2.1 Specimen collection and experimental setup

Three individuals of the scyphozoan jellyfish Rhopilema nomadica (Galil et al., 1990) of medium size (bell diameter 20–25 cm) were collected on 29 July 2019, at Tel Shikmona, Haifa, near the Israel Oceanographic and Limnological Research Institute, on the shore of the easternmost Mediterranean Sea (lat. 32^∘49^′32^′′ N, long. 34^∘57^′26^′′ E). The specimens were weighed and cut to pieces of 4–5 g to ensure representation of all body parts. Processed 25 g wet weight (approximately 1.25 g dry weight) of R. nomadica (including umbrellas, tentacles, and oral arms, following Qu et al., 2015) were placed each in three Perspex cylinders (9.45 cm internal diameter; 50 cm length) that were filled up to 10 cm height with coastal sediments (Fig. 2), that were collected 1 week prior to the experiment, allowing the re-establishment of natural sediment profiles. Three additional cylinders with sediments did not include jellyfish and functioned as controls. The setup was completed by topping off the cylinders with oxygen saturated Mediterranean coastal water (approximately 3.14 L) pumped from 1 m depth and pre-filtered to remove large-size zooplankton (67 µm). The cores were sealed with gas-tight sealing caps and placed in a lab with a relatively constant temperature of 27–28 ^∘C, which is similar to the summer mean coastal water temperatures of the easternmost EMS (Raveh et al., 2015). The setup was acclimatized for 24 h to insure similar initial conditions in the chambers before jellyfish addition. Nutrient fluxes were measured using the whole-core incubation technique previously described by Denis et al. (2001). Although restricting this study for testing short-term responses, this method follows the best practices for measuring oxygen and nutrient fluxes and dynamics at the sediment–water interface (Glud, 2008; Hammond et al., 2004; Pratihary et al., 2014; Skoog and Arias-Esquivel, 2009). Pre-filtered coastal water was transferred to a reserve tank and stored under the same conditions as the incubated cores. The incubation cores were connected by tubing to the dedicated reserve tank, which replaced the water in the incubation chambers during each sampling. The cores were incubated under PAR = 100 µmol photons m⁻² s⁻¹ with a photoperiodicity of 14:10 (L:D).

Figure 2Experimental setup. Incubation cylinders including jellyfish treatment (a, N=3) and controls (b, N=3).

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Within each chamber, the overlying water was continuously mixed with a magnetic stirrer fixed 10 cm below the upper cap (75 rpm, Hammond et al., 2004) and was sampled at the following intervals: 0, 5, 10, 18, 26, 34, and 44 h, with dedicated sampling tubing. The reserve tank was sampled only at three intervals (0, 20, and 44 h). At each sampling, 200 mL water samples were transferred to acid-washed transparent Nalgene bottles (250 mL) and subsampled by filtering (Minisart^® 0.45 µm) for the following chemical analyses: PO₄, NO₂+NO₃ (NO_x), Si(OH)₄, NH₄, TDP (DOP), and TN (DON). Nutrient samples were immediately frozen after collection for later analysis. Biological measurements were collected using unfiltered water for picophytoplankton (Synechococcus, Prochlorococcus, pico- and nanoeukaryotes), heterotrophic bacterial abundance, and bacterial production measurements. Oxygen consumption rates at the sediment–water interface were continuously monitored using oxygen sensor spots (FireSting, PyroScience, Germany) adapted for measuring oxygen in closed containers through a transparent window (plastic or glass). The sensor spots were fixed to the inner side of the window with silicone glue. Four optical fibers continuously measured the oxygen in the three jellyfish chambers and one of the control incubations. The system was calibrated with saturated de-ionized water (DIW). The pH was measured with a sensor (MultiLine WTW, Germany) calibrated with National Bureau of Standards (NBS) buffers.

Fluxes (mmol m⁻² d⁻¹) were determined by regressing the change in overlying water concentration (C) through time multiplied by the chamber height (Volume∕Area), following Eq. (1):

A correction for water replacement from the reserve tank was not applied, as the consequent error was less than 5 %.

2.2 Inorganic and organic nutrient analysis

Nutrient concentrations were determined using a three-channel segmented flow auto-analyzer system (AA-3 Seal Analytical) following Kress et al. (2014). The limit of detection (LOD), measured as 3 times the standard deviation of 10 measurements of the blank (low-nutrient seawater collected from the offshore EMS), was 8 nM for PO₄, 50 nM for total dissolved phosphorus (TDP) and Si(OH)₄, 80 nM for NO₂+NO₃ (NO_x) 90 nM for NH₄, and 0.74 µmol for total dissolved phosphorus (TDN). The accuracy of the analyses was determined using certified reference materials (CRMs): MOOS 3 (seawater certified reference material for nutrients) (PO₄, NO_x and Si(OH)₄), VKI (Visual Kinematics, USA) 4.1 (NO_x), and VKI 4.2 (PO₄ and Si(OH)₄). Results were accepted when measured CRMs were within ±5 % of the certified values.

