Seasonality of top-down control of bacterioplankton at two central Red Sea sites with different trophic status

(1)

Seasonality of top-down control of bacterioplankton at two central Red Sea sites with different trophic status

Item Type Article

Authors Sabbagh, Eman I.; Calleja Cortes, Maria de Lluch; Daffonchio, Daniele; Moran, Xose Anxelu G.

Citation Sabbagh, E. I., Calleja, M. Ll., Daffonchio, D., & Morán, X. A. G.

(2023). Seasonality of top-down control of bacterioplankton at two central Red Sea sites with different trophic status. Environmental Microbiology. Portico. https://doi.org/10.1111/1462-2920.16439 Eprint version Publisher's Version/PDF

DOI 10.1111/1462-2920.16439

Publisher Wiley

Journal Environmental microbiology

Rights Archived with thanks to Environmental microbiology under a Creative Commons license, details at: http://

creativecommons.org/licenses/by-nc/4.0/

Download date 21/06/2023 18:03:10

Item License http://creativecommons.org/licenses/by-nc/4.0/

Link to Item http://hdl.handle.net/10754/692596

(2)

R E S E A R C H A R T I C L E

Seasonality of top-down control of bacterioplankton at two central Red Sea sites with different trophic status

Eman I. Sabbagh¹ | Maria Ll. Calleja^1,2 | Daniele Daffonchio¹ | Xosé Anxelu G. Moran¹

1Red Sea Research Center (RSRC), Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

2Department of Climate Geochemistry, Max Plank Institute for Chemistry (MPIC), Mainz, Germany

Correspondence

Eman I. Sabbagh and Xosé Anxelu G. Moran, Red Sea Research Center (RSRC), Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

Email:[email protected]and [email protected];xelu.moran@

ieo.es

Present address

Xosé Anxelu G. Moran, Centro Oceanografico de Gijon/Xixon (IEO, CSIC), Gijon, Spain.

Abstract

The role of bottom-up (nutrient availability) and top-down (grazers and viruses mortality) controls on tropical bacterioplankton have been rarely investigated simultaneously from a seasonal perspective. We have assessed them through monthly samplings over 2 years in inshore and offshore waters of the central Red Sea differing in trophic status. Flow cytometric analysis allowed us to distinguish five groups of heterotrophic bacteria based on physiological properties (nucleic acid content, membrane integrity and active respiration), three groups of cyanobacteria (two populations of Synechococcus and Prochlorococcus), heterotrophic nanoflagellates (HNFs) and three groups of viruses based on nucleic acid content.

The dynamics of bacterioplankton and their top-down controls varied with season and location, being more pronounced in inshore waters. HNFs abundances showed a strong preference for larger prey inshore (r=0.62 to0.59,p=0.001–0.002). Positive relationships between viruses and heterotrophic bacterioplankton abundances were more marked inshore (r=0.67,p< 0.001) than offshore (r=0.44,p=0.03). The negative correlation between HNFs and viruses abundances (r=0.47,p=0.02) in shallow waters indicates a persistent seasonal switch between protistan grazing and viral lysis that maintains the low bacterioplankton stocks in the central Red Sea area.

INTRODUCTION

Bacterioplankton, including autotrophic and heterotrophic groups, represent the largest living biomass in aquatic environments (Whitman et al.,1998). Determin- ing the factors controlling bacterioplankton biomass and growth is crucial to comprehend the trophic relationships between the different components of planktonic communities and ultimately their role in marine food webs and biogeochemical cycles. Heterotrophic bacteria are the major consumers of dissolved organic matter (DOM) and are consequently responsible for the bulk of nutrient remineralisation (Azam, 1998), while autotrophic members of phylum Cyanobacteria are key primary producers, forming the base of the microbial

food web in vast areas of the ocean, especially in oligotrophic environments (Chisholm,1992) where they may contribute up to 40% of carbon production and energy transfer (Ducklow et al., 1993). The variations in the abundance and activity of planktonic bacteria are caused by a combination of bottom-up (inorganic and organic substrates availability) and top-down (losses due to viral lysis and protistan grazing) factors, as well as other physico-chemical drivers such as temperature (e.g., Moran et al.,2017), light (e.g., Mary et al.,2008) and salinity (e.g., Pedros-Alio et al.,2000).

Although the supply of fresh resources ultimately derived from autotrophic primary production is the key process for properly assessing bottom-up control of heterotrophic bacteria (Ducklow & Carlson, 1992),

This is an open access article under the terms of theCreative Commons Attribution-NonCommercialLicense, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Environ Microbiol.2023;1–18. wileyonlinelibrary.com/journal/emi 1

(3)

wide-scale comparative studies have frequently used chlorophylla as a proxy of phytoplankton biomass and productivity (Bouvier et al., 2007). Consequently, the lack (Gomes et al., 2015) or the existence of weak (Sabbagh et al., 2020) relationships between bottom- up controls and bacterioplankton biomass and activity have sometimes been claimed as evidence of strong top-down control (Tsai et al.,2013).

Heterotrophic protists and viruses are deemed as the most significant factors causing bacterioplankton losses in the ocean (Giorgio et al.,1996; Suttle,2007) through size-selective grazing and host-specific lysis activities, respectively, regulating the flow of energy and organic matter through the planktonic food web (Liu et al., 2015). Attempts have been made to determine the impact of top-down control on prokaryotic communities in both marine and fresh water ecosystems [e.g., the coastal Mediterranean Thau lagoon in Bouvy et al., 2011; or groups of temperate lakes in France (Ram et al.,2014)], suggesting that the relative impact of heterotrophic nanoflagellates (HNFs) and viruses on bacterioplankton communities can vary in different environments, often resulting in spatial (e.g., Kopylov et al., 2021) and temporal (e.g., Kaikkonen et al., 2020) variability. Tsai et al. (2013) suggested that grazing by HNF was the main source of bacterioplankton mortality during summertime, while viral lysis was responsible for most bacterial losses during the colder months in the subtropical W Pacific Ocean.

Another study recently conducted in the Southern Ocean showed the opposite trend with a dominance of viral lysis over HNF grazing during the warmer months and vice-versa (Christaki et al.,2020). In the theoreti- cal analysis of aquatic ecosystems, Thingstad et al.

(2014) suggested that bacterial production and growth rates are related to bottom-up controls, while top-down controls regulate their abundance and biomass.

