{"paper_id":"1d2afe2b-6491-41ec-a348-2b7a9457feb3","body_text":"Flatfish intestinal microbiota depend on various host 1 \ntraits, and vary with sediment type and bottom 2 \ntrawling effort  3 \n  4 \nMichelle Gwinner1* (michelle.gwinner@uol.de), Holger Haslob2 5 \n(holger.haslob@thuenen.de), Hermann Neumann2 6 \n(hermann.neumann@thuenen.de), Sahar Khodami3 7 \n(sahar.khodami@senckenberg.de), Peter J. Schupp1,4 (peter.schupp@uol.de), Guido 8 \nBonthond1,2* (guido.bonthond@uol.de) 9 \n  10 \n1  Institute for Chemistry and Biology of the Marine Environment (ICBM), School of 11 \nMathematics and Science, Carl von Ossietzky Universität Oldenburg, 12 \nAmmerländer Heerstraße 114-118, 26129 Oldenburg, Germany  13 \n2  Thünen Institute of Sea Fisheries, Herwigstraße 31, 27572 Bremerhaven, 14 \nGermany  15 \n3  Senckenberg am Meer Wilhelmshaven, German Centre for Marine Biodiversity 16 \nResearch, Südstrand 44, 26382 Wilhelmshaven, Germany  17 \n4 Helmholtz Institute for Functional Marine Biodiversity at the University of 18 \nOldenburg (HIFMB), Ammerländer Heerstraße 231, D-26129 Oldenburg  19 \n  20 \n* Correspondence: michelle.gwinner@uol.de, guido.bonthond@uol.de  21 \n  22 \nRunning title: Intestinal microbiota of flatfish   23 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nAbstract  24 \nThe intestinal microbiota of fishes support digestion, nutrient uptake and play an 25 \nimportant role in the immune system, development and reproduction. Flatfish live in 26 \nclose contact with the seafloor, and are particularly exposed to anthropogenic 27 \ndisturbances such as bottom trawling. Bottom trawling impacts the ecosystem in 28 \nvarious ways and it recent evidence indicates that the microbial composition and 29 \ndiversity in marine sediments varies with fishing intensity. It is presently unknown 30 \nwhether this trawling signal applies to the seafloor alone, or may also extend to 31 \nmicrobiota of marine holobionts inhabiting it, such as flatfishes. Here, three flatfish 32 \nspecies (Buglossidium luteum , Limanda limanda and Pleuronectes platessa ) were 33 \nsampled across the southeastern North Sea. We characterized the intestinal 34 \nmicrobiota using 16S rDNA metabarcoding of 162 individuals, and disentangled how 35 \nintestinal microbial composition and diversity are jointly shaped by various host traits 36 \n(species, sex, age, weight, and condition factor) and environmental factors (sediment 37 \ntype and trawling intensity). Intestinal diversity varied among species and changed 38 \nwith age, weight and sediment type. Community composition was dependent on 39 \nspecies, age, condition factor and sediment type. In addition, we found that trawling 40 \nintensity explained shifts in intestinal microbial community composition, suggesting 41 \nthat the known impacts of bottom trawling on the benthic environment may cascade 42 \nto intestinal microbiota of flatfish. Our findings provide important insight into host-43 \nmicrobiota interactions in marine ecosystems and highlight the interplay between 44 \nhost traits and environment as drivers of intestinal microbial diversity and community 45 \ncomposition in flatfish.  46 \n 47 \nKeywords: bottom trawling, fishing impacts, intestinal microbiota, gut microbiome, 48 \nfish holobiont 49 \n 50 \nHighlights 51 \n• Flatfish microbiota are shaped by host traits and environmental factors 52 \n• Host species and age affect intestinal microbial diversity and composition 53 \n• Intestinal microbiota vary along a bottom trawling intensity gradient 54 \n• Bottom trawling impacts may cascade from sediments to fish intestinal microbiota 55 \n 56 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\n1. Introduction  57 \nComprising a diverse and complex ecosystem of bacteria, archaea, fungi, 58 \nprotozoa, and viruses, the intestinal microbiota play a critical role in animal 59 \nphysiology in general, and in fish particularly (Arun & Midhun 2023; Rombout et al. 60 \n2011). For instance, the intestinal microbiota influence immune functions, 61 \nhomeostasis, food digestion, growth and reproduction (Nayak 2010; Nie et al. 2017; 62 \nRay et al. 2012; Rolig et al.  2017; Tarnecki et al.  2017; Wu et al. 2012). In humans, 63 \nwhere intestinal microbiota have been studied most extensively, healthy intestinal 64 \nmicrobiota have even been shown to enhance brain health and stress response 65 \n(Dinan & Cryan 2016). Due to their shorter generation times and higher genetic 66 \nvariability, microbiota can respond more rapidly to environmental changes, thereby 67 \ninfluencing the adaptive responses of their marine hosts (Leray et al. 2021; Wilkins et 68 \nal. 2019). 69 \nThe factors that shape the composition and diversity of these microbial 70 \ncommunities through space and time are less well understood, but appear to be 71 \nhabitat, species and tissue specific (Ghotbi et al. 2022; Huang et al.  2020; Kanika et 72 \nal. 2025; Li et al.  2017), and correlate with variables such as diet, age, pollutants, 73 \nsalinity or temperature (Adamovsky et al.  2018; Bolnick et al.  2014; Lozupone & 74 \nKnight 2007; Nayak 2010; Ringoe & Birkbeck 1999).   75 \nAs the largest and most diverse group of vertebrates comprising more than 76 \n30,000 species (Fan et al.  2020), fish play an essential role in a variety of aquatic 77 \necosystems. Notably, they contribute to maintaining the biodiversity, stability and 78 \nresilience of those ecosystems. Further, as a fundamental part of aquatic food webs, 79 \nthey ensure the recycling and transport of diverse nutrients, even between aquatic 80 \nand terrestrial ecosystems (Holmlund & Hammer 1999). As consumers in marine 81 \nfood webs, fish contribute to the biological carbon pump by transporting carbon to the 82 \nseafloor, thereby helping to mitigate the impacts of climate change (Saba et al.  83 \n2021). Beyond providing fundamental ecosystem services, fish account for important 84 \neconomic value and contribute to global nutrition (FAO 2024; Viana et al.  2023). 85 \nDespite the high species diversity, only a small fraction is used commercially as food 86 \nfish (FAO 2022). For instance, in the North Sea, home to approximately 197 fish 87 \nspecies, only a small number of species accounts for the majority of total landings 88 \n(Froese & Pauly 2024; ICES 2022). Demersal fish, such as flatfish, account for a 89 \nsignificant proportion of these landings, highlighting their importance in the region's 90 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nfisheries (ICES 2022; Link et al. 2002). With the rapid decline of fish populations and 91 \nthe collapse of entire commercially exploited stocks, such as the North Sea herring 92 \n(Clupea harengus ) in the 1970ies, gaining a deeper understanding of population 93 \ndynamics and fish health is increasingly important (Dickey-Collas et al. 2010; FAO 94 \n2024; Zhu et al. 2021).  95 \nFishing practices are known to impact the marine environment, with bottom 96 \ntrawling being the most pervasive anthropogenic disturbance of seabed habitats 97 \n(ICES 2022; Kaiser et al. 2002). Shallow shelf sea regions are especially subject to 98 \ntrawling, with the central and south-eastern North Sea being among the most heavily 99 \ntrawled areas in the world (Amoroso et al.  2018). Besides the removal of flatfish 100 \ndirectly, trawling may also indirectly impact the remaining flatfish populations, by 101 \naltering the communities of seabed organisms that partially form their diet, or by 102 \nresuspending the upper sediment layers, stirring up fine particulate matter and 103 \ncontaminants (Kaiser et al.  2002). Furthermore, recent evidence suggests that 104 \ntrawling may also alter the microbiota of the sediments (Bruce et al. 2022; Bonthond 105 \net al.  2023). Both diet and sediment serve as crucial microbial sources for the 106 \nmicrobial communities associated with flatfishes and other seafloor-inhabiting 107 \nholobionts, playing a primary role in shaping the composition and diversity of their 108 \nintestinal microbiota. However, whether trawling impacts on flatfish diet and sediment 109 \nmicrobiota may be indirectly affecting the microbiota of seafloor dwelling holobionts, 110 \nincluding flatfishes, remains unknown. 111 \nTo gain better insight into the flatfish intestinal microbiota and improve our 112 \nunderstanding of wild flatfish populations, we analyzed the intestinal microbiota of 113 \nthree flatfish species (Buglossidium luteum, Risso 1810; Limanda limanda, Linnaeus 114 \n1758; and Pleuronectes platessa , Linnaeus 1758) that were collected in the 115 \nsoutheastern North Sea. Using 16S rDNA metabarcoding, we characterized the 116 \nprokaryotic communities of 162 fish. Subsequently, we used uni- and multivariate 117 \ngeneralized linear models to test the predictive potential of different variables on the 118 \nstructure and diversity of the intestinal microbiota, each representing individual 119 \nhypotheses. This included a combination of host variables (i.e., species, sex, 120 \ncondition factor, age and weight), and environmental variables (i.e., sediment type 121 \nand trawling intensity). In addition, we analyzed differential abundances associated 122 \nwith these variables, to identify microbial markers that potentially fulfil important roles 123 \nin the flatfish intestinal microbiota.  