TDN and TDP were measured following potassium persulfate digestion and ultraviolet (UV) photo-oxidation, using a digestion block system (Seal Analytical, UK). The reproducibility of the analyses was examined with VKI 4.2 and deep seawater reference (DSR) material. One of the TDP samples was lost (t=44 h). DON concentrations were determined by subtracting NO_x and NH₄ from TDN concentrations and DOP concentrations were determined by subtracting PO₄ from TDP concentrations.

2.3 Pico-/nanophytoplankton and heterotrophic bacterial abundance

Samples (1.8 mL) were fixed with flow-cytometry-grade glutaraldehyde (0.02 % final concentration, G7651, Sigma-Aldrich, USA), frozen in liquid nitrogen, and stored at −80 ^∘C until analysis within 2 weeks. Synechococcus and Prochlorococcus, autotrophic pico-/nanoeukaryotes (maximal size ∼ 70 µm), and heterotrophic bacterial abundances were determined using an Attune^® Acoustic Focusing Flow Cytometer (Applied Biosystems, USA) as described in Bar-Zeev and Rahav (2015). Samples of Synechococcus, Prochlorococcus, and pico-/nanoeukaryotes were run at 100 µL min⁻¹. Their taxonomic discrimination for based on the orange fluorescence of phycoerythrin (585 nm), the red fluorescence of chlorophyll a (630 nm), side scatter (SSC, a proxy of cell volume), and forward scatter (FSC, a proxy of cell size). Heterotrophic bacterial samples were run at 25 µL min⁻¹ using a discrimination threshold of green fluorescence (520 nm) and FSC. Beads (0.93 µm, Polysciences) were run in parallel as a size standard. Blank samples of sterile seawater (0.2 µm) were also run and their reads were removed from the total bacterial counts.

2.4 Bacterial production (BP)

Bacterial production was estimated using the ³H-leucine incorporation method (Perkin Elmer, specific activity 123 Ci mmol⁻¹) followed by microcentrifugation (Simon, 1990). Samples (1.7 mL) were incubated with 10 nmol leucine L⁻¹ for 4–5 h under ambient temperature in the dark. Triplicate additions of trichloroacetic acid (TCA) were performed at each time point and served as controls. The incubations were terminated with 100 µL of concentrated (100 %) TCA. After adding 1 mL of scintillation cocktail (Ultima-Gold, PerkinElmer, USA) to each vial, the samples were counted using a TRI-CARB 2100 TR (Packard Biocience, USA) scintillation counter. A conversion factor of 3 kg C mol⁻¹ per every mole leucine incorporated was used, assuming an isotopic dilution of 2.0 (Simon and Azam, 1989).

2.5 DNA extraction and sequencing

Approximately 300 mL of overlying seawater were collected with a sterile syringe and passed through 0.22 µm Sterivex filter. The membranes were removed from the cases, cut into pieces under sterile conditions, and transferred into the extraction tubes. A total of 250 mg from 0–1 and 1–2 cm sediment sections were transferred into the extraction tube. DNA was extracted from water and sediment using the DNeasy PowerSoil Kit (Qiagen, California, USA) using the manufacturer's protocol that included a FastPrep-24™ (MPBIO, Ohio, USA) bead-beating step (2×40 s at 5.5 m s⁻¹, with a 5 min interval). The V4 region of the 16S rRNA gene was amplified using the modified primer pair 515F-806R (Apprill et al., 2015; Parada et al., 2016) in combination with CS1/CS2 tags (CS1_515Fc 5'-ACACTGACGACATGGTTCTACA GTGYCAGCMGCCGCGGTAA, CS2_806Rc 5'-TACGGTAGCAGAGACTTGGTCT GGACTACNVGGGTWTCTAAT), using the following polymerase chain reaction (PCR) amplification protocol: initial denaturation at 94 ^∘C for 45 s, 30 cycles of denaturation (94 ^∘C for 15 s), annealing (15 cycles at 50 ^∘C and 15 cycles at 60 ^∘C for 20 s), and extension (72 ^∘C for 30 s). The 18S rRNA gene sequences were amplified using the 1391f-EukBr primer pair (Amaral-Zettler et al., 2009; Stoeck et al., 2010) in combination with CS1/CS2 tags (1391fc 5'-ACACTGACGACATGGTTCTACA GTACACACCGCCCGTC, EukBr 5'-TACGGTAGCAGAGACTTGGTCT TGATCCTTCTGCAGGTTCACCTAC), using the following PCR amplification protocol: initial denaturation at 94 ^∘C for 45 s, 30 cycles of denaturation (94 ^∘C for 15 s), annealing (60 ^∘C for 20 s), and extension (72 ^∘C for 30 s). Library preparation from the PCR products and sequencing of 2×250 bp Illumina MiSeq reads were performed at HyLabs (Israel).