Recent report conducted in a shallow site of the central Red Sea (Silva et al., 2019) agree with this view;

the stocks of heterotrophic bacteria were jointly controlled by HNFs and viruses, which seem to alternate their prevalence along the year (Sabbagh et al., 2020). Sampling 1 year in microbial ecology studies is frequently insufficient for understanding the comprehensive long-term patterns of seasonality (Turk et al.,2020), we further investigated the spatio- temporal dynamics of bacterioplankton in the Red Sea coastal environment by extending on our previous work conducted in the coastal waters of King Abdullah University of Science and Technology (KAUST) Har- bor (Sabbagh et al., 2020) to two additional years of observations. Moreover, the inclusion of an offshore, more pristine location, allow us to identify variations not only arising from seasonality, but also from physico-chemical gradients. Monthly values of surface heterotrophic and autotrophic bacterial stocks and cell-specific physiological properties together with

ancillary physico-chemical variables, as well the abundances of HNFs and viruses, were assessed at KAUST Harbor (KH) and at Abu Shusha (AS), a coral reef located 7 km to the northeast of KAUST, from January 2018 to December 2019. Based on the results obtained from our weekly study conducted at KH in 2017 (Sabbagh et al., 2020), we hypothesised that: (i) bacterioplankton standing stocks are top-down controlled at the offshore site, (ii) the flow cytometric groups of low and high nucleic acid content (LNA and HNA, respectively, Gasol & Moran,2015) are consistently subjected to size-selection grazing by HNF along the inshore-offshore gradient, and (iii) bacterioplankton might respond differently to top- down controls at different trophic state and environmental conditions.

EXPERIMENTAL PROCEDURES Study sites and sample collection

The study sites are located north of Thuwal, Saudi Arabia. The inshore KH station is within the semi-enclosed KAUST Harbor at 2 m deep (22 18.412⁰ N, 39 6.172⁰ E, Figure 1), while the offshore AS station near Abu Shusha reef (60 m deep) is located westwards 7 km away from KAUST (22 19.104⁰N 39 01.230⁰ E, Figure1). Sampling was car- ried out on a monthly basis from January 2018 to December 2019 on board of the KAUST RV Explorer.

Surface water from each location was collected in 9 L acid-washed polycarbonate carboys and carefully transported within 1 h to the laboratory at KAUST, where it was immediately processed for the subsequent analyses described below.

Physico-chemical parameters

Continuous measurements of temperature and salinity were taken from the surface of both locations using a conductivity and temperature portable probe (YSI Pro- fessional Plus).

Samples for inorganic nutrients were collected in 15 mL Falcon tubes after pre-filtering through pre- combusted (470C for 5 h) glass fibre filters (Whatman GF/F, 0.7μm nominal pore-size) and kept frozen until further processing (except for ammonium). A seg- mented flow analyser from Seal Analytical (Analytical Core Lab, KAUST) was used to measure the concentration of nitrate (NO3), nitrite (NO2) and phosphate (PO4

3) using the standard methods described in Han- sen and Koroleff (1999). The concentration of ammonium (NH4+), only measured during 2019, was determined in fresh samples using a Shimadzu spec- trofluorometer (ICEOPS 2 and ATOS II) following the

(4)

methods mentioned in Kérouel and Aminot (1997). Dis- solved inorganic nitrogen (DIN) was calculated as NO3+NO2+NH4+.

Samples for dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) were collected in 40 mL amber dark glass vials after filtering the seawater through pre-combusted (470C for 5 hours) glass fibre filters (Whatman GF/F, 0.7μm nominal pore-size).

Samples were acidified with 200μL of 85% phosphoric acid and stored in a+4C fridge. High-temperature catalytic oxidation (HTCO) on a Shimadzu TOC-L with a total nitrogen unit was used for the analysis. Refer- ence material of deep-sea carbon (42–45μmol C L¹ and 31–33μmol N L¹) and low carbon water (1– 2μmol C L¹) were used to monitor the ultimate

accuracy of DOC and TDN measurements. Dissolved organic nitrogen (DON) concentration was calculated as TDNDIN.

Chlorophyll a concentration (Chl a) was used as a proxy of phytoplankton biomass. 200 mL of seawater were sequentially filtered through three polycarbonate filters (Millipore, 47 mm diameter) of decreasing pore- size: 20, 2 and 0.2μm. After filtration, Chl a pigment was extracted by sonicating the filters in 90% acetone and incubating them in a+4C fridge for 24 hours. Chl afluorescence was measured using a Trilogy fluorome- ter (Turner design) calibrated with a chlorophyll standard (Anacystis nidulans, Sigma-Aldrich). Total Chl awas calculated as the sum of the Chlaconcentration measured in each size-fraction.

F I G U R E 1 Map of the Red Sea showing the locations of the two sites; Abu-Shusha (AS) and KAUST Harbor (KH).

(5)

Microbial abundances

Samples for determining the abundance and cellular characteristics of the different bacterioplankton groups were analysed with a FACSCanto II flow cytometer (BD Biosciences). Latex fluorescent beads (Molecular Probes, ref. 17154–10) of 1μm diameter were used as an internal standard for the different signals of fluorescence and right angle light scatter (RALS). The actual flow rate (μL min¹), necessary for calculating absolute abundances (cells mL¹), was measured during each analysis by weighing 1 mL of Milli-Q water before and after 5 min running in the flow cytometer. All cytograms were analysed using FCS Express 5 (De Novo Software).

Autotrophic bacteria were enumerated by running 600μL of fresh seawater in the flow cytometer after the addition of 10μL of bead solution. We distinguished Prochlorococcus from Synechococcus in plots of red (620 nm, corresponding to Chl a, common to both genera of cyanobacteria) versus orange (530 nm, corresponding to phycoerythrin, present only in Syne- chococcus) fluorescence. Prochlorococcus were also characterized by lower RALS signals. Two groups of Synechococcuswere distinguished based on their relative orange fluorescence signal, referred to as LF and HF (from low and high fluorescence, respectively), as in Sabbagh et al. (2020).

We distinguished five groups of heterotrophic bacteria based on their physiological state (Giorgio &

Gasol, 2008), namely the relative nucleic acid content, the presence of healthy membranes and the respiratory activity, using the methods described in detail in Gasol and Moran (2015). Two groups of heterotrophic bacteria were always identified based on their relative green fluorescence signal after staining them with a fluorescent dye: low nucleic acid bacteria (LNA) and high nucleic acid bacteria (HNA). Samples (1800μL of seawater) were fixed with 180μL of paraformaldehyde+glutaraldehyde at final concentrations of 1% and 0.5%, respectively, and incubated in the dark for 10 min before storing them in a 80C freezer until analysis. For analysis, 400μL of the thawed samples were stained with 4μL of SYBR Green I (Molecular Probes), incubated in the dark for 10 min and analysed in the flow cytometer after the addition of 10μL of bead solution. LNA and HNA bacteria were identified based on their relative signals of green fluorescence (530 nm) and RALS. The size of LNA and HNA cells was estimated by converting their RALS signals (relative to that of the fluorescent beads) to cell diameter, and ultimately to biovolume following the method of Calvo-Díaz and Moran (2006). The ratio of bacterial abundance per unit Chlawas used as a proxy of resources available per bacterial cell (Li et al.,2004).

