Assessing the ecological status of seagrasses using morphology, biochemical descriptors and microbial community analyses. A study in Halophila stipulacea (Forsk.) Aschers meadows in the northern Red Sea

Ecological Indicators 60 (2016) 1150–1163

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Assessing the ecological status of seagrasses using morphology, biochemical descriptors and microbial community analyses. A study in Halophila stipulacea (Forsk.) Aschers meadows in the northern Red Sea Astrid Y. Mejia a,∗ , Alice Rotini a , Federica Lacasella b , Revital Bookman c , Maria Cristina Thaller a , Rachamim Shem-Tov d , Gidon Winters d , Luciana Migliore a a

Department of Biology, Tor Vergata University, Via della Ricerca Scientifica snc, I-00133 Rome, Italy Department of Entomology, University of Wisconsin, Wisconsin Energy Institute, 1552 University Avenue, Madison, WI, United States c The Dr. Moses Strauss Department of Marine Geosciences, Leon H. Charney School of Marine Sciences, University of Haifa, Mt. Carmel, Haifa 31905, Israel d The Dead Sea-Arava Science Center, Tamar Regional Council, Neve Zohar 86910, Israel b

a r t i c l e

i n f o

Article history: Received 22 February 2015 Received in revised form 4 September 2015 Accepted 5 September 2015 Keywords: Seagrass monitoring Halophila stipulacea Epiphytic microbial community Plant morphometrics Total phenols Photosynthetic pigments

a b s t r a c t Seagrasses are one of the most valuable marine ecosystems on earth, yet they are declining worldwide at alarming rates. With most of seagrass monitoring based on long term responses to environmental pressures, there is growing interest in developing alternative diagnostic tools that more effectively identify changes in seagrass ecological status at an early stage. Besides morphological indicators, functional and biochemical descriptors may provide a good understanding of plant’s responses to environmental changes. Moreover, the epiphytic microbial communities of seagrasses may also shift in response to changes in environmental conditions, although these have been seldom used as a descriptor of environmental change. In this study three Halophila stipulacea (Forsk.) Aschers meadows, found in the Gulf of Aqaba (northern Red Sea), were characterized using an integrated approach to highlight possible differences in the meadows ecological status. Plant descriptors, including leaves morphometrics (leaf size, leaf number/plant, leaves with lost apex), photosynthetic pigments (Chlorophylls, Carotenoids) and total phenols contents, were investigated and coupled with the plants’ epiphytic microbial community structure and composition, studied using pyrosequencing. The entire suite of descriptors highlighted differences among the meadows ecological status based on changes in plants’ morphology and biochemistry, and their associated microbial communities, in response to the different environmental conditions (water column turbidity, seawater and sediment nutrients) and the geomorphological features (bottom slope, granulometry) of the stations. Leaf morphology and photosynthetic pigment content were modulated in H. stipulacea in response to light availability and hydrodynamics in the Gulf of Aqaba. The highest leaf surface area and photosynthetic pigment contents were observed at the lowest irradiance and hydrodynamics/granulometry among stations. Total phenol content showed differences among stations with increasing concentrations from north to south. The microbial communities showed differences among stations and plant compartments, with high incidence of Gammaproteobacteria and Bacteroidetes in light limiting conditions, while Cyanobacteria and Rhodobacteraceae thrived in conditions of high light availability and hydrodynamics. The mutual response of the seagrass plants and the microbial communities provided evidence of their functional relationship, which undoubtedly needs further investigation. To the best of our knowledge, this is the first time that such descriptors have been used in an integrated approach. We provide evidence of their effectiveness in discriminating seagrass ecological status, even at small spatial scales. This work constitutes a new approach to the assessment of seagrasses and a stepping stone in the application of microbial communities as a putative marker in a changing environment. © 2015 Published by Elsevier Ltd.

∗ Corresponding author. Tel.: +39 0672595984; fax: +39 062023500; mobile: +39 3291749796. E-mail address: [email protected] (A.Y. Mejia). http://dx.doi.org/10.1016/j.ecolind.2015.09.014 1470-160X/© 2015 Published by Elsevier Ltd.

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1. Introduction Seagrasses perform essential biological functions and represent one of the most valuable marine ecosystems on earth, with an estimated value of US$ 2.8 × 106 yr−1 km−2 (Costanza et al., 2014). They provide an essential habitat for economically important fish and crustaceans as well as crucial ecological services, including the production and burial of organic carbon, nutrient cycling and sediment stabilization (reviewed by Fourqurean et al., 2012; Orth et al., 2006; Waycott et al., 2009). Seagrasses have been legally recognized in the European Union (EU) Water Framework Directive (WFD, Directive 2000/60/EC) as key coastal ecosystems and identified as bioindicators of ecosystem quality (Marbà et al., 2013). However, they are experiencing worldwide declines mainly due to anthropogenic and natural threats (Björk et al., 2008; Short et al., 2011; Waycott et al., 2009). Human-induced climate change may also impact seagrasses as sea level rises and severe storms become more frequent (Green and Short, 2003). Implementing monitoring programs has been an important step in assessing the conservation status of seagrass meadows and identifying the causes and effects of potential stressors to thereby improve their management and conservation. Large scale efforts through programs such as Seagrass-Watch (www.seagrasswatch.org; McKenzie et al., 2000) and Seagrass Net (www.seagrassnet.org; Short et al., 2004, 2005) had been carried out worldwide to monitor changes in the health status of seagrass meadows. The identification of suitable parameters to effectively assess seagrasses health has become more critical and necessary to prompt timely actions for their protection and conservation (Marbà et al., 2013; Unsworth et al., 2014). Traditionally, most seagrass monitoring approaches have been based on the follow-up of changes in community based parameters such as bed composition, percent cover and biomass (Buia et al., 2004; Cabac¸o et al., 2007). These descriptors, however, represent relatively slow responses to environmental changes (Marbà et al., 2013). Plant morphometry is also widely used in seagrasses monitoring because leaves, rhizomes and roots are highly plastic organs that change to cope with environmental variation (Duarte, 1991). Morphometric variations have been described in many seagrass species in response to stress caused by tidal exposure, light reduction, nutrient load increase, among others (Peralta et al., 2005, 2006; Cabac¸o et al., 2009). There is a growing interest in developing new diagnostic tools to describe more effectively and rapidly the current state of seagrass meadows (Romero et al., 2007). Indeed, functional and biochemical descriptors focusing on plant physiology are increasingly being applied on seagrasses to assess plant responses to environmental changes (Arnold et al., 2012; Migliore et al., 2007; Rotini et al., 2011, 2013a,b; Silva et al., 2013). Biochemical responses such as the production of photosynthetic pigments and the synthesis of secondary metabolites (total phenols) are influenced by changes in the environment and thus reflect the plants’ ecological conditions. To keep photosynthetic efficiency, plants change their photosynthetic pigments concentrations in response to differences in water quality and/or light regimes (Beer et al., 2014; Campbell et al., 2003; Ralph et al., 2007). Phenol compounds are widely abundant in seagrasses (Agostini et al., 1998; Cariello et al., 1979; Quakenbush et al., 1986; Zapata and McMillan, 1979). Changes in phenol content have been observed in response to several biotic and abiotic pressures, including pollution and disease (Ferrat et al., 2003; Migliore et al., 2007; Rotini et al., 2011, 2013a,b; Vergeer and Develi, 1997), ocean acidification (Arnold et al., 2012, 2014; Migliore et al., 2012), competition (Pergent et al., 2008), herbivory (Arnold et al., 2008; Darnell and Heck, 2013; Vergés et al., 2008) and light reduction (Silva et al., 2013). Similarly, microbial community shifts are being increasingly studied in the marine environment to better understand the

