www.sciencemag.org/content/355/6326/731/suppl/DC1

Supplementary Materials for Seagrass ecosystems reduce exposure to bacterial pathogens of humans, fishes,

and invertebrates

Joleah B. Lamb,* Jeroen A. J. M. van de Water, David G. Bourne, Craig Altier, Margaux Y. Hein, Evan A. Fiorenza, Nur Abu, Jamaluddin Jompa, C. Drew Harvell

*Corresponding author. Email: joleah.lamb@cornell.edu

Published 17 February 2017, Science 355, 731 (2017)

DOI: 10.1126/science.aal1956

This PDF file includes:

Materials and Methods Figs. S1 to S4 Tables S1 to S10 References

Materials and Methods

Study location and sampling sites All sampling took place in the Spermonde Archipelago, located along the western coast of Sulawesi in central Indonesia (fig. S1). The archipelago consists of 160 fringing, barrier and patch reefs adjacent to the city of Makassar. Approximately 54 coral atolls are inhabited in the archipelago, including all that have an autochthonous water source (9). The freshwater lenses of these islands are often no more than 10–20 cm thick due to their low height and small size (23). The surface soils are extremely thin and have a low water retention quality, therefore the nearby marine environment is particularly susceptible to pollution from solid waste, sewage and wastewater (9, 10). Maximum tidal amplitude in the region is 1.8 m, with an average of 0.8 m (24). Based on distinct marine plant and stony coral distributions across the archipelago , we identified four inhabited islands situated on the eastern edge of the outer shelf with similar intertidal to shallow subtidal reef flats inhabited by mixed species seagrass meadows and rimmed by living coral (fig. S1). Human population densities on the islands have even distributions and range between 96 and 325 people hectare-1 (9). Seagrass removal and natural processes on biogenic sand cays have lead the paucity of sediment accumulation processes that result in unsuitable stable substrates for seagrass recolonization (24), therefore each of the selected islands had paired sites with seagrass meadows present or absent on the intertidal flat (fig. S1). Each set of paired sites consisted of an approximate shoreline width of 40 m with replicate sampling locations in the intertidal flats and reefs equidistant from shore (table S1). To ensure similar characteristics between paired sites, we quantified abiotic seawater parameters, including temperature, dissolved oxygen, pH, salinity, total dissolved solids, chlorophyll-a, and turbidity at two locations in triplicate approximately 10 cm below the water surface using a multiparameter sonde (Exo2, Xylem Inc., New York, USA). Seagrass meadows have been shown to slow water flow velocity (25) and alter seawater chemistry (5), including dissolved oxygen, turbidity, and pH. Productivity in seagrass meadows often peak around the time of highest levels of photosynthetically available radiation (PAR) (26). We measured PAR using a quantum meter (Logan MQ-200, Apogee Instruments) and collected samples during approximately the same time of day during (11am – 2pm, PAR range = 1500 – 2100 µmol m-2 s-1, table S1), which coincided with the lowest tidal fluctuations and water motion. Water flow velocity was measured using a Flow Probe (FP311, Global Water). Flow velocity was reduced by 45% within meadows (mean speed inside seagrass meadows = 4.14 m min-1 versus 7.35 m m-1 in paired control without seagrass, n = 6 each), which is consistent with other studies (25). Seagrass cover and composition was estimated using three, 1 m2 replicate quadrats at three locations separated by approximately 20 m at each island. Seagrass assemblages were similar among the four islands and composed of 6 species with cover ranging between 80 – 93% (fig. S2). This region experiences the highest total rainfall and the highest precipitation in Southeast Asia (27) and a monsoonal climate. The archipelago experiences an annual wet season from December to March and a dry season from June to September (28). We conducted our study prior to the wet season between 2 and 27 November 2014.

2

Fig. S1. Study islands located in Sulawesi, Indonesia. (A) Satellite (Spot 5) image of the central section of the Spermonde Archipelago, showing the barrier reef along the westernmost edge of the shelf (Br) and submerged reefs (Sr) in the lagoon. Several of the reefs in the barrier complex and many of the platform reefs within the lagoon have intertidal reef flats (Rf) that are vegetated by seagrass ecosystems and contain a coral cay (Cc) with terrestrial vegetation (red). The positions of 4 islands used in this study are indicated by name with other islands evaluated and deemed inappropriate for the study numbered in yellow. Composite image was created for (24) by Irendra Radjawli and used with permission. (B) Aerial image of a single study island depicting sampling locations with seagrass meadows present (Sg) and sandy substrates with seagrass meadows absent (Sd). (C) A young boy bathes in an expansive mixed species seagrass meadow adjacent to a study site.