LiveandDeadheterotrophic bacteria were identified using the nucleic acid double staining protocol of Gré- gori et al. (2003). Fresh seawater (400μL) were stained

with 4μL of SYBR Green I and 4μL of propidium iodide (PI, Molecular Probes). After 10 min in the dark, samples were analysed in the flow cytometer after the addition of 10μL of bead solution.Live andDead bacteria were detected in green (530 nm) versus red (620 nm) fluorescence plots. While both Live or membrane-intact and Dead or membrane-damaged cells yielded a green fluorescent due to SYBR Green staining, only Dead cells (stained with both SYBR Green I and PI) yielded a red fluorescent signal.

We used 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) dye to enumerate actively respiring bacteria or CTC+. CTC competes with the electron transport chain in the most active organisms, which reduce it to an insoluble fluorescent compound. Fresh seawater samples (250μL) were stained with 28μL CTC, incubated in the dark for 90 min and then analysed in the flow cytometer after the addition of 10μL of bead solution. The red fluorescence (620 nm) of CTC+ bacteria was distinguished from naturally red fluorescent (due to Chl a) Synechococcus cyanobacteria because the latter were also fluorescent in the orange wavelength (530 nm) due to their phycoerythrin content.

HNFs were identified following the protocol of Chris- taki et al. (2011). Seawater samples (4400μL) were fixed with 113μL of glutaraldehyde at 2% final concentration, incubated for 10 minutes in the dark and stored in a 80C freezer. 1000μL of the thawed samples were stained with 10μL of SYBR Green I, incubated in the dark for 10 minutes and analysed in the flow cytometer after the addition of 10μL of bead solution. After successive gatings, the HNF population was detected in red (620 nm) versus green (530 nm) fluorescence plots since only autotrophic nanoflagellates yielded a strong red fluorescence signal due to their Chl acontent.

Viruses were estimated as described in the protocol optimised by Brussaard (2004). Three groups of viruses were defined based on their nucleic acid content. V1 cor- responds to viruses with the lowest nucleic acid content, while V2 and V3 correspond to viruses with a medium and high nucleic acid content, respectively. Sea water samples (1500μL) were fixed with 30μL of glutaraldehyde at 2% final concentration, incubated in the dark for 10 min and stored in a 80C freezer. 25μL of the thawed samples were diluted in 475μL of 1X Tris-EDTA buffer (TE). Samples were stained with 5μL of SYBR Green I, incubated at 80C water bath for 10 min, cooled down to room temperature and analysed in the flow cytometer after the addition of 10μL of bead solution. A control was performed by running 500μL of TE buffer and 5μL of SYBR Green I. After the analysis, the num- ber of particles counted in the control was subtracted from each sample count. The three viruses subgroups (V1, V2 and V3) were discriminated based on their green fluorescence (530 nm) and RALS signals.

(6)

Data analysis

Abundances were logarithmically transformed to comply with homogeneity of variance for further calcu- lations. JMP (Pro 15) was used to build figures and to perform common statistical tests including pairedt-tests and Pearson’s coefficient of correlation (r). A non- parametric test (Kruskal-Wallis test and post-hoc Dunn’s test) was used to detect seasonal patterns.

Ocean Data View (version 5.5.1) software was used to create the map figure.

RESULTS

Environmental properties

Most physico-chemical variables (seasonal mean in Table 1) differed between the two sites. Surface temperature and salinity in the inshore waters of KAUST Harbor (KH) were tightly coupled (r=0.89,p< 0.0001, n=24), with values ranging from 23.3 to 34.4C and from 39.6 to 40.8, respectively (Figure S1A,C), and were significantly higher (paired t-tests, p=0.01 and <0.0001, n=24, respectively) than the values found at the offshore station of Abu Shusha reef (AS), which ranged from 24.5 to 32.4C and from 39.2 to 39.8, respectively (Figure S1B,D).

Nitrate also showed significantly higher (pairedt-test, p< 0.0001) concentrations at KH (ranging from 0.74 to 4.71μmol L¹, Figure S2A) than at AS (below 1μmol L¹ except on one occasion, Figure S2B). The concentration of nitrite was low at both sites (mean ± SD, KH: 0.12

± 0.07 and AS: 0.02 ± 0.07μmol L¹), resulting in the same mean contribution of 4 ± 5% to NO3+NO2

values. Consequently, the concentration of NO3+NO2

at KH was significantly higher than at AS (KH: 2.42

± 1.05μmol L¹, AS: 0.28 ± 0.38μmol L¹, pairedt-test, p< 0.0001). Ammonium (only measured during 2019) showed a mean concentration of 1.96 ± 0.75μmol L¹at KH and 1.59 ± 0.92μmol L¹at AS. Phosphate concentration did not show any clear seasonal pattern at KH, with a mean of 0.036 ± 0.021μmol L¹(Figure S2C), while at AS it varied from 0.001 in fall to 0.118μmol L¹in winter (Figure S2D). The inorganic N:P ratio was typically higher than 16 at KH (except in October 2019), averaging 100

± 72 at KH, while values at AS were much lower (14 ± 34).

DOC concentration averaged 96.0 ± 17.8μmol C L¹(Figure S2E) at KH and showed significantly higher values (paired t-test, p=0.0001) than those found at AS, where they averaged 83.7 ± 15.4μmol C L¹ (Figure S2F). DON concentration ranged from 4.1 to 23.8μmol N L¹ at KH (Figure S2G) and from 0.3 to 27.2μmol N L¹ at AS (Figure S2H). DOM C:N ratios therefore varied from 5 to 21 at KH and from 4 to 61 at

AS, showing no clear seasonal pattern. 111TABLE1Seasonalvariability(mean±SD)oftemperature(C),salinity,inorganicnutrientsconcentrations;nitrate,nitriteandphosphate(μmolL),DOC(μmolCL),DON(μmolNL) 1 andtotalchlorophylla(totalChla,μgL)atKHandAS. KHAS WinterSpringSummerFallWinterSpringSummerFall Temperature26.2±3.328.4±2.333.2±1.231.6±2.527.1±1.627.1±1.930.9±1.330.9±1.0 Salinity39.8±0.440.0±0.040.5±0.140.4±0.339.4±0.239.3±0.239.5±0.139.6±0.1 Nitrate1.88±0.92.00±0.673.32±1.032.00±0.940.47±0.560.14±0.130.32±0.430.08±0.06 Nitrite0.10±0.070.17±0.130.13±0.050.12±0.050.02±0.010.06±0.14ndnd Phosphate0.04±0.030.03±0.020.04±0.020.03±0.010.05±0.040.06±0.030.03±0.010.03±0.01 DOC82.9±4.494.1±16.2103.5±13.8103.4±25.177.2±5.379.4±7.592.3±13.685.9±24.9 DON7.5±2.29.1±3.811.4±3.314.3±6.73.7±4.08.3±5.713.3±8.26.6±7.6 TotalChla0.49±0.110.40±0.180.48±0.140.50±0.200.39±0.040.21±0.070.15±0.040.31±0.08 Note:ndrepresentsvaluesthatwerenotdetected.

