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Study setupSix adults and six elderly, who were included in a previously conducted in vivo GOS intervention study [11], donated their faecal material for the current study (Fig. S1) at their first visit or at least 4 weeks after the intervention period. Each participant defecated into a stool collector (Excretas Medical BV, Enschede, the Netherlands). Directly after defecation, faecal material was divided into two portions. A small portion (~0.5 g) was frozen immediately. The remaining faeces was anoxically cryo-conserved and used as inoculum for the in vitro incubations. The viability of different microbial groups in the anoxically cryo-conserved faecal material was determined with propidium monoazide (PMA) dye. The in vitro incubations lasted for 24 h with samples collected in duplicate to compare microbiota composition, carbohydrate degradation and metabolite production between age groups (adults vs elderly). The degrading capacity for two typical bifidogenic carbohydrates, i.e., GOS and 2′-FL, was determined for the microbiota of all six adults and six elderly and compared to a non-carbohydrate control. To further extend these experiments, we also studied the degradation of other typical bifidogenic carbohydrates, i.e. FOS, inulin, and IMMP, using the faecal inocula of three adults and three elderly for which sufficient material was still available.ParticipantsThe six adults (20–30 yrs) and six elderly participants (70–85 yrs) of the intervention study [11] were randomly contacted and participated in the current study, who differed significantly in age, but not in sex, BMI, alcohol consumption, smoking, medication use or dietary fibre intake (Table 1). None of the participants took acid inhibitors (e.g., proton pump inhibitors), nor antibiotics 90 days prior to the study, nor did any of the participants have a chronic disorder or major surgery, as these factors potentially could have limited participation, completion of the study, or interfered with the study outcomes. Detailed description of the inclusion and exclusion criteria has been provided previously [11]. Subject codes as shown in the results were randomly assigned in the data analysis phase and cannot be traced back to individual subjects without the specific randomization key. The study was approved by the medical Ethics Committee of the Maastricht University Medical Center+ and registered in the US National Library of Medicine (http://www.clinicaltrials.gov) with the registration number NCT03077529 [11].Table 1 Characteristics of adults (n = 6) and elderly (n = 6) included in this study.Full size tableDietary intakeParticipants in the current study completed the dietary records on 3 consecutive days, after instructed to record their food, beverage and dietary supplement intake based on standard household units. Their nutrient intake was analyzed using the online dietary assessment tool of The Netherlands Nutrition Centre (www.voedingcentrum.nl).CarbohydratesFive different carbohydrates, i.e., GOS, 2′-FL, FOS, inulin and IMMP were used as sole carbon sources in this study. GOS and the human milk oligosaccharide 2′-FL (Fucα1-2Galβ1-4Glc) were kindly provided by Friesland Campina (Amersfoort, The Netherlands). In order to mimic the actual portion of GOS utilized by intestinal microbiota, purified GOS with  0.05) to explain the observed difference, using the prc function in the vegan package [30]. As for the metabolite data, redundancy analysis (RDA) in combination with Monte Carlo permutation was performed to assess to what extent explanatory variables, i.e., incubation time, subject- and carbohydrate-specificity, could explain the overall variation in metabolite data, using the rda function in the vegan package [30]. To assess the effect of age group (adult vs elderly) on the degradation of carbohydrates/concentration of metabolites during incubation, we analyzed the data using two-way mixed ANOVA, with one between-subjects factor (age group) and one within-subjects factor (incubation time), using the anova_test function in the rstatix package [31]. False discovery rate (FDR) correction according to the Benjamini–Hochberg procedure was applied for multiple testing when applicable. A corrected P value < 0.05 was considered to indicate significant difference. More