124 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\n 125 \n2. Material and Methods   126 \n 127 \n2.1 Sampling   128 \nFlatfishes were sampled during an expedition with the German Research Vessel 129 \nSolea from 07.05.2021 to 18.05.2021 (Cruise number: SOL791). The expedition took 130 \nplace in the Natura 2000 area in the Southeastern North Sea (Figure 1) and was 131 \ncarried out within the frame of a systematic survey on demersal fish and epibenthic 132 \nfauna in the southeastern North Sea. A 2-meter beam trawl with a 20 mm mesh size 133 \nouter net and 4 mm mesh size codend, and a 7-meter beam trawl with a 70 mm 134 \nmesh size forenet and a 20 mm mesh size codend were used.. At each station, the 135 \ntwo-meter beam trawl had a towing time of five minutes on ground, while the seven-136 \nmeter beam trawl remained on the bottom for 15 minutes. Flatfish were caught 137 \nexclusively during the day and for the present study the species 138 \nPleuronectes/i1platessa (European plaice), Limanda /i1limanda (Common dab) and 139 \nBuglossidium luteum (Solenette) were selected from the catch. When possible, one 140 \nmale and one female were randomly selected at each station. The fish were placed in 141 \nplastic bags and frozen at 20/i1 °C on board. After the cruise, the fish were transferred 142 \nto the laboratory in cooling boxes, where they were again stored at 20 /i1 °C. In total 143 \n183 individuals of the species P. /i1platessa, L./i1limanda and B./i1luteum were 144 \nanalyzed. Prior to dissection of fish individuals, the sex of the thawed fish was re-145 \ndetermined. Then, the length of the fish was measured from the head to the end of 146 \nthe tail fin and the weight was recorded. Scissors, forceps, and scalpels were 147 \ncleaned and sterilized with a burner before the dissection of each fish and all 148 \nprocedures were carried out in a sterile hood.   149 \nTo determine the age of the fish, the otoliths were removed, cleaned and stored 150 \nin individually in tubes, and the age was determined at the Thünen Institute of Sea 151 \nFisheries (Bremerhaven, Germany). Subsequently, the stomach, intestines, liver and 152 \nin females also the gonads were removed, and their weight was measured 153 \nindividually. Intestines were then collected in separate tubes and stored at -20/i1 °C for 154 \nDNA extraction.   155 \n 156 \n2.2 DNA extraction and library preparation  157 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nTo allow optimal DNA extraction from intestinal prokaryotes, intestines were cut 158 \ninto small fragments with sterile scissors, resulting in a homogeneous suspension. 159 \nFrom this suspension, subsamples of 100 to 125 mg were used for DNA extraction 160 \nwith the ZYMO Quick-DNA™ Fecal/Soil MicroPrep Kit (D6012; ZYMO Research) 161 \naccording to the manufacturer's protocol. To verify potential contamination during the 162 \nextraction process, DNA was also extracted from several blanks. Amplicon library 163 \npreparation followed the two-step PCR protocol from (Gohl et al. 2016). The 16S-V4 164 \nregion was amplified using the primers 515F (S-*-Univ-0515-a-S-19) and 806R (S-D-165 \nArch-0786-a-A-20, Klindworth et al., 2013), using the Phusion Green Hot Start II 166 \nHigh-Fidelity PCR Master Mix (ThermoFisher Scientific), and 1 /i1 µl of DNA template 167 \nper reaction. PCRs were also conducted on Mock communities (ZYMO-D311) and 168 \nnegative controls. The program of the first PCR included an initial denaturation step 169 \nof 98/i1 °C for 3:00 min, followed by 30 cycles of 98 /i1 °C for 0:30 min, 55 /i1 °C for 0:30 170 \nmin and 72/i1 °C for 0:30 min and a final elongation step of 72 /i1 °C for 5:00 min. PCR 171 \nproducts were run through a 1% agarose gel electrophoresis and samples without a 172 \nvisible product underwent 5 additional PCR cycles. Then, PCR products were diluted 173 \n1:100 and used as templates for the second PCR, which was conducted with the 174 \nsame reagents and the indexing primers from Gohl et al. (2016). This PCR included 175 \n10/i1 cycles but was otherwise identical to the PCR /i1 1. The final amplicons were then 176 \nnormalized, purified and pooled using sequelPrep plates (ThermoFisher Scientific). 177 \nThe sequencing, on the Illumina MiSeq platform with the MiSeq Reagent /i1 Kit/i1 v3 178 \n(600-cycle), was done in the German Centre for Marine Biodiversity Research 179 \n(DZMB; Wilhelmshaven, Germany).   180 \n  181 \n2.3 Data preparation and processing  182 \nThe raw sequencing reads were quality filtered and clustered into OTUs based 183 \non the 97% similarity criterion, using the OptiClust algorithm (Westcott & Schloss 184 \n2017) using the software MOTHUR (v.1.48.0, Schloss et al., 2009) and classified with 185 \nthe SILVA database (v138.1, Quast et al., 2012). Finally, samples were purged of 186 \nreads classified as chloroplasts, mitochondria or eukaryotes, as well as those without 187 \na domain level classification, and excluded from downstream analyses if the 188 \nremaining total read count was below 1000. The final dataset was rarefied by 189 \naveraging 100 replicated count tables that were subsampled to 3500 read counts.  190 \n  191 \n2.4 Statistical analysis  192 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nAll analyses were performed in R (v4.4.2, see for rendered scripts 193 \nhttps://github.com/gbonthond/flatfish_microbiota). We considered a total of eight host 194 \nand environmental variables. The host variables included species, sex, age (in 195 \nyears), length (in cm), total weight (in grams) and Fulton's condition factor (total 196 \nweight/length³, Nash et al., 2006). The weight and age were transformed with a 197 \nnatural log and the median grain size with a log-base 2. The environmental variables 198 \nthat were considered for the analysis were the median grain size (in µm) as a proxy 199 \nfor habitat type and the trawling intensity in swept area ratio (SAR) per year. Median 200 \ngrain size data was obtained from the NOAH Habitat atlas portal (NOAH 2015). As a 201 \nmeasure for the trawling intensity, we used fishing intensity of the subsurface (≥ 2 cm) 202 \npenetrating gears from the OSPAR data & information management system (OSPAR 203 \n2017). Based on spearman correlation coefficients, which revealed a strong 204 \ncorrelation between length and weight (Spearman’s rank coefficient = 0.99, Figure 205 \nS1, and see Figures S2-5 for more details on the host variables), it was decided to 206 \nuse the variable weight as an indicator of size and exclude length from downstream 207 \nanalyses.   208 \nTo evaluate the impact of the remaining predictors on the diversity of the 209 \nintestinal microbial community, we calculated the OTU richness and the effective 210 \nnumber of OTUs (Jost 2006). On both responses, we fitted linear mixed models 211 \n(LMMs) using the R package lme4 (v1.1-35.5, Bates et al., 2015). First a global 212 \nmodel was fitted with the same structure as used for the PERMANOVA (i.e., the main 213 \neffects and all possible interactions with the factor species identity) and with the 214 \nstation identity as a random effect. Model assumptions were assessed using 215 \ndiagnostic plots. Model selection was then performed for both response variables by 216 \ncomparing the global model to all possible simpler models, keeping the random effect 217 \nfixed. The best model was selected based on the corrected Akaike Information 218 \nCriterion (AICc).  219 \nThe community composition of the intestinal microbiota was analyzed with 220 \nPERMANOVA (Anderson 2001) with the adonis2() function in the R package vegan 221 \n(v2.6.8 (Oksanen et al.  2013), based on Bray Curtis distances and 9999 222 \npermutations. To test whether predictors had general effects applicable to all species, 223 \nas well as species-specific effects, all seven predictors were included along with all 224 \npossible second order interactions with the factor species identity. To visualize 225 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\ncommunity similarity patterns, we conducted non-metric multidimensional scaling 226 \n(nMDS) based on Bray Curtis distances, using the R package vegan (v2.6.8).   227 \nSubsequently, a differential abundances analysis was conducted to identify 228 \nmicrobial markers for each of the predictors. First, the OTU dataset was reduced to 229 \nOTUs with at least 1% occurrence and at least 0.1% relative abundance. Second, a 230 \nmultivariate generalized linear model (mGLM) was fitted on the reduced community 231 \nmatrix with the package mvabund (v4.2.1, Wang et al., 2012). This model included all 232 \nseven predictors and assumed a negative binomial distribution. Third, the estimated 233 \nmodel coefficients were used to identify marker OTUs for each predictor, with 234 \ncoefficients considered significant if the respective 95% confidence intervals did not 235 \noverlap with zero. To identify species-specific OTUs, the mGLM was fitted three 236 \ntimes, each time using a different species as the reference level, allowing us to obtain 237 \npairwise estimates of species differences. The two coefficients and standard errors of 238 \nthe differences with both other species were then pooled to obtain estimates and 239 \nconfidence intervals for species-specific OTUs.  240 \n 241 \n 242 \n3. Results  243 \nAfter quality filtering a total of 162 samples remained. These belonged to 65 244 \nindividuals of the species B./i1luteum, 51 of L. /i1limanda, and 46 of P. /i1platessa, and 245 \nincluded 96 females and 66 males. The rarefied community matrix counted 12,676 246 \nOTUs. Pseudomonadota is the most abundant phylum followed by Actinomycetota, 247 \nPlanctomycetota, Bacillota, and Verrucomicrobiota. At the family level, Pirellulaceae, 248 \nIlumatobacteraceae, Paracoccaceae, Hyphomicrobiaceae, and 249 \nActinomarinales_uncultured were the five most abundant groups, accounting for 250 \n36.7% of the total OTU abundance (Figure 2). 251 \nWhile the intestinal microbiota were generally dominated by a combination of 252 \ntaxa from the phyla Pseudomonadota, Planctomycetota and Actinomycetota, some 253 \nsamples deviated from this pattern and only counted few taxa (e.g., 254 \nMycoplasmataceae or Hyphomicrobiaceae), or taxa that were not among the 30 most 255 \nabundant families. 