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Supplementary online material
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Materials and Methods
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Origin, preparation and transport of samples: Samples of Lissoclinum patella were carefully lifted from
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the coral substratum into a bucket containing ambient seawater. Specimens were kept in a shaded aquarium
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(<200 µmol photons m-2 s-1) with a continuous supply of fresh seawater (26-28°C) prior to sub-sampling.
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Relatively flat and homogeneous pieces of L. patella with a surface area of ~2x2 cm were cut with a scalpel,
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and immediately rinsed and submerged in filtered seawater. Vertical slices (~0.5-1 mm thick), cut with
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sterilized razor blades, were mounted on microscope slides in seawater or, alternatively, fixed with an
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embedding medium (Confocal Matrix, Micro-Tech-Lab, Austria) for further inspection. After imaging of
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samples, three microbial consortia were sampled from: i) the upper surface layer, ii) the underside of L.
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patella, both of which were collected using a sterilized razorblade, and iii) the middle section containing the
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cloacal cavity harboring the deep green Prochloron spp. symbiont, which was collected using a pipette and
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gentle squeezing. During microscopy and imaging, samples were kept submerged in sterile-filtered seawater
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at all times.
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Samples used for subsequent DNA extraction were immediately submerged in RNAlater (Ambion, Applied
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Biosystems, USA), incubated in a refrigerator overnight and then frozen at -80°C the next morning. These
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samples were transported back to the laboratory on dry ice and stored at -80°C upon arrival.
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Microsensor measurements: A whole colony of L. patella (area of ~5x5 cm and ~0.5-1 cm thick) was
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mounted on a piece of neoprene with thin stainless steel dissection needles and submerged in a flow-chamber
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flushed with pre-heated (28ºC) and aerated seawater. The ascidian was illuminated vertically from above
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with a fiber-optic halogen lamp (KL2500, Schott GmbH, Germany). Light intensity could be varied neutrally
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by a built-in filterwheel with varying pin-hole numbers and sizes. Downwelling scalar irradiance at the level
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of the ascidian was measured for defined lamp settings with a miniature scalar irradiance sensor (3 mm
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diameter; Walz GmbH, Germany), which was connected and calibrated for use with a quantum irradiance
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meter (LI-250, LiCor, USA).
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Depth profiles of O2 concentration were measured in darkness and under defined irradiance levels with
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electrochemical O2 microsensors (OX25 and OX50, Unisense, Denmark (Revsbech 1989) connected to a
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microsensor multimeter (Unisense) and mounted on a motorized micromanipulator system (Unisense)
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connected to a PC controlling data acquisition and sensor positioning with dedicated software (SensorTrace
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Pro, Unisense).
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To be able to insert the fragile O2 microsensors into the tough test material of the ascidian, the test was pre-
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drilled with a thin sterile hypodermic needle (29G) at a zenith angle of ~40° relative to the vertically incident
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light. Thereafter, the microsensor was positioned at the surface of the ascidians test using a dissection scope
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(Olympus, Japan). After determination of the surface position (z=0) the microprofiling was done in steps of
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0.2 mm through the ascidian tissue down to a depth of ~4-6 mm; deeper profiling was not attempted to avoid
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breaking the fragile microsensor.
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Depth profiles of spectral scalar irradiance were measured with a fiber-optic scalar irradiance microprobe
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mounted in the motorized micromanipulator and connected to a fiber-optic spectrometer (QE65000, Ocean
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Optics, USA) (Kühl 2005). Measured scalar irradiance spectra were normalized to the spectral downwelling
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irradiance measured at a position equivalent to the animal tissue surface and determined by replacing the
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ascidian with a black non-reflective beaker.
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Hyperspectral imaging: The top and underside of pre-cut 2x2 cm pieces of ascidian tissues were imaged
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under a dissection microscope (SZ X16, Olympus) equipped with a digital CCD camera (DP-71, Olympus)
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using a fiber-optic halogen lamp (LG-PG2, Olympus) for homogeneous illumination of the field of view. We
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used the same microscope and light source for hyperspectral imaging, by replacing the CCD camera with a
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hyperspectral image scan unit (100T-VNIR, Themis-Vision, USA) (Kühl and Polerecky 2008). The
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hyperspectral system was controlled via a PC running the software Hypervisual 2.2 (Themis-Vision).
