Spatio-temporal fish gill microbiota analysis as indicators in estuarine fish health monitoring

Spatio-temporal fish gill microbiota analysis as indicators in estuarine fish health monitoring | 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 Spatio-temporal fish gill microbiota analysis as indicators in estuarine fish health monitoring Raphael Koll, Elena Hauten, Jesse Theilen, Corinna Bang, Michelle Bouchard, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4846387/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 Coastal marine and estuarine systems are subject to enormous endogenous and exogenous pressures, particularly climate change, while at the same time being highly productive sources and nurseries for fish populations. Interactions between host and microbiome are increasingly recognized for their importance for fish health, with growing evidence indicating that the abundance and virulence of pathogenic bacteria are rapidly increasing. The microbial composition of the gill mucus reflects environmental conditions and represents a major entry route for pathogens into the fish body. High-throughput sequencing of prokaryotic populations from 250 samples of two fish species with highly different habitat preferences, as well as seasonal and spatial distributions in the Elbe estuary system, allowed us to describe the variation of the microbiota along a salinity gradient and under fluctuating environmental conditions. The analysis of estuarine fish core microbiota in relation to variable bacterial components indicated dysbiotic states under sustained hypoxia and high nutrient loads largely driven by the takeover of opportunistic pathogens ( Acinetobacter , Shewanella , Aeromonas ). By correlating bacterial abundances with environmental and physiological parameters in a co-occurrence network approach, we describe plasticity in microbiota composition, identify biomarkers of fish health and reconstruct movement patterns of the fish. Our results will help to shape future non-invasive and cost-effective monitoring programs, and identify factors that might be controlled in the estuary to promote fish and stock health. Wildlife Biology General Microbiology Marine and Freshwater Ecology Bioinformatics Microbiota Estuary Monitoring Hypoxia Dysbiosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Communities of microbes colonize the mucosal surfaces of fish from hatching on, influencing the health of their host (Legrand et al., 2020) and responding as holobiont to seasonal variation in nutrients, salinity and temperature. Physiological stress however, impacting the cross talk between host and microbiome, might create opportunists or pathogens from otherwise commensal species (Kelly & Salinas, 2017). Changes in salinity (François-Étienne et al., 2023), temperature (Ghosh et al., 2022; Matanza & Osorio, 2018; Morshed & Lee, 2023; S. Yang et al., 2022) and dissolved oxygen (DO) (Fan et al., 2020; Shi et al., 2021; Song et al., 2023; Wang et al., 2021) have been shown to impact, or otherwise be modulated by, both the host physiology and the nature of the microbial biofilm, increasing the likelihood of pathogenic species and reducing fish health and survivability. Of particular concern are recent reports of hypoxia-induced immune suppression, making it difficult to assess the impact on fish populations in the light of climate change (Abdel-Tawwab et al., 2019; Leeuwis et al., 2024). Globally, increased frequency of extreme events and shifts in climate patterns have already intensified pathogen loads and disease outbreaks in aquaculture (Harrison et al., 2022; Samsing & Barnes, 2024; Vezzulli et al., 2013) and estuaries (Suzzi, Stat, Gaston, Siboni, et al., 2023). Estuarine ecosystems constitute important nursery areas and population sources for fish (Pasquaud et al., 2015; Seitz et al., 2014; Tournois et al., 2017), and are characterized by intense fluctuations in these physicochemical conditions (Elliott & Quintino, 2007) . Climate change is expected to increase frequency and intensity of heatwaves, oxygen minimum zones (OMZ) and change salinity regimes affecting inhabiting fish fauna (Breitburg et al., 2018; Cottingham et al., 2018; Diaz & Rosenberg, 2008; Lauchlan & Nagelkerken, 2020; Little et al., 2017; Sampaio et al., 2021) . The Elbe estuary in Germany, is one of the largest in Europe and has long been exposed to endogenous pressures. For example, over the last decades the Elbe has experienced increasing turbidity due to dredging (Heininger et al., 2015; Reese et al., 2019; van Maren et al., 2015), expansion of oxygen minimum zones (OMZ) (Colombano et al., 2021), intensified stratification of the well-mixed water column and eutrophication (Pein et al., 2021). The combined effect of these various stress factors is associated with a drastic decline in fish biomass in the system of more than 90 % over the last decade (Koll et al., 2024; Scholle & Schuchardt, 2020; Theilen et al., 2024) . Knowledge on fish associated microbiota is largely biased towards farmed fish and gastrointestinal communities (Egerton et al., 2018). Only a few studies, all but one dealing with internal microbiota, have focused on the highly productive intersection between land and marine ecosystems in estuaries (de Macedo et al., 2024; Koll et al., 2024; Suzzi et al., 2022; Suzzi, Stat, Gaston, & Huggett, 2023; Suzzi, Stat, Gaston, Siboni, et al., 2023; Wei et al., 2018) . Although the external microbial composition, with its immediate exchange with the environment, has a huge potential for monitoring, studies including external microbiomes of wild fish are mostly focused on marine or freshwater habitats (Amill et al., 2024; Itay et al., 2022; Minich et al., 2020, 2022; Pratte et al., 2018; F. É. Sylvain et al., 2020; Varela et al., 2024). The fish gill, with critical roles in respiration, osmoregulation and waste exchange, forms not only a unique habitat for the microbes but further constitutes a major route for pathogen invasion (Pratte et al., 2018; Salinas, 2015). A gill associated lymphoid tissue (GIALT) (Salinas, 2015) composed of adaptive and innate immune cell populations enables cross-talk between microbes and host (Gomez et al., 2013; Kaetzel, 2014) and discrimination between beneficial and pathogenic bacteria keeping the microbiome in homeostasis (Nakanishi et al., 2015; Yu et al., 2021). In this study, we aimed now to determine spatio-temporal variation in composition in a seasonal time-series of estuarine fish gill microbiota. Here we focus on two fish species with highly different life-styles and habitat usage to generate a larger picture on underlying dynamics in the estuarine system. Anadromous smelt ( Osmerus eperlanus L.) is a key species in food webs of a number of European riverine ecosystems (Illing et al., 2024) and constitute up to 96 % of the fish abundance in the Elbe estuary (Eick & Thiel, 2014; Theilen et al., 2024) . They begin annual spawning migrations to estuarine freshwater sites at the end of their second year of life (Thiel & Thiel, 2015) gathering in autumn at the mouth of the estuary and migrating upstream as temperatures drop to 3 - 6°C (Freyhof & Kottelat, 2007) . Within a month they reach spawning grounds upstream Hamburg and spawn until march (Borchardt, 1998; Eick, 2015; Thiel & Thiel, 2015) . Hatched larvae drift to shallow habitats downstream of the port, where oxygen deficiency can increase mortality (Diercking & Wehrmann, 1991; Thiel et al., 1995; Thiel & Thiel, 2015) . Adults avoid low-oxygen areas and accumulate where oxygen exceeds 5 mg/l (Möller & Scholz, 1991) . In spring, all life stages occupy the estuary before the majority of adults migrate to the ocean (Thiel & Thiel, 2015) . The ruffe ( Gymnocephalus cernua L.) on the other hand, is a rather undemanding bentho-pelagic freshwater species common in oligo- to mesohaline (0 -12 psu) and eutrophic estuaries (Gutsch & Hoffman, 2016) . It is the third most common fish in the estuary (Eick & Thiel, 2014) and important benthic predator of invertebrates (Hölker & Thiel, 1998). Ruffe migrate to shallow waters in summer and deeper areas in winter (Gutsch & Hoffman, 2016). They mature at 1 - 3 years and spawn in early and possibly again in late summer in warm waters (>12°C) where embryos require high oxygen levels (Gutsch & Hoffman, 2016; Ogle, 1998) . Recent studies demonstrated the special importance of time-series in contrast to single measurements to gain overview of biotic interaction and drivers that shape it (Minich et al., 2020; Scheifler et al., 2022). We generate here a comprehensive spatio-temporal dataset of the bacterial community via 16S rRNA sequencing linked to environmental conditions and physiological fish data. The objective is to study the responsiveness and plasticity of the microbiota under multiple pressures, especially along a salinity gradient and under deoxygenation and eutrophic conditions. Therefore, we determine core taxa and variable components in the microbial composition and relate them to driving forces as means of determining disruptive situations and biomarkers for life history and health situation of fish stocks in this understudied estuarine habitat. Materials and Methods 3.1. Sample Collection Fish were caught with a stow-net fishing vessel (opening area of 135 m², mesh size of 10 mm at the cod end) at five stations along the Elbe estuary (Fig. 1) in Summer (25-29.08.21), Autumn (17-21.11.21), Winter (08-12.03.22), Spring (31.05-04.06.22). Sampling stations were chosen within different estuarine sections classified by dominating abiotic drivers (Amann et al., 2012): Station ML-Elbe kilometer 663 and TW-Ekm 651 within the OMZ (stream km 620 – 650, low oxygen, summer < 2 mg/L, salinity 0.5 1 < 20 psu). Sampling procedures followed the standards described in the German Animal Welfare Act (§4 TierSchG). Exemptions to the ordinances on nature reserves were obtained (see Permits). Seven individuals per species (when possible) from ebb and flood hauls at each station (OE n = 129, GC n = 90) were processed immediately on board in the following standardized manner: Fish were measured and weighed before recovering gill bacteria with sterile cotton swabs from the middle part of the second and third arch. White muscle tissue from the dorsoventral site of each individual was dissected and rinsed with distilled water for latter stable isotope measurements. Bacterioplankton samples (n = 20) obtained from the water column were vacuum filtered onto 0.2 µm polycarbonate membranes. All samples were kept on dry ice on board until they were moved to -80°C until further processing. Whole fish samples were frozen at -20°C and further analyzed in the lab: body indices (Fulton’s body condition (FCF), hepato-somatic - (HSI), spleno-somatic (SSI) and gonado-somatic indices (GSI)) were determined (Petitjean et al., 2020), stomach content was weighed and age determination from otoliths and scales was performed. Abiotic conditions (oxygen, salinity, Secchi depth, temperature, pH) were measured at start and end of each haul using a multi-probe (Hanna HI 9829 and Secchi disc) at the water surface. In addition, abiotic data (PO 4 , NH 4 , NO 3 , NO 2 , suspended particular matter (SPM), O 2 , pH) from continuous national measurement programs ( www.fgg-elbe.de ) were downloaded for appropriate times and stations close by where the fishing took place and included in the analyses. 3.2. DNA extraction & sequencing DNA extraction, sequencing and bioinformatics were performed as explained elsewhere (Koll et al., 2024). In short CTAB/chlorophorm/phenol extraction was performed followed by amplification of the V3-V4 variable regions of the 16S rRNA gene in a one-step PCR using the primer pair 341F-806R (dual-barcoding approach (Kozich et al., 2013); primer sequences: 5’-CCTACGGGAGG-CAGCAG-30 and 5’-GGACTACHVGGGTWTCTAAT-30). PCR-products were verified via gel electrophoresis, normalized (Sequal Prep Normalization Plate Kit; Thermo Fisher Scientific, Waltham, USA), equimolar pooled and sequenced on a MiSeq platform (MiSeqFGx; Illumina, San Diego, USA) with v3 chemistry (2x300 bp). The settings for demultiplexing were 0 mismatches in the barcode sequences. 3.3. DNA Bioinformatics Microbiota analysis was performed on the University Hamburg Hummel high performance cluster. Read files were quality controlled, adapter, quality and length filtered via TrimGalore (v 0.6.10) (Krueger, 2015). All downstream analyses were performed in R (v.4.3.0) using visualization packages ggplot2 (v.3.4.2) and cowplot (v.1.1.1). For Amplicon sequence variant (ASV) prediction and taxonomic identification DADA2 (v.1.29.0) (Callahan et al., 2016) was used. Quality profiles of paired reads were inspected and truncated at 270 and 190 for forward and reverse reads. ASV inference was performed with pooling method and taxonomy assignment used the SILVA SSU v138 taxonomic database (Quast et al., 2013). To ensure contamination free and accurate analyses, mock community samples (ZYMO research) were added along the whole process from DNA extraction until taxonomic assignment (Fig. A. 7). ASV table and sample data were parsed to phyloseq (v 1.45.0) (Mcmurdie & Holmes, 2012) removing ASVs taxonomically assigned to non-bacteria. Low abundance taxa in each of the individual fish and bacterioplankton datasets were filtered at a value reducing the number of zeros in the datasets by ca. 50 % while minimally reducing the number of overall counts (Fig. A. 1) to improve interpretability and minimize the risk of spurious correlations. The filtering value was determined as sum of counts lower than 0.005 % of total sum of all counts. Alpha diversity measures were calculated via vegan package (v. 2.6.4) (Oksanen et al., 2022) and the core microbiota were determined from relative abundance data via microbiome package (v. 1.23.0) (Lahti & Shetty, 2017) with filtering detection threshold to 0.0001 % within samples and 90 % prevalence. Centered log-ratio (CLR) transformation to the ASV matrix was applied following best practices for handling of compositional data (Gloor et al., 2017). The transformed data were used for visualization and network analyses. 3.4. Stable isotope analysis Additional stable isotope analysis of d 13 C was conducted to determine movement directions of investigated fish. Tissue samples were freeze-dried for 24 hours and grinded to powder using a cell lyser. Samples were weighed (0.8 – 1.2 mg) and folded into tin capsules (Hekatech). d 13 C ratios in permille (‰) were measured by the UC Davis Stable Isotope Facility of the University of California using a continuous flow isotope ratio mass spectrometer (IRMS) PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Ratios are expressed relative to the international standard VPDB (Vienna Pee Dee Belemnite) for carbon using the delta notation (d 13 ) (Fry, 1988, 2013). We predicted estuarine d 13 C ranges for resident fish using stable isotope information of prey specimens of mysid shrimp from MG-Ekm 713 and ML-Ekm 633, which inhabit distinct d 13 C ratios that enables spatial determination of food origin (Guelinckx et al., 2006). Stable isotope ratios of mysid shrimp were pooled, and median and standard deviations were calculated. Due to the carnivorous feeding preferences of mysids in estuaries (Modéran et al., 2012), we assume similar trophic levels as in fish, thus trophic enrichment factors on the data could be neglected during our analysis. d 13 C above and below these limits indicated riverine and marine derived sources that was stored in the flesh of the consumer. 