1
Supporting Information
2
3
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Patterns and drivers of bacterial α- and β-diversity across vertical
5
profiles from surface to subsurface sediments
6
7
Luna G.M.1, Corinaldesi C. 2, Rastelli E. 2, Danovaro, R. 2, *
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9
1
National Research Council - Institute of Marine Sciences (CNR - ISMAR), Castello 1364/a, 30122
10
Venezia, Italy
11
2
12
Bianche, 60131 Ancona, Italy
Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce
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14
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Supplementary Materials and Methods
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Figures S1 – S2.
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Tables S1 – S4.
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21
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25
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1
Supplementary Materials and Methods
2
Study areas and sampling activities
3
Sediment sampling was performed in two areas in the Mediterranean Sea, one located in the
4
Southern Adriatic Sea (41°34’33’’N, 16°02’71’’E) and the other in the Aegean Sea (37°39’23’’ N,
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23°58’10’’E). The first area was characterized by the presence of soft, non vegetated sediments,
6
mostly characterized by silts and clays (>90%). These sediments were collected in a gulf
7
characterized by a limited water circulation, high sedimentation rates and high water column
8
productivity. The area in the Aegean Sea was located on the coast south of Attiki, in a strait formed
9
between the coast and the small island of Patroklos, and was characterized by the presence of the
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seagrass Posidonia oceanica, with an average shoot density of ca. 357–372 m-2 (Díaz-Almela et al.,
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2008), and by mostly carbonate, sandy sediments (silt/clay contributing to <5%; Apostolaki et al.,
12
2007). From each site, three replicated cores were collected. In the Southern Adriatic Sea,
13
sediments were collected in October 2002, at 15-meters depth, using a gravity corer (Carmacoring,
14
model SW 104). In the Aegean Sea, sediments were collected in June 2003 in the Sounion Bay, in a
15
station located at ca. 500 m distance from the coast. In Sounion, sediments were collected by scuba
16
divers, at a depth of 16 meters, using a long, sterile steel corer which was gently pushed into the
17
sediment (internal diameter ca. 8 cm).
18
Immediately after sampling, the cores were kept in the dark at in situ temperature and
19
transported into the laboratory. Each core was then sliced, using sterile spatulas, into layers from the
20
surface down to 1 meter below the surface (0–1 cm, 1–2 cm, 2–3 cm, 3–4 cm, 4–5 cm, 5–10 cm,
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10–15 cm, 15–20 cm, 20–30 cm, 30–40 cm, 40–50 cm, 50–60 cm, 60–70 cm and 70–100 cm). The
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sediments were processed according to the specific protocol required for each of the environmental
23
and microbiological variables. All the samples were collected from the central part of the cores to
24
avoid any possible contamination or contact with the adjacent layers. Temperature, pH and redox
25
potential were measured in each sediment horizon using punch-in probes during the slicing
26
procedure. For trophic variables (chloroplastic pigments, total organic matter, protein,
1
1
carbohydrates, lipids and biopolymeric carbon), sediments were put into sterile Petri dishes and
2
stored at -20°C until analyses. For total prokaryotic abundance determinations, sediment samples
3
(ca. 1 cm3) were transferred into sterile test tubes, fixed with 4 ml of pre-filtered (0.2 µm) seawater
4
added with buffered formalin (final concentration 2%) and stored at 4°C until analyses (within 1
5
week). For estimates of prokaryotic heterotrophic carbon production rates, aliquots of sediment
6
were collected from the sediment core using sterile syringes and immediately analysed as described
7
below. For molecular analyses of bacterial diversity, aliquots (ca. 10 cm 3) of sediments were put
8
into polystirene sterile 50-ml test tubes using sterile spatulas, immediately frozen and then stored at
9
-20°C until DNA extraction (within one week). From each core, one sediment sample was collected
10
for each variable in each layer, and the subsequent analyses were performed in triplicate. This
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results in a total of nine analyses for each layer and each habitat. The resulting data are presented as
12
the average of the nine replicates ± standard deviation.
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14
Environmental and trophic variables
15
Sediment water content was calculated as the difference between wet and dry weight (at 60°C until
16
a constant weight was achieved) and expressed as percentage. Total sediment organic matter (OM)
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was determined as the difference between dry weight (60°C, 24–48 hours) of the sediment and
18
weight of the residue after combustion at 450°C (2 hours). Sediment chlorophyll–a and
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phaeopigments were extracted in 90% acetone overnight and analyzed fluorometrically according to
20
the method described elsewhere (Pusceddu et al. 1999; 2009). Chloroplastic pigment equivalents
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were calculated as the sum of Chl–a and phaeopigment concentrations. The biopolymeric carbon
22
content was determined as the sum of carbon equivalents of proteins, carbohydrates, and lipids. All
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biochemical components were determined spectrophotometrically according to the methods
24
described by Dell’Anno et al. (2002).