Model: network structureCommunities are simulated using a modified version of the evolutionary food web models developed in Allhoff et al. (2015) and Allhoff & Drossel (2016), which build on previous models25,26 to show that biodiversity can be maintained in multitrophic networks despite ongoing species turnover when feeding traits are allowed to evolve independent of body mass. The model includes consumptive and competitive interactions, where interaction strengths are determined by the traits of consumer species and their resources. All species possess three traits, a body mass or size ((m)) (used interchangeably), which places them on a body size trait axis, a feeding center ((f)) and feeding range ((s)), which determine the shape and placement of their feeding curve along the axis (Fig. 1a). While the (s) parameter specifically represents one standard deviation of a species’ feeding curve, we refer to (s) throughout as simply the feeding range. The feeding curve represents the hypothetical, fundamental feeding niche of species and shows the potential strength of a consumer’s attack rate for a given resource located along the body size trait axis. Because interactions are determined through these Gaussian curves, our networks are technically fully connected. However, when resources are far from consumer’s feeding centers, interaction strengths become asymptotically small, having a negligible effect on dynamics. Additionally, a basal resource drives energy flow in the food web (Fig. 1a). A summary of all model parameters and variables is provided in Table 2.Model: population dynamicsDynamics are governed by a bioenergetics consumer-resource model, where parameters are scaled to the body mass of species, following previous developments in Yodzis & Innes (1992) and Brose et al. (2006). The rate of change of consumer biomass (({B}_{i})) is given by:$$frac{{dB_{i} }}{dt} = mathop sum limits_{j = resources} e_{j} g_{ij} B_{i} B_{j} – mathop sum limits_{j = consumers} g_{ji} B_{i} B_{j} – mathop sum limits_{j = competitors} c_{ij} B_{i} B_{j} – x_{i} B_{i}$$
(1)
where ({e}_{j}) represents the efficiency of biomass conversion of resource (j) by consumers, ({g}_{ij}) is the mass-specific consumption rate of resource (i) by consumer (j), ({c}_{ij}) is the interference competition between consumer (i) and (j), and ({x}_{i}) is the mass-specific biomass loss from respiration and mortality for consumer (i). The rate of change in basal resource biomass (({B}_{0})) is described by:$$frac{{dB_{0} }}{dt} = n_{0} – mathop sum limits_{j = consumers} g_{j0} B_{j} B_{0} – lB_{0}$$
(2)
where ({n}_{0}) represents the constant influx of resource biomass and (l) the outflow rate. The time scale of the whole system is therefore defined by setting the constant resource influx rate ({n}_{0}=1), meaning that all other rates in the system, and consequently also consumer lifespans, must be interpreted in relation to ({n}_{0}). The basal resource is given a constant body mass trait value of ({m}_{0}=1) which does not evolve. The mass-specific consumption rate is given by:$${g}_{ij} = frac{1}{{m}_{i}} frac{{a}_{ij}}{1+{sum }_{k=prey}{h}_{i}{a}_{ik}{B}_{k}}$$
(3)
where,$${a}_{ij}= {m}_{i}^{0.75}cdot {N}_{ij}={m}_{i}^{0.75}cdot frac{1}{{s}_{i}sqrt{2pi }}cdot mathrm{exp}left[-frac{{left({log}_{10}left({f}_{i}right)-{log}_{10}({m}_{j})right)}^{2}}{2{s}_{i}^{ 2}}right]$$
(4)
describes the mass-specific attack rate of consumer (i) on resource (j), given the feeding kernel (({N}_{ij})) of consumer (i). Gaussian feeding kernels are calculated from consumer (i)’s feeding range (({s}_{i})), feeding center (({f}_{i})), and resource j’s body mass (({m}_{i})), such that resources which occur close to consumer feeding center on the body size trait axis result in the highest attack rates (Fig. 1a). The mass-specific handling time for consumers is given by ({h}_{i}=0.4cdot {m}_{i}^{-0.25}). Interference competition between consumer (i) and (j) is described by:$${c}_{ij}= {c}_{0}cdot frac{{I}_{ij}}{{I}_{ii}} text{ for }ine j$$
(5)
where,$${I}_{ij}= int {N}_{ik}cdot {N}_{jk}dleft({log}_{10}{(m}_{k})right)$$
(6)
describes the overlap in resources (k) between two competing consumers (i) and (j), such that consumers with similar feeding traits will have greater overlap between their feeding kernels resulting in higher competition coefficients.Model: community assembly & network evolutionCommunity assembly of food webs occurs through a combination of ecological and evolutionary dynamics (Fig. 1b). All ecological dynamics are described by the consumer-resource model above, where species with viable biomass densities persist in communities and species whose biomass falls below a fixed extinction threshold ((varepsilon = {10}^{-8})) are removed from the network. New species are introduced probabilistically into the network at fixed intervals through either mutation