2.6 Statistical and bioinformatic analyses

Demultiplexed paired-end reads were processed in Quantitative Insights Into Microbial Ecology (QIIME2) V2019.7 environment (Bolyen et al., 2018). Reads were truncated based on quality plots, checked for chimeras, merged, and grouped into amplicon/environmental sequence variants (A/ESVs) with DADA2 (Callahan et al., 2016), as implemented in QIIME2. After removing the low-quality sequences, a total of 361 335 (106 169 in 6 and 255 166 in 12 seawater and sediment samples, respectively) high-quality 16S rRNA gene amplicon reads with an average length of 260 bp, and a total of 658 251 (162 313 in 6 and 495 938 in 12 seawater and sediment samples, respectively) high-quality 18S rRNA gene amplicon reads with an average length of 207 bp, were generated. The 16S and 18S amplicons were classified with the naïve Bayes classifiers that were trained on the Silva 132 database, clustered at 99 % (515F/806R region for the 16S and full-length sequences for the 18S rRNA gene amplicons). Downstream statistical analyses, calculation of alpha diversity indices (Chao1, Shannon, and Simpson), and plotting were performed in R (Core Team, 2020), using packages phyloseq (McMurdie and Holmes, 2013), ampvis2 (Andersen et al., 2018) and ggplot2 (Wickham, 2016). Systematic changes across experimental conditions were estimated with DESeq2 (Love et al., 2014). The metabolic functions and pathways of the bacterial communities were predicted using Tax4Fun2 based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Wemheuer et al., 2018). Pearson correlations and similarity percentage (SIMPER) analysis (to assess the contribution of KEGG pathways to the dissimilarity between treatments and controls) were performed in R using packages Hmisc (Harrell, 2004) and vegan (Oksanen et al., 2010). Principal component analysis (PCA) of the metabolic functions was performed with PAST V4 (Hammer et al., 2001).

3.1 Dissolved oxygen and pH dynamics

Dissolved oxygen (DO) levels in the jellyfish treatments decreased from an initial average concentration of 261.5 ± 4.5 µmol L⁻¹ to null within 40 h, at an average rate of 5.9 ± 0.1 µmol L⁻¹ h⁻¹, whereas the DO levels in the control chambers decreased slightly at an average rate of 0.7 ± 0.1 µmol L⁻¹ h⁻¹ (mean ± SD, Fig. 3a). The variability within the treatment replicates and within the controls was small and non-significant (treatment replicates: $F_{(\mathrm{2},\mathrm{18})}=\mathrm{0.017}$, p=0.98; controls: $F_{(\mathrm{2},\mathrm{18})}=\mathrm{0.055}$, p=0.59). The calculated average DO flux from the water column in the jellyfish treatment was −56.9 ± 1.0 mmol m⁻² d⁻¹ versus −6.7 ± 0.3 mmol m⁻² d⁻¹ in the controls (Table 1). In accordance with the decrease in DO, pH levels in the jellyfish treatments decreased from an initial average level of 8.10 ± 0.02 to 7.88 ± 0.01 and remained relatively stable (8.10–8.15) in the controls (Fig. 3b).

Figure 3The decomposition of the jellyfish R. nomadica leads to oxygen depletion and acidification in the seawater overlying the sediment. (a) Continuous dissolved oxygen (DO) record in the experimental cylinders enriched with carcasses of the jellyfish R. nomadica (JF1–JF3) and in the controls. (b) pH dynamics in the experimental cylinders, including jellyfish and in the controls. N=3. The temperature was kept relatively constant at 27–28 ^∘C. The slight increases in DO concentrations throughout the incubation period indicate water compensation during discrete sampling events.

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Table 1Daily oxygen consumption and nutrient release rates standardized to jellyfish (R. nomadica) biomass (µmol g WW⁻¹ d⁻¹). The average wet weight of the whole jellyfish was 1.5±0.4 kg. N=3.

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3.2 Nutrient dynamics

Nutrient levels significantly increased in the jellyfish-enriched chambers, whereas in the controls they remained stable and low (Fig. 4). These increases were non-linear and characterized by multiple phases, including in some cases changes of the flux direction. Therefore, nutrient flux rates were calculated in different linear phases over time.

Figure 4Changes in the concentrations (µmol L⁻¹) of organic and inorganic nutrients in the experimental cylinders enriched with carcasses of the jellyfish R. nomadica and in the controls: (a) ammonium, (b) nitrite, (c) orthophosphate, (d) DON, (e) DOP, and (f) TDN : TDP ratio. (N=3).