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26
2
1
Total prokaryotic abundance
2
Total prokaryotic abundance was determined according to the Sybr Green Direct Count (SGDC)
3
procedure described by Luna et al. (2002). Samples were sonicated three times (Branson Sonifier
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2200, 60W) for 1 minute, properly diluted with 0.2 µm pre-filtered formalin (2% final
5
concentration) and then concentrated on 0.2 µm pore-size Al2O3 Anodisc filters (Whatman). Filters
6
were then stained with SYBR Green I (Molecular Probes) by adding, on each filter, 20 µl of the
7
stock solution (previously diluted 1:20 with filtered [0.2-µm-pore-size] Milli-Q water), washed
8
twice with 3 ml sterilized Milli-Q water and mounted onto microscope slides. Filters were analyzed
9
using epifluorescence microscopy (Zeiss Axioskop 2MOT, magnification × 1,000). For each filter,
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at least 20 microscope fields were observed and at least 400 cells counted. Data were normalized to
11
sediment dry weight after desiccation (48 hours at 60°C).
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13
Total viral abundance
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Viral abundance was determined under epifluorescence microscopy according to the procedure
15
described by Danovaro et al. (2001). Sediments were treated using pyrophosphate (5 mM final
16
concentration) and ultrasound treatment (three times for 1 min, Branson 2200, 60 W) to increase the
17
extraction yields. In order to eliminate uncertainties in virus counting due to extracellular DNA
18
interference, sub-samples were supplemented with DNase I from bovine pancreas (10 U mL−1 final
19
concentration) and incubated for 15 min at room temperature. Sediment samples were diluted 250
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times with 0.02-μm-pre-filtered Milli-Q sterile water, filtered onto 0.02-μm-pore-size Al2O3 filters
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(Anodisc; diameter 25 mm) and then stained with 20 μl of SYBR Green I stock solution diluted
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1:20 with 0.02-µm-pore-size Milli-Q water). Filters were incubated in the dark for 20 minutes,
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rinsed three times with 3 ml 0.02-µm-pore-size Milli-Q water, dried under laminar flow hood and
24
then mounted on glass slides with 20 μl of antifade solution (50% phosphate buffer (pH 7.8) and
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50% glycerol containing 0.5% ascorbic acid). Viral counts were obtained by epifluorescence
26
microscopy (Zeiss Axioskop 2MOT, magnification × 1000) examining at least 20 fields per slide, in
3
1
order to count at least 400 viral particles per filter. Viral abundance was expressed as the number of
2
viruses per gram of sediment dry weight (after desiccation, 60°C for 48 h). The virus–to–prokaryote
3
ratios (VPR) were calculated by dividing viral abundances by total prokaryotic abundances.
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5
Prokaryotic heterotrophic carbon production
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The determination of prokaryotic heterotrophic carbon production was carried out using the method
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of 3[H]–leucine incorporation, according to the procedure described for coastal marine sediments by
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Luna et al. (2002). An aqueous solution of 3[H]–leucine (Amersham) was added to triplicate
9
sediment subsamples (200 µL) at the final concentration 1 µM. Samples were then incubated at in
10
situ temperature for 1 hour in the dark. After incubation, samples were supplemented with ethanol
11
(80%) to stop prokaryotic incorporation of leucine. For each sample, blanks were run by adding 1.7
12
mL of 80% ethanol to sediment immediately before 3[H]–leucine addition. After two washes of the
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samples with ethanol (80%) by mixing, centrifuging at 16000 × g and removing the supernatant, the
14
sediment was resuspended once more in ethanol (80%) and the whole sample was filtered onto a
15
white polycarbonate filter (0.2 µm pore size, Whatman). Subsequently, the filters were washed four
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times with 5% trichloroacetic acid. Filters were treated with 2N NaOH for 2 hours in a dry bath at
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100°C. One ml of supernatant was transferred to scintillation vials containing 10 ml of scintillation
18
liquid. Measurements of radioactivity were carried out using a liquid scintillation counter (Packard,
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Tric-Carb 2100 TR). Prokaryotic carbon production was then normalized to sediment dry weight
20
after desiccation (60°C, 48 h) and expressed as ngC g-1 h-1.