256 \n 257 \n3.1 Diversity  258 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nFrom the comparison of models fitted on the effective number of OTUs (ENO) the 259 \nbest model yielded only trawling intensity as informative predictor (Table S1). 260 \nHowever, the effect of trawling intensity on ENO was not significant ( χ ²1 = 3.06, p = 261 \n0.080, Table S2) and the model explained < 2% of the variation (R 2m = R2c = 0.019). 262 \nIn contrast, for OTU richness, the best model (R2m = 0.151, R2c = 0.225) included the 263 \nvariables species ( χ ²2 = 7.33, p = 0.026), age ( χ ²1 = 5.05, p = 0.025), weight ( χ ²1 = 264 \n3.89, p = 0.049), the median grain size ( χ ²1 = 7.77, p = 0.005) and the interaction 265 \nbetween the median grain size and species ( χ ²2 = 6.74, p = 0.034, (Table S1-S2). 266 \nPost-hoc pairwise comparisons (adjusting p-values to control for familywise error 267 \nrates with the Holm method) among species, revealed significant differences in OTU 268 \nrichness between B./i1luteum and P./i1platessa (t167 = -2.525, p = 0.030) and between 269 \nL./i1limanda and P./i1platessa (t158 = -2.605, p = 0.030), and identified that of species-270 \nspecific changes in OTU richness with the median grain size, only L./i1limanda was 271 \nsignificant (t116.9 = -3.209, p = 0.005, Figure 3, Table S3). 272 \n 273 \n3.2 Community composition  274 \nNMDS (based on Bray-Curtis distances) did not reveal strong clustering patterns, 275 \nbut indicated that community composition varied with age, condition factor, median 276 \ngrain size and trawling intensity (Figure4A), and subtly differed among species 277 \n(Figure 4B).   278 \nThese patterns were confirmed by the PERMANOVA analysis, which could 279 \nexplain only 16.9% of the overall variation in community composition, but resolved 280 \nthe variables species (F2,141 = 1.678, p/i1 </i1 0.001, R2 = 0.020), condition factor (F1,141 281 \n= 1.561, p/i1 =/i1 0.015, R 2 = 0.015), the interaction between condition factor and 282 \nspecies (F2,141 = 1.311, p/i1 =/i1 0.036, R2 = 0.036), age (F2,141 = 1.834, p = 0.003, R2 = 283 \n0.011), median grain size (F 1,141 = 3.621, p /i1 </i1 0.001, R 2 = 0.021) and trawling 284 \nintensity (F 1,141 = 3.488, p /i1 </i1 0.001, R 2 = 0.021) as significant predictors of 285 \ncommunity composition. 286 \n 287 \n3.3 Differential abundances   288 \nThe differential abundance analysis (Figure 5A) identified various OTUs that 289 \nwere negatively or positively associated with trawling intensity (negative: 29, positive: 290 \n24, Figure 5B), and median grain size (negative: 12, positive 35, Figure 5C). The host 291 \nvariable with the highest amount of differentially abundant OTUs was the condition 292 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nfactor (negative: 13, positive: 11, Figure 5D), followed by age (negative: 3, positive: 293 \n10, Figure 5E). For B. luteum (Figure 5F), L. limanda (Figure 5G), and P. platessa 294 \n(Figure 5H), the relative abundance of 6, 10, and 10 OTUs, respectively, increased 295 \nexclusively with each species. Additionally, 16 OTUs showed significantly lower 296 \nrelative abundance in B. luteum, 12 in L. limanda, and 2 in P. platessa. 297 \n 298 \n4. Discussion 299 \nThis study provides new insights into the intestinal microbial community of three 300 \nflatfish species from the Southeastern North Sea, highlighting the influence of 301 \nenvironmental and host predictors on bacterial diversity and community composition. 302 \nOverall, the predictors evaluated in this study were able to account for a portion of 303 \nthe total variation, but our models also indicated substantial variation to remain 304 \nunexplained, indicating that other variables, not examined in the present study, 305 \nimportantly contribute as well to microbial composition and diversity in the fish 306 \nintestines. The significance of sediment properties (i.e., measured by the median 307 \ngrain size) and trawling intensity emphasizes the important role of environmental 308 \nfactors, which is consistent with previous studies (Hovda et al.  2012; Leray et al.  309 \n2021; MacFarlane et al.  1986; Ramírez & Romero 2017; Smith et al.  2015). 310 \nAdditionally, we identified several host variables (species, age, condition factor and 311 \nweight) that contribute to diversity as well as to community composition. However, 312 \nwhile we expected that intestinal microbiota would vary between sexes our study 313 \nfound no significant differences between females and males on either diversity or 314 \ncommunity composition, suggesting that other factors, such as environmental 315 \ninfluences or host genetics, play a more dominant role in shaping the microbial 316 \ncommunity. 317 \nIt is important to emphasize that an interplay of different factors shapes the 318 \nintestinal microbial community (Egerton et al.  2018; Kanika et al.  2025; Xie et al.  319 \n2024). Some environmental variables such as temperature (Givens 2014; Neuman et 320 \nal. 2016), salinity (Dehler et al. 2017; Hieu et al. 2022; Lozupone & Knight 2007), 321 \nwater microbiota (Giatsis et al. 2015; Xiong et al. 2019), pollutants (Adamovsky et al. 322 \n2018; Mulcahy 2002; Spilsbury et al. 2022; Suzzi et al. 2022) or diet (Bolnick et al., 323 \n2014; Li et al., 2017; Liu et al., 2016a; Xia et al., 2014), were not considered in this 324 \nstudy, although they are known to affect the intestinal microbiota of fish. Diet 325 \nespecially, is known to have a primary influence on the diversity and composition of 326 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nintestinal microbiota (Bolnick et al., 2014; Li et al., 2017; Liu et al., 2016a; Xia et al., 327 \n2014). While variation in diet may be partially captured by both host and 328 \nenvironmental variables, we did not have direct information of dietary intake of 329 \nindividual flatfishes, which makes other dietary factors likely candidate sources for 330 \nthe variation that this study could not explain.  331 \nThe presence of several similar bacterial taxa in the intestinal microbiota of one 332 \nor more fish species from different populations suggests that these taxa play an 333 \nimportant role in host intestinal functions (Roeselers et al. 2011). As reviewed in the 334 \nstudy by Rombout et al. (2011), the phylum Pseudomonadota is the most common in 335 \nthe intestinal microbial community in fish, as it is in B. luteum, L. limanda, and 336 \nP. platessa in this study comprising about 29% of all reads. Another review by 337 \nGhanbari et al. (2015) found that the phyla Pseudomonadota, Bacteroidetes, and 338 \nBacillota make up to 90% of the fish intestinal microbiota. For the here studied flatfish 339 \nspecies, these phyla account for only ~40% of all amplicon reads. Instead, the phyla 340 \nActinomycetota and Planctomycetota make up a significantly larger share, with the 341 \nmost abundant three phyla accounting for around 70% of the reads found.  342 \n 343 \n4.1 Intestinal microbiota vary among flatfish species 344 \nIt is well known that the intestinal microbiota are species dependent and often 345 \nvary even among closely related species (Miyake et al. 2015; Navarrete et al. 2012; 346 \nSmith et al. 2015; Xie et al. 2024). For instance, Miyake et al. (2015) showed that 347 \nintestinal microbial community composition differed even among closely related 348 \nspecies within the same genus ( Acanthurus). The three species analyzed in this 349 \nstudy are more distantly related, belonging to the same order (Pleuronectiformes): 350 \nP. platessa and  L. limanda are both members of the family Pleuronectidae, while 351 \nB. luteum belongs to the family Soleidae (Ahyong et al. 2023). Given that 352 \nevolutionary distance is positively correlated with differences in intestinal microbiota, 353 \nit is expected that these species have distinct intestinal microbiota (Li et al., 2017). 354 \nMoreover, Smith et al. (2015) examined the intestinal microbiota of different 355 \npopulations of threespine stickleback ( Gasterosteus aculeatus ) and found that its 356 \ncomposition and diversity varied, with more genetically divergent populations having 357 \nmore distinct intestinal microbiota. Our results are in line with this, showing that both 358 \ncommunity composition and OTU-richness varied among the three flatfish species. In 359 \naddition, we found that changes in composition with the condition factor were 360 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nspecies-specific, and changes in OTU richness with sediment type were different 361 \namong species. 362 \n Our study identified 6 OTUs that were significantly more abundant in B. luteum, 363 \nand 10 OTUs in both P. platessa and L. limanda. Interestingly, one of the L. limanda 364 \nspecific OTUs was classified to the genus Endozoicomonas, which is recognized as 365 \na diverse symbiont, found across a wide range of marine animals, including sponges, 366 \ncorals, mollusks, and fish (Neave et al. 2016). While the flatfish species studied here 367 \nare benthic predators, there are subtle dietary differences. A study by Schückel et al. 368 \n(2012), examined the dietary overlap among four flatfish species, including the 369 \nflatfish studied here, throughout the German Bight and found differences in prey 370 \nselection. This prey resource partitioning among species likely contributes to the 371 \ndifferences in prokaryotic community composition and diversity found here among 372 \nspecies. Beyond species-specific dietary differences, other unique traits may further 373 \ncontribute to the distinct composition of the intestinal microbiota. Even though all 374 \nthree species live in marine demersal habitats, only L. limanda and P. platessa  375 \ntolerate brackish water. Since salinity is an important factor influencing the 376 \ncomposition of the microbiota this may contribute to differences found in intestinal 377 \ncommunity composition within species (Dehler et al. 2017; Hieu et al. 2022; 378 \nLozupone & Knight 2007). Further, there are likely differences in migratory behavior 379 \namong the flatfishes studied here (Rijnsdorp et al. 1992; Marriott et al. 2016), as it 380 \ninfluences both physiological changes and environmental exposure, and may 381 \ntherefore also contribute to differences among species (Llewellyn et al.  2016; 382 \nHamilton et al. 2019; Liu et al. 2021). 