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impacts of changing environmental conditions, particularly when associated with eukaryotes, such as corals (Ainsworth et al., 2010; Rosenberg et al., 2007), macroalgae (Crawford and Clardy, 2011; Dubilier et al., 2008; Egan et al., 2012; Singh and Reddy, 2014; Wahl et al., 2012) and sponges (Hentschel et al., 2012; Webster and Bourne, 2012). Differences in epiphytic microbial communities have been associated with deteriorating health of corals and sponges in response to different CO2 concentrations (Meron et al., 2011) and temperature conditions (Webster et al., 2008). Differences in microbial community assemblages have been recently found on biofilms associated with the leaves of the seagrass Enhalus acroides (Hassenrück et al., 2015), when exposed to natural acidification in CO2 -rich vents in Papua New Guinea. In addition, marine biofilm forming microbial taxa attached to artificial substrates in coral reef habitats have been associated with particular environmental conditions: the phyla Alphaproteobacteria and Cyanobacteria have been correlated with oligotrophic conditions (low impacted sites with high light, low nutrients and low Chlorophyll a concentrations), while Gammaproteobacteria and Bacteroidetes have been correlated to sites with eutrophication (Witt et al., 2012). This suggests the need to explore further the resilience of plants and their associated microbial communities in a changing climate. Seagrasses are known to harbor biofilms of highly diversified microbial communities (Bagwell et al., 2002; Crump and Koch, 2008; Hamisi et al., 2009; Weidner et al., 2000) which actively participate in the recycling of nutrients (Duarte et al., 2005). The epiphytic microbial communities of seagrasses may shift in response to environmental changes and both (plants and microbes) may respond in synchrony to changes in environmental conditions. While the functional relationship between seagrasses and microbial communities has not yet properly been established and understood, their characterization may help to understand the possible links between seagrasses and their surrounding environment. Although famous for its coral reefs, the Red Sea also supports vast meadows of tropical seagrasses, with up to 12 species reported for this region (El Shaffai, 2011). In the northern most tip of the Red Sea, at the Gulf of Aqaba (GOA), Halophila stipulacea (Forsk.) Aschers is the most widespread seagrass species (Angel et al., 1995; Al-Rousan et al., 2011; El Shaffai, 2011; Green and Short, 2003), forming discontinuous meadows along the Israeli coastline. The GOA comprises a short coastal stretch (∼11 km long) influenced by different anthropogenic levels, geomorphological features and environmental conditions. In this study, three H. stipulacea meadows in the northern GOA were selected to assess their ecological status employing a new integrated approach based on plant descriptors (morphometry, photosynthetic pigments, phenol content) and the plant’s associated microbiome, on a small geographical scale. Results may provide a better understanding of the ecological traits of H. stipulacea under environmental fluctuations and confirm the effectiveness of this approach for seagrass health monitoring, and of the unexplored application of microbial communities as a putative descriptor. 2. Materials and methods 2.1. Study site Three seagrass beds of H. stipulacea were selected for sampling: North Beach (NB), Tur Yam (TY), and South Beach (SB) (Fig. 1). These beds are situated about 3.5 km from each other and have different geomorphological features and environmental conditions (Table 1). The meadows are also exposed to different levels of anthropogenic pressures, suggested by the surface area occupied by human infrastructure on the coastline and human water activities

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Fig. 1. Map of the study area (a), northern Gulf of Aqaba, Red Sea, Israel (coordinates correspond to the Israel transverse Mercator) showing the three sampling stations, North Beach (NB), Tur Yam (TY) and South Beach (SB), with the corresponding percent cover of the Halophila stipulacea meadows at each station (shown as a green scale), and the area of nearshore human use within 300 m from the shoreline, in front of each meadow (NB, blue; TY, yellow; SB, orange, see data in Table 1). Shore-facing images of the three sites show the different levels of coastal anthropogenic pressure/human use nearby to each of the meadows (b–d). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

(Fig. 1 and Table 1). The NB meadow is the most extensive meadow in the GOA and exposed to the greatest human pressure, involving highly populated beaches with the highest density of related coastal infrastructures in a 300 m buffer zone (Fig. 1a). For about 8 years two commercial fish farms were operational in the area, discharging nutrients onto the seawater, threatening coral recruitment (Loya et al., 2004). In this site corals are practically absent. Seawater activities consist mainly of nearshore swimming. The TY station is part of a medium-sized meadow that extends northwards into a local oil terminal. This meadow has relatively medium anthropogenic pressures, with a low-frequented beach, low intensity diving and a small marina. Although this site is close to a local oil terminal, for the past 20 years or more, this terminal is seldom used (>10 ships/year). Corals are sparsely found at this station. The SB station is part of a medium-sized meadow that extends southwards along the coast and grows down to at least 48 m deep at some sites (Sharon et al., 2011). This site comprises a pristine shoreline with hardly any infrastructure but busy diving activities given the high density of corals. 2.2. Sample collection and analyses Samples were collected in October 2013, except for Total Organic Carbon (TOC %) samples which were collected in October

2013 and 2014. At each station, sampling was conducted by SCUBA diving at randomly selected sites along a 50 m transect line placed at 9 m depth. Samples of seawater (collected right above the plants) and pore water (10 cm into the sediment) were collected with 50 ml syringes at two sites along the transect line. Sediment samples were also collected at the same two sites using minicorers (3.0 cm diameter and 50 ml volume). In parallel, H. stipulacea plants were collected separately for morphometrics, biochemical analyses and microbial community characterization (see more details below). The plants were carefully dug out to keep their root system intact. After collection, the samples (plants, sediment and water samples) were immediately transported to the lab and kept cold in the shade until further processing. 2.3. Environmental variables 2.3.1. Water analysis In the lab, the seawater samples were transferred from the syringes to 50 ml tubes and stored at 4 ◦ C awaiting nutrient analysis. The pore water samples were first centrifuged at 5000 rpm for 5 min, transferred to 50 ml tubes and stored at 4 ◦ C. The nutrient concentrations (NO2 − , NO3 − , and PO4 3− ) of all samples were measured according to the colorimetric method described by Grasshoff et al. (1999), using the Flow Injection QuikChem 8500 Autoanalyser

Table 1 Main geomorphological features and human uses of the three sampling stations in the Gulf of Aqaba (Red Sea, Israel). Secchi depthb (m)

Kd PARc

Presence of coral reef

Nearshore human use aread (m2 )

Potential anthropogenic pressure

2.55◦

23.13 ± 2.69

0.196

No

1,919,250

High

67,365

5.26◦

25.63 ± 1.65

0.102

Partial

856,800

Medium

61,900

◦

26.38 ± 2.16

0.155

Yes

387,750

Low

Sampling station

Location

Meadow areaa (m2 )