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Ammonium was the dominant form of dissolved inorganic nitrogen in the experimental chambers. During the first 10 h from the onset of the experiment, NH₄ levels increased at a rate of 0.39 ± 0.12 µmol L⁻¹ h⁻¹, after which (10–26 h) the rate of NH₄ release slowed to 0.26 ± 0.10 µmol L⁻¹ h⁻¹ and then (26–44 h) sharply increased to 1.33 ± 0.31 µmol L⁻¹ h⁻¹ (Fig. 4a). NO₂ levels steadily increased at a rate of $\mathrm{5.5}\times {\mathrm{10}}^{-\mathrm{3}}\pm \mathrm{2.0}\times {\mathrm{10}}^{-\mathrm{3}}$ µmol L⁻¹ h⁻¹ and decreased to background levels after 34 h (Fig. 4b). NO₃ levels were generally higher in the jellyfish treatment than in the controls but did not present any significant trend over time (Appendix A, Fig. A1). One of the jellyfish treatments (JF2) showed higher (2-fold) concentrations of NO₃ throughout the experiment, likely due to a different initial NO₃ content derived from the mixture of jellyfish tissue, as some parts have shown to include higher concentrations of dissolved nitrogen (MacKenzie et al., 2017). Nevertheless, this has not affected the overall nutrient fluxes nor triggered different responses to the microbial communities (thus, the same direction and strength of responses were observed in all jellyfish addition treatments). Silicic acid concentrations remained overall stable throughout the experiment, and higher in two of the jellyfish-enriched chambers (Appendix A, Fig. A1).

Within the first 5 h following the jellyfish enrichment, orthophosphate levels increased by 2 orders of magnitude from 0.02 ± 0.01 to 1.02 ± 0.13 µmol L⁻¹ (Fig. 4c). Throughout the rest of the experiment, PO₄ was fully consumed and its levels decreased to the background levels within 34 h (0.04 µmol L⁻¹), after which an increase was recorded (0.30 µmol L⁻¹).

The majority of TDN and TDP released from the jellyfish was organic, where 84 % of the TDN was DON, (Fig. 4d), and 71 % of the TDP was DOP (Fig. 4e). Both organic nutrient levels significantly increased in the jellyfish enriched chambers, whereas their concentrations in the control chambers remained stable and low. During the incubation period, DON concentrations increased 12-fold in the jellyfish treatment compared to the controls (Fig. 4d) and DOP concentrations increased 18-fold (Fig. 4e). The ratio between TDN and TDP (TDN : TDP) decreased from an initial average value of $\mathrm{96}\pm \mathrm{18}:\mathrm{1}$ to an average value of $\mathrm{23}\pm \mathrm{7}:\mathrm{1}$ in the jellyfish treatments, whereas in the controls it decreased to $\mathrm{57}\pm \mathrm{3}:\mathrm{1}$ (Fig. 4f).

The rates of nutrient release (remineralization rates) standardized to jellyfish biomass are detailed in Table 1, and the calculated nutrient fluxes (mmol m⁻² d⁻¹) in the jellyfish enriched cylinders and in the controls are summarized in Table 2.

Table 2Calculated oxygen and nutrient fluxes in the seawater of jellyfish (R. nomadica)-enriched and control experimental cylinders. Positive flux represents water column enrichment (source); negative flux represents removal from the water column (sink). N=3. SD denotes standard deviation. NA – not available.

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3.3 Autotrophic and heterotrophic abundance and bacterial production

Heterotrophic bacterial abundance increased linearly in the jellyfish treatments (R²=0.98, p<0.01) and reached $\mathrm{1.5}\times {\mathrm{10}}^{\mathrm{7}}\pm \mathrm{1.9}\times {\mathrm{10}}^{\mathrm{5}}$ cells mL⁻¹ after 44 h, whereas the controls remained stable at a concentration of $\mathrm{2.0}\times {\mathrm{10}}^{\mathrm{6}}\pm \mathrm{6.7}\times {\mathrm{10}}^{\mathrm{4}}$ (Fig. 5a). Synechococcus abundance dropped in both jellyfish-enriched and control cylinders; however, after 44 h, the number of Synechococcus cells in the jellyfish treatment was 5-fold larger compared to the controls (Fig. 5b). Prochlorococcus cell numbers increased in both jellyfish-enriched and control cylinders, and after 44 h it was lower in the jellyfish treatment (Fig. 5c). Both cell numbers of pico- and nanoeukaryotes dropped throughout the experiment; nonetheless, they were higher in the jellyfish treatment than in the controls by 50 % (Fig. 5d–e).

Figure 5Microbial abundance (cell mL⁻¹) and production (µg C L⁻¹ h⁻¹) in the jellyfish R. nomadica-enriched (blue) and control (black) experimental cylinders over the experimental period: (a) total bacterial abundance, (b) Synechococcus, (c) Prochlorococcus, (d) picoeukaryotes, (e) nanoeukaryotes, and (f) bacterial production. N=3, the error bars denote standard deviation.

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Bacterial production remained stable in the jellyfish treatments at a rate of 3.1 ± 0.3 µg C L⁻¹ h⁻¹ during the first 26 incubation hours, increased to 4.3 ± 0.1 µg C L⁻¹ h⁻¹, and decreased again after 34 h. In contrast, in the controls, the bacterial production decreased immediately from the onset of the experiment and reached a rate of 0.4 ± 0.2 µg C L⁻¹ h⁻¹ after 18 h that remained stable until the experiment ended (Fig. 5f).