3.5. Statistical analyses First, we visualized the gill bacterial community according to the fish species, sampling season and location using stacked barplots (Fig. 1). Then we computed PCoAs based on pairwise averaged subsampled Bray-Curtis dissimilarity between all gill mucus and bacterioplankton samples (N = 245) to study how the sample cluster according to origin and season. The distance matrix was further used to test for significant differences in community structure between the aforementioned factors via permutational analyses of variance (PERMANOVA) with 999 permutations. R vegan package and post hoc pairwise t-test pairwiseAdonis (P. Martinez Arbizu, 2020) were applied. Secondly, we identified bacterial biomarkers on ASV level discriminant for bacterioplankton or gill mucus assemblage, the host species identity, the seasonal influence as well as selected locational effects. We used the multipatt function from the IndicSpecies package (v 1.7.14) (De Cáceres & Legendre, 2009) with indicator value > 0.7 and P-value < 0.05 after 999 permutations based on the read abundance of the ASV table with internal correction for group size inequality. Relative abundance (%) of these biomarkers are shown in the supplementary material Tab. A. 4, relative abundance of overall ASV counts were visualized as heatmap in Fig. A. 2B. Thirdly, we analyzed the response of the fish mucus communities to seasonal and spatial gradients and in relation to physiological measurements first by computing average Bray-Curtis distance-based redundancy analyses (dbRDA) (Legendre & Anderson, 1999) via vegan capscale function following the workflow described in (F.-É. Sylvain et al., 2022). Environmental and physiological variables were selected by stepwise model building for constrained ordination ( ordistep ) and assessment of multicollinearity between variables by measuring variance inflation factors (VIF) keeping ordistep selected parameters with VIF < 10 as explicative variables. Association strength between explicative variables and bacterial assemblage was then measured by fitting environmental vectors on the reduced model ordination using vegan envfit (Tab. A. 3). Finally, Mantel test was used on the distance matrices to gain initial overview of association between fish mucus assemblages, bacterioplankton communities and environmental and physiological measures (Fig. 4E, Fig. A. 4B). We than applied a weighted gene co-expression network analysis (WGCNA) (v.1.77-1) (Langfelder & Horvath, 2008) to infer networks of co-abundant bacterial taxa and relate them to physiological and environmental data in an integrated heatmap analysis approach (Koll et al., 2024; Strand et al., 2021) (Fig. 5, Fig A 6 & 7). Parameter list: networkType = "signed", TOMType = "signed", corType = "bicor", minModuleSize = 3, minKMEtoStay = 0.5, deepSplit = 2/3 (for fish mucus / water filter WF respectively), mergeCutHeight = 0.15/0.3 (Fish/WF), maxPOutliers_value = 0.05/0.1 (Fish/WF). Initial module detection by hierarchical clustering is controlled by deepSplit parameter (1 - 4, last being most sensitive). The minModuleSize defines the minimum size for the initial module where ASVs with correlation to the module eigenmode (KME) smaller than minKMEtoStay are removed. Different modules whose eigennodes correlate higher than 1 – mergeCutHeight are merged. The parameters chosen here seek to detect modules with highly correlated nodes but not losing interesting profiles represented by only a few taxa. The parameters are adjusted to account for the different sampling sizes. Module eigengenes/ASVs where correlated between the different networks and to physiological traits of the fish and external abiotic factors via Pearson correlation and corrected for multiple testing via FDR. Results Approximately 15 million trimmed, filtered and merged reads (mean 58,000 reads) across 139 O. eperlanus (OE), 89 G. cernua (GC) gill mucus and 20 bakterioplankon (WF) samples resulted in 42.000 ASVs. The datasets were divided for filtering retaining 1552, 1393, 2364 ASVs for the individual datasets. Species accumulation analysis indicated that by the exclusion of rare taxa the microbiota were sufficiently captured in the datasets (see Fig A.1). 4.1. Bacterial gill communities deviate from bacterioplankton Gill mucus communities were dominated by six phyla (> 1 % overall abundance) including Proteobacteria (54 %), Bacteroidota (27 %), Actinobacteriota (7 %), Firmicutes (5 %), Verrucomicrobiota (4 %), Deinococcota (2 %) at similar proportions between species (Tab. A.1). The bacterioplankton in contrast was composed of additional phyla Desulfobacterota (1.5 %), Acidobacteriota (2,3 %), Cyanobacteria (1,4 %), Nitrospirota (1,9 %), Gemmatimonadota (1,4 %), Chloroflexi (1,2 %) but lacked high abundances of Firmicutes (0.3 %) and Deinococcota (0.1 %). Analyses of alpha diversity showed significantly higher richness and Shannon indices in free-living bacteria samples compared to the fish mucus communities, but no overall significant difference between the two fish species (Fig A. 3A & B) The principal coordinates analysis in Fig. 1 showed a strong distinction between the bacterioplankton and the fish gill mucus community along Axis 1 and 2 summarizing 18.3 und 6.8 % variance. PERMANOVA indicated a significant distinction between fish (OE: F 1, 143 = 28.775, R 2 = 16 %, p = 0.001, GC: F 1, 103 = 33.134, R 2 = 25 %, p = 0.001) and bacterioplankton samples. Presence-absence analyses indicated that roughly half of the detected ASVs (> 1600) are unique to the bacterioplankton while about 20 % (ca. 500) are shared between both fish and surrounding water (Fig. A 2A). The shared taxa make up about 50 ± 5 % in relative abundance on all three biomes. Taxa with the highest overlap between the biota (> 0.5 % relative abundance in both) comprise Luteolibacter , Persicirhabdus , Flavobacterium , TRA3-20 , Ilumatobacter and Halioglobus , adding Polynucleobacter between GC and bacterioplankton (Tab. A. 2). Between free-living and fish-associated biota 1447 and 385 bacterial biomarkers at ASV level were determined, for the latter however the strains with significant abundances (> 0.2 % relative abundance) constituted all core taxa in the fish mucus (see next chapter). Although significantly distinct (F 1, 224 = 5.8978 R 2 = 2.6 % p = 0.001), the two fish species showed a strong overlap with largest distinction indicated by the seasonal sampling along Axis 1. OE samples collected in autumn formed a prominently distinct cluster. Presence-absence analyses indicated that both species share more then 1000 ASVs while about 474 were unique to OE and 326 were unique to GC. Comparing the seasonal samplings for each species, relatively low amounts of ASVs (< 5 %) were unique to single seasons. Comparing the fish species, no indicator taxa were specific to OE, and only strains of Verticiella , Polynucleobacter and Candidatus Megaira were determined specific for GC at relevant abundance levels (> 0.2 %). In the temporal-spatial comparison within each fish species, few taxa appeared significant, only Acinetobacter strains were determined indicative for summer conditions in the estuary in both fish associated communities ( see Tab. A. 4 ). 4.2. Stable estuarine core gill microbiota The core microbiota (determined by prevalence) over seasonal and spatial samplings comprised 27 ASVs from 13 orders (21 genera) accounting for 30 ± 12 % in OE and 64 ASVs from 11 orders (30 genera) accounting for 50 ± 22 % of the overall relative microbiota abundance in GC (Fig. 3). Only a fraction of these taxa was present in the bacterioplankton (33 % OE, 20 % GC) accounting for < 1% of the bacterioplankton relative abundance (Fig. 3D). Elizabethkingia , the dominant bacterial taxon in the fish microbiota accounts for only 0.06 % of the relative abundance in the free-living community. The gill mucus core microbiota of both fish species were composed by only four phyla, in GC Proteobacteria (almost entirely composed of Enterobacterales) made up 56 % followed by Bacteroidota (almost entirely represented by Flavobacterales) with 36 % and Actinobacteria 5% and Deinococcota 2 %. In OE Bacteroidota made up 60 % followed by Proteobacteria 27 %, Actinobacteria 9 %, Deinococcota 3 %. OE showed a strong seasonal variation in autumn samples driven by the low abundance of Enterobacterales and Elizabethkingia . Most prominent, both fish species showed a strong decline in the abundance of core taxa in freshwater and Hamburg Port area only in summer (Ekm 651 – 633) (OE 18 %, GC 22 %) (Fig. 3B & D, highlighted). 4.3. Drivers in gill mucus bacterial composition Redundancy analysis revealed that bacterial composition in the anadromous species O. eperlanus was strongly influenced by seasonal effects in environmental and biometric measures while a more pronounced spatial pattern appeared in the stationary species G. cernua (Fig. 4 A & C). The variances explained by the first two RDA dimensions varied considerably between the two species (OE 32 % and GC 17 %). Fitting environmental variables (envfit results Tab. A. 2) showed significant effects of temperature, salinity, PO 4 , NO 3 , NO 2 and TOC on the gill mucus microbial communities in both species. OE samples showed a strong seasonal clustering for autumn along SPM, PO 4 and GSI values, winter samples aligned along TOC and a combined cluster of spring and summer samples group along temperature where samples from the upper estuary (ML-633, TW-651) deviated with PO 4 . GC samples instead showed a stronger overlap for seasonal effects, again summer samples from the upper estuary (ML-633, TW-651) deviated strongest along with PO 4 , NO 2 and Temperature axes. Interestingly physiological parameters other than age in GC and GSI in OE, were not significantly correlated to the bacterial composition in the parametric analysis. In addition, we evaluated the association between bacterial sample distances and physiological and environmental data as well correlation between bacterioplankton and fish mucus communities via individual non-parametric Mantel tests (Fig. 4 E, all results Tab. A 3). The results showed associations (r = 0.20 – 0.55) between bacterioplankton and environmental parameters (salinity, temperature, O 2 , SPM, NO 3 , PO 4 ), however these were weaker for both fish species. OE was most strongly correlated with GSI (r = 0.67). There was overall overall no significant association between the bacterioplankton community and GC, and a slight overall trend between OE and free-living bacteria. A spatio-temporally resolved analysis for this species showed that only the microbiota of the autumn smelt were significantly associated with bacterioplankton (Fig. A. 4B). 4.4. Movement patterns & microbiota plasticity The stable isotope ratios of δ 13 C from fish muscle reflects their long-term feeding preferences of several weeks to months depending on metabolic activity of the tissue and growth rates of the fish (Buchheister & Latour, 2010) that further enables the reconstruction of migration. These data indicated that OE gathering in the estuary autumn just arrived from the North Sea. As described above, the bacterial core taxa in this group were rare (Fig. 3B), while mantel tests indicated a significant association to the bacterioplankton communities (Fig. A. 4B). Analyzing the bacterial taxa shared between with the estuarine bacterioplankton while excluding the core taxa in the fish mucus community, showed a steady increase in abundance in upstream direction from 15 % in the estuarine mouth to 26 – 29 % in the middle section until 47 % in the harbor area (Fig. A. 4A). OE caught in winter (3 months after the autumn animals) completed the spawning process (as indicated by low GSI indices) but still showed a strong marine isotope signal. The core microbiota and abundance of shared taxa with bacterioplankton however aligned to summer and spring animals (Fig. 3 A & B). In contrast, GC samples range all year within the signal boundaries of the estuary and show a strong spatial pattern for sampling station along the course of the estuary (Fig. 3 D & E). 4.5. Bacterial network response We performed separate network analyses on the centered-log ratio (CLR) transformed bacterial data from the gill mucus of both fish host species and water samples to explore patterns in interacting taxa and relate sub-networks (modules) to environmental drivers. The role of individual taxa in mediating environmentally driven functions was explored via intra-network connectivity (K in ) and abundance correlation to prevailing pressures. For the analysis we focus on the main factors determined by RDA in section 4.3. WGCNA clustered the gill mucus taxa into 13 and 16 sub-networks for OE and GC, respectively, and 24 for the bacterioplankton (Fig. 5C, Fig A. 6). Both fish mucus assemblage networks resulted in highly overlapping sub-networks correlated to the dominant environmental drivers. The prevalence determined core-taxa (section 4.2) were also reflected within networks of both species in the dominant modules OE6 and GC8 overlapping in 70.5 % and 90.1 % in abundance with prevalence determined taxa (Fig. 5 & Fig. A. 5) and by 80 % and 96 % with each other. OE6 summarized 33 – 37 % in overall abundance in summer, spring and winter but only 14 % in autumn while GC8 summarizes 36 – 60 % over all seasons. OE6 is dominated by Elizabethkingia (40.9 % within module abundance), Enterobacteriaceae (24.8 %), Enterobacter (11 %), Citrobacter (10.8 %), identical to GC8 with Elizabethkingia (37.4 %), Enterobacteriaceae (27.2 %), Enterobacter (12.3 %), Citrobacter (12.5 %). Uncorrelated taxa (OE0 & GC0) make up 20 % of the overall microbial abundance in both species driven by Luteolibacter , Asinibacterium , Clostridium sensu stricto 1 strains and Rhodobacteraceae in GC. In migrating autumn smelt the amount of uncorrelated taxa raised to 49 %. 4.5.1. Salinity Freshwater conditions in the estuary are correlated with ruffe network module GC11 (r – 0.36 to salinity, P **, 3 – 20% bacterial abundance in upstream direction) characterized by Polynucleobacter (54% module abundance proportion), Verticiella (27 %) and Candidatus Megaira (10 %). In smelt (OE4, r – 0.54, P ***, 2.5 – 17 % in upstream direction) showed uprises in Rhizobiales Incertae Sedis (7.8 %), Xanthobacteraceae (4.5 %) and nitrobacteria Ellin6067 (8 %), Hyphomicrobium (7.5 %), Gaiella (5.9 %). The mesohaline conditions on the contrary are correlated with Persicirhabdus (33.9 %), Ilumatobacter (10.5 %) and Halioglobus (10.4 %) in OE3 (r 0.72, P ***, 17 – 0 % in upstream direction), Less abundant, GC7 (r 0.77, P ***, 3 – 0.1% in upstream direction) is composed of Halioglobus (17.6%), Persicirhabdus (8.1 %), Ilumatobacter (6.7 %), Candidatus Symbiobacter (4.9 %) and Luteolibacter (4.6 %). 