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Hyperspectral image stacks were obtained for the reflected light from samples, the reflected light from a
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spectrally neutral reflectance standard (Spectralon, Labsphere, USA), and background noise under dark
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conditions. Data were corrected (in Hypervisual 3.0, Themis-Vision) to % reflectance by subtracting
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background noise and normalizing the sample reflectivity to the reflectivity from the reflectance standard.
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Reflectance spectra averaged over particular AOI’s were subsequently calculated and extracted from the
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hyperspectral image stack by the system software. After file format conversion in ENVI 4.8 (ITT Visual
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Information Solutions, UK), further processing of corrected data were done according to (Polerecky et al
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2009): The fourth derivative of the spectral reflectance [R(λ)] was used to determine the presence or absence
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of pigments characterized by an absorption peak centered at λc, i.e. R4(λc). This was done using the freeware
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Look@MOSI (http://www.microsen-wiki.net/doku.php?id=lookatmosi_howto). False color coded images
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were used to determine relative pigment abundances using Adobe Photoshop CS5 Extended (Adobe
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Systems, San Jose, CA, USA) by evaluating the coverage area of selected pixel intensities. This was
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performed by a pixel wise color range selection, standardized to specific color range values of red, thus only
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selecting the pixel intensities of interest. Coverage percentages were then calculated as the percentage of
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selected pixels compared to the total number of pixels contained in the specimen image. Mounted cross-
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sections of samples were also inspected using an Olympus BX51 fluorescence microscope system equipped
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with a DP71 digital camera (Olympus).
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Variable chlorophyll fluorescence imaging: For measurements on the photosynthetic activity of biofilms,
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freshly cut samples were placed into seawater filled petri dishes. After focusing onto the area of interest
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using the non-actinic infrared imaging function of the system, the sample was allowed to dark-adapt for 15
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min before further measurements. After acquiring images of the reflectivity from the sample when irradiated
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with red (R) and near-infrared (NIR) light, an image of the absorptivity of red light was calculated as A=1-
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(R/NIR). The pulse-modulated measuring light was sufficiently weak (<1 μmol photons m s ) to be
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considered non-actinic during assessment of the minimal fluorescence yield, F , of the dark-adapted sample.
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Using the saturation-pulse-method, images of the maximal quantum yield of PSII photosynthetic energy
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conversion, (ΦPSII)
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measured at defined levels of actinic light, PAR. Relative rates of PSII-driven electron transport were
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calculated as: rETR = A ΦPSII ×PAR. Automated measurements of these parameters were done with the
-2
-1
0
’
’
m
m
= (F -F )/F and of the effective quantum yield of PSII, ΦPSII = (F -F)/F , were
max
m
o
m
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imaging systems using a range of pre-defined irradiance levels. The sample was exposed to each actinic light
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level for 10 s before a saturation pulse measurement, which was followed by a step-up to the next actinic
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light level. With this procedure rapid light curves were obtained, giving a snapshot of the photosynthetic
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light acclimation of the sample (Kühl et al 2001, Ralph and Gademann 2005).
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Molecular analysis
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Sequence analysis: A total of 237.988 amplicon sequence reads were obtained from pyrosequencing. Pre-
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processing of sequencing reads, which included binning reads according to sample-barcodes, discarding
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reads that were considered either too short (<150 bp) or too long (>350 bp), of too low quality (mean Phred
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quality score <25), or contained any ambiguous bases or nucleotide homopolymers longer than 7 bp was
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performed using the split_libraries.py script included in the quantitative insights into microbial ecology
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software package (QIIME, version 1.2.1, http://qiime.sourceforge.net/). Additionally, reads in which either of
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the amplification primers varied by more than 3 bases from the used primers were discarded. Primers were
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removed from both ends, and in instances where reads did not contain the 806R primer sequence these
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sequences were retained as long as the read length exceeded the minimum length of 150 bp. In total, 161.499
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sequences were left for further sequence analysis. The following QIIME workflow was employed to generate
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the OTU table: (1) sequences were clustered at 97% identity using the standard UCLUST parameters (--
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stepwords=20, --word_length=12, --max_accepts=20, --max_rejects=500), employed from version 1.2.1 of
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QIIME.
