whereas the other three ASVs were related to uncultured organisms, previously detected in marine sediment (ASV-67), intertidal outcrops (ASV-37), and the coral Galaxea fascicularis (ASV-36, > 99% sequence similarity, Supplementary Table 8). With the exception of ASV-67, all of these ASVs were also found in water (ASVs 5 and 36) and/or host organisms (ASVs 5, 25, 36, 37) across sampling events.Figure 5 Relative abundance of the 50 most abundant ASVs, which are colour-coded according to their phylum-level classification. The circle size of the ASV is proportional to the mean percentage of sequences per biotope and sampling event. The y-axis lists the ASVs with their respective id number. Sd: sediment, Wt: water, Md: M. digitata, Mc: M. capricornis, Sg: S. glaucum, Zs: Zoanthus sp., Cs: Chondrilla sp.

Ten ASVs, assigned to the Alpha- and Gammaproteobacteria (ASVs 2, 11, 19, 23, 39, 41, 52, 55, 3 and 142) were consistently abundant in sediment at days 8, 29 and 34 but were not detected in Pre sediment. They also occurred in water and invertebrate hosts at various sampling events. ASVs 2, 11, 52 and 142 were related (100% sequence similarity) to isolates classified as Tritonibacter litoralis, Leisingera sp., Ruegeria sp. strain and Alteromonas sp. detected in seawater, a coral, marine sediments and estuarine soil, respectively. ASVs 3, 19, 23, 39, 41 and 55 were related to (> 97% sequence similarity) uncultured organisms detected in seawater, the coral Fungia granulosa, the coral Alcyonium digitatum, the sponge Haliclona sp., endolithic communities and marine sediment, respectively. Three ASVs (30, 43 and 186), assigned to the classes Gracilibacteria and ABY1, were not detected in sediment and host Pre samples, but were abundant across water and sediment at days 29 and 34 and associated with several invertebrate hosts at day 34. Additionally, eleven of the 50 most abundant ASVs showed a biotope specific association (only recorded in a single biotope). For example, ASV-42 (Entoplasmatales) was only recorded in S. glaucum, ASV-10 (Endozoicomonas) in Zoanthus sp., and ASVs 26 (BD2-11 terrestrial group) and 32 (Cyanobiaceae) in Chondrilla sp.. These ASVs were similar to uncultured organisms previously detected in the sea anemone Nematostella vectensis, a deep-sea octocoral, and the sponges Xestospongia muta and Haliclona sp., respectively (Supplementary Table 8).

Predicted functional analysis

The fraction of ASVs and sequences used in the prediction of metagenomic content varied from 12.9 ± 1.5% ASVs and 22.3 ± 1.0% of sequences in day 29 sediment samples to 43.4 ± 11.2% ASVs and 57.4 ± 2.04% sequences in Pre M. digitata samples (Supplementary Table 9). The predicted relative abundances of 7 of the 12 tested KEGG categories remained relatively stable across all sampling events. Variation was observed in the nitrogen metabolism category, which significantly declined from Pre to day 8, but increased from day 8 to days 29 and 34 to similar values found in Pre sediments (P < 0.0006, Fig. 6 and Supplementary Table 10). Additionally, predicted gene count abundances of the cAMP signalling and two-component system categories significantly (P < 0.0006) decreased, and the carbon metabolism and quorum sensing categories significantly increased from Pre to day 8 and thereafter remained relatively stable from days 8 to 34 (emmeans, Supplementary table 9). In water, 6 of the categories remained stable. Variation was observed among the carbon metabolism, secondary metabolites and antibiotic biosynthesis categories, which increased from day 8 to 34, whereas the quorum sensing category decreased (emmeans, Supplementary Table 10). The terpenoid backbone biosynthesis category increased from day 8 to 29, but there was no significant difference between day 8 and 34. The cAMP signalling category significantly increased from day 8 to 29 and subsequently significantly decreased from day 29 to 34 (emmeans, Supplementary Table 10). In host biotopes, none of the functional categories investigated differed significantly between sampling events.Figure 6 Predicted mean relative gene count abundance of the KEGG level 1 categories of biotopes at the different sample events. Error bars represent standard deviations of the mean. Sd: sediment, Wt: water, Md: M. digitata, Mc: M. capricornis, Sg: S. glaucum, Zs: Zoanthus sp., Cs: Chondrilla sp.