383 \n 384 \n4.2 Microbiota change with age in composition and become more diverse 385 \nMicrobial community assembly of the intestine in fish can be divided into 386 \ncolonization and persistence (Smith et al.  2015). The interplay between colonization 387 \nand persistence begins immediately after hatching when microbes start to colonize 388 \nthe intestine (Blanch et al. 1997; Lauzon et al.  2010). In addition to the skin or gills, 389 \nthe intestine acts as an entry point, allowing prokaryotes in the surrounding 390 \nenvironment and the first food to colonize it (Legrand et al.  2020). With each 391 \nsuccessive food or water intake, the intestinal microbiota of the fish becomes more 392 \ndifferent from the environment (Stephens et al. 2016) and diversifies further (Hansen 393 \n& Olafsen 1999). Accordingly, the diversity of intestinal microbiota tends to increase 394 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nwith age (Li et al. 2017; Llewellyn et al. 2016; Zhang et al. 2018). However, Yan et al. 395 \n(2016) found that diversity decreased with age in freshwater fish species. Especially 396 \nin early life phases, environmental factors can have a particularly strong and long-397 \nlasting impact on the composition of the microbiota (Stephens et al.  2016). 398 \nConsidering that fish are exposed to various factors throughout their life that can 399 \ninfluence their bacterial community, it is natural to expect a gradual and consistent 400 \nchange with age. Our results are in line with many other studies, who found that 401 \nmicrobial diversity in the intestines increases with age in fishes (Ringoe & Birkbeck 402 \n1999; Zhang et al. 2018), as well as in humans (Nayak, 2010). In the studied flatfish 403 \nspecies, we found a similar increasing trend in OTU richness (but not on the effective 404 \nspecies number). Furthermore, community composition changed with age, and we 405 \nidentified 6 OTUs that were significantly less abundant while 7 OTUs decreased with 406 \nage.  407 \n 408 \n4.3 Intestinal microbiota do not differ between females and males  409 \nOur data showed no evidence for differences in diversity between sexes. These 410 \nfindings are somewhat contrasting to other studies on fish intestinal microbiota where 411 \nmore pronounced differences have been detected between females and males 412 \n(Chen et al. 2022; Liu et al. 2016a; Piazzon et al. 2019). In a study by Martyniuk et al. 413 \n(2022) a comparison between females and males in zebrafish found also no 414 \ndifferences in diversity between sexes, but several individual taxa were more 415 \ncommon in either males or females. For example, male zebrafish showed higher 416 \nabundance in the families Erythrobacteraceae and Lamiaceae (Martyniuk et al.  417 \n2022). In another study by Li et al. (2016) on largemouth bronze gudgeon (Coreius 418 \nguichenoti), certain taxa were found to differ in abundance between sexes. 419 \nPseudomonadota was the most abundant phylum in males, whereas females 420 \nexhibited dominance of five different phyla. Additionally, Li et al. (2016) found 421 \nsignificant differences in diversity. Such sex-associated differences may be explained 422 \nby dietary variations between females and males (Bolnick et al. 2014), but could also 423 \nbe driven by physiological factors, such as differences in hormone production (Liu et 424 \nal. 2016b; He et al. 2021).  425 \n 426 \n4.4 Richness decreases with size and composition changes 427 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nFew studies have examined the relationship between size and the intestinal 428 \nmicrobiota in fish. Since length and weight were highly correlated, we used weight as 429 \na proxy for size. Size is linked to other physiological variables such as length or age. 430 \nEven though a fish's weight increases with age, especially in early life stages, growth 431 \nrate reduces with age. This is one of the reasons that makes size and age different 432 \nfrom each other and potentially impact the intestinal microbiota differently. Our study 433 \nwas able to disentangle these effects to some extent, as we found that OTU-richness 434 \nincreased with age, whereas a decreasing trend in richness was found for weight, 435 \nafter correcting for age. This result, however, is contrasting with the general 436 \nobservation made across vertebrates (Xie et al. 2024) that larger organisms, which 437 \nconsequently have a longer intestine, exhibiting higher intestinal microbial diversity. 438 \nLikewise, a study by Zhao et al. (2023) on Gymnocypris chilianensis  found that 439 \ndiversity was higher in larger individuals (>300g) than in smaller ones. These results 440 \nalign with the island biogeography theory (MacArthur & Wilson 2001), which has 441 \nbeen linked to diversity of intestinal microbiota, predicting that as an isolated 442 \necosystem, the intestine, can support greater diversity as it increases in size (Ramos 443 \nSarmiento et al.  2024). Considering the different habitats in the intestines that are 444 \nassociated with distinct microbial communities, this correlation gets even more 445 \nevident (McCallum & Tropini 2023). First of all, the intestine is composed of the 446 \nforegut, midgut, and hindgut, each hosting distinct microbial communities (Egerton et 447 \nal. 2018; Hovda et al. 2007; Ktari et al. 2012; Minich et al.  2022; Ringø et al. 2006). 448 \nFurthermore, allochthonous microbiota are transient and associated with digesta, 449 \nwhile autochthonous microbiota colonize the mucosal surface, forming the core 450 \ncommunity Interestingly, our study suggests a size-diversity relationship for flatfish 451 \nintestinal microbiota that is opposite from what island theory predicts. Exceptions, 452 \nhowever, exist as for instance a study on largemouth bronze gudgeon found no effect 453 \nof weight on diversity (Li et al. 2016).  454 \n 455 \n4.5 Fulton’s condition factor is associated with compositional changes 456 \nIn this study, we demonstrated a significant effect of the variable \"condition 457 \nfactor\", as well as an interaction between species and condition factor on community 458 \ncomposition. However, no significant effect was found on diversity. Among all studied 459 \nhost variables, condition factor was found to have most differently abundant OTUs 460 \nconsisting of 13 negatively differently abundant OTUs and 11 positively differently 461 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nabundant OTUs. While weight provides a measure of size, the condition factor, as a 462 \nratio of weight to length, offers a relative measure that reflects a fish's health and 463 \nfood availability (Heino & Kaitala 1999). Also in this case, diet, which is known as an 464 \nimportant factor influencing the composition of the intestinal microbiota across 465 \nvertebrates, and specifically in fish, may be associated with the condition factor. In 466 \nparticular, food availability, or more precisely periods of starvation, are a prime 467 \nexample that affects the condition factor and the intestinal microbiota simultaneously. 468 \nConsequently, Xia et al. (2014) observed shifts in bacterial communities in Asian 469 \nseabass, with a significant enrichment of Bacteroidetes and a significant depletion of 470 \nBetaproteobacteria as a result of starvation. Although comparable studies that 471 \nanalyze the relationship between the condition factor and the intestinal microbiota in 472 \nfish are rare, community composition of the human intestinal microbiota has been 473 \nreported to vary with the body mass index, which can be considered equivalent to 474 \nFulton’s condition factor (Dominianni et al.  2015; Goodrich et al.  2014). The 475 \nrelationship between the intestinal microbiota and the condition factor is reciprocal, 476 \nas some prokaryotes of the intestinal microbiota are able to break down complex 477 \nsugars and provide essential short-chain fatty acids and energy as well as other 478 \nnutrients, directly influencing the nutritional and health condition of the fish (Talwar et 479 \nal. 2018). Due to this fundamental importance of the microbiota for the condition of a 480 \nfish, the intestinal microbial diversity has been used a biomarker for fish health and 481 \nmetabolic capacity (Xiong et al. 2019). 482 \n 483 \n4.6 Microbial composition and diversity vary with sediment type 484 \nWith their bottom-dwelling way of life and their food, which largely consists of 485 \norganisms living in the sediment (Schückel et al. 2012), flatfish are in close contact 486 \nwith both the sediment and its specific microbiota. Sediment properties (i.e., median 487 \ngrain size, mud content and organic matter content) are primary drivers of microbial 488 \ncommunity composition and diversity of the top sediment layer (Bonthond et al.  489 \n2023). Moreover, also the organisms that make up the flatfish diet and pass through 490 \ntheir intestines vary across habitat types and are thus strongly dependent on 491 \nsediment properties (Huys et al.  1992; Neumann et al.  2017; Reiss et al.  2010). 