(7)

Total chlorophyll a concentration was consistently low at both sites (Table 1), never reaching 1μg L¹, although values were significantly higher at KH (0.19– 0.80μg L¹, pairedt-test,p< 0.0001, Figure S2I) than at AS. At AS waters, Chl a varied from 0.09 to 0.45μg L¹and showed a consistent unimodal distribution, with minimum values as low as 0.1–0.2μg L¹ in summer and maximum in early winter and late fall (Kruskal-Wallis test,p< 0.05, Dunn’s test, winter=fall-

> summer=spring, Figure S2J). Site differences in the three size-classes contribution to Chl a were also detected (Table S1), with a significantly higher relative contribution of the picoplankton size-class at AS than at KH (65 ± 12% vs. 51 ± 15%, paired t-test, p=0.001), while those of the nanoplankton (KH: 22 ± 8%, AS:

13 ± 11%) and microplankton (KH: 26 ± 10%, AS:

23 ± 18%) size-classes were conversely significantly higher at KH (paired t-tests, p=0.04 and p=0.01, respectively).

Bacterioplankton abundance

Picophytoplankton were dominated by cyanobacteria of the genusSynechococcusat both sites, withProchloro- coccus being consistently observed only at AS (Figure 2). Although at KH Prochlorococcus were detected more frequently in 2019 than in 2018 (10 versus only four occasions), they presented very low abundances (maximum 1.4210⁴ cells mL¹, Figure 2A) there compared to AS, where abundances ranged from 0.32 to 8.5410⁴ cells mL¹ (Figure 2B) and showed a consistent, pronounced seasonality with noticeably higher values from January through June, followed by a decrease during the rest of the year with less than half the values found in the first 6 months (Figure 2B). The total abundance of Synechococcus (i.e., LF+HF groups) was significantly higher at KH (pairedt-test,p=0.004) than at AS, ranging from 0.44 to 1.6610⁵cells mL¹(Figure2C). At AS, totalSyne- chococcus abundance did not show much seasonal variation, ranging from 0.2 to 1.2110⁵ cells mL¹ (Figure2D). The contribution of the LF group was different at both sites (Figure2E,F). At KH, LFSynechococ- cus displayed a consistent, remarkable seasonal pattern, averaging 87 ± 19% of total Synechococcus with minima found at the beginning and the end of the year and clearly dominating the community during summer (Figure2E). The contribution of LFSynechococcus at AS was on average 46 ± 16% of total Synechococ- cus abundance and did not show any clear seasonal pattern (Figure 2F). The LF fraction ofSynechococcus abundance at KH showed a significant positive correlation with salinity (r=0.49, p=0.01, n=24), but this relationship was not detected at AS.

Total heterotrophic bacterial abundance (i.e., the sum of LNA and HNA cells) showed similar ranges at

the two sites (KH: 2.84–8.7910⁵ cells mL¹, AS:

3.33–8.7910⁵cells mL¹, Figure3A,B, respectively), displaying as expected similar values to the sum ofLive and Dead bacteria. Live cells dominated at both sites year-round, with the same mean annual contribution (85%, Figure 3C,D). Although HNA bacteria showed similar mean abundances at both sites (KH: 2.36

± 0.6910⁵ cells mL¹, AS: 2.08 ± 0.6910⁵ cells mL¹) their contribution to total numbers was significantly higher (paired t-test, p=0.0004, n=24) at AS (64 ± 7%, Figure 3F) than at KH (55 ± 7%, Figure3E).

The contribution of actively respiring (i.e., CTC+) cells was low at both locations (maximum of 23%), but it showed a significantly higher value at KH (14 ± 4%

vs. 10 ± 5% at AS, paired t-test, p=0.01, n=24, Figure3G,H, respectively). Total heterotrophic bacterial abundance was positively correlated with Chl a at KH only (r=0.40, p=0.04,n=23). Overall, the bacterial abundance per unit Chl a (Figure S3) at KH varied widely from 0.56 to 2.2310⁶ cells μg Chl a¹, and was significantly higher at AS (1.00–5.3510⁶ cells μg Chla¹, pairedt-test,p=0.0007,n=22).

None of the autotrophic and heterotrophic bacterial group abundances showed any significant correlation with DOC, DON and inorganic nutrients concentrations at either KH or AS.

NHFs and viral abundances

The abundance of HNFs (overall range 746–2808 cells mL¹) varied significantly between the two areas (paired t-test, p=0.004, n=24), with higher values found at KH than at AS (1632 ± 459 and 1290 ± 307 cells mL¹, respectively, Figure 4A,B). At KH, HNFs displayed a consistent unimodal seasonal distribution during both years (Kruskal-Wallis test, p< 0.05), with minima in summer and maxima in winter and fall (Dunn’s test, winter=fall > spring=summer, Figure 4A), while at AS HNFs did not show any clear seasonal pattern (Figure4B).

As expected from the previous study at KH (Sabbagh et al., 2020), we consistently detected three subgroups of viruses at the two sites. Total viral abundance (V1+V2+V3) ranged from 0.86 to 2.0910⁷ particles mL¹ at KH, a range significantly higher (pairedt-test,p< 0.0001) than that found at AS (0.49– 1.4810⁷particles mL¹, Figure4C,D). At KH, viruses displayed a consistent temporal pattern, increasing from January to April, with generally high values throughout spring and summer (>1.510⁷ particles mL¹) followed by a decrease towards the end of the year (Kruskal-Wallis, p=0.01, Dunn’s test, spring=summer > winter=fall, Figure 4C). At AS, viruses did not display any clear seasonal distribution (Figure 4D). However, the three viruses subgroups exhibited different contributions in the 2 years

(8)

(Table S2). At KH, V1 was the dominant group in 2018, with a mean contribution of 57 ± 7%, followed by V2 with 35 ± 5%, and V3 with just 8 ± 3%, while in 2019, V2 represented on average 60 ± 6% of total viruses counts followed by V1 (35 ± 5%) and V3 (5 ± 2%, Table S2). Similarly to KH, at AS V1 was the most abundant subgroup (51 ± 7%), followed by V2 (38

± 5%), and V3 contributed to only 11 ± 2% of total counts during 2018. However, in 2019 the V2 subgroup became also the dominant group (54 ± 15% vs. 31 ±-

11% of V1 and 15 ± 5% of V3, Table S2).

