{"paper_id":"4b5f9741-271b-4ff3-bbf0-98eb6d4fca5d","body_text":"Coral-associated denitrification is seasonally variable and species-specific | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Coral-associated denitrification is seasonally variable and species-specific Claudia E. L. Hill, Arjen Tilstra, Yusuf C. El-Khaled, Neus Garcias-Bonet, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8588779/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Nitrogen (N) plays a critical role in coral growth, but maintaining an N-limited state is essential for coral-algal symbiosis stability. Coral-associated denitrifiers are microbes that live in association with the coral host and may help regulate excess N, though denitrification in corals remains poorly understood. We investigated year-long denitrification dynamics in four Red Sea corals, using acetylene inhibition assays alongside physiological and environmental measurements. All species exhibited measurable denitrification activity, ranging from 0–0.8 nmol N cm − 2 h − 1 for Stylophora pistillata and Acropora sp., 0–0.4 nmol N cm − 2 h − 1 for Millepora dichotoma , and 0–2.0 nmol N cm − 2 h − 1 for Tubastrea coccinea . We observed seasonal trends in denitrification activity, with generally higher rates in the spring/summer compared to autumn/winter, and identified temperature, dissolved organic carbon (DOC) and nitrate availability as key environmental drivers. Lastly, we observed up to 5 times higher denitrification rates in the fully heterotrophic azooxanthellate species T. coccinea than in the three mixotrophic zooxanthellate species. Our findings show that denitrifiers use both photosynthetically derived and environmental C, with DOC central in maintaining tight coupling of C and N cycling in coral holobionts. Additionally, denitrification is modulated by environmental conditions, highlighting its vulnerability to environmental change. Marine and Freshwater Ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Nitrogen (N) is essential for corals, supporting protein synthesis, reproduction and photosynthetic efficiency 1 – 3 . Yet, corals thrive in N - poor oligotrophic waters. Therefore, to sustain their productivity in these environments, corals employ a multifaceted approach to efficiently acquire, process and retain N. They can meet much of their N demand through heterotrophic feeding on N-rich prey and particulate organic matter, if available 4 . Additionally, corals exist as holobionts, living in association with microorganisms such as bacteria, viruses and many other taxa 5 , 6 . Diazotrophic bacteria form part of this intricate microbial community, playing a crucial role in N-fixation and contributing to the coral’s N budget. Specifically, diazotrophic bacteria convert atmospheric N 2 into bioavailable ammonium that can be used by the coral 7 – 10 In addition, many stony and soft corals harbour symbiotic dinoflagellates from the family Symbiodiniaceae and are colloquially known as zooxanthellate species 11 , 12 . The symbionts are capable of taking up nitrate, a process that the coral host itself cannot perform directly as it lacks the appropriate enzymes to reduce it into the ammonium bioavailable form 13 , 14 . The symbionts supply the coral host with carbon (C) – rich and N - poor photosynthates 11 . The symbionts also recycle metabolic waste products from the host, such as ammonium 15 , converting these into amino acids and other nitrogenous compounds that are partially translocated to the coral host 16 , 17 . In contrast, azooxanthellate corals do not host Symbiodiniaceae 18 and therefore rely solely on heterotrophic feeding and N-fixation for their N supply, without the added benefit of symbiotic N assimilation and C supply. Under contrasting conditions, corals can be negatively affected when N is available in excess. When more in hospite N is available, symbionts allocate more C to their own growth rather than to the coral host, promoting symbiont proliferation and a transition towards parasitism which can trigger coral bleaching 19 – 26 . The outcome can depend on nutrient stoichiometry, particularly the balance of N and phosphate (P) 3,27–30 . When N enrichment occurs without a corresponding increase in P, the coral-algal symbiosis can break down because the symbionts are starved of P, causing light and heat induced bleaching 30 . The effects of excess N can also vary by form of N, for example urea-exposed corals recover faster than those exposed to excess nitrate 27 . Additionally, excess ammonium has mixed effects, offering potential benefits to photosynthesis and calcification of corals at moderate concentrations, yet becoming toxic in higher concentrations 31 . Conversely, nitrate may negatively affect both photosynthesis and calcification processes as its conversion into bioavailable ammonium is energetically – costly 32 – 34 . One mechanism by which the coral holobiont mitigates excess N is the process of denitrification 3 , 35 , 36 . Denitrification is a microbial process where denitrifying microbes, within the coral holobiont sequentially reduce nitrate to nitrite, nitric oxide, nitrous oxide and eventually to dinitrogen gas that is released into the atmosphere 37 . This process has received increased attention in recent years, and preliminary insights into denitrification in coral reefs are now emerging. For example, recent studies have revealed that denitrification is an active pathway in multiple stony and soft Red Sea corals 36 , 38 stony Cuban corals 39 and Great Barrier Reef corals 40 . Denitrification has also been identified as an active pathway among several benthic reef substrates such as coral rubble, biogenic rock, turf algae and reef sediment 35 , 41 . These studies have also revealed that there are apparent susbstrate and coral species-specific differences in denitrification activity. Additionally, rates of denitrification and the opposing pathway N 2 fixation, were found to correlate with algal symbiont density and with each other, leading to speculation that the pathways may have some similarities 36 . For example, authors hypothesised that the heterotrophic bacteria that govern the two pathways may share a supply of organic C from the algal symbionts 36 . However, although denitrification activity has now been detected broadly, the significance of the pathway in overall N removal in stony corals is debated. For example, Glaze and colleagues 40 postulated that denitrification has limited importance compared to other N removal pathways like anaerobic ammonium oxidation, whereas Yang and colleagues 42 found denitrification accounted for ~ 90% of N 2 production in stony corals. However, it is important to acknowledge that different techniques have been used to quantify denitrification among existing studies. These include molecular techniques that quantify copy numbers of denitrifying genes such as nirS which can be used as a proxy for denitrification 36 , 40 and varying physiological techniques such as tracer experiments (direct) 39 , 40 , 42 and acetylene assays (indirect) 36 , 38 , 41 complicating direct comparisons between studies. Significant knowledge gaps remain in our understanding of coral-associated denitrification. While previous studies have quantified the denitrification rates of several Red Sea corals, these measurements were conducted under nitrate-enriched conditions to determine denitrification potential 35 , 36 . Consequently, it remains unclear how denitrification activity responds to natural environmental conditions in the Red Sea. In particular, we do not yet know how coral-associated denitrification varies seasonally. Seasonal cycles in the Red Sea are pronounced, with cooler temperatures (~ 24 °C) and higher nutrient concentrations (e.g., inorganic N) during winter and spring due to vertical mixing, and warmer temperatures (~ 32 °C) but lower nutrient availability during the stratified summer months 43 , 44 . The seasonal influence on an alternate N-cycling pathway (N 2 fixation) has been studied previously, which found significantly higher N 2 fixation of the reef in spring/summer than autumn/winter 45 . Likewise, in another Red Sea study, higher N 2 fixation rates were measured in the summer for Stylophora pistillata across water depths of 5, 10 and 20 m 46 . Furthermore, previous studies have speculated that there may be a link between denitrification activity and the trophic strategy of the coral host 36 , 38 , this has also not been directly investigated, and there has yet to be an assessment of denitrification across species with varying trophic strategies. Considering these knowledge gaps, we asked three key questions: i) What is the influence of seasonal change on coral-associated denitrification rates and which environmental factors drive this process? Secondly, ii) How do internal nutrient dynamics modulate denitrification activity? Lastly, iii) How does the host trophic strategy influence denitrification rates? To assess this, we selected a suite of Red Sea corals that, according to literature, differ in their trophic strategy. We included zooxanthellate corals that have a greater reliance on autrotrophy such as Stylophora pistillata, Acropora sp. and Millepora dichotoma , and an azooxanthellate coral Tubastrea coccinea that is fully heterotrophic 47 – 50 . We sampled these corals bimonthly (once every two months) over a complete year and measured denitrification rates, assessed various physiological parameters and monitored environmental conditions. We hypothesised that denitrification rates of corals would be lower in winter months compared to summer months as bacterial metabolisms are slowed down by low temperatures 51 . In fact, this pattern in denitrification activity has been observed in seagrass sediments with higher rates measured in summer compared to winter in the central Red Sea 52 . Secondly, we hypothesised that the internal nutrient dynamics of the coral host would influence denitrification activity, given that denitrification activity has been found to correlate with symbiont cell densities 36 , suggesting a link to internal nutrient cycling. Lastly, we anticipated that more autotrophic coral species would exhibit higher denitrification rates than those that are more heterotrophic. This hypothesis stems from previous studies that suggest that denitrifiers may rely on autotrophically-derived C 36,38 . Filling these knowledge gaps is crucial for comprehending both the natural dynamics of denitrification and the potential impacts of environmental stressors, such as ocean warming and eutrophication, on microbial community structure and function of corals. Furthermore, these findings will shed light on species-specific differences in denitrification and enhance our understanding of how particular species may withstand global changes. Material and methods 3 .1 Collection of corals We carried out coral collections in the central Red Sea at the “Al Fahal Reef”, or also known as “The Coral Probiotics Village” (22.30518N, 38.96468E), a mid-shore reef located 15 km offshore from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia 53 . The sampling area is shallow, with a maximum water depth of 10 m. We identified four species of Red Sea corals that differ in their trophic strategy, including three zooxanthellate species S. pistillata , Acropora sp., and M. dichotoma that exhibit mixotrophic feeding and an azooxanthellate species T. coccinea that has a fully heterotrophic lifestyle 47 – 50 . We sampled five separate colonies (n = 5) of each species using SCUBA between 1–5 m water depth, every second month over a one-year timespan, generating six timepoints i.e., April 2022, June 2022, August 2022, October 2022, December 2022, and February 2023. We consistently carried out sampling in the first two weeks of every other month, sampling M. dichotoma and Acropora sp. in the first week, and S. pistillata and T. coccinea in the second week. From each colony, we cut three fragments using pliers and placed them into labelled sampling bags, filled with seawater. Out of the three fragments, we used two for incubations (~ 5 cm length) and one for isotope and elemental analysis (~ 5 cm length). In the case of T. coccinea , colonies were too small to sample multiple fragments from, so instead, we sampled 15 polyps bimonthly. On the boat, we stored the fragments for incubations in recirculation aquaria filled with seawater from the sampling site, equipped with an air pump to maintain water circulation and oxygen availability. We placed all aquaria in the shade to prevent heat/light stress during transport, and we kept the fragments for physiological assessments on ice and later stored them at -20°C in the lab. The collections were conducted as part of a collaboration between KAUST and the University of Bremen, in which a subset of the dataset (isotopic and elemental data) for two species ( S. pistillata and M. dichotoma ) was analysed separately. 3.2 Quantification of denitrification rates On the same day as sampling, we quantified denitrification rates via acetylene blockage/inhibition assays. This indirect method has been successfully applied to investigate coral reef associated denitrification activities 54 . Acetylene blocks the activity of the enzyme nitrous oxide (N 2 O) reductase within the denitrification pathway, leading to the accumulation of N 2 O which can be used as a proxy for the relative activity of denitrification of the coral holobiont 54 – 57 . The efficacy of acetylene assays has been validated in previous work which used both acetylene assays and nirS gene copy numbers to quantify denitrification 36 . The observed patterns of nirS gene abundance corresponded closely with the denitrification rates measured using the acetylene method, providing evidence that the acetylene method is accurate and can be relied upon 36 . To set up the acetylene assays, we secured each coral fragment to a stand using rubber bands and placed them inside a gas-tight glass beaker (Figure S1a). We specially adapted the beakers for acetylene assays by having an 8 mm hole drilled into the lid. The hole was sealed with a gas-tight rubber stopper, and a hypodermic needle (hypodermic needle with polypropylene hub 30G x 3/4\", Tyco Healthcare group, MonojectTM) was permanently inserted through the stopper into the jar. On the outside of the jar, the needle hub was attached to a 2-way stopcock with a Luer lock connection (two-way stopcock, BraunTM DiscofixTM) where a gas syringe (50 ml gastight syringe model 1050 TLL PTFE Luer Lock, Hamilton) could be later fitted when required, for gas samples to be taken (Figure S1a). We filled each beaker with seawater (taken from the sampling site the same morning) to 80% of its capacity, leaving a 20% headspace. We then replaced ten percent of the seawater volume with acetylene enriched seawater, and likewise, replaced 10% of the beaker headspace with acetylene gas (Yoshinari & Knowles, 1976) (Figure S1a). We made acetylene gas freshly on the same day prior to the incubations (detailed in the Supplementary). For the incubations, we secured corals to a stand in gas-tight glass beakers filled with site-collected temperature-controlled seawater. We placed beakers into water baths equipped with thermostats (3613 aquarium heater. 75W 220–240 V; EHEIM GmbH and Co.KG) and temperature controllers (Schego Temperature Controller TRD, max. 1000W) that heated the water to the corresponding in situ temperature per sampling month. The water bath was then placed on top of magnetic stir plates which powered magnetic stir bars in the base of each beaker to ensure adequate water circulation (~ 220 rpm). we incubated corals for 12 h in the dark followed by 12 h in the light. During the light incubation, we supplied light at the same intensity as the in situ conditions of the respective sampling month. However, we used new fragments for the following light incubation to minimise the potential impact of stress on the coral’s denitrification rates. We included four control beakers containing no corals in each incubation run to account for potential background denitrification activity in the seawater. We took gas samples (3 ml) from the beaker headspace at the beginning (T0) and end (T12) of each incubation, using a gas syringe (50 ml gastight syringe model 1050 TLL PTFE Luer Lock, Hamilton) and stored the samples in gas tight vials until further measurement. Gas samples generated from the acetylene assays are typically measured using a gas chromatograph (GC) fitted with an electron capture detector (ECD). However, in our study, we measured the gas samples using a N 2 O microsensor (custom-made, Unisense). Although a GC with ECD was tested, it was not used as microsensors outperformed it in several aspects. For example, the electrochemical microsensor has high sensitivity (detection limit: 25 nM) and higher throughput compared to GC-based methods. We connected the microsensor to a multi-channel (fx-6 UniAmp multi-channel 110394, Unisense) and calibrated it every day prior to usage with a two-point calibration curve consisting of a low point (ambient air: 0.009 µmol L − 1 ) and a high point (a known standard: 0.575 µmol L − 1 ) at a consistent room temperature (21°C) and pressure (1 bar). To record and visualise measurements, we synced the N 2 O microsensor with the Sensor Trace Suite software (v.1.13) on a computer desktop. Further technical details about the microsensor and the steps to use the microsensor and normalise the data can be found in the supplementary material. While the acetylene inhibition technique has historically been popular to quantify denitrification rates 36 , 38 , 41 , it has several limitations to be aware of 54,58 . Sometimes incomplete inhibition of N 2 O reductase occurs, meaning that N 2 is produced as normal instead of accumulating as N 2 O, causing an underestimation of denitrification rates 54 . Further underestimation of denitrification may arise from the inhibition effect of acetylene on the nitrification pathway 59 . Nitrification is the preceding step in the N-cycle that provides nitrate as a substrate for denitrification. Therefore, denitrification rates may be underestimated due to substrate limitation 58 – 60 . Another common technique is to run 15N tracer incubations 40 which measures the actual N 2 production directly. The benefits of this technique include the ability to distinguish between pathways and the lack of an inhibitory effect on related pathways. However, in our case, using the acetylene inhibition technique was more suitable, as it allowed us to measure denitrification activity under ambient conditions, without artificially enriching with 15N-labelled substrates. 3.3 Elemental and isotopic analysis of carbon and nitrogen Firstly, we blasted the tissue off the coral skeleton. To do so, we held the fragment within a sterile clear sampling bag (Whirl-pack sample bag) and used an airbrush (model S68 with dual action siphon feed, Master Airbrush) to remove the tissue with high pressure air and MilliQ water. We always sterilised the airbrush with 70% ethanol and rinsed it with MilliQ water between samples. We then stored the tissue slurries in Falcon tubes at − 20°C and defrosted them in prior to the next stage. Once samples had defrosted, we separated the tissue slurry into host and symbiont fractions via centrifugation (2.5 minutes at 500 x g) and then washed and resuspended the symbiont pellet in 3 ml of MilliQ until it was clean (i.e., free from white residue), which typically required three washes. Following this, we filtered each fraction through 0.7 µm GF/F filters (fitted to a vacuum assembly), treated to remove skeletal contaminants (2 ml x1N HCl), rinsed with MilliQ, and immediately dried the samples at 60°C for 48 h. We then scraped the dried mass into tin cups and weighed and analysed the samples for δ13C, δ15N and mass percent C and N via EA-IRMS at the Natural History Museum, Berlin, using a Flash 1112 EA and Thermo Scientific Delta V IRMS (Berlin, Germany). We report the isotopic data using the conventional delta notations (δ) and express in ‰ relative to the international standards (Vienna Pee Dee Belemnite) for δ13C (0.01118) and atmospheric N2 for δ15N (0.00368) 61 . Within-run standard deviations (SD) of the standards were < 0.15 per mil (‰) for δ13C and δ15N and the SD of replicate measurements of the lab standard are < 3% of the concentration analysed. 3.4 Measurement of environmental parameters Twenty four hours before sampling for nutrients, we acid-washed all water sampling equipment in a 4% HCl bath to minimise potential contaminations. On the same day as coral fragments were collected, we e collected water samples from the average study site depth (~ 3–5 m depth) using 2 x 5 L Niskin bottles. On the boat, we aliquoted water for separate nutrient measurements into Falcon tubes. Immediately on the boat, water aliquots for nitrite and nitrate were filtered (0.22 µM Millex®-GV), yet we performed no immediate filtration steps for ammonium, DOC and chl a (stored in opaque bottles). We kept all water samples on ice during transport and then stored samples for nitrite, nitrate and ammonium at − 20°C, and stored samples for chl a and DOC analysis at + 4°C. For the measurement of nitrate and nitrite, we analysed samples with a segmented flow analyser (Model AA3 HR, SEAL Analytical IC). We performed a calibration prior to every run and accepted upon the criteria that R 2 > 0.99. To prepare the calibration standards, we used a ready-made stock standard of 1000 ppm nitrite and nitrate to create low (5, 20, 50 and 100 ppb) and high (100, 200, 500, 1000 ppb) standards. The instrument detection limit for nitrate was 0.0322 µmol L − 1 and 0.0217 µmol L − 1 for nitrite. For the measurement of ammonium, we analysed samples using a fluorometer (Turner Designs, Trilogy Fluorometer) via Orthophthaldialdehyde (OPA) derivatisation. We performed a calibration prior to every run and accepted upon the criteria that R 2 > 0.99. We prepared a mother solution of ammonium chloride to make standards of 0.0, 0.03, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3 µmol L − 1 . The detection limit of the instrument was 0.058 µmol L − 1 . For DOC analysis, we filtered 500 ml of water through 0.7 µm GF/F filters (pre-combusted at 450°C for 4.5 h). We divided the filtrate into sterile amber glass vials, that we then acidified with 0.1 ml of 85% phosphoric acid to prevent bacterial activity and analysed them on a TOC Analyser (TOC-L, Total Organic Carbon Analyser, Shimadzu, Kyoto, Japan). We performed a calibration before each run using a standard addition curve of Potassium Hydrogen Phthalate (0; 33; 25; 50; 62; 83; 100; 125; 167; 250; 500 µmol C L − 1 ). We prepared internal controls using Consensus Reference Materials (CRM; Batch 12; 2012; DOC: 42–45 µmol L − 1) provided by DA Hansell and W Chen (University of Miami). The average analytical variation of the instrument was < 3.5% for DOC based on 5–7 injections per sample. On the same day as collection, we filtered water samples for chl a (2 L in duplicates) through 0.7 µm GF/F filters and then stored them at − 80°C until further processing. We prepared samples for measurement adapting a protocol from Erar & Collins, (1997) 62 and a protocol used in the California Operative Oceanic Fisheries Investigations. In brief, we soaked the filters in 10 ml of 90% acetone, vortexed, sonicated in an ice bath and stored them at 4°C overnight in the dark. The following morning, we repeated the sonication and vortexing steps twice more, and then centrifuged the samples at 2500 rpm at 4°C for 10 min. Next, we measured the samples fluorometrically (Turner Designs, Trilogy Fluorometer) on a fluorometer fitted with a chl a module (Turner Designs, Wide-Chlorophyll a Acidification Module). We calibrated the instrument prior to use with standards of 0.5, 1.0, 2.5, 5.0, 10.0, 20.0, 100.0 and 200.0 µg L − 1 which generated a calibration curve of R 2 > 0.99. Following this, we validated the instrument calibration with two solid secondary standards adjusted to 2.5 and 20 µg L − 1 (Turner Designs, Adjustable Solid Secondary Standard - Red) and ran “blanks” of 90% acetone at the beginning, and after every few samples. Lastly, we measured seawater temperature continuously throughout the sampling period using an Onset Hobo pendant temperature logger deployed at the reef. We deployed the logger at 1–2 m depth, and measured temperature at 10 minute intervals throughout the sampling period April 2022 – February 2023. We wrapped the logger in white electrical tape to minimise solar bias and achieve better measurement accuracy 63 . For light intensity, we extracted data from Copernicus ERA5. The data are hourly measurements under direct clear sky radiation from the sampling site between April 2022 – February 2023, that we converted into µmol photons m − 2 s − 1 . 