4.5.2. Temperature Elevated temperatures were highly correlated with smelt module OE5 (r 0.61, P ***, 6.5 % overall abundance) composed of Chryseobacterium (22.3 % module abundance proportion), Alkanindiges (9.9 %), Psychrobacter (9.5 %), Deinococcus ( 7.8 %) and Paracoccus (6.5 %). Likewise, ruffe GC5 (r 0.4, P ***, 7.1 % overall abundance) consisted of Chryseobacterium (12.8 %), Paracoccus (10.4 %), Deinococcus (10.4 %), Ornithinicoccus (6 %). In both fish species the same strains of Flavobacterium (r 0.7) and Ornithobacterium (r 0.6) showed the highest correlations to elevated temperature values. On the contrary, lowered temperatures were highly correlated to OE1 (r -0.77, P ***, 21.2% in winter) dominated by Chryseobacterium (17.1 %), Flavobacterium (13.6 %), Deinococcus (9.3 %). GC6 (r -0.63, P ***, 6 % in winter) consists of Escherichia-Shigella (6.9 %), Sphingomonas (9.1 %), Thermomonas (5.2 %), Deinococcus (9.3 %), Weeksellaceae (4.8 %), Flavobacterium (8.9 %). 4.5.3. Desoxygenation In both fish species, highly similar modules correlated with deoxygenation and nutrient levels (OE7: DO -0.53***, PO 4 0.53***, NO 2 0.34 *** and GC2: DO -0.49***, PO 4 0.54***, NO2 0.56***) overlapping in 63 % of the ASVs. OE7 was largely dominated by Acinetobacter (66.4%, A. lwoffii 31.1 %, A. johnsonii 10.17 %), Exiguobacterium (5.5 %), Macrococcus (4.6 %) and Pseudomonas (4.4%). Similarly, GC2 was composed by Acinetobacter (54.2%, A. lwoffii 20.6 %, A. johnsonii 10.7%), Macrococcus (7.9 %), Shewanella (5.4 %, S. baltica 2 %, S. putrefaciens 0.6 %), Chryseobacterium (4.6 %), Aeromonas (4.5 %) and Pseudomonas (3.8 %). While these taxa account for only 4% in overall microbiota abundance in both species, sampling groups from the upper estuary (Ekm 651 – 633) in late summer reached 31.7 % and 22.2 % with individual samples peaking above 60% relative abundance. Discussion Through amplicon sequencing of gill mucus bacteria in two highly different estuarine key fish species, we aimed to gain insights into the plasticity of the host associated microbiota, movement patterns of the fish and developing indicators for stressful conditions in the host. Many studies have dealt with the influence of abiotic stress on the gut and skin microbiota in fish, usually under artificial rearing conditions (Bell et al., 2024). However, despite its huge potential for health monitoring, gill compositions are largely overlooked (Sehnal et al., 2021; Xavier et al., 2024), especially along physicochemical gradients in an estuarine system. 5.1. Life history strategies & microbiota plasticity Albeit surrounded by water, a clear distinction existed between bacterioplankton and gill mucus microbiota communities suggesting selective colonization (Koll et al., 2024; Legrand et al., 2018; Minich et al., 2020; Pratte et al., 2018; Rosado et al., 2021). Though many taxa are shared between the biomes, a few core taxa, rare in the water column, dominate the fish gill mucus. The spatial and temporal resolution of the dataset revealed insights into the dynamics of microbiota plasticity. In autumn, anadromous O. eperlanus migrate to the estuary. Their microbiota composition showed a diminished amount of estuarine core but increased estuarine bacterioplankton taxa, suggesting a two-step microbiome adaptation: an initial increase of bacterioplankton-shared taxa that compete best for niches in the host environment at a given salinity (Schmidt et al., 2015) rising with residence time during the one-month ascent process (Borchardt, 1998). In here, Luteolibacter, a marine and freshwater taxon (Ji et al., 2021) associated with bacterial dysbiosis in fish (Kakakhel et al., 2023; Mondal et al., 2022; Sun et al., 2021) became dominant. The taxon was shown to antagonize pathogen growth contributing to microflora resilience by niche colonization and might further be involved in epithelial tissue repair (Nakatani & Hori, 2021) which could serve beneficial roles during deep-reorganization in migrating smelt. In the second, slower step, the estuarine core taxa became dominant along the entire salinity gradient. The indicator analysis showed overall no loss or gain of abundant taxa but a shift in abundance ratios as underlying mechanism. In contrast, G. cernua's stable isotope data reflect a stationary lifestyle indicating residence for several weeks to month dependent on metabolic activity (Buchheister & Latour, 2010; Grønkjær et al., 2013) matching with microbiota composition mainly influenced by spatial drivers. In accordance with the stable isotope signal, a few bacterial indicator taxa ( Verticiella, Polynucleobacter and Candidatus Megaira ) are specific for freshwater residency in G. cernua . In O. eperlanus decreasing salinity correlated with nitrogen-metabolizing taxa ( Hyphomicrobium, Gaiella, Ellin6067, TRA3-20, GOUTA6, Rhizobiales, Xanthobacteraceae ) (Chen et al., 2022; Xiao et al., 2022; C. Yang et al., 2023), potentially preventing toxic ammonia buildup at the gills (Legrand et al., 2018; van Kessel et al., 2016) in the anadromous species less adapted to high loads of nitrogen compounds in estuarine freshwater areas. In the bacterioplankton these conditions were associated with an increase in nitrogen metabolizing Nitrospira and TRA3-20 while in ruffe gill mucus all these taxa were omnipresent. Mesohaline indicators ( Persicirhabdus, Ilumatobacter, Halioglobus ) co-occur in fish and bacterioplankton and have been found in association with various marine organisms, although their function is poorly understood (Amin et al., 2022; Han et al., 2021; Scheifler et al., 2023). 5.2. Estuarine gill core microbiota Core taxa are assumed to serve beneficial roles in the host (Sehnal et al., 2021) and the dominant taxa here, Elizabethkingia (Jacobs & Chenia, 2011) , Enterobacteriaceae , Lelliottia (Salgueiro et al., 2020), Arthrobacter (Tsoukalas et al., 2023), Deinococcus (Amill et al., 2024), Asinibacterium , Knoellia (Zou et al., 2023) are all well described from external fish microbiota. Asinibacterium and Enterobacter taxa are even considered probiotic, inhibiting pathogen growth (Halet et al., 2007; Schubiger et al., 2015). The genus Citrobacter contains pathogens of marine and freshwater fish (Liu et al., 2024; Sato et al., 1982), but was found here as a constant compartment of the gill microbiota in both fish species. Overall, the core microbiota of the two fish species were almost identical and highly concordant with another top predatory fish species ( Sander lucioperca L.) in the estuarine system (Koll et al., 2024). These data indicated that the gill mucus core microbiota of estuarine fish were similarly shaped regardless of trophic level, life history and habitat use, and the estuarine-specific signal was acquired within a time frame of less than three months in migratory fish. This suggests that the strong fluctuations in physiochemical conditions in the estuarine system allowed colonization of only a small number of generalist species or strong host selection for critical ecological functioning (Luan et al., 2023; Roeselers et al., 2011; Sharpton et al., 2021). The size of the core microbiota with ~30 genera appeared to be low in estuarine fish compared to hundreds in other studies (Lorgen-Ritchie et al., 2022; Sharpton et al., 2021), but at 30 – 50% high in terms of abundance similar to gut microbiota studies (Wilkes Walburn et al., 2019). Determining the average core abundance proportions in this larger dataset enabled the identification of animals with strong deviation. In both species, we found groups of animals with core abundance < 20 %, which could indicate profound reorganization or dysbiosis and possible disease states (Sehnal et al., 2021). In this context, analyzing the prevalence of potentially pathogenic taxa in overlap with signs of dysbiosis of the overall microbiota composition is probably more informative, as potential pathogens may also be part of the core community of apparently healthy individuals (Itay et al., 2022; Yajima et al., 2023). 5.3. Deoxygenation & dysbiosis in estuarine fish Dysbiosis, the disturbance of microbiome homeostasis (abnormal taxonomic structure and metagenomic function) by uprise of opportunistic taxa (Egan & Gardiner, 2016; Levy et al., 2017) , affects microbiome functionality and host physiology and is associated with disease states in fish (Legrand et al., 2020; Mougin & Joyce, 2023) . Understanding these processes and identifying biomarkers for dysbiotic processes before physical signs of infection are important not only in fish farming (Mougin & Joyce, 2023; Xavier et al., 2024), but also for monitoring the health of wild fish populations and ecosystems. While dysbiosis is often defined as a reduction in diversity and microbial richness, measurements of alpha diversity show contradicting results even in controlled conditions in fish with clear disease signs (Karlsen et al., 2017; Liu et al., 2024; Mougin & Joyce, 2023; Vasemägi et al., 2017; Xavier et al., 2024; X. Zhang et al., 2018) . Changes in Proteobacteria to Bacteroidetes (P:B) ratios were also suggestively linked to disease states in different fish microbiomes (Xavier et al., 2024). The P:B ratio in estuarine fish gill mucus was at 3:1 higher than in marine taxa (10:1) (Legrand et al., 2018) and decreased slightly during periods of deep reorganization (autumn smelt 7:1) and prolonged oxygen depletion in late summer (OE 4.6:1, GC 4:1). Overall, however, we did not identify clear patterns in alpha diversity measures associated with environmental factors and in agreement with recent reviews, did not consider them suitable for detecting perturbations in microbial homeostasis (Xavier et al., 2024). We observed a strong increase in opportunistic Acinetobacter coinciding with the decrease in the abundance of core microbiota in both fish species under persistent hypoxia (< 5mg/L) and elevated nutrient loads (nitrates, nitrites and phosphates). Particularly noteworthy, this pattern was not seen in spring when oxygen levels just start declining at similar nutrient loads. Increases in abundance of Acinetobacter, albeit less prevalent (< 1 %) were observed in the surrounding water column matching described functions in nitrification and nitrogen as well as phosphorous compound assimilation (Carr et al., 2003; Zhong et al., 2023). Acinetobacter are repeatedly described as part of the core microbiota of marine fish (Lorgen-Ritchie et al., 2022; Varela et al., 2024), although they can occur as pathogens in a number of freshwater fish (Kozińska et al., 2014; Malick et al., 2020; Visca et al., 2011; M. Zhang et al., 2023). Several strains of A. johnsonii and A. Iwoffii were shown to cause lesions and hemorrhage especially in gill and liver tissues (Bi et al., 2023; Cao et al., 2018). Notably co-infections of Acinetobacter species (Malick et al., 2020), similar to Shewanella (Erfanmanesh et al., 2019) and Aeromonas (Chandrarathna et al., 2018; Wise et al., 2024) can significantly influence the severity and course of diseases in fish through synergistic interactions like immunosuppressive effects (Kotob et al., 2016; Okon et al., 2023). We detected a strong co-occurrence of different opportunistic Acinetobacter strains ( A. iwoffii , A. johnsonii ) with Aeromonas and Pseudomonas and in GC also with Shewanella ( S. baltica & S. putrefaciens ) in both fish species independently becoming a significant component of the microbiota. No physiological measurements (i.e. lesions) of gills or other tissues were performed in this study and no correlations with biometric markers (FCF, HSI, SSI) were detected. Thus, no direct consequences for fish health can be determined in the present data set. Against the background of wild sampling, however, these physiological endpoint makers are highly variable and do not allow conclusions to be drawn about the influence on health with the sample sizes available here. In a previous study we identified strong correlations of the immune response and cellular stress response in the gill and liver of another estuarine species in response to an increase in Shewanella and Acinetobacter (Koll et al., 2024). Conclusion Our data showed the potential of non-invasive gill microbiomes for long-term monitoring the health of estuarine fish in the context of changes occurring in estuaries due to multiple anthropogenic stressors. Combined with a previous study, we have been able to show, for three species of fish in the Elbe Estuary, that an easily identifiable core microbiome associated with healthy fish. Further, for each species, we show that changes in these core microbiota, either as a consequence of migration, seasonality or stress can increase the likelihood of opportunistic and pathogenic species which may impact fish survivability. Whilst, additional targeted studies, would be necessary to clarify the pathogenic nature of these strains, and the capacity of the gill microbiome to recover after alleviation of stress inducing conditions, these results are of global significance and concern. Estuaries should be considered protected spawning and nursery areas, ensuring marine fish stocks are well maintained, though our data and others suggest this is not the case. However, increased monitoring of estuarine fish and the water column, might provide a solution by identifying areas of reduced impact that can be restored or conserved, ensuring that estuaries continue to provide this much needed ecosystem function. Declarations 7. Data availability The sequence data are deposited in the ENA Sequence Read Archive under the study PRJEB77621. The complete analysis is available in stepwise R markdown files, including metadata and supplementary lists as well as visualizations as HTMLs at DOI: 10.5281/zenodo.12819246 8. Funding This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Research Training Group 2530: “Biota-mediated effects on Carbon cycling in Estuaries” (project number 407270017; contribution to Universität Hamburg and Leibniz-Institut für Gewässerökologie und Binnenfischerei im Forschungsverbund Berlin e.V.). This work was also supported by the DFG research grand “Large Scale Sequencing to Unravel Carbon Cycling in the Elbe estuary (Micro)biota” Project number: 496691966 / FA 1568. and by the project “Blue Estuaries” funded by the Federal Ministry for Education and Research under funding code 03F0864F. Microbiota sequencing received infrastructure support from the DFG Excellence Cluster 2167 "Precision Medicine in Chronic Inflammation" (PMI) and the DFG Research Unit 5042 „miTarget". 9. Permits Sampling procedures were according to the standards described in the German Animal Welfare Act (§4 TierSchG). After being brought on board, the fish were stunned with a blow to the head, before being killed with a heart stab. The implementation of the stow-net fishing for scientific purposes is approved by the Authority for the Environment Climate, Energy and Agarwirtschaft, by the State Fisheries Office Bremerhaven and by the State Office for Agriculture, Environment and Rural Areas of Schleswig-Holstein. Exemptions to the ordinances on nature reserves Mühlenberger Loch/Neßsand as well as a nature conservation permit to conduct research fishing in protected areas in the NSG "Rhinplate und Elbufer südlich Glückstadt"/FHH area DE 2393-393 from the Office of Environmental Protection were obtained. 