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of the Greengenes database (http://greengenes.lbl.gov) to verify that they were 16S rRNA sequences, (3) and
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hypervariable columns were subsequently filtered out according to the greengenes lanemask and a
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phylogenetic tree was constructed in FastTree (http://www.microbesonline.org/fasttree/). (4) Representatives
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from each generated OTU were picked and assigned to appropriated taxa using BLASTN against a non-
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redundant set of Greengenes reference sequences (gg_99_otus_4feb_2011) truncated to only contain the
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V3/V4 hypervariable regions. All previous results were combined into a single OTU table. All OTUs
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identified as chloroplasts were subsequently removed from the OTU table, reducing the total number of
(2) Representatives from each OTU were picked and aligned against V3/V4 truncated core alignment
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sequences in the dataset to 140.871. Species richness and diversity estimators were calculated on a rarefied
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read library, previously cleaned for chloroplasts and archaea, to accommodate for the lowest number of reads
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in our sample library (=4009) using the script implemented in Qiime.
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Visualization and metabolic assignments: Only OTUs summing to >100 sequences across all samples
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were used for further log10+1 transformation and visualization in the heatmap using the taxonomy
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assignments described. OTU clustering is shown along the Y-axis, where dendrogram distances are based
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upon relative abundances within the data matrix and not on phylogenetic relationships. The top dendrogram
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is based on Euclidean distances and represents clustering according to relative abundances within the data
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matrix. The heatmap was created in R (http://www.r-project.org/) using the pheatmap package.
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Metabolic assignments are based on the same OTUs as displayed in the heatmap. Relative percentages of
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OTU associated sequences were calculated for each layer (underside, surface and cloacal cavity) as well as
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depth (deep, intermediate, shallow). Functional assignments were based on a thorough literature search
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(reference list available upon request) and are based upon conservation of key properties within major
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taxonomic groups. C- metabolism is classified as either phototrophy or chemotrophy, whereas O2
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metabolism and their associated bacteria are classified as aerobes, anaerobes, facultative anaerobes and
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obligate anaerobes.
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Supplementary references:
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Kühl M, Glud RN, Borum J, Roberts R, Rysgaard S (2001). Photosynthetic performance of surface-
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associated algae below sea ice as measured with a pulse amplitude-modulated (PAM) fluorometer and O2
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microsensors. Marine Ecology Progress Series 223: 1-14.
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Kühl M (2005). Optical Microsensors for Analysis of Microbial Communities. Methods in Enzymology.
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Academic Press. pp 166-199.
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Kühl M, Polerecky L (2008). Functional and structural imaging of phototrophic microbial communities and
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symbioses. Aquatic Microbial Ecology 53: 99-118.
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Polerecky L, Bissett A, Al-Najjar M, Faerber P, Osmers H, Suci PA et al (2009). Modular Spectral Imaging
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System for Discrimination of Pigments in Cells and Microbial Communities. Applied and Environmental
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Microbiology 75: 758-771.
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Ralph PJ, Gademann R (2005). Rapid light curves: A powerful tool to assess photosynthetic activity. Aquatic
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Botany 82: 222-237.
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Revsbech NP (1989). An Oxygen Microsensor with a Guard Cathode. Limnology and Oceanography 34:
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474-478.
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Supplementary figure and table legends:
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Fig. S1: Classification of the ten most abundant phyla for the three depths and sampling locations presented
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as pie charts. Relative percentages of the OTUs are given and are based on two (intermediate depth) or three
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independent biological replicates (deep and shallow site). Chloroplasts have been removed but Archea have
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been left for the analysis.
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Fig. S2: Relative abundances of taxonomically classified OTUs and their respective metabolisms. The same
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OTUs as displayed in the heatmap (see Fig. 4) were used to calculate the relative percentage of associated
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sequences for each layer (“underside”, “surface” and “cloacal cavity”) and depth (“deep”, “intermediate”,
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“shallow”). Associated bacterial OTUs were assigned metabolic classifications based on a thorough literature
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research of their key conserved metabolic pathways. (A) Carbon metabolism classified as either phototrophy
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or chemotrophy. (B) Oxygen metabolism with the four categories: aerobes, anaerobes, facultative anaerobes
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and obligate anaerobes.
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Table S1: General amplicon sequencing statistics. “Raw reads” denotes sequences that were not quality
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checked. “Filtered reads” are sequences after quality checking. Bacteria, Archaea and Chloroplast are the
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OTUs to which filtered sequences were assigned. “Total assigned” denotes those sequences that were
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assigned to one of the OTUs mentioned above.
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