3

Tab

le S

1. S

eaw

ater

col

lect

ion

loca

tions

and

env

ironm

enta

l par

amet

er m

eans

(n =

6 e

ach)

.

Is

land

Lo

catio

n Sh

ore

dist

ance

(m

)

Side

of

isla

nd

Latit

ude

Long

itude

PA

R

(µm

ol m

-2 s-1

) Te

mp.

(ºC

) D

O

(mg

L-1)

TDS

(mg

L-1)

Salin

ity

(ppt

) pH

Fl

ow

velo

city

(m

min

-1)

Turb

idity

(F

NU

) C

hl

(µg

L-1)

Seag

rass

Abs

ent

Cad

di

Shor

e 0

E 5°

4'5

4.89

"S

119°

19'1

5.29

"E

1924

.7

31.5

7.

2 32

846.

7 32

.9

8.3

2.5

16

.41

8.1

C

addi

Fl

at

14

E 5°

4'5

5.36

"S

119°

19'1

5.62

"E

1910

.5

31.3

6.

9 32

825.

0 32

.9

8.2

5.8

13.8

3 6.

9

Cad

di

Ree

f 78

E

5° 4

'56.

43"S

11

9°19

'17.

50"E

19

25.9

31

.0

8.1

3277

0.8

32.9

8.

2 3.

4 4.

88

2.1

Lo

mpo

Sh

ore

0 SE

5°

3'0

3.68

"S

119°

19'4

9.49

"E

1904

.9

32.2

7.

7 32

640.

8 32

.7

8.3

2.3

10.4

4 7.

1

Lom

po

Flat

22

SE

5°

3'0

4.35

"S

119°

19'5

0.03

"E

1855

.6

31.6

6.

0 32

375.

0 32

.4

8.2

8.3

9.87

5.

2

Lom

po

Ree

f 20

1 SE

5°

3'0

7.96

"S

119°

19'5

4.51

"E

1573

.5

31.0

6.

2 32

565.

0 32

.7

8.2

8.0

4.04

2.

1

Bon

etam

bung

Sh

ore

0 N

W

5° 2

'10.

22"S

11

9°16

'36.

61"E

19

04.9

32

.2

7.7

3264

0.8

32.7

8.

3 2.

3 7.

79

6.4

B

onet

ambu

ng

Flat

27

N

W

5° 2

'09.

85"S

11

9°16

'35.

75"E

18

55.6

31

.6

6.0

3237

5.0

32.4

8.

2 8.

3 4.

22

4.5

B

onet

ambu

ng

Ree

f 13

7 N

W

5° 2

'07.

93"S

11

9°16

'32.

83"E

14

73.5

31

.0

6.2

3256

5.0

32.7

8.

2 8.

0 3.

56

2.2

K

ondi

ngar

eng

Shor

e 0

SE

5° 9

'00.

73"S

11

9°15

'56.

36"E

19

23.5

32

.1

8.1

3292

2.5

32.7

8.

3 5.

7 6.

68

3.2

K

ondi

ngar

eng

Flat

57

SE

5°

9'0

2.38

"S

119°

15'5

7.80

"E

1963

.6

31.7

8.

3 32

955.

0 33

.1

8.3

7.0

2.98

1.

8

Kon

ding

aren

g R

eef

170

SE

5° 9

'03.

81"S

11

9°16

'00.

62"E

20

34.6

31

.2

9.0

3293

3.3

33.0

8.

3 5.

6 3.

36

1.8

Seag

rass

Pr

esen

t

Cad

di

Shor

e 0

W

5° 4

'51.

95"S

11

9°19

'10.

17"E

19

37.7

32

.6

10.5

32

814.

2 32

.8

8.5

1.5

10.0

3 4.

4

Cad

di

Flat

14

W

5°

4'5

1.84

"S

119°

19'0

9.74

"E

1868

.5

32.4

8.

9 32

825.

0 32

.9

8.4

0.4

9.19

3.4

C

addi

R

eef

82

W

5° 4

'51.

19"S

11

9°19

'07.

61"E

18

38.9

31

.2

6.5

3284

6.7

32.9

8.

2 4.

0 1.

72

1.4

Lo

mpo

Sh

ore

0 SW

5°

3'0

3.47

"S

119°

19'3

9.13

"E

1916

.7

31.8

7.

1 32

835.

8 32

.9

8.3

0.8

8.58

2.

4

Lom

po

Flat

25

SW

5°

3'0

3.45

"S

119°

19'3

7.97

"E

1613

.0

31.5

8.

4 32

922.

5 33

.0

8.3

5.7

8.60

1.

4

Lom

po

Ree

f 19

4 SW

5°

3'0

5.09

"S

119°

19'3

2.73

"E

1592

.0

31.7

7.

2 32

922.