The temporal dynamics of DO and nutrient concentrations strongly correlated with total bacterial abundance but not with bacterial production (Appendix B, Table B1).

3.4 Microbial diversity

Bacterial alpha diversity (Fig. 6) was significantly lower in the jellyfish-enriched seawater than in the controls (p<0.05), but in the sediment samples there was no significant difference (p>0.05). The vast majority (93 %–97 %) of the 18S sequence variants in seawater (Appendix C, Fig. C2) belonged to Scyphozoa, hindering alpha diversity evaluation. In the sediment, no significant difference (p>0.05) in alpha diversity was observed between treatments (Appendix C, Fig. C1). These findings were confirmed with rarefaction curves (Appendix C, Figs. C3, C4).

Figure 6Bacterial alpha diversity indices (Chao, Shannon, Simpson) in water and sediment samples from experimental cylinders enriched with carcasses of the jellyfish and in the controls (N=3).

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The distribution of the 30 most abundant bacterial genera measured in seawater in the jellyfish-enriched and control chambers is presented in a heatmap (as inferred from read abundance estimates, Fig. 7). Lineages for which significant changes in abundance (p<0.05) between the treatment and control were detected by DESeq2 (Fig. 7, yellow star symbols: lineages more abundant in the controls, purple star symbols: lineages more abundant in the jellyfish treatment). Nine lineages were significantly more abundant in the jellyfish treatment, whereas 12 lineages were significantly more abundant in the controls. The relative abundance of the common marine bacteria, including the primary producers Synechococcus and the heterotrophic bacteria SAR11, HIMB11, and SAR86 (Dupont et al., 2012; Durham et al., 2014; Giovannoni, 2017), has all diminished following jellyfish additions. Mostly opportunistic lineages (Kordiimonadaceae, Pseudoalteromonadaceae, Saccharospirillaceae, and Nitrincolaceae) that use multiple carbon sources, including xenobiotics, were enriched in jellyfish-amended incubations and are often associated with oil discharge (Yakimov et al., 2007). Algicola (Pseudoalteromonadaceae) and Kordiimonas (Kordiimonadaceae) appear to be the most abundant degraders of the jellyfish biomass based on the marked change observed in the abundance of their relative amplicon sequence variants.

Figure 7Diversity of bacteria in the jellyfish-enriched and control experimental cylinder seawater. The 30 most abundant lineages are presented and organized by hierarchical clustering. Color scale denotes the relative abundance of reads (%). The star symbols on the right-side panel indicate lineages significantly more abundant in the jellyfish treatment (in purple) or the controls (in yellow) based on DESeq2 estimations.

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Heatmap showing the distribution of the 30 most abundant genera in the sediment, measured in the 0–1 cm below-surface layer and in the 1–2 cm below-surface layer (inferred from 16S sequences), in the jellyfish-enriched and control chambers is presented in Fig. C1 (Appendix C). Among the 30 most abundant taxa, only Fusimonas and Algicola genera were significantly more abundant in the jellyfish treatments in the 0–1 cm layer; however, in the 1–2 cm layer, there was no significant difference between the treatments and controls.

The distribution of the 30 most abundant eukaryotic genera (inferred from the 18S rRNA amplicon read abundance) measured in seawater and sediment in the jellyfish-enriched and control chambers is presented in Fig. C2 (Appendix C). Both sediment layers showed no difference between treatment and controls, whereas in the seawater samples, four lineages of dinoflagellates, Ciliophora, and Labyrinthulomycetes were more abundant in the jellyfish than the controls.

Predicted functions were classified as KEGG orthologs (KOs) resulting in the identification of 346 KOs across all samples, 160 of which were associated with prokaryotic functions. The principal component analysis (including 324 KOs across all samples, after removal of rare KOs that appear in only one of the replicates to avoid zero-inflated dimensionality) showed that jellyfish-treated and control samples significantly differed based on microbial predicted functions (Fig. 8). Photosynthesis (ko00195) and carbon fixation in photosynthetic organisms (ko00710) were enriched in controls, while catabolic functions, such as fatty acid degradation (ko00071), valine, leucine and isoleucine degradation (ko00362), and xenobiotic degradation pathways, benzoate degradation (ko00650) in particular, were enriched in jellyfish additions (Fig. 8). SIMPER analysis (Appendix C, Table C1) showed that the pathways mostly contributing to the difference between the jellyfish treatments and controls were signal transduction two-component system (ko02020) and ABC transporters (ko02010), contributing to 13 % and 10 % of the dissimilarity between the groups, respectively.