492 \nUsing the median grain size as a simple measure for sediment type (e.g., mud, sand 493 \nor gravel), we found that microbial diversity and composition within the flatfish 494 \nintestine is indeed linked with local sediment properties. 495 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nThat the sediment is an important source of microbiota found in the intestine is 496 \nalso suggested by others. Although grass carp primarily inhabit the mid to upper 497 \nwater column, Wu et al. (2012) found that their intestinal microbiota composition 498 \nmainly originates from the surrounding water and sediment. Species dwelling closer 499 \nto the bottom sediment, such as flatfish, may be even more influenced by it. Besides 500 \nthe highly significant association with OTU-richness and community composition, the 501 \nmedian grain size also yielded a large number of differentially abundant OTUs, with 502 \n47 OTUs decreasing with median grain size, and 35 OTUs increasing.  503 \n 504 \n4.7 Intestinal microbial community composition varies with trawling intensity 505 \nWe found that trawling intensity explained small but significant changes in 506 \nintestinal community composition. In total, we detected 24 OTUs to increase with 507 \ntrawling intensity, while 29 OTUs decreased. While trawling activity does not impact 508 \nthe isolated microbial ecosystems in the intestines of individual flatfishes directly, we 509 \npropose three potential indirect pathways that may explain this observed trend.   510 \nFirst, mobile bottom-contacting fishing gears impact the environment physically. 511 \nBeam trawls penetrate several centimeters deep into the seafloor, resuspending 512 \nlarge amounts of sediment and organic matter, and modifying the seabed 513 \nmorphology (Puig et al.  2012). This affects both the water column and the seafloor, 514 \nwith which flatfish live in close contact.  515 \nSecond, trawling alters benthic faunal communities. In the North Sea, up to 70% 516 \nof benthic invertebrates, including bivalves, polychaetes, echinoderms or ophiuroids, 517 \ndie when they get dragged by a trawl (reviewed in Eigaard et al.  2017, Sciberras et 518 \nal. 2018). Many of these animals naturally influence the remineralization of organic 519 \nmatter and the regeneration of nutrients by microorganisms (Hooper et al.  2005; 520 \nOlsgard et al.  2008), but also make up the flatfish diet, which importantly impacts 521 \nintestinal microbiota composition and diversity (Ringø et al.  2006; Xie et al.  2024). 522 \nLink et al. (2002) analyzed dietary data of flatfish in the Northwest Atlantic over 25 523 \nyears and found that the average weight of stomach contents of flatfish decreased in 524 \nheavily fished areas. This supports that trawling can influence the diet of flatfishes. 525 \nMoreover, besides changes in the identity of organisms in the flatfish diet, periods of 526 \nreduced food availability and starvation are known to alter the intestinal microbial 527 \ncommunity (Xia et al. 2014). Therefore, trawling driven changes in flat fish diet may 528 \nimpact the intestinal microbiota in different ways and present a possible cause of the 529 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nhere observed association between intestinal microbial community composition and 530 \ntrawling intensity.  531 \nThird, the demersal flatfish species studied here live in close contact with the 532 \nsediment, which also acts as a source of microbes that colonize their intestines (Wu 533 \net al. 2012). Sediment microbiota have been shown to vary with bottom trawling effort 534 \nas well, showing a decrease in alpha diversity and change in overall community 535 \ncomposition (Bruce et al.  2022; Bonthond et al.  2023). Therefore, trawling related 536 \nshifts in benthic microbiota may offer another potential pathway through which 537 \ntrawling intensity could indirectly impact the intestinal microbial community of flatfish.  538 \nWhile physical alterations to the seabed morphology, changes in faunal 539 \ncommunities and therefore diet, and shifts in sediment microbiota, present interesting 540 \nhypotheses for how trawling may indirectly affect flat fish intestinal microbiota, they 541 \ncurrently remain speculative. Nonetheless, they currently offer the best explanation 542 \nfor the observed association between bottom trawling intensity and intestinal 543 \nmicrobiota composition. Moreover, they highlight the need for further investigation 544 \nand serve as a hypothetical basis for future research on the effects of environmental 545 \ndisturbance caused by fishing activities on fish health and fish intestinal microbiota in 546 \nparticular.  547 \n 548 \n5 Conclusions 549 \nHere, we disentangled how species identity, age, size, condition factor, and 550 \nenvironmental factors (i.e., sediment type and trawling intensity), contribute to 551 \nshaping the intestinal microbiota of the demersal flatfish species B. luteum, 552 \nL. limanda and P. platessa  in the southeastern North Sea. The strong effects of 553 \nsediment type and trawling intensity indicate that the environment plays a 554 \ndeterminant role in shaping the intestinal microbiota of these flatfishes. To the best of 555 \nour knowledge, this study is the first to detect an association with bottom trawling 556 \nintensity. This adds on previous work, that found benthic microbiota to vary along a 557 \ntrawling gradient (Bonthond et al. 2023), and may hint that such effects could extent 558 \nto higher trophic levels. However, we note that substantial variation could not be 559 \nexplained by our models, indicating that other processes, not captured by the 560 \nvariables examined here, importantly influence fish intestinal microbiota as well. 561 \nDietary factors likely account for a substantial portion of the unexplained variation in 562 \nboth composition and diversity of the flatfish intestinal microbiota. Another important 563 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nfactor that we were not able to account for is the migratory history of the flatfish 564 \nstudied (Marriott et al. 2016; Liu et al. 2021).  565 \nWhile many studies have focused on captive fish due to their economic 566 \nimportance in aquaculture, wild populations remain understudied (Kanika et al.  567 \n2025). Given that Ramírez & Romero (2017) found differences in intestinal microbial 568 \ncommunities between wild and captive Fine Flounder (Paralichthys adspersus ), and 569 \nXie et al. (2024) documented such differences across vertebrates in a meta-analysis, 570 \nthe focus of this study on wild populations contributes to narrowing this knowledge 571 \ngap. Furthermore, this study helps to compensate for a geographic bias, as most 572 \nstudies on this topic to date have come from North America or East Asia, while 573 \nCentral Europe and Africa tend to be underrepresented (Kanika et al. 2025). 574 \nThese findings contribute to our understanding of how host variables as well as 575 \nenvironmental and anthropogenic processes may directly or indirectly affect host-576 \nassociated microbial communities, with potential ecological and evolutionary 577 \nimplications. As this study is the first to observe an association of trawling effort and 578 \nmicrobial community composition of flatfish intestines, it merits for more research to 579 \nidentify the mechanisms that underly this trend, gain insight into the long-term 580 \nimpacts, and potential consequences for fish health and population dynamics. 581 \n 582 \nAcknowledgements 583 \nWe thank the crew of the RV Solea for all their support during the fieldwork. 584 \nBirgit Brinkmann and Petra Schwarz are thanked for their help at the ICBM lab. We 585 \nare particular grateful to Valeria Adrian-Schütte and Jana Bäger (Thünen Institute of 586 \nSea Fisheries) for determining the age of individual fishes through otolith analysis.  587 \nFurther, we express our gratitude to Jessica Van der Maesen for illustrating the 588 \nflatfish species depicted in Figure 1. This  is pu blicat ion num ber 106 th at us es dat a from 589 \nthe  Sencken berg am M ee r M e tabarcod ing an d SNG labor at or y .  590 \nThis study was funded by the German Federal Ministry of Education and 591 \nresearch (BMBF), through DAM: MGF North Sea I and II (Grant numbers 03F0847B 592 \nand 03F0936C).  593 \n 594 \n 595 \nData availability 596 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nThe de-multiplexed V4-16S gene amplicon reads and associated metadata are 597 \navailable from the European Nucleotide Archive under the Bioproject accession 598 \nnumber PRJEB88596. Data and R-scripts used for the analyses are available on 599 \nGitHub at https://github.com/gbonthond/flatfish_microbiota. 600 \n 601 \nAuthor contributions 602 \nHH, HN, PJS and GB conceptualized the study. Field collections were 603 \nconducted by HH, HN and GB. MG, SK and GB conducted laboratory work. MG and 604 \nGB processed data and carried out the formal analysis. MG and GB drafted the 605 \nmanuscript. All authors contributed to revising the manuscript. 606 \n 607 \nCompeting interests 608 \nThe authors declare that no competing interests exist 609 \n 610 \nEthics statement 611 \nAnimals sampled for this study were obtained from trawling catches collected 612 \nduring regular monitoring by the Thünen Institute of Sea Fisheries aboard the 613 \nGerman Research Vessel Solea, which is operated by the Federal Office for 614 \nAgriculture and Food (BLE), the authority responsible for regulating fishing activities 615 \nin German waters. Thereby, fishing was conducted with permission from German 616 \nauthorities. Since the animals experienced no additional stress beyond standard 617 \ncommercial fishing practices, no further authorization or ethics approval was 618 \nrequired. The species studied are neither protected by legislation nor classified as 619 \nthreatened or endangered. All research complied with the European directive 620 \n2010/63/EU on the protection of animals used for scientific purposes. 