Discussion

The current study aimed to develop and validate an ELSS to study coral reef microbial communities under controlled conditions. To this end, we assessed bacterial community composition (sediment, water, and host-associated communities), coral photosynthetic efficiency, and physicochemical parameters (e.g., salinity, pH, temperature, PAR and UV light, inorganic nutrients and DOC) within the ELSS. Sediment bacterial communities within the microcosms were compared to those in natural environmental sediments. Additionally, we compared host-associated bacterial communities within the ELSS to those in their original culture aquarium to examine the impact of transplantation.

Physicochemical parameters, photosynthetic efficiency and their resemblance to natural conditions.

Average values of the water temperature, dissolved oxygen, pH and salinity in the microcosms fell within the range of values measured at shallow coral reef sites34,54,55. Daily fluctuations were observed among water temperatures (increased by 2 degrees during the course of the day), oxygen and pH (higher in the morning and lower in the afternoon). At reefs, similar temperature fluctuations have been observed due to sun exposure34,54, whereas oxygen and pH can fluctuate as a result of net-photosynthesis during the day and respiration at night55. Given the average temperature of ~ 27 °C and daily temperature fluctuations, the conditions in the ELSS were most comparable to conditions observed at shallow coral reef sites during summer months in Australian and North Pacific reefs34,55. Due to their close proximity to the equator, water temperature fluctuations at Indo-pacific reefs are less driven by summer and winter patterns but vary as a consequence of (monsoon induced) upwelling35. Overall, the temperature conditions within the ELSS were most comparable to the conditions in January, February, August and September of Indonesian reefs35.

Inorganic nutrients in the water column and sediment pore water (NO3−, NO2−, NH4+ and PO43−) remained low (< 0.2, < 0.01, < 0.15 and < 0.05 mg L−1, respectively) throughout the experiment. Inorganic nutrient concentrations below these values were previously observed in pacific coral reefs as well56. Porewater sulphate concentrations of surface sediments have been observed to vary between 2300–2880 mg L−1 57,58. At the first time point, sulphate was relatively high (2940.0 ± 16.33 mg L−1) in the microcosms, but fell to levels previously observed in coral reef environments57,58. Although we detected relatively low DOC in the ELSS at the beginning of the experiment (1.77 ± 0.18 mg C L−1), DOC concentrations increased to 4.97 ± 2.59 mg C L−1 at day 34. These values were in the range of DOC concentrations (~ 3 to 8 mg C L−1) in surface sediment pore water at Great Barrier Reef sites (measured using 0.4 μm membrane filters)59. It should also be noted that the membrane pore size of our samplers was 0.6 μm, which included a slightly larger fraction of total organic matter compared to the measurements taken at the Great Barrier Reef.

The photosynthetic efficiency of M. capricornis, S. glaucum and Zoanthus sp. did not change significantly following 34 days in the microcosms, which indicates that the photo-physiology of these organisms was stable. The photosynthetic efficiency of M. digitata, however, was significantly higher at day 34 compared to Pre conditions. The increased photosynthetic efficiency can indicate a certain level of photo-acclimation of the photosynthetic endosymbionts of M. digitata to the light conditions in the ELSS60,61. The photosynthetic efficiency may also vary due to shifts in auto- versus heterotrophic feeding ratios, influenced by factors such as organic matter availability.