events ((p)) or as invaders ((1-p)), where (p) can be manipulated to increase the frequency of either mutation or invasion events. The traits of new mutant species are drawn probabilistically from a Gaussian distribution set around the traits of a selected extant parent species in the network. Invader species traits are generated in a similar fashion but using a Gaussian distribution with a greater standard deviation. The standard deviation of this trait range is set with the invader strangeness parameter (z), which can be manipulated to increase the range of potential traits for invader species. Thus, a larger (z) value increases the probability that new invader species will appear “strange” compared to other species already in the community. For mutant species, (z) is always set to 0.1.Parents of mutants are chosen probabilistically, where species with greater individual density (species biomass/body mass) are more likely to generate new mutant species. The parents of invader species are chosen randomly, with equal probability given to all extant species in the community. Both mutants and invaders are introduced into the system at the extinction threshold biomass ((varepsilon ={10}^{-8})). For mutants the initial biomass is removed from the biomass of the parent species’ populations, while for invaders this biomass is added into the system without affecting the parent species’ biomass pool; however, this difference did not significantly impact our results.Communities are initialized with a single ancestor species (starting biomass (varepsilon ={10}^{-8})) and the basal resource (starting biomass (=frac{{n}_{0}}{l}=2.0)) (Fig. 1b). The ancestor species is given a body mass of (m=100), feeding center of (f=1), and feeding range of (s=0.4). Upon initialization, the system is a run with only the ancestor species consuming the basal resource until a new species is introduced at 100 time steps. Thereafter, new species are introduced every 100 time steps, with ecological dynamics occurring between each species introduction. Additionally, species biomass is assessed at each 100 time step interval and non-viable species populations that fall below the biomass extinction threshold are removed. This process is repeated cyclically over the course of simulations (Fig. 1b), with many new species being generated and many removed due to extinction. The persistence of individual species is thus determined by their individual traits and overall resource availability given the composition of the rest of the community. With this dynamical approach to simulating evolving food webs, similar models have been shown to generate viable communities with both multi-trophic diversity and constant species turnover27,28, making this framework useful for testing the evolutionary impacts of species invasion and disturbance on community composition.Simulation experimentsSimulations were conducted in C, where numerical integration of differential equations was performed using the Runge–Kutta–Fehlberg algorithm from the GNU Scientific library29. Simulations were run for 25 million time steps, with 250,000 novel species introductions (mutants or invaders) for each simulation. To test if invasion would increase disturbance and variability in communities and drive the evolution of more generalized species, we conducted simulations where invaders were introduced with an increased probability of having trait values that were divergent from parent species. We controlled this by manipulating the invader strangeness parameter ((z)) across a range from (z=0.1) (invader and mutant trait values are equivalent) to (z=5.0). Invasion frequency ((p)) was fixed at 0.2 for all simulations, making mutation events more likely to occur than invasion.We hypothesized that introducing invaders with traits that are very different from parent species and from the community should result in greater disturbance in food webs because these species would be more likely to occupy novel niche space along the body size trait axis, which could result in the overexploitation of resources either through superior feeding strategies or by allowing invaders to avoid consumption by other consumers. Together, this should increase the probability of disrupted consumer-resource dynamics and secondary species extinction occurring with the introduction of strange invaders, both resulting in increased variability of biomass in the community. As a result, this increased variation should favor the survival of more generalist species in the community if they can buffer variability by consuming a greater range of resources.This