10. Author contributions RK: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Validation, Writing- original draft, Visualization, Project administration. EH: Investigation, Visualization, Formal analysis, Data curation, Review & Editing. JT: Investigation, Permit acquisition, Review & Editing. CB: Data curation, Resources, Review & Editing. MB: Investigation, Data curation. RT & CM: Resources, Review & Editing. JW: Supervision, Validation, Review & Editing. AF: Funding acquisition, Conceptualization, Supervision, Validation, Review & Editing, Project administration. 11. Acknowledgements We would like to thank Claus & Harald Zeeck and Dirk Stumpe for their efforts in collecting the samples and the hundreds of hours spent together on their fishing vessel. We thank Prof. Dr. Kathrin Dausmann for mentoring the project. 12. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work RK used DeepL/translate/write in order to improve language of the manuscript. After using this tool, RK reviewed and edited the content as needed and takes full responsibility for the content of the publication. References Abdel-Tawwab, M., Monier, M. N., Hoseinifar, S. H., & Faggio, C. (2019). Fish response to hypoxia stress: growth, physiological, and immunological biomarkers. Fish Physiology and Biochemistry , 45 (3), 997–1013. https://doi.org/10.1007/S10695-019-00614-9 Amann, T., Weiss, A., & Hartmann, J. (2012). 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Scientific Reports . https://doi.org/10.1038/s41598-018-20120-x Wilkes Walburn, J., Wemheuer, B., Thomas, T., Copeland, E., O’Connor, W., Booth, M., Fielder, S., & Egan, S. (2019). Diet and diet-associated bacteria shape early microbiome development in Yellowtail Kingfish (Seriola lalandi). Microbial Biotechnology , 12 (2), 275–288. https://doi.org/10.1111/1751-7915.13323 Wise, A. L., LaFrentz, B. R., Kelly, A. M., Liles, M. R., Griffin, M. J., Beck, B. H., & Bruce, T. J. (2024). Coinfection of channel catfish (Ictalurus punctatus) with virulent Aeromonas hydrophila and Flavobacterium covae exacerbates mortality. Journal of Fish Diseases . https://doi.org/10.1111/JFD.13912 Xavier, R., Severino, R., & Silva, S. M. (2024). Signatures of dysbiosis in fish microbiomes in the context of aquaculture. Reviews in Aquaculture , 16 (2), 706–731. https://doi.org/10.1111/RAQ.12862 Xiao, Z., Zhang, S., Yan, P., Huo, J., & Aurangzeib, M. (2022). Microbial Community and Their Potential Functions after Natural Vegetation Restoration in Gullies of Farmland in Mollisols of Northeast China. Land , 11 (12), 2231. https://doi.org/10.3390/LAND11122231/S1 Yajima, D., Fujita, H., Hayashi, I., Shima, G., Suzuki, K., & Toju, H. (2023). Core species and interactions prominent in fish-associated microbiome dynamics. Microbiome , 11 (1), 1–15. https://doi.org/10.1186/S40168-023-01498-X/FIGURES/6 Yang, C., Zhang, H., Feng, Y., Hu, Y., Chen, S., Guo, S., & Zeng, Z. (2023). Effect of microbial communities on nitrogen and phosphorus metabolism in rivers with different heavy metal pollution. Environmental Science and Pollution Research , 30 (37), 87398–87411. https://doi.org/10.1007/S11356-023-28688-2/FIGURES/6 Yang, S., Xu, W., Tan, C., Li, M., Li, D., Zhang, C., Feng, L., Chen, Q., Jiang, J., Li, Y., Du, Z., Luo, W., Li, C., Gong, Q., Huang, X., Du, X., Du, J., Liu, G., & Wu, J. (2022). Heat Stress Weakens the Skin Barrier Function in Sturgeon by Decreasing Mucus Secretion and Disrupting the Mucosal Microbiota. Frontiers in Microbiology , 13 , 860079. https://doi.org/10.3389/FMICB.2022.860079/BIBTEX Yu, Y. Y., Ding, L. G., Huang, Z. Y., Xu, H. Y., & Xu, Z. (2021). Commensal bacteria-immunity crosstalk shapes mucosal homeostasis in teleost fish. Reviews in Aquaculture , 13 (4), 2322–2343. https://doi.org/10.1111/RAQ.12570 Zhang, M., Dou, Y., Xiao, Z., Xue, M., Jiang, N., Liu, W., Xu, C., Fan, Y., Zhang, Q., & Zhou, Y. (2023). Identification of an Acinetobacter lwoffii strain isolated from diseased hybrid sturgeon (Acipenser baerii♀× Acipenser schrenckii♂). Aquaculture , 574 , 739649. https://doi.org/10.1016/J.AQUACULTURE.2023.739649 Zhang, X., Ding, L., Yu, Y., Kong, W., Yin, Y., Huang, Z., Zhang, X., & Xu, Z. (2018). The Change of Teleost Skin Commensal Microbiota Is Associated With Skin Mucosal Transcriptomic Responses During Parasitic Infection by Ichthyophthirius multifillis. Frontiers in Immunology , 9 . https://doi.org/10.3389/FIMMU.2018.02972 Zhong, Y. ;, Xia, H., Zhong, Y., & Xia, H. (2023). Characterization of the Nitrogen Removal Potential of Two Newly Isolated Acinetobacter Strains under Low Temperature. Water 2023, Vol. 15, Page 2990 , 15 (16), 2990. https://doi.org/10.3390/W15162990 Zou, Y., Wu, D., Wei, L., Xiao, J., Zhang, P., Huang, H., Zhang, Y., & Guo, Z. (2023). Mucus-associated microbiotas among different body sites of wild tuna from the South China Sea. Frontiers in Marine Science , 9 , 1073264. https://doi.org/10.3389/FMARS.2022.1073264/BIBTEX Additional Declarations The authors declare no competing interests. Supplementary Files GA.png Graphical Abstract AppendixA.Supplementarydata25.07.24.docx 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-4846387","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335079942,"identity":"2a3ac14a-a476-47d3-82db-90f9cd60239e","order_by":0,"name":"Raphael Koll","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-4072-3841","institution":"University of Hamburg; Institute of Cell- and Systems Biology of Animals, Molecular Animal Physiology","correspondingAuthor":true,"prefix":"","firstName":"Raphael","middleName":"","lastName":"Koll","suffix":""},{"id":335079943,"identity":"3b3ae0b4-57bc-472a-8c0b-77d14a64c729","order_by":1,"name":"Elena Hauten","email":"","orcid":"","institution":"University of Hamburg; Institute of Marine Ecosystem and Fishery Science, Marine ecosystem dynamics","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Hauten","suffix":""},{"id":335079944,"identity":"e6e4b383-8e90-4b77-95c4-10569594a2ef","order_by":2,"name":"Jesse Theilen","email":"","orcid":"","institution":"University of Hamburg; Department of Biology, Biodiversity Research","correspondingAuthor":false,"prefix":"","firstName":"Jesse","middleName":"","lastName":"Theilen","suffix":""},{"id":335079945,"identity":"7490a0ba-9c31-4ecb-88a6-966f595593a5","order_by":3,"name":"Corinna Bang","email":"","orcid":"","institution":"Kiel University, Institute of Clinical Molecular Biology, Germany","correspondingAuthor":false,"prefix":"","firstName":"Corinna","middleName":"","lastName":"Bang","suffix":""},{"id":335079946,"identity":"a226d4ec-26e4-4f8e-bf95-35dbdc63fbcc","order_by":4,"name":"Michelle Bouchard","email":"","orcid":"","institution":"University of Hamburg; Institute of Cell- and Systems Biology of Animals, Molecular Animal Physiology","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Bouchard","suffix":""},{"id":335079947,"identity":"5da84120-8a75-45bd-960d-123877a6a7bb","order_by":5,"name":"Ralf Thiel","email":"","orcid":"","institution":"Independent researcher, Lübeck, Germany","correspondingAuthor":false,"prefix":"","firstName":"Ralf","middleName":"","lastName":"Thiel","suffix":""},{"id":335079948,"identity":"7bf7a99d-da89-4601-850b-7286a1593748","order_by":6,"name":"Christian Möllmann","email":"","orcid":"","institution":"University of Hamburg; Institute of Marine Ecosystem and Fishery Science, Marine ecosystem dynamics","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Möllmann","suffix":""},{"id":335079949,"identity":"330daa5d-8846-48ae-9d53-971b319e8fe1","order_by":7,"name":"Jason Nicholas Woodhouse","email":"","orcid":"","institution":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Microbial and phytoplankton Ecology","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"Nicholas","lastName":"Woodhouse","suffix":""},{"id":335079950,"identity":"2478a1c1-b39d-4366-a07f-2b48f915a47f","order_by":8,"name":"Andrej Fabrizius","email":"","orcid":"","institution":"University of Hamburg; Institute of Cell- and Systems Biology of Animals, Molecular Animal Physiology","correspondingAuthor":false,"prefix":"","firstName":"Andrej","middleName":"","lastName":"Fabrizius","suffix":""}],"badges":[],"createdAt":"2024-08-02 07:16:08","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4846387/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4846387/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61830685,"identity":"e8188912-f2ec-4577-89d4-4a928eaa0c92","added_by":"auto","created_at":"2024-08-06 04:31:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235582,"visible":true,"origin":"","legend":"\u003cp\u003eOverview map of sampling locations along the course of the estuary (A) and abiotic conditions (oxygen levels, temperature and salinity) in the water column in a seasonal and spatial view. Sampling stations are marked by colored dots while seasonal sampling periods are indicated by symbols.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/96a4e099cd91ec1df5cb839c.png"},{"id":61831235,"identity":"08935ac5-5cbd-4a45-b94b-7d5e69dff39e","added_by":"auto","created_at":"2024-08-06 04:39:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":479365,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 1 Overview of spatio-temporal patterns in bacterioplankton and bacterial gill mucus communities. \u003c/strong\u003ePrincipal coordinates analyses (PCoA) of gill bacterial community based on averaged subsampled Bray-Curtis dissimilarity (middle panel). Ellipses display the sample kinds (OE, GC, WF), coloration by season of sampling. Stacked barplots (top \u0026amp; bottom panel) depict relative abundance of the most abundant genera within the three biomes (OE upper panel, GC \u0026amp; WF bottom panels). Samples are ordered by season and faceted sampling station along the course of the estuary (left = most downstream, right = most upstream). Coloration follows a phylum code (top right legend) and individual colors by genus (right legend\u003cem\u003e, \u003c/em\u003egrey resembles unspecified taxa). The 34 overall most abundant genera are shown.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/8768a62796c35326e3f1ad98.png"},{"id":61830684,"identity":"69853cb6-d729-419c-8b50-df90b8e39a19","added_by":"auto","created_at":"2024-08-06 04:31:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":298785,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCore microbiota of Elbe estuarine fish. \u003c/strong\u003eMicrobial taxa present in 90 % of the samples per species respectively and their contribution to the overall microbial community per sample are shown on the stacked barplots (\u003cstrong\u003eB\u003c/strong\u003e \u0026amp; \u003cstrong\u003eD\u003c/strong\u003e). Sample groups are ordered by season and facets within season by sampling location (mesohaline Ekm 713 to freshwater Ekm 633). The right (\u003cstrong\u003eD\u003c/strong\u003e) panel also contains the relative contributions to the bacterioplankton from seasonal and spatial sampling (dark blue). Areas marked by red squares and enlarged depict sampling groups with deviating abundance in bacterial core taxa. The most abundant taxa on lowest determined taxonomic level and their overall relative contribution (\u0026gt; 0.2 % relative abundance) to the respective datasets are shown in list (\u003cstrong\u003eC\u003c/strong\u003e) for both species respectively. The stable isotope signal for δ\u003csup\u003e13\u003c/sup\u003e C from fish muscle tissue (\u003cstrong\u003eA\u003c/strong\u003e \u0026amp; \u003cstrong\u003eE\u003c/strong\u003e) indicates in which region inside and outside the estuary the individual animals fed. The limits of the estuary are -20.2 ± 0.6 (lower reaches) and -25.6 ± 0.9 (upper reaches). Values above this come from the marine, below this from the freshwater reaches upstream.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/97aba226dcd78d4d4d7e4284.png"},{"id":61830689,"identity":"fc941510-a709-4a8f-9e05-14156247e92e","added_by":"auto","created_at":"2024-08-06 04:31:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":217681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnvironmental and physiological drivers in gill mucus bacterial composition. \u003c/strong\u003eAverage Bray-Curtis distance-based redundancy analyses (dbRDA) on gill mucus bacterial community in two host species \u003cem\u003eO. eperlanus\u003c/em\u003e \u003cem\u003eOE\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e) and \u003cem\u003eG. cernua GC\u003c/em\u003e (\u003cstrong\u003eC\u003c/strong\u003e).\u003cstrong\u003e \u003c/strong\u003eDatapoints indicate 16S rRNA gill mucus microbiota samples colored for season of sampling while the shape indicates\u003cstrong\u003e \u003c/strong\u003esampling location along the course of the estuary. Physiological and environmental samples were selected by stepwise model building for constrained ordination (ordistep) and VIF \u0026lt; 10. Abiotic data from water column in spatio-temporal course are depicted (\u003cstrong\u003eB\u003c/strong\u003e). TOC stands for total organic carbon (mg/L) and SPM (suspended particular matter). Oxygen levels were removed from dbRDA by VIF control from both analyses for multicollinearity with temperature and nutrient loads. Biometric data sampled per fish species in spatio-temporal course (\u003cstrong\u003eD \u003c/strong\u003e\u0026amp;\u003cstrong\u003e F\u003c/strong\u003e). GSI (gonado-somatic index), HSI (hepato-somatic index), FCF (Fulton’s condition factor). Global matrix correlation between fish gill microbiota and bacterioplankton to environmental and biometric data as well as between bacterioplankton ASV matrix (WF) and the fish gill microbiota matrices (OE, GC) tested by Mantel test (\u003cstrong\u003eE\u003c/strong\u003e). Significance levels are depicted as \u0026lt; 0.05 (*), \u0026lt; 0.01 (**) and \u0026lt; 0.001 (***).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/2f49be728ac5788bc60cf178.png"},{"id":61830683,"identity":"a10313a4-8749-40b1-b714-0268943f94ae","added_by":"auto","created_at":"2024-08-06 04:31:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":306359,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between bacterial co-occurrence networks, biometric measurements and abiotic conditions for Elbe estuarine smelt OE\u003c/strong\u003e.\u0026nbsp; Integrated heatmap of gill bacterial mucus network analysis showing correlation between ASV module eigengenes (\u003cstrong\u003eB\u003c/strong\u003e: OE-1-13), physiological traits (\u003cstrong\u003eA\u003c/strong\u003e: HSI, SSI, GSI, FCF, fill level, age, length) and relevant abiotic factors (\u003cstrong\u003eA\u003c/strong\u003e: NH\u003csub\u003e4\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e3\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e, PO\u003csub\u003e4\u003c/sub\u003e, TOC, temperature, SPM, salinity) as well as bacterioplankton module eigenASVs (\u003cstrong\u003eC \u003c/strong\u003eWF1-23). \u003cstrong\u003eB\u003c/strong\u003e shows Z-score of eigengene values per module as rows for individual fish (n = 129) ordered by season and faceted within season by sampling location in upstream direction as columns. Overall relative abundance per module is shown in the barplot on the left side. \u0026nbsp;\u003cstrong\u003eA \u003c/strong\u003eand \u003cstrong\u003eC\u003c/strong\u003e show FDR corrected Pearson correlation strength between eigenASVs and host/external traits, the right panel shows FDR corrected Pearson correlation to WF-SSU eigenASVs (brown representing positive, zero white and green negative correlation) and statistical significance indicated by stars: * P \u0026lt; 0.05, ** P \u0026lt;= 0.01, *** P \u0026lt;= 0.001. Only correlations r \u0026gt; 0.3 are shown. Taxa with highest intra-modular abundance (%) are shown in barplots (\u003cstrong\u003eD\u003c/strong\u003e) colored by phylum identity. For network analysis on ruffe GC see Fig. A. 5.