ELSS bacterial communities

Community composition and diversity

In line with previous studies, our analysis showed that bacterial evenness and richness were lowest in the sponge Chondrilla sp. and highest in the sediment62,63. In both water and sediment, there were significant shifts in bacterial evenness, and composition from day 8 to 29, but not from day 29 to 34, suggesting microbiome stabilization. Additionally, sediment bacterial evenness declined significantly from Pre to day 8 and subsequently increased from day 8 to 29 and 34. Sediment bacterial richness, however, significantly declined from day 29 to 34. Previously, Coelho et al.14 also observed lower bacterial richness in microcosms compared to natural sediment. They attributed this to the more stable and less heterogeneous conditions in microcosms. Community composition in Pre sediments was compositionally distinct from all ELSS sediments. The PCO analysis showed, however, that communities at day 29 and 34 deviated less from Pre than day 8 communities. Note that some community variation in Pre compared to microcosm sediments might also be related to the use of different preservation methods (96% alcohol and immediate freezing, respectively)64,65.

Bacterial richness and evenness in the present study fell within ranges observed at natural reef sites for all host-associated biotopes with the exception of Zoanthus sp., where relatively low richness values were observed compared to previous findings66–68. Richness and evenness at specific time points varied more among individuals of hard and soft coral biotopes than among individuals of the sponge Chondrilla sp., in line with previous in situ comparisons of corals and sponges63. Evenness only differed significantly between sampling events (Pre and day 34) in M. digitata, increasing substantially from Pre to day 34. Bacterial composition, in turn, differed significantly between Pre and day 34 for M. digitata and Chondrilla sp., but not for the other host-associated biotopes. This finding suggests that the bacterial communities of M. digitata and Chondrilla sp. were responsive to changing environmental conditions as exemplified in the present case by transplantation between aquaria. Previous studies have shown that the bacterial communities of certain coral species change along with shifting environmental conditions, a trait, which may be part of the process of host adaptation to new environmental conditions24,70,71. Moreover, we found Pre host-specific and Pre sediment-specific ASVs in both the bacterial communities of the hosts and the sediment at day 34, suggesting bacterial exchange between biotopes in the ELSS. In reef environments, host-associated biotopes were also found to share a significant percentage of ASVs with sediments33. Our findings suggest a mutual exchange, where members from the sediment community colonize hosts and vice versa. Overall community variance among host individuals, however, was similar in Pre compared to day 34 conditions and samples clustered according to their respective host species in both conditions. This indicates that the bacterial communities of individuals of the same species maintained their relatedness despite their cultivation in separate aquaria for 34 days.

Bacterial higher taxonomic composition

Dominant classes and phyla observed in the present study were similar to those observed in situ in several coral reef studies33,62,67,69. Across biotopes, the most abundant higher taxa consisted of Alpha- and Gammaproteobacteria, Bacteriodota, Verrucomicrobiota (with exception of sponges) and Planctomycetota (with the exception of water and sponges). Firmicutes were most abundant in corals, whereas Cyanobacteria, Acidobacteriota and Gemmatimonadota were most abundant in sponges and Patescibacteria in water. In sediment and water, the abundance of several higher taxonomic groups differed significantly among sampling events, whereas no significant variation was observed among host organisms. In sediment, the relative abundance of the phylum Desulfobactereota was significantly higher in Pre compared to days 8, 29 and 34. Bacterial members of this phylum were mainly assigned to the orders Desulfobulbales, Desulfobacterales and Sva1033. These orders include a variety of presumably, anearobic sulfate-reducing bacteria, which are widespread and abundant in marine sediments where they oxidize organic matter by reducing sulfate to sulfide under anoxic conditions72–74. This finding indicates that the microcosm sediment layers may have been exposed to higher oxygen concentrations than sediment retrieved from coral reefs, thereby favouring aerobic (e.g., Rhodobacteraceae members) groups. Longer incubation times might be needed for the re-establishment of anaerobic sediment regions. The abundance of the phylum Chloroflexi was significantly lower at day 8 compared to Pre, but increased to abundances similar to Pre at later time points. Although not significant, the same pattern was observed for Acidobacteriota and Gemmatimonadota, and indicates recolonization by these bacterial groups after an initial disturbance. In water, the relative abundance of Verrucomicrobiota and Planctomycetota increased from day 8 to day 29 and day 34. Members of these bacterial phyla are often observed within marine bacterioplankton communities where they have been associated with microalgal blooms75,76. Recently, genomic and proteomic data indicated that small, coccoid, free-living Verrucomicrobiota specialise in the degradation of fucoidan-like substrates during spring algal blooms in the North Sea73.