621 \n  622 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nReferences 623 \nAdamovsky, O., Buerger, A.N., Wormington, A.M., Ector, N., Griffitt, R.J., Bisesi, J.H., 624 \net al. (2018). The gut microbiome and aquatic toxicology: An emerging 625 \nconcept for environmental health: The microbiome and aquatic toxicology. 626 \nEnviron. Toxicol. Chem., 37, 2758–2775. 627 \nAhyong, S., Boyko, C.B., Bailly, N., Bernot, J., Bieler, R., Brandão, S.N., et al. (2023). 628 \nWorld Register of Marine Species (WoRMS). 629 \nAmoroso, R.O., Pitcher, C.R., Rijnsdorp, A.D., McConnaughey, R.A., Parma, A.M., 630 \nSuuronen, P., et al. (2018). Bottom trawl fishing footprints on the world’s 631 \ncontinental shelves. Proc. Natl. Acad. Sci., 115. 632 \nAnderson, M.J. (2001). A new method for non-parametric multivariate analysis of 633 \nvariance: NON-PARAMETRIC MANOVA FOR ECOLOGY. Austral Ecol., 26, 634 \n32–46. 635 \nArun, D. & Midhun, S.J. (2023). Microbiome of fish. In: Recent Advances in 636 \nAquaculture Microbial Technology. Elsevier, pp. 15–33. 637 \nBates, D., Mächler, M., Bolker, B. & Walker, S. (2015). Fitting Linear Mixed-Effects 638 \nModels Using lme4. J. Stat. Softw., 67. 639 \nBlanch, A.R., Alsina, M., Simón, M. & Jofre, J. (1997). Determination of bacteria 640 \nassociated with reared turbot (  Scophthalmus maximus  ) larvae. J. Appl. 641 \nMicrobiol., 82, 729–734. 642 \nBolnick, D.I., Snowberg, L.K., Hirsch, P.E., Lauber, C.L., Org, E., Parks, B., et al. 643 \n(2014). Individual diet has sex-dependent effects on vertebrate gut microbiota. 644 \nNat. Commun., 5, 4500. 645 \nBonthond, G., Beermann, J., Gutow, L., Neumann, A., Rafael Barboza, F., 646 \nDesiderato, A., et al. (2023). Benthic microbial biogeographic trends in the 647 \nNorth Sea are shaped by an interplay of environmental drivers and bottom 648 \ntrawling effort. ISME Commun. 649 \nBruce, S.A., Aytur, S.A., Andam, C.P. & Bucci, J.P. (2022). Metagenomics to 650 \ncharacterize sediment microbial biodiversity associated with fishing exposure 651 \nwithin the Stellwagen Bank National Marine Sanctuary. Sci. Rep., 12, 9499. 652 \nChen, Z.-W., Jin, X.-K., Gao, F.-X., Gui, J.-F., Zhao, Z. & Shi, Y . (2022). Comparative 653 \nanalyses reveal sex-biased gut microbiota in cultured subadult pufferfish 654 \nTakifugu obscurus. Aquaculture, 558, 738366. 655 \nDe La Cuesta-Zuluaga, J., Kelley, S.T., Chen, Y ., Escobar, J.S., Mueller, N.T., Ley, 656 \nR.E., et  al.  (2019). Age- and Sex-Dependent Patterns of Gut Microbial 657 \nDiversity in Human Adults. mSystems, 4, e00261-19. 658 \nDehler, C.E., Secombes, C.J. & Martin, S.A.M. (2017). Seawater transfer alters the 659 \nintestinal microbiota profiles of Atlantic salmon (Salmo salar L.). Sci. Rep., 7, 660 \n13877. 661 \nDickey-Collas, M., Nash, R.D.M., Brunel, T., Payne, M.R., Corten, A., Geffen, A.J., et 662 \nal. (2010). Lessons learned from stock collapse and recovery of North Sea 663 \nherring: a review. Int. Counc. Explor. Sea. 664 \nDinan, T.G. & Cryan, J.F. (2016). Mood by microbe: towards clinical translation. 665 \nDominianni, C., Sinha, R., Goedert, J.J., Pei, Z., Yang, L., Hayes, R.B., et al. (2015). 666 \nSex, Body Mass Index, and Dietary Fiber Intake Influence the Human Gut 667 \nMicrobiome. PLOS ONE, 10, e0124599. 668 \nEgerton, S., Culloty, S., Whooley, J., Stanton, C. & Ross, R.P. (2018). The Gut 669 \nMicrobiota of Marine Fish. Front. Microbiol., 9, 873. 670 \nEigaard, O.R., Bastardie, F., Hintzen, N.T., Buhl-Mortensen, L., Buhl-Mortensen, P ., 671 \nCatarino, R., et al. (2017). The footprint of bottom trawling in European waters: 672 \ndistribution, intensity, and seabed integrity. ICES J. Mar. Sci., 74, 847–865. 673 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nFan, G., Song, Y ., Yang, L., Huang, X., Zhang, S., Zhang, M., et al. (2020). Initial data 674 \nrelease and announcement of the 10,000 Fish Genomes Project (Fish10K). 675 \nGigaScience, 9. 676 \nFAO. (2022). The State of World Fisheries and Aquaculture 2022 - Towards Blue 677 \nTransformation. FAO - Food and Agriculture Organisation of the United 678 \nNations, Rome. 679 \nFAO. (2024). The State of World Fisheries and Aquaculture 2024 – Blue 680 \nTransformation in action. Rome. 681 \nFroese, R. & Pauly, Y. (2024). Species in the North Sea. 682 \nGhanbari, M., Kneifel, W. & Domig, K.J. (2015). A new view of the fish gut 683 \nmicrobiome: Advances from next-generation sequencing. Aquaculture, 448, 684 \n464–475. 685 \nGhotbi, M., Kelting, O., Blümel, M. & Tasdemir, D. (2022). Gut and Gill-Associated 686 \nMicrobiota of the Flatfish European Plaice (Pleuronectes platessa): Diversity, 687 \nMetabolome and Bioactivity against Human and Aquaculture Pathogens. Mar. 688 \nDrugs, 20, 573. 689 \nGiatsis, C., Sipkema, D., Smidt, H., Heilig, H., Benvenuti, G., Verreth, J., et al. (2015). 690 \nThe impact of rearing environment on the development of gut microbiota in 691 \ntilapia larvae. Sci. Rep., 5, 18206. 692 \nGivens, C.E. (2014). A Fish Tale:  Comparison of the Gut Microbiome of 15 Fish 693 \nSpecies and the Influence of Diet and Temperature on its Composition. Dep. 694 \nMar. Sci. Univ. Ga. USA, 232. 695 \nGohl, D.M., Vangay, P ., Garbe, J., MacLean, A., Hauge, A., Becker, A., et al. (2016). 696 \nSystematic improvement of amplicon marker gene methods for increased 697 \naccuracy in microbiome studies. Nat. Biotechnol., 34, 942–949. 698 \nGoodrich, J.K., Waters, J.L., Poole, A.C., Sutter, J.L., Koren, O., Blekhman, R., et al. 699 \n(2014). Human Genetics Shape the Gut Microbiome. Cell, 159, 789–799. 700 \nHamilton, E.F., Element, G., Van Coeverden De Groot, P ., Engel, K., Neufeld, J.D., 701 \nShah, V., et al. (2019). Anadromous Arctic Char Microbiomes: Bioprospecting 702 \nin the High Arctic. Front. Bioeng. Biotechnol., 7, 32. 703 \nHansen, G.H. & Olafsen, J.A. (1999). Bacterial Interactions in Early Life Stages of 704 \nMarine Cold Water Fish. Microb. Ecol., 38, 1–26. 705 \nHe, S., Li, H., Yu, Z., Zhang, F., Liang, S., Liu, H., et al. (2021). The Gut Microbiome 706 \nand Sex Hormone-Related Diseases. Front. Microbiol., 12, 711137. 707  \nHeino & Kaitala. (1999). Evolution of resource allocation between growth and 708 \nreproduction in animals with indeterminate growth. J. Evol. Biol., 12, 423–429. 709 \nHieu, D.Q., Hang, B.T .B., Lokesh, J., Garigliany, M.-M., Huong, D.T .T., Yen, D.T ., et 710 \nal. (2022). Salinity significantly affects intestinal microbiota and gene 711 \nexpression in striped catfish juveniles. Appl. Microbiol. Biotechnol., 106, 3245–712 \n3264. 713 \nHolmlund, C.M. & Hammer, M. (1999). Ecosystem services generated by fish 714 \npopulations. Ecol. Econ., 29, 253–268. 715 \nHooper, D.U., Chapin, F .S., Ewel, J.J., Hector, A., Inchausti, P., Lavorel, S., et al. 716 \n(2005). EFFECTS OF BIODIVERSITY ON ECOSYSTEM FUNCTIONING: A 717 \nCONSENSUS OF CURRENT KNOWLEDGE. Ecol. Monogr., 75, 3–35. 718 \nHovda, M.B., Fontanillas, R., McGurk, C., Obach, A. & Rosnes, J.T. (2012). Seasonal 719 \nvariations in the intestinal microbiota of farmed Atlantic salmon (Salmo salar 720 \nL.): Seasonal variations in the intestinal microbiota of Salmo salar L. Aquac. 721 \nRes., 43, 154–159. 722 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nHovda, M.B., Lunestad, B.T., Fontanillas, R. & Rosnes, J.T. (2007). Molecular 723 \ncharacterisation of the intestinal microbiota of farmed Atlantic salmon (Salmo 724 \nsalar L.). Aquaculture, 272, 581–588. 725 \nHuang, Q., Sham, R.C., Deng, Y., Mao, Y., Wang, C., Zhang, T., et al. (2020). 726 \nDiversity of gut microbiomes in marine fishes is shaped by host‐ related 727 \nfactors. Mol. Ecol., 29, 5019–5034. 728 \nHuys, R., Herman, P .M.J., Heip, C.H.R. & Soetaert, K. (1992). The meiobenthos of 729 \nthe North Sea: density, biomass trends and distribution of copepod 730 \ncommunities. ICES J. Mar. Sci., 49, 23–44. 731 \nICES. (2022). Greater North Sea ecoregion – fisheries overview, 13045887 Bytes. 732 \nJost, L. (2006). Entropy and diversity. Oikos, 113, 363–375. 733 \nKaiser, M.J., Collie, J.S., Hall, S.J., Jennings, S. & Poiner, I.R. (2002). Modification of 734 \nmarine habitats by trawling activities: prognosis and solutions. F H F H E R E 735 \nS. 736 \nKanika, N.H., Liaqat, N., Chen, H., Ke, J., Lu, G., Wang, J., et al. (2025). Fish gut 737 \nmicrobiome and its application in aquaculture and biological conservation. 738 \nFront. Microbiol., 15, 1521048. 739 \nKlindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., et al. 740 \n(2013). Evaluation of general 16S ribosomal RNA gene PCR primers for 741 \nclassical and next-generation sequencing-based diversity studies. Nucleic 742 \nAcids Res., 41, e1–e1. 743 \nKtari, N., Jridi, M., Bkhairia, I., Sayari, N., Ben Salah, R. & Nasri, M. (2012). 744 \nFunctionalities and antioxidant properties of protein hydrolysates from muscle 745 \nof zebra blenny (Salaria basilisca) obtained with different crude protease 746 \nextracts. Food Res. Int., 49, 747–756. 747 \nLauzon, H.L., Gudmundsdottir, S., Petursdottir, S.K., Reynisson, E., Steinarsson, A., 748 \nOddgeirsson, M., et al. (2010). Microbiota of Atlantic cod (Gadus morhua L.) 749 \nrearing systems at pre- and posthatch stages and the effect of different 750 \ntreatments: Microbiota of cod rearing systems. J. Appl. Microbiol., no-no. 751 \nLegrand, T.P.R.A., Wynne, J.W., Weyrich, L.S. & Oxley, A.P.A. (2020). A microbial sea 752 \nof possibilities: current knowledge and prospects for an improved 753 \nunderstanding of the fish microbiome. Rev. Aquac., 12, 1101–1134. 