5 33

.0

8.2

7.0

1.26

1.

3

Bon

etam

bung

Sh

ore

0 N

E 5°

2'1

0.19

"S

119°

16'4

2.27

"E

1993

.2

31.8

9.

3 32

825.

0 32

.9

8.4

1.5

9.82

1.

7

Bon

etam

bung

Fl

at

26

NE

5° 2

'09.

59"S

11

9°16

'43.

01"E

19

51.2

31

.4

8.1

3286

8.3

33.0

8.

3 6.

5 10

.43

1.1

B

onet

ambu

ng

Ree

f 13

7 N

E 5°

2'0

7.61

"S

119°

16'4

5.98

"E

1958

.6

31.1

8.

3 32

792.

5 32

.9

8.2

4.9

0.75

1.

0

Kon

ding

aren

g Sh

ore

0 SW

5°

9'0

0.06

"S

119°

15'4

9.39

"E

1654

.3

32.9

8.

8 32

987.

5 33

.1

8.4

0.9

7.75

2.2

K

ondi

ngar

eng

Flat

49

SW

5°

8'5

9.86

"S

119°

15'4

7.83

"E

1780

.9

32.8

10

.2

3303

0.8

33.1

8.

5 4.

0 7.

64

1.4

K

ondi

ngar

eng

Ree

f 17

7 SW

5°

8'5

9.71

"S

119°

15'4

3.23

"E

1575

.3

31.1

7.

1 32

955.

0 33

.1

8.3

6.6

0.62

1.

0 A

bbre

viat

ions

: PA

R –

Pho

tosy

nthe

tical

ly a

ctiv

e ra

diat

ion

DO

– D

isso

lved

oxy

gen

TDS

– To

tal d

isso

lved

solid

s C

hl –

Chl

orop

hyll

4

Fig. S2. Seagrass cover (mean ±SE) sand composition at each island. Island abbreviations: Barrang Caddi (BC), Bonetambung (B), Barrang Lompo (BL) and Kodingareng Lompo (K). Other species consist of Halophilia ovalis, Halodule uninervis, and Syringodium isoetifoloium. n = 9 each. Enterococcus assays Enterococci are currently the only enteric indicator bacteria recommended by the U.S. Environmental Protection Agency for brackish and marine waters, since they correlate better with human health outcomes than other enteric indicator bacteria, such as fecal coliforms or Escherichia coli (11). Enterococci may also attach to aquatic vegetation and detritus (11), therefore we sampled carefully from surface waters in a small boat. Replicate samples of seawater were collected in 100 ml sterile bottles and immediately transported on ice to Hasanuddin University and processed using a standard method for determining intestinal enterococci (International Organization for Standardization method 7899-2 2000, Geneva, Switzerland [(29)]). Briefly, each sample was filtered using a sterile aseptic filter system (Millepore Sterifil, EMD Millepore, MA, USA) onto a 0.45 µm hydrophilic mixed cellulose ester GN-6 Metricel® gridded and sterile 47 mm membrane filter (Pall Life Sciences, MI, USA; lot no. T32678). Filters were placed on sterile Slanetz and Bartley selective medium (Oxoid, Basingstoke, UK) inside 50 mm sterile petri dishes (Pall Life Sciences) and covered. Plates were immediately incubated at 37.0 ± 1.0 °C for 4.0 ± 0.5 hours followed by 44.0 ± 0.5 °C for 40 ± 4 hours and then counted. DNA isolation from seawater Replicate 2-liter seawater samples were collected from surface waters and filtered through 0.22 µm polyethersulfone sterivex cartridges (EMD Millepore, Billerica, MA, USA) using sterile 50 ml polypropelene syringes (Terumo). Excess seawater was forced from filters and filters were subsequently filled with RNAlater stabilization solution (Ambion, Life Technologies, Carlsbad, CA, USA). Samples werestored in the dark on ice (4°C) in sterile plastic bags until being processed at the laboratory at the Australian Institute for Marine Science for DNA isolation (8 days later). Cartridges were capped after collection to prevent contamination and sterile nitrile gloves were worn during collection to reduce contamination.