Figure 8Principle component analysis biplot of functional diversity based on taxonomy-based functional predictions using Tax4Fun2. JF indicates jellyfish samples; C indicates control samples. The vectors present KEGG pathways. The within-group similarity is 95 % between the jellyfish treatments (red) and 97 % between the controls (light blue). The following KEGG pathways are shown: ko00071 – fatty acid degradation; ko00190 – oxidative phosphorylation; ko00195 – photosynthesis; ko00270 – cysteine and methionine metabolism; ko00280 – valine, leucine, and isoleucine degradation; ko00362 – benzoate degradation; ko00520 – amino sugar and nucleotide sugar metabolism; ko00630 – glyoxylate and dicarboxylate metabolism; ko00650 – butanoate metabolism; ko00680 – methane metabolism; ko00710 – carbon fixation in photosynthetic organisms; ko00920 – sulfur metabolism; ko01110 – biosynthesis of secondary metabolites; ko01120 – microbial metabolism in diverse environments; ko01130 – biosynthesis of antibiotics; ko01200 – carbon metabolism; ko01220 – degradation of aromatic compounds; ko01230 – biosynthesis of amino acids; ko02010 – ABC transporters; ko02020 – two-component system.

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4.1 The effects of R. nomadica decomposition on oxygen and nutrient fluxes

Jellyfish blooms trigger substantial changes in dissolved oxygen, inorganic carbon, and nutrient concentrations in the water column (Condon et al., 2011; Pitt et al., 2009). Post-bloom processes, by comparison, modify the oxygen, carbon, and nutrient fluxes in the benthic boundary layer and the sediment–water interface (Chelsky et al., 2015; Lebrato and Jones, 2011; Qu et al., 2015; West et al., 2008). Here, we found that the decomposition of the invasive jellyfish Rhopilema nomadica triggered deoxygenation of the seawater overlying the sediment to hypoxic and eventually anoxic levels, although the complete dissipation of oxygen is likely due to the experimental conditions. Similarly, increased sediment oxygen demand following jellyfish decomposition was measured by West et al. (2008) in Catostylus mosaicus and by Tinta et al. (2016) in the moon jellyfish (Aurelia aurita). Qu et al. (2015) that studied the effects of Cyanea nozakii decomposition in the Yellow Sea using incubations found that oxygen was depleted in both sediment and seawater. They hypothesized that the metabolism and propagation of heterotrophic bacteria led to enhanced oxygen consumption. Indeed, our experimental results support this hypothesis, as bacterial abundance was strongly correlated with oxygen levels, whereas the abundance of autotrophic cyanobacteria decreased as they were likely outcompeted by the heterotrophic bacteria (Sisma-Ventura and Rahav, 2019; Thingstad et al., 2005). Thus, jelly-falls can generate hypoxic areas on the seabed and overlying waters (Pitt et al., 2009), and affect the benthic infauna (Chelsky et al., 2016). Although the Eastern Mediterranean coastal waters are well oxygenated (Kress et al., 2014), the collapse of massive R. nomadica blooms could potentially create local hypoxic or even anoxic hotspots on the seabed, thereby affecting the surrounding biota (Feely et al., 2010).

In addition to deoxygenation, our experiment showed a significant reduction in pH to levels that are considered detrimental to various organisms, mainly calcifies (Kroeker et al., 2010; Zunino et al., 2017). Acidification as a result of jellyfish decomposition was also observed by Qu et al. (2015) that speculated that the release of amino acids and fatty acids from proteins and lipid metabolism of jellyfish tissue is the root cause for the observed decrease in pH. Nonetheless, hypoxia and acidification are biogeochemically coupled via the production of inorganic carbon in the process of respiration (Feely et al., 2010; Gobler and Baumann, 2016). In addition, the increase in NH₄, as was measured in our experiment, increases total alkalinity and pH, whereas nitrate and silicate decrease pH, but they were comparably scarce. Based on oxygen-to-carbon conversion (1:1.3) and alkalinity change due to NH₄ addition, it is estimated that the observed decrease in pH in our experiment can be solely attributed to inorganic carbon and carbonic acid production (due to bacterial respiration) and ammonium release. The combination of hypoxia and acidification may have synergistic additive negative effects on the benthic fauna (Gobler et al., 2014; Melzner et al., 2013). Furthermore, ammonium in high concentrations may have toxic effects on various marine organisms, from bacteria to fish (Brun et al., 2002; Eddy, 2005; Ferretti and Calesso, 2011; Müller et al., 2006).