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22
Bacterial diversity and community composition
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The T–RFLP fingerprinting technique was utilized for estimating bacterial OTU richness (α-
24
diversity) and β–diversity (turnover diversity) in the entire set of sediments. DNA was extracted
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from 1 g aliquots of sediment by the UltraClean Soil DNA Isolation kit (MoBio Laboratoires Inc.,
26
California, USA). Three replicated DNA extractions were performed for each sediment layer and
4
1
then analysed as follows. Extracted DNA was quantified spectrofluorimetrically using SYBR Green
2
I. The 16S rDNA was amplified from standardized DNA quantities (5 ng) using universal primers
3
27F and 907R. The primer 27F was fluorescently labelled at the 5’ end with the fluorochrome HEX
4
(MWGspa BIOTECH). Polymerase chain reactions were performed using a thermalcycler
5
(Biometra) in a final volume of 50 µl and using the MasterTaq ® kit (Eppendorf), according to the
6
manufacturer’s instructions. We used 30 PCR-cycles, each cycle consisting of 94°C for 1 min, 55°C
7
for 1 min and 72°C for 2 min, preceded by 3 min of denaturation at 94°C and followed by a final
8
extension of 10 min at 72°C. Negative controls, containing only the PCR-reaction mixture without
9
DNA templates, were run. PCR-products were checked on agarose-TBE gel (1%), containing
10
ethidium bromide for DNA staining and visualization. For each DNA sample, four 50-µl PCRs were
11
performed on each extracted DNA sample. The four PCR products resulting from each DNA sample
12
were pooled together to minimize stochastic PCR biases and then purified using a Wizard PCR
13
clean-up system (Promega). Purified 16S rDNA products was then digested, in duplicate, in a 20 µl
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reaction volume with 10 U of either Alu I or Rsa I (Promega) at 37°C for 3 hours. Restriction
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digestions were stopped by incubating at 65°C for 20 minutes, and samples were then kept frozen at
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–20°C until analysis. Standard aliquots (2 µl) of each digest were mixed with appropriate internal
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size standard (GS1000-ROX; Applied Biosystems, Foster City, Calif.) and fragments were analysed
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in an ABI Prism 3100 Genetic Analyzer (Applied Biosystems). For each digestion, two replicates
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were analysed as described. Terminal restriction fragment sizes between 35 and 900 bp were
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determined using Peak Scanner version 1.0 (Applied Biosystems). For interpretation of T-RFLP
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profiles and the identification of each ribotype or OTU within electropherograms, the procedure
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described by Danovaro et al. (2006) and Luna et al. (2006) was adopted, which included binning of
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peaks, elimination of “shoulder” and non-replicated peaks, and cut-off criterion. We then calculated
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bacterial α–diversity (expressed as number of bacterial OTUs), and β–diversity (as percentage % of
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Bray-Curtis dissimilarity on a presence/absence basis) using the SIMPER tool of the PRIMER 6+
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software (Plymouth Marine Laboratory, UK).
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1
Statistical analyses
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Differences between different sediment layers within each habitat were assessed for the investigated
3
environmental, trophic and microbiological variables (total prokaryotic abundance, total viral
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abundance, prokaryotic carbon production and bacterial α-diversity) using a one-way analysis of
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variance (ANOVA). When significant differences (P<0.05) were observed, a post-hoc Student–
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Newman–Kuels’ test (SNK) was also performed. ANOVA was carried out using the GMAV
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software (University of Sidney). To assess the presence of statistical differences between the two
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habitats (non vegetated sediments from the Manfredonia Gulf and vegetated sediments from the
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Sounion Bay), we used the analysis of similarity (ANOSIM) tool. The analysis was performed on a
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dataset composed of all the environmental (temperature, redox potential, water content) and trophic
11
variables (chlorophyll–a, phaeopigments, total organic matter, biopolymeric carbon and protein to
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carbohydrate ratio), based on a Bray–Curtis similarity matrix.
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Data of bacterial community composition obtained from the 16S rDNA T–RFLP analyses
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were analysed using multivariate statistics tools, to test the hypothesis that statistical differences
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exist in bacterial community composition between sediment layers and habitats (non vegetated vs.