Enhalus acoroidesThalassia hemprichiiCymodocea rotundataOther

Mea

n se

agra

ss c

over

(%)

100

80

60

40

20

0BC B BL K

Species

5

DNA from seawater cartridges was isolated following a modified phenol:chloroform extraction method. In short, 1.8 ml lysis buffer (50 mM Tris-HCl (pH 8.0), 40 mM EDTA (pH 8.0) and 0.75 M sucrose in ultrapure MilliQ water; 0.22 µm filter-sterilised) and 18 µl of lysozyme (100 mg/ml) was added to the Sterivex filter and incubated for 1 hr at 37°C. Proteinase K was added to the lysate at final concentration of 0.2 mg/ml. After 1 hr incubation at 37°C, the lysate was recovered and divided over 2 sterile 1.5 ml tubes. Equal volumes of phenol:choloroform:isoamyl alcohol (25:24:1) were added to the lysate, mixed and centrifuged for 10 min at 16,000 xg. The aqueous phase was recovered, and equal volumes of chloroform/isoamyl alcohol (24:1) were added. The extract was mixed and centrifuged for 10 min at 16,000 xg. The aqueous phase was collected and DNA was precipitated by adding 1000 µl of isopropanol (incubate for 15 min at room temperature) followed by centrifugation for 30 min at 2,000 xg at 4°C. The supernatant was discarded and the pellet was washed with 500 µl 70% EtOH and centrifuged for 10 min at 20.000 xg. The supernatant was removed and the pellet was air-dried for 30 min. The DNA pellet was resuspended in 30 µl of Tris-HCl (pH 8.0) overnight at 4°C.

16S rRNA gene amplicon pyrosequencing and bioinformatics Extracted DNA was shipped to MR DNA (Molecular Research LP: Shallowater, Texas, USA) for 16S rRNA gene amplicon library preparation and sequencing. Bacterial 16S rRNA amplicons were generated using the 28F/519R primer set, and libraries were sequenced on an Illumina MiSeq. Sequence .fasta and quality files were obtained from MR DNA and processed using the QIIME pipeline (30). The split_libraries.py script was used to remove poor quality (<25) sequences, reads of <200 bp or >550 bp in length, primers and barcodes and to add a sample identification to each sequence. Using UCHIME (31), chimeric sequences were identified against the chimera-free GreenGenes 16S rRNA gene sequence database and removed. Using the quality-filtered .fasta file, sequences of 97% similarity were clustered using UCLUST (Edgar 2010), with each cluster representing an operational taxonomic unit (OTU). The most abundant sequence per cluster was selected as representative sequence and Green Genes taxonomy (database gg_13_5) was assigned using BLAST (75% similarity). OTU tables were generated and reads classified as chloroplasts, mitochondria or Unassigned as well as singletons were removed. Filtered OTU tables were subsampled to 46,148 reads per sample and analysed for the relative abundance of OTUs and bacterial taxa. Quantification of total bacteria Quantitative real time PCR (qPCR) was performed using a StepOnePlusTM Real-Time PCR System (Applied Biosystems, California, USA) by adding 1µl of sample volume to 10µl of TaqMan® Universal PCR Master Mix (Life Technologies, catalog no. 4304437) prepared according to the manufacturer’s specifications. Standards were prepared using 10-fold serial dilutions of known bacteria quantity to extinction (fig. S3). Relative quantity of total bacteria was generated by deltaCT comparison to the standard curve (table S6). Coral health and disease surveys At each site, we surveyed coral health on three 15 x 2 m belt transects along reef contours at 3-4 m in depth and approximately 20 m apart using globally standardised protocols (32). Within each 30 m2

belt transect, we identified each coral colony over 5 cm in diameter to genus and further classified each coral as either healthy (no disease observed) or affected by one or more of six common Indo-Pacific coral diseases or seven indicators of compromised coral health according to (33). Coral disease prevalence was calculated as the number of affected colonies divided by the total colonies on each transect.

6

Statistical analyses Differences in levels of Enterococcus were analyzed using a generalized linear mixed-effects model with a negative binomial error distribution and log link function glm.nb and glmer.nb functions of the MASS package. Seagrass status (presence vs absence) and location (shore, flat and reef) were treated as fixed factors and island as a random factor to maintain consistency with the study design. Location was treated as a continuous variable to account for natural dilution of pathogens from the island point source. Model selection was based on Akaike information criterion (34) (table S2). Pairwise comparisons were tested using the lsmeans function in lsmeans package (35) with a Bonferroni correction. Differences in the relative abundance of total pathogens were analyzed using a general linear model with a negative binomial error distribution and log link function glm.nb and glmer.nb functions of the MASS package. Seagrass status and location were treated as fixed factors. Location was treated as a continuous variable to account for natural dilution of pathogens from the island point source. Model selection was based on Akaike information criterion (34) (table S7). Pairwise comparisons were tested using the lsmeans function in lsmeans package (35) with a Bonferroni correction. Differences in prevalence levels of coral disease were analysed using generalized linear mixed-effects models with a binomial error distribution and logit link (glmer function of the lme4 package) (36). Seagrass status was treated as a fixed effect, while transect was nested into island and treated as a random effect. We compared each model to the null model using a likelihood ratio test. Heatmaps were generated using the ggplot2 package (37) and the heatmap.2 function in the gplots package with dendrogram clustering set at default (38). Relative abundance of pathogenic bacteria was calculated in each replicate sample as a proportion of the total bacteria for the corresponding sample. Replicates were standardized for each genus as percentiles of relative abundance. Univariate analyses were performed using R v. 3.3.1 (39). Differences in overall pathogen assemblages between locations with seagrass present and absent were investigated using a fixed factor permutational multivariate analysis of variance (40). The analysis was based on a zero-adjusted Bray-Curtis similarity matrix with a type III partial sum of squares and 999 random permutations of the residuals under the reduced model. To identify pathogens contributing most to the patterns in multivariate space, we used a principal coordinates ordination analysis performed on a Euclidean distance matrix on square root transformed data due to strong linear pairs of variables (40). We calculated Pearson correlations of the ordination axes with the data, and strong correlations (defined as ≥ 0.6) were then overlaid as vectors on a bi-plot. Hierarchical clusters were overlaid from dendrograms based on a Bray-Curtis similarity matrix. Multivariate analyses and modeling were performed using Primer v. 6 (41).