3.5 Data analyses We assessed denitrification data for statistically significant differences between species, between months per species and between light and dark incubations per month and species. All denitrification rate data were tested for normality via Shapiro-wilk tests, revealing that data were not normally distributed, even following transformations. Therefore, the non-parametric Kruskal-Wallis test was used followed by a Dunn’s test with Bonferroni p-value adjustment for post-hoc analysis. Secondly, we employed a random forest model to identify key environmental variables that may influence denitrification rates across different coral species. The random forest regression model consisted of 500 trees, with 3 variables tried at each split. The variable importance for predicting denitrification was determined by the mean squared error (%MSE), where a high %MSE indicates that the variable is important, as if it were removed, the model’s error would significantly increase. As a follow-up to the random forest analysis, we plotted partial dependence plots (PDPs) for the top 3 most influential parameters per species. This provided insight into how these parameters interacted with denitrification, by displaying how changes in one parameter influences the predicted outcome, while averaging the influence of all other parameters. Next, we correlated the denitrification rates of each species with its measured biogeochemical signatures, including isotope (δ15N and δ13C) and elemental data (C:N) of both the host and symbiont. For this, we used a non-parametric Spearman rank correlation, as data were not normally distributed following Shapiro-wilk testing. Data were pooled across the sampling year and cleaned prior to analysis. This included removing samples below the instrument’s detection limit of 0.015 mg, followed by identifying and excluding statistical outliers. Lastly, the baseline trophic strategies and niche widths for the three zooxanthellate corals ( S. pistillata, Acropora sp., and M. dichotoma ) were estimated following the approach of Jackson et al., (2011) 64 adapted for corals by Conti-Jerpe et al., (2020) 47 and Fox et al., (2023) 65 . The isotope data (δ15N and δ13C) were pooled and cleaned in the same way as the correlation analysis (above), with the added criterion that only paired δ15N and δ13C values for both host and symbiont fractions were included. This resulted in sample sizes of n = 18 for S. pistillata , n = 14 for Acropora sp., and n = 16 for M. dichotoma . Following this, we visualised the isotopic niches of each species, by plotting the standard ellipse areas of the coral host and symbiont fractions. Next, standard ellipse areas (SEAb) were calculated directly from posterior sampling of theSIBER model, and niche widths were summarised using the posterior mode along with 50% and 95% credible intervals. Thirdly, we used bootstrapped estimates (n = 10,000) of SEAc overlap between the host and symbiont fractions as a proxy for trophic strategy (Conti-Jerpe et al., 2020). In addition, we calculated Layman’s metrics 66 which serve as useful descriptions of data dispersion, offering insight into trophic diversity. The software R (version 4.3.2) (R Core Team, 2023) was used to generate figures using packages ‘ggplot2’ 67 , ‘ggpubr’ 68 , ‘dplyr’ 69 , ‘RColorBrewer’ 70 , ‘gridExtra’ 71 , ‘cowplot’ 72 . Likewise, statistics were also computed in R, using packages ‘rstatix’ 73 , ‘dunn.test’ 74 , ‘tidyverse’ 75 , ‘randomForest’ 76 , ‘pdp’ 77 , ‘SIBER’ 78 and ‘rjags’ 79 . Results 4.1 Denitrification rates among four Red Sea corals Denitrification was detected in all four Red Sea coral species (Fig. 1 a). Averaged over the year, denitrification rates of the three zooxanthellate species ( S. pistillata , Acropora sp. and M. dichotoma ) were similar at 0.09 ± 0.16, 0.10 ± 0.16 and 0.08 ± 0.10 nmol N cm − 2 h − 1 respectively and therefore did not significantly differ (p > 0.05; Fig. 1 a). However, the average denitrification rate of the azooxanthellate species T. coccinea was significantly higher than all three zooxanthellate species (p < 0.05), being 5-fold higher than both S. pistillata and M. dichotoma and 4-fold higher than Acropora sp. at 0.43 ± 0.61nmol N cm − 2 h − 1 (Fig. 1 a). 4.2 Seasonal variation in denitrification rates Monthly denitrification rates significantly differed across the year in all four species (Kruskal-Wallis; S. pistillata : H = 18.2, p < 0.01; Acropora sp.: H = 14.7, p < 0.05; M. dichotoma : H = 13.2, p < 0.05; T. coccinea : H = 14.6, p < 0.01), with a trend of higher denitrification activity during the spring and summer months compared to the autumn and winter months (Fig. 1 b - e). In S. pistillata , denitrification rates were significantly higher in April (0.20 ± 0.20 nmol N cm − 2 h − 1 ) and June (0.23 ± 0.26 nmol N cm − 2 h − 1 ) compared to August (0.02 ± 0.04 nmol N cm − 2 h − 1 ), October (0.03 ± 0.05 nmol N cm − 2 h − 1 ), December (0.01 ± 0.02 nmol N cm − 2 h − 1 ) and February (0.05 ± 0.10 nmol N cm − 2 h − 1 ), all p < 0.05 (Fig. 1 b). In Acropora sp., rates were significantly higher in June (0.17 ± 0.10 nmol N cm − 2 h − 1 ) than in October (0.02 ± 0.03 nmol N cm − 2 h − 1 ) and December (0.03 ± 0.04 nmol N cm − 2 h − 1 ), all p < 0.05 (Fig. 1 c). In M. dichotoma , denitrification rates were significantly higher in June (0.18 ± 0.10 nmol N cm − 2 h − 1 ), than in April (0.05 ± 0.07 nmol N cm − 2 h − 1 ) and August (0.03 ± 0.06 nmol N cm − 2 h − 1 ), all p < 0.05 (Fig. 1 d). Lastly, in T. coccinea , denitrification rates were significantly higher in April (0.89 ± 0.86 nmol N cm − 2 h − 1 ) and June (0.69 ± 0.71 nmol N cm − 2 h − 1 ) than in December (0.02 ± 0.06 nmol N cm − 2 h − 1 ), all p < 0.05 (Fig. 1 e). 4.3 Denitrification in the light and dark incubations Significant differences between denitrification rates measured in light and dark incubations per month were observed in all four species (Fig. 1 b - e). Mostly, denitrification rates were significantly higher in the dark than in the light incubations, with this significant trend observed in 11 out of 13 of the significant pairings identified. More specifically, in S. pistillata denitrification rates were 23-fold higher in April in the dark versus the light (Kruskal-Wallis, H = 6.00, p < 0.01) and 0.06 in the dark versus 0 nmol N cm − 2 h − 1 in the light in October (Kruskal-Wallis, 5.54, p < 0.05; Fig. 1 b). In Acropora sp., denitrification rates were 2-fold higher in June (Kruskal-Wallis, H = 3.94, p < 0.05) and 39-fold higher in August in the dark compared to the light (Kruskal-Wallis, H = 6.99, p < 0.01). In October, denitrification rates were 0.05 in the dark versus 0 nmol N cm − 2 h − 1 in the light (Kruskal-Wallis, H = 5.54, p < 0.05; Fig. 1 c). In M. dichtoma , denitrification rates were 2-fold higher in June (Kruskal-Wallis, H = 4.81, p < 0.05) and 8-fold higher in October in the dark compared to the light incubations (Kruskal-Wallis, H = 6.90, p < 0.01; Fig. 1 d). Lastly, in T. coccinea , rates were 17-fold higher in April (Kruskal-Wallis, H = 6.99, p < 0.01), 7-fold higher in June (Kruskal-Wallis, H = 5.77, p < 0.05) and 53-fold higher in October in the dark compared to the light (Kruskal-Wallis, H = 6.21, p < 0.05), while in August rates were 0.06 in the dark versus 0 nmol N cm − 2 h − 1 in the light (Kruskal-Wallis, H = 7.76, p < 0.01; Fig. 1 e). However, the reverse trend was observed in 2 out of 13 significant pairings, where denitrification was higher in the light incubation compared to the dark incubation. This was observed only during February in Acropora sp. where rates were 382-fold higher in the light versus the dark (Kruskal-Wallis, H = 7.26, p < 0.01), and T. coccinea where rates were 0.3 in the light versus 0 nmol N cm − 2 h − 1 in the dark (Kruskal-Wallis, H = 7.20, p < 0.01;Figure 1 c & e). 4.4 Determining the environmental drivers of denitrification The measured environmental variables (nitrate, ammonium, DOC, water chl a and temperature) were used in a random forest model, to determine the most influential variables over denitrification rates, for each species (Fig. 2 ). The amount of denitrifying variation that the environmental variables explained, varied between species. For example, the environmental variables explained 12% of denitrifying variation in S. pistillata , 57% in Acropora sp., 38% in M. dichtoma and 50% in T. coccinea . In S. pistillata , DOC, nitrate and ammonium were identified as the top three most influential variables over denitrification rates (Fig. 2 a). DOC availability was the strongest predictor of denitrification in S. pistillata (15% IncMSE), with higher levels promoting denitrification (Fig. 2 b). Low nitrate availability was the second most influential over denitrification rates in S. pistillata (12.5% IncMSE; Fig. 2 a, 2 c). Moderate ammonium availability also influenced denitrification rates of S. pistillata , yet to a much lesser extent (3.6% IncMSE; Fig. 2 a, 2 d). In Acropora sp., temperature, ammonium and water chl a were most influential over denitrification rates and to similar extents (Fig. 2 e). High temperature was the top predictor of denitrification in Acropora sp. (14.4% IncMSE, Fig. 2 e, 2 f), followed by moderate ammonium availability (13.5% IncMSE, Fig. 2 e, 2 g) and water chl a (13.3% IncMSE; Fig. 2 e, 2 h). In M. dichotoma , denitrification rates were evenly influenced by moderate temperature (13.6% IncMSE, Fig. 2 i, 2 j), high DOC availability (13.3% IncMSE, Fig. 2 i, 2 k) and moderate water chl a concentrations (13.3% IncMSE; Fig. 2 i, 2 l). Lastly, in T. coccinea , low nitrate availability was the most influential variable over denitrification rates (21.1% IncMSE, Fig. 2 m, 2 n), followed by high DOC availability (12.7% IncMSE; Fig. 2 m, 2 o). High water chl a concentration (5.8% IncMSE, Fig. 2 m, 2 p) was ranked third but influenced denitrification to a lesser extent than the top two variables. Overall, each species was influenced by a unique combination of environmental variables. However, we also identified environmental drivers that are common across multiple species, such as DOC availability and temperature. 4.5 The influence of internal nutrient dynamics on denitrification In S. pistillata , we found a significant positive correlation between denitrification rates (0.09 ± 0.16 nmol N cm − 2 h − 1 ) and symbiont δ13C (-14.16 ± 0.77‰; rho : 0.44; p < 0.05), as well as a significant negative correlation with symbiont δ15N (2.55 ± 0.54‰; rho : − 0.45; p < 0.05; Fig. 3 ). In Acropora sp., we found significant positive correlations between denitrification rates (0.10 ± 0.16 nmol N cm − 2 h − 1 ) and host δ13C (-15.34 ± 1.03‰; rho : 0.44; p < 0.05) and symbiont δ13C (-14.56 ± 0.48‰; rho : 0.51; p < 0.05; Fig. 3 ). In M. dichotoma , no significant relationships were detected between denitrification rates and biogeochemical signatures (p > 0.05; Fig. 3 ). Additionally, in T. coccinea , no significant relationships were detected between denitrification rates and biogeochemical signatures (p > 0.05; Fig. 3 ). Yet only one relationship (denitrification and host δ13C ) could be analysed since symbiont-related parameters were not applicable for this azooxanthellate species, and the other parameters had too few samples for a reliable analysis (Fig. 3 ). 4.6 Determining the trophic strategy of each coral species We used Bayesian analysis of isotopic niches 64 adapted for corals 47 , 65 (Fig. 4 ) combined with Layman metrics of trophic diversity 66 (Table 1 ) to quantify and compare the isotopic niches of the three zooxanthellate corals S. pistillata , Acropora sp. and M. dichotoma . The three species showed similarly broad isotopic niche areas (Fig. 4 a). However, M. dichotoma exhibited the largest Bayesian standard ellipse area (SEAb) of the host fraction (2.87‰ 2 , 95% CI: 1.67–4.70), compared to S. pistillata (2.15‰ 2 , 95% CI: 1.31–3.51) and Acropora sp. (1.34‰ 2 , 95% CI: 0.86–2.54, as well as the largest Bayesian standard ellipse area (SEAb) of the symbiont fraction (2.34‰ 2 , 95% CI: 1.14–3.18), compared to S. pistillata (1.05‰ 2 , 0.66–1.73) and Acropora sp. (0.76‰ 2 , CI: 0.38–1.18; Fig. 4 b). This was also supported by Layman’s metrics, where M. dichotoma had the largest NR, CR and TA (Table 1 ). However, M. dichotoma also had the highest NND and SDNND (Table 1 ), indicating higher variability between samples. Lastly, when we quantified the relative reliance of heterotrophy versus autotrophy as the percentage overlap between host and symbiont ellipse areas (corrected for sample size (SEAc), the three species all exhibited a mixotrophic feeding strategy, with no marked differences in mean SEAc (Fig. 4 c). Although sample sizes were limited, the resampling errors stabilised and converged, rather than spanning the full possible range (0–100%), and patterns were consistent across coral species. Therefore, we believe our interpretations are conservative and robust, despite these constraints. The trophic strategy of T. coccinea could not be quantified because this analysis requires isotope data from both host and symbiont fractions, and since T. coccinea lacks symbionts, the method was not applicable. Though, as an azooxanthellate coral, its feeding mode is already known to be entirely heterotrophic. We were, however, able to measure host parameters for T. coccinea and the mean host δ13C was – 24.01 ± 2.08‰, which was higher than the mean host δ13C of S. pistillata (− 14.72 ± 0.75‰), Acropora sp. (− 15.21 ± 0.60‰), and M. dichotoma (-15.3 ± 0.80‰; Figure S2). Unfortunately, host δ15N could not be reliably quantified because too few samples remained following data cleaning steps (detailed above in section 3.5 Data analyses). Table 1 Layman metrics 66 of the three zooxanthellate species Stylophora pistillata , Acropora sp., and Millepora dichotoma . Description and interpretation guidelines are adapted from Layman et al. (2007) (Layman et al., 2007). Values are shown for the host and symbiont (Sym) fraction. The lowest value of the sample groups is italicised, while the highest value is indicated with an asterisk (*). S. pistillata Acropora sp. M. dichotoma Metric Description Interpretation Host Sym Host Sym Host Sym δ 15 N range (NR) Maximum δ 15 N – minimum δ 15 N Larger NR indicates more trophic diversity. 3.03 2.12 3.55 1.58 5.14* 3.61 δ 13 C range (CR) Maximum δ 13 C - minimum δ 13 C Larger CR indicates more trophic diversity with varying C sources. 2.70 2.89 1.98 1.83 3.03* 2.19 Total Area (TA) A measure of the amount of niche space occupied Larger TA indicates a higher extent of trophic diversity. [Can be influenced by extreme/outlier values]. 6.71 3.30 3.45 1.59 8.01* 5.50 Mean distance to centroid (CD) Mean Euclidean distance of samples to the δ 13 C - δ 15 N centroid. The centroid is the mean δ 13 C and δ 15 N of samples Larger CD indicates a higher average degree of trophic diversity. [Less influenced by extreme/outlier values]. 1.07 0.72 0.91 0.57 1.28* 1.12 Nearest neighbour distance (NND) Mean of the Euclidean distances between samples in biplot space Small NND indicates similar trophic ecologies between samples. 0.53 0.34 0.42 0.27 0.56* 0.46 Standard deviation (SD) of NND Evenness of spacing between samples Small SDNND indicates a more even distribution. 0.34 0.26 0.35 0.16 0.53* 0.28 Discussion Among four coral species, we observed seasonal patterns in denitrification activity, demonstrating how the N-cycling pathway is highly influenced by environmental conditions. In addition, denitrification rates were influenced by the internal nutrient dynamics of the coral holobiont and may be linked to the trophic strategy of the coral host. 5.1 The effect of seasonality on coral denitrification rates The denitrification rates measured here are comparable to the ranges presented in previous studies on Red Sea corals, once appropriate conversions are applied (Fig. 1 a) 36 . Rates also significantly varied throughout the year for all four species, with a general seasonal trend of higher rates in the spring/summer compared to the autumn/winter months of the year (Fig. 1 b). The effect of seasonality on denitrification has not been investigated before in corals, but studies on other systems such as estuaries and marshlands have reported similar seasonal patterns to our study, finding higher denitrification rates and, in addition, higher denitrifier diversity in spring 80 , 81 . The seasonal trend observed in these former studies and our own can be explained by the sensitivity of the denitrification pathway to environmental conditions that fluctuate over a year (Figure S3). Temperature emerged as the primary driver of denitrification in Acropora sp., and M. dichotoma , though its influence differed between the two species. In Acropora sp., denitrification rates increased linearly with temperature. This relationship reflects the general principle of higher temperatures stimulating microbial metabolism and activity 51 . The same pattern was observed in Red Sea seagrass sediments 52 . Additionally, among corals, elevated temperatures increase the activity of diazotrophs that govern N 2 fixation 82 introducing more in hospite N available for denitrification. Furthermore, a recent study by Rädecker et al. (2021) 83 demonstrated that under high temperatures, the coral catabolises amino acids, again introducing more in hospite N available for denitrification. For M. dichotoma , however, denitrification rates peaked at moderate temperatures and decreased at higher temperatures These differences may reflect the response (and presence of) species-specific denitrifying microbiomes, as has been found between corals in previous work 84 . While there is no literature directly comparing the denitrifying microbial communities between Acropora sp. and M. dichotoma , studies reveal that Acropora sp. hosts a greater bacterial diversity than M. dichotoma 85 , indicating a potential for differences. High DOC availability was identified as the top driver of denitrification in S. pistillata , and the second driver in M. dichotoma and T. coccinea . The positive relationship between DOC and denitrification has been widely observed in other environments i.e., sediments and freshwater systems 86 – 89 and is attributed to the role of DOC as a key element supporting heterotrophic microbial growth and activity 90 . Therefore, our study provides evidence that denitrifiers in coral holobionts are also capable of utilising environmental DOC as a source of C, and may not solely rely on symbiont derived C. One study investigating the influence of DOC on octocoral denitrification reported contrasting results, where excess DOC (supplied as glucose) reduced denitrifier abundance by an order of magnitude in Xenia umbellata , but had no effect on Pinnigorgia flava 91 . This discrepancy may be due to variations in the composition and concentration of DOC 91 . Against expectations, low nitrate availability was another key driver of denitrification. Nitrate is essential to the denitrification process, acting as an electron acceptor for denitrifying bacteria, which sequentially reduces it to dinitrogen gas 92 . Consequently, one may expect that the more available nitrate, the more is taken up by the coral symbionts (among zooxanthellate corals) and the higher the denitrification rates. However, the opposite relationship was apparent in our study, with denitrification rates linked to low nitrate availability (Fig. 2 ). This may simply be because denitrification activity on an ecosystem scale is sufficiently high to reduce nitrate concentrations in the surrounding water column. Alternatively, a study by El-Khaled et al. (2020) 35 demonstrated that N cycling is nuanced, with opposing pathways like N 2 fixation and denitrification increasing together under higher environmental N. In the oligotrophic Red Sea, where nitrate stays low year-round (0.2–1.3 µmol L-1; Figure S3), N 2 fixation may rise in response to low N while denitrification simultaneously increases. This suggests denitrification becomes dominant only at higher nitrate concentrations, whereas at lower concentrations it may co-occur with N 2 fixation as previously found 35 . To fully explain this finding, however, further investigation into denitrification and N 2 fixation activity in response to a range of DIN concentrations is required. While the random forest analysis highlighted key environmental drivers of denitrification, a portion of denitrifying variability remained unexplained by the environmental parameters in our study. For example, 12% of denitrifying variation was explained by environmental conditions for S. pistillata , 57% for Acropora sp., 38% for M. dichotoma and 50% for T. coccinea (Fig. 2 ), emphasising the potential influence of additional factors beyond the scope of our study. Furthermore, denitrification rates were significantly higher during dark incubations than under light conditions in the acetylene assays (Fig. 1 b - e). This likely reflects reduced oxygen availability in the dark where the absence of photosynthesis and continued respiration creates conditions that favour denitrification by anaerobic microbes 93 . Therefore, our study also confirms that denitrification is a more active pathway under low oxygen conditions, aligning with findings of former studies 41 . 5.2 The influence of internal nutrient dynamics on coral denitrification rates By correlating denitrification rates with species-specific biogeochemical signatures, we found a significant negative correlation between denitrification rates and symbiont δ15N in S. pistillata (Fig. 3 ). This is an indication that denitrification may enhance or maintain internal N-limitation within the coral holobiont, as expected 3 . However, this relationship was not observed for the other zooxanthellate species Acropora sp., and M. dichotoma , challenging the functional role of denitrification in these species, which we expand upon later. Furthermore, we found a significant positive relationship between denitrification rates and symbiont δ13C in Acropora sp. and S. pistillata , and an additional significant positive correlation between denitrification and host δ13C in Acropora sp. (Fig. 3 ). These findings provide evidence that denitrifiers utilise autotrophically derived-C, as seen in former studies 36 , 38 , 91 . However, we of course also provide that evidence that denitrifiers utilise environmental-DOC (Fig. 2 ). Naturally, this raises the question of how host trophic strategy might further influence denitrification activity. 