Relationships between bacterioplankton and their top-down controls

The empirical model proposed by Gasol (1994) shown in Figure 5A,B was used to examine the regulation of

heterotrophic bacteria by HNFs. In brief, points closer to the Maximum Attainable Abundance (MAA) line represent a strong bottom-up control of HNFs by heterotrophic bacteria, while points closer to the Mean Realised Abundance (MRA) line would suggest increased top- down control on HNFs. No significant correlation was found between both datasets at any of the sites, although their abundances covaried positively. How- ever, for a given value of heterotrophic bacterial abundance HNF abundances tended to be higher at KH than at AS. All of our data were located above the MRA line (Figure5A,B), with some values on or even above the MAA line at KH. At AS, all points except one were distributed between the two lines (Figure 5B). Alto- gether, these results suggest little top-down control on HNFs themselves at both sites.

At KH, the increase in the abundance of HNFs was mirrored by concomitant decreases in the cell size of

F I G U R E 2 Temporal variations of the abundances of cyanobacteria at KH (left panels) and AS (right panels);Prochlorococcus(A and B), totalSynechococcus(i.e., sum of cells belonging to the LF and HF groups, C and D) and the contribution of LFSynechococcusto total Synechococcusabundance (E and F).

(9)

both heterotrophic bacterial groups, slightly more marked in LNA cells (with a slope of0.060 vs.0.045 slope for HNA cells, Figure 5A). This relationship was not detected at AS, characterized by smaller bacteria but fewer HNFs (Figure 5B). The ratio of bacteria to

heterotrophic nanoflagellates (bacteria:HNF) ranged from 154 to 691 and from 233 to 850 at KH and AS, respectively, with no coherent seasonal variation either at KH or AS. Bacteria:HNF ratios were significantly higher at AS than at KH (paired t-test, p=0.001,

F I G U R E 3 Temporal variations of the abundances of heterotrophic bacteria at KH (left panels) and AS (right panels); total heterotrophic bacteria (i.e., sum of LNA and HNA, A and B), the contribution ofLivecells (i.e.,Live/Live+dead, C and D), the contribution of HNA cells (i.e., HNA/LNA+HNA, E and F) and the contribution of CTC+bacteria (i.e., CTC+/LNA+HNA, G and H).

(10)

n=24). At KH, the bacteria:HNF ratio of bacteria:HNF showed a significant increase with the increasing in temperature at KH (r=0.48,p=0.01,n=23).

Total viral abundance at KH showed positive correlations with the total abundances of Synechococcus (r=0.62, p=0.001, n=23) and heterotrophic bacteria (r=0.67,p=0.0004,n=23, Figure 5C). The correlation between the abundances of viruses and heterotrophic bacteria was also significant but weaker at AS (r=0.44,p=0.03,n=23, Figure5D). The virus to heterotrophic bacteria ratios (VBR) were significantly higher at KH (paired t-test,p=0.0001,n=24), where they ranged from 18 to 58 compared with 8–43 at AS. No discernable seasonal pattern was found at any of the sites (Kruskal-Wallis test, p> 0.05). Similarly to the bacteria:HNF ratio, the VBR showed a significant increase with temperature with all data pooled (r=0.61, p=0.002, n=23, Figure S4). The abundances of the three subgroups of viruses were differen- tially correlated with the abundances of their potential hosts. Thus, viruses with the lowest nucleic acid content (V1) showed a significant positive correlation only with HNA abundance at KH (r=0.47, p=0.01, n=24, Table 2), but with LNA at AS (r=0.41, p=0.04,n=24, Table2). The V2 subgroup showed a significant correlation with LF Synechococcus abundance (r=0.50,p=0.01,n=24, Table2) at KH, and with Prochlorococcus abundance at AS (r=0.47,

p=0.02,n=24, Table2). The subgroup V3 characterized by the highest nucleic acid content showed a significant relationship with picoplankton Chl a (r=0.47, p=0.02,n=24) at AS. As suggested from the above results, at KH the seasonal increase in HNF abundance was accompanied by a decrease in viruses and vice- versa (Figure4A,C), thus resulting in a significant, negative relationship between HNF and total viral stocks at that site (r=0.47,p=0.02,n=22, Figure7).

DISCUSSION

With the exception of its northernmost region, the microbial food web in the Red Sea has remained little explored. In this study, we added the deeper, more pristine waters of Abu Shusha reef to the shallow ecosystem of KAUST Harbor, which we had previously described using a weekly sampling frequency (Ansari et al., 2022; Sabbagh et al., 2020). Our new dataset agrees with some recent studies highlighting the importance of protistan grazers and viruses as controls of bacterial assemblages (Ashy et al., 2021; Sabbagh et al.,2020; Silva et al.,2019) in these tropical waters, but also provides new insights into how top-down control varies both spatially and temporally.

Temperature and salinity covaried tightly at KH (Figure S1A,C), like previously described in weekly

F I G U R E 4 Temporal variations of the abundances of heterotrophic nanoflagellates (HNFs, A and B) and total viruses (i.e., sum of V1, V2 and V3, C and D) at KH (left panels) and AS (right panels).

(11)

(Ansari et al.,2022; Sabbagh et al.,2020) and monthly (Silva et al.,2019) assessments. The location of KH, in the vicinity of campus with over 8000 residents, may explain the high concentrations of nitrate that are occa- sionally found at this site (Sabbagh et al., 2020; Silva et al., 2019). The significantly lower concentrations at AS (89% on average, Table1and Figure S2A,B) were similar to the values measured elsewhere in the Red

Sea (e.g., Badran, 2001). However, ammonium concentrations in 2019 were surprisingly high in the two areas, up to 4–12-fold higher than values reported for the Gulf of Aqaba (Badran,2001). Contrary to inorganic nitrogen, phosphate concentrations were similarly low in the two areas and comparable to other central Red Sea values (Wickham et al., 2015). Even without including the relatively high ammonium concentrations,

T A B L E 2 Pearson’s correlation coefficients between the log10-transformed abundances of viruses subgroups (V1, V2 and V3) and both the autotrophic (ProchlorococcusandSynechococcus) and selected single-cell physiological groups of heterotrophic bacteria (LNA and HNA bacteria) at KH and AS.

KH AS

V1 V2 V3 V1 V2 V3

Prochlorococcus na na na 0.04 0.45* 0.10

LFSynechococcus 0.39 0.50* 0.43* 0.03 0.13 0.16

HFSynechococcus na na na 0.08 0.19 0.16

LNA 0.22 0.36 0.23 0.41* 0.05 0.02

HNA 0.51* 0.05 0.38 0.23 0.21 0.37

Note: Significant correlations are represented in bold (*p< 0.05). na, not available due to the absence ofProchlorococcusand HFSynechococcusat KH during most of the sampling period.