7

Table S2. Generalized linear mixed-effects model selection used to assess the effects of seagrass ecosystems (presence vs. absence) on levels of Enterococcus in seawater.

[1] Full [2] Additive [3] Status [4] Location [5] Island [6] Null

Intercept Estimate 6.625*** 7.249*** 5.550*** 6.721*** 5.592*** 5.614*** Intercept SE 0.202 0.188 0.198 0.144 0.169 0.102 Distance Estimate -1.369*** -1.817*** - -1.663*** NA NA Distance SE 0.135 0.098 NA 0.096 NA NA Status Estimate 0.188 -0.802*** 0.082 NA NA NA Status SE 0.277 0.169 0.207 NA NA NA Location*Status Estimate -0.786*** NA NA NA NA NA Location *Status SE 0.202 NA NA NA NA NA link - - - - - log df 6 5 4 4 3 2 logLik -1993.326 -2001.948 -2124.404 -2012.78 -2124.482 -2125.888 AICc 3998.879 4014.058 4256.914 4033.669 4255.027 4255.807 delta 0 15.179 258.036 34.789 256.148 256.928 weight 0.999 0.0005 9.29E-57 2.79E-08 2.39E-56 1.62E-56

*** P < 0.001 Model equations: [1]ln(%&'&()*&+,-(./(-01)~45|789:;< + 4>?&@A0-&( + 4BC0A0D1 + 4E?&@A0-&( ∗ C0A0D1 [2] ln(%&'&()*&+,-(./(-01)~45|789:;< + 4>?&@A0-&( + 4BC0A0D1 [3] ln(%&'&()*&+,-(./(-01)~45|789:;< + 4>C0A0D1 [4]ln(%&'&()*&+,-(./(-01)~45|789:;< + 4>?&@A0-&( [5]ln(%&'&()*&+,-(./(-01)~45|789:;< [6] ln(%&'&()*&+,-(./(-01)~45 Table S3. Pairwise comparisons of Enterococcus levels following the generalized linear mixed-effects model among locations with seagrass meadows present and absent. Tests are performed on the log scale with a Bonferroni correction.

Location Estimate SE Z-ratio P-value Shore -0.188 0.277 -0.679 0.4968 Flat 0.598 0.162 3.683 0.0002*** Reef 1.385 0.211 6.547 <0.0001***

8

Table S4. Review of potentially pathogenic bacteria of marine fishes (F), invertebrates (I) mammals (M) that are viable in seawater or cause human (H) illness through the consumption of seafood or skin contact (42).1Genus Species Taxa Disease Reference

1. Aerococcus A. viridans I Red tail disease in lobster (43) 2. Aeromonas A. salmonicida F Hemorrhagic septicemia, Furunculosis,

Erythrodermatitis, ulcer disease (44)

A. jandaei F Eel disease (45) 3. Arcobacter A. cryaerophilus.

Spp.1 H

F Trout degenerated opercula and gills, liver Emerging generalist foodborne pathogens associated with enteric diseases & zoonoses

(45) (46-48)

4. Bacillus Spp. F I Sponge brown lesion necrosis and fish ulcers

(45, 49)

5. Bordetella B. bronchiseptica Spp.

M M

Bronchopneumonia

(44, 50)

6. Brucella B. cetaceae M Brucellosis (44) 7. Chryseobacterium C. balustinum F Flavobacteriosis (45) C. scophthalmum

Spp.