754 \nLeray, M., Wilkins, L.G.E., Apprill, A., Bik, H.M., Clever, F., Connolly, S.R., et al. 755 \n(2021). Natural experiments and long-term monitoring are critical to 756 \nunderstand and predict marine host–microbe ecology and evolution. PLOS 757 \nBiol., 19, e3001322. 758 \nLi, T., Long, M., Li, H., Gatesoupe, F.-J., Zhang, X., Zhang, Q., et al. (2017). Multi-759 \nOmics Analysis Reveals a Correlation between the Host Phylogeny, Gut 760 \nMicrobiota and Metabolite Profiles in Cyprinid Fishes. Front. Microbiol., 8. 761 \nLi, X., Yan, Q., Ringø, E., Wu, X., He, Y . & Yang, D. (2016). The influence of weight 762  \nand gender on intestinal bacterial community of wild largemouth bronze 763 \ngudgeon (Coreius guichenoti, 1874). BMC Microbiol., 16, 191. 764 \nLink, J.S., Bolles, K. & Milliken, C.G. (2002). The Feeding Ecology of Flatfish in the 765 \nNorthwest Atlantic. J. Northwest Atl. Fish. Sci., 30, 1–17. 766 \nLiu, H., Guo, X., Gooneratne, R., Lai, R., Zeng, C., Zhan, F., et al. (2016a). The gut 767 \nmicrobiome and degradation enzyme activity of wild freshwater fishes 768 \ninfluenced by their trophic levels. Sci. Rep., 6, 24340. 769 \nLiu, Y ., Li, X., Li, J. & Chen, W. (2021). The gut microbiome composition and 770 \ndegradation enzymes activity of black Amur bream ( Megalobrama terminalis ) 771 \nin response to breeding migratory behavior. Ecol. Evol., 11, 5150–5163. 772 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nLiu, Y ., Yao, Y ., Li, H., Qiao, F., Wu, J., Du, Z., et al. (2016b). Influence of 773 \nEndogenous and Exogenous Estrogenic Endocrine on Intestinal Microbiota in 774 \nZebrafish. PLOS ONE, 11, e0163895. 775 \nLlewellyn, M.S., McGinnity, P ., Dionne, M., Letourneau, J., Thonier, F., Carvalho, 776 \nG.R., et al. (2016). The biogeography of the atlantic salmon ( Salmo salar ) gut 777 \nmicrobiome. ISME J., 10, 1280–1284. 778 \nLozupone, C.A. & Knight, R. (2007). Global patterns in bacterial diversity. Proc. Natl. 779 \nAcad. Sci., 104, 11436–11440. 780 \nMacArthur, R.H. & Wilson, E.O. (2001). The theory of island biogeography. Princeton 781 \nlandmarks in biology. 13th printing and first Princeton landmarks in biology ed. 782 \nPrinceton university press, Princeton Oxford. 783 \nMacFarlane, R.D., McLaughlin, J.J. & Bullock, G.L. (1986). Quantitative and 784 \nQualitative Analysis of Gut Flora in Striped Bass from Estuarine and Coastal 785 \nMarine Habitats. J. Wildl. Dis., 22, 344–348. 786 \nMarriott, A., McCarthy, I., Ramsay, A. & Chenery, S. (2016). Discriminating nursery 787 \ngrounds of juvenile plaice (Pleuronectes platessa) in the south-eastern Irish 788 \nSea using otolith microchemistry. Mar. Ecol. Prog. Ser., 546, 183–195. 789 \nMartyniuk, C.J., Buerger, A.N., Vespalcova, H., Rudzanova, B., Sohag, S.R., Hanlon, 790 \nA.T., et al. (2022). Sex-dependent host-microbiome dynamics in zebrafish: 791 \nImplications for toxicology and gastrointestinal physiology. Comp. Biochem. 792 \nPhysiol. Part D Genomics Proteomics, 42, 100993. 793 \nMcCallum, G. & Tropini, C. (2023). The gut microbiota and its biogeography. Nat. 794 \nRev. Microbiol. 795 \nMinich, J.J., Härer, A., Vechinski, J., Frable, B.W., Skelton, Z.R., Kunselman, E., et al. 796 \n(2022). Host biology, ecology and the environment influence microbial 797 \nbiomass and diversity in 101 marine fish species. Nat. Commun., 13, 6978. 798 \nMiyake, S., Ngugi, D.K. & Stingl, U. (2015). Diet strongly influences the gut 799 \nmicrobiota of surgeonfishes. Mol. Ecol., 24, 656–672. 800 \nMulcahy, M.F. (2002). Diseases of flatfish. Department of Zoology and Animal 801 \nEcology, University College Cork, Ireland. 802 \nNash, R.D.M., Valencia, A.H. & Geffen, A.J. (2006). The origin of Fulton’s condition 803 \nfactor - Setting the record straight. 804 \nNavarrete, P ., Magne, F., Araneda, C., Fuentes, P ., Barros, L., Opazo, R., et al. 805 \n(2012). PCR-TTGE Analysis of 16S rRNA from Rainbow Trout (Oncorhynchus 806 \nmy\nkiss) Gut Microbiota Reveals Host-Specific Communities of Active Bacteria. 807 \nPLoS ONE, 7, e31335. 808 \nNayak, S.K. (2010). Role of gastrointestinal microbiota in fish: Role of gastrointestinal 809 \nmicrobiota in fish. Aquac. Res., 41, 1553–1573. 810 \nNeave, M.J., Apprill, A., Ferrier-Pagès, C. & Voolstra, C.R. (2016). Diversity and 811 \nfunction of prevalent symbiotic marine bacteria in the genus Endozoicomonas. 812 \nAppl. Microbiol. Biotechnol., 100, 8315–8324. 813 \nNeuman, C., Hatje, E., Zarkasi, K.Z., Smullen, R., Bowman, J.P . & Katouli, M. (2016). 814 \nThe effect of diet and environmental temperature on the faecal microbiota of 815 \nfarmed Tasmanian Atlantic Salmon ( Salmo salar L.). Aquac. Res., 47, 660–816 \n672. 817 \nNeumann, H., Diekmann, R., Emeis, K.-C., Kleeberg, U., Moll, A. & Kröncke, I. 818 \n(2017). Full-coverage spatial distribution of epibenthic communities in the 819 \nsouth-eastern North Sea in relation to habitat characteristics and fishing effort. 820 \nMar. Environ. Res., 130, 1–11. 821 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nNie, L., Zhou, Q.-J., Qiao, Y . & Chen, J. (2017). Interplay between the gut microbiota 822 \nand immune responses of ayu (Plecoglossus altivelis) during Vibrio 823 \nanguillarum infection. Fish Shellfish Immunol., 68, 479–487. 824 \nNOAH. (2015). NOAH Habitat atlas portal: Porosity of Marine Sediments. 825 \nOksanen, J., Simpson, G.L., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P .R., et 826 \nal. (2013). vegan: Community Ecology Package. 827 \nOlsgard, F., Schaanning, M.T., Widdicombe, S., Kendall, M.A. & Austen, M.C. (2008). 828 \nEffects of bottom trawling on ecosystem functioning. J. Exp. Mar. Biol. Ecol., 829 \n366, 123–133. 830 \nOSPAR. (2017). OSPAR Bottom Fishing Intensity - Surface & Subsurface. 831 \nPiazzon, M.C., Naya-Català, F., Simó-Mirabet, P ., Picard-Sánchez, A., Roig, F.J., 832 \nCalduch-Giner, J.A., et al. (2019). Sex, Age, and Bacteria: How the Intestinal 833 \nMicrobiota Is Modulated in a Protandrous Hermaphrodite Fish. Front. 834 \nMicrobiol., 10, 2512. 835 \nPuig, P., Canals, M., Company, J.B., Martín, J., Amblas, D., Lastras, G., et al. (2012). 836 \nPloughing the deep sea floor. Nature, 489, 286–289. 837 \nQuast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2012). 838 \nThe SILVA ribosomal RNA gene database project: improved data processing 839 \nand web-based tools. Nucleic Acids Res., 41, D590–D596. 840 \nRamírez, C. & Romero, J. (2017). Fine Flounder (Paralichthys adspersus) 841 \nMicrobiome Showed Important Differences between Wild and Reared 842 \nSpecimens. Front. Microbiol., 08. 843 \nRamos Sarmiento, K., Carr, A., Diener, C., Locey, K.J. & Gibbons, S.M. (2024). Island 844 \nbiogeography theory provides a plausible explanation for why larger 845 \nvertebrates and taller humans have more diverse gut microbiomes. ISME J., 846 \n18, wrae114. 847 \nRay, A.K., Ringoe, E. & Ghosh, K. (2012). Enzymeproducing bacteria isolated from 848 \nfish gut: a review. Aquac. Nutr., 18, 465–492. 849 \nReiss, H., Degraer, S., Duineveld, G.C.A., Kröncke, I., Aldridge, J., Craeymeersch, 850 \nJ.A., et al. (2010). Spatial patterns of infauna, epifauna, and demersal fish 851 \ncommunities in the North Sea. ICES J. Mar. Sci., 67, 278–293. 852 \nRijnsdorp, A.D., Vethaak, A.D. & van Leeuwen, P.I. (1992). Population biology of dab 853 \nLimanda limanda in the southeastern North Sea. Mar. Ecol. Prog. Ser., 91, 854 \n19–35. 855 \nRingø, E., Sperstad, S., Myklebust, R., Refstie, S. & Krogdahl, Å. (2006). 856 \nCharacterisation of the microbiota associated with intestine of Atlantic cod 857 \n(Gadu\ns morhua L.). Aquaculture, 261, 829–841. 858 \nRingoe, E. & Birkbeck, T. (1999). Intestinal microflora of fish larvae and fry. Aquac. 859 \nRes., 30, 73–93. 860 \nRoeselers, G., Mittge, E.K., Stephens, W.Z., Parichy, D.M., Cavanaugh, C.M., 861 \nGuillemin, K., et al. (2011). Evidence for a core gut microbiota in the zebrafish. 862 \nISME J., 5, 1595–1608. 863 \nRolig, A.S., Mittge, E.K., Ganz, J., Troll, J.V., Melancon, E., Wiles, T.J., et al. (2017). 864 \nThe enteric nervous system promotes intestinal health by constraining 865 \nmicrobiota composition. PLOS Biol., 15, e2000689. 866 \nRombout, J.H.W.M., Abelli, L., Picchietti, S., Scapigliati, G. & Kiron, V. (2011). Teleost 867 \nintestinal immunology (online first). Fish Shellfish Immunol. 2010. 868 \nSaba, G.K., Burd, A.B., Dunne, J.P ., Hernández‐ León, S., Martin, A.H., Rose, K.A., et 869 \nal. (2021). Toward a better understanding of fish‐ based contribution to ocean 870 \ncarbon flux. Limnol. Oceanogr., 66, 1639–1664. 871 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nSchloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., et 872 \nal. (2009). Introducing mothur: Open-Source, Platform-Independent, 873 \nCommunity-Supported Software for Describing and Comparing Microbial 874 \nCommunities. Appl. Environ. Microbiol., 75, 7537–7541. 875 \nSchückel, S., Sell, A.F., Kröncke, I. & Reiss, H. (2012). Diet overlap among flatfish 876 \nspecies in the southern North Sea. J. Fish Biol., 80, 2571–2594. 877 \nSciberras, M., Hiddink, J.G., Jennings, S., Szostek, C.L., Hughes, K.M., Kneafsey, B., 878 \net al. (2018). Response of benthic fauna to experimental bottom fishing: A 879 \nglobal meta‐ analysis. Fish Fish., 19, 698–715. 880 \nSmith, C.C.R., Snowberg, L.K., Gregory Caporaso, J., Knight, R. & Bolnick, D.I. 881 \n(2015). Dietary input of microbes and host genetic variation shape among-882 \npopulation differences in stickleback gut microbiota. ISME J., 9, 2515–2526. 