North Beach (NB)

29.546150◦ N 34.964819◦ E 29.516527◦ N 34.927205◦ E 29.497664◦ N 34.912737◦ E

343,032

Tur Yam (TY) South Beach (SB)

Bottom slopea

17.92

a Bottom slopes and surface areas of meadows were obtained from ongoing efforts to map seagrasses along the Israeli coast of the northern Gulf of Aqaba (Winters et al., personal comm.). b Data correspond to the average values of Secchi depths from October 2006 to 2013 obtained from the Israel’s National Monitoring of the Gulf of Eilat – NMP (2014); the NMP’s monitoring sites differ slightly from the sites in this study (∼400 m difference max.), the averaged Secchi depth data used correspond to the following NMP sites: Taba (200 m from SB), Navy, (some 400 m from NB) and the water control station (some 300 m from TY). c PAR measurements were taken in each sampling site during plant collection at noon at 0–9 m and the corresponding diffuse attenuation coefficient (Kd) based on PAR reading at 2 and 9 m depth, using the 2␲-quantum sensor of the Diving-PAM (pulse amplitude modulated) fluorometer (Walz, Germany). d Surface area occupied by human infrastructure within a 300 m zone measured from the waterline (marked out in Fig. 1).

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(LACHAT Instruments, CO, USA) at the Inter University Institute Center in Eilat (IL).

pellets using the Power Soil® DNA isolation kit (Mo Bio, Carlsbad, CA, USA) according to the manufacturer’s instructions.

2.3.2. Sediment analysis Granulometric composition was performed on 50 ml sediment samples (two sediment samples from each site). For this, sediment samples were dried for 24 h at 60 ◦ C in a drying oven, weighted using an analytical scale, and then transferred to a set of sieves (ASTM D6913, ASTM International, USA) which were shaken for 10 min. This method separates sediment particles into seven size ranges of: >2000, 2000–1000, 1000–500, 500–250, 250–125, 125–63 and <63 ␮m. Following this shaking, the mass of particles of each size range (sieve) was weighed and calculated as relative percent of the weight of the initial sample. Total Organic Carbon (TOC %) in the sediments was measured by Skalar PrimacsSLC TOC Analyser (Skalar Analytical BV, Netherlands). Dry samples were ground and split into two fractions. One burned at 1050 ◦ C for Total Carbon measurement and the second was titrated with 20% phosphoric acid to detect the inorganic carbon fraction. The subtraction yielded the TOC %.

2.5.3. 454 pyrosequencing Pure DNA bacterial extracts were sent to the Molecular Research LP in TX, USA (MR DNA, http://www.mrdnalab.com/ , Dowd et al., 2008) for PCR amplification of the 16S rRNA gene and 454 pyrosequencing. In brief, universal primers, Com1 (forward, 5 -CAGCAGCCGCGGTAATAC-3 ) and Com2 (reverse, 5 -CCGTCAATTCCTTTGAGTTT-3 ), were used to amplify a 407 bp fragment encompassing the phylogenetically highly variable regions, V4 and V5 of the 16S rRNA gene (Schwieger and Tebbe, 1998; Schmalenberger et al., 2000). Amplification of the metagenome was obtained from a single-step 30 cycle PCR using HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA) with an initial denaturation step of 94 ◦ C for 3 min, followed by 28 cycles of 94 ◦ C for 30 s; 53 ◦ C for 40 s and 72 ◦ C for 1 min, with a final extension step of 72 ◦ C for 5 min. Amplicon products were mixed in equal concentrations and purified using Agencourt Ampure beads (Agencourt Bioscience Corporation, MA, USA). Samples were then sequenced utilizing Roche 454 FLX titanium instruments and reagents following the manufacturer’s guidelines.

2.4. Plant descriptors 2.4.1. Leaf count and morphometrics Five plants were used to determine the mean leaf number and leaf surface area per station. For this, leaves were scanned (Canon Lide 110 scanner) and the images were then imported into ImageJ software (version 1.47; Abramoff et al., 2004). The images were used to measure morphometrics (leaf length, width and area), and to calculate percentage of leaves with lost apex. 2.4.2. Biochemical analyses Biochemical analyses were performed on 8 plants per station. Total phenols were extracted in duplicate from the leaves (200 mg fresh weight) and rhizomes (200 mg fresh weight) of each plant, and quantified according to Migliore et al. (2007). Photosynthetic pigments (Chlorophyll a and Chlorophyll b, total Carotenoids) were extracted in duplicate from the leaves (250 mg) of each plant, according to Wellburn (1994), modified by Rotini et al. (2013a). Extracts were read twice using a Shimadzu UV2600 spectrophotometer. Both phenols and photosynthetic pigment contents were expressed as mg/g of fresh weight (FW). 2.5. Microbial communities 2.5.1. Sample collection For the microbial community characterization, two plants were collected simultaneously at four different sites along the 50 m transect line and divided evenly to obtain two replicates per sampling station. The plant samples were separated underwater into aboveground (leaves) and belowground (rhizomes with roots) compartments. Each replicate of the aboveground compartment was composed of at least 32 leaves and the belowground of about 16 cm of rhizomes with roots. Two replicates per plant compartment and station were used for microbial community analysis. 2.5.2. Bacterial pellet and DNA extraction To get a bacterial pellet, each sample was first washed with 10 ml of washing solution (200 mM Tris–HCl pH 8, 10 mM EDTA, and 0.24% Triton X-100; Kadivar and Stapleton, 2003). The plant parts were gently rubbed against the collection tubes walls and vortexed (3× 30 s) to detach and collect the bacteria. The plant parts were removed with sterile tweezers and the tubes containing the washing solution were centrifuged at 5000 rpm for 20 min to obtain the bacterial pellet. Metagenomic bacterial DNA was extracted from the

2.5.4. 454 pyrosequencing data processing The 454-pyrosequencing raw data were processed with the open source software MOTHUR (http://www.mothur.org/), according to the 454 Standard Operating Procedure pipeline (Schloss et al., 2009) available online at the software’s website. Briefly, in the pipeline, quality trimming was performed, sequences were depleted of their barcodes and those <200 bp and with ambiguous base calls were removed. These sequences were then dereplicated, aligned against the greengenes core-set template alignment, and screened to make them overlap in the same region. Putative chimeras were identified with the “uchime” algorithm and removed. Finally, high quality sequences were classified with naïve Bayesian classifier in MOTHUR using the RDP taxonomy reference database-trainset 9 032012. Sequences classified at phylum level as unknown, archaea and chloroplast were removed. Operational Taxonomic Units (OTUs) were defined by clustering of sequences at 3% divergence (97% similarity). A final matrix of the OTUs assigned to each sample with their taxonomic identification was built in MOTHUR and used in downstream analyses. To describe the bacterial composition at different taxonomic levels, the final matrix was filtered to include only OTUs with taxonomic identities of ≥80% confidence at the phylum and class levels and that had at least 10 sequence reads per sample. The complete set of raw sequences obtained in this study has been deposited in GenBank at the Sequence Read Archive (SRA) under the study accession no. SRP057388, BioProject no. PRJNA281491. 2.5.5. Bacterial community analyses Microbial community structure and composition were analyzed in MOTHUR as follows: (i) rarefaction curves were built to evaluate differences in sampling effort; (ii) Venn diagrams were built to state the relationship between unique and shared OTUs; (iii) the Shannon index (H ) was used to evaluate bacterial diversity, (iv) histograms were built to visualize the bacterial community composition and their relative abundances. Multivariate analyses of OTU data were also performed: (a) unconstrained UPGMA clustering using the UniFrac weighted metric within MOTHUR and (b) n-MDS analysis of OTU profiles by plant compartments and stations was built in PRIMER. In addition, the SIMilarity PERcentage (SIMPER) test was used to identify the taxonomic groups (phylotypes) contributing most to the dissimilarity between the samples. To standardize differences in