F F

Gill disease; generalized septicemia

(45)

8. Clostridium C. perfringens1 H Foodborne illness (51) C. botulinum1 H Botulinum neurotoxin (51) Spp.1 H M Emerging foodborne pathogens, clostridial

myositis (52)

9. Corynebacterium Spp. I Bilateral exophthalmia (abnormal protrusion of the eye)

(53)

10. Cytophaga Spp. I Ligament erosion (54) 11. Dermatophilus D. congolensis M Streptothricosis (55) 12. Enterococcus

E. seriolicida and Spp. Spp.

H

F Yellowtail and eel, fish farms Common foodborne and nosocomial pathogens, high mortality rates (up to 61%), bacteraemia

(56-58) (59)

13. Erysipelothrix E. rhusiopathiaeis, M I Diamond skin disease (50) 14. Escherichia E. coli1 H Gastrointestinal illness (51) 15. Flavobacterium F. branchiophila F Bacterial gill disease (55) F. columnare F Columnaris disease (55) F. psychrophilum

Spp.

F

Coldwater disease Opportunistic pathogens, tissue infection in numerous global fish species

(55) (60)

16. Francisella F. noatunensis F Francisellosis (55) 17. Halomonas H. cupida

Spp. H

F Aquaculture opportunist Opportunistic human pathogen

(45) (61)

18. Lactocococcus L. garvieae Spp.

F F

Streptococcosis

(45, 51) (62)

19. Leptospira L. interrogans H M Leptospirosis (51) Spp. H M Leptospirosis (55) 20. Listeria L. monocytogenes1 H Listeriosis (51) 21. Micrococcus M. luteus I Micrococcis (63) 22. Morganella M. morganii M Cystitis and enteritis of beluga whales (64) 23. Mortitella M. marina F Skin lesions (45) M. viscosa F Winter ulcer syndrome (45) 24. Mycobacterium Spp. M F Fish tuberculosis, Mycobacteriosis (45, 50, 55) 25. Mycoplasma M. mobile F Red Disease (45) Spp. M I Respiratory disease in marine mammals, (50, 65)

9

crustacean infection of the pancreas and bivalves

26. Norcardia Spp. F I Chronic and persistent infections; Nocardiosis (fishes and giant clams)

(45, 66)

27. Pasteurella Spp. M Multiple stranded marine mammals (50) 28. Phormidium P. corallyticum

Spp. I

I Black band disease of corals Pathogenic cyanobacterium

(67, 68)

29. Photobacterium P. damselae H F Hemorrhagic septicemia, vibriosis (69) P. rosenbergii 30. Pseudoalteromonas Spp. I Opportunistic pathogen in aquaculture (70) 31. Pseudomonas P. anguilliseptica F Red spot disease (45) Spp. H I

Skin and soft tissue infections of humans (bacteraemia), sponge brown lesion necrosis (consortia of Pseudomonas spp.)

(71) (49, 72)

32. Renibacterium R. salmoninarum F Bacterial kidney disease, ocular oedema (45) 33. Rickettsia Spp. F I Hepatopancreas infection (65) 34. Salmonella S. enteritidis1 H Gastrointestinal illness (51) Spp.1 H Gastrointestinal illness (51) 35. Serratia S. liquefaciens F Septicemia (45) S. marcescens I White pox (73) 36. Shewanella S. putrefaciens F Septicemia in rabbitfishes (45) Spp.

H I White plague in corals,

Emerging marine pathogen of humans (74, 75)

37. Shigella S. dysenteriae1 H Dysentery (51) 38. Streptococcus S. iniae F Streptocecosis (45) S. parauberis

Spp. F

F Mariculture diseases of fish

Lesions and signs of streptococosis (76) (77)

39. Tenacibaculum T. maritimus F Gill disease, black patch necrosis (45) T. ovolyticus F Larval and egg mortalities (45) 40. Thalassomonas T. loyana I White plague (78) 41. Vibrio V. alginolyticus I Eye disease (fishes), epidermal vibriosis

and skin ulceration disease (echinoderms) (43, 45, 51,

79) V. cholerae1 H F Dysentery (humans), septicemia (fishes) (45) V. vulnificus1 H F Septicemia (fishes) (45, 51) V. anguillarum F I Vibriosis (fishes), infected epithelium

(echinoderms) (45, 65)

V. harveyi F I Eye disease and blindness (fishes), Body wall lesions & skin ulceration disease (echinoderms) systemic (crustaceans), white plague (corals)

(43, 45, 65, 79, 80)

V. ichthyoenteri F Intestinal necrosis (fishes) (45) V. logei F Skin lesions (45) V. ordalii F Vibriosis (45) V. pelagius F Egg mortality (45) V. splendidus F Septicemia (45) V. salmonicida F Hitra disease (45) V. coralliityticus I Bacterial bleaching (81) V. charcharvina I White band II (82) V. shiloi I Bacterial bleaching (83) V. clyclitrophicus I Skin ulceration disease (79) V. coryllyticus I White syndromes (84) V. gigantis I Skin ulceration disease (79) V. natriengens I Skin ulceration disease (85) V. nigripulchritudo I Body wall lesions (86) V. parahaemolyticus1 H I Gastrointestinal illness in humans (87)