5.3 The influence of the host trophic strategy on denitrification activity We also examined the influence of host trophic strategy on denitrification rates. Unexpectedly, denitrification rates of the fully heterotrophic species T. coccinea were significantly higher than those of all other species in our study, being 5-fold higher than both S. pistillata and M. dichotoma and 4-fold higher than Acropora sp. (Fig. 1 a). This result contradicts our initial hypothesis where we predicted higher denitrification rates in the more autotrophic species based on findings from former studies 36 , 38 , 91 . Therefore, our findings suggest that environmental C may even fuel denitrification at a faster rate than photosynthetic C, given the exceptionally high rates measured in T. coccinea . However, such high rates may also be attributed to other factors beyond the C source. For example, T. coccinea may host a more diverse and efficient denitrifying community 84 . We also need to measure the denitrification activity of additional heterotrophic species to see whether this finding is unique to T. coccinea or applies broadly to heterotrophic species. Furthermore, since our species were all mixotrophic (Fig. 4 ), we could not assess the denitrification activity in more autotrophic species and would suggest future work to include a species that exhibits a greater reliance on autotrophy. However, since we had to pool the isotopic data over the year due to limited sample sizes, we may have missed seasonal shifts towards greater autotrophy in our species. Therefore, we would recommend higher sample sizes at each seasonal time point. Furthermore, by mitigating excess N, denitrification is proposed to sustain N-limitation within the coral holobiont, which is critical to the stability of the coral-algal symbiosis 3 . However, the occurrence of denitrification in an azooxanthellate coral, challenges this proposed functional role. Our findings suggest that denitrification is not an adaptive trait among azooxanthellate corals, but rather a passive, opportunistic response to a suite of environmental conditions that favour its activity. This interpretation aligns with previous research on denitrification in octocorals 91 and tropical scleractinian corals 40 . 5.4 Interpretations in the context of the Red Sea Having addressed the specific research questions of our study, it is important to interpret these results in the context of the Red Sea’s unique characteristics for a broader perspective of their ecological relevance. The Red Sea is one of the warmest and saltiest seas on Earth, exhibiting strong temporal and spatial gradients 43 . Spatially, the temperature, nutrient availability and chl a concentration differs between the north and south of the Red Sea, with the highest temperature, DIN and chl a occurring in the south, and decreasing northwards 43 , 94 , the nutrient availability in the shallow zone is very low and increases with depth. Our study was conducted in shallow waters of the central Red Sea where environmental conditions were found to have a strong influence over denitrification activity. Broadly, temperature and nutrient availability emerged as key drivers of denitrification. Based on our findings, we anticipate that corals of the same species may exhibit spatial variation in denitrification activity across the Red Sea in response to environmental conditions. For example, denitrification activity in Acropora sp. may increase southwards as temperature and DIN increases. However, caution should be taken when extrapolating our seasonal findings beyond the Red Sea, as seasonal dynamics differ among coral reef regions worldwide. Some regions experience weak seasonality such as the equatorial Indo-Pacific. Therefore, it is likely that the same spike in denitrification activity in the spring/summer seasons as measured in our study, may not be seen globally. 5.5 Conclusions Our study shows that the denitrification pathway is seasonally variable in corals, exhibiting generally higher rates in the spring and summer months compared to the autumn and winter months (Fig. 1 b), which can be explained by the high sensitivity of denitrification to environmental conditions (Fig. 2 ). Our study identified species-specific environmental drivers of denitrification (Fig. 2 ). The top driver in S. pistillata was DOC, providing evidence that denitrifiers do not exclusively rely on photosynthetic C for energy (Fig. 2 ). For Acropora sp., and M. dichotoma , the top driver was temperature, although its influence differed between the two species (Fig. 2 ). In Acropora sp., we found a linear relationship between denitrification and temperature, whereas in M. dichotoma , denitrification activity increased with temperature until an upper threshold, beyond which it declined (Fig. 2 ). For T. coccinea , we unexpectedly identified low nitrate availability as a driver of denitrification (Fig. 2 ), yet this is likely the effect not the cause of high denitrification activity, or this is due to the co-occurrence of denitrification with N 2 fixation when nitrate levels are low. Our study also showed that for all four species, denitrification activity was higher under dark conditions where oxygen levels are reduced (Fig. 1 b), thus favouring anaerobic microbial activity 93 as previously found for other reef substrates 41 . Furthermore, our study demonstrated that denitrification is also influenced by the internal nutrient dynamics of the coral holobiont, as the positive relationship between denitrification and host ∂13C in S. pistillata , and both host and symbiont ∂13C in Acropora sp. (Fig. 3 ), suggest that denitrifiers utilise symbiont-derived C as shown in former studies 95 . Lastly, we found that denitrification rates were affected by host trophic strategy, finding significantly higher denitrification in the azooxanthellate, fully heterotrophic coral T. coccinea , being 5-fold higher than both S. pistillata and M. dichotoma and 4-fold higher than Acropora sp. (Fig. 1 a) that we determined were all mixotrophic (Fig. 4 ). This suggests that environmental C may even fuel denitrification at a faster rate than photosynthetic C, or points to the influence of distinct denitrifying communities that may exhibit different rates. However, the exceptionally high denitrification rates measured in the azooxanthellate species challenge the functional significance of N removal, since no coral-algal symbiosis is present to necessitate such regulation. Therefore, we suspect that denitrification may play a more passive role than previously suspected among Red Sea corals, reinforcing findings from coral-associated denitrification research in other geographic regions 40 and on octocorals 91 . Overall, our study established a valuable baseline for seasonal denitrification rates in the Red Sea, and an understanding of its environmental drivers across four species. This foundation offers a springboard for future research to explore the influences of environmental change on N cycling in greater detail. Declarations Competing interests The authors declare that they have no known competing interests that may have influenced the work reported in this paper. Author Contributions C. E.L. Hill : data collection, data analysis, visualisation, writing – original draft, writing – review and editing. A. Tilstra : writing – review and editing, conceptualisation, supervision. Y. C. El-Khaled : data collection, writing – review and editing. N. Garcias-Bonet : data collection, writing- review and editing. V. A. Bonacker : data collection, data analysis, writing – review and editing. A. Novoa Lamprea : data collection, data analysis, writing – review and editing. W. A. Rich : data collection, data analysis, writing – review and editing. M. Ostendarp: data analysis, writing: review and editing. M. D. Fox : writing – review and editing, data analysis, supervision. S. Carvalho : writing – review and editing, conceptualisation, supervision, funding. R. S. Peixoto : writing – review and editing, conceptualisation, supervision, funding. C. Wild : writing – review and editing, conceptualisation, supervision, funding. Acknowledgements This study came together with the help and support of many people. We thank Livia A. Hott for her support in the lab with running incubations and sample processing. We again thank Livia A. Hott, as well as Patricia Sanchez-Lopez and Gerard Clancy who dedicated a lot of time to troubleshooting and refining the protocols for gas measurements of nitrous oxide. We thank Vijayalaxmi Dasari who performed the ammonium and inorganic nutrient analysis, Doaa Baker and Daria Vashuinina who performed the DOC analysis, and João Curdia who assisted with the water chl a analysis. We also thank Prof Dr Ulrich Stuck for processing the stable isotope and elemental data. We also thank CMR and the boat captains for their support in fieldwork. Lastly, the authors acknowledge the funding support from KAUST grant number BAS/1/1095-01-01 and BAS/1/1109-01-01 and the German Research Foundation (DFG) grant Wi 2677/16 − 1. Data availability statement All data is available in the public repository Zenodo (Hill et al. doi: https://doi.org/10.5281/zenodo.17951369 ). The isotopic and elemental data shared with Thobor et al. is available in Zenodo (Hill et al. doi: https://doi.org/10.5281/zenodo.17849457 ) References Fiore CL, Jarett JK, Olson ND, Lesser MP (2010) Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol 18:455–463 Li M, Sheng H-X, Dai M, Kao S-J (2023) Understanding nitrogen dynamics in coral holobionts: comprehensive review of processes, advancements, gaps, and future directions. Front Mar Sci 10:1203399 Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C (2015) Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol 23:490–497 Houlbrèque F (2009) Ferrier-Pagès, C. Heterotrophy in Tropical Scleractinian Corals. Biol Rev 84:1–17 Robbins SJ et al (2019) A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nat Microbiol 4:2090–2100 Voolstra CR et al (2024) The coral microbiome in sickness, in health and in a changing world. Nat Rev Microbiol 22:460–475 Cardini U et al (2015) Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc. R. Soc. B. 282, 20152257 Lesser M et al (2007) Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar Ecol Prog Ser 346:143–152 Rädecker N, Meyer F, Bednarz V, Cardini U, Wild C (2014) Ocean acidification rapidly reduces dinitrogen fixation associated with the hermatypic coral Seriatopora hystrix. Mar Ecol Prog Ser 511:297–302 Shashar N, Cohen Y, Loya Y, Sar N (1994) Nitrogen fixation (Acetylene reduction) in stony corals: evidence for coral-bacteria interactions. Mar Ecol Prog Ser 111 Falkowski PG, Dubinsky Z, Muscatine L, Porter JW (1984) Light and the Bioenergetics of a Symbiotic Coral. BioScience 34, 705–709 LaJeunesse TC et al (2018) Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral Endosymbionts. Curr Biol 28:2570–2580e6 Grover R, Maguer J-F, Allemand D (2003) Ferrier-Pagés, C. Nitrate uptake in the scleractinian coral Stylophora pistillata . Limnol Oceanogr 48:2266–2274 Miller D, Yellowlees D (1989) Inorganic nitrogen uptake by symbiotic marine cnidarians: a critical review. Proc. R. Soc. Lond. B 237, 109–125 Rahav O, Dubinsky Z, Achituv Y, Falkowski PG (1989) Ammonium metabolism in the zooxanthellate coral, stylophora pistillata . Proc. R. Soc. Lond. B 236, 325–337 Reynaud S et al (2009) Effect of light and feeding on the nitrogen isotopic composition of a zooxanthellate coral: role of nitrogen recycling. Mar Ecol Prog Ser 392:103–110 Wang JT, Douglas AE (1999) Essential amino acid synthesis and nitrogen recycling in an alga-invertebrate symbiosis. Mar Biol 135:219–222 Dawson J (2002) Biogeography of azooxanthellate corals in the Caribbean and surrounding areas. Coral Reefs 21:27–40 Baker DM, Freeman CJ, Wong JCY, Fogel ML (2018) Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J 12:921–930 Buckingham MC et al (2022) Impact of nitrogen (N) and phosphorus (P) enrichment and skewed N:P stoichiometry on the skeletal formation and microstructure of symbiotic reef corals. Coral Reefs 41:1147–1159 Cunning R, Baker AC (2013) Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat Clim Change 3:259–262 Ezzat L, Maguer J-F, Grover R, Ferrier-Pagès C (2015) New insights into carbon acquisition and exchanges within the coral–dinoflagellate symbiosis under NH 4 + and NO 3 – supply. Proc. R. Soc. B. 282, 20150610 Krueger T et al (2020) Intracellular competition for nitrogen controls dinoflagellate population density in corals. Proc. R. Soc. B. 287, 20200049 Marubini F, Davies PS (1996) Nitrate increases zooxanthellae population density and reduces skeletogenesis in corals. Mar Biol 127:319–328 Muscatine L (1990) The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs Wooldridge SA (2017) Instability and breakdown of the coral–algae symbiosis upon exceedence of the interglacial pCO2 threshold (> 260 ppmv): the missing Earth-System feedback mechanism. Coral Reefs 36:1025–1037 Burkepile DE et al (2020) Nitrogen Identity Drives Differential Impacts of Nutrients on Coral Bleaching and Mortality. Ecosystems 23:798–811 Lesser MP (2021) Eutrophication on Coral Reefs: What Is the Evidence for Phase Shifts, Nutrient Limitation and Coral Bleaching. Bioscience 71:1216–1233 Vega Thurber RL et al (2014) Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob Change Biol 20:544–554 Wiedenmann J et al (2013) Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat Clim Change 3:160–164 Hoegh-Guldberg O (1994) Population dynamics of symbiotic zooxanthellae in the coral Pocillopora damicornis exposed to elevated ammonium [(NH4) 2 SO4] concentrations Marangoni FDB, Ferrier-Pagès L, Rottier C, Bianchini C, A., Grover R (2020) Unravelling the different causes of nitrate and ammonium effects on coral bleaching. Sci Rep 10:11975 Shantz AA, Burkepile DE (2014) Context-dependent effects of nutrient loading on the coral–algal mutualism. Ecology 95:1995–2005 Zhao H et al (2021) Impacts of nitrogen pollution on corals in the context of global climate change and potential strategies to conserve coral reefs. Sci Total Environ 774:145017 El-Khaled Y et al (2020) In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser 645:55–66 Tilstra A et al (2019) Denitrification Aligns with N2 Fixation in Red Sea Corals. Sci Rep 9:19460 Knowles R, Denitrification (1982) Microbiol Rev 46:43–70 El-Khaled YC et al (2021) Nitrogen fixation and denitrification activity differ between coral- and algae-dominated Red Sea reefs. Sci Rep 11:11820 Babbin AR et al (2021) Discovery and quantification of anaerobic nitrogen metabolisms among oxygenated tropical Cuban stony corals. ISME J 15:1222–1235 Glaze TD, Erler DV, Siljanen H (2022) M. P. Microbially facilitated nitrogen cycling in tropical corals. ISME J 16:68–77 El-Khaled YC et al (2021) High plasticity of nitrogen fixation and denitrification of common coral reef substrates in response to nitrate availability. Mar Pollut Bull 168:112430 Yang Q et al (2024) Microbial nitrogen removal in reef-building corals: A light-sensitive process. Chemosphere 359:142394 Berumen ML et al (2019) The Red Sea: Environmental Gradients Shape a Natural Laboratory in a Nascent Ocean. In: Voolstra CR, Berumen ML (eds) Coral Reefs of the Red Sea, vol 11. Springer International Publishing, Cham, pp 1–10 Roik A et al (2016) Year-Long Monitoring of Physico-Chemical and Biological Variables Provide a Comparative Baseline of Coral Reef Functioning in the Central Red Sea. PLoS ONE 11:e0163939 Cardini U et al (2016) Budget of Primary Production and Dinitrogen Fixation in a Highly Seasonal Red Sea Coral Reef. Ecosystems 19:771–785 Bednarz VN et al (2018) Contrasting seasonal responses in dinitrogen fixation between shallow and deep-water colonies of the model coral Stylophora pistillata in the northern Red Sea. PLoS ONE 13:e0199022 Conti-Jerpe IE et al (2020) Trophic strategy and bleaching resistance in reef-building corals. Sci Adv 6:eaaz5443 Creed JC et al (2017) The invasion of the azooxanthellate coral Tubastraea (Scleractinia: Dendrophylliidae) throughout the world: history, pathways and vectors. Biol Invasions 19:283–305 Einbinder S et al (2009) Changes in morphology and diet of the coral Stylophora pistillata along a depth gradient. Mar Ecol Prog Ser 381:167–174 Imbs AB, Dang LTP, Nguyen KB, Luu HV, Pham LQ (2020) Annual Dynamics of the Composition of Polar Lipids, Storage Lipids, and Fatty Acid Markers in the Hydrocoral Millepora dichotoma Forskål, 1775 from Coastal Waters of Vietnam. Russ J Mar Biol 46:221–225 Jørgensen BB (2000) Bacteria and Marine Biogeochemistry. In: Schulz HD, Zabel M (eds) Marine Geochemistry. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 173–207. doi: 10.1007/978-3-662-04242-7_5 . Garcias-Bonet N et al (2018) High denitrification and anaerobic ammonium oxidation contributes to net nitrogen loss in a seagrass ecosystem in the central Red Sea. Biogeosciences 15:7333–7346 Garcias-Bonet N et al (2025) The Coral Probiotics Village: An Underwater Laboratory to Tackle the Coral Reefs Crisis. Ecol Evol 15:e71558 El-Khaled YC et al (2020) Simultaneous measurements of dinitrogen fixation and denitrification associated with coral reef substrates: advantages and limitations of a combined acetylene assay. Front Mar Sci 7:411 Balderston WL, Sherr B, Payne W (1976) Blockage by acetylene of nitrous oxide reduction in Pseudomonas perfectomarinus. Appl Environ Microbiol 31:504–508 Fedorova R, Milekhina E, Il’Yukhina N (1973) Evaluation of the method of gas metabolism for detecting extraterrestrial life. Identification of nitrogen-fixing microorganisms. Izv Akad Nauk SSSR Ser Biol 6:797–806 Yoshinari T, Knowles R (1976) Acetylene inhibition of nitrous oxide reduction by denitrifying bacteria. Biochem Biophys Res Commun 69:705–710 Groffman PM et al (2006) METHODS FOR MEASURING DENITRIFICATION: DIVERSE APPROACHES TO A DIFFICULT PROBLEM. Ecol Appl 16:2091–2122 Oremland RS, Capone DG (1988) Use of Specific Inhibitors in Biogeochemistry and Microbial Ecology. In: Marshall KC (ed) Advances in Microbial Ecology, vol 10. Springer US, Boston, MA, pp 285–383 Seitzinger SP (1993) Denitrification and nitrification rates in aquatic sediments. Handbook of methods in aquatic microbial ecology. CRC, pp 633–641 Mariotti A (1983) Atmospheric nitrogen is a reliable standard for natural 15N abundance measurements. Nature 303:685–687 Erar EJ, Collins GB (1997) Method 445.0 In Vitro Determination of Chlorophyll a and Pheophytin ain Marine and Freshwater Algae by Fluorescence Rich WA et al (2024) Widespread inconsistency in logger deployment methods in coral reef studies may bias perceptions of thermal regimes. PLOS Clim 3:e0000517 Jackson AL, Inger R, Parnell AC, Bearhop S (2011) Comparing isotopic niche widths among and within communities: SIBER - Stable Isotope Bayesian Ellipses in R: Bayesian isotopic niche metrics. J Anim Ecol 80:595–602 Fox MD et al (2023) Ocean currents magnify upwelling and deliver nutritional subsidies to reef-building corals during El Niño heatwaves. Sci Adv 9:eadd5032 Layman CA, Arrington DA, Montaña CG, Post DM (2007) CAN STABLE ISOTOPE RATIOS PROVIDE FOR COMMUNITY-WIDE MEASURES OF TROPHIC STRUCTURE? Ecology 88, 42–48 Wickham H (2016) Data Analysis. Springer Kassambara A (2020) ggpubr:ggplot2 based publication ready plots. R package version 0 4 0 438 Wickham H, François R, Henry L, Müller K (2018) dplyr: A Grammar of Data Manipulation. R package version 0.7. 6. Computer software]. https://CRAN. R-project. org/package = dplyr Neuwirth E (2014) Package ‘RColorBrewer’. ColorBrewer Palettes Auguie B, Antonov A (2017) gridExtra: miscellaneous functions for grid graphics. R package version 2:602 Wilke CO (2015) cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. https://doi.org/10.32614/CRAN.package.cowplot . 1.1.3 Kassambara A (2023) Pipe-Friendly Framework for Basic Statistical Tests. https://CRAN.R-project.org/package=rstatix Dinno A (2024) Dunn’s Test of Multiple Comparisons Using Rank Sums. https://CRAN.R-project.org/package=dunn.test Wickham H et al (2019) Welcome to the Tidyverse. JOSS 4:1686 Liaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2:18–22 Greenwell BM (2017) pdp: An R Package for Constructing Partial Dependence Plots. R J 9:421–436 Jackson A, Parnell A, Siber (2023) Stable isotope bayesian ellipses in R. R package version 2.1. 6 Plummer M, Stukalov A, Denwood M (2016) Bayesian graphical models using MCMC. R package version 46 Smith J, Wagner-Riddle C, Dunfield K (2010) Season and management related changes in the diversity of nitrifying and denitrifying bacteria over winter and spring. Appl Soil Ecol 44:138–146 Song K, Kang H, Zhang L, Mitsch WJ (2012) Seasonal and spatial variations of denitrification and denitrifying bacterial community structure in created riverine wetlands. Ecol Eng 38:130–134 Bednarz V, Cardini U, Van Hoytema N, Al-Rshaidat M, Wild C (2015) Seasonal variation in dinitrogen fixation and oxygen fluxes associated with two dominant zooxanthellate soft corals from the northern Red Sea. Mar Ecol Prog Ser 519:141–152 Rädecker N et al (2021) Heat stress destabilizes symbiotic nutrient cycling in corals. Proc. Natl. Acad. Sci. U.S.A. 118, e2022653118 Yang S, Sun W, Zhang F, Li Z (2013) Phylogenetically Diverse Denitrifying and Ammonia-Oxidizing Bacteria in Corals Alcyonium gracillimum and Tubastraea coccinea. Mar Biotechnol 15:540–551 Delgadillo-Ordoñez N et al (2022) Red Sea Atlas of Coral-Associated Bacteria Highlights Common Microbiome Members and Their Distribution across Environmental Gradients—A. Syst Rev Microorganisms 10:2340 Bernard-Jannin L, Sun X, Teissier S, Sauvage S, Sánchez-Pérez J-M (2017) Spatio-temporal analysis of factors controlling nitrate dynamics and potential denitrification hot spots and hot moments in groundwater of an alluvial floodplain. Ecol Eng 103:372–384 Hill AR, Devito KJ, Campagnolo S, Sanmugadas K (2000) Subsurface denitrification in a forest riparianzone: Interactions between hydrology and supplies ofnitrate and organic carbon. Biogeochemistry 51:193–223 Steinberg C, Steinberg (2013) Christian. Ecology of humic substances in freshwaters: determinants from geochemistry to ecological niches. in (Springer Science & Business Media Zhou W, Xia L, Yan X (2017) Vertical distribution of denitrification end-products in paddy soils. Sci Total Environ 576:462–471 Wetzel RG (1992) Gradient-dominated ecosystems: sources and regulatory functions of dissolved organic matter in freshwater ecosystems. Hydrobiologia 229:181–198 Xiang N et al (2022) Contrasting Microbiome Dynamics of Putative Denitrifying Bacteria in Two Octocoral Species Exposed to Dissolved Organic Carbon (DOC) and Warming. Appl Environ Microbiol 88:e01886–e01821 Rajta A, Bhatia R, Setia H, Pathania P (2020) Role of heterotrophic aerobic denitrifying bacteria in nitrate removal from wastewater. J Appl Microbiol 128:1261–1278 Zumft WG (1997) Cell biology and molecular basis of denitrification. Microbiol Mol Biol Rev 61:533–616 Pearman JK et al (2017) Microbial planktonic communities in the Red Sea: high levels of spatial and temporal variability shaped by nutrient availability and turbulence. Sci Rep 7:6611 Xiang N et al (2022) Presence of algal symbionts affects denitrifying bacterial communities in the sea anemone Aiptasia coral model. ISME Commun 2:105 Weiss R, Price B (1980) Nitrous oxide solubility in water and seawater. Mar Chem 8:347–359 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarymaterial.docx Supplementary Material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8588779\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":573741979,\"identity\":\"20b448ba-3ac2-4b42-8665-de15fdd9d227\",\"order_by\":0,\"name\":\"Claudia E. L. Hill\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACdiDmMWBIYGNnbjjw4YcNkMvYeACvFmaYFmbGxoMze9JAWhqI0MLAkMDAzNh8mIPtMFgQrxb+ZuZnEm8K7PL4mBkbDjPwnLdb234YaEuNTTQuLRKH2cwk5xgkF7OBtBRY3E7ediYRqOVYWm4DDi0GzAxm0jwGzIltIC0zeG4nmx0AagGy8Whh/wbUUg/RwsN2Ltns/ENCWnhAthyGaTlgZ3aDgC0Sh3mKLecYHAdrAQZycoLZDaAtCXj8wt/evvHGmz/VifPbmw9/+PDDzt7sfPrDBx9qbHBqwQCJYJUJxCoHAXtSFI+CUTAKRsHIAAB5W2BU22Ps6gAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0000-0001-5355-8567\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Claudia\",\"middleName\":\"E. L.