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/ba82918cbbeb04778b0568fb.png"},{"id":61830691,"identity":"f7f62967-829a-44d1-820d-19846849408d","added_by":"auto","created_at":"2024-08-06 04:31:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":365680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOpportunistic pathogens thrive under prolonged hypoxia in the Hamburg Port area (Ekm 651 – 633)\u003c/strong\u003e. Dominant bacterial species in smelt OE and ruffe GC network modules with highest negative correlation to oxygen values are shown as network modules OE7 and GC2. Stacked barplots depict relative abundance of these taxa in spatio-temporal course. Dotplots indicate individual correlation of ASVs to DO levels and intra-module correlation.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/d278de1d9eb43127c3c9031b.png"},{"id":61832114,"identity":"46cea904-9aea-4b33-9a3f-0b11840bfc6f","added_by":"auto","created_at":"2024-08-06 04:47:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2846675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/4e8de49c-9658-4462-a29e-a6b942e37260.pdf"},{"id":61830688,"identity":"c333fdb5-f6a3-4165-808b-cacac896c6b2","added_by":"auto","created_at":"2024-08-06 04:31:17","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":595117,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/5073bd1dcb315d391a60c38e.png"},{"id":61830687,"identity":"edfbc25f-3062-4361-b726-8a326aeb2c4e","added_by":"auto","created_at":"2024-08-06 04:31:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6503626,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.Supplementarydata25.07.24.docx","url":"https://assets-eu.researchsquare.com/files/rs-4846387/v1/7bcf7ba2e5275c05e095bff3.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSpatio-temporal fish gill microbiota analysis as indicators in estuarine fish health monitoring\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCommunities of microbes colonize the mucosal surfaces of fish from hatching on, influencing the health of their host\u0026nbsp;(Legrand et al., 2020)\u0026nbsp;and responding as holobiont to seasonal variation in nutrients, salinity and temperature. Physiological stress however, impacting the cross talk between host and microbiome, might create opportunists or pathogens from otherwise commensal species\u0026nbsp;(Kelly \u0026amp; Salinas, 2017). Changes in salinity\u0026nbsp;(Fran\u0026ccedil;ois-\u0026Eacute;tienne et al., 2023),\u0026nbsp;temperature\u0026nbsp;(Ghosh et al., 2022; Matanza \u0026amp; Osorio, 2018; Morshed \u0026amp; Lee, 2023; S. Yang et al., 2022)\u0026nbsp;and dissolved oxygen (DO)\u0026nbsp;(Fan et al., 2020; Shi et al., 2021; Song et al., 2023; Wang et al., 2021)\u0026nbsp;have been shown to impact, or otherwise be modulated by, both the host physiology and the nature of the microbial biofilm, increasing the likelihood of pathogenic species and reducing fish health and survivability.\u0026nbsp;Of particular concern are recent reports of hypoxia-induced immune suppression, making it difficult to assess the impact on fish populations in the light of climate change (Abdel-Tawwab et al., 2019; Leeuwis et al., 2024). Globally, increased frequency of extreme events and shifts in climate patterns have already intensified pathogen loads and disease outbreaks in aquaculture \u003csup\u003e(Harrison et al., 2022; Samsing \u0026amp; Barnes, 2024; Vezzulli et al., 2013)\u003c/sup\u003e and estuaries (Suzzi, Stat, Gaston, Siboni, et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEstuarine ecosystems constitute important nursery areas and population sources for fish (Pasquaud et al., 2015; Seitz et al., 2014; Tournois et al., 2017), and are characterized by intense fluctuations in these physicochemical conditions \u003csup\u003e(Elliott \u0026amp; Quintino, 2007)\u003c/sup\u003e. Climate change is expected to increase frequency and intensity of heatwaves, oxygen minimum zones (OMZ) and change salinity regimes affecting inhabiting fish fauna \u003csup\u003e(Breitburg et al., 2018; Cottingham et al., 2018; Diaz \u0026amp; Rosenberg, 2008; Lauchlan \u0026amp; Nagelkerken, 2020; Little et al., 2017; Sampaio et al., 2021)\u003c/sup\u003e.\u0026nbsp;The Elbe estuary in Germany, is one of the largest in Europe and has long been exposed to endogenous pressures. For example, over the last decades the Elbe has experienced increasing turbidity due to dredging (Heininger et al., 2015; Reese et al., 2019; van Maren et al., 2015),\u0026nbsp;expansion of oxygen minimum zones (OMZ) (Colombano et al., 2021), intensified\u0026nbsp;stratification of the well-mixed water column and eutrophication (Pein et al., 2021). The combined effect of these various stress factors is associated with a drastic decline in fish biomass in the system of more than 90 % over the last decade \u003csup\u003e(Koll et al., 2024; Scholle \u0026amp; Schuchardt, 2020; Theilen et al., 2024)\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKnowledge on fish associated microbiota is largely biased towards farmed fish and gastrointestinal communities (Egerton et al., 2018). Only a few studies, all but one dealing with internal microbiota, have focused on the highly productive intersection between land and marine ecosystems in estuaries \u003csup\u003e(de Macedo et al., 2024; Koll et al., 2024; Suzzi et al., 2022; Suzzi, Stat, Gaston, \u0026amp; Huggett, 2023; Suzzi, Stat, Gaston, Siboni, et al., 2023; Wei et al., 2018)\u003c/sup\u003e. Although the external microbial composition, with its immediate exchange with the environment, has a huge potential for monitoring, studies including external microbiomes of wild fish are mostly focused on marine or freshwater habitats (Amill et al., 2024; Itay et al., 2022; Minich et al., 2020, 2022; Pratte et al., 2018; F. \u0026Eacute;. Sylvain et al., 2020; Varela et al., 2024). The fish gill, with critical roles in respiration, osmoregulation and waste exchange, forms not only a unique habitat for the microbes but further constitutes a major route for pathogen invasion\u0026nbsp;(Pratte et al., 2018; Salinas, 2015). A gill associated lymphoid tissue (GIALT) (Salinas, 2015)\u0026nbsp;composed of adaptive and innate immune cell populations enables cross-talk between microbes and host (Gomez et al., 2013; Kaetzel, 2014)\u0026nbsp;and discrimination between beneficial and pathogenic bacteria keeping the microbiome in homeostasis (Nakanishi et al., 2015; Yu et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed now to determine spatio-temporal variation in composition in a seasonal time-series of estuarine fish gill microbiota. Here we focus on two fish species with highly different life-styles and habitat usage to generate a larger picture on underlying dynamics in the estuarine system. Anadromous smelt (\u003cem\u003eOsmerus eperlanus\u0026nbsp;\u003c/em\u003eL.) is a key species in food webs of a number of European riverine ecosystems (Illing et al., 2024) and constitute up to 96 % of the fish abundance in the Elbe estuary \u003csup\u003e(Eick \u0026amp; Thiel, 2014; Theilen et al., 2024)\u003c/sup\u003e. They begin annual spawning migrations to estuarine freshwater sites at the end of their second year of life \u003csup\u003e(Thiel \u0026amp; Thiel, 2015)\u003c/sup\u003e gathering in autumn at the mouth of the estuary and migrating upstream as temperatures drop to 3 - 6\u0026deg;C \u003csup\u003e(Freyhof \u0026amp; Kottelat, 2007)\u003c/sup\u003e. Within a month they reach spawning grounds upstream Hamburg and spawn until march \u003csup\u003e(Borchardt, 1998; Eick, 2015; Thiel \u0026amp; Thiel, 2015)\u003c/sup\u003e. Hatched larvae drift to shallow habitats downstream of the port, where oxygen deficiency can increase mortality \u003csup\u003e(Diercking \u0026amp; Wehrmann, 1991; Thiel et al., 1995; Thiel \u0026amp; Thiel, 2015)\u003c/sup\u003e. Adults avoid low-oxygen areas and accumulate where oxygen exceeds 5 mg/l \u003csup\u003e(M\u0026ouml;ller \u0026amp; Scholz, 1991)\u003c/sup\u003e. In spring, all life stages occupy the estuary before the majority of adults migrate to the ocean \u003csup\u003e(Thiel \u0026amp; Thiel, 2015)\u003c/sup\u003e. The ruffe (\u003cem\u003eGymnocephalus cernua\u003c/em\u003e L.) on the other hand, is a rather undemanding bentho-pelagic freshwater species common in oligo- to mesohaline (0 -12 psu) and eutrophic estuaries \u003csup\u003e(Gutsch \u0026amp; Hoffman, 2016)\u003c/sup\u003e. It is the third most common fish in the estuary \u003csup\u003e(Eick \u0026amp; Thiel, 2014)\u003c/sup\u003e and important benthic predator of invertebrates (H\u0026ouml;lker \u0026amp; Thiel, 1998). Ruffe migrate to shallow waters in summer and deeper areas in winter (Gutsch \u0026amp; Hoffman, 2016). They mature at 1 - 3 years and spawn in early and possibly again in late summer in warm waters (\u0026gt;12\u0026deg;C) where embryos require high oxygen levels \u003csup\u003e(Gutsch \u0026amp; Hoffman, 2016; Ogle, 1998)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRecent studies demonstrated the special importance of time-series in contrast to single measurements to gain overview of biotic interaction and drivers that shape it (Minich et al., 2020; Scheifler et al., 2022). We generate here a comprehensive spatio-temporal dataset of the bacterial community via 16S rRNA sequencing linked to environmental conditions and physiological fish data. The objective is to study the responsiveness and plasticity of the microbiota under multiple pressures, especially along a salinity gradient and under deoxygenation and eutrophic conditions. Therefore, we determine core taxa and variable components in the microbial composition and relate them to driving forces as means of determining disruptive situations and biomarkers for life history and health situation of fish stocks in this understudied estuarine habitat.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003e3.1.\u0026nbsp; Sample Collection\u003c/h2\u003e\n\u003cp\u003eFish were caught with a stow-net fishing vessel (opening area of 135 m\u0026sup2;, mesh size of 10 mm at the cod end) at five stations along the Elbe estuary (Fig. 1) in Summer (25-29.08.21), Autumn (17-21.11.21), Winter (08-12.03.22), Spring (31.05-04.06.22). Sampling stations were chosen within different estuarine sections classified by dominating abiotic drivers (Amann et al., 2012): Station ML-Elbe kilometer 663 and TW-Ekm 651 within the OMZ (stream km 620 \u0026ndash; 650, low oxygen, summer \u0026lt; 2 mg/L, salinity \u0026lt; 0.5 psu), stations SS-Ekm 665 and BB-Ekm-692 within the maximum turbidity zone (MTZ) (stream-km 650 \u0026ndash; 705, high loads of suspended matter, salinity \u0026gt; 0.5 \u0026lt; 5 psu) and station MG-Ekm 715 within post-MTZ (stream km 705 \u0026ndash; 730, transition full marine, salinity \u0026gt; 1 \u0026lt; 20 psu).\u0026nbsp; Sampling procedures followed the standards described in the German Animal Welfare Act (\u0026sect;4 TierSchG). Exemptions to the ordinances on nature reserves were obtained (see Permits).\u003c/p\u003e\n\u003cp\u003eSeven individuals per species (when possible) from ebb and flood hauls at each station (OE n = 129, GC n = 90) were processed immediately on board in the following standardized manner: Fish were measured and weighed before recovering gill bacteria with sterile cotton swabs from the middle part of the second and third arch. White muscle tissue from the dorsoventral site of each individual was dissected and rinsed with distilled water for latter stable isotope measurements. Bacterioplankton samples (n = 20) obtained from the water column were vacuum filtered onto 0.2 \u0026micro;m polycarbonate membranes. All samples were kept on dry ice on board until they were moved to -80\u0026deg;C until further processing. Whole fish samples were frozen at -20\u0026deg;C and further analyzed in the lab: body indices (Fulton\u0026rsquo;s body condition (FCF), hepato-somatic - (HSI), spleno-somatic (SSI) and gonado-somatic indices (GSI)) were determined (Petitjean et al., 2020), stomach content was weighed and age determination from otoliths and scales was performed. Abiotic conditions (oxygen, salinity, Secchi depth, temperature, pH) were measured at start and end of each haul using a multi-probe (Hanna HI 9829 and Secchi disc) at the water surface. In addition, abiotic data (PO\u003csub\u003e4\u003c/sub\u003e, NH\u003csub\u003e4\u003c/sub\u003e, NO\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, suspended particular matter (SPM), O\u003csub\u003e2\u003c/sub\u003e, pH) from continuous national measurement programs (\u003ca href=\"http://www.fgg-elbe.de\"\u003ewww.fgg-elbe.de\u003c/a\u003e) were downloaded for appropriate times and stations close by where the fishing took place and included in the analyses.\u003c/p\u003e\n\u003ch2\u003e3.2.\u0026nbsp; DNA extraction \u0026amp; sequencing\u003c/h2\u003e\n\u003cp\u003eDNA extraction, sequencing and bioinformatics were performed as explained elsewhere (Koll et al., 2024). In short CTAB/chlorophorm/phenol extraction was performed followed by amplification of the V3-V4 variable regions of the 16S rRNA gene in a one-step PCR using the primer pair 341F-806R\u0026nbsp;(dual-barcoding approach (Kozich et al., 2013); primer sequences: 5\u0026rsquo;-CCTACGGGAGG-CAGCAG-30 and 5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-30). PCR-products were verified via gel electrophoresis, normalized (Sequal Prep Normalization Plate Kit; Thermo Fisher Scientific, Waltham, USA), equimolar pooled and sequenced on a MiSeq platform (MiSeqFGx; Illumina, San Diego, USA) with v3 chemistry\u0026nbsp;(2x300 bp). The settings for demultiplexing were 0 mismatches in the barcode sequences.\u003c/p\u003e\n\u003ch2\u003e3.3.