is tested against the assumption that specialist consumers are more efficient than generalist consumers (generalist trade-off hypothesis2,12), which is built into our model given the formulation of the attack rate parameter ((a)), where specialist species achieve higher optimal peaks in attack rates, given their smaller feeding ranges ((s)). Thus, under conditions of low variability, our model results in communities being composed of mostly very specialized species, with narrow feeding ranges. To counter this trend toward extreme specialization, we set a floor for minimum feeding range values for all species of (s=0.3). Given these tendencies, we expected the persistence (lifespan) of more specialist species to be greatest under conditions of low variability (low invader strangeness) and that the relative persistence of more generalist species compared to specialists should increase with disturbance due to increasingly strange invaders.To test the robustness of these predictions, we replicated the (z) parameter sweep 100 times using random initial seed sets, resulting in 5000 simulations total, which collectively generated over 1.25e+09 unique species across all simulations. Data from these simulations was extracted at three different time intervals. We assessed species traits and lifespan data for all species generated in simulations at every 100 time steps, excluding data from the first 50,000 time steps to avoid including transient dynamics. Community level data, including community biomass and basal resource biomass were extracted at every 50,000 time steps (excluding time 0 from analysis). Species turnover data was extracted at every 10,000 time steps. In the infrequent event that simulations did not complete (community level extinction or crashed runs) we reran simulations with different random seed sets but identical parameter values.Data & statistical analysisDo resource and community variation increase with invader strangeness?To assess whether the addition of increasingly strange invaders into food web communities resulted in increased variation we analyzed several metrics of community and resource variability. We calculated the standard deviation (SD) of the basal resource biomass across time for each simulation and pooled these data for all simulation replicates across the invader strangeness parameter sweep. To assess variation at the community level, we used a similar approach to calculate variability in community biomass. For this metric, we summed the population biomasses of all species in the community for each given time interval output (excluding species introduced at that time step) and calculated the SD of these values across time for each individual simulation.Finally, to further assess community variability and to determine if increasing invader strangeness drives increased extinction in communities, we calculated species turnover for each time output. Species turnover was measured as the percentage change in the composition of species in communities between each time output (10,000 time steps). We then calculated mean species turnover over time for each simulation replicate and pooled all data together. To account for the non-linearity observed in our variation data (see “Results”) we conducted generalized additive models (GAM) to determine if increasing invader strangeness resulted in a significant increase in variability. GAMs were fit using a gamma error distribution with a log link function to account for continuous data constrained to positive values.Does the degree of generalism in communities increase with invader strangeness?To determine if the degree of generalism and the proportion of generalist species in food webs increased as invader strangeness increased, we calculated the mean and median feeding range ((s)) (Table 1) of species which occurred in communities for each simulation. We included all species that were generated and that survived for at least 100 time-steps in simulations, to remove the many non-viable species which immediately go extinct. Additionally, we included only mutant species for this metric to avoid the influence of the traits of invaders species, which we directly manipulated through the invader strangeness parameter. We reasoned this would provide a more independent metric of feeding range trends in communities. Mean and median feeding range were calculated for all simulation replicates and the impact of invader strangeness was assessed with GAMs (gamma error distribution with a log link function) to account for non-linear data (see “Results”).Additionally, we calculated a measure