F I G U R E 5 Relationships between the abundances of total heterotrophic bacteria and Heterotrophic nanoflagellates (HNFs, A and B) and total viruses (C and D) at KH (left panels) and AS (right panels). The two lines drawn in A and B represent the maximum attainable abundance (MAA) and the mean realised abundance (MRA) of the predator–prey model by Gasol (1994). Fitted lines in c and d represent the ordinary least square (OLS) linear regressions (coefficient of determination and significance level provided). The shaded points were considered outliers and therefore excluded from the regression. Data pooled from the 2 years sampled.

(12)

which were only measured in the second year, the N:P ratio was generally well above 16 at KH, indicating strong P limitation at this site, similar to other reports in the Gulf of Aqaba (Wickham et al.,2015). However, the more offshore waters of AS were characterized by a mean value of 14 ± 34, indicative of N rather than P limitation of planktonic assemblages for most (66%) of the year. N limitation was also previously reported in central Red Sea open waters (Calleja et al., 2019; Kürten et al.,2016). This variation between the inshore and offshore waters would indicate a complex nature of mac- ronutrient limitation in the central Red Sea. DOC and DON concentrations at KH were similar to previous years (Ansari et al., 2022; Sabbagh et al., 2020; Silva et al.,2019), while values at AS were also comparable to the nearby open waters off KAEC (Al-Otaibi et al., 2020). The seasonal variability of Chlaat AS is fully consistent with remote sensing observations of the surface Red Sea (Raitsos et al.,2013). The picoplankton size-class was clearly dominant year-round at both sites (Table S1), matching previous in the area (Ansari et al., 2022; Silva et al., 2022) and elsewhere in the Red Sea (Kheireddine et al.,2017; Qurban et al.,2019).

This trophic gradient from shallow to deeper waters also drove changes in microbial plankton communities.

Although Prochlorococcus and Synechococcus are known to thrive in tropical ecosystems (DuRand et al., 2001; Partensky et al., 1999; Ting et al., 2002), the former cyanobacteria were absent or appeared in very low numbers at KH (<1.410⁴cells mL¹, Figure 2A), which is consistent with prior weekly assessments at the same site (0.37–5.7910⁴cells mL¹, Ansari et al.,2022), perhaps linked to the significantly higher nitrate concentrations (Figure S2A,B) (Partensky et al., 1999). The abundance of Prochloro- coccus at AS was similar to the numbers detected off KAEC station (1.1–5.810⁴cells mL¹, Al-Otaibi et al., 2020), but were however well below the maximum abundances found in the Indian (2.510⁵ Flombaum et al., 2013), Pacific (HOT station, 1.7 10⁵cells mL¹, Campbell et al., 1997) and Atlantic (BATS station, 2.610⁵cells mL¹, DuRand et al., 2001) Oceans. The total abundance ofSynechococcus in both areas fell within the range found in the Red Sea (Al-Otaibi et al., 2020; Post et al.,2011). The contribution of the two populations of Synechococcus characterized by differing phycoerythrin (PE) content (Katano & Nakano, 2006) was remarkably predictable at KH (Figure 2E), but not at AS (Figure2F). The com- plete disappearance of HF Synechococcus during the warmest months in the shallower waters of KH had already been identified (Sabbagh et al.,2020), but they were present year-round in the open waters of the station off KAEC (Al-Otaibi et al., 2020) relatively close to our AS site. An earlier study in the Red Sea revealed the presence of twoSynechococcusstrains that vary in

phycoerythrin content due to different ratios of phycour- obilin and phycoerythrobilin chromophores, which in turn was highly dependent on light conditions (Fuller et al.,2003). Although we might expect higher turbidity in KH waters, we do not have detailed irradiance data to compare between the two sites. However, Li et al.

(2019) suggested that LF and HF-Synechococcushave different salinity preferences, with LF Synechococcus more commonly found in more saline areas. Therefore, the significantly higher salinity waters of KH might be partly responsible for its substantially higher contribution of LFSynechococcusto total numbers.

Heterotrophic bacterial abundances have been monitored in the shallow waters of KAUST Harbor since 2015 (Ansari et al.,2022). In this study, the mean abundances of heterotrophic bacteria found there (5.2410⁵ cells L¹) were notably higher than in the previous 2 years (65%–75% lower abundances than the present study, Ansari et al., 2022; Sabbagh et al., 2020; Silva et al., 2019). This difference could also be related to the higher viral abundances observed in the current study (see below). However, our abundances still lie towards the lower range of values expected for tropical and subtropical oligotrophic waters (e.g., Cavender-Bares et al., 2001; Moran et al., 2017; Van Wambeke et al., 2008). Chlorophyll a has been widely used as a proxy for bacterial resource supply when direct rate measurements are not available (Ducklow & Carlson,1992). The total bacterial abundance:Chlaratios found in this region of the Red Sea, with mean annual values ranging from 1 (this study at KH) to more than 210⁶cellsμg Chla¹(Al- Otaibi et al.,2020), are still five-fold lower than the maximum ratios reviewed by Li et al. (2004). Together with the fact that the linear regression of bacterial abundance vs. Chl a was only significant at KH, but explained just 16% of its variance, our results suggest a weak control of heterotrophic bacterial stocks by phytoplankton as direct supply of substrates in the area.

While total heterotrophic bacterial abundances were similar at the two sites (non significant 14% higher numbers on average at AS), the single-cell physiological structure did show significant variations between inshore and offshore waters (Figure3). OurLivebacteria values (generally well above 70% of the total, Figure 3B,D) were similar to previous studies at the same area (Silva et al., 2019; Sabbagh et al., 2020;

Ansari et al., 2022). Although the range of variation of cells with different nucleic acid content was also consistent with previous reports (Al-Otaibi et al., 2020;

Sabbagh et al.,2020), the significantly higher (pairedt- test, p=0.0004) contribution of HNA cells at AS (9%

higher on average, Figure 3F) was unexpected since this value tends to decrease along the inshore-offshore gradient (Calvo-Díaz & Moran, 2006; Franco-Vidal &

Moran, 2011), which could be related to the stronger pressure of HNF at KH. In this regard, Silva et al. (2019)

(13)

found clearly higher bacterial carrying capasities in grazer-free incubations at the same site. Site-specific differences were also noticeable in the contribution of actively respiring bacteria (CTC+) with higher values at KH (Figure3G,H, though slightly lower than the ca. 20%

found in 2016 and 2017, Silva et al., 2019; Sabbagh et al., 2020), reflecting the expected increase from the nutrient-poor open ocean to more productive systems (Giorgio & Scarborough,1995; Hu et al.,2020).