\",\"lastName\":\"Hill\",\"suffix\":\"\"},{\"id\":573742008,\"identity\":\"cfa0a227-53f3-4e5b-8c85-781bf3417b9f\",\"order_by\":1,\"name\":\"Arjen Tilstra\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Arcadis\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Arjen\",\"middleName\":\"\",\"lastName\":\"Tilstra\",\"suffix\":\"\"},{\"id\":573742060,\"identity\":\"743e95a3-f877-49f2-9bcd-390a68e888bd\",\"order_by\":2,\"name\":\"Yusuf C. El-Khaled\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yusuf\",\"middleName\":\"C.\",\"lastName\":\"El-Khaled\",\"suffix\":\"\"},{\"id\":573742093,\"identity\":\"eb374449-7af3-4d9d-ba32-71fe42a6c3b9\",\"order_by\":3,\"name\":\"Neus Garcias-Bonet\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Neus\",\"middleName\":\"\",\"lastName\":\"Garcias-Bonet\",\"suffix\":\"\"},{\"id\":573742318,\"identity\":\"96e6a9bf-7b51-4283-bfbd-5da06cf52848\",\"order_by\":4,\"name\":\"Vivian A. Bonacker\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Groningen\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Vivian\",\"middleName\":\"A.\",\"lastName\":\"Bonacker\",\"suffix\":\"\"},{\"id\":573742374,\"identity\":\"948b6349-acbf-4868-b4be-8097daf113c7\",\"order_by\":5,\"name\":\"Andres Novoa-Lamprea\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Andres\",\"middleName\":\"\",\"lastName\":\"Novoa-Lamprea\",\"suffix\":\"\"},{\"id\":573742414,\"identity\":\"765de32c-1078-4311-bd0c-71f4d5ee221e\",\"order_by\":6,\"name\":\"Walter A. Rich\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Walter\",\"middleName\":\"A.\",\"lastName\":\"Rich\",\"suffix\":\"\"},{\"id\":573742484,\"identity\":\"89f38c21-7dc2-402a-8d94-2df28d1f3e4d\",\"order_by\":7,\"name\":\"Malte Ostendarp\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Bremen\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Malte\",\"middleName\":\"\",\"lastName\":\"Ostendarp\",\"suffix\":\"\"},{\"id\":573742528,\"identity\":\"26db13cf-9aa7-473e-9f50-e9d0cff15887\",\"order_by\":8,\"name\":\"Michael D. Fox\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Michael\",\"middleName\":\"D.\",\"lastName\":\"Fox\",\"suffix\":\"\"},{\"id\":573748255,\"identity\":\"e2c15eae-eca1-40fc-ae13-e1fe4ebe0e9b\",\"order_by\":9,\"name\":\"Susana Carvalho\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Susana\",\"middleName\":\"\",\"lastName\":\"Carvalho\",\"suffix\":\"\"},{\"id\":573748531,\"identity\":\"7f52ad5c-a9b8-4e20-ae92-7eb30f8e25fd\",\"order_by\":10,\"name\":\"Raquel S. Peixoto\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Raquel\",\"middleName\":\"S.\",\"lastName\":\"Peixoto\",\"suffix\":\"\"},{\"id\":573748547,\"identity\":\"c536a67b-a09c-4916-a0d9-223fcaac1391\",\"order_by\":11,\"name\":\"Christian Wild\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Bremen\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Christian\",\"middleName\":\"\",\"lastName\":\"Wild\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-13 07:42:59\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-8588779/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8588779/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":100200030,\"identity\":\"4f9e5512-3c66-48e3-b4c4-e07ac08b4685\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":2834676,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Cleanversionfinal.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/3a76b7ac328b80c6c247fe92.docx\"},{\"id\":100369071,\"identity\":\"a6242209-f735-4e16-84f7-51741771407f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:58:41\",\"extension\":\"json\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":342,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"rs8588779.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/0ee93c3cb9df19e46ef0c4a6.json\"},{\"id\":100200027,\"identity\":\"695a061b-0c60-4cca-92c2-588293f6678b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":211439,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"rs85887790enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/352b209111e3948d0cdc5bb6.xml\"},{\"id\":100369900,\"identity\":\"ccbc85f4-e8d8-4d25-9ac5-09446e18191c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:59:36\",\"extension\":\"png\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":1167923,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/60fbaf7a5a73ae000f084bb8.png\"},{\"id\":100200031,\"identity\":\"c70481a5-4da2-4765-959d-0ba8b1e5038a\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":381282,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/1e4518f6c807ac6fb3923816.png\"},{\"id\":100200036,\"identity\":\"edfe3106-5561-4b64-bcaf-6885e0cbca8f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":165443,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/13c52ad6dd58d97736f9ab37.png\"},{\"id\":100200032,\"identity\":\"1ec7c5f4-82e4-4f62-8eac-d3160ed409c1\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"jpeg\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":412423,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/ea364d964ffb6b646f24a0e2.jpeg\"},{\"id\":100369198,\"identity\":\"ffe7964d-64b5-492c-bd6b-c659801d2fda\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:58:47\",\"extension\":\"png\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":199342,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/1e16a985b8a76f03344b8221.png\"},{\"id\":100200028,\"identity\":\"5b74fdd2-950c-4340-ad9f-dd33be4a4c81\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":64702,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/51a9bdf85578c888d9118181.png\"},{\"id\":100200040,\"identity\":\"91721576-3311-497b-b006-8dd27748d902\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":32097,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/3d5ea2fef34744833f4ffed3.png\"},{\"id\":100370429,\"identity\":\"b7ba3a8a-f21b-4f5c-b4db-9400ac61028c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 08:05:47\",\"extension\":\"png\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":123216,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/3381f4f79d095901d0bd468d.png\"},{\"id\":100200033,\"identity\":\"4cfd2b51-b285-4488-89e2-60ca56065c46\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":64746,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/ff4a74e62daf401e276c48b0.png\"},{\"id\":100200037,\"identity\":\"ebc86a56-baf1-40d8-8c5c-36c0bb98ce11\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":90761,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/f8eecb9be149e990162151b7.png\"},{\"id\":100200038,\"identity\":\"544946f9-57f5-46e0-a245-3a97a735ff3a\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":16,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":66088,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/5cb9898af4d81ec0f3619b3e.png\"},{\"id\":100200041,\"identity\":\"1b42b926-1711-4535-b7fc-1b711320231d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"xml\",\"order_by\":17,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":209939,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"rs85887790structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/204306f101e573c990b3bfc4.xml\"},{\"id\":100200042,\"identity\":\"ad6a8238-1932-452c-ad39-7a9212596a9a\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"html\",\"order_by\":18,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":227096,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/bbb998c106f7c7daf184cbbe.html\"},{\"id\":100200024,\"identity\":\"dc88dcb3-4a74-469f-8710-36ec7df29a4a\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3486155,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePanel a) Denitrification rates of \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e, \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., \\u003cem\\u003eMillepora dichotoma\\u003c/em\\u003eand \\u003cem\\u003eTubastrea coccinea\\u003c/em\\u003e pooled across one year (April 2022 – February 2023). Diamonds indicate mean values. Species with the same letter do not differ significantly whereas different letters denote significant differences between species. Panels b - e) Denitrification rates of each species across sampling months, under light and dark incubations. Bars show the mean of five biological replicates (n = 5), with standard error. Note that panel e (\\u003cem\\u003eT. coccinea\\u003c/em\\u003e) has a larger y axis scale than the other species. Brackets indicate significant differences between the light and dark incubations within each month, with asterisks denoting the strength of significance (* p \\u0026lt; 0.05; ** p \\u0026lt; 0.01). Months sharing the same letter do not differ significantly, whereas months with different letters show significant differences. All images are taken by Vivan A. Bonacker.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/00ee7f7bf698fd1643a548d1.png\"},{\"id\":100200023,\"identity\":\"5d9a8eeb-1fbc-4af5-afdc-6696f0b98d4d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":800200,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEnvironmental drivers of denitrification rates for each species: \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e (a – d), \\u003cem\\u003eAcropora\\u003c/em\\u003esp. (e – h), \\u003cem\\u003eMillepora dichotoma\\u003c/em\\u003e (i - l) and \\u003cem\\u003eTubastrea coccinea\\u003c/em\\u003e(m - p). For each species (each row), the leftmost bar plot shows the ranked importance of five environmental variables in explaining denitrifying variation, as identified by random forest analysis. The cumulative contribution of each variable to the overall variation is shown by the auxiliary curve (aligned with the upper x axis). Th line graphs for each species are partial dependence plots (PDPs) for the three most influential variables, illustrating how each variable affects denitrification rates. Abbreviations: “DOC” = dissolved organic carbon, “IncMSE” = increase in mean squared error and “Water Chl-a” = water chlorophyll \\u003cem\\u003ea\\u003c/em\\u003e.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/9eb3c6283f03ec17a4516207.png\"},{\"id\":100369196,\"identity\":\"208c846f-b83e-4390-890d-591af839d344\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:58:47\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":284384,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA correlation matrix relating the annual denitrification rates of \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e, \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., \\u003cem\\u003eMillepora dichotoma\\u003c/em\\u003e and \\u003cem\\u003eTubastrea coccinea\\u003c/em\\u003eto their respective biogeochemical signatures. Significant relationships are indicated by an asterisk * (p \\u0026lt; 0.05).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/4a302a4592ce0b3087a01466.png\"},{\"id\":100200035,\"identity\":\"7968d6a2-893c-4a4b-b844-5e688b4042ab\",\"added_by\":\"auto\",\"created_at\":\"2026-01-14 04:37:59\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":652333,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDetermination of the trophic strategies of three zooxanthellate corals \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e, \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., \\u003cem\\u003eMillepora dichotoma\\u003c/em\\u003e. a) Standard ellipse areas for the coral host (circles, thick ellipse outline) and Symbiodiniaceae (triangles, thin ellipse outline) representing the core 40% of the isotopic niche. Greater overlap between host and Symbiodiniaceae ellipses indicates higher reliance on autotrophy, while less overlap indicates higher reliance on heterotrophy. b) Standard ellipse area estimates (SEAb), calculated from posterior sampling of the Bayesian SIBER model, summarised using the posterior mode (coloured circle) along with 50% (thick line) and 95% (thin line) credible intervals. c) Bootstrapped estimates (n = 10,000) of SEAc overlap between the host and symbiont fractions as a proxy for trophic strategy. Coral trophic strategy cutoffs are indicated by dashed lines and labels, according to Conti-Jerpe et al. (2020) \\u003csup\\u003e47\\u003c/sup\\u003e. The distribution of the 10, 000 overlap estimates are displayed above the plot.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/05d6e0f80b5a0b82bcab2750.png\"},{\"id\":100383120,\"identity\":\"67165123-7565-4c66-886f-93c3f586dc02\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 10:46:08\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6321051,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/6bb24551-91bf-49ec-9b0e-c8abc0bb1024.pdf\"},{\"id\":100370648,\"identity\":\"5beb14bc-4bd9-4299-8730-c949cdee5792\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 08:07:09\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":700333,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Material\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarymaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8588779/v1/c92f8b18dfcacb7d9eb57b94.docx\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eCoral-associated denitrification is seasonally variable and species-specific\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eNitrogen (N) is essential for corals, supporting protein synthesis, reproduction and photosynthetic efficiency \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. Yet, corals thrive in N - poor oligotrophic waters. Therefore, to sustain their productivity in these environments, corals employ a multifaceted approach to efficiently acquire, process and retain N. They can meet much of their N demand through heterotrophic feeding on N-rich prey and particulate organic matter, if available \\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e. Additionally, corals exist as holobionts, living in association with microorganisms such as bacteria, viruses and many other taxa \\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. Diazotrophic bacteria form part of this intricate microbial community, playing a crucial role in N-fixation and contributing to the coral\\u0026rsquo;s N budget. Specifically, diazotrophic bacteria convert atmospheric N\\u003csub\\u003e2\\u003c/sub\\u003e into bioavailable ammonium that can be used by the coral \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR8 CR9\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e In addition, many stony and soft corals harbour symbiotic dinoflagellates from the family Symbiodiniaceae and are colloquially known as zooxanthellate species \\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. The symbionts are capable of taking up nitrate, a process that the coral host itself cannot perform directly as it lacks the appropriate enzymes to reduce it into the ammonium bioavailable form \\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e. The symbionts supply the coral host with carbon (C) \\u0026ndash; rich and N - poor photosynthates \\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. The symbionts also recycle metabolic waste products from the host, such as ammonium \\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e, converting these into amino acids and other nitrogenous compounds that are partially translocated to the coral host \\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e. In contrast, azooxanthellate corals do not host Symbiodiniaceae \\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e and therefore rely solely on heterotrophic feeding and N-fixation for their N supply, without the added benefit of symbiotic N assimilation and C supply.\\u003c/p\\u003e \\u003cp\\u003eUnder contrasting conditions, corals can be negatively affected when N is available in excess. When more \\u003cem\\u003ein hospite\\u003c/em\\u003e N is available, symbionts allocate more C to their own growth rather than to the coral host, promoting symbiont proliferation and a transition towards parasitism which can trigger coral bleaching \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR20 CR21 CR22 CR23 CR24 CR25\\\" citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. The outcome can depend on nutrient stoichiometry, particularly the balance of N and phosphate (P) \\u003csup\\u003e3,27\\u0026ndash;30\\u003c/sup\\u003e. When N enrichment occurs without a corresponding increase in P, the coral-algal symbiosis can break down because the symbionts are starved of P, causing light and heat induced bleaching \\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e. The effects of excess N can also vary by form of N, for example urea-exposed corals recover faster than those exposed to excess nitrate \\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e. Additionally, excess ammonium has mixed effects, offering potential benefits to photosynthesis and calcification of corals at moderate concentrations, yet becoming toxic in higher concentrations \\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e. Conversely, nitrate may negatively affect both photosynthesis and calcification processes as its conversion into bioavailable ammonium is energetically \\u0026ndash; costly \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR33\\\" citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eOne mechanism by which the coral holobiont mitigates excess N is the process of denitrification \\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. Denitrification is a microbial process where denitrifying microbes, within the coral holobiont sequentially reduce nitrate to nitrite, nitric oxide, nitrous oxide and eventually to dinitrogen gas that is released into the atmosphere \\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. This process has received increased attention in recent years, and preliminary insights into denitrification in coral reefs are now emerging. For example, recent studies have revealed that denitrification is an active pathway in multiple stony and soft Red Sea corals \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e stony Cuban corals \\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e and Great Barrier Reef corals \\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e. Denitrification has also been identified as an active pathway among several benthic reef substrates such as coral rubble, biogenic rock, turf algae and reef sediment \\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e. These studies have also revealed that there are apparent susbstrate and coral species-specific differences in denitrification activity. Additionally, rates of denitrification and the opposing pathway N\\u003csub\\u003e2\\u003c/sub\\u003e fixation, were found to correlate with algal symbiont density and with each other, leading to speculation that the pathways may have some similarities \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. For example, authors hypothesised that the heterotrophic bacteria that govern the two pathways may share a supply of organic C from the algal symbionts \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. However, although denitrification activity has now been detected broadly, the significance of the pathway in overall N removal in stony corals is debated. For example, Glaze and colleagues \\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e postulated that denitrification has limited importance compared to other N removal pathways like anaerobic ammonium oxidation, whereas Yang and colleagues \\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e found denitrification accounted for ~\\u0026thinsp;90% of N\\u003csub\\u003e2\\u003c/sub\\u003e production in stony corals. However, it is important to acknowledge that different techniques have been used to quantify denitrification among existing studies. These include molecular techniques that quantify copy numbers of denitrifying genes such as \\u003cem\\u003enirS\\u003c/em\\u003e which can be used as a proxy for denitrification \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e and varying physiological techniques such as tracer experiments (direct) \\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e and acetylene assays (indirect) \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e complicating direct comparisons between studies.\\u003c/p\\u003e \\u003cp\\u003eSignificant knowledge gaps remain in our understanding of coral-associated denitrification. While previous studies have quantified the denitrification rates of several Red Sea corals, these measurements were conducted under nitrate-enriched conditions to determine denitrification potential \\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. Consequently, it remains unclear how denitrification activity responds to natural environmental conditions in the Red Sea. In particular, we do not yet know how coral-associated denitrification varies seasonally. Seasonal cycles in the Red Sea are pronounced, with cooler temperatures (~\\u0026thinsp;24 \\u0026deg;C) and higher nutrient concentrations (e.g., inorganic N) during winter and spring due to vertical mixing, and warmer temperatures (~\\u0026thinsp;32 \\u0026deg;C) but lower nutrient availability during the stratified summer months \\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e. The seasonal influence on an alternate N-cycling pathway (N\\u003csub\\u003e2\\u003c/sub\\u003e fixation) has been studied previously, which found significantly higher N\\u003csub\\u003e2\\u003c/sub\\u003e fixation of the reef in spring/summer than autumn/winter \\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e. Likewise, in another Red Sea study, higher N\\u003csub\\u003e2\\u003c/sub\\u003e fixation rates were measured in the summer for \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e across water depths of 5, 10 and 20 m \\u003csup\\u003e46\\u003c/sup\\u003e. Furthermore, previous studies have speculated that there may be a link between denitrification activity and the trophic strategy of the coral host \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e, this has also not been directly investigated, and there has yet to be an assessment of denitrification across species with varying trophic strategies.