of the realized feeding range of consumers (distinct from the fixed fundamental feeding range ((s)) (Table 1)) to determine if more species were functioning as feeding generalists in communities. For this metric, we calculated the attack rate of each consumer on all other species in the community (including the basal resource and the focal consumer) for each time output (every 50,000 time steps from our community data, excluding species introduced at that time step). We then calculated the proportion of the attack rate on each species compared to the focal species’ maximum possible attack rate (an ideal prey at the exact center of the consumer’s feeding kernel). We then excluded all values below a threshold of 0.1 and from this calculated the proportion of species consumed out of the total number of species in the community. This metric correlated positively with the fundamental feeding range ((s)) of consumer species (Supplementary Fig. S3) and we refer to it throughout as the realized feeding range (Table 1) of consumer species. For our statistical analysis, we calculated the mean realized feeding range of species per simulation across invader strangeness ((z)) and ran a GLM with a quasibinomial error distribution and logit link function to account for proportional data.Does the persistence of generalist species increase with invader strangeness?To determine the persistence of species in our simulations we assessed the lifespan of individual species in simulated communities across time. For a given species, lifespan was measured as the number of time steps it persisted in a simulation after its initial introduction. We used this data to determine the relationship between species persistence and feeding range traits in two ways. First, we assessed the lifespan of all species in individual simulations continuously given the feeding range trait values across species. From this, a regression coefficient was calculated from the log10-scaled data, using a GLM with a gamma error distribution (log link), to determine the trend or “lifespan slope” for each simulation under different levels of invader strangeness (Fig. 4b). These lifespan slope values were then assessed for all simulation replicates across the full range of the invader strangeness parameter. Because more specialized species have higher maximal attack rates and are typically more efficient in our model, we expected that the lifespans of specialist species would be longer than more generalized species and that lifespan slopes should be negative under conditions of low variation. Given this, we expected to observe a positive trend in lifespan slope values across the invader strangeness parameter sweep if disturbance was increased in simulations as (z) became higher. We tested for this positive trend in the lifespan slope data by conducting a GAM (Gaussian distribution and the identity link function) to manage the observed non-linear trend in our data (see “Results”).For the second approach, we aimed to determine the relative persistence of species by binning “generalist” and “specialist” species based on feeding range traits and comparing species lifespans between these groups. For this analysis we split species into bins, where specialist species included all species with feeding range (sle) 0.32 and generalists as all species with feeding range values (sge) 0.39 (species with intermediate feeding range values were excluded from the analysis). We performed a robustness check of bin cutoffs but found no qualitative or statically significant differences in our results for a range of bin cutoff values. To assess how the relative persistence of generalists compared to specialists was influenced by invader strangeness, we then calculated the mean life span of all species falling into either of these categories per simulation and determined how these values were influenced by (z) for all simulation replicates. To assess whether mean lifespan was different between each of these groups across the invader strangeness sweep, we conducted a GLM with species type (generalist or specialist) and invader strangeness ((z)) as fixed effect terms and tested for the statistical significance of their interaction on mean species lifespan. The GLM was run using a Poisson distribution to account for discrete lifespan count data with a log link function. All GLMs and GAMs were performed in R using the “glm” and “mgcv” functions30, respectively, and all non-linear parameters in GAMs were fit using generalized cross validation (GCV). More