\u0026nbsp; DNA Bioinformatics\u003c/h2\u003e\n\u003cp\u003eMicrobiota analysis was performed on the University Hamburg Hummel high performance cluster. Read files were quality controlled, adapter, quality and length filtered via TrimGalore (v 0.6.10) (Krueger, 2015). All downstream analyses were performed in R (v.4.3.0) using visualization packages ggplot2 (v.3.4.2) and cowplot (v.1.1.1). For Amplicon sequence variant (ASV) prediction and taxonomic identification DADA2 (v.1.29.0) (Callahan et al., 2016) was used. Quality profiles of paired reads were inspected and truncated at 270 and 190 for forward and reverse reads. ASV inference was performed with pooling method and taxonomy assignment used the SILVA SSU v138 taxonomic database (Quast et al., 2013). To ensure contamination free and accurate analyses, mock community samples (ZYMO research) were added along the whole process from DNA extraction until taxonomic assignment (Fig. A. 7). ASV table and sample data were parsed to phyloseq (v 1.45.0) \u003csup\u003e(Mcmurdie \u0026amp; Holmes, 2012)\u003c/sup\u003e removing ASVs taxonomically assigned to non-bacteria. Low abundance taxa in each of the individual fish and bacterioplankton datasets were filtered at a value reducing the number of zeros in the datasets by ca. 50 % while minimally reducing the number of overall counts (Fig. A. 1) to improve interpretability and minimize the risk of spurious correlations. The filtering value was determined as sum of counts lower than 0.005 % of total sum of all counts. Alpha diversity measures were calculated via vegan package (v. 2.6.4) (Oksanen et al., 2022) and the core microbiota were determined from relative abundance data via microbiome package (v. 1.23.0) \u003csup\u003e(Lahti \u0026amp; Shetty, 2017)\u003c/sup\u003e with filtering detection threshold to 0.0001 % within samples and 90 % prevalence. Centered log-ratio (CLR) transformation to the ASV matrix was applied following best practices for handling of compositional data (Gloor et al., 2017). The transformed data were used for visualization and network analyses.\u003c/p\u003e\n\u003ch2\u003e3.4.\u0026nbsp; Stable isotope analysis\u003c/h2\u003e\n\u003cp\u003eAdditional stable isotope analysis of d\u003csup\u003e13\u003c/sup\u003eC was conducted to determine movement directions of investigated fish. Tissue samples were freeze-dried for 24 hours and grinded to powder using a cell lyser. Samples were weighed (0.8 \u0026ndash; 1.2 mg) and folded into tin capsules (Hekatech). d\u003csup\u003e13\u003c/sup\u003eC ratios in permille (\u0026permil;) were measured by the UC Davis Stable Isotope Facility of the University of California using a continuous flow isotope ratio mass spectrometer (IRMS) PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Ratios are expressed relative to the international standard VPDB (Vienna Pee Dee Belemnite) for carbon using the delta notation (d\u003csup\u003e13\u003c/sup\u003e) (Fry, 1988, 2013). We predicted estuarine d\u003csup\u003e13\u003c/sup\u003eC ranges for resident fish using stable isotope information of prey specimens of mysid shrimp from MG-Ekm 713 and ML-Ekm 633, which inhabit distinct d\u003csup\u003e13\u003c/sup\u003eC ratios that enables spatial determination of food origin (Guelinckx et al., 2006). Stable isotope ratios of mysid shrimp were pooled, and median and standard deviations were calculated. Due to the carnivorous feeding preferences of mysids in estuaries (Mod\u0026eacute;ran et al., 2012), we assume similar trophic levels as in fish, thus trophic enrichment factors on the data could be neglected during our analysis. d\u003csup\u003e13\u003c/sup\u003eC above and below these limits indicated riverine and marine derived sources that was stored in the flesh of the consumer.\u003c/p\u003e\n\u003ch2\u003e3.5.\u0026nbsp; Statistical analyses\u003c/h2\u003e\n\u003cp\u003eFirst, we visualized the gill bacterial community according to the fish species, sampling season and location using stacked barplots (Fig. 1). Then we computed PCoAs based on pairwise averaged subsampled Bray-Curtis dissimilarity between all gill mucus and bacterioplankton samples (N = 245) to study how the sample cluster according to origin and season. The distance matrix was further used to test for significant differences in community structure between the aforementioned factors via permutational analyses of variance (PERMANOVA) with 999 permutations. R vegan package and post hoc pairwise t-test pairwiseAdonis (P. Martinez Arbizu, 2020) were applied.\u003c/p\u003e\n\u003cp\u003eSecondly, we identified bacterial biomarkers on ASV level discriminant for bacterioplankton or gill mucus assemblage, the host species identity, the seasonal influence as well as selected locational effects. We used the \u003cem\u003emultipatt \u003c/em\u003efunction from the \u003cem\u003eIndicSpecies\u003c/em\u003e package (v 1.7.14) \u003csup\u003e(De C\u0026aacute;ceres \u0026amp; Legendre, 2009)\u003c/sup\u003e with indicator value \u0026gt; 0.7 and P-value \u0026lt; 0.05 after 999 permutations based on the read abundance of the ASV table with internal correction for group size inequality. Relative abundance (%) of these biomarkers are shown in the supplementary material Tab. A. 4, relative abundance of overall ASV counts were visualized as heatmap in Fig. A. 2B. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThirdly, we analyzed the response of the fish mucus communities to seasonal and spatial gradients and in relation to physiological measurements first by computing average Bray-Curtis distance-based redundancy analyses (dbRDA) \u003csup\u003e(Legendre \u0026amp; Anderson, 1999)\u003c/sup\u003e via vegan \u003cem\u003ecapscale\u003c/em\u003e function following the workflow described in (F.-\u0026Eacute;. Sylvain et al., 2022). Environmental and physiological variables were selected by stepwise model building for constrained ordination (\u003cem\u003eordistep\u003c/em\u003e) and assessment of multicollinearity between variables by measuring variance inflation factors (VIF) keeping \u003cem\u003eordistep\u003c/em\u003e selected parameters with VIF \u0026lt; 10 as explicative variables. Association strength between explicative variables and bacterial assemblage was then measured by fitting environmental vectors on the reduced model ordination using vegan \u003cem\u003eenvfit\u003c/em\u003e (Tab. A. 3).\u003c/p\u003e\n\u003cp\u003eFinally, Mantel test was used on the distance matrices to gain initial overview of association between fish mucus assemblages, bacterioplankton communities and environmental and physiological measures (Fig. 4E, Fig. A. 4B). We than applied a weighted gene co-expression network analysis (WGCNA) (v.1.77-1) \u003csup\u003e(Langfelder \u0026amp; Horvath, 2008)\u003c/sup\u003e to infer networks of co-abundant bacterial taxa and relate them to physiological and environmental data in an integrated heatmap analysis approach (Koll et al., 2024; Strand et al., 2021) (Fig. 5, Fig A 6 \u0026amp; 7). Parameter list: networkType = \"signed\", TOMType = \"signed\", corType = \"bicor\", minModuleSize = 3, minKMEtoStay = 0.5, deepSplit = 2/3 (for fish mucus / water filter WF respectively), mergeCutHeight = 0.15/0.3 (Fish/WF), maxPOutliers_value = 0.05/0.1 (Fish/WF). Initial module detection by hierarchical clustering is controlled by deepSplit parameter (1 - 4, last being most sensitive). The minModuleSize defines the minimum size for the initial module where ASVs with correlation to the module eigenmode (KME) smaller than minKMEtoStay are removed. Different modules whose eigennodes correlate higher than 1 \u0026ndash; mergeCutHeight are merged. The parameters chosen here seek to detect modules with highly correlated nodes but not losing interesting profiles represented by only a few taxa. The parameters are adjusted to account for the different sampling sizes. Module eigengenes/ASVs where correlated between the different networks and to physiological traits of the fish and external abiotic factors via Pearson correlation and corrected for multiple testing via FDR.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eApproximately 15 million trimmed, filtered and merged reads (mean 58,000 reads) across 139 \u003cem\u003eO. eperlanus \u003c/em\u003e(OE), 89 \u003cem\u003eG. cernua\u003c/em\u003e (GC) gill mucus and 20 bakterioplankon (WF) samples resulted in 42.000 ASVs. The datasets were divided for filtering retaining 1552, 1393, 2364 ASVs for the individual datasets. Species accumulation analysis indicated that by the exclusion of rare taxa the microbiota were sufficiently captured in the datasets (see Fig A.1).\u003c/p\u003e\n\u003ch2\u003e4.1. Bacterial gill communities deviate from bacterioplankton\u003c/h2\u003e\n\u003cp\u003eGill mucus communities were dominated by six phyla (\u0026gt; 1 % overall abundance) including Proteobacteria (54 %), Bacteroidota (27 %), Actinobacteriota (7 %), Firmicutes (5 %), Verrucomicrobiota (4 %), Deinococcota (2 %) at similar proportions between species (Tab. A.1). The bacterioplankton in contrast was composed of additional phyla Desulfobacterota (1.5 %), Acidobacteriota (2,3 %), Cyanobacteria (1,4 %), Nitrospirota (1,9 %), Gemmatimonadota (1,4 %), Chloroflexi (1,2 %) but lacked high abundances of Firmicutes (0.3 %) and Deinococcota (0.1 %).\u003c/p\u003e\n\u003cp\u003eAnalyses of alpha diversity showed significantly higher richness and Shannon indices in free-living bacteria samples compared to the fish mucus communities, but no overall significant difference between the two fish species (Fig A. 3A \u0026amp; B)\u003c/p\u003e\n\u003cp\u003eThe principal coordinates analysis in Fig. 1 showed a strong distinction between the bacterioplankton and the fish gill mucus community along Axis 1 and 2 summarizing 18.3 und 6.8 % variance. PERMANOVA indicated a significant distinction between fish (OE: F\u003csub\u003e1, 143 \u003c/sub\u003e= 28.775, R\u003csup\u003e2\u003c/sup\u003e = 16 %, p = 0.001, GC: F\u003csub\u003e1, 103 \u003c/sub\u003e= 33.134, R\u003csup\u003e2\u003c/sup\u003e = 25 %, p = 0.001) and bacterioplankton samples.\u003c/p\u003e\n\u003cp\u003ePresence-absence analyses indicated that roughly half of the detected ASVs (\u0026gt; 1600) are unique to the bacterioplankton while about 20 % (ca. 500) are shared between both fish and surrounding water (Fig. A 2A). The shared taxa make up about 50 \u0026plusmn; 5 % in relative abundance on all three biomes. Taxa with the highest overlap between the biota (\u0026gt; 0.5 % relative abundance in both) comprise \u003cem\u003eLuteolibacter\u003c/em\u003e, \u003cem\u003ePersicirhabdus\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eTRA3-20\u003c/em\u003e, \u003cem\u003eIlumatobacter \u003c/em\u003eand \u003cem\u003eHalioglobus\u003c/em\u003e, adding \u003cem\u003ePolynucleobacter \u003c/em\u003ebetween GC and bacterioplankton (Tab. A. 2). Between free-living and fish-associated biota 1447 and 385 bacterial biomarkers at ASV level were determined, for the latter however the strains with significant abundances (\u0026gt; 0.2 % relative abundance) constituted all core taxa in the fish mucus (see next chapter).\u003c/p\u003e\n\u003cp\u003eAlthough significantly distinct (F\u003csub\u003e1, 224 \u003c/sub\u003e= 5.8978 R\u003csup\u003e2\u003c/sup\u003e = 2.6 % p = 0.001), the two fish species showed a strong overlap with largest distinction indicated by the seasonal sampling along Axis 1. OE samples collected in autumn formed a prominently distinct cluster. Presence-absence analyses indicated that both species share more then 1000 ASVs while about 474 were unique to OE and 326 were unique to GC. Comparing the seasonal samplings for each species, relatively low amounts of ASVs (\u0026lt; 5 %) were unique to single seasons. Comparing the fish species, no indicator taxa were specific to OE, and only strains of \u003cem\u003eVerticiella\u003c/em\u003e, \u003cem\u003ePolynucleobacter \u003c/em\u003eand \u003cem\u003eCandidatus Megaira\u003c/em\u003e were determined specific for GC at relevant abundance levels (\u0026gt; 0.2 %). In the temporal-spatial comparison within each fish species, few taxa appeared significant, only \u003cem\u003eAcinetobacter \u003c/em\u003estrains were determined indicative for summer conditions in the estuary in both fish associated communities (\u003cstrong\u003esee Tab. A. 4\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e4.2. Stable estuarine core gill microbiota\u003c/h2\u003e\n\u003cp\u003eThe core microbiota (determined by prevalence) over seasonal and spatial samplings comprised 27 ASVs from 13 orders (21 genera) accounting for 30 \u0026plusmn; 12 % in OE and 64 ASVs from 11 orders (30 genera) accounting for 50 \u0026plusmn; 22 % of the overall relative microbiota abundance in GC (Fig. 3). Only a fraction of these taxa was present in the bacterioplankton (33 % OE, 20 % GC) accounting for \u0026lt;\u0026nbsp;1% of the bacterioplankton relative abundance (Fig. 3D). \u003cem\u003eElizabethkingia\u003c/em\u003e, the dominant bacterial taxon in the fish microbiota accounts for only 0.06 % of the relative abundance in the free-living community. The gill mucus core microbiota of both fish species were composed by only four phyla, in GC Proteobacteria (almost entirely composed of Enterobacterales) made up 56 % followed by Bacteroidota (almost entirely represented by Flavobacterales) with 36 % and Actinobacteria 5% and Deinococcota 2 %. In OE Bacteroidota made up 60 % followed by Proteobacteria 27 %, Actinobacteria 9 %, Deinococcota 3 %. OE showed a strong seasonal variation in autumn samples driven by the low abundance of Enterobacterales and \u003cem\u003eElizabethkingia\u003c/em\u003e. Most prominent, both fish species showed a strong decline in the abundance of core taxa in freshwater and Hamburg Port area only in summer (Ekm 651 \u0026ndash; 633) (OE 18 %, GC 22 %) (Fig. 3B \u0026amp; D, highlighted).\u003c/p\u003e\n\u003ch2\u003e4.3. Drivers in gill mucus bacterial composition\u003c/h2\u003e\n\u003cp\u003eRedundancy analysis revealed that bacterial composition in the anadromous species \u003cem\u003eO. eperlanus\u003c/em\u003e was strongly influenced by seasonal effects in environmental and biometric measures while a more pronounced spatial pattern appeared in the stationary species \u003cem\u003eG. cernua\u003c/em\u003e (Fig. 4 A \u0026amp; C). The variances explained by the first two RDA dimensions varied considerably between the two species (OE 32 % and GC 17 %). Fitting environmental variables (envfit results Tab. A. 2) showed significant effects of temperature, salinity, PO\u003csub\u003e4\u003c/sub\u003e, NO\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and TOC on the gill mucus microbial communities in both species. OE samples showed a strong seasonal clustering for autumn along SPM, PO\u003csub\u003e4 \u003c/sub\u003eand GSI values, winter samples aligned along TOC and a combined cluster of spring and summer samples group along temperature where samples from the upper estuary (ML-633, TW-651) deviated with PO\u003csub\u003e4\u003c/sub\u003e. GC samples instead showed a stronger overlap for seasonal effects, again summer samples from the upper estuary (ML-633, TW-651) deviated strongest along with PO\u003csub\u003e4\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and Temperature axes. Interestingly physiological parameters other than age in GC and GSI in OE, were not significantly correlated to the bacterial composition in the parametric analysis. In addition, we evaluated the association between bacterial sample distances and physiological and environmental data as well correlation between bacterioplankton and fish mucus communities via individual non-parametric Mantel tests (Fig. 4 E, all results Tab. A 3). The results showed associations (r = 0.20 \u0026ndash; 0.55) between bacterioplankton and environmental parameters (salinity, temperature, O\u003csub\u003e2\u003c/sub\u003e, SPM, NO\u003csub\u003e3\u003c/sub\u003e, PO\u003csub\u003e4\u003c/sub\u003e), however these were weaker for both fish species. OE was most strongly correlated with GSI (r = 0.67). There was overall overall no significant association between the bacterioplankton community and GC, and a slight overall trend between OE and free-living bacteria. A spatio-temporally resolved analysis for this species showed that only the microbiota of the autumn smelt were significantly associated with bacterioplankton (Fig. A. 4B).