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vegetated sediments). Bacterial community composition data were ordinated by multidimensional
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scaling analysis (MDS), based on a Bray-Curtis similarity matrix on a presence/absence basis. The
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Bray-Curtis similarity (%) coefficient allows to assess the degree of similarity between T-RFLP
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profiles obtained from different samples, thus allowing the comparison of bacterial assemblage
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composition between layers and habitat type. To do this, a similarity matrix, containing all possible
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pairwise comparisons, was generated and used to produce a MDS plot, to visually represent the
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Bray Curtis similarity between samples. PERMANOVA, ANOSIM and MDS analyses were
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performed using the PRIMER 6+ software (Plymouth Marine Laboratory, UK).
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In order to determine the extent to which the environmental and biotic variables investigated
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(temperature, redox potential, pH [only in non vegetated sediments], water content, chlorophyll-a
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and phaeopigments, total organic matter, biopolymeric carbon, the protein to carbohydrate ratio and
6
1
viral abundance) explained the patterns in prokaryotic abundance, heterotrophic production and α-
2
and β-bacterial diversity, a non-parametric multivariate multiple regression analysis was carried out,
3
based on Bray-Curtis dissimilarities, using the routine DISTLM forward (McArdle and Anderson,
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2001). The forward selection of the predictor variables was carried out with tests by permutation. P
5
values were obtained using 4,999 permutations of raw data for the marginal tests (tests of individual
6
variables), while for all of the conditional tests, the routine used 4,999 permutations of residuals
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under a reduced model. For abundance, production and α-diversity, analyses were performed using
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the software DISTLM forward (http://www.stat.auckland.ac.nz/~mja/). For β-diversity, analyses
9
were carried out using the DISTLM function provided in the PERMANOVA+ package of the
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PRIMER 6+ software. For β-diversity analyses, pH and redox potential were not included due to
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lack of data in some sediment layers.
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13
References
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1. Apostolaki, E.T., Tsagaraki, T., Tsapakis, M., Karakassis, I. (2007) Fish farming impact on
15
sediments and macrofauna associated with seagrass meadows in the Mediterranean. Est
16
Coast Shelf Sci 75: 408-416.
17
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2. Danovaro, R., Dell’Anno A., Trucco, A., Serresi M., and Vanucci S. (2001) Determination
of virus abundance in marine sediments. Appl Environ Microbiol 67: 1384–1387.
19
3. Dell'Anno, A. Mei, M.L, Pusceddu, A., and Danovaro, R. (2002) Assessing the trophic state
20
and eutrophication of coastal marine systems: a new approach based on the biochemical
21
composition of sediment organic matter. Mar Pollut Bull 44: 611-622.
22
4. Danovaro, R., Luna, G.M., Dell’Anno, A., and Pietrangeli, B. (2006) Comparison of two
23
fingerprinting techniques, Terminal Restriction Fragment Length Polymorphism and
24
Automated Ribosomal Intergenic Spacer Analysis, for determination of bacterial diversity in
25
aquatic environments. Appl Environ Microb 72: 5982–5989.
7
1
5. Díaz-Almela, E., Marbà, N., Álvarez, E., Santiago, R., Holmer, M., Grau, A., Mirto, S.,
2
Danovaro, R., Petrou, A., Argyrou, M., Karakassis, I., Duarte, C.M. (2008) Benthic input
3
rates predict seagrass (Posidonia oceanica) fish farm-induced decline. Mar Pollut Bull 56:
4
1332–1342.
5
6. Luna, G.M., Manini, E., and Danovaro, R. (2002) Large fraction of dead and inactive
6
bacteria in coastal marine sediments: comparison of protocols for determination and
7
ecological significance. Appl Environ Microbiol 68: 3509–3513.
8
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7. Luna, G.M., Dell’Anno, A., and Danovaro, R. (2006) DNA extraction procedure: a critical
issue for bacterial diversity assessment in marine sediments. Environ Microbiol 8: 308–320.
10
8. Pusceddu, A., Sarà. G., Armeni, M., Fabiano, M., and Mazzola, A. (1999) Seasonal and
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spatial changes in the sediment organic matter of a semi-enclosed marine system (W-
12
Mediterranean Sea). Hydrobiol 397: 59–70.
13
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9. McArdle, B.H., and Anderson, M.J. (2001) Fitting multivariate models to community data: a
comment on distance-based redundancy analysis. Ecology 82: 290–297.
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10. Pusceddu, A., Dell’Anno, A., Fabiano, M., and Danovaro, R. (2009) Quantity and
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bioavailability of sediment organic matter as signatures of benthic trophic status. Mar Ecol
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Prog Ser 375: 41–52.