The decomposition of dead R. nomadica tissue generated an immediate rapid release of organic and inorganic phosphate after which the inorganic phosphate (PO₄) was completely consumed, while the efflux of organic and inorganic (mostly ammonium) nitrogenous compounds gradually increased throughout the experiment. Similar dynamics were observed in C. mosaicus by West et al. (2008) and Chelsky et al. (2015), and by Tinta et al. (2010) in Aurelia solida, where organic and inorganic phosphate peaked and completely abolished within 24 h, presumably due to bacterial uptake. The production of NO_x in our experiment was evident only in the jellyfish treatment while oxygen levels were conducive, suggesting that nitrification plays an important role in nutrient dynamics following jellyfish decomposition, as was found in different jellyfish species (Hubot et al., 2020; Welsh et al., 2009). The non-linearity of nutrient fluxes that was evident in our experiment (especially in the multi-rate flux of NH₄ and in the bi-directional flux of NO₂ and PO₄) as well as in other jellyfish degradation studies (Blanchet et al., 2015; Chelsky et al., 2016; Qu et al., 2015; Tinta et al., 2010), indicating a sequential nature of decomposition, likely due to microbial colonization and non-linear growth rates. The shift from nitrate production to nitrate consumption 36 h from the onset of the experiment likely reflects the shift from aerobic to anaerobic processes due to the low, hypoxic (and eventually anoxic) levels and may be regarded as an experimental artifact, although such changes were previously showed in surface sediments (Chelsky et al., 2016). The stoichiometric relationship between TDN and TDP decreased from 57:1 to 23:1 as a result of R. nomadica decomposition, as was also found by West et al. (2009) and Qu et al. (2015). This decrease can be explained by the elemental body composition of scyphozoan jellyfish, in general, is 2.48 N % DW (dry weight) and 0.22 P % DW and hence has an N:P ratio of 25:1 (Lucas et al., 2011).

The rates of nutrient release from R. nomadica decomposition found in this study were comparable to jellyfish-decomposition-driven rates found in former studies (e.g., Blanchet et al., 2015; Pitt et al., 2009; Qu et al., 2015; Tinta et al., 2012, 2016; Titelman et al., 2006; West et al., 2008). Ammonium release rate in R. nomadica (1.96 µmol g⁻¹ WW d⁻¹) was slightly higher than the rate measured by Tinta et al. (2012) in Rhizostoma pulmo (1.6 µmol g⁻¹ WW d⁻¹), another common Mediterranean scyphozoan. Reported densities of R. nomadica aggregations from the EMS are 1.6×10⁵ km⁻² on the Israeli coast (Lotan et al., 1992, 1994), 1×10⁶ km⁻² on the Lebanese coast (Lakkis and Zeidane, 1991), and 9×10⁵ km⁻² on the Egyptian Mediterranean coast (Madkour et al., 2019). The average wet weight of R. nomadica changes seasonally, 1340 ± 953 g ind⁻¹ during summer and 2450 ± 1854 g ind⁻¹ during winter (N=40, Guy-Haim, unpublished data), yielding approximately 1.3 kt km⁻². We can therefore estimate that the collapse of R. nomadica bloom potentially releases ammonium and phosphate in concentrations of 2.5 and 0.8 kmol km⁻², respectively.

Nutrient remineralization during jelly-fall decomposition, as was found in this study and others, can be inhibitory or toxic to some organisms (e.g., dissolved sulfides and ammonium in Chelsky et al., 2016), but on the other hand, they can stimulate primary production and induce algal blooms in the water column and on the sediment. Møller and Riisgård (2007) found that following blooms of A. aurita, peak concentrations of chlorophyll a were measured in a heavily eutrophic Danish fjord. Using mesocosm experiments, West et al. (2009) found that excretion of jellyfish C. mosaicus led to a 10-fold increase in diatom abundance. In the EMS, R. nomadica typically peaks in the summer months and collapses at the end of July (Edelist et al., 2020), whereas peak chlorophyll a concentrations in the water column are measured during wintertime (Ignatiades et al., 2009; Rahav et al., 2018a, 2015). This may result from the competitive exclusion of phytoplankton by heterotrophic bacteria (Sisma-Ventura and Rahav, 2019). Thus, fertilization of the water column due to nutrient release from R. nomadica decomposition may fail to trigger an algal bloom in the EMS. In contrast to the water column, maximum chlorophyll concentrations were measured in the sediment of the shallow Israeli coastal shelf during the late spring–summer (Hyams-Kaphzan et al., 2009; Tadir et al., 2017). This discrepancy was explained by the spring bloom of benthic primary producers. However, the results of this study could provide another plausible explanation for the high summer chlorophyll concentrations in the sediment, which may be the post-bloom nutrient boost to the benthic ecosystem.

4.2 Decomposition-induced shifts in bacterial community abundance, production, composition, and functionality

Heterotrophic bacteria are major consumers of dissolved organic matter (DOM) in marine ecosystems and can therefore benefit from jellyfish decomposition. Previous studies have demonstrated a significant increase in bacterial abundance triggered by jellyfish degradation (Blanchet et al., 2015; Condon et al., 2011; Dinasquet et al., 2012; Frost et al., 2012; Kramar et al., 2019; Tinta et al., 2016, 2010; Titelman et al., 2006; West et al., 2009). Our study found that the decomposition of R. nomadica induced an increase of 2 orders of magnitude in the heterotrophic bacteria abundance. Autotrophic cyanobacteria, on the other hand, decreased (Synechococcus), or increased to a lower level than the unamended control (Prochlorococcus), likely due to deoxygenation (Bagby and Chisholm, 2015) or outcompetition by heterotrophic bacteria (Sisma-Ventura and Rahav, 2019; Thingstad et al., 2005).