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Table 2 Sea and pore water nutrient concentrations (Total Oxidized Nitrogen = TON, NO2 + NO3 , Phosphate = PO4 ) and the sediments Total Organic Carbon (TOC), at the three sampling stations: North Beach (NB), Tur Yam (TY) and South Beach (SB), in the Gulf of Aqaba (Red Sea, Israel). Values are means ± SD of two measurements. Seawater

NB TY SB

Pore water

Sediment

TON (␮mol/l)

PO4 3− (␮mol/l)

TON (␮mol/l)

PO4 3− (␮mol/l)

TOC (%)

0.154 ± 0.064 0.112 ± 0.000 0.530 ± 0.297

0.024 ± 0.004 0.018 ± 0.000 0.043 ± 0.004

0.430 ± 0.015 0.145 ± 0.078 0.718 ± 0.220

0.690 ± 0.043 0.508 ± 0.131 1.403 ± 0.403

0.425 ± 0.053 0.260 ± 0.008 0.595 ± 0.020

sampling effort among samples, the complete OTUs dataset was normalized by random subsampling to a common depth (the lowest number of sequences produced by the samples). The latter allows for adequate comparisons at community level (Shannon diversity index, n-MDS analysis and UniFrac.weighted measurements). 2.6. Statistical analyses Environmental variables: - Water nutrient and sediment analyses: Kruskal–Wallis test was used to evaluate significant differences in nutrients and %TOC concentrations among stations. Granulometric characterization of each station was performed by calculating the percent frequency of seven different class sizes. Differences in granulometric composition among sampling stations were evaluated by two-way ANOVA. For each sample the modal class (the most represented class of granulometry) was detected and used in Pearson’s correlation analysis to evaluate relationships with other environmental data. - Leaf morphometrics: ANOVA was employed to evaluate significant differences in the mean leaf surface area of the plants among stations. Multivariate non-parametric analysis of similarities (ANOSIM) on Euclidean distances from normalized data was used to further evaluate differences in Leaf biometry (variables: leaf area, width and length). Differences in mean leaf number/plant and the percentage of leaves with lost apex were evaluated using Kruskal–Wallis and Mann–Whitney tests. - Photosynthetic pigment contents: Chlorophyll a, Chlorophyll b and total Carotenoids were analyzed for significant differences among stations using Mann–Whitney test. - Phenol content: Mann–Whitney and Kruskal–Wallis tests were used to evaluate significant differences in total phenol content between plant compartments and among stations. - Microbial community analyses: The UniFrac weighted algorithm was used in MOTHUR to evaluate if the microbial community structure of the stations differed significantly based on their phylogenetic relationships and the abundances of the taxonomic groups present in the samples, and statistical significance was defined at p < 0.01. n-MDS ordination was conducted on a Bray–Curtis distance matrix calculated between sampling plots with log(x + 1)-transformed OTUs abundance data. ANOSIM (n = 999 randomizations; Clarke and Warwick, 2001) was employed to test for significant differences in microbial communities between plant compartments. The univariate analyses were performed using PAST v. 3.1 software (Hammer and Ryan, 2009), while multivariate analyses were performed using PRIMER v.6 (PRIMER-E, Plymouth Marine Laboratory, UK). Pearson’s correlation coefficient was used to evaluate the relationships among environmental variables and plant descriptors (on log transformed data). Statistical significance was defined at p < 0.05 (see Supporting information, Table S1).

3. Results 3.1. Environmental variables During the sampling period (1 week), the average sea surface water temperature in all stations was 24 ◦ C and salinity was 40.70 PSU (NMP, 2014). The water column showed differences among stations based on diffuse attenuation coefficients, KdPAR (Table 1). Light attenuated the fastest in NB, followed by SB and TY; this was also confirmed by Secchi depth values (Table 1), which in NB was 2–3 m shallower than in TY and SB stations. Nutrients concentrations (Table 2) in seawater (above plants) were highest in SB, while NB and TY showed comparable values, although differences were not significant (Kruskal–Wallis, n.s.). The sediment compartment showed the same trend in both the pore water nutrient concentrations (Table 2) and Total Organic Carbon content, with the highest values in SB (Kruskal–Wallis, n.s.). The granulometric composition (Fig. 2) changed dramatically among stations (two-way ANOVA, F = 2.2504, p < 0.002) from fine-medium sand in NB (modal class: 250–125 ␮m, in both replicates) to coarse sand-gravel/coral rubble in SB (modal class: >2000 ␮m, in both replicates). TY showed a fair combination of coarse and very coarse sand (shown by different modal classes of two replicates: 1000–2000 and 250–125 ␮m, respectively). 3.2. Plant descriptors 3.2.1. Leaves morphometrics The mean leaf surface area of the plants (Table 3) decreased continuously from NB to SB and differed significantly among stations (ANOVA, F = 54.17, p < <0.01). The leaves in NB were the widest and longest (entailing larger surface areas) followed by TY and SB. A pairwise ANOSIM analysis including mean leaf surface area, leaf width and height highlighted significant differences between NB and SB stations (ANOSIM, R = 0.351, p < 0.01). The mean leaf number/plant (Table 3) showed also a decreasing trend from NB to SB, although differences among stations were not significant (Kruskal–Wallis, n.s.). The percentage of leaves with lost apex due to grazing and/or mechanical damage was significantly higher in NB than in TY and SB (Mann–Whitney, NB vs. TY: p < 0.02; NB vs. SB: p < 0.01). Pearson’s coefficient highlighted significant and negative relationships between leaf morphology and slope (R = −0.86, p = 0.02) and with Secchi disc (R = −0.91, p = 0.01; see Supporting information, Table S1). 3.2.2. Biochemical analyses The photosynthetic pigments content (Fig. 3a), namely Chl a and Chl b, were significantly higher in NB than in the other two stations (Mann–Whitney, p < 0.01), while total Carotenoid content remained relatively constant among stations (n.s., Mann–Whitney). The ratio Chltot /Cartot showed the highest mean values in NB (5.72 ± 0.48), and comparable values were found between TY (4.13 ± 0.79) and SB (4.09 ± 0.70). The mean total phenol content in leaves was higher than in rhizomes-roots in the three stations, with significant differences