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Virus to Prokaryote Abundance Ratio
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0.0
4
1.0
2.0
3.0
0.0
5
0-1
0-1
6
1-2
2-3
3-4
4-5
5-10
1-2
2-3
3-4
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8
9
10
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12
13
14
15
20-30
30-40
4-5
5-10
10-15
15-20
20-30
30-40
40-50
50-60
60-70
40-50
50-60
60-70
70-100
70-100
10-15
15-20
1.0
2.0
3.0
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18
19
20
21
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Figure S1. Vertical patterns of the virus-to-prokaryote abundance ratio (VPR) along the vertical
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profiles in the two investigated habitats. A = non vegetated sediments; B = vegetated sediments.
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27
28
29
30
31
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Base Pairs
1
Non vegetated 0-1 cm
2
3
4
5
6
Vegetated 0-1 cm
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8
9
10
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Non vegetated 2-3 cm
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13
14
15
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Vegetated 2-3 cm
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18
19
20
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Non vegetated 60-70 cm
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25
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Vegetated 60-70 cm
27
28
29
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Figure S2. T-RFLP electropherograms in non vegetated and vegetated sediments in different
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sediment horizons and habitats. Shown are the results obtained using the enzyme Rsa I.
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1
Table S1. Environmental and biotic drivers of prokaryotic abundance in the sediments from the two
2
habitats, based on DISTLM analysis outputs. Data are from the forward selection procedure with
3
the conditional tests (i.e. fitting each variable one at a time, conditional on the variables that are
4
already included in the model). SS: Sum of square. p-F: pseudo-F. Prop %: percentage of variance
5
explained by each variable. The cumulative percentage of variance is also reported. P value is
6
expressed as * (statistical significance at p<0.05 in the marginal test), ** (p<0.01) and ***
7
(p<0.001). n.s.: not significant. pH data are not available for vegetated sediments.
8
Non vegetated
sediments
Variable
SS
p-F
P
prop %
cumulative %
Temperature
3573.14
7.487
***
5.97
5.97
Redox Potential
9340.95
16.09
***
15.61
21.58
pH
294.43
1.292
n.s.
-
-
Water Content
1663.31
6.554
**
2.78
24.36
Chlorophyll-a
2288.51
7.480
***
3.83
28.19
Phaeopigments
33070.04 38.32
***
55.28
83.47
OM content
880.41
3.850
*
1.47
84.94
Biopolymeric Carbon
181.81
0.788
n.s.
-
-
Protein:Carbohydrate
3290.29
8.733
***
5.5
90.44
601.99
1.828
n.s.
-
-
Redox Potential
496.51
1.537
n.s.
-
-
Water Content
5199.51
13.03
***
10.86
10.86
Chlorophyll-a
125.83
0.395
n.s.
-
-
Phaeopigments
800.20
2.626
n.s.
-
-
OM content
30729.60 55.50
***
64.16
75.02
Biopolymeric Carbon
2143.17
6.328
***
4.47
79.49
Protein:Carbohydrate
157.39
0.507
n.s.
-
-
Vegetated sediments Temperature
9
10
11
12
13
14
15
16
11
1
Table S2. Environmental and biotic drivers of prokaryotic heterotrophic production in the
2
sediments from the two habitats, based on DISTLM analysis outputs. Data are from the forward
3
selection procedure with the conditional tests (i.e. fitting each variable one at a time, conditional on
4
the variables that are already included in the model). SS: Sum of square. p-F: pseudo-F. Prop %:
5
percentage of variance explained by each variable. The cumulative percentage of variance is also
6
reported. P value is expressed as * (statistical significance at p<0.05 in the marginal test), **
7
(p<0.01) and *** (p<0.001). n.s.: not significant. pH data are not available for vegetated sediments.
8
9
Non vegetated
sediments
Variable
SS
Temperature
P
prop %
cumulative %
33692.88 38.45
***
55.36
55.36
Redox Potential
12448.94 25.38
***
20.46
75.82
pH
1407.83
4.593
*
2.31
78.13
Water Content
564.80
2.033
n.s.
-
-
Chlorophyll-a
767.50
2.658
n.s.
-
-
Phaeopigments
1560.78
4.513
*
2.56
80.69
OM content
509.92
1.902
n.s.
-
-
Biopolymeric Carbon
548.53
2.144
n.s.