\u003c/p\u003e\n\u003ch2\u003e4.4. Movement patterns \u0026amp; microbiota plasticity\u003c/h2\u003e\n\u003cp\u003eThe stable isotope ratios of \u0026delta;\u003csup\u003e13\u003c/sup\u003eC from fish muscle reflects their long-term feeding preferences of several weeks to months depending on metabolic activity of the tissue and growth rates of the fish \u003csup\u003e(Buchheister \u0026amp; Latour, 2010)\u003c/sup\u003e that further enables the reconstruction of migration. These data indicated that OE gathering in the estuary autumn just arrived from the North Sea. As described above, the bacterial core taxa in this group were rare (Fig. 3B), while mantel tests indicated a significant association to the bacterioplankton communities (Fig. A. 4B). Analyzing the bacterial taxa shared between with the estuarine bacterioplankton while excluding the core taxa in the fish mucus community, showed a steady increase in abundance in upstream direction from 15 % in the estuarine mouth to 26 \u0026ndash; 29 % in the middle section until 47 % in the harbor area (Fig. A. 4A). OE caught in winter (3 months after the autumn animals) completed the spawning process (as indicated by low GSI indices) but still showed a strong marine isotope signal. The core microbiota and abundance of shared taxa with bacterioplankton however aligned to summer and spring animals (Fig. 3 A \u0026amp; B). In contrast, GC samples range all year within the signal boundaries of the estuary and show a strong spatial pattern for sampling station along the course of the estuary (Fig. 3 D \u0026amp; E).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e4.5. Bacterial network response\u003c/h2\u003e\n\u003cp\u003eWe performed separate network analyses on the centered-log ratio (CLR) transformed bacterial data from the gill mucus of both fish host species and water samples to explore patterns in interacting taxa and relate sub-networks (modules) to environmental drivers. The role of individual taxa in mediating environmentally driven functions was explored via intra-network connectivity (K\u003csub\u003ein\u003c/sub\u003e) and abundance correlation to prevailing pressures. For the analysis we focus on the main factors determined by RDA in section 4.3. WGCNA clustered the gill mucus taxa into 13 and 16 sub-networks for OE and GC, respectively, and 24 for the bacterioplankton (Fig. 5C, Fig A. 6). Both fish mucus assemblage networks resulted in highly overlapping sub-networks correlated to the dominant environmental drivers.\u003c/p\u003e\n\u003cp\u003eThe prevalence determined core-taxa (section 4.2) were also reflected within networks of both species in the dominant modules OE6 and GC8 overlapping in 70.5 % and 90.1 % in abundance with prevalence determined taxa (Fig. 5 \u0026amp; Fig. A. 5) and by 80 % and 96 % with each other. OE6 summarized 33 \u0026ndash; 37 % in overall abundance in summer, spring and winter but only 14 % in autumn while GC8 summarizes 36 \u0026ndash; 60 % over all seasons. OE6 is dominated by \u003cem\u003eElizabethkingia\u003c/em\u003e (40.9 % within module abundance), Enterobacteriaceae (24.8 %), \u003cem\u003eEnterobacter\u003c/em\u003e (11 %), \u003cem\u003eCitrobacter \u003c/em\u003e(10.8 %), identical to GC8 with \u003cem\u003eElizabethkingia \u003c/em\u003e(37.4 %), Enterobacteriaceae (27.2 %), \u003cem\u003eEnterobacter\u003c/em\u003e (12.3 %),\u003cem\u003e Citrobacter \u003c/em\u003e(12.5 %).\u003c/p\u003e\n\u003cp\u003eUncorrelated taxa (OE0 \u0026amp; GC0) make up 20 % of the overall microbial abundance in both species driven by \u003cem\u003eLuteolibacter\u003c/em\u003e, \u003cem\u003eAsinibacterium\u003c/em\u003e, \u003cem\u003eClostridium sensu stricto 1\u003c/em\u003e strains and \u003cem\u003eRhodobacteraceae \u003c/em\u003ein GC. In migrating autumn smelt the amount of uncorrelated taxa raised to 49 %.\u003c/p\u003e\n\u003ch3\u003e4.5.1. Salinity\u003c/h3\u003e\n\u003cp\u003eFreshwater conditions in the estuary are correlated with ruffe network module GC11 (r \u0026ndash; 0.36 to salinity, P **, 3 \u0026ndash; 20% bacterial abundance in upstream direction) characterized by \u003cem\u003ePolynucleobacter\u003c/em\u003e (54% module abundance proportion),\u003cem\u003e Verticiella \u003c/em\u003e(27 %) and \u003cem\u003eCandidatus Megaira\u003c/em\u003e (10 %). In smelt (OE4, r \u0026ndash; 0.54, P ***, 2.5 \u0026ndash; 17 % in upstream direction) showed uprises in \u003cem\u003eRhizobiales Incertae Sedis\u003c/em\u003e (7.8 %), \u003cem\u003eXanthobacteraceae\u003c/em\u003e (4.5 %) and nitrobacteria \u003cem\u003eEllin6067\u003c/em\u003e (8 %), \u003cem\u003eHyphomicrobium\u003c/em\u003e (7.5 %), \u003cem\u003eGaiella\u003c/em\u003e (5.9 %).\u003c/p\u003e\n\u003cp\u003eThe mesohaline conditions on the contrary are correlated with \u003cem\u003ePersicirhabdus\u003c/em\u003e (33.9 %), \u003cem\u003eIlumatobacter\u003c/em\u003e (10.5 %) and \u003cem\u003eHalioglobus\u003c/em\u003e (10.4 %) in OE3 (r 0.72, P ***, 17 \u0026ndash; 0 % in upstream direction), Less abundant, GC7 (r 0.77, P ***, 3 \u0026ndash; 0.1% in upstream direction) is composed of \u003cem\u003eHalioglobus\u003c/em\u003e (17.6%), \u003cem\u003ePersicirhabdus \u003c/em\u003e(8.1 %), \u003cem\u003eIlumatobacter \u003c/em\u003e(6.7 %), \u003cem\u003eCandidatus Symbiobacter\u003c/em\u003e (4.9 %) and Luteolibacter (4.6\u0026nbsp;%).\u003c/p\u003e\n\u003ch3\u003e4.5.2. Temperature\u003c/h3\u003e\n\u003cp\u003eElevated temperatures were highly correlated with smelt module OE5 (r 0.61, P ***, 6.5 % overall abundance) composed of \u003cem\u003eChryseobacterium\u003c/em\u003e (22.3 % module abundance proportion), \u003cem\u003eAlkanindiges \u003c/em\u003e(9.9\u0026nbsp;%), \u003cem\u003ePsychrobacter \u003c/em\u003e(9.5 %), \u003cem\u003eDeinococcus (\u003c/em\u003e7.8 %) and \u003cem\u003eParacoccus \u003c/em\u003e(6.5 %). Likewise, ruffe GC5 (r 0.4, P ***, 7.1 % overall abundance) consisted of \u003cem\u003eChryseobacterium\u003c/em\u003e (12.8 %), \u003cem\u003eParacoccus\u003c/em\u003e (10.4 %), \u003cem\u003eDeinococcus \u003c/em\u003e(10.4 %), \u003cem\u003eOrnithinicoccus \u003c/em\u003e(6 %). In both fish species the same strains of \u003cem\u003eFlavobacterium \u003c/em\u003e(r 0.7) and \u003cem\u003eOrnithobacterium \u003c/em\u003e(r 0.6) showed the highest correlations to elevated temperature values.\u003c/p\u003e\n\u003cp\u003eOn the contrary, lowered temperatures were highly correlated to OE1 (r -0.77, P ***, 21.2% in winter) dominated by \u003cem\u003eChryseobacterium \u003c/em\u003e(17.1 %), \u003cem\u003eFlavobacterium\u003c/em\u003e (13.6 %), \u003cem\u003eDeinococcus\u003c/em\u003e (9.3 %). GC6 (r -0.63, P ***, 6 % in winter) consists of \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (6.9 %), \u003cem\u003eSphingomonas\u003c/em\u003e (9.1 %), \u003cem\u003eThermomonas\u003c/em\u003e (5.2 %), \u003cem\u003eDeinococcus \u003c/em\u003e(9.3 %), Weeksellaceae (4.8 %), \u003cem\u003eFlavobacterium\u003c/em\u003e (8.9 %).\u003c/p\u003e\n\u003ch3\u003e4.5.3. Desoxygenation\u003c/h3\u003e\n\u003cp\u003eIn both fish species, highly similar modules correlated with deoxygenation and nutrient levels (OE7: DO -0.53***, PO\u003csub\u003e4\u003c/sub\u003e 0.53***, NO\u003csub\u003e2\u003c/sub\u003e 0.34 *** and GC2: DO -0.49***, PO\u003csub\u003e4\u003c/sub\u003e 0.54***, NO2 0.56***) overlapping in 63 % of the ASVs. OE7 was largely dominated by \u003cem\u003eAcinetobacter\u003c/em\u003e (66.4%, \u003cem\u003eA. lwoffii\u003c/em\u003e 31.1\u0026nbsp;%, \u003cem\u003eA. johnsonii\u003c/em\u003e 10.17 %), \u003cem\u003eExiguobacterium \u003c/em\u003e(5.5 %), \u003cem\u003eMacrococcus \u003c/em\u003e(4.6 %) and \u003cem\u003ePseudomonas \u003c/em\u003e(4.4%). Similarly, GC2 was composed by \u003cem\u003eAcinetobacter\u003c/em\u003e (54.2%, \u003cem\u003eA. lwoffii\u003c/em\u003e 20.6\u0026nbsp;%, \u003cem\u003eA. johnsonii\u003c/em\u003e 10.7%), \u003cem\u003eMacrococcus \u003c/em\u003e(7.9 %), \u003cem\u003eShewanella\u003c/em\u003e (5.4 %, \u003cem\u003eS. baltica\u003c/em\u003e 2 %, \u003cem\u003eS. putrefaciens\u003c/em\u003e 0.6 %), \u003cem\u003eChryseobacterium\u003c/em\u003e (4.6 %), \u003cem\u003eAeromonas \u003c/em\u003e(4.5 %) and \u003cem\u003ePseudomonas\u003c/em\u003e (3.8 %). While these taxa account for only 4% in overall microbiota abundance in both species, sampling groups from the upper estuary (Ekm 651 \u0026ndash; 633) in late summer reached 31.7 % and 22.2 % with individual samples peaking above 60% relative abundance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough amplicon sequencing of gill mucus bacteria in two highly different estuarine key fish species, we aimed to gain insights into the plasticity of the host associated microbiota, movement patterns of the fish and developing indicators for stressful conditions in the host. Many studies have dealt with the influence of abiotic stress on the gut and skin microbiota in fish, usually under artificial rearing conditions (Bell et al., 2024). However, despite its huge potential for health monitoring, gill compositions are largely overlooked (Sehnal et al., 2021; Xavier et al., 2024), especially along physicochemical gradients in an estuarine system.\u003c/p\u003e\n\u003ch2\u003e5.1. Life history strategies \u0026amp; microbiota plasticity\u003c/h2\u003e\n\u003cp\u003eAlbeit surrounded by water, a clear distinction existed between bacterioplankton and gill mucus microbiota communities suggesting selective colonization (Koll et al., 2024; Legrand et al., 2018; Minich et al., 2020; Pratte et al., 2018; Rosado et al., 2021). Though many taxa are shared between the biomes, a few core taxa, rare in the water column, dominate the fish gill mucus. The spatial and temporal resolution of the dataset revealed insights into the dynamics of microbiota plasticity. In autumn, anadromous \u003cem\u003eO. eperlanus \u003c/em\u003emigrate to the estuary. Their microbiota composition showed a diminished amount of estuarine core but increased estuarine bacterioplankton taxa, suggesting a two-step microbiome adaptation: an initial increase of bacterioplankton-shared taxa that compete best for niches in the host environment at a given salinity (Schmidt et al., 2015) rising with residence time during the one-month ascent process (Borchardt, 1998). In here, \u003cem\u003eLuteolibacter,\u003c/em\u003e a marine and freshwater taxon (Ji et al., 2021) associated with bacterial dysbiosis in fish (Kakakhel et al., 2023; Mondal et al., 2022; Sun et al., 2021) became dominant. The taxon was shown to antagonize pathogen growth contributing to microflora resilience by niche colonization and might further be involved in epithelial tissue repair \u003csup\u003e(Nakatani \u0026amp; Hori, 2021)\u003c/sup\u003e which could serve beneficial roles during deep-reorganization in migrating smelt. In the second, slower step, the estuarine core taxa became dominant along the entire salinity gradient. The indicator analysis showed overall no loss or gain of abundant taxa but a shift in abundance ratios as underlying mechanism.\u003c/p\u003e\n\u003cp\u003eIn contrast, \u003cem\u003eG. cernua's\u003c/em\u003e stable isotope data reflect a stationary lifestyle indicating residence for several weeks to month dependent on metabolic activity \u003csup\u003e(Buchheister \u0026amp; Latour, 2010; Gr\u0026oslash;nkj\u0026aelig;r et al., 2013)\u003c/sup\u003e matching with microbiota composition mainly influenced by spatial drivers. In accordance with the stable isotope signal, a few bacterial indicator taxa (\u003cem\u003eVerticiella, Polynucleobacter\u003c/em\u003e and \u003cem\u003eCandidatus Megaira\u003c/em\u003e) are specific for freshwater residency in \u003cem\u003eG. cernua\u003c/em\u003e. In \u003cem\u003eO. eperlanus\u003c/em\u003e decreasing salinity correlated with nitrogen-metabolizing taxa (\u003cem\u003eHyphomicrobium, Gaiella, Ellin6067, TRA3-20, GOUTA6, Rhizobiales, Xanthobacteraceae\u003c/em\u003e) (Chen et al., 2022; Xiao et al., 2022; C. Yang et al., 2023), potentially preventing toxic ammonia buildup at the gills (Legrand et al., 2018; van Kessel et al., 2016) in the anadromous species less adapted to high loads of nitrogen compounds in estuarine freshwater areas. In the bacterioplankton these conditions were associated with an increase in nitrogen metabolizing \u003cem\u003eNitrospira\u003c/em\u003e and \u003cem\u003eTRA3-20\u003c/em\u003e while in ruffe gill mucus all these taxa were omnipresent. Mesohaline indicators (\u003cem\u003ePersicirhabdus, Ilumatobacter, Halioglobus\u003c/em\u003e) co-occur in fish and bacterioplankton and have been found in association with various marine organisms, although their function is poorly understood (Amin et al., 2022; Han et al., 2021; Scheifler et al., 2023).\u003c/p\u003e\n\u003ch2\u003e5.2. Estuarine gill core microbiota\u003c/h2\u003e\n\u003cp\u003eCore taxa are assumed to serve beneficial roles in the host (Sehnal et al., 2021) and the dominant taxa here, \u003cem\u003eElizabethkingia\u003c/em\u003e\u003csup\u003e(Jacobs \u0026amp; Chenia, 2011)\u003c/sup\u003e, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eLelliottia\u003c/em\u003e (Salgueiro et al., 2020), \u003cem\u003eArthrobacter\u003c/em\u003e (Tsoukalas et al., 2023), \u003cem\u003eDeinococcus\u003c/em\u003e (Amill et al., 2024), \u003cem\u003eAsinibacterium\u003c/em\u003e, \u003cem\u003eKnoellia\u003c/em\u003e (Zou et al., 2023) are all well described from external fish microbiota. \u003cem\u003eAsinibacterium \u003c/em\u003eand \u003cem\u003eEnterobacter \u003c/em\u003etaxa are even considered probiotic, inhibiting pathogen growth (Halet et al., 2007; Schubiger et al., 2015). The genus\u003cem\u003e Citrobacter\u003c/em\u003e contains pathogens of marine and freshwater fish (Liu et al., 2024; Sato et al., 1982), but was found here as a constant compartment of the gill microbiota in both fish species.