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Fig. 2. Granulometric compositions as a percentage of dry weight of the sediments collected in the three sampling stations, North Beach (NB), Tur Yam (TY) and South Beach (SB). Values are means of two measurements. The main granulometric size classes are indicated according to Wentworth (1922): fine-medium sand (<63 to 250 ␮m), coarse sand (250 to <2000 ␮m) and granules/gravel (>2000 ␮m). Table 3 Leaf morphometry and leaf counts of Halophila stipulacea collected in the three sampling stations, North Beach (NB), Tur Yam (TY) and South Beach (SB). Mean values (±SD) of leaf width, length and surface area (n = 30–60 leaves from a total of 5 plants) are shown. Mean values (±SD) of leaf number per plant and percentage of leaves with lost apex are also shown (n = 5 plants). Station

Leaf width (mm)

Leaf length (mm)

Leaf surface area (mm2 )

Leaf number/plant

Leaves with lost apex (%)

NB TY SB

13.5 ± 4.2 11.6 ± 3.8 9.6 ± 3.1

42.7 ± 7.8 39.4 ± 8.5 35.2 ± 8.7

302.4 ± 61.7 227.9 ± 76.8 183.5 ± 63.1

20.8 ± 11.1 14.8 ± 10.0 12.6 ± 4.2

40.4 9.5 13.3

in NB and TY (Mann–Whitney, p < 0.05 and p < 0.001, respectively; Fig. 3b). In SB, the differences between leaves and rhizomes-roots seemed more pronounced but these are not statistically significant due to data dispersion. Significant differences in the total phenol content of leaves (Kruskal–Wallis, H = 7.085, p < 0.05) and also of rhizomes-roots (Kruskal–Wallis, H = 7.065, p < 0.05) were found among stations, with the highest values found always in SB. 3.3. Microbial community analysis 3.3.1. 454 pyrosequencing output The raw output of the 454 pyrosequencing resulted in a total of 109,622 bacterial sequences, comprising all samples and stations. The processing of sequences with the MOTHUR software

resulted in a total of 75,028 sequences, assigned to a total of 5752 OTUs, including singletons and doubletons. On average, each individual sample was represented by 6252 sequences, ranging from 740 to 9146 sequences. The rarefaction curves analysis (Fig. 4) showed that some samples did not reach the asymptote in OTU richness; in those cases, more sequences would be required to recover the full taxonomic diversity. To standardize differences in sampling effort among samples, the dataset was normalized by random subsampling to a common depth of 726 sequences reads per sample. 3.3.2. Microbial community diversity The Shannon diversity index (H ) of the stations was evaluated using the normalized dataset (Table 4) and complete dataset

Fig. 3. Biochemical descriptors in Halophila stipulacea plants (n = 8) from the three sampling stations: North Beach (NB), Tur Yam (TY) and South Beach (SB). (a) Mean photosynthetic pigment content of leaves (Chl a = Chlorophyll a; Chl b = Chlorophyll b; Car = total Carotenoid) and (b) mean total phenol content of leaves and rhizomes.

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Fig. 4. Rarefaction curves of the microbial communities associated with the different plant compartments, aboveground (a = leaves) and belowground (b = rhizomes and roots) of Halophila stipulacea in the three sampling stations: North Beach (NB), Tur Yam (TY) and South Beach (SB). Two replicates per sampling station are shown. No. of OTUs = Number of Operational Taxonomic Units; Seqs = sequences.

Table 4 Shannon diversity index (H ) of the microbial communities associated with Halophila stipulacea above and belowground compartments in the three sampling station: North Beach (NB), Tur Yam (TY) and South Beach (SB). Values are means ± SD of two replicates, using the normalized dataset. Shannon diversity (H )

Station

NB TY SB

Aboveground

Belowground

4.85 ± 0.05 4.49 ± 0.44 4.05 ± 0.21

4.83 ± 0.09 4.49 ± 0.02 4.91 ± 0.04

(see Supporting information, Table S2). In general, higher diversity index values were obtained when using the complete dataset; however, the same trend in diversity was observed regardless of the dataset used. The samples aboveground showed a decreasing trend from NB to SB, while belowground no clear trend among stations was observed. Belowground, SB showed the highest diversity value among stations. The UniFrac weighted analysis (Table 5), which measures the difference between collections of sequences as the amount of evolutionary history that is unique to each one (Lozupone et al., 2011), evaluated differences in microbial community structure based on the OTUs phylogenetic relationships and abundance, and highlighted differences by stations. The pairwise comparisons performed using the normalized OTUs dataset showed statistically significant differences among the stations aboveground (NB vs. TY vs. SB; p < 0.001) while belowground, differences were significant only between NB vs. TY (p < 0.001). The nonMetric multi-Dimensional Scaling (n-MDS; Fig. 5) analysis, which evaluated differences in microbial community structure based only on the OTUs abundances, showed a separation between plant compartments. Significant differences between above- and

Fig. 5. n-MDS analysis of the microbial community associated with different plant compartments of Halophila stipulacea, aboveground (a = leaves) and belowground (b = rhizomes and roots) in the three sampling stations: North Beach (NB), Tur Yam (TY) and South Beach (SB). The analysis was performed using the normalized OTUs dataset. Two replicates per station are shown.

belowground communities were highlighted by ANOSIM (pairwise test R: 0.74; p < 0.01). 3.3.3. Specific and shared OTUs Venn diagrams (Fig. 6) showed a lower number of observed OTUs in the stations aboveground (2635) than belowground (3923). A total of 1260 OTUs were shared among all the samples in the study and of these 68% were common to both plant compartments. In all the samples, the number of unique OTUs was higher than the shared ones, with only 7% of the OTUs being shared among the stations

Table 5 The UniFrac weighted scores (WScore) and statistical significance (**p < 0.001, *p < 0.05) of the evaluation of the microbial communities based on the OTUs abundance and phylogenetic relationships, among stations (North Beach, NB; Tur Yam, TY; South Beach, SB) by plant compartment (aboveground, a; belowground, b). This analysis was performed in MOTHUR.

NBa TYa SBa NBb TYb

NBa

TYa

SBa

NBb

TYb

SBb

– – – – –

0.885509** – – – –

0.936209** 0.870773** – – –

1* n.s. 1** – –

0.956254** 0.956254* 0.956254** n.s. –

1* 1* 1** n.s. 0.888104**

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Fig. 6. Venn diagrams of the bacterial OTUs (specific and shared) associated with Halophila stipulacea above and belowground compartments in the three sampling stations: North Beach (NB), Tur Yam (TY) and South Beach (SB). The data of two replicates per station are shown.

aboveground and 8% belowground. The taxonomic identification of the shared OTUs revealed the ‘core microbiome’ composition of both plant compartments (Table 6). The phyla Proteobacteria and Planctomycetes represented >70% of the shared OTUs on both plant compartments. 3.3.4. Microbial community composition Proteobacteria was the dominant phylum found associated with H. stipulacea, both aboveground and belowground, in all stations (Fig. 7, in blue). This phylum accounted for at least 70%

of bacteria composition of each sample. Within this phylum, Alphaproteobacteria, Gammaproteobacteria and Deltaproteobacteria were the most abundant classes found, and these were differently represented in the above and belowground compartments (Fig. 8, pie charts). Aboveground, Alphaproteobacteria (68% of the total community composition) was the dominant class in all stations. Belowground, there was not a single dominant class but it was distributed among Gammaproteobacteria (32%), Alphaproteobacteria (31%), and Deltaproteobacteria (21%).