Two more years of samplings at KH confirm that the seasonality in the abundance of HNFs first identified by Sabbagh et al. (2020) is highly consistent (Figure4A).

Differences in HNF abundance between inshore and offshore waters such as the 40% significantly lower values found at AS were previously reported in surveys conducted in the western (Zhang et al., 2017) and southern (Safi & Hall,1997) Pacific Ocean. Overall, our HNF abundances agree well with values found in other oligotrophic environments (1.310³cells mL¹in the E Mediterranean Sea, Christaki et al.,2001and 1.810³ cells mL¹ in the W Pacific Ocean, Tsai et al., 2015), including earlier reports in the Red Sea (1.210³cells mL¹, Berninger & Wickham,2005and 1.210³cells mL¹, Weisse,1989). The positive relationship between heterotrophic bacteria and HNFs abundances, widely detected in marine regions (Simo-Matchim et al., 2020;

Thomson et al.,2010), including the Red Sea (El-Serehy et al.,2013; Sabbagh et al.,2020), was not significant at any of the two sites in this study (Figure5A,B). A possi- ble explanation is that the direct effect of HNFs predation was obscured by the monthly temporal resolution, as opposed to the weekly sampling of KH in 2017 (Sabbagh et al., 2020). As in our previous study, we used Gasol (1994) empirical quantitative predator–prey model to determine the main control of HNFs (Figure 5A,B). Most data fell between the MAA and MRA lines at both sites, with some points lying notably closer or even above the MAA line at KH, suggesting that HNFs were strongly dependent on their bacterial prey, especially at the shallowest site. Remarkably, the stronger effect of HNFs on heterotrophic bacteria at KH became even more visible when we examined the relationship between HNFs abundances and the cell size of their prey (Figure 6A), confirming our previous annual study (Sabbagh et al.,2020) conducted at that site, similar to other studies in tropical regions (e.g., Hu et al., 2020; Landry & Kirchman, 2002). The lack of a significant covariation of bacterial size and HNFs abundance at AS (Figure 6B) suggests that heterotrophic bacteria were more loosely controlled by HNFs in the offshore waters of Abu Shusha reef.

To further investigate the relationship between HNFs and heterotrophic bacterial abundances, the bacteria:HNF ratio can be used (Christaki et al., 2011) to indicate whether heterotrophic bacterial abundance is bottom-up or top-down controlled (Tanaka &

Rassoulzadegan, 2002). Thus, a high bacteria:HNF

ratio (i.e., >1000) can be interpreted as uncoupling between bacteria and HNFs (Fenchel,1986; Krstulovic et al., 1998). Our mean bacteria:HNF ratios were consistently lower than that value (344 ± 128 at KH and 484 ± 156 at AS), indicating that heterotrophic bacteria were indeed to a large degree controlled by HNFs. The significantly higher bacteria:HNF ratio at the offshore site suggests that heterotrophic bacteria were less controlled by HNF in an overall less productive ecosystem, as suggested by Gasol and Vaque (1993). Moreover, the significant relationship we found between bacteria:HNF ratio and temperature at KH, suggests a lower consumption of bacteria by HNFs in warmer conditions. As discussed in more detail below we suggest that heterotrophic bacteria were predominantly controlled by HNFs during colder months and by viruses during warmer months.

Overall, our viral abundances fit well with the range found for other oligotrophic, low latitude waters (Gainer et al.,2017; Hwang & Cho,2002; Lara et al.,2017) and the significantly higher abundances at KH compared to AS match other observations made along inshore- offshore gradients (Alongi et al., 2015; Ordulj

F I G U R E 6 Relationships between the cell size of LNA and HNA bacteria and the abundance of heterotrophic nanoflagellates (HNFs) at KH (A) and AS (B). Fitted lines in A represent the OLS linear regression (coefficient of determination and significance level provided). Data pooled from the 2 years.

(14)

et al., 2015). The consistent seasonal pattern in total viral abundances at KH (Figure 4C), agrees with our previous weekly observations at that site (Sabbagh et al.,2020). This seasonality is also in accordance with previous reports from other regions (Johannessen et al.,2017; Parsons et al.,2012).

Viruses are known to infect both autotrophic and heterotrophic bacteria (Brussaard et al.,2008; Sullivan et al., 2003; Suttle, 2007). The positive correlation between total viruses andSynechococcusabundances at KH might indicate that a large fraction of viruses were cyanophages, which tend to increase in more productive systems (Bettarel et al., 2004; Johannessen et al., 2017). The analysis of the 3 different subgroups based on nucleic acid content (V1, V2 and V3, Table S2) consistently observed in our samples, as in most oceanic regions (e.g., Johannessen et al., 2017;

Mojica & Brussaard, 2020) can shed more light into specific associations with distinct bacterial groups. The observed changes in the relative contribution of the 3 subgroups between the two years (Table S2) are likely related to the change in absolute abundances, as also observed in other studies (Marston et al., 2013;

Marston & Sallee, 2003). The coupling between the abundances of the V1 group and HNA bacteria (Table2) found at KH had been also observed recently in the NW Atlantic Ocean (Mojica et al., 2019). Since HNA bacteria are consistently larger than their LNA counterparts in the central Red Sea (Al-Otaibi et al.,2020) and also have higher specific growth rates (Silva et al., 2019), our results suggest that V1 viruses at KH were infecting the more actively growing heterotrophic bacterial taxa (Mojica & Brussaard, 2014;

Weinbauer, 2004). In contrast, the fact that the abundance of the V1 viruses was only significantly correlated to that of LNA bacteria (Table 2) at AS could be explained by differences in the actual composition of the V1 subgroup in offshore waters. Martínez et al.

(2014) have shown that the V1 subgroup mostly con- sists of bacteriophages from the family Myoviridae and Podoviridae that frequently infect members of the SAR11 cells. Members of this bacterial clade generally dominates the LNA group in oligotrophic ecosystems (Schattenhofer et al., 2011). Another interesting obser- vation is that the abundance of the V2 subgroup covaried positively with the most abundant cyanobacteria at each site, LFSynechococcusat KH andProchlorococ- cus at AS (Table 1), strongly suggesting that the V2 subgroup was mainly made of cyanophages, as also shown in other studies (Li & Dickie, 2001; Payet &

Suttle, 2008). The abundance of the V3 subgroup was also correlated with LF Synechococcusabundance at KH and with picophytoplankton Chl a(of which cyanobacteria are the dominant component in these waters, Al-Otaibi et al., 2020) at AS, suggesting that the flow cytometric group of viruses with the largest nucleic acid content infect small primary producers (dominated by

cyanobacteria in tropical ecosystems, Sullivan et al., 2003; Zeng & Chisholm, 2012) rather than heterotrophic prokaryotes. Although several studies have considered V3 eukaryotic algal viruses (Martínez et al., 2014), Mojica et al. (2016) detected a tight coupling between the abundance of V3 viruses and the picocyanobacterial community, suggesting that the highest nucleic acid content group may also contain many cyanophages (Payet et al., 2014). In conclusion, our results agree with the finding that bacterioplankton are not subjected to infection by the same viral groups in inshore and offshore waters (Parsons et al.,2012).