\\u003c/p\\u003e \\u003cp\\u003eConsidering these knowledge gaps, we asked three key questions: i) What is the influence of seasonal change on coral-associated denitrification rates and which environmental factors drive this process? Secondly, ii) How do internal nutrient dynamics modulate denitrification activity? Lastly, iii) How does the host trophic strategy influence denitrification rates? To assess this, we selected a suite of Red Sea corals that, according to literature, differ in their trophic strategy. We included zooxanthellate corals that have a greater reliance on autrotrophy such as \\u003cem\\u003eStylophora pistillata, Acropora\\u003c/em\\u003e sp. and \\u003cem\\u003eMillepora dichotoma\\u003c/em\\u003e, and an azooxanthellate coral \\u003cem\\u003eTubastrea coccinea\\u003c/em\\u003e that is fully heterotrophic \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR48 CR49\\\" citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. We sampled these corals bimonthly (once every two months) over a complete year and measured denitrification rates, assessed various physiological parameters and monitored environmental conditions. We hypothesised that denitrification rates of corals would be lower in winter months compared to summer months as bacterial metabolisms are slowed down by low temperatures \\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e. In fact, this pattern in denitrification activity has been observed in seagrass sediments with higher rates measured in summer compared to winter in the central Red Sea \\u003csup\\u003e\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e. Secondly, we hypothesised that the internal nutrient dynamics of the coral host would influence denitrification activity, given that denitrification activity has been found to correlate with symbiont cell densities \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e, suggesting a link to internal nutrient cycling. Lastly, we anticipated that more autotrophic coral species would exhibit higher denitrification rates than those that are more heterotrophic. This hypothesis stems from previous studies that suggest that denitrifiers may rely on autotrophically-derived C \\u003csup\\u003e36,38\\u003c/sup\\u003e. Filling these knowledge gaps is crucial for comprehending both the natural dynamics of denitrification and the potential impacts of environmental stressors, such as ocean warming and eutrophication, on microbial community structure and function of corals. Furthermore, these findings will shed light on species-specific differences in denitrification and enhance our understanding of how particular species may withstand global changes.\\u003c/p\\u003e\"},{\"header\":\"Material and methods\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003e3 .1 Collection of corals\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe carried out coral collections in the central Red Sea at the \\u0026ldquo;Al Fahal Reef\\u0026rdquo;, or also known as \\u0026ldquo;The Coral Probiotics Village\\u0026rdquo; (22.30518N, 38.96468E), a mid-shore reef located 15 km offshore from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia \\u003csup\\u003e\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e. The sampling area is shallow, with a maximum water depth of 10 m. We identified four species of Red Sea corals that differ in their trophic strategy, including three zooxanthellate species \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e that exhibit mixotrophic feeding and an azooxanthellate species \\u003cem\\u003eT. coccinea\\u003c/em\\u003e that has a fully heterotrophic lifestyle \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR48 CR49\\\" citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. We sampled five separate colonies (n\\u0026thinsp;=\\u0026thinsp;5) of each species using SCUBA between 1\\u0026ndash;5 m water depth, every second month over a one-year timespan, generating six timepoints i.e., April 2022, June 2022, August 2022, October 2022, December 2022, and February 2023. We consistently carried out sampling in the first two weeks of every other month, sampling \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e and \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. in the first week, and \\u003cem\\u003eS. pistillata\\u003c/em\\u003e and \\u003cem\\u003eT. coccinea\\u003c/em\\u003e in the second week. From each colony, we cut three fragments using pliers and placed them into labelled sampling bags, filled with seawater. Out of the three fragments, we used two for incubations (~\\u0026thinsp;5 cm length) and one for isotope and elemental analysis (~\\u0026thinsp;5 cm length). In the case of \\u003cem\\u003eT. coccinea\\u003c/em\\u003e, colonies were too small to sample multiple fragments from, so instead, we sampled 15 polyps bimonthly. On the boat, we stored the fragments for incubations in recirculation aquaria filled with seawater from the sampling site, equipped with an air pump to maintain water circulation and oxygen availability. We placed all aquaria in the shade to prevent heat/light stress during transport, and we kept the fragments for physiological assessments on ice and later stored them at -20\\u0026deg;C in the lab.\\u003c/p\\u003e \\u003cp\\u003eThe collections were conducted as part of a collaboration between KAUST and the University of Bremen, in which a subset of the dataset (isotopic and elemental data) for two species (\\u003cem\\u003eS. pistillata\\u003c/em\\u003e and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e) was analysed separately.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Quantification of denitrification rates\\u003c/h2\\u003e \\u003cp\\u003eOn the same day as sampling, we quantified denitrification rates via acetylene blockage/inhibition assays. This indirect method has been successfully applied to investigate coral reef associated denitrification activities \\u003csup\\u003e\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e. Acetylene blocks the activity of the enzyme nitrous oxide (N\\u003csub\\u003e2\\u003c/sub\\u003eO) reductase within the denitrification pathway, leading to the accumulation of N\\u003csub\\u003e2\\u003c/sub\\u003eO which can be used as a proxy for the relative activity of denitrification of the coral holobiont \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR55 CR56\\\" citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e. The efficacy of acetylene assays has been validated in previous work which used both acetylene assays and \\u003cem\\u003enirS\\u003c/em\\u003e gene copy numbers to quantify denitrification \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. The observed patterns of \\u003cem\\u003enirS\\u003c/em\\u003e gene abundance corresponded closely with the denitrification rates measured using the acetylene method, providing evidence that the acetylene method is accurate and can be relied upon \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eTo set up the acetylene assays, we secured each coral fragment to a stand using rubber bands and placed them inside a gas-tight glass beaker (Figure S1a). We specially adapted the beakers for acetylene assays by having an 8 mm hole drilled into the lid. The hole was sealed with a gas-tight rubber stopper, and a hypodermic needle (hypodermic needle with polypropylene hub 30G x 3/4\\\", Tyco Healthcare group, MonojectTM) was permanently inserted through the stopper into the jar. On the outside of the jar, the needle hub was attached to a 2-way stopcock with a Luer lock connection (two-way stopcock, BraunTM DiscofixTM) where a gas syringe (50 ml gastight syringe model 1050 TLL PTFE Luer Lock, Hamilton) could be later fitted when required, for gas samples to be taken (Figure S1a). We filled each beaker with seawater (taken from the sampling site the same morning) to 80% of its capacity, leaving a 20% headspace. We then replaced ten percent of the seawater volume with acetylene enriched seawater, and likewise, replaced 10% of the beaker headspace with acetylene gas (Yoshinari \\u0026amp; Knowles, 1976) (Figure S1a). We made acetylene gas freshly on the same day prior to the incubations (detailed in the Supplementary). For the incubations, we secured corals to a stand in gas-tight glass beakers filled with site-collected temperature-controlled seawater. We placed beakers into water baths equipped with thermostats (3613 aquarium heater. 75W 220\\u0026ndash;240 V; EHEIM GmbH and Co.KG) and temperature controllers (Schego Temperature Controller TRD, max. 1000W) that heated the water to the corresponding \\u003cem\\u003ein situ\\u003c/em\\u003e temperature per sampling month. The water bath was then placed on top of magnetic stir plates which powered magnetic stir bars in the base of each beaker to ensure adequate water circulation (~\\u0026thinsp;220 rpm). we incubated corals for 12 h in the dark followed by 12 h in the light. During the light incubation, we supplied light at the same intensity as the \\u003cem\\u003ein situ\\u003c/em\\u003e conditions of the respective sampling month. However, we used new fragments for the following light incubation to minimise the potential impact of stress on the coral\\u0026rsquo;s denitrification rates. We included four control beakers containing no corals in each incubation run to account for potential background denitrification activity in the seawater. We took gas samples (3 ml) from the beaker headspace at the beginning (T0) and end (T12) of each incubation, using a gas syringe (50 ml gastight syringe model 1050 TLL PTFE Luer Lock, Hamilton) and stored the samples in gas tight vials until further measurement.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eGas samples generated from the acetylene assays are typically measured using a gas chromatograph (GC) fitted with an electron capture detector (ECD). However, in our study, we measured the gas samples using a N\\u003csub\\u003e2\\u003c/sub\\u003eO microsensor (custom-made, Unisense). Although a GC with ECD was tested, it was not used as microsensors outperformed it in several aspects. For example, the electrochemical microsensor has high sensitivity (detection limit: 25 nM) and higher throughput compared to GC-based methods. We connected the microsensor to a multi-channel (fx-6 UniAmp multi-channel 110394, Unisense) and calibrated it every day prior to usage with a two-point calibration curve consisting of a low point (ambient air: 0.009 \\u0026micro;mol L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and a high point (a known standard: 0.575 \\u0026micro;mol L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) at a consistent room temperature (21\\u0026deg;C) and pressure (1 bar). To record and visualise measurements, we synced the N\\u003csub\\u003e2\\u003c/sub\\u003eO microsensor with the Sensor Trace Suite software (v.1.13) on a computer desktop. Further technical details about the microsensor and the steps to use the microsensor and normalise the data can be found in the supplementary material.\\u003c/p\\u003e \\u003cp\\u003eWhile the acetylene inhibition technique has historically been popular to quantify denitrification rates \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e, it has several limitations to be aware of \\u003csup\\u003e54,58\\u003c/sup\\u003e. Sometimes incomplete inhibition of N\\u003csub\\u003e2\\u003c/sub\\u003eO reductase occurs, meaning that N\\u003csub\\u003e2\\u003c/sub\\u003e is produced as normal instead of accumulating as N\\u003csub\\u003e2\\u003c/sub\\u003eO, causing an underestimation of denitrification rates \\u003csup\\u003e\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e. Further underestimation of denitrification may arise from the inhibition effect of acetylene on the nitrification pathway \\u003csup\\u003e\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e\\u003c/sup\\u003e. Nitrification is the preceding step in the N-cycle that provides nitrate as a substrate for denitrification. Therefore, denitrification rates may be underestimated due to substrate limitation \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR59\\\" citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e\\u003c/sup\\u003e. Another common technique is to run 15N tracer incubations \\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e which measures the actual N\\u003csub\\u003e2\\u003c/sub\\u003e production directly. The benefits of this technique include the ability to distinguish between pathways and the lack of an inhibitory effect on related pathways. However, in our case, using the acetylene inhibition technique was more suitable, as it allowed us to measure denitrification activity under ambient conditions, without artificially enriching with 15N-labelled substrates.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Elemental and isotopic analysis of carbon and nitrogen\\u003c/h2\\u003e \\u003cp\\u003eFirstly, we blasted the tissue off the coral skeleton. To do so, we held the fragment within a sterile clear sampling bag (Whirl-pack sample bag) and used an airbrush (model S68 with dual action siphon feed, Master Airbrush) to remove the tissue with high pressure air and MilliQ water. We always sterilised the airbrush with 70% ethanol and rinsed it with MilliQ water between samples. We then stored the tissue slurries in Falcon tubes at \\u0026minus;\\u0026thinsp;20\\u0026deg;C and defrosted them in prior to the next stage. Once samples had defrosted, we separated the tissue slurry into host and symbiont fractions via centrifugation (2.5 minutes at 500 x g) and then washed and resuspended the symbiont pellet in 3 ml of MilliQ until it was clean (i.e., free from white residue), which typically required three washes. Following this, we filtered each fraction through 0.7 \\u0026micro;m GF/F filters (fitted to a vacuum assembly), treated to remove skeletal contaminants (2 ml x1N HCl), rinsed with MilliQ, and immediately dried the samples at 60\\u0026deg;C for 48 h. We then scraped the dried mass into tin cups and weighed and analysed the samples for δ13C, δ15N and mass percent C and N via EA-IRMS at the Natural History Museum, Berlin, using a Flash 1112 EA and Thermo Scientific Delta V IRMS (Berlin, Germany). We report the isotopic data using the conventional delta notations (δ) and express in \\u0026permil; relative to the international standards (Vienna Pee Dee Belemnite) for δ13C (0.01118) and atmospheric N2 for δ15N (0.00368) \\u003csup\\u003e\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u003c/sup\\u003e. Within-run standard deviations (SD) of the standards were \\u0026lt;\\u0026thinsp;0.15 per mil (\\u0026permil;) for δ13C and δ15N and the SD of replicate measurements of the lab standard are \\u0026lt;\\u0026thinsp;3% of the concentration analysed.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Measurement of environmental parameters\\u003c/h2\\u003e \\u003cp\\u003eTwenty four hours before sampling for nutrients, we acid-washed all water sampling equipment in a 4% HCl bath to minimise potential contaminations. On the same day as coral fragments were collected, we e collected water samples from the average study site depth (~\\u0026thinsp;3\\u0026ndash;5 m depth) using 2 x 5 L Niskin bottles. On the boat, we aliquoted water for separate nutrient measurements into Falcon tubes. Immediately on the boat, water aliquots for nitrite and nitrate were filtered (0.22 \\u0026micro;M Millex\\u0026reg;-GV), yet we performed no immediate filtration steps for ammonium, DOC and chl \\u003cem\\u003ea\\u003c/em\\u003e (stored in opaque bottles). We kept all water samples on ice during transport and then stored samples for nitrite, nitrate and ammonium at \\u0026minus;\\u0026thinsp;20\\u0026deg;C, and stored samples for chl \\u003cem\\u003ea\\u003c/em\\u003e and DOC analysis at +\\u0026thinsp;4\\u0026deg;C.\\u003c/p\\u003e \\u003cp\\u003eFor the measurement of nitrate and nitrite, we analysed samples with a segmented flow analyser (Model AA3 HR, SEAL Analytical IC). We performed a calibration prior to every run and accepted upon the criteria that R\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.99. To prepare the calibration standards, we used a ready-made stock standard of 1000 ppm nitrite and nitrate to create low (5, 20, 50 and 100 ppb) and high (100, 200, 500, 1000 ppb) standards. The instrument detection limit for nitrate was 0.0322 \\u0026micro;mol L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 0.0217 \\u0026micro;mol L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for nitrite. For the measurement of ammonium, we analysed samples using a fluorometer (Turner Designs, Trilogy Fluorometer) via Orthophthaldialdehyde (OPA) derivatisation. We performed a calibration prior to every run and accepted upon the criteria that R\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.99. We prepared a mother solution of ammonium chloride to make standards of 0.0, 0.03, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3 \\u0026micro;mol L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. The detection limit of the instrument was 0.058 \\u0026micro;mol L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. For DOC analysis, we filtered 500 ml of water through 0.7 \\u0026micro;m GF/F filters (pre-combusted at 450\\u0026deg;C for 4.5 h). We divided the filtrate into sterile amber glass vials, that we then acidified with 0.1 ml of 85% phosphoric acid to prevent bacterial activity and analysed them on a TOC Analyser (TOC-L, Total Organic Carbon Analyser, Shimadzu, Kyoto, Japan). We performed a calibration before each run using a standard addition curve of Potassium Hydrogen Phthalate (0; 33; 25; 50; 62; 83; 100; 125; 167; 250; 500 \\u0026micro;mol C L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). We prepared internal controls using Consensus Reference Materials (CRM; Batch 12; 2012; DOC: 42\\u0026ndash;45 \\u0026micro;mol L\\u0026thinsp;\\u0026minus;\\u0026thinsp;1) provided by DA Hansell and W Chen (University of Miami). The average analytical variation of the instrument was \\u0026lt;\\u0026thinsp;3.5% for DOC based on 5\\u0026ndash;7 injections per sample.\\u003c/p\\u003e \\u003cp\\u003eOn the same day as collection, we filtered water samples for chl \\u003cem\\u003ea\\u003c/em\\u003e (2 L in duplicates) through 0.7 \\u0026micro;m GF/F filters and then stored them at \\u0026minus;\\u0026thinsp;80\\u0026deg;C until further processing. We prepared samples for measurement adapting a protocol from Erar \\u0026amp; Collins, (1997) \\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e and a protocol used in the California Operative Oceanic Fisheries Investigations. In brief, we soaked the filters in 10 ml of 90% acetone, vortexed, sonicated in an ice bath and stored them at 4\\u0026deg;C overnight in the dark. The following morning, we repeated the sonication and vortexing steps twice more, and then centrifuged the samples at 2500 rpm at 4\\u0026deg;C for 10 min. Next, we measured the samples fluorometrically (Turner Designs, Trilogy Fluorometer) on a fluorometer fitted with a chl \\u003cem\\u003ea\\u003c/em\\u003e module (Turner Designs, Wide-Chlorophyll \\u003cem\\u003ea\\u003c/em\\u003e Acidification Module). We calibrated the instrument prior to use with standards of 0.5, 1.0, 2.5, 5.0, 10.0, 20.0, 100.0 and 200.0 \\u0026micro;g L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e which generated a calibration curve of R\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.99. Following this, we validated the instrument calibration with two solid secondary standards adjusted to 2.5 and 20 \\u0026micro;g L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Turner Designs, Adjustable Solid Secondary Standard - Red) and ran \\u0026ldquo;blanks\\u0026rdquo; of 90% acetone at the beginning, and after every few samples.\\u003c/p\\u003e \\u003cp\\u003eLastly, we measured seawater temperature continuously throughout the sampling period using an Onset Hobo pendant temperature logger deployed at the reef. We deployed the logger at 1\\u0026ndash;2 m depth, and measured temperature at 10 minute intervals throughout the sampling period April 2022 \\u0026ndash; February 2023. We wrapped the logger in white electrical tape to minimise solar bias and achieve better measurement accuracy \\u003csup\\u003e\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003e. For light intensity, we extracted data from Copernicus ERA5. The data are hourly measurements under direct clear sky radiation from the sampling site between April 2022 \\u0026ndash; February 2023, that we converted into \\u0026micro;mol photons m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Data analyses\\u003c/h2\\u003e \\u003cp\\u003eWe assessed denitrification data for statistically significant differences between species, between months per species and between light and dark incubations per month and species. All denitrification rate data were tested for normality via Shapiro-wilk tests, revealing that data were not normally distributed, even following transformations. Therefore, the non-parametric Kruskal-Wallis test was used followed by a Dunn\\u0026rsquo;s test with Bonferroni p-value adjustment for post-hoc analysis.\\u003c/p\\u003e \\u003cp\\u003eSecondly, we employed a random forest model to identify key environmental variables that may influence denitrification rates across different coral species. The random forest regression model consisted of 500 trees, with 3 variables tried at each split. The variable importance for predicting denitrification was determined by the mean squared error (%MSE), where a high %MSE indicates that the variable is important, as if it were removed, the model\\u0026rsquo;s error would significantly increase. As a follow-up to the random forest analysis, we plotted partial dependence plots (PDPs) for the top 3 most influential parameters per species. This provided insight into how these parameters interacted with denitrification, by displaying how changes in one parameter influences the predicted outcome, while averaging the influence of all other parameters.\\u003c/p\\u003e \\u003cp\\u003eNext, we correlated the denitrification rates of each species with its measured biogeochemical signatures, including isotope (δ15N and δ13C) and elemental data (C:N) of both the host and symbiont. For this, we used a non-parametric Spearman rank correlation, as data were not normally distributed following Shapiro-wilk testing. Data were pooled across the sampling year and cleaned prior to analysis. This included removing samples below the instrument\\u0026rsquo;s detection limit of 0.015 mg, followed by identifying and excluding statistical outliers.\\u003c/p\\u003e \\u003cp\\u003eLastly, the baseline trophic strategies and niche widths for the three zooxanthellate corals (\\u003cem\\u003eS. pistillata, Acropora\\u003c/em\\u003e sp., and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e) were estimated following the approach of Jackson et al., (2011) \\u003csup\\u003e\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e adapted for corals by Conti-Jerpe et al., (2020) \\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e and Fox et al., (2023) \\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e. The isotope data (δ15N and δ13C) were pooled and cleaned in the same way as the correlation analysis (above), with the added criterion that only paired δ15N and δ13C values for both host and symbiont fractions were included. This resulted in sample sizes of n\\u0026thinsp;=\\u0026thinsp;18 for \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, n\\u0026thinsp;=\\u0026thinsp;14 for \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., and n\\u0026thinsp;=\\u0026thinsp;16 for \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e. Following this, we visualised the isotopic niches of each species, by plotting the standard ellipse areas of the coral host and symbiont fractions. Next, standard ellipse areas (SEAb) were calculated directly from posterior sampling of theSIBER model, and niche widths were summarised using the posterior mode along with 50% and 95% credible intervals. Thirdly, we used bootstrapped estimates (n\\u0026thinsp;=\\u0026thinsp;10,000) of SEAc overlap between the host and symbiont fractions as a proxy for trophic strategy (Conti-Jerpe et al., 2020). In addition, we calculated Layman\\u0026rsquo;s metrics \\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e\\u003c/sup\\u003e which serve as useful descriptions of data dispersion, offering insight into trophic diversity.