Table 6 The core microbiome associated with the above and belowground compartments of Halophila stipulacea in the Gulf of Aqaba, Red Sea. No. OTUs = Number of Operational Taxonomic Units. Tables were built using all OTUs found in two replicates per station. Phylum Aboveground Actinobacteria Bacteroidetes Cyanobacteria Planctomycetes Proteobacteria

Verrucomicrobia Unclassified Total Belowground Acidobacteria Actinobacteria Bacteroidetes

Chlorobi Chloroflexi Cyanobacteria Firmicutes Planctomycetes Proteobacteria

Unclassified WS3 Total

No. OTUs 4 7 24 25 117

2 10

Class Actinobacteria Sphingobacteria Flavobacteria Cyanobacteria Planctomycetacia Phycisphaerae Alphaproteobacteria Deltaproteobacteria Gammaproteobacteria Proteobacteria unclassified Verrucomicrobiae Unclassified

189

3 10 11

1 3 21 1 25 194

43 1 313

Acidobacteria Actinobacteria Sphingobacteria Bacteriodetes unclassified Flavobacteria Ignavibacteria Anaerolineae Chlorofexi unclassified Cyanobacteria Clostridia Planctomycetacia Phycisphaerae Alphaproteobacteria Betaproteobacteria Deltaproteobacteria Epsilonproteobacteria Gammaproteobacteria Proteobacteria unclassified Unclassified WS3 incertae sedis

No. OTUs

No. sequences

4 3 4 24 24 1 83 7 24 3 2 10

92 233 423 2068 1558 22 19,280 1600 1365 62 38 305

189

27,046

3 10 4 1 6 1 2 1 21 1 23 2 77 1 46 1 63 6 43 1

40 192 189 14 320 272 13 15 1125 98 440 11 7322 7 5608 94 7699 67 1392 5

313

24,923

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Fig. 7. The bacterial community composition at phylum level associated with Halophila stipulacea, aboveground (a) and belowground (b), in the three sampling stations (NB = North Beach, TY = Tur Yam, SB = South Beach). Values are means of two replicates from each sampling station. (For interpretation of the references to color in the text, the reader is referred to the web version of the article.)

Several phyla made up the less represented (rare) bacterial community components (Fig. 7, top of the bars), including Planctomycetes (4.0%), Bacteroidetes (2.8%), Chlorobi (0.5%), Firmicutes (0.3%), Actinobacteria (0.3%), Verrucomicrobia (0.2%) and Acidobacteria (0.1%). Cyanobacteria were assigned to 4.8% of the sequences, while unclassified bacteria accounted for 3.8% of the sequences. This component included many classes, which combined represented 9% aboveground and 7% belowground (Fig. 8, bar plot). Specifically, classes Planctomycetacia, Flavobacteria, Actinobacteria and Cyanobacteria were found in all the stations, showing different relative abundance above and belowground. Classes Epsilonproteobacteria, Ignavibacteria, Clostridia and Bacilli were only present belowground. The sampling stations showed differences in the presence, absence and relative abundance of microbial phylotypes at lower

taxonomic levels. Aboveground, NB showed the highest number of different phylotypes (28), followed by TY (26) and SB (23). Belowground, SB had the highest number of different phylotypes (33), followed by NB (28) and TY (26). Some phylotypes were only found in SB, including Alteromonadaceae, Cytophagaceae, Bacilli and Epsilonbacteria. Similarly, the order Sphingomonadales was found only in NB and the order Clostridiales was only found in TY. The SIMPER test analysis showed a high dissimilarity between plant compartments (55.95%) with five phylotypes contributing to >70% of the differences: Rhodobacteraceae, Gammaproteobacteria, Alphaproteobacteria, Desulfobulbaceae and Planctomycetaceae. A general analysis among stations showed the greatest dissimilarity between TY and SB (47.86%), followed by NB vs. SB (44.44%) and TY vs. NB (38.98%). The analysis by plant compartment also showed the greatest differences aboveground between SB and

Fig. 8. The bacterial community composition at class level associated with Halophila stipulacea, aboveground and belowground, in the three sampling stations (NB = North Beach, TY = Tur Yam, SB = South Beach). The pie charts represent the dominant bacterial component while the bars the rare components. Values are mean of two replicates from each sampling station.

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both, NB and TY, with average dissimilarity values of 33.71% and 31.82%, respectively. On the contrary, the dissimilarity value between NB vs. TY was considerably low (14.61%). The main phylotypes contributing to differences with SB included Cyanobacteria and Flavobacteriaceae, in addition to the phylotypes mentioned above for differences between compartments. Belowground, the dissimilarity values among stations were comparable, with the highest value found between SB vs. NB (36.76%), followed by SB vs. TY (36.45%) and NB vs. TY (32.12%). The phylotypes contributing to >70% of the differences included Desulfobulbaceae, Gammaproteobacteria, Cyanobacteria, Myxococcales, Sphingobacteriales, Rhodobacteraceae and Desulfobacteraceae.

4. Discussion The present study assessed the ecological status of three H. stipulacea meadows, North Beach (NB), Tur Yam (TY), and South Beach (SB), in the northern tip of the Gulf of Aqaba (GOA) employing a new integrated approach based on fast-response plant descriptors (morphometrics, photosynthetic pigments and phenol content) and the plants’ associated microbiome. This approach was able to depict and highlight differences among the meadows, even though the spatial distances between the meadows were relatively small (<10 km between one site and another). The meadows selected in this study are potentially exposed to an increasing water quality gradient from north to south, as suggested by the near shore human uses, water column turbidity and coral reef occurrence. However, the environmental heterogeneity in the study area was determined mainly by the local topography (i.e. bottom slope) and hydrodynamics, which in turn influence the sediment composition, presence of corals, light availability and water turbidity. NB, which is the site located at the northernmost tip of the Gulf, extends over a large platform that gently rises toward the coast, dissipating the energy of tidal currents and accumulating predominantly fine grained sand. TY, the intermediate meadow, has a gentle slope slightly higher than NB, where a fair mix of medium to coarse sand accumulates. On the contrary, the SB meadow is settled on a steeper platform, where the tidal current is stronger and washes away the fine sediments; thus granulometry is predominantly composed by coarse gravel and coral rubble. The nutrient concentrations (N and P) in the water column and pore water as well as the organic matter content in the sediment, did not show significant differences among stations. Unexpectedly, slightly higher concentrations of N, P and TOC were found in SB; this is possibly related to the water current regimes in the GOA, which may cause a faster dilution of nutrients in NB as the current moves southwards, accumulating more nutrients in SB (Abelson et al., 1999). The higher concentrations of N, P and TOC in SB may be also due to the high density of coral reef colonies, which may add nutrients to the surrounding water and nearby seagrass, as shown by Silverman et al. (2007). Leaf morphology showed differences among stations with a gradient from north to south. Our results showed a clear differentiation in H. stipulacea morphology along the Gulf, indicative of the species acclimative plasticity to cope with the local environmental gradient (i.e. bottom slope, turbidity). Seagrasses can change leaf morphology in response to light availability or irradiance (Duarte, 1991; reviewed by Ralph et al., 2007). H. stipulacea displayed significant negative relationships between leaf morphology and slope, with the longest and widest leaves and largest leaf surface area at NB where irradiance was the lowest and water turbidity the highest among stations, in agreement with other structurally small seagrasses (Cabac¸o et al., 2009; García-Marín et al., 2013; Peralta et al., 2005). TY showed lower leaf sizes and surface area than NB but higher than SB, correspondingly with differences in light availability and water turbidity among stations. On the contrary, plants

