National-scale biogeography and function of river and stream bacterial biofilm communities

Domain: earth_science
Published: 2025-11-26
Editorial outcome: minor_revision

Abstract
Biofilm-dwelling microorganisms coat the surfaces of stones in rivers and streams, forming diverse communities that are fundamental to biogeochemical processes and ecosystem functioning. Flowing water (lotic) ecosystems face mounting pressures from changes in land use, chemical pollution, and climate change. Despite their ecological importance, the taxonomic and functional diversity of river biofilms and their responses to environmental change are poorly understood at large spatial scales. We conducted a national-scale assessment of bacterial diversity and function using metagenomic sequencing from rivers and streams across England. We recovered 1,014 metagenome-assembled genomes (MAGs) from 450 biofilms collected across England’s extensive river network. Substantial taxonomic novelty was identified, with ~20% of the MAGs representing novel genera. Here we show that biofilm communities, dominated by generalist bacteria, exhibit remarkable functional diversity and metabolic versatility, and likely play a significant role in nutrient cycling with the potential for contaminant transformation. Measured environmental drivers collectively explained an average of 71% of variation in the relative abundance of bacterial MAGs, with geology and land cover contributing most strongly. These findings highlight the importance of river biofilms and establish a foundation for future research on the roles of biofilms in ecosystem health and resilience to environmental change.

Introduction
It is estimated that up to 80% of all bacterial and archaeal cells on Earth exist within biofilms1. These biofilms dominate microbial life in freshwater streams and rivers2, forming complex, interacting assemblages of taxonomically and functionally diverse heterotrophic and phototrophic microbes. These include bacteria, algae, fungi, protozoa, and viruses2, existing within an extracellular polymeric matrix that adheres to the surfaces of stones, aquatic plants, and sediments1. Biofilms are hotspots of metabolic activity, driving essential biogeochemical processes such as nutrient cycling, primary production, and respiration3. They play key roles in degrading pollutants, regulating water quality4, and supporting energy flow to higher trophic levels5. Microbes within biofilms are therefore critical to the functioning of river ecosystems6, which are under increasing pressure from pollution and climate change7. Monitoring biofilm microbial communities offers valuable opportunities to assess ecosystem health, detect early signs of pollution, and inform restoration strategies, such as water quality monitoring, bioremediation, and improved wastewater treatment8. Consequently, a detailed understanding of the diversity and function of biofilm microbial communities in rivers is essential for the effective management and conservation of these vital ecosystems4.

Diverse microbial communities have been detected in river biofilms2,6. Their composition and functionality are likely influenced by a complex interplay of factors, including physiochemical parameters such as temperature, pH, and dissolved oxygen9–11; the concentrations of nutrients, organic matter, and pollutants12–14; flow dynamics15; land use16,17; ecological processes such as selection and dispersal18; and biotic interactions including cooperation and competition between microbes, and grazing by macroinvertebrates19,20. By shaping microbial community composition and function, environmental changes can significantly affect biogeochemical fluxes and overall ecosystem health.

Despite their ecological importance, the diversity of microbial communities and their functional roles in river biofilms remains poorly understood. Furthermore, identifying the environmental drivers that influence biofilm community dynamics and assessing their resilience to pressures, such as nutrient pollution, presents ongoing challenges. These challenges include the complex and heterogenous nature of biofilm communities6, which are shaped not only by a multitude of external environmental factors, but also by the unique microenvironment and the intricate network of microbe-microbe interactions occurring within the biofilm3,21. Few studies capture a sufficiently comprehensive suite of environmental covariates to fully resolve these relationships. Moreover, much of the existing research on river biofilm communities has focused on individual rivers or catchments, providing valuable insights into local environmental drivers of microbial diversity and community dynamics22–24. However, these studies typically comprise a relatively small number of samples and cover a limited geographic area.