\\u003c/p\\u003e \\u003cp\\u003eThe software R (version 4.3.2) (R Core Team, 2023) was used to generate figures using packages \\u0026lsquo;ggplot2\\u0026rsquo; \\u003csup\\u003e67\\u003c/sup\\u003e, \\u0026lsquo;ggpubr\\u0026rsquo; \\u003csup\\u003e68\\u003c/sup\\u003e, \\u0026lsquo;dplyr\\u0026rsquo; \\u003csup\\u003e69\\u003c/sup\\u003e, \\u0026lsquo;RColorBrewer\\u0026rsquo; \\u003csup\\u003e70\\u003c/sup\\u003e, \\u0026lsquo;gridExtra\\u0026rsquo; \\u003csup\\u003e71\\u003c/sup\\u003e, \\u0026lsquo;cowplot\\u0026rsquo; \\u003csup\\u003e72\\u003c/sup\\u003e. Likewise, statistics were also computed in R, using packages \\u0026lsquo;rstatix\\u0026rsquo; \\u003csup\\u003e73\\u003c/sup\\u003e, \\u0026lsquo;dunn.test\\u0026rsquo; \\u003csup\\u003e74\\u003c/sup\\u003e, \\u0026lsquo;tidyverse\\u0026rsquo; \\u003csup\\u003e75\\u003c/sup\\u003e, \\u0026lsquo;randomForest\\u0026rsquo; \\u003csup\\u003e76\\u003c/sup\\u003e, \\u0026lsquo;pdp\\u0026rsquo; \\u003csup\\u003e77\\u003c/sup\\u003e, \\u0026lsquo;SIBER\\u0026rsquo; \\u003csup\\u003e78\\u003c/sup\\u003e and \\u0026lsquo;rjags\\u0026rsquo; \\u003csup\\u003e79\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Denitrification rates among four Red Sea corals\\u003c/h2\\u003e \\u003cp\\u003eDenitrification was detected in all four Red Sea coral species (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). Averaged over the year, denitrification rates of the three zooxanthellate species (\\u003cem\\u003eS. pistillata\\u003c/em\\u003e, \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e) were similar at 0.09 \\u0026plusmn; 0.16, 0.10 \\u0026plusmn; 0.16 and 0.08 \\u0026plusmn; 0.10 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e respectively and therefore did not significantly differ (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). However, the average denitrification rate of the azooxanthellate species \\u003cem\\u003eT. coccinea\\u003c/em\\u003e was significantly higher than all three zooxanthellate species (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), being 5-fold higher than both \\u003cem\\u003eS. pistillata\\u003c/em\\u003e and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e and 4-fold higher than \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. at 0.43 \\u0026plusmn; 0.61nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Seasonal variation in denitrification rates\\u003c/h2\\u003e \\u003cp\\u003eMonthly denitrification rates significantly differed across the year in all four species (Kruskal-Wallis; \\u003cem\\u003eS. pistillata\\u003c/em\\u003e: H\\u0026thinsp;=\\u0026thinsp;18.2, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; \\u003cem\\u003eAcropora\\u003c/em\\u003e sp.: H\\u0026thinsp;=\\u0026thinsp;14.7, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e: H\\u0026thinsp;=\\u0026thinsp;13.2, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; \\u003cem\\u003eT. coccinea\\u003c/em\\u003e: H\\u0026thinsp;=\\u0026thinsp;14.6, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), with a trend of higher denitrification activity during the spring and summer months compared to the autumn and winter months (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb - e). In \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, denitrification rates were significantly higher in April (0.20 \\u0026plusmn; 0.20 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and June (0.23 \\u0026plusmn; 0.26 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) compared to August (0.02 \\u0026plusmn; 0.04 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), October (0.03 \\u0026plusmn; 0.05 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), December (0.01 \\u0026plusmn; 0.02 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and February (0.05 \\u0026plusmn; 0.10 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb). In \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., rates were significantly higher in June (0.17 \\u0026plusmn; 0.10 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) than in October (0.02 \\u0026plusmn; 0.03 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and December (0.03 \\u0026plusmn; 0.04 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec). In \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, denitrification rates were significantly higher in June (0.18 \\u0026plusmn; 0.10 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), than in April (0.05 \\u0026plusmn; 0.07 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and August (0.03 \\u0026plusmn; 0.06 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ed). Lastly, in \\u003cem\\u003eT. coccinea\\u003c/em\\u003e, denitrification rates were significantly higher in April (0.89 \\u0026plusmn; 0.86 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and June (0.69 \\u0026plusmn; 0.71 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) than in December (0.02 \\u0026plusmn; 0.06 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ee).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Denitrification in the light and dark incubations\\u003c/h2\\u003e \\u003cp\\u003eSignificant differences between denitrification rates measured in light and dark incubations per month were observed in all four species (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb - e). Mostly, denitrification rates were significantly higher in the dark than in the light incubations, with this significant trend observed in 11 out of 13 of the significant pairings identified. More specifically, in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e denitrification rates were 23-fold higher in April in the dark versus the light (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;6.00, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) and 0.06 in the dark versus 0 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in the light in October (Kruskal-Wallis, 5.54, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb). In Acropora sp., denitrification rates were 2-fold higher in June (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;3.94, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and 39-fold higher in August in the dark compared to the light (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;6.99, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). In October, denitrification rates were 0.05 in the dark versus 0 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in the light (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;5.54, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec). In \\u003cem\\u003eM. dichtoma\\u003c/em\\u003e, denitrification rates were 2-fold higher in June (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;4.81, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and 8-fold higher in October in the dark compared to the light incubations (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;6.90, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ed). Lastly, in \\u003cem\\u003eT. coccinea\\u003c/em\\u003e, rates were 17-fold higher in April (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;6.99, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), 7-fold higher in June (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;5.77, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and 53-fold higher in October in the dark compared to the light (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;6.21, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), while in August rates were 0.06 in the dark versus 0 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in the light (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;7.76, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ee). However, the reverse trend was observed in 2 out of 13 significant pairings, where denitrification was higher in the light incubation compared to the dark incubation. This was observed only during February in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. where rates were 382-fold higher in the light versus the dark (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;7.26, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), and T. coccinea where rates were 0.3 in the light versus 0 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in the dark (Kruskal-Wallis, H\\u0026thinsp;=\\u0026thinsp;7.20, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01;Figure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec \\u0026amp; e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 Determining the environmental drivers of denitrification\\u003c/h2\\u003e \\u003cp\\u003eThe measured environmental variables (nitrate, ammonium, DOC, water chl \\u003cem\\u003ea\\u003c/em\\u003e and temperature) were used in a random forest model, to determine the most influential variables over denitrification rates, for each species (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The amount of denitrifying variation that the environmental variables explained, varied between species. For example, the environmental variables explained 12% of denitrifying variation in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, 57% in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., 38% in \\u003cem\\u003eM. dichtoma\\u003c/em\\u003e and 50% in \\u003cem\\u003eT. coccinea\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, DOC, nitrate and ammonium were identified as the top three most influential variables over denitrification rates (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). DOC availability was the strongest predictor of denitrification in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e (15% IncMSE), with higher levels promoting denitrification (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). Low nitrate availability was the second most influential over denitrification rates in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e (12.5% IncMSE; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). Moderate ammonium availability also influenced denitrification rates of \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, yet to a much lesser extent (3.6% IncMSE; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed). In \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., temperature, ammonium and water chl \\u003cem\\u003ea\\u003c/em\\u003e were most influential over denitrification rates and to similar extents (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee). High temperature was the top predictor of denitrification in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (14.4% IncMSE, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef), followed by moderate ammonium availability (13.5% IncMSE, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eg) and water chl \\u003cem\\u003ea\\u003c/em\\u003e (13.3% IncMSE; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eh). In \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, denitrification rates were evenly influenced by moderate temperature (13.6% IncMSE, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ei, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ej), high DOC availability (13.3% IncMSE, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ei, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ek) and moderate water chl \\u003cem\\u003ea\\u003c/em\\u003e concentrations (13.3% IncMSE; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ei, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003el). Lastly, in \\u003cem\\u003eT. coccinea\\u003c/em\\u003e, low nitrate availability was the most influential variable over denitrification rates (21.1% IncMSE, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003em, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003en), followed by high DOC availability (12.7% IncMSE; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003em, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eo). High water chl \\u003cem\\u003ea\\u003c/em\\u003e concentration (5.8% IncMSE, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003em, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ep) was ranked third but influenced denitrification to a lesser extent than the top two variables. Overall, each species was influenced by a unique combination of environmental variables. However, we also identified environmental drivers that are common across multiple species, such as DOC availability and temperature.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5 The influence of internal nutrient dynamics on denitrification\\u003c/h2\\u003e \\u003cp\\u003eIn \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, we found a significant positive correlation between denitrification rates (0.09 \\u0026plusmn; 0.16 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and symbiont δ13C (-14.16 \\u0026plusmn; 0.77\\u0026permil;; \\u003cem\\u003erho\\u003c/em\\u003e: 0.44; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), as well as a significant negative correlation with symbiont δ15N (2.55 \\u0026plusmn; 0.54\\u0026permil;; \\u003cem\\u003erho\\u003c/em\\u003e: \\u0026minus;\\u0026thinsp;0.45; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). In \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., we found significant positive correlations between denitrification rates (0.10 \\u0026plusmn; 0.16 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and host δ13C (-15.34 \\u0026plusmn; 1.03\\u0026permil;; \\u003cem\\u003erho\\u003c/em\\u003e: 0.44; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and symbiont δ13C (-14.56 \\u0026plusmn; 0.48\\u0026permil;; \\u003cem\\u003erho\\u003c/em\\u003e: 0.51; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). In \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, no significant relationships were detected between denitrification rates and biogeochemical signatures (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Additionally, in \\u003cem\\u003eT. coccinea\\u003c/em\\u003e, no significant relationships were detected between denitrification rates and biogeochemical signatures (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Yet only one relationship (denitrification and host δ13C ) could be analysed since symbiont-related parameters were not applicable for this azooxanthellate species, and the other parameters had too few samples for a reliable analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.6 Determining the trophic strategy of each coral species\\u003c/h2\\u003e \\u003cp\\u003eWe used Bayesian analysis of isotopic niches \\u003csup\\u003e\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e adapted for corals \\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) combined with Layman metrics of trophic diversity \\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e\\u003c/sup\\u003e (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) to quantify and compare the isotopic niches of the three zooxanthellate corals \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e. The three species showed similarly broad isotopic niche areas (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea). However, \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e exhibited the largest Bayesian standard ellipse area (SEAb) of the host fraction (2.87\\u0026permil;\\u003csup\\u003e2\\u003c/sup\\u003e, 95% CI: 1.67\\u0026ndash;4.70), compared to \\u003cem\\u003eS. pistillata\\u003c/em\\u003e (2.15\\u0026permil;\\u003csup\\u003e2\\u003c/sup\\u003e, 95% CI: 1.31\\u0026ndash;3.51) and \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (1.34\\u0026permil;\\u003csup\\u003e2\\u003c/sup\\u003e, 95% CI: 0.86\\u0026ndash;2.54, as well as the largest Bayesian standard ellipse area (SEAb) of the symbiont fraction (2.34\\u0026permil;\\u003csup\\u003e2\\u003c/sup\\u003e, 95% CI: 1.14\\u0026ndash;3.18), compared to \\u003cem\\u003eS. pistillata\\u003c/em\\u003e (1.05\\u0026permil;\\u003csup\\u003e2\\u003c/sup\\u003e, 0.66\\u0026ndash;1.73) and \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (0.76\\u0026permil;\\u003csup\\u003e2\\u003c/sup\\u003e, CI: 0.38\\u0026ndash;1.18; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb). This was also supported by Layman\\u0026rsquo;s metrics, where \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e had the largest NR, CR and TA (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). However, \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e also had the highest NND and SDNND (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), indicating higher variability between samples. Lastly, when we quantified the relative reliance of heterotrophy versus autotrophy as the percentage overlap between host and symbiont ellipse areas (corrected for sample size (SEAc), the three species all exhibited a mixotrophic feeding strategy, with no marked differences in mean SEAc (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec). Although sample sizes were limited, the resampling errors stabilised and converged, rather than spanning the full possible range (0\\u0026ndash;100%), and patterns were consistent across coral species. Therefore, we believe our interpretations are conservative and robust, despite these constraints.\\u003c/p\\u003e \\u003cp\\u003eThe trophic strategy of \\u003cem\\u003eT. coccinea\\u003c/em\\u003e could not be quantified because this analysis requires isotope data from both host and symbiont fractions, and since \\u003cem\\u003eT. coccinea\\u003c/em\\u003e lacks symbionts, the method was not applicable. Though, as an azooxanthellate coral, its feeding mode is already known to be entirely heterotrophic. We were, however, able to measure host parameters for \\u003cem\\u003eT. coccinea\\u003c/em\\u003e and the mean host δ13C was \\u0026ndash; 24.01 \\u0026plusmn; 2.08\\u0026permil;, which was higher than the mean host δ13C of \\u003cem\\u003eS. pistillata\\u003c/em\\u003e (\\u0026minus;\\u0026thinsp;14.72 \\u0026plusmn; 0.75\\u0026permil;), \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (\\u0026minus;\\u0026thinsp;15.21 \\u0026plusmn; 0.60\\u0026permil;), and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e (-15.3 \\u0026plusmn; 0.80\\u0026permil;; Figure S2). Unfortunately, host δ15N could not be reliably quantified because too few samples remained following data cleaning steps (detailed above in section \\u003cspan refid=\\\"Sec6\\\" class=\\\"InternalRef\\\"\\u003e3.5\\u003c/span\\u003e Data analyses).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eLayman metrics \\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e\\u003c/sup\\u003e of the three zooxanthellate species \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e, \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., and \\u003cem\\u003eMillepora dichotoma\\u003c/em\\u003e. Description and interpretation guidelines are adapted from Layman et al. (2007) (Layman et al., 2007). Values are shown for the host and symbiont (Sym) fraction. The lowest value of the sample groups is italicised, while the highest value is indicated with an asterisk (*).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. pistillata\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAcropora\\u003c/em\\u003e sp.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eM. dichotoma\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMetric\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDescription\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eInterpretation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHost\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSym\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHost\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSym\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHost\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSym\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eδ\\u003csup\\u003e15\\u003c/sup\\u003eN range (NR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMaximum δ\\u003csup\\u003e15\\u003c/sup\\u003eN \\u0026ndash; minimum δ\\u003csup\\u003e15\\u003c/sup\\u003eN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLarger NR indicates more trophic diversity.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e1.58\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e5.14*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eδ\\u003csup\\u003e13\\u003c/sup\\u003eC range (CR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMaximum δ\\u003csup\\u003e13\\u003c/sup\\u003eC - minimum δ\\u003csup\\u003e13\\u003c/sup\\u003eC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLarger CR indicates more trophic diversity with varying C sources.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e1.83\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e3.03*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal Area (TA)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eA measure of the amount of niche space occupied\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLarger TA indicates a higher extent of trophic diversity. [Can be influenced by extreme/outlier values].\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e1.59\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e8.01*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e5.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean distance to centroid (CD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean Euclidean distance of samples to the δ\\u003csup\\u003e13\\u003c/sup\\u003eC - δ\\u003csup\\u003e15\\u003c/sup\\u003eN centroid. The centroid is the mean δ\\u003csup\\u003e13\\u003c/sup\\u003eC and δ\\u003csup\\u003e15\\u003c/sup\\u003eN of samples\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLarger CD indicates a higher average degree of trophic diversity. [Less influenced by extreme/outlier values].\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e0.57\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.28*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNearest neighbour distance (NND)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean of the Euclidean distances between samples in biplot space\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSmall NND indicates similar trophic ecologies between samples.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e0.27\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.56*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStandard deviation (SD) of NND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEvenness of spacing between samples\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSmall SDNND indicates a more even distribution.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e0.16\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.53*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eAmong four coral species, we observed seasonal patterns in denitrification activity, demonstrating how the N-cycling pathway is highly influenced by environmental conditions. In addition, denitrification rates were influenced by the internal nutrient dynamics of the coral holobiont and may be linked to the trophic strategy of the coral host.