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in SB had the lowest leaf sizes under conditions of high irradiance and water clarity. Leaf plasticity resulting in reduced morphology has been related to strong hydrodynamics (Peralta et al., 2006) and disturbance (García-Marín et al., 2013), while increased morphology to limiting light (Cabac¸o et al., 2009), in agreement with our findings. Based on our results, we can conclude that leaf size in H. stipulacea is negatively related to light availability and hydrodynamics in the GOA. Accordingly with the lowest light regimes, the photosynthetic pigment contents (Chl a and Chl b) of leaves were the highest in NB among stations, since seagrasses may increase their overall production of total chlorophyll as a way to adjust their photosynthetic efficiency under light limiting conditions (Longstaff et al., 1999; Rotini et al., 2013a; Silva et al., 2013). On the contrary, in TY and SB lower total chlorophyll content was found under conditions of higher irradiance and water clarity. The stations also showed differences in total Chlorophyll to Carotenoid content ratios (Chltot /Cartot ), with a decreasing trend from north to south. In response to high light intensities plants will often increase their total Carotenoid content to total Chlorophyll in a way to increase photoprotection (Lichtenthaler and Babani, 2004). NB leaves showed the lowest need for photoprotection (with the lowest total Carotenoid content to total Chlorophyll), and at this station the high canopy biomass may also provide self-shading and leaf photoprotection, thereby reducing light stress, as observed in highly illuminated Thalassia testudinum meadows (Schubert et al., 2015). On the contrary, SB showed the lowest Chltot /Cartot ratio value (the highest total Carotenoid content per total Chlorophyll) and thus leaves with the highest photoprotection capacity among stations, indicative of higher light stress conditions at this station. Photosynthetic efficiency adjustments in response to changes in light availability have been observed in many seagrass species (Longstaff et al., 1999; Rotini et al., 2013a; Silva et al., 2013) and are known to be among the first physiological responses, preceding morphological changes and eventual biomass loss (Collier et al., 2009, 2012) in seagrass meadows. Although some seagrass species have shown less clear responses to changes in light availability (Enriquez et al., 2004; Cayabyab and Enríquez, 2007), H. stipulacea’s high physiological plasticity and efficiency in photoacclimatory adjustments in response to light fluctuations (Sharon and Beer, 2008; Sharon et al., 2009) are well documented. In here we found that photosynthetic pigment content was able to highlight the influence of changes in light availability, supporting the use of this descriptor in H. stipulacea to quickly detect light reduction events, which are known to affect seagrass photosynthesis, morphology, growth and survival (reviewed by Ralph et al., 2007). Seagrasses also have other self-protection mechanisms that can respond rapidly to stress, such as the production of phenolic compounds, which are known to have defensive properties and to be influenced by many abiotic and biotic stressors both in terrestrial and aquatic plants (Bennett and Wallsgrove, 1994; Sieg and Kubanek, 2013). To our knowledge, this is the first time that total phenol content has been quantified in H. stipulacea tissues (leaves and rhizomes). Phenol contents showed differences among stations with a gradient of increasing concentrations from north to south. NB and TY showed comparable and lower phenol concentrations in the leaves and rhizomes-roots than SB. The latter suggests the presence of more stressful conditions at SB. This station is characterized by coarser-grain sediment granulometry, high hydrodynamics and the presence of corals, which may interfere with plant’s growth and colonization, thereby causing stress. Increased phenol concentrations in seagrasses may result from changing patterns in carbon-based resource allocation (Steele et al., 2005; Vergeer et al., 1995; Vergés et al., 2007). Higher herbivory pressure, suggested by the large number of leaves with lost apex found at SB, can also result in higher phenols production (Aragones et al., 2006; Arnold et al., 2008; Steele and Valentine, 2012). In addition, high

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phenolic concentrations at SB may be also related to light availability, given that a positive effect of high light intensity on the phenolic biosynthesis was observed in other species (Rotini et al., 2013a; Vergeer et al., 1995). The phenol content descriptor highlighted biochemical changes among plants of the different stations, in response to the local environmental variations in the GOA. Results showed advantages for its inclusion in seagrass monitoring programs, although further investigations, considering different stress sources and comparisons with other species, are still necessary to better understand the role and the dynamics of phenol compounds in this and other fast-growing species. 4.1. Microbial community structure is a promising putative descriptor Microbial communities showed differences in diversity, richness, structure and community composition among stations and plant compartment, based on OTUs membership, abundance and phylogenetic relationships, the latter highlighted by the UniFrac metrics. NB showed the highest microbial diversity and richness on the leaves among stations, which may be associated with the high levels of Chlorophyll a and the availability of resources in the turbid water column. This is suggested by the high incidence of the opportunistic phyla, Gammaproteobacteria and Bacteroidetes (Flavobacteria), which were found on the leaves of this station, as these phylotypes are known to thrive under high Chlorophyll a levels (Witt et al., 2012) and with the increase influx of organic nutrients (Teira et al., 2010). TY showed a comparable diversity and a similar community composition to NB, especially aboveground. This station showed the highest dissimilarity to SB based on the differences of the relative abundance of the phylotypes, Rhodobacteraceae, Gammaproteobacteria, Desulfobulbaceae and Planctomycetaceae. SB showed the highest diversity and richness belowground among stations and was highly dissimilar in bacterial community composition to TY and NB, as highlighted by the SIMPER analysis. Aboveground, it showed the highest incidence of Alphaproteobacteria and Cyanobacteria, which may be related to the high levels of available light and low Chlorophyll a levels (Witt et al., 2012) found at this station. Belowground, this station had the highest incidence of Deltaproteobacteria (and thus, sulfur-reducing bacteria), which are known to increase with high nutrients as well as Cyanobacteria (possibly attached to the rhizomes) which are known to increase with high light availability and water clarity (Witt et al., 2012). The high incidence of Cyanobacteria associated with the belowground compartment may be related to granulometry, since the large sediment sizes of granules allow a deeper light penetration than fine sediments (Stal et al., 1985). The Rhodobacteraceae was the most abundant phylotype aboveground among stations, particularly at SB. This phylotype comprises chemo-organotrophs and photoheterotrophic bacteria, which are dominant and ubiquitous primary surface colonizers in coastal waters, with some groups known to deter the attachment of other bacteria through the production of antibacterial compounds (Dang et al., 2008). These features may enable them to both persist and adapt rapidly to changing environmental conditions and be dominant epiphytic microbiota in coastal biofilms. This phylotype has been found before on the leaves of H. stipulacea in the Red Sea (Weidner et al., 2000), as well as on seagrass roots (Crump and Koch, 2008) and biofilms in coral reef dominated waters (Elifantz et al., 2013; Witt et al., 2011). A second abundant group, the Gammaproteobacteria, which comprises halotolerant/halophilic nitrate reducing and sulfur oxidizing marine bacteria, known to withstand changes in nutrient conditions, was present in both plant compartments among stations but was more abundant belowground in NB, as observed