Most abundant ASVs and closely related organisms

In sediment, abundant ASVs were assigned to the genera Ruegeria (family Rhodobacteraceae), Methyloceanibacter (family Methyloligellaceae) and Filomicrobium (family Hyphomicrobiaceae). Members of these families were previously found to be abundant in marine sediment and are known to play an important role in nutrient remineralization26,72,77,78. Recently, Ruegeria strains isolated from marine sediment were observed to utilize various organic and inorganic compounds, oxidize sulfur, and perform complete denitrification by reducing nitrate to molecular nitrogen77. The Methyloligellaceae and Hyphomicrobiaceae families include bacterial species, which are able to reduce one-carbon compounds (methylotrophs) and oxidize methane under aerobic conditions78⁠. Additionally, Methyloceanibacter members may be involved in nitrogen cycling72,78.

ASV-2, assigned to the Rhodobacteraceae, was particularly abundant in water and had 100% sequence similarity to an organism identified as Tritonibacter litoralis isolated from coastal surface seawater79. Tritonibacter members are globally distributed and primarily found in surface waters80. They are also often found in association with seaweeds due to their ability to metabolize dimethylsulfoniopropionate (DMSP)79,80. Moreover, certain Tritonibacter strains (producers of the antibacterial compound Tropodithietic acid) can inhibit the growth of fast-growing bacteria and fish pathogens81. ASV-33, assigned to the Pirellulaceae, was abundant in all cnidarian biotopes in the present study, but absent from other biotopes. Members of this family have previously been detected in deep-sea octocorals and sponges and are believed to play a role in nitrogen cycling82,83. ASV-10, assigned to the genus Endozoicomonas, was only recorded in Zoanthus sp.. Endozoicomonas spp. have been recorded across a range of hosts including corals. Previous studies have suggested that they play important roles in host nutrient cycling and influence microbiome structure84,85. ASVs 1 and 32, assigned to the Ca. Synechococcus spongiarum, were specifically enriched in Chondrilla sp.. Members of this genus have been previously detected in 28 sponge species and may play a role in carbon assimilation and transfer to their sponge host86,87. ASVs 18 and 42, assigned to the Ca. Hepatoplasma (Tenericutes) were only detected in S. glaucum. Members of this genus have been suggested to play roles in the degradation of complex organic molecules in the digestive tracts of terrestrial and marine isopods88. Notably, three ASVs assigned to the phylum Patescibacteria (ASVs 30, 43, 186) were found across a variety of biotopes from day 29 onward, but were not detected in natural sediment or host-associated bacterial communities sampled prior to addition to the ELSS (Pre). Patescibacteria currently lack isolated representatives but have been detected in marine surface water89, sediment88 and reef sponges91. Members of this phylum are characterized by reduced cell and genome sizes with limited metabolic capacities92. It has been suggested that they live in symbiosis with other bacteria93 and presumably play a role in oceanic carbon cycling, a finding substantiated by the detection of genes required for carbon fixation in nano-sized patescibacterial members94.

Putative functional profiles of bacterial communities

Despite some variation among biotopes, the predicted functional profiles (e.g., degradation of aromatic compounds, nutrient metabolism, and biosynthesis of secondary metabolites and antibiotics) of bacterial communities in water, sediment and host biotopes were relatively uniform at different sampling events. The predicted gene counts of the cAMP signalling and two-component system categories were, however, significantly lower at all time points from day 8 onward in the sediment biotope compared to Pre, whereas the reverse held for the quorum sensing category. The two-component system and cAMP signalling categories help bacteria to adapt to changing environmental conditions95–97. Quorum sensing, in turn, allows groups of bacteria to synchronously alter gene expression in response to changes in population density and environmental cues98. In addition, quorum sensing has been predicted to give bacteria a competitive advantage under nutrient limiting conditions99. These conditions were likely present during the early stages of the experiment (day 8) and match the prevalence of the quorum sensing pathway. Previous studies have shown that the two-component system category was enriched in members of the phylum Desulfobacterota100, whereas, the quorum sensing category was enriched in members of the class Alphaproteobacteria101–103. These findings suggest an association between higher taxonomic abundance and predicted function. Note that the predictive functional profiles only included ASVs of which metagenomic data was available in the database, as opposed to presenting a complete view of the functional pathway abundance in the dataset.