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 The effect of seasonality on coral denitrification rates\\u003c/h2\\u003e \\u003cp\\u003eThe denitrification rates measured here are comparable to the ranges presented in previous studies on Red Sea corals, once appropriate conversions are applied (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea) \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. Rates also significantly varied throughout the year for all four species, with a general seasonal trend of higher rates in the spring/summer compared to the autumn/winter months of the year (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb). The effect of seasonality on denitrification has not been investigated before in corals, but studies on other systems such as estuaries and marshlands have reported similar seasonal patterns to our study, finding higher denitrification rates and, in addition, higher denitrifier diversity in spring \\u003csup\\u003e\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e\\u003c/sup\\u003e. The seasonal trend observed in these former studies and our own can be explained by the sensitivity of the denitrification pathway to environmental conditions that fluctuate over a year (Figure S3).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTemperature emerged as the primary driver of denitrification in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, though its influence differed between the two species. In \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., denitrification rates increased linearly with temperature. This relationship reflects the general principle of higher temperatures stimulating microbial metabolism and activity \\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e. The same pattern was observed in Red Sea seagrass sediments \\u003csup\\u003e\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e. Additionally, among corals, elevated temperatures increase the activity of diazotrophs that govern N\\u003csub\\u003e2\\u003c/sub\\u003e fixation \\u003csup\\u003e\\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e\\u003c/sup\\u003e introducing more \\u003cem\\u003ein hospite\\u003c/em\\u003e N available for denitrification. Furthermore, a recent study by R\\u0026auml;decker et al. (2021) \\u003csup\\u003e\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e83\\u003c/span\\u003e\\u003c/sup\\u003e demonstrated that under high temperatures, the coral catabolises amino acids, again introducing more \\u003cem\\u003ein hospite\\u003c/em\\u003e N available for denitrification. For \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, however, denitrification rates peaked at moderate temperatures and decreased at higher temperatures These differences may reflect the response (and presence of) species-specific denitrifying microbiomes, as has been found between corals in previous work \\u003csup\\u003e\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e\\u003c/sup\\u003e. While there is no literature directly comparing the denitrifying microbial communities between \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, studies reveal that \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. hosts a greater bacterial diversity than \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e \\u003csup\\u003e\\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e85\\u003c/span\\u003e\\u003c/sup\\u003e, indicating a potential for differences.\\u003c/p\\u003e \\u003cp\\u003eHigh DOC availability was identified as the top driver of denitrification in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, and the second driver in \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e and \\u003cem\\u003eT. coccinea\\u003c/em\\u003e. The positive relationship between DOC and denitrification has been widely observed in other environments i.e., sediments and freshwater systems \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR87 CR88\\\" citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e86\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR89\\\" class=\\\"CitationRef\\\"\\u003e89\\u003c/span\\u003e\\u003c/sup\\u003e and is attributed to the role of DOC as a key element supporting heterotrophic microbial growth and activity \\u003csup\\u003e\\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e90\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, our study provides evidence that denitrifiers in coral holobionts are also capable of utilising environmental DOC as a source of C, and may not solely rely on symbiont derived C. One study investigating the influence of DOC on octocoral denitrification reported contrasting results, where excess DOC (supplied as glucose) reduced denitrifier abundance by an order of magnitude in \\u003cem\\u003eXenia umbellata\\u003c/em\\u003e, but had no effect on \\u003cem\\u003ePinnigorgia flava\\u003c/em\\u003e \\u003csup\\u003e\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e\\u003c/sup\\u003e. This discrepancy may be due to variations in the composition and concentration of DOC \\u003csup\\u003e\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eAgainst expectations, low nitrate availability was another key driver of denitrification. Nitrate is essential to the denitrification process, acting as an electron acceptor for denitrifying bacteria, which sequentially reduces it to dinitrogen gas \\u003csup\\u003e\\u003cspan citationid=\\\"CR92\\\" class=\\\"CitationRef\\\"\\u003e92\\u003c/span\\u003e\\u003c/sup\\u003e. Consequently, one may expect that the more available nitrate, the more is taken up by the coral symbionts (among zooxanthellate corals) and the higher the denitrification rates. However, the opposite relationship was apparent in our study, with denitrification rates linked to low nitrate availability (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). This may simply be because denitrification activity on an ecosystem scale is sufficiently high to reduce nitrate concentrations in the surrounding water column. Alternatively, a study by El-Khaled et al. (2020) \\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e demonstrated that N cycling is nuanced, with opposing pathways like N\\u003csub\\u003e2\\u003c/sub\\u003e fixation and denitrification increasing together under higher environmental N. In the oligotrophic Red Sea, where nitrate stays low year-round (0.2\\u0026ndash;1.3 \\u0026micro;mol L-1; Figure S3), N\\u003csub\\u003e2\\u003c/sub\\u003e fixation may rise in response to low N while denitrification simultaneously increases. This suggests denitrification becomes dominant only at higher nitrate concentrations, whereas at lower concentrations it may co-occur with N\\u003csub\\u003e2\\u003c/sub\\u003e fixation as previously found \\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e. To fully explain this finding, however, further investigation into denitrification and N\\u003csub\\u003e2\\u003c/sub\\u003e fixation activity in response to a range of DIN concentrations is required.\\u003c/p\\u003e \\u003cp\\u003eWhile the random forest analysis highlighted key environmental drivers of denitrification, a portion of denitrifying variability remained unexplained by the environmental parameters in our study. For example, 12% of denitrifying variation was explained by environmental conditions for \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, 57% for \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., 38% for \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e and 50% for \\u003cem\\u003eT. coccinea\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), emphasising the potential influence of additional factors beyond the scope of our study.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, denitrification rates were significantly higher during dark incubations than under light conditions in the acetylene assays (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb - e). This likely reflects reduced oxygen availability in the dark where the absence of photosynthesis and continued respiration creates conditions that favour denitrification by anaerobic microbes \\u003csup\\u003e\\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e93\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, our study also confirms that denitrification is a more active pathway under low oxygen conditions, aligning with findings of former studies \\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 The influence of internal nutrient dynamics on coral denitrification rates\\u003c/h2\\u003e \\u003cp\\u003eBy correlating denitrification rates with species-specific biogeochemical signatures, we found a significant negative correlation between denitrification rates and symbiont δ15N in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). This is an indication that denitrification may enhance or maintain internal N-limitation within the coral holobiont, as expected \\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. However, this relationship was not observed for the other zooxanthellate species \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, challenging the functional role of denitrification in these species, which we expand upon later. Furthermore, we found a significant positive relationship between denitrification rates and symbiont δ13C in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. and \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, and an additional significant positive correlation between denitrification and host δ13C in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). These findings provide evidence that denitrifiers utilise autotrophically derived-C, as seen in former studies \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e\\u003c/sup\\u003e. However, we of course also provide that evidence that denitrifiers utilise environmental-DOC (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Naturally, this raises the question of how host trophic strategy might further influence denitrification activity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.3 The influence of the host trophic strategy on denitrification activity\\u003c/h2\\u003e \\u003cp\\u003eWe also examined the influence of host trophic strategy on denitrification rates. Unexpectedly, denitrification rates of the fully heterotrophic species \\u003cem\\u003eT. coccinea\\u003c/em\\u003e were significantly higher than those of all other species in our study, being 5-fold higher than both \\u003cem\\u003eS. pistillata\\u003c/em\\u003e and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e and 4-fold higher than \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). This result contradicts our initial hypothesis where we predicted higher denitrification rates in the more autotrophic species based on findings from former studies \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, our findings suggest that environmental C may even fuel denitrification at a faster rate than photosynthetic C, given the exceptionally high rates measured in \\u003cem\\u003eT. coccinea\\u003c/em\\u003e. However, such high rates may also be attributed to other factors beyond the C source. For example, \\u003cem\\u003eT. coccinea\\u003c/em\\u003e may host a more diverse and efficient denitrifying community \\u003csup\\u003e\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e\\u003c/sup\\u003e. We also need to measure the denitrification activity of additional heterotrophic species to see whether this finding is unique to \\u003cem\\u003eT. coccinea\\u003c/em\\u003e or applies broadly to heterotrophic species. Furthermore, since our species were all mixotrophic (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), we could not assess the denitrification activity in more autotrophic species and would suggest future work to include a species that exhibits a greater reliance on autotrophy. However, since we had to pool the isotopic data over the year due to limited sample sizes, we may have missed seasonal shifts towards greater autotrophy in our species. Therefore, we would recommend higher sample sizes at each seasonal time point.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, by mitigating excess N, denitrification is proposed to sustain N-limitation within the coral holobiont, which is critical to the stability of the coral-algal symbiosis \\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. However, the occurrence of denitrification in an azooxanthellate coral, challenges this proposed functional role. Our findings suggest that denitrification is not an adaptive trait among azooxanthellate corals, but rather a passive, opportunistic response to a suite of environmental conditions that favour its activity. This interpretation aligns with previous research on denitrification in octocorals \\u003csup\\u003e\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e\\u003c/sup\\u003e and tropical scleractinian corals \\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.4 Interpretations in the context of the Red Sea\\u003c/h2\\u003e \\u003cp\\u003eHaving addressed the specific research questions of our study, it is important to interpret these results in the context of the Red Sea\\u0026rsquo;s unique characteristics for a broader perspective of their ecological relevance. The Red Sea is one of the warmest and saltiest seas on Earth, exhibiting strong temporal and spatial gradients \\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e. Spatially, the temperature, nutrient availability and chl \\u003cem\\u003ea\\u003c/em\\u003e concentration differs between the north and south of the Red Sea, with the highest temperature, DIN and chl \\u003cem\\u003ea\\u003c/em\\u003e occurring in the south, and decreasing northwards \\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR94\\\" class=\\\"CitationRef\\\"\\u003e94\\u003c/span\\u003e\\u003c/sup\\u003e, the nutrient availability in the shallow zone is very low and increases with depth. Our study was conducted in shallow waters of the central Red Sea where environmental conditions were found to have a strong influence over denitrification activity. Broadly, temperature and nutrient availability emerged as key drivers of denitrification. Based on our findings, we anticipate that corals of the same species may exhibit spatial variation in denitrification activity across the Red Sea in response to environmental conditions. For example, denitrification activity in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. may increase southwards as temperature and DIN increases. However, caution should be taken when extrapolating our seasonal findings beyond the Red Sea, as seasonal dynamics differ among coral reef regions worldwide. Some regions experience weak seasonality such as the equatorial Indo-Pacific. Therefore, it is likely that the same spike in denitrification activity in the spring/summer seasons as measured in our study, may not be seen globally.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.5 Conclusions\\u003c/h2\\u003e \\u003cp\\u003eOur study shows that the denitrification pathway is seasonally variable in corals, exhibiting generally higher rates in the spring and summer months compared to the autumn and winter months (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb), which can be explained by the high sensitivity of denitrification to environmental conditions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Our study identified species-specific environmental drivers of denitrification (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The top driver in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e was DOC, providing evidence that denitrifiers do not exclusively rely on photosynthetic C for energy (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). For \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, the top driver was temperature, although its influence differed between the two species (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). In \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., we found a linear relationship between denitrification and temperature, whereas in \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e, denitrification activity increased with temperature until an upper threshold, beyond which it declined (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). For \\u003cem\\u003eT. coccinea\\u003c/em\\u003e, we unexpectedly identified low nitrate availability as a driver of denitrification (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), yet this is likely the effect not the cause of high denitrification activity, or this is due to the co-occurrence of denitrification with N\\u003csub\\u003e2\\u003c/sub\\u003e fixation when nitrate levels are low. Our study also showed that for all four species, denitrification activity was higher under dark conditions where oxygen levels are reduced (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb), thus favouring anaerobic microbial activity \\u003csup\\u003e\\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e93\\u003c/span\\u003e\\u003c/sup\\u003e as previously found for other reef substrates \\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e. Furthermore, our study demonstrated that denitrification is also influenced by the internal nutrient dynamics of the coral holobiont, as the positive relationship between denitrification and host \\u0026part;13C in \\u003cem\\u003eS. pistillata\\u003c/em\\u003e, and both host and symbiont \\u0026part;13C in \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), suggest that denitrifiers utilise symbiont-derived C as shown in former studies \\u003csup\\u003e\\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e95\\u003c/span\\u003e\\u003c/sup\\u003e. Lastly, we found that denitrification rates were affected by host trophic strategy, finding significantly higher denitrification in the azooxanthellate, fully heterotrophic coral \\u003cem\\u003eT. coccinea\\u003c/em\\u003e, being 5-fold higher than both \\u003cem\\u003eS. pistillata\\u003c/em\\u003e and \\u003cem\\u003eM. dichotoma\\u003c/em\\u003e and 4-fold higher than \\u003cem\\u003eAcropora\\u003c/em\\u003e sp. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea) that we determined were all mixotrophic (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). This suggests that environmental C may even fuel denitrification at a faster rate than photosynthetic C, or points to the influence of distinct denitrifying communities that may exhibit different rates. However, the exceptionally high denitrification rates measured in the azooxanthellate species challenge the functional significance of N removal, since no coral-algal symbiosis is present to necessitate such regulation. Therefore, we suspect that denitrification may play a more passive role than previously suspected among Red Sea corals, reinforcing findings from coral-associated denitrification research in other geographic regions \\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e and on octocorals \\u003csup\\u003e\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e\\u003c/sup\\u003e. Overall, our study established a valuable baseline for seasonal denitrification rates in the Red Sea, and an understanding of its environmental drivers across four species. This foundation offers a springboard for future research to explore the influences of environmental change on N cycling in greater detail.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCompeting interests\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare that they have no known competing interests that may have influenced the work reported in this paper.\\u003c/p\\u003e \\u003ch2\\u003eAuthor Contributions\\u003c/h2\\u003e \\u003cp\\u003e \\u003cb\\u003eC. E.L. Hill\\u003c/b\\u003e: data collection, data analysis, visualisation, writing \\u0026ndash; original draft, writing \\u0026ndash; review and editing. \\u003cb\\u003eA. Tilstra\\u003c/b\\u003e: writing \\u0026ndash; review and editing, conceptualisation, supervision. \\u003cb\\u003eY. C. El-Khaled\\u003c/b\\u003e: data collection, writing \\u0026ndash; review and editing. \\u003cb\\u003eN. Garcias-Bonet\\u003c/b\\u003e: data collection, writing- review and editing. \\u003cb\\u003eV. A. Bonacker\\u003c/b\\u003e: data collection, data analysis, writing \\u0026ndash; review and editing. \\u003cb\\u003eA. Novoa Lamprea\\u003c/b\\u003e: data collection, data analysis, writing \\u0026ndash; review and editing. \\u003cb\\u003eW. A. Rich\\u003c/b\\u003e: data collection, data analysis, writing \\u0026ndash; review and editing. M. Ostendarp: data analysis, writing: review and editing. \\u003cb\\u003eM. D. Fox\\u003c/b\\u003e: writing \\u0026ndash; review and editing, data analysis, supervision. \\u003cb\\u003eS. Carvalho\\u003c/b\\u003e: writing \\u0026ndash; review and editing, conceptualisation, supervision, funding. \\u003cb\\u003eR. S. Peixoto\\u003c/b\\u003e: writing \\u0026ndash; review and editing, conceptualisation, supervision, funding. \\u003cb\\u003eC. Wild\\u003c/b\\u003e: writing \\u0026ndash; review and editing, conceptualisation, supervision, funding.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e \\u003cp\\u003eThis study came together with the help and support of many people. We thank Livia A. Hott for her support in the lab with running incubations and sample processing. We again thank Livia A. Hott, as well as Patricia Sanchez-Lopez and Gerard Clancy who dedicated a lot of time to troubleshooting and refining the protocols for gas measurements of nitrous oxide. We thank Vijayalaxmi Dasari who performed the ammonium and inorganic nutrient analysis, Doaa Baker and Daria Vashuinina who performed the DOC analysis, and Jo\\u0026atilde;o Curdia who assisted with the water chl \\u003cem\\u003ea\\u003c/em\\u003e analysis. We also thank Prof Dr Ulrich Stuck for processing the stable isotope and elemental data. We also thank CMR and the boat captains for their support in fieldwork. Lastly, the authors acknowledge the funding support from KAUST grant number BAS/1/1095-01-01 and BAS/1/1109-01-01 and the German Research Foundation (DFG) grant Wi 2677/16\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.\\u003c/p\\u003e\\u003ch2\\u003eData availability statement\\u003c/h2\\u003e \\u003cp\\u003eAll data is available in the public repository Zenodo (Hill et al. doi: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.5281/zenodo.17951369\\u003c/span\\u003e\\u003cspan address=\\\"10.5281/zenodo.17951369\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The isotopic and elemental data shared with Thobor et al. is available in Zenodo (Hill et al. doi: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.5281/zenodo.17849457\\u003c/span\\u003e\\u003cspan address=\\\"10.5281/zenodo.17849457\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e)\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eFiore CL, Jarett JK, Olson ND, Lesser MP (2010) Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol 18:455\\u0026ndash;463\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi M, Sheng H-X, Dai M, Kao S-J (2023) Understanding nitrogen dynamics in coral holobionts: comprehensive review of processes, advancements, gaps, and future directions. Front Mar Sci 10:1203399\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eR\\u0026auml;decker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C (2015) Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol 23:490\\u0026ndash;497\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHoulbr\\u0026egrave;que F (2009) Ferrier-Pag\\u0026egrave;s, C. Heterotrophy in Tropical Scleractinian Corals. Biol Rev 84:1\\u0026ndash;17\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRobbins SJ et al (2019) A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nat Microbiol 4:2090\\u0026ndash;2100\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVoolstra CR et al (2024) The coral microbiome in sickness, in health and in a changing world. Nat Rev Microbiol 22:460\\u0026ndash;475\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCardini U et al (2015) Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. \\u003cem\\u003eProc. R. Soc. B.