\u003c/p\u003e\n\u003cp\u003eOverall, the core microbiota of the two fish species were almost identical and highly concordant with another top predatory fish species (\u003cem\u003eSander lucioperca\u003c/em\u003e L.) in the estuarine system (Koll et al., 2024). These data indicated that the gill mucus core microbiota of estuarine fish were similarly shaped regardless of trophic level, life history and habitat use, and the estuarine-specific signal was acquired within a time frame of less than three months in migratory fish. This suggests that the strong fluctuations in physiochemical conditions in the estuarine system allowed colonization of only a small number of generalist species or strong host selection for critical ecological functioning (Luan et al., 2023; Roeselers et al., 2011; Sharpton et al., 2021). The size of the core microbiota with ~30 genera appeared to be low in estuarine fish compared to hundreds in other studies (Lorgen-Ritchie et al., 2022; Sharpton et al., 2021), but at 30 \u0026ndash; 50% high in terms of abundance similar to gut microbiota studies (Wilkes Walburn et al., 2019). Determining the average core abundance proportions in this larger dataset enabled the identification of animals with strong deviation. In both species, we found groups of animals with core abundance \u0026lt; 20 %, which could indicate profound reorganization or dysbiosis and possible disease states (Sehnal et al., 2021). In this context, analyzing the prevalence of potentially pathogenic taxa in overlap with signs of dysbiosis of the overall microbiota composition is probably more informative, as potential pathogens may also be part of the core community of apparently healthy individuals (Itay et al., 2022; Yajima et al., 2023).\u003c/p\u003e\n\u003ch2\u003e5.3. Deoxygenation \u0026amp; dysbiosis in estuarine fish\u003c/h2\u003e\n\u003cp\u003eDysbiosis, the disturbance of microbiome homeostasis (abnormal taxonomic structure and metagenomic function) by uprise of opportunistic taxa \u003csup\u003e(Egan \u0026amp; Gardiner, 2016; Levy et al., 2017)\u003c/sup\u003e, affects microbiome functionality and host physiology and is associated with disease states in fish \u003csup\u003e(Legrand et al., 2020; Mougin \u0026amp; Joyce, 2023)\u003c/sup\u003e. Understanding these processes and identifying biomarkers for dysbiotic processes before physical signs of infection are important not only in fish farming (Mougin \u0026amp; Joyce, 2023; Xavier et al., 2024), but also for monitoring the health of wild fish populations and ecosystems.\u003c/p\u003e\n\u003cp\u003eWhile dysbiosis is often defined as a reduction in diversity and microbial richness, measurements of alpha diversity show contradicting results even in controlled conditions in fish with clear disease signs \u003csup\u003e(Karlsen et al., 2017; Liu et al., 2024; Mougin \u0026amp; Joyce, 2023; Vasem\u0026auml;gi et al., 2017; Xavier et al., 2024; X. Zhang et al., 2018)\u003c/sup\u003e. Changes in Proteobacteria to Bacteroidetes (P:B) ratios were also suggestively linked to disease states in different fish microbiomes (Xavier et al., 2024). The P:B ratio in estuarine fish gill mucus was at 3:1 higher than in marine taxa (10:1) (Legrand et al., 2018) and decreased slightly during periods of deep reorganization (autumn smelt 7:1) and prolonged oxygen depletion in late summer (OE 4.6:1, GC 4:1). Overall, however, we did not identify clear patterns in alpha diversity measures associated with environmental factors and in agreement with recent reviews, did not consider them suitable for detecting perturbations in microbial homeostasis (Xavier et al., 2024).\u003c/p\u003e\n\u003cp\u003eWe observed a strong increase in opportunistic \u003cem\u003eAcinetobacter \u003c/em\u003ecoinciding with the decrease in the abundance of core microbiota in both fish species under persistent hypoxia (\u0026lt;\u0026nbsp;5mg/L) and elevated nutrient loads (nitrates, nitrites and phosphates). Particularly noteworthy, this pattern was not seen in spring when oxygen levels just start declining at similar nutrient loads. Increases in abundance of \u003cem\u003eAcinetobacter, \u003c/em\u003ealbeit less prevalent (\u0026lt; 1 %) were observed in the surrounding water column matching described functions in nitrification and nitrogen as well as phosphorous compound assimilation (Carr et al., 2003; Zhong et al., 2023). \u003cem\u003eAcinetobacter\u003c/em\u003e are repeatedly described as part of the core microbiota of marine fish (Lorgen-Ritchie et al., 2022; Varela et al., 2024), although they can occur as pathogens in a number of freshwater fish (Kozińska et al., 2014; Malick et al., 2020; Visca et al., 2011; M. Zhang et al., 2023). Several strains of \u003cem\u003eA. johnsonii\u003c/em\u003e and \u003cem\u003eA. Iwoffii\u003c/em\u003e were shown to cause lesions and hemorrhage especially in gill and liver tissues (Bi et al., 2023; Cao et al., 2018). Notably co-infections of \u003cem\u003eAcinetobacter \u003c/em\u003especies (Malick et al., 2020), similar to \u003cem\u003eShewanella\u003c/em\u003e (Erfanmanesh et al., 2019) and \u003cem\u003eAeromonas\u003c/em\u003e (Chandrarathna et al., 2018; Wise et al., 2024) can significantly influence the severity and course of diseases in fish through synergistic interactions like immunosuppressive effects (Kotob et al., 2016; Okon et al., 2023).\u003c/p\u003e\n\u003cp\u003eWe detected a strong co-occurrence of different opportunistic \u003cem\u003eAcinetobacter\u003c/em\u003e strains (\u003cem\u003eA. iwoffii\u003c/em\u003e, \u003cem\u003eA. \u003c/em\u003e\u003cem\u003ejohnsonii\u003c/em\u003e) with \u003cem\u003eAeromonas \u003c/em\u003eand \u003cem\u003ePseudomonas\u003c/em\u003e and in GC also with \u003cem\u003eShewanella\u003c/em\u003e (\u003cem\u003eS. baltica \u003c/em\u003e\u0026amp;\u003cem\u003e S. putrefaciens\u003c/em\u003e) in both fish species independently becoming a significant component of the microbiota. No physiological measurements (i.e. lesions) of gills or other tissues were performed in this study and no correlations with biometric markers (FCF, HSI, SSI) were detected. Thus, no direct consequences for fish health can be determined in the present data set. Against the background of wild sampling, however, these physiological endpoint makers are highly variable and do not allow conclusions to be drawn about the influence on health with the sample sizes available here. In a previous study we identified strong correlations of the immune response and cellular stress response in the gill and liver of another estuarine species in response to an increase in \u003cem\u003eShewanella\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e (Koll et al., 2024).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur data showed the potential of non-invasive gill microbiomes for long-term monitoring the health of estuarine fish in the context of changes occurring in estuaries due to multiple anthropogenic stressors. Combined with a previous study, we have been able to show, for three species of fish in the Elbe Estuary, that an easily identifiable core microbiome associated with healthy fish. Further, for each species, we show that changes in these core microbiota, either as a consequence of migration, seasonality or stress can increase the likelihood of opportunistic and pathogenic species which may impact fish survivability. Whilst, additional targeted studies, would be necessary to clarify the pathogenic nature of these strains, and the capacity of the gill microbiome to recover after alleviation of stress inducing conditions, these results are of global significance and concern. Estuaries should be considered protected spawning and nursery areas, ensuring marine fish stocks are well maintained, though our data and others suggest this is not the case. However, increased monitoring of estuarine fish and the water column, might provide a solution by identifying areas of reduced impact that can be restored or conserved, ensuring that estuaries continue to provide this much needed ecosystem function.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e7. Data availability\u003c/h2\u003e\n\u003cp\u003eThe sequence data are deposited in the ENA Sequence Read Archive under the study PRJEB77621. The complete analysis is available in stepwise R markdown files, including metadata and supplementary lists as well as visualizations as HTMLs at DOI: 10.5281/zenodo.12819246\u003c/p\u003e\n\u003ch2\u003e8. Funding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Research Training Group 2530: \u0026ldquo;Biota-mediated effects on Carbon cycling in Estuaries\u0026rdquo; (project number 407270017; contribution to Universit\u0026auml;t Hamburg and Leibniz-Institut f\u0026uuml;r Gew\u0026auml;sser\u0026ouml;kologie und Binnenfischerei im Forschungsverbund Berlin e.V.). This work was also supported by the DFG research grand \u0026ldquo;Large Scale Sequencing to Unravel Carbon Cycling in the Elbe estuary (Micro)biota\u0026rdquo; Project number: 496691966 / FA 1568. and by the project \u0026ldquo;Blue Estuaries\u0026rdquo; funded by the Federal Ministry for Education and Research under funding code 03F0864F. Microbiota sequencing received infrastructure support from the DFG Excellence Cluster 2167 \"Precision Medicine in Chronic Inflammation\" (PMI) and the DFG Research Unit 5042 \u0026bdquo;miTarget\".\u003c/p\u003e\n\u003ch2\u003e9. Permits\u003c/h2\u003e\n\u003cp\u003eSampling procedures were according to the standards described in the German Animal Welfare Act (\u0026sect;4 TierSchG). After being brought on board, the fish were stunned with a blow to the head, before being killed with a heart stab. The implementation of the stow-net fishing for scientific purposes is approved by the Authority for the Environment Climate, Energy and Agarwirtschaft, by the State Fisheries Office Bremerhaven and by the State Office for Agriculture, Environment and Rural Areas of Schleswig-Holstein. Exemptions to the ordinances on nature reserves M\u0026uuml;hlenberger Loch/Ne\u0026szlig;sand as well as a nature conservation permit to conduct research fishing in protected areas in the NSG \"Rhinplate und Elbufer s\u0026uuml;dlich Gl\u0026uuml;ckstadt\"/FHH area DE 2393-393 from the Office of Environmental Protection were obtained.\u003c/p\u003e\n\u003ch2\u003e10. Author contributions\u003c/h2\u003e\n\u003cp\u003eRK: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Validation, Writing- original draft, Visualization, Project administration. EH: Investigation, Visualization, Formal analysis, Data curation, Review \u0026amp; Editing. JT: Investigation, Permit acquisition, Review \u0026amp; Editing. CB: Data curation, Resources, Review \u0026amp; Editing. MB: Investigation, Data curation. RT \u0026amp; CM: Resources, Review \u0026amp; Editing. JW: Supervision, Validation, Review \u0026amp; Editing. AF: Funding acquisition, Conceptualization, Supervision, Validation, Review \u0026amp; Editing, Project administration.\u003c/p\u003e\n\u003ch2\u003e11. Acknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to thank Claus \u0026amp; Harald Zeeck and Dirk Stumpe for their efforts in collecting the samples and the hundreds of hours spent together on their fishing vessel. We thank Prof. Dr. Kathrin Dausmann for mentoring the project.\u003c/p\u003e\n\u003ch2\u003e12. Declaration of generative AI and AI-assisted technologies in the writing process\u003c/h2\u003e\n\u003cp\u003eDuring the preparation of this work RK used DeepL/translate/write in order to improve language of the manuscript. After using this tool, RK reviewed and edited the content as needed and takes full responsibility for the content of the publication.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdel-Tawwab, M., Monier, M. N., Hoseinifar, S. H., \u0026amp; Faggio, C. (2019). Fish response to hypoxia stress: growth, physiological, and immunological biomarkers. \u003cem\u003eFish Physiology and Biochemistry\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(3), 997\u0026ndash;1013. https://doi.org/10.1007/S10695-019-00614-9\u003c/li\u003e\n\u003cli\u003eAmann, T., Weiss, A., \u0026amp; Hartmann, J. (2012). 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Mucus-associated microbiotas among different body sites of wild tuna from the South China Sea. \u003cem\u003eFrontiers in Marine Science\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 1073264. https://doi.org/10.3389/FMARS.2022.1073264/BIBTEX\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"daf5f68d-236e-45b8-8f75-72f93b87a013","identifier":"10.13039/501100001659","name":"Deutsche Forschungsgemeinschaft","awardNumber":"407270017","order_by":0},{"identity":"16822846-9169-4784-9f61-a3e8860bdcf0","identifier":"10.13039/501100001659","name":"Deutsche Forschungsgemeinschaft","awardNumber":"496691966 / FA 1568","order_by":1},{"identity":"30fe7697-61cd-43ce-8135-75f75b39ac6b","identifier":"10.13039/501100002347","name":"Bundesministerium für Bildung und Forschung","awardNumber":"03F0864F","order_by":2}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universität Hamburg","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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":"Microbiota, Estuary, Monitoring, Hypoxia, Dysbiosis","lastPublishedDoi":"10.21203/rs.3.rs-4846387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4846387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal marine and estuarine systems are subject to enormous endogenous and exogenous pressures, particularly climate change, while at the same time being highly productive sources and nurseries for fish populations. Interactions between host and microbiome are increasingly recognized for their importance for fish health, with growing evidence indicating that the abundance and virulence of pathogenic bacteria are rapidly increasing. The microbial composition of the gill mucus reflects environmental conditions and represents a major entry route for pathogens into the fish body. High-throughput sequencing of prokaryotic populations from 250 samples of two fish species with highly different habitat preferences, as well as seasonal and spatial distributions in the Elbe estuary system, allowed us to describe the variation of the microbiota along a salinity gradient and under fluctuating environmental conditions. The analysis of estuarine fish core microbiota in relation to variable bacterial components indicated dysbiotic states under sustained hypoxia and high nutrient loads largely driven by the takeover of opportunistic pathogens (\u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e). By correlating bacterial abundances with environmental and physiological parameters in a co-occurrence network approach, we describe plasticity in microbiota composition, identify biomarkers of fish health and reconstruct movement patterns of the fish. Our results will help to shape future non-invasive and cost-effective monitoring programs, and identify factors that might be controlled in the estuary to promote fish and stock health.\u003c/p\u003e","manuscriptTitle":"Spatio-temporal fish gill microbiota analysis as indicators in estuarine fish health monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-06 04:31:12","doi":"10.21203/rs.3.rs-4846387/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"f97a1e44-a7f7-4f6e-a82f-68988caca6bf","owner":[],"postedDate":"August 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35486336,"name":"Wildlife Biology"},{"id":35486337,"name":"General Microbiology"},{"id":35486338,"name":"Marine and Freshwater Ecology"},{"id":35486339,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2024-08-06T04:31:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-06 04:31:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4846387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4846387","identity":"rs-4846387","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}