10

V. shilonii I Bacterial bleaching (88) V. splendidus I Skin ulceration disease (65) V.tapetis I Brown ring disease (65) Spp. I Bald sea urchin disease, spotting disease,

skin ulceration disease (79)

I Conchiolin infection (65) I General infections of crustaceans (43, 89) 42. Yersinia

Y. enterocolitica1

Y. ruckeri H

F

Enterocolitis Enteric redmouth disease of salmonids & trout

(42) (90)

Table S5. Potentially pathogenic bacterial genera sequenced in seawater samples (see Table S4 for detailed list). Asterisk indicates genera excluded from final analysis due to absence in all replicate seawater samples collected from the intertidal flat and reef.

Detected Not detected Aerococcus* Aeromonas Arcobacter Bordetella Bacillus Brucella Chryseobacterium Dermatophilus Clostridium+ Erysipelothrix Corynebacterium Escherichia+ Cytophaga* Francisella Enterococcus Listeria+ Flavobacterium Mortitella Halomonas* Norcardia Lactococcus Pasteurella Leptospira* Renibacterium Micrococcus* Salmonella+ Morganella* Shigella+ Mycobacterium* Yersinia Mycoplasma* Phormidium

Photobacterium Pseudoalteromonas Pseudomonas Rickettsia Serratia* Shewanella

Streptococcus Tenacibaculum Thalassomonas Vibrio+

+ infection of humans from seafood consumption

11

Fig. S3. Standard distribution curve used to generate the relative quantity of total bacteria in Table S6.

R = 0.998

0

5

10

15

20

25

30

35

0 2000 4000 6000 8000 10000

Con

trol M

ean

CT

Control quantity (CFU)

C Mean Quantity19.22 1000019.22 1000019.22 1000022.68 100022.68 100022.68 100026.04 10026.04 10026.04 10029.76 1029.76 1029.76 1032.51 132.51 132.51 1

12

Table S6. Colony forming units (CFU) quantified in each replicate seawater sample using quantitative PCR.

Location Replicate Mean CT

a Predicted CFU µl-1

Concentration µg ul-1

b DNA isolated

µg CFU µl-1 CFU

sample-1

Seagrass Absent Shore 1 23.79 46607.2 225.9 6777 206.31 699108

2

3 23.48 57549.7 219.0 6570 262.78 863246

4 25.48 14620.7 207.0 6210 70.63 219311

5 24.24 34323.9 235.9 7077 145.50 514858

Flat 1 24.42 30367.7 246.2 7386 123.35 455515

2 24.34 32050.4 277.2 8316 115.62 480757

3 25.46 14885.6 307.1 9213 48.47 223283

4

5 24.79 23521.1 297.2 8916 79.14 352816 Reef 1 24.67 25527.2 244.2 7326 104.53 382907

2 24.84 22811.0 251.8 7554 90.59 342164

3 24.55 27771.4 255.7 7671 108.61 416571

4 24.49 28942.7 252.1 7563 114.81 434141

5 24.90 21841.8 257.1 7713 84.95 327627

Seagrass Present Shore 1 25.41 15389.9 228.1 6843 67.47 230848

2 26.05 9924.9 307.6 9228 32.27 148873

3 26.76 6104.7 282.8 8484 21.59 91569

4 26.55 7039.5 235.1 7053 29.94 105591

5 23.74 48155.1 65.8 1974 731.84 722325

Flat 1 23.81 46075.5 187.6 5628 245.61 691132

2 24.05 39011.4 206.6 6198 188.83 585171

3 24.02 40015.4 155.9 4677 256.67 600230

4 24.11 37460.2 164.4 4932 227.86 561902

5 23.98 40910.7 154.2 4626 265.31 613659

Reef 1 23.33 64172.2 283.5 8505 226.36 962582

2

3 23.83 45343.0 319.8 9594 141.78 680145

4 24.06 38882.6 286.9 8607 135.52 583238

5 24.07 38583.4 250.5 7515 154.03 578750

Notes: a 1 µg of sample used in each qPCR reaction b µg DNA isolated from 2L seawater with pellet dissolved in 30µl

13

Table S7. Generalized linear model selection used to assess the effects of seagrass ecosystems (presence vs. absence) on the relative abundance of total potentially pathogenic bacteria in seawater.