883 \nSpilsbury, F., Foysal, M.J., Tay, A. & Gagnon, M.M. (2022). Gut Microbiome as a 884 \nPotential Biomarker in Fish: Dietary Exposure to Petroleum Hydrocarbons and 885 \nMetals, Metabolic Functions and Cytokine Expression in Juvenile Lates 886 \ncalcarifer. Front. Microbiol., 13, 827371. 887 \nStephens, W.Z., Burns, A.R., Stagaman, K., Wong, S., Rawls, J.F., Guillemin, K., et 888 \nal. (2016). The composition of the zebrafish intestinal microbial community 889 \nvaries across development. ISME J., 10, 644–654. 890 \nSuzzi, A.L., Stat, M., MacFarlane, G.R., Seymour, J.R., Williams, N.LR., Gaston, T.F., 891 \net al. (2022). Legacy metal contamination is reflected in the fish gut 892 \nmicrobiome in an urbanised estuary. Environ. Pollut., 314, 120222. 893 \nTalwar, C., Nagar, S., Lal, R. & Negi, R.K. (2018). Fish Gut Microbiome: Current 894 \nApproaches and Future Perspectives. Indian J. Microbiol., 58, 397–414. 895 \nTarnecki, A.M., Burgos, F.A., Ray, C.L. & Arias, C.R. (2017). Fish intestinal 896 \nmicrobiome: diversity and symbiosis unravelled by metagenomics. J. Appl. 897 \nMicrobiol., 123, 2–17. 898 \nViana, D.F., Zamborain-Mason, J., Gaines, S.D., Schmidhuber, J. & Golden, C.D. 899 \n(2023). Nutrient supply from marine small-scale fisheries. Sci. Rep., 13, 900 \n11357. 901 \nWang, Y., Naumann, U., Wright, S. & Warton, D. (2012). Wang Y , Naumann U, Wright 902 \nST, Warton DI.. mvabund - an R package for model-based analysis of 903 \nmultivariate abundance data. Methods Ecol Evol 3: 471-474. Methods Ecol. 904 \nEvol., 3, 471. 905 \nWestcott, S.L. & Schloss, P.D. (2017). OptiClust, an Improved Method for Assigning 906 \nAmplicon-Based Sequence Data to Operational Taxonomic Units. mSphere, 2, 907 \ne00073-17. 908 \nWilkins, L.G.E., Leray, M., O’Dea, A., Yuen, B., Peixoto, R.S., Pereira, T.J., et al. 909 \n(2019). Host-associated microbiomes drive structure and function of marine 910 \necosystems. PLOS Biol., 17, e3000533. 911 \nWu, S., Wang, G., Angert, E.R., Wang, W., Li, W. & Zou, H. (2012). Composition, 912 \nDiversity, and Origin of the Bacterial Community in Grass Carp Intestine. PLoS 913 \nONE, 7, e30440. 914 \nXia, J.H., Lin, G., Fu, G.H., Wan, Z.Y., Lee, M., Wang, L., et al. (2014). The intestinal 915  \nmicrobiome of fish under starvation. 916 \nXie, Y ., Xu, S., Xi, Y ., Li, Z., Zuo, E., Xing, K., et al. (2024). Global meta‐ analysis 917 \nreveals the drivers of gut microbiome variation across vertebrates. 918 \niMetaOmics, 1, e35. 919 \nXiong, J.-B., Nie, L. & Chen, J. (2019). Current understanding on the roles of gut 920 \nmicrobiota in fish disease and immunity. Zool. Res., 40, 70–76. 921 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nYan, Q., Li, J., Yu, Y., Wang, J., He, Z., Van Nostrand, J.D., et al. (2016). 922 \nEnvironmental filtering decreases with fish development for the assembly of 923 \ngut microbiota. Environ. Microbiol., 18, 4739–4754. 924 \nZhang, Z., Li, D., Refaey, M.M., Xu, W., Tang, R. & Li, L. (2018). Host Age Affects the 925 \nDevelopment of Southern Catfish Gut Bacterial Community Divergent From 926 \nThat in the Food and Rearing Water. Front. Microbiol., 9, 495. 927 \nZhao, Z., Zhao, H., Zhang, L., Huang, Z., Ke, H., Liu, Y., et al. (2023). Integrated 928 \nanalysis of how gender and body weight affect the intestinal microbial diversity 929 \nof Gymnocypris chilianensis. Sci. Rep., 13, 8811. 930 \nZhu, L., Wang, J. & Bahrndorff, S. (2021). Editorial: The Wildlife Gut Microbiome and 931 \nIts Implication for Conservation Biology. Front. Microbiol., 12, 697499. 932 \n 933 \n  934 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nFigures  935 \n 936 \nFigure 1: Sampling stations in the Sylter Outer Reef area (green outline) in the 937 \nSoutheastern North Sea and target species below. Red circles highlight the sampling 938 \nstations where the fish were caught. Drawings of the species studied here can be 939 \nseen below the map. 940 \n 941 \nFigure 2: Stacked bar plot of the 30 most abundant prokaryotic families across all 942 \nsamples, sorted by species and sex. Each color represents a family, which are sorted 943 \nby phylum. The length of the bars corresponds to the proportional abundance in the 944 \nmicrobiome of a fish's intestine. 945 \n 946 \nFigure 3: Diversity represented as OTU richness for species, median grain size 947 \n(separated for species), age and weight. 948 \n 949 \nFigure 4: nMDS plots based on Bray-Curtis distances display compositional 950 \ndissimilarities of the intestinal microbial communities. Variables that significantly 951 \nexplain dissimilarities in community composition are shown for continuous variables 952 \nand displayed as vectors (A) and for species (B), with ellipses drawn around the 953 \ncentroids based on the standard deviation of the data points. 954 \n 955 \nFigure 5: (A) The number of OTUs that significantly differ in abundance, either 956 \npositively (grey or colored) or negatively (white) in response to environmental and 957 \nhost predictors. Positive responses of differentially abundant OTUs are displayed 958 \nseparately for each species in a Venn diagram, where overlapping areas indicate the 959 \nnumber of OTUs that show an increase in abundance in both species. In forest plots, 960 \nthe differentially abundant OTUs in relation to trawling intensity (B), median grain size 961 \n(C), condition factor (D), age (E) and species (F-H) are illustrated. For each 962 \ndifferentially abundant OTU, a negative fold change represents a negative response 963 \nand a positive fold change value a positive response. The length of the bars indicates 964 \nthe 95% confidence intervals.  965 \n 966 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nSampling Stations in\nthe Sylt Outer Reef,\nGerman Bight\nSampling Stations (ID)\nSylt Outer Reef\nNATURA 2000\nGerman border\nLegend\nData Source: own data, NATURA\nBuglossidium luteum\n2000 Network viewer, BKG\nBackground map: EsriTopo\nESPG 3857\nOverview Map\nLimanda limanda\n Pleuronectes platessa\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nP eM15_fa\nSolirubrobacterales_67−14\nGaiellales_uncultured\nFlavobacteriaceae\nBacteria_unclassified\nActinomycetota\nBacteroidota\nUnclassified\nMycobacteriaceae\nActinomycetota_unclassified\nMicrotrichaceae\nMicrotrichales_uncultured\nIlumatobacteraceae\nActinomarinales_uncultured\nThermoanaerobaculaceae\nAcidobacteriota\nChloroflexota_KD4−96\nClostridiaceae\nMycoplasmataceae\nLactobacillales_unclassified\nSynechococcaceae\nChloroflexota\nBacillota\nCyanobacteriota\nCoxiellaceae\nParacoccaceae\nRhizobiaceae\nMethyloligellaceae\nHyphomicrobiaceae\nDesulfobulbaceae\nPirellulaceae\nPlanctomycetota\nRubritaleaceae\nVerrucomicrobiota\nChthoniobacteraceae\nMoraxellaceae\nPseudomonadota\nHalieaceae\nLegionellaceae\nVibrionaceae\n0.00\n0.25\n0.50\n0.75\nfemale male female male female male\nB. luteum L. limanda P . platessa\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nspecies\nrichnessrichness\nweight (g)\ngrain size (µm)\n0 50 100 150 200\nage (years)\n0\n100\n200\n300\n400\n500\n600\n5 10 15\nB. luteum L. limanda P . platessa\n0\n100\n200\n300\n400\n500\n600\n100 200 300 400 500 100 200 300 400 500 100 200 300 400 500\nA B\nDC\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\nL. limanda ♀\nL. limanda ♂\nP . platessa ♀\nP . platessa ♂\nB. luteum ♀\nB. luteum ♂\nA Bstress = 0.165\n−6 −4 −2 0 2 4\n−6 −4 −2 0 2 4 6\nNMDS1\nNMDS2\n0.2\n0.4\n0.6\n0.8\nswept area ratio\n1.0\ntrawling intensity\ngrain\ncondition factor\nage\n−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5\n−1.0 −0.5 0.0 0.5 1.0\nNMDS1\nNMDS2\n−1.5 1.5\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint \n\n0 5 10 15 20\nOtu00163 Pirellulaceae\nOtu00087 Oceanirhabdus\nOtu00192 Chlamydiales\nOtu00116 T epidibacter\nOtu00124 Rhizobiales\nOtu00039 Micromonospora\nOtu00151 Blastopirellula\nOtu00102 Meiothermus\nOtu00016 Lactobacillales\nOtu00158 Thermicanus\nOtu00091 Thiogranum\nOtu00095 Sandaracinaceae\nOtu00062 Lutimonas\nOtu00167 Sva0996_marine_group\nOtu00112 Corynebacterium\nOtu00074 Candidatus_Arthromitus\nOtu00004 Pirellulaceae\nOtu00082 Mycobacterium\nOtu00009 Rickettsiella\nOtu00060 Staphylococcus\nOtu00125 Psychrobacter\nOtu00054 Shewanella\nOtu00123 Geobacillus\nOtu00153 P edobacter\nOtu00084 Endozoicomonas\nOtu00016 Lactobacillales\nFold change\nP . platessa\nL. limanda\nB. luteum\nOtu00015 Filomicrobium\nOtu00020 67−14\nOtu00024 KD4−96\nOtu00021 Filomicrobium\nOtu00005 Methyloceanibacter\nOtu00004 Pirellulaceae\nOtu00022 Actinomarinales\nOtu00003 Tateyamaria\nOtu00012 Chthoniobacteraceae\nOtu00009 Rickettsiella\nT rawling intensity\n0 5 10 15\nB\nFold change\n−40 −20 0 20\nD\nFold change\nCondition factor\nOtu00054 Shewanella\nOtu00070 Rickettsiella\nOtu00026 Legionellaceae\nOtu00036 Gaiellales\nOtu00008 P eM15\nOtu00067 Cloacibacterium\nOtu00023 Mycoplasmataceae\nOtu00045 Massilia\nOtu00074 Candidatus_Arthromitus\nL. limanda\nP . platessa\nB. luteum\nTrawling intensity\nGrain size\nCondition factor\nAge\n10\n1012\n11\n3\n16\n12 35\n29 24\n13\n10\n6\n2 10 10\n6\n12\n2 16\npositive responsesnegative responses\nA\nH\nF\nG\nOtu00083 Ornithinimicrobium\nE\nFold change\nAge\nOtu00142 Legionella\nOtu00015 Filomicrobium\nOtu00016 Lactobacillales\nOtu00151 Blastopirellula\nOtu00102 Meiothermus\nOtu00054 Shewanella\nOtu00039 Micromonospora\nOtu00103 Subgroup_17\nOtu00132 Pir4_lineage\nOtu00042 Catellicoccus\n−10 0 10\nFold change\nGrain size\nOtu00012 Chthoniobacteraceae\nOtu00009 Rickettsiella\nOtu00018 Actinomarinales\nOtu00020 67−14\nOtu00010 Blastopirellula\nOtu00017 Rubripirellula\nOtu00005 Methyloceanibacter\nOtu00003 Tateyamaria\nOtu00026 Legionellaceae\nOtu00016 Lactobacillales\n−7.5 −5.0 −2.5 0.0\nC\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted June 2, 2025. ; https://doi.org/10.1101/2025.05.30.657041doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}