A negative relationship between viral abundance and that of their heterotrophic bacterial hosts was found from April to December at KH (Sabbagh et al.,2020) as well as in a nearby coastal lagoon (Ashy & Agustí,2020). A similar negative relationship was also observed at BATS (Parsons et al., 2012), which could be explained by a direct effect of viruses on their susceptible bacteria hosts as indicated by the ‘Killing-the-winner’ hypothesis (Thingstad,2000). In this study, although a positive relationship between viruses and total heterotrophic bacterial abundances was found at both sites (Figure 5C,D), the higher slope of the linear regression at KH (explaining 45% of the variance in viruses numbers), suggests that top-down control by viral lysis was stronger in KH than in AS waters. A new hypothesis coined as‘Piggyback-the- winner’(Silveira & Rohwer,2016) suggests a switch in viruses life strategy from lytic to lysogenic cycle at higher bacterial abundances and growth rates, in which viruses integrate into the hosts genome preventing that a closely related virus infects the same bacterial cell. Since the abundances of viruses and heterotrophic bacteria at KH in 2017 were significantly (t-test,p< 0.05) lower (on average 2.3 and 1.7-fold, respectively, Sabbagh et al.,2020) than those found in this study, we suggest that the change in the relationship between bacteria and viruses from negative in 2017 (Sabbagh et al.,2020) to positive in the current study, besides the potential effect of a different sampling frequency, might be partly due to the simulta- neous increase in bacteria and viruses abundances, which would induce viruses to switch their cycle from lytic to lysogenic and can potentially limit the growth of less abundant microbes and further reduce microbial diversity (Chen et al.,2019). Moreover, Ashy et al. (2021) demon- strated that lysogeny is the favoured viral replication strategy at a nearby coastal site in the central Red Sea.

Interannual changes in bulk abundances of bacteria and viruses have been also observed in other oceanic areas (Marston et al., 2013; Parsons et al., 2012) and were attributed to changes in viral taxa and host composition (Marston & Sallee,2003). However, additional research is required to test this hypothesis.

The virus to bacteria ratio (VBR) is a common metric used as a proxy of viral dynamics in relation to potential hosts in marine ecosystems (Lara et al., 2017). Our VBR values agree with the typical ranges

(15)

found in other pelagic environments (Ordulj et al.,2015;

Parikka et al.,2017; Weinbauer et al.,2020). The significantly higher VBR at KH compared to AS would reflect a higher viral pressure on heterotrophic bacteria lead- ing to comparatively lower host density in inshore waters, as reported for other areas (Bettarel et al., 2004; Parikka et al.,2017). As viral abundance and life strategy are tightly linked to their potential host dynamics, increasing temperature will likely influence the virus-host interactions as indicated by Danovaro et al.

(2011). This is supported by the positive relationship found between temperature and VBR in our inshore site (Figure S4). Early, Murray and Jackson (1992) reported that higher temperature increases the contact rates of viruses to their host cells. Also, Bettarel et al. (2009) suggested that viruses are capable to adapt to higher environmental temperatures in order to survive and thus increase their ability to encounter their host cells.

Our data suggest that further warming may switch the top-down control of bacterial assemblages towards greater relative importance of viral lysis at the expense of grazing by HNF. Bacterial abundances in the Red Sea coastal waters may thus be kept low by warming- related increases in viral infection, in agreement with recent studies highlighting an increase in viral infection rates under warmer conditions, within temperature ranges (from 23 to 27C, Frenken et al.,2020and from 18 to up to 22C, Courboulès et al., 2021) significantly below the range found in the Red Sea (Table1).

Indeed, the abundances of viruses and HNFs showed a highly consistent and inverse seasonal distribution at KH (Sabbagh et al., 2020), with concurrent minima in HNF abundance and maxima in total viral abundance during summer and vice-versa in winter and fall, which is confirmed in this study (Figure4). The consistent negative association between the abundances of both mortality agents found in this coastal embayment (Figure 7) likely reflects a temporal switch between viral infection and HNFs grazing in regulating heterotrophic bacterial stocks in Red Sea inshore waters. This novel dataset confirms the ubiquitous nature of these two periods, roughly equivalent to half of the year each, with a distinct prevailing mode of top- down control first described in Sabbagh et al. (2020).

Thus, in tropical waters with reduced turnover such as our enclosed site at KH, grazing by HNFs would prevail in the coldest months (i.e., from December to February) while viral lysis would increase during the rest of the year, with maximum values between March and September. This opposite mode of bacterial top-down control has also been observed in other locations, such as the Arctic (Vaqué et al., 2019) and NE Pacific Oceans (Pasulka et al.,2015), suggesting that the seasonal succession between viral and grazing mortality might be a general feature of microbial food webs.

In conclusion, this study brings further supports to our previous findings that strong top-down

control is a persistent characteristic of central Red Sea coastal waters, largely responsible cause for lower than expected bacterial stocks. However, interesting differences arose from the actual location along the inshore-offshore gradient. Both the absolute top-down control and the temporal segre- gation between viruses and protistan grazers as the prevailing agents of bacterial mortality was much more noticeable in the same enclosed area.

The positive relationship of the VBR with temperature suggests that the importance of viral lysis as the major source of mortality of tropical heterotrophic bacteria may increase in response to warming.

Currently, viral lysis and protistan grazing alternate in regulating bacterial abundances along the annual cycle in the shallowest ecosystem with a higher water residence time, where HNFs preferentially grazed on larger cells of both LNA and HNA groups. Although sharing an oligotrophic condition, since most physico-chemical variables changed significantly as we moved offshore, our results suggest that tropical microbial communities might be structured differently only 7 km apart.

A U T H O R C O N T R I B U T I O N S

Eman I. Sabbagh:Conceptualization (equal); resources (lead); software (lead); visualization (lead);

writing–original draft (lead); writing–review and editing (equal). Maria Ll. Calleja:Data curation (equal); formal analysis (equal); writing – review and editing (equal).

Daniele Daffonchio:Data curation (equal); supervision (equal); writing – review and editing (equal). Xosé Anxelu G. Moran:Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); resources (equal); supervision (lead); writing–review and editing (equal).

F I G U R E 7 Relationship between the abundances of total viruses and heterotrophic nanoflagellates (HNFS) at KH. Fitted line

represents the OLS linear regression with data pooled from the two years sampled (coefficient of determination and significance level provided).