[1] Full [2] Additive [3] Status [4] Location [5] Null

Intercept Estimate -5.1875*** -4.6531*** -5.4295*** -4.9324*** -5.3970*** Intercept SE 0.212 0.2124 0.2089 0.1795 0.1451 Location Estimate -0.2438 -0.6816*** NA -0.5912*** NA Location SE 0.1561 0.1335 - 0.139 - Status Estimate 0.4178 -0.3962 0.0618 NA NA Status SE 0.2855 0.2181 0.2902 - - Location*Status Estimate -0.7897*** NA NA NA NA Location*Status SE 0.2209 - - - - df 5 4 3 3 2 logLik -213.695 -219.275 -228.186 -220.612 -228.208 AICc 440.247 448.368 463.416 448.267 460.917 delta 0 8.121 23.169 8.021 20.671 weight 0.966 0.017 8.99E-06 0.0175 3.14E-05

*** P < 0.001 Model equations: [1] ln(G&0A'HA0ℎ&.J(1)~45 + 4>?&@A0-&( + 4BC0A0D1 + 4E?&@A0-&( ∗ C0A0D1 + ln(G&0A'KA@0J+-A) [2] ln(G&0A'HA0ℎ&.J(1)~45 + 4>?&@A0-&( + 4BC0A0D1 + ln(G&0A'KA@0J+-A) [3] ln(G&0A'HA0ℎ&.J(1)~45 + 4>C0A0D1 + ln G&0A'KA@0J+-A [4] ln(G&0A'HA0ℎ&.J(1)~45 + 4>?&@A0-&( + ln G&0A'KA@0J+-A [5] ln(G&0A'HA0ℎ&.J(1)~45 + ln(G&0A'KA@0J+-A) Table S8. Pairwise comparisons of relative abundance of total potentially pathogenic bacteria in seawater following the generalized linear model among locations with seagrass meadows present and absent. Tests are performed on the log scale with a Bonferroni correction.

Location Estimate SE Z-ratio P-value Shore -0.418 0.286 -1.463 0.1434 Flat 0.372 0.181 2.061 0.0393* Reef 1.162 0.285 4.078 < 0.0001***

14

Table S9. Pearson correlation values representing relative contributions of 18 genera of potential bacterial pathogens on the observed separation between paired sites with seagrass present and absent.

Genus PCO1

94.8% PCO2

1% Arcobacter 0.59 0.12 Bacillus -0.14 0.17 Chryseobacterium 0.34 0.28 Clostridium -0.20 -0.56 Corynebacterium 0.69 -0.04 Enterococcus 0.50 0.27 Flavobacterium 0.99 0.03 Lactococcus 0.09 -0.17 Phormidium 0.46 0.19 Photobacterium 0.51 -0.38 Pseudoalteromonas 0.54 0.29 Pseudomonas 0.09 -0.03 Rickettsia 0.62 0.36 Shewanella 0.61 0.54 Streptococcus 0.28 0.32 Tenacibaculum 0.31 0.66 Thalassomonas 0.18 -0.26 Vibrio 0.64 0.04

Fig. S4. Dendrogram denotes the percent similarity among bacterial assemblages (range = 0 –100) based on a Bray–Curtis similarity matrix.

Samples

100

95

90

85

80

75

70

Sim

ilarit

y

FlatFlat ReefReefSeagrass presentSeagrass absent

15

Table S10. Mean prevalence (±SE) of coral disease or other causes of tissue mortality and results of generalized linear mixed-effects models (GLMM) comparing prevalence levels between seagrass present and absent locations.

Variable Seagrass Present Seagrass Absent GLMM

mean ± SE prevalence mean ± SE

prevalence Z-ratio P-value

Total disease 1.6 ± 0.6 5.9 ± 1.1 -4.213 <0.001 Black band 0.5 ± 0.3 1.6 ± 1.0 -3.977 <0.001 White syndrome 0.2 ± 0.1 1.4 ± 0.4 -4.791 <0.001 Skeletal eroding band 0.5 ± 0.3 0.4 ± 0.1 1.588 0.112 Growth anomalies 0.4 ± 0.2 0.5 ± 0.3 -0.111 0.912 Brown band 0.2 ± 0.2 b - - -

Sediment damage 4.6 ± 1.7 16.0 ± 9.3 -13.592 <0.001 Bleaching 2.0 ± 0.1 4.8 ± 1.8 -5.497 <0.001

a Mean prevalence calculated as the percentage of colonies with disease for each disease type or as a percentage of the total number of diseased corals per transect. Analyses performed on binomial data. b Only 8 cases of disease recorded, model could not be performed. Model equations: [1] Full model '&.-0(′M-1JA1JN)~45|789:;<OPQ:;8RST + 4>C0A0D1 [2]Nullmodel'&.-0(′M-1JA1JN)~45|789:;<OPQ:;8RST

16

17

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