\\u003c/em\\u003e 282, 20152257\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLesser M et al (2007) Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar Ecol Prog Ser 346:143\\u0026ndash;152\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eR\\u0026auml;decker N, Meyer F, Bednarz V, Cardini U, Wild C (2014) Ocean acidification rapidly reduces dinitrogen fixation associated with the hermatypic coral Seriatopora hystrix. Mar Ecol Prog Ser 511:297\\u0026ndash;302\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShashar N, Cohen Y, Loya Y, Sar N (1994) Nitrogen fixation (Acetylene reduction) in stony corals: evidence for coral-bacteria interactions. Mar Ecol Prog Ser 111\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFalkowski PG, Dubinsky Z, Muscatine L, Porter JW (1984) Light and the Bioenergetics of a Symbiotic Coral. \\u003cem\\u003eBioScience\\u003c/em\\u003e 34, 705\\u0026ndash;709\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLaJeunesse TC et al (2018) Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral Endosymbionts. Curr Biol 28:2570\\u0026ndash;2580e6\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGrover R, Maguer J-F, Allemand D (2003) Ferrier-Pag\\u0026eacute;s, C. Nitrate uptake in the scleractinian coral \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e. Limnol Oceanogr 48:2266\\u0026ndash;2274\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMiller D, Yellowlees D (1989) Inorganic nitrogen uptake by symbiotic marine cnidarians: a critical review. \\u003cem\\u003eProc. R. Soc. Lond. B\\u003c/em\\u003e 237, 109\\u0026ndash;125\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRahav O, Dubinsky Z, Achituv Y, Falkowski PG (1989) Ammonium metabolism in the zooxanthellate coral, \\u003cem\\u003estylophora pistillata\\u003c/em\\u003e. \\u003cem\\u003eProc. R. Soc. Lond. B\\u003c/em\\u003e 236, 325\\u0026ndash;337\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eReynaud S et al (2009) Effect of light and feeding on the nitrogen isotopic composition of a zooxanthellate coral: role of nitrogen recycling. Mar Ecol Prog Ser 392:103\\u0026ndash;110\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang JT, Douglas AE (1999) Essential amino acid synthesis and nitrogen recycling in an alga-invertebrate symbiosis. Mar Biol 135:219\\u0026ndash;222\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDawson J (2002) Biogeography of azooxanthellate corals in the Caribbean and surrounding areas. Coral Reefs 21:27\\u0026ndash;40\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBaker DM, Freeman CJ, Wong JCY, Fogel ML (2018) Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J 12:921\\u0026ndash;930\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBuckingham MC et al (2022) Impact of nitrogen (N) and phosphorus (P) enrichment and skewed N:P stoichiometry on the skeletal formation and microstructure of symbiotic reef corals. Coral Reefs 41:1147\\u0026ndash;1159\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCunning R, Baker AC (2013) Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat Clim Change 3:259\\u0026ndash;262\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEzzat L, Maguer J-F, Grover R, Ferrier-Pag\\u0026egrave;s C (2015) New insights into carbon acquisition and exchanges within the coral\\u0026ndash;dinoflagellate symbiosis under NH \\u003csub\\u003e4\\u003c/sub\\u003e \\u003csup\\u003e+\\u003c/sup\\u003e and NO \\u003csub\\u003e3\\u003c/sub\\u003e \\u003csup\\u003e\\u0026ndash;\\u003c/sup\\u003e supply. \\u003cem\\u003eProc. R. Soc. B.\\u003c/em\\u003e 282, 20150610\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKrueger T et al (2020) Intracellular competition for nitrogen controls dinoflagellate population density in corals. \\u003cem\\u003eProc. R. Soc. B.\\u003c/em\\u003e 287, 20200049\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMarubini F, Davies PS (1996) Nitrate increases zooxanthellae population density and reduces skeletogenesis in corals. Mar Biol 127:319\\u0026ndash;328\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMuscatine L (1990) The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWooldridge SA (2017) Instability and breakdown of the coral\\u0026ndash;algae symbiosis upon exceedence of the interglacial pCO2 threshold (\\u0026gt;\\u0026thinsp;260 ppmv): the missing Earth-System feedback mechanism. Coral Reefs 36:1025\\u0026ndash;1037\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBurkepile DE et al (2020) Nitrogen Identity Drives Differential Impacts of Nutrients on Coral Bleaching and Mortality. Ecosystems 23:798\\u0026ndash;811\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLesser MP (2021) Eutrophication on Coral Reefs: What Is the Evidence for Phase Shifts, Nutrient Limitation and Coral Bleaching. Bioscience 71:1216\\u0026ndash;1233\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVega Thurber RL et al (2014) Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob Change Biol 20:544\\u0026ndash;554\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWiedenmann J et al (2013) Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat Clim Change 3:160\\u0026ndash;164\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHoegh-Guldberg O (1994) Population dynamics of symbiotic zooxanthellae in the coral Pocillopora damicornis exposed to elevated ammonium [(NH4) 2 SO4] concentrations\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMarangoni FDB, Ferrier-Pag\\u0026egrave;s L, Rottier C, Bianchini C, A., Grover R (2020) Unravelling the different causes of nitrate and ammonium effects on coral bleaching. Sci Rep 10:11975\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShantz AA, Burkepile DE (2014) Context-dependent effects of nutrient loading on the coral\\u0026ndash;algal mutualism. Ecology 95:1995\\u0026ndash;2005\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhao H et al (2021) Impacts of nitrogen pollution on corals in the context of global climate change and potential strategies to conserve coral reefs. Sci Total Environ 774:145017\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEl-Khaled Y et al (2020) In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser 645:55\\u0026ndash;66\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTilstra A et al (2019) Denitrification Aligns with N2 Fixation in Red Sea Corals. Sci Rep 9:19460\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKnowles R, Denitrification (1982) Microbiol Rev 46:43\\u0026ndash;70\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEl-Khaled YC et al (2021) Nitrogen fixation and denitrification activity differ between coral- and algae-dominated Red Sea reefs. Sci Rep 11:11820\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBabbin AR et al (2021) Discovery and quantification of anaerobic nitrogen metabolisms among oxygenated tropical Cuban stony corals. ISME J 15:1222\\u0026ndash;1235\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGlaze TD, Erler DV, Siljanen H (2022) M. P. Microbially facilitated nitrogen cycling in tropical corals. ISME J 16:68\\u0026ndash;77\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEl-Khaled YC et al (2021) High plasticity of nitrogen fixation and denitrification of common coral reef substrates in response to nitrate availability. Mar Pollut Bull 168:112430\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang Q et al (2024) Microbial nitrogen removal in reef-building corals: A light-sensitive process. Chemosphere 359:142394\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBerumen ML et al (2019) The Red Sea: Environmental Gradients Shape a Natural Laboratory in a Nascent Ocean. In: Voolstra CR, Berumen ML (eds) Coral Reefs of the Red Sea, vol 11. Springer International Publishing, Cham, pp 1\\u0026ndash;10\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRoik A et al (2016) Year-Long Monitoring of Physico-Chemical and Biological Variables Provide a Comparative Baseline of Coral Reef Functioning in the Central Red Sea. PLoS ONE 11:e0163939\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCardini U et al (2016) Budget of Primary Production and Dinitrogen Fixation in a Highly Seasonal Red Sea Coral Reef. Ecosystems 19:771\\u0026ndash;785\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBednarz VN et al (2018) Contrasting seasonal responses in dinitrogen fixation between shallow and deep-water colonies of the model coral Stylophora pistillata in the northern Red Sea. PLoS ONE 13:e0199022\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eConti-Jerpe IE et al (2020) Trophic strategy and bleaching resistance in reef-building corals. Sci Adv 6:eaaz5443\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCreed JC et al (2017) The invasion of the azooxanthellate coral Tubastraea (Scleractinia: Dendrophylliidae) throughout the world: history, pathways and vectors. Biol Invasions 19:283\\u0026ndash;305\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEinbinder S et al (2009) Changes in morphology and diet of the coral Stylophora pistillata along a depth gradient. Mar Ecol Prog Ser 381:167\\u0026ndash;174\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eImbs AB, Dang LTP, Nguyen KB, Luu HV, Pham LQ (2020) Annual Dynamics of the Composition of Polar Lipids, Storage Lipids, and Fatty Acid Markers in the Hydrocoral Millepora dichotoma Forsk\\u0026aring;l, 1775 from Coastal Waters of Vietnam. Russ J Mar Biol 46:221\\u0026ndash;225\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJ\\u0026oslash;rgensen BB (2000) Bacteria and Marine Biogeochemistry. In: Schulz HD, Zabel M (eds) Marine Geochemistry. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 173\\u0026ndash;207. doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/978-3-662-04242-7_5\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/978-3-662-04242-7_5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGarcias-Bonet N et al (2018) High denitrification and anaerobic ammonium oxidation contributes to net nitrogen loss in a seagrass ecosystem in the central Red Sea. Biogeosciences 15:7333\\u0026ndash;7346\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGarcias-Bonet N et al (2025) The Coral Probiotics Village: An Underwater Laboratory to Tackle the Coral Reefs Crisis. Ecol Evol 15:e71558\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEl-Khaled YC et al (2020) Simultaneous measurements of dinitrogen fixation and denitrification associated with coral reef substrates: advantages and limitations of a combined acetylene assay. Front Mar Sci 7:411\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBalderston WL, Sherr B, Payne W (1976) Blockage by acetylene of nitrous oxide reduction in Pseudomonas perfectomarinus. Appl Environ Microbiol 31:504\\u0026ndash;508\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFedorova R, Milekhina E, Il\\u0026rsquo;Yukhina N (1973) Evaluation of the method of gas metabolism for detecting extraterrestrial life. Identification of nitrogen-fixing microorganisms. Izv Akad Nauk SSSR Ser Biol 6:797\\u0026ndash;806\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYoshinari T, Knowles R (1976) Acetylene inhibition of nitrous oxide reduction by denitrifying bacteria. Biochem Biophys Res Commun 69:705\\u0026ndash;710\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGroffman PM et al (2006) METHODS FOR MEASURING DENITRIFICATION: DIVERSE APPROACHES TO A DIFFICULT PROBLEM. Ecol Appl 16:2091\\u0026ndash;2122\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOremland RS, Capone DG (1988) Use of Specific Inhibitors in Biogeochemistry and Microbial Ecology. In: Marshall KC (ed) Advances in Microbial Ecology, vol 10. Springer US, Boston, MA, pp 285\\u0026ndash;383\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSeitzinger SP (1993) Denitrification and nitrification rates in aquatic sediments. Handbook of methods in aquatic microbial ecology. CRC, pp 633\\u0026ndash;641\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMariotti A (1983) Atmospheric nitrogen is a reliable standard for natural 15N abundance measurements. Nature 303:685\\u0026ndash;687\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eErar EJ, Collins GB (1997) Method 445.0 In Vitro Determination of Chlorophyll a and Pheophytin ain Marine and Freshwater Algae by Fluorescence\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRich WA et al (2024) Widespread inconsistency in logger deployment methods in coral reef studies may bias perceptions of thermal regimes. PLOS Clim 3:e0000517\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJackson AL, Inger R, Parnell AC, Bearhop S (2011) Comparing isotopic niche widths among and within communities: SIBER - Stable Isotope Bayesian Ellipses in R: Bayesian isotopic niche metrics. J Anim Ecol 80:595\\u0026ndash;602\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFox MD et al (2023) Ocean currents magnify upwelling and deliver nutritional subsidies to reef-building corals during El Ni\\u0026ntilde;o heatwaves. Sci Adv 9:eadd5032\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLayman CA, Arrington DA, Monta\\u0026ntilde;a CG, Post DM (2007) CAN STABLE ISOTOPE RATIOS PROVIDE FOR COMMUNITY-WIDE MEASURES OF TROPHIC STRUCTURE? \\u003cem\\u003eEcology\\u003c/em\\u003e 88, 42\\u0026ndash;48\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWickham H (2016) Data Analysis. Springer\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKassambara A (2020) ggpubr:ggplot2 based publication ready plots. R package version 0 4 \\u003cem\\u003e0\\u003c/em\\u003e 438\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWickham H, Fran\\u0026ccedil;ois R, Henry L, M\\u0026uuml;ller K (2018) dplyr: A Grammar of Data Manipulation. R package version 0.7. 6. \\u003cem\\u003eComputer software]. https://CRAN. R-project. org/package\\u0026thinsp;=\\u0026thinsp;dplyr\\u003c/em\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNeuwirth E (2014) Package \\u0026lsquo;RColorBrewer\\u0026rsquo;. ColorBrewer Palettes\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAuguie B, Antonov A (2017) gridExtra: miscellaneous functions for grid graphics. R package version 2:602\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWilke CO (2015) cowplot: Streamlined Plot Theme and Plot Annotations for \\u0026lsquo;ggplot2\\u0026rsquo;. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.32614/CRAN.package.cowplot\\u003c/span\\u003e\\u003cspan address=\\\"10.32614/CRAN.package.cowplot\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. 1.1.3\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKassambara A (2023) Pipe-Friendly Framework for Basic Statistical Tests. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://CRAN.R-project.org/package=rstatix\\u003c/span\\u003e\\u003cspan address=\\\"https://CRAN.R-project.org/package=rstatix\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDinno A (2024) Dunn\\u0026rsquo;s Test of Multiple Comparisons Using Rank Sums. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://CRAN.R-project.org/package=dunn.test\\u003c/span\\u003e\\u003cspan address=\\\"https://CRAN.R-project.org/package=dunn.test\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWickham H et al (2019) Welcome to the Tidyverse. JOSS 4:1686\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2:18\\u0026ndash;22\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGreenwell BM (2017) pdp: An R Package for Constructing Partial Dependence Plots. R J 9:421\\u0026ndash;436\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJackson A, Parnell A, Siber (2023) Stable isotope bayesian ellipses in R. R package version 2.1. 6\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePlummer M, Stukalov A, Denwood M (2016) Bayesian graphical models using MCMC. R package version 46\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSmith J, Wagner-Riddle C, Dunfield K (2010) Season and management related changes in the diversity of nitrifying and denitrifying bacteria over winter and spring. Appl Soil Ecol 44:138\\u0026ndash;146\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSong K, Kang H, Zhang L, Mitsch WJ (2012) Seasonal and spatial variations of denitrification and denitrifying bacterial community structure in created riverine wetlands. Ecol Eng 38:130\\u0026ndash;134\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBednarz V, Cardini U, Van Hoytema N, Al-Rshaidat M, Wild C (2015) Seasonal variation in dinitrogen fixation and oxygen fluxes associated with two dominant zooxanthellate soft corals from the northern Red Sea. Mar Ecol Prog Ser 519:141\\u0026ndash;152\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eR\\u0026auml;decker N et al (2021) Heat stress destabilizes symbiotic nutrient cycling in corals. \\u003cem\\u003eProc. Natl. Acad. Sci. U.S.A.\\u003c/em\\u003e 118, e2022653118\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang S, Sun W, Zhang F, Li Z (2013) Phylogenetically Diverse Denitrifying and Ammonia-Oxidizing Bacteria in Corals Alcyonium gracillimum and Tubastraea coccinea. Mar Biotechnol 15:540\\u0026ndash;551\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDelgadillo-Ordo\\u0026ntilde;ez N et al (2022) Red Sea Atlas of Coral-Associated Bacteria Highlights Common Microbiome Members and Their Distribution across Environmental Gradients\\u0026mdash;A. Syst Rev Microorganisms 10:2340\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBernard-Jannin L, Sun X, Teissier S, Sauvage S, S\\u0026aacute;nchez-P\\u0026eacute;rez J-M (2017) Spatio-temporal analysis of factors controlling nitrate dynamics and potential denitrification hot spots and hot moments in groundwater of an alluvial floodplain. Ecol Eng 103:372\\u0026ndash;384\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHill AR, Devito KJ, Campagnolo S, Sanmugadas K (2000) Subsurface denitrification in a forest riparianzone: Interactions between hydrology and supplies ofnitrate and organic carbon. Biogeochemistry 51:193\\u0026ndash;223\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSteinberg C, Steinberg (2013) Christian. Ecology of humic substances in freshwaters: determinants from geochemistry to ecological niches. in (Springer Science \\u0026amp; Business Media\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhou W, Xia L, Yan X (2017) Vertical distribution of denitrification end-products in paddy soils. Sci Total Environ 576:462\\u0026ndash;471\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWetzel RG (1992) Gradient-dominated ecosystems: sources and regulatory functions of dissolved organic matter in freshwater ecosystems. Hydrobiologia 229:181\\u0026ndash;198\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXiang N et al (2022) Contrasting Microbiome Dynamics of Putative Denitrifying Bacteria in Two Octocoral Species Exposed to Dissolved Organic Carbon (DOC) and Warming. Appl Environ Microbiol 88:e01886\\u0026ndash;e01821\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRajta A, Bhatia R, Setia H, Pathania P (2020) Role of heterotrophic aerobic denitrifying bacteria in nitrate removal from wastewater. J Appl Microbiol 128:1261\\u0026ndash;1278\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZumft WG (1997) Cell biology and molecular basis of denitrification. Microbiol Mol Biol Rev 61:533\\u0026ndash;616\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePearman JK et al (2017) Microbial planktonic communities in the Red Sea: high levels of spatial and temporal variability shaped by nutrient availability and turbulence. Sci Rep 7:6611\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXiang N et al (2022) Presence of algal symbionts affects denitrifying bacterial communities in the sea anemone Aiptasia coral model. ISME Commun 2:105\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWeiss R, Price B (1980) Nitrous oxide solubility in water and seawater. Mar Chem 8:347\\u0026ndash;359\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[{\"identity\":\"783f4aef-fe19-4dc0-b2eb-64dc14b20549\",\"identifier\":\"10.13039/501100001659\",\"name\":\"Deutsche Forschungsgemeinschaft\",\"awardNumber\":\"Wi 2677/16-1\",\"order_by\":0},{\"identity\":\"c577e845-08ed-45e1-96cc-03db7a58d7a6\",\"identifier\":\"10.13039/501100004052\",\"name\":\"King Abdullah University of Science and Technology\",\"awardNumber\":\"BAS/1/1095-01-01\",\"order_by\":1},{\"identity\":\"bf621e5d-ae8c-4f9e-ad91-35c7ecbcf9a3\",\"identifier\":\"10.13039/501100004052\",\"name\":\"King Abdullah University of Science and Technology\",\"awardNumber\":\"BAS/1/1109-01-01 \",\"order_by\":2}],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"King Abdullah University of Science and Technology\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8588779/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8588779/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eNitrogen (N) plays a critical role in coral growth, but maintaining an N-limited state is essential for coral-algal symbiosis stability. Coral-associated denitrifiers are microbes that live in association with the coral host and may help regulate excess N, though denitrification in corals remains poorly understood. We investigated year-long denitrification dynamics in four Red Sea corals, using acetylene inhibition assays alongside physiological and environmental measurements. All species exhibited measurable denitrification activity, ranging from 0\\u0026ndash;0.8 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for \\u003cem\\u003eStylophora pistillata\\u003c/em\\u003e and \\u003cem\\u003eAcropora\\u003c/em\\u003e sp., 0\\u0026ndash;0.4 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for \\u003cem\\u003eMillepora dichotoma\\u003c/em\\u003e, and 0\\u0026ndash;2.0 nmol N cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for \\u003cem\\u003eTubastrea coccinea\\u003c/em\\u003e. We observed seasonal trends in denitrification activity, with generally higher rates in the spring/summer compared to autumn/winter, and identified temperature, dissolved organic carbon (DOC) and nitrate availability as key environmental drivers. Lastly, we observed up to 5 times higher denitrification rates in the fully heterotrophic azooxanthellate species \\u003cem\\u003eT. coccinea\\u003c/em\\u003e than in the three mixotrophic zooxanthellate species. Our findings show that denitrifiers use both photosynthetically derived and environmental C, with DOC central in maintaining tight coupling of C and N cycling in coral holobionts. Additionally, denitrification is modulated by environmental conditions, highlighting its vulnerability to environmental change.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Coral-associated denitrification is seasonally variable and species-specific\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-14 04:37:54\",\"doi\":\"10.21203/rs.3.rs-8588779/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a42c76f3-2202-4e23-be44-13b3dd612118\",\"owner\":[],\"postedDate\":\"January 14th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":61045183,\"name\":\"Marine and Freshwater Ecology\"}],\"tags\":[],\"updatedAt\":\"2026-01-14T04:37:54+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-14 04:37:54\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8588779\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8588779\",\"identity\":\"rs-8588779\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}