Conclusion

The current study described and validated an ELSS to study host and free-living coral reef bacterial communities. The physical and chemical conditions and bacterial communities of the ELSS were similar to those of coral reef ecosystems. Sediment bacterial diversity and composition were more similar between Pre and day 29 and day 34 than between Pre and day 8. These results suggest that a recovery period is necessary for ELSS setups before meaningful comparisons to environmental conditions can be made. Similarities between Pre and day 29 and day 34 communities suggest an adequate recovery period occurred in the presented setup. By changing the pre-sets of our ELSS values with respect to temperature and UV, it is possible to simulate several scenarios of global climate change, such as the predicted increase in temperature and UVB. Temperature can be regulated using water-baths, which allow temperature control in blocks of four independent microcosms. The transparent polyester films that blocked UVB radiation in our validation trial can be removed from individual microcosms. Additionally, irradiance values can be manipulated by varying lamp intensity and time of operation. The modular design of the ELSS developed here offers a multitude of statistically robust experimental designs. In this way, the system enables researchers to establish cause–effect relationships of the individual and interactive effects of a suite of environmental conditions on marine bacterial communities.

Supplementary Information

Supplementary Table S1.

Supplementary Table S2.

Supplementary Table S3.

Supplementary Table S4.

Supplementary Table S5.

Supplementary Table S6.

Supplementary Table S7.

Supplementary Table S8.

Supplementary Table S9.

Supplementary Table S10.

Supplementary Information 11.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-69514-0.

Acknowledgements

We are grateful to F. Coelho and V. Oliveira for their help during lab work and FOLEX COATING GMBH in Germany for providing samples of their specialized polyester film. Also, we are thankful to J. Hilgendorf for her assistance in the creation of Fig. 2. The research was conducted at the Laboratory of Molecular Studies and Marine Environment, situated at the Centre for Environmental and Marine Studies (CESAM), University of Aveiro, Portugal.

Author contributions

T.M.S: Methodology, Investigation, Data analysis, Writing—original draft, review & editing; D.F.R.C: Supervision, Conceptualization, Data analysis, Writing—original draft, review & editing; R.J.M.R.: Resources, Conceptualization, Writing—review & editing; A.R.M.P.: Investigation, Writing—review & editing; D.A.M.S.: Conceptualization, Investigation, Writing—review & editing; J.C.F.: Resources, Writing—review & editing; A.L.: Conceptualization, Methodology, Investigation, Writing—review & editing; Y.M.H.: Resources, Writing—review & editing; N.J.dV: Supervision, Conceptualization, Resources, Writing—review & editing; N.C.M.G.: Supervision, Conceptualization, Resources, Methodology, Writing—original draft, review & editing.

Funding

This study is funded by 4D-REEF (www.4d-reef.eu). 4D-REEF receives funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813360. Additional funding was received from the Ministry of Science and Technology, Taiwan to Y. M. Huang (MOST 110-2621-B-346-001). Ana R.M. Polónia was supported by a postdoctoral scholarship (SFRH/BPD/117563/2016) funded by the Portuguese Foundation for Science and Technology (FCT)/national funds (MCTES) and by the European Social Fund (ESF)/EU. Davide A.M. Silva was supported by a PhD grant (2020.05774.BD) funded by the Portuguese Foundation for Science and Technology (FCT), national funds (MCTES), and the European Social Fund POPH-QREN programme. Jörg C. Frommlet was supported by national funds (OE), through FCT, in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. We acknowledge financial support to CESAM by FCT/MCTES (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020), through national funds. Additional funding was received from the research programme NWO-VIDI with project number 16.161.301, which is financed by the Netherlands Organisation for Scientific Research (NWO).

Data availability

Sequences generated in this study can be downloaded from the NCBI Sequence Read Archive under the BioProject accession number PRJNA904682.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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