-
-
Protein:Carbohydrate
3472.28
8.955
***
5.71
86.40
1404.87
13.81
***
7.47
7.47
Redox Potential
40.19
0.480
n.s.
-
-
Water Content
1470.03
10.13
***
7.82
15.29
Chlorophyll-a
77.67
0.959
n.s.
-
-
Phaeopigments
512.91
5.893
*
2.73
18.02
OM content
12973.9
69.05
***
69.02
87.04
Biopolymeric Carbon
54.84
0.669
n.s.
-
-
Protein:Carbohydrate
254.38
3.147
n.s.
-
-
Vegetated sediments Temperature
p-F
10
11
12
13
14
15
16
17
12
1
Table S3. Environmental and biotic drivers of α-diversity patterns in the sediments from the two
2
habitats, based on DISTLM analysis outputs. Data are from the forward selection procedure with
3
the conditional tests (i.e. fitting each variable one at a time, conditional on the variables that are
4
already included in the model). SS: Sum of square. p-F: pseudo-F. Prop %: percentage of variance
5
explained by each variable. The cumulative percentage of variance is also reported. P value is
6
expressed as * (statistical significance at p<0.05 in the marginal test), ** (p<0.01) and ***
7
(p<0.001). n.s.: not significant. pH data are not available for vegetated sediments.
8
9
Non vegetated
sediments
Variable
SS
p-F
P
prop %
cumulative %
Temperature
676.23
2.791
n.s.
-
-
Redox Potential
1117.46
5.357
*
3.94
3.94
pH
1174.58
15.55
***
4.14
8.08
Water Content
1873.28
13.19
***
6.61
14.69
Chlorophyll-a
312.46
4.827
*
1.10
15.79
Phaeopigments
15994.56 40.13
***
56.42
72.21
TOM
2978.58
9.528
***
10.51
82.72
Biopolymeric Carbon
1128.28
3.966
*
3.98
86.70
Protein:Carbohydrate
638.88
5.267
*
2.25
88.95
Viral abundance
1033.20
4.008
*
3.64
92.59
206.64
3.687
*
2.61
2.61
Redox Potential
107.08
2.597
n.s.
-
-
Water Content
22.961
0.567
*
2.75
5.36
Chlorophyll-a
137.01
2.207
n.s.
-
-
Phaeopigments
74.69
1.884
n.s.
-
-
TOM
2620.76
13.82
***
33.05
38.41
Biopolymeric Carbon
859.13
13.22
***
10.83
49.24
Protein:Carbohydrate
2761.38
29.26
***
34.82
84.06
Viral abundance
330.76
7.502
***
4.17
88.23
Vegetated sediments Temperature
10
11
12
13
14
15
13
1
Table S4. Environmental and biotic drivers of β-diversity patterns in the sediments from the two
2
habitats, based on DISTLM analysis outputs. Data are from the forward selection procedure with
3
the conditional tests (i.e. fitting each variable one at a time, conditional on the variables that are
4
already included in the model). SS: Sum of square. p-F: pseudo-F. Prop %: percentage of variance
5
explained by each variable. The cumulative percentage of variance is also reported. P value is
6
expressed as * (statistical significance at p<0.05 in the marginal test), ** (p<0.01) and ***
7
(p<0.001). n.s.: not significant.
8
9
Non vegetated
sediments
Variable
SS
p-F
P
prop %
cumulative %
Temperature
2070.3
0.807
n.s.
-
-
Water Content
2221.7
0.895
n.s.
-
-
Chlorophyll-a
1369.0
0.431
n.s.
-
-
Phaeopigments
2864.5
0.861 n.s.
-
-
TOM
3408.4
1.402
n.s.
-
-
Biopolymeric Carbon
1932.9
0.710
n.s.
-
-
Protein:Carbohydrate
2331.5
0.954
n.s.
-
-
Viral abundance
5031.9
1.982
**
18.04
18.04
6010.1
1.731
**
17.79
17.79
Water Content
5601.7
1.589
**
16.58
34.37
Chlorophyll-a
3605.1
0.955
n.s.
-
-
Phaeopigments
5064.6
1.411
n.s.
-
-
TOM
5914.9
1.698
*
17.51
51.88
Biopolymeric Carbon
4501.1
1.229
n.s.
-
-
Protein:Carbohydrate
4534.4
1.239
n.s.
-
-
Viral abundance
4003.7
1.075
n.s.
-
-
Vegetated sediments Temperature
10
11
12
13
14
15
16
14