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A framework to estimate unowned cat abundanceIn the following sections, we describe the application of an IAM, a hierarchical modelling approach, which estimates unowned cat abundance in discrete geographical units from spatially replicated citizen data, in combination with expert data obtained from 162 sites across five urban areas in England. In doing so, we explored key predictors of unowned cat abundance. We then estimated unowned cat abundance across urban areas in England and the UK with respect to the modelling results. We used WinBUGS53 and R54 for all data analysis via the R package R2Winbugs55 and QGIS56 for plotting maps.Data collation and preparationA database of unowned cat count data were compiled from citizen science data and expert data collected throughout a one-year period that began between 2016 and 2018 across five urban areas in the UK. Areas included Beeston, Bradford, Bulwell, Dunstable & Houghton Regis and Everton (Fig. 1). These data were collected as part of Cat Watch, a community partnership project set-up by Cats Protection, a UK feline welfare charity, to control cat numbers39,57. Two distinct forms of citizen science data were collected: (1) the first consisted of an initial cross-sectional random-sample door-to-door survey carried out with approximately 10% of households. At that stage, residents were asked how many cats they know of locally and how many they think were owned in the form of a multiple-choice question with the following options; none, 1–2, 3–4, 5–9, 10 or more, from which the number of unowned cats were derived. When a range was selected the central value was taken; for ten or more we used 15 (the average from reports when 10 or more was specified was 14.7). Location data were available for 3101 survey responses, within which there were estimates of 4411 unowned cats; (2) throughout the project, residents were able to report unowned cats in their area directly via social media or through a mobile application. During the study period, 877 reports were received reporting on the locations of 2790 unowned cats. These data were collected according to the study protocol approved by University of Bristol Faculty of Health Science Research Ethics Committee approval number 38661. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was secured in advance of survey participation. Residents provided report data voluntarily, with no identifying information collected. No experimental protocols were used.Expert data were obtained from an experienced community team (CT) that recorded when and where an unowned cat was found or confirmed the lack of presence of an unowned cat. The CT carried out extensive door-to-door surveillance across both reported hot spot and cold spot areas. These data are considered of higher quality, due to the ability of the CT to correctly identify an unowned cat and with no risk of double counting the same individual. Unowned cats can be either stray or feral. Protocols to accurately identify a stray cat included; scanning for a microchip, attaching a paper collar to notify potential owners, advertising online, door-to-door notifications, local posters and contacting other animal welfare organisations, including veterinary practices. If no owner was found during this process it was identified as unowned. Feral cats were more likely to be identified via behavioural means; as they have not been socialised to humans, they will be more fearful and will not approach humans47. If they have already been neutered they may also have their left ear “tipped”. During the study period, there were 601 records from the CT, reporting on the location of 605 confirmed unowned cats. All three of these data sources provided detailed location data (postcodes and/or addresses) enabling geo-referencing of unowned cat location data.To account for duplicate sightings, the citizen science data required clustering to account for neighbours in close-proximity reporting the same cats. There is limited understanding of urban unowned cats in the UK, however studies of urban unowned cats in other areas indicate home range sizes between 3.7 and 10.4 ha for urban areas58,59. Studies on unowned cats in the UK indicate that home ranges vary between 10 and 15 hectares60. We assume a maximum 20 ha home range, equivalent to a circular area with a diameter of 504 m. Consequently, we apply a 500 m cluster function in R that derives clusters of cat sightings that are within 500 m of each other. The individual records were maintained as replicate counts within each cluster. Clustering of 500 m has also been shown to provide reasonable estimates in