The fate of jellyfish DOM consumed by bacteria depends on bacterial growth efficiency – the ratio of bacterial production to substrate assimilation (i.e., the sum of bacterial production and respiration) (Condon et al., 2011). While some studies have found that the succession of bacterial production mirrored bacterial abundance and respiration (Blanchet et al., 2015; Titelman et al., 2006), in our study, bacterial production reduced in the controls, whereas under jellyfish enrichment remained at a steady, 8-fold-higher level. This decoupling between bacterial abundance and production may indicate a shift in the functional diversity and metabolic demands of the jellyfish-associated bacterial communities during the experiment. In the shallow coastal waters of the EMS, bacterial production levels peak in winter and summer (Raveh et al., 2015), coinciding with, and potentially contributed by, the seasonal aggregations of R. nomadica (Edelist et al., 2020).

A significant reduction in the microbial alpha diversity indices of seawater during jellyfish decomposition was observed in this as well as in former studies (Blanchet et al., 2015; Kramar et al., 2019; Tinta et al., 2012). The decline in diversity can be attributed to the specialization of surface-colonizing bacteria, having the competitive advantage for settling from the surrounding seawater (Kramar et al., 2019) and was thus less evident in the sediment samples. Additionally, changes in bacterial diversity may result from bacterial antagonism, i.e., the production of antagonistic compounds and sensitivity or resilience to them (Titelman et al., 2006). In this study, we found a significant increase in the relative abundance of the Alphaproteobacterium Kordiimonas and the Gammaproteobacteria Algicola in the seawater enriched with R. nomadica. Similarly, the predominance of Alphaproteobacterium and Gammaproteobacteria stimulated by jellyfish decomposition was found in different studies (Basso et al., 2019; Blanchet et al., 2015; Condon et al., 2011; Dinasquet et al., 2012; Kramar et al., 2019; Tinta et al., 2012; Titelman et al., 2006). Gammaproteobacteria are conspicuous particle colonizers (Bižić-Ionescu et al., 2015; Simon et al., 2002), capable of degrading high-molecular-weight organic compounds (Cottrell and Kirchman, 2000; Reichenbach, 1992; Woyke et al., 2009), e.g., hydrocarbons (Niepceron et al., 2013). Kramar et al. (2019) found that Alphaproteobacteria and Gammaproteobacteria dominated the body surface of Aurelia, especially during the senescent phase. Blanchet et al. (2015) found a succession of bacterial diversity during the degradation of Aurelia and concluded that Alphaproteobacteria and Gammaproteobacteria have a major role in the succession of jellyfish DOM degradation. The link between the bacterial diversity of living R. nomadica at different life phases and the diversity of bacteria associated with its decomposed DOM is yet to be investigated.

Both genetic and functional diversity analyses of bacterial communities demonstrated a shift under R. nomadica degradation. We found that the predicted functions that dominated the decomposed jellyfish communities were signal transduction (two-component system), catabolic functions, such as fatty acid degradation, valine, leucine and isoleucine degradation, xenobiotic degradation pathways, and benzoate degradation. In the control communities, predominating functions were photosynthesis and carbon fixation in photosynthetic organisms. This functional shift can be explained by the fact that autotrophic cyanobacteria may be outcompeted by biodegrading heterotrophic bacteria. Once the jellyfish bloom decomposes, populations of these intrinsic microbial biodegraders become dominant and active, exploiting the carbon and nutrients released from the jellyfish. The 16S rRNA amplicon data for predicting functional profiles are a powerful tool for assessing bacterial functional diversity; nonetheless, their accuracy and resolution are dependent on the representation of sampled organisms in the 16S rRNA and KEGG databases (Sun et al., 2020; Wemheuer et al., 2018). Likely, jellyfish degraders are underrepresented in these databases. Further research using omics (e.g., whole-genome sequencing) will elucidate the metabolic potential of microbial degraders of the jellyfish necromass.

Although not to the same extent as bacterial diversity, eukaryotic diversity had also shifted during the decomposition of R. nomadica to a more flagellate-dominated community. Marine ciliates and parasitic protists (Labyrinthulomycetes) were also more abundant in the jellyfish decomposed community. Flagellate bacterivory represents the primary mechanism for the reintroduction of jellyfish carbon into the planktonic food web (Condon et al., 2011; Gasol and Kirchman, 2018). The increase in ciliates can be attributed to a “bottom-up” effect, where with the increase in flagellates, the abundance of their predators (e.g., ciliates) also increases (Epstein et al., 1992). Since jellyfish consume ciliates (Kamiyama, 2018; Stoecker et al., 1987), the flagellate carbon could be assimilated and recycled by the jellyfish, creating a positive-feedback loop termed as the “jelly-loop” (Condon et al., 2011; Lebrato and Jones, 2011).