in other species (Crump and Koch, 2008). The family Desulfobulbaceae of the phylum Deltaproteobacteria abundant belowground, particularly at SB, includes obligate anaerobic sulfate reducing bacteria, commonly found on the roots of seagrasses (GarcíaMartínez et al., 2009) and in seagrass marine sediments (Boer and de, 2007; Jensen et al., 2007; Kerfahi et al., 2014). The classes Planctomycetacia and Clostridia showed the highest abundance in TY aboveground (as found by Weidner et al., 2000) and belowground, respectively. The phylum Planctomycetes comprises very interesting marine bacteria, some of which are able to oxidize ammonia without oxygen, playing an important role in nitrogen cycling (Fuerst, 2010). The class Clostridia belongs to the phylum Firmicutes and plays an important role in the nitrogen and carbon cycles in seagrass sediments (Devereux, 2005; Küsel et al., 1999). The variations in the relative abundance of some bacterial phylotypes among stations in response to the local environmental condition suggest their possible use as descriptors in seagrass meadows. This is the first time that microbial communities are being proposed as descriptors, yet future work is necessary to fully understand their response to changing environmental conditions and their link to plant health. Since host traits may be more influential at driving microbial community structure and composition than environmental conditions, geographical location or spatial scales, as observed in seaweeds (Marzinelli et al., 2015). A possible starting point in establishing this descriptor may be the monitoring of some of the phyla detected in this study which showed shifts in response to environmental variations (i.e. Alphaproteobacteria, Gammaproteobacteria, Cyanobacteria, Clostridia, Planctomycetacia), and their relative frequency ratios. As highlighted by the UniFrac metrics, the stations showed differences in the phylogenetic lineages of the bacterial phylotypes found, suggesting the importance of incorporating phylogenetic analyses to help discriminate microbial communities among environmental samples. Moreover, it is also necessary to properly determine the most suitable way in which microbial communities can be used as a descriptor in seagrass meadows (i.e. phylotype frequency ratio, index, diversity, etc.). A further milestone will be the identification of the most efficient suite of technical and statistical tools to be applied in the study of seagrass host associated microbial communities. Future studies should focus on long term monitoring of bacterial shifts under different temporal and spatial scales, as well as in the identification of the plant physiological and metabolic variations associated with bacterial shifts. This could help understand the most influential drivers of the seagrass holobiont dynamics, as is emerging from other marine organisms, like corals (Meron et al., 2011; Rosenberg et al., 2007), sponges (Fan et al., 2012; Webster et al., 2008) and seaweeds (Marzinelli et al., 2015; Singh and Reddy, 2014). 5. Conclusions The integrated approach applied in this study successfully detected differences in the plants’ ecological status and the associated epiphytic microbiome, in response to the local environmental conditions. Both the morphological (leaf morphometrics, number of leaves/shoot and percentage of leaves with lost apex) and the biochemical descriptors (foliar photosynthetic pigments and total phenol content) showed how morphological plasticity and biochemical adaptation of plants occur as they cope with different environmental conditions. This study provides evidence about the suitability of these descriptors to depict together the ecological status of seagrass meadows, even at a small spatial scale. Significantly different microbial communities were also found among stations, in association with the plant’s ecological status and the local environmental conditions. This indicates the functional relationship that exists between seagrasses and their associated microbial

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communities, which needs further investigation. This work constitutes a new approach to the monitoring of seagrasses and a stepping stone in the application of microbial communities as a putative marker in a changing environment. With the growing concerns on the well being of seagrass meadows worldwide, we believe that these diagnostic descriptors can increase the effectiveness of monitoring programs and consequently improve the conservation efforts of these important habitats. Conflict of interest The authors have no conflict of interest to declare. Acknowledgements This work was partly financed by the Dead Sea-Arava Science Center, Central Arava Branch. GW was partially funded by the grant 121-1-4 awarded to by the Israeli Ministry of Environmental Protection. AYM and AR were recipients of a Short Term Scientific Mission grant from the COST Action scientific programme (ES0906) on “Seagrass productivity: from genes to ecosystem management” to travel to Israel and conduct sampling activities (COST-STSM-ES0906-06445 and ES0906-15001). AR, AYM and LM were supported by the European Community – Research Infrastructure Action under the FP7 “Capacities” Specific Programme, ASSEMBLE project “Free-living coral associated Vibrio spp. identification through molecular and bioinformatics tools. Trial on Eilat Vibrio isolates”, 8th call, ASSEMBLE grant agreement no. 227799. AYM was funded by a PhD grant (International PhD program) from the University of Tor Vergata (XXVII Cycle). AR was funded by a Postdoctoral grant from the University of Tor Vergata/Regione Lazio Research Program “Certificazione di prodotti agroalimentari di qualità: dal DNA barcoding al naso elettronico – Area tematica DTB: Bioscienze & Biotec Verdi”. The authors are grateful to the staff at the Interuniversity Institute for Marine Sciences (IUI) Eilat for their logistic support. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2015. 09.014. References Abelson, A., Shteinman, B., Fine, M., Kaganovsky, S., 1999. Mass transport from pollution sources to remote coral reefs in Eilat (Gulf of Aqaba, Red Sea). Mar. Pollut. Bull. 38 (1), 25–29, http://dx.doi.org/10.1016/S0025-326X(99)80008-3. Abramoff, M.D., Magalhaes, P.J., Ram, S.J., 2004. Image processing with ImageJ. Biophotonics Int. 11 (7), 36–42. Agostini, S., Desjobert, J., Pergent, G., 1998. Distribution of phenolic compounds in the seagrass Posidonia oceanica. Phytochemistry 48, 611–617. Ainsworth, T.D., Vega Thurber, R., Gates, R.D., 2010. The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25 (4), 233–240, http://dx.doi.org/10.1016/j. tree.2009.11.001. Al-Rousan, S., Al-Horani, F., Eid, E., Khalaf, M., 2011. Assessment of seagrass communities along the Jordanian coast of the Gulf of Aqaba, Red Sea. Mar. Biol. Res. 7, 93–99, http://dx.doi.org/10.1080/17451001003660319. Angel, D.L., Eden, N., Susel, L., 1995. The influence of environmental variables on Halophila stipulacea growth. In: Rosenthal, H., Moav, B., Gordin, H. (Eds.), Improving the Knowledge Base in Modern Aquaculture. European Aquaculture Society Special Publication No. 25. , pp. 103–128. Aragones, L.V., Lawler, I.R., Foley, W.J., Marsh, H., 2006. Dugong grazing and turtle cropping: grazing optimization in tropical seagrass systems? Oecologia 149, 635–647, http://dx.doi.org/10.1007/s00442-006-0477-1. Arnold, T., Freundlich, G., Weilnau, T., Verdi, A., Tibbetts, I.R., 2014. Impacts of groundwater discharge at Myora Springs (North Stradbroke Island, Australia) on the phenolic metabolism of eelgrass, Zostera muelleri, and grazing by the juvenile rabbitfish, Siganus fuscescens. PLOS ONE 9 (8), e104738, http://dx.doi. org/10.1371/journal.pone.0104738.

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