an urban area with high expert coverage (91%), where you would not anticipate cat numbers to be significantly inflated above those observed by experts25. In the absence of expert data, the effect of violating this assumption (i.e. reporting them as replicate sightings when they are not) would result in lower estimates of cats. However, where expert data is available, the effect of violating this assumption would result in bias in the observation parameters, not estimates of the cats themselves, which are also inferred from the expert data that do not contain duplicate sightings.Data analysisWe applied an integrated abundance model (IAM) within a Bayesian framework that combines count data across sites from two forms of citizen science data and expert data25. The hierarchical structure of the IAM enables it to borrow strength from the sites with expert data to inform detection biases of citizen science data, including detection probability of an unowned cat and false positives due to misidentification of an owned cat as unowned. The goal of the inference is to estimate the abundance of unowned cats within each site and explore covariates as predictors of population density.Specifically, observed citizen science counts at each site i and during each replicate survey j are linked to true site-specific population sizes (Ni) via a detection probability (p) and the expected number of misidentifications (m). We apply a Poisson distribution to account for additional stochasticity in spatial replicates not accounted for in the systematic biases (m and p). Each type of citizen science data is modelled separately to account for the different biases in collection methods between the survey data (y) and report data (u):$$ {y_{i,j}}sim {text{ Poisson }}({N_i}{p_y} + {m_y}) $$$$ {u_{i,j}}sim {text{ Poisson }}({N_i}{p_u} + {m_u}) $$Expert consensus (wi) was available on the abundance of individuals for 104 sites and linked to true population sizes via a Poisson observation error.$$ {w_i}sim {text{ Poisson }}left( {N_i} right) $$We additionally assume that where expert counts are available they are accurate at the level of presence or absence.$$ {z_i}sim {text{Bernoulli}}left( Omega right) $$$$ {N_i}_= {z_i}{lambda_i} $$whereby zi is a binary measure of occurrence, with each of the i sites occupied or not, that is modelled as a Bernoulli random variable determined by occupancy probability (Ω). True site-specific population sizes (Ni) are therefore a function of whether a site is occupied or not and a site-specific mean λi. When expert data on occurrence can be inferred from expert consensus this was included in zi.We extend the original development of an IAM25 described above to model the log the site specific mean (λi) as a linear function of covariates (x) using the following linear relationship:$$ log{lambda }_{i} = mu +sum_{j=1}^{n}{beta }_{j};{x}_{j,i}+{varepsilon }_{i}$$$$varepsilon sim N(0,{sigma }^{2})$$where xj, I are the values of the jth covariate across sites i, βs are the regression coefficients for each covariate and ɛ is the residual site-specific variation providing estimates of unexplained variance. We also fitted a model without covariate effects to gain an estimate of total site-specific variance. The proportional reduction in the residual site-specific variation component is a measure for the proportion of the site-specific variance in abundance explained by that covariate or covariates.To assess the credibility of covariate effects we calculated the probability that their effects were positive [P(β  > 0)] or negative [P(β  More

Genome sequencingWe sequenced the genomes of 35 strains of B. pseudocatenulatum (Supplementary Table S1). These strains were isolated at the Yakult Central Institute and the species were identified based on the 16S rRNA gene sequence analysis. These strains have been isolated in the course of various studies over the past few decades, including many studies on infants and adults. B. pseudocatenulatum cultures were anaerobically incubated in modified Gifu anaerobic medium (Nissui Pharmaceutical, Tokyo, Japan) supplemented with lactose and glucose (both 0.5% wt/vol) at 37 °C for 16 h. These culture conditions were applied throughout the study unless stated otherwise. The detailed procedures for genomic DNA extraction, library preparation for MiSeq (Illumina, San Diego, CA, USA), MinION (Oxford Nanopore Technologies, Oxford, UK) and PacBio RS2 (Pacific Biosciences, Menlo Park, CA, USA), and sequencing are described in the Supplementary Methods.Genome assembly, gene prediction and pangenome analysisWe used Unicycler [26] with default parameters for both short-read and hybrid assembly, and Prokka [27] with default parameters for annotating the reconstructed genomes and those downloaded from the RefSeq database. The annotated genomes were then processed with Roary [28] with a default gene identity cut-off parameter of 95% for species level pangenome analysis. A representative sequence from each gene cluster was translated into a protein sequence, and CAZymes were identified using the dbCAN2 server [29]. Proteins were considered CAZymes if they were identified using HMMER, DIAMOND and Hotpep with default parameters. We then built a CAZyme gene distribution matrix (Supplementary Table S2) based on the gene presence-absence table determined using Roary.Carbohydrate utilisation assaysStrains of B. pseudocatenulatum were cultured until they reached the exponential phase, centrifuged, and then, the resulting pellets were suspended to an OD600 of 0.2 in modified peptone yeast extract (PY) medium (100 mM PIPES, pH 6.7, 2 g/L peptone, 2 g/L BBL trypticase peptone, 2 g/L bacto-yeast extract, 8 mg/L CaCl2, 19.2 mg/L MgSO4 ∙ 7H2O, 80 mg/L NaCl, 4.9 mg/L hemin, 0.5 g/L L-cysteine hydrochloride and 100 ng/L vitamin K1). These suspension cultures were inoculated (1% vol/vol) into modified PY medium supplemented with 0.5% (wt/vol) XOS (Xylo-Oligo95P, B Food Science, Aichi, Japan) (PY-XOS), wheat arabinoxylan (Megazyme, Bray, Ireland) (PY-AX) or beechwood xylan (Sigma-Aldrich, Darmstadt, Germany) (PY-XY) and covered with sterile mineral oil (50 μL) to prevent evaporation. Growth was monitored anaerobically by measuring the OD600 using a PowerWave 340 plate reader (BioTek, Winooski, VT, USA) every 30 min in an anaerobic chamber for 48 h. The organic acids produced in PY-XY were analysed using high-pressure liquid chromatography as described [8].Cloning, expression and purification of recombinant BpXyn10AThe GH10 domain of the BpXyn10A gene was amplified by PCR using the primers xynA-GH-F (5’-CATCATCATCATCATGCGGAAGGCGACGCCGTA-3’) and xynA-GH-R (5’-AGCAGAGATTACCTAATCCTTGAATGCGTTCATGC-3’), with the genomic DNA of YIT 11027 as a template. A linearised vector was synthesised by PCR using primers pColdII-F (5’-GTAATCTCTGCTTAAAAGCACAGAATCTA-3’) and pColdII-R (5’-ATGATGATGATGATGATGCACTTTGT-3’), and the pColdII vector (Takara Bio, Otsu, Japan) as a template. These fragments were ligated using In-Fusion HD Cloning Kits (Takara Bio, Otsu, Japan), resulting in pColdII-xynA. Escherichia coli BL21 was transformed with pColdII-xynA and cultured to express recombinant BpXyn10A as described by the manufacturer. Bacterial cells were harvested by centrifugation and lysed with B-PER Bacterial Cell Lysis Reagent (Thermo Fisher Scientific, Waltham, MA, USA) containing lysozyme at 100 µg/mL and 10 U/mL of DNase I. Recombinant BpXyn10A was further purified using Ni-NTA Spin Column (Qiagen, Hilden, Germany) and analysed by SDS-PAGE.Endo-xylanase activity assayB. pseudocatenulatum YIT 11027, YIT 11952 and YIT 4072T cells were grown anaerobically in PY-AX or PY-XOS medium for 16 h. Cultures (1.5 mL) were centrifuged (8000× g for 2 min at room temperature); then, supernatants were sterilised by passage through a 0.22-μm filter. Pelleted cells were washed with modified PY medium and resuspended in 1.5 mL of the same medium. The endo-xylanase activity of the supernatant and the cell fractions were assayed using Xylanase Assay kits (XylX6 method) (Megazyme, Bray, Ireland) as described by the manufacturer. According to the manufacturer, this kit is designed to specifically detect only endo-xylanase activity, and not xylosidase or exo-xylanase enzyme activity.Purified BpXyn10A-added cultureB. pseudocatenulatum YIT 4072T and Ba. ovatus YIT 6161T cells were cultured anaerobically until they reached the exponential phase. Thereafter, cultures (200 μL) were centrifuged (8000× g for 2 min at room temperature), then pelleted cells were resuspended in modified PY medium (500 μL), and inoculated (1% vol/vol) into PY-AX medium supplemented with 0, 10, 100 and 1000 ng/mL purified recombinant BpXyn10A. Growth was monitored anaerobically by measuring the OD600 using the PowerWave 340 plate reader.RNA-seq analysisB. pseudocatenulatum YIT 11952 was cultured in modified PY medium supplemented with 0.5% (wt/vol) lactose, xylose, XOS, beechwood xylan or arabinoxylan and harvested at mid- to late-log phase. The detailed procedures for total RNA extraction, rRNA removal and sequencing using MiSeq are described in the Supplementary Methods. We obtained a total of 23 million paired-end reads. Low-quality bases (average quality More