In recent years, national-scale studies have been undertaken to capture the wider diversity of freshwater microbes and explore their biogeographic patterns across a broad range of environments25–28. These national-scale datasets demonstrate the value of molecular approaches, including metagenomics, for uncovering both the taxonomic and functional diversity of microbial communities, and highlight community dynamics and adaptations in response to environmental drivers across large spatial scales. Collectively, national-scale datasets can provide a more comprehensive understanding of the ecological dynamics of freshwater microbes and their drivers at a global scale. However, there are few examples of such national-scale studies of river biofilm communities, and, to our knowledge, no national-scale metagenomic assessment of river microbes currently exists in England.

The Environment Agency (EA) established the River Surveillance Network (RSN) as a national initiative to monitor and assess long-term changes in the health of English rivers. The RSN comprises 1600 sites, representing the diversity of England’s extensive river network. As part of this initiative, 450 river biofilms were systematically collected over a three-year period from 146 RSN sites spatially distributed across England for metagenomic sequencing (Fig. 1A). This comprehensive dataset spanned a latitudinal gradient of 645 km. It encompassed all major land cover types present in the country, ranging from woodlands and grasslands to arable land and urban areas, and represented a variety of catchment geologies (Supplementary Table 1, Supplementary Data 1). In addition to the diverse catchments, the RSN sites spanned considerable variability in their physicochemical conditions, including water temperature, alkalinity, pH, dissolved oxygen, dissolved organic carbon, orthophosphate, nitrate-N, and nitrite-N (Supplementary Table 2, Supplementary Data 1).

This systematic investigation of the river network, coupled with detailed environmental data, enables a robust assessment of microbial biofilms, comparable to existing large-scale efforts25,26,28,29, capturing their biogeographic distribution, taxonomic diversity, metabolic and functional potential, and environmental drivers. Specifically, this study addresses three fundamental research questions:

(i) How are river biofilm bacterial communities structured across England’s river network?
(ii) What metabolic capabilities and functional traits underpin their roles in ecological processes such as biogeochemical cycling?
(iii) How do environmental drivers shape river biofilm bacterial communities at a national-scale?

By addressing these questions, this study offers novel insights into the biogeography of river biofilm bacteria and their critical roles in ecosystem functioning. These findings help provide a foundation for effective monitoring, management, and conservation of river ecosystems.

Results
Diversity and composition of river biofilm microbial communities

River biofilms are cosmopolitan in their distribution and known to host high microbial diversity, supported by continuous nutrient and community inputs from upstream6. However, our understanding of freshwater biofilm microbial communities is significantly less advanced than that of microbial communities in the water column25 or sediments9. Although several studies have explored the taxonomic composition of river biofilms at the local scale10,11,23, the overall abundance, composition, and genomic traits of these communities across large spatial scales remain poorly understood.

Using metagenomic data averaging 66.80 million reads per sample (±28.5 million reads SD), we found that bacterial sequences comprised the majority (85.17%) of all metagenomic reads in the river biofilms. Eukaryotes and Archaea represented 11.56% and 2.64% of the reads, respectively (Supplementary Data 2). Because of their dominance in biofilms, we focused our analysis on bacterial communities. The assembled reads were used to reconstruct a total of 4027 bacterial bins (archaeal bins were not recovered), which were dereplicated into 1014 metagenome-assembled genomes (MAGs) (also known as species-level genome bins). Of these, 329 MAGs (32%) were identified as near-complete, 491 (48%) as medium-quality, and 194 (19%) as low-quality (Fig. 1B) with a mean completeness of 86.70% (±9.3% SD) and a mean contamination of 3.57% (±4.1% SD). Mean genome size was estimated to be 3.66 Mbp (±1.4 Mbp SD) and mean GC content was 52.66% (±11.7% SD) (Supplementary Fig. 1, Supplementary Data 3).

The 820 medium or high-quality bacterial MAGs retained for downstream analysis encompassed a diverse range of taxa, including 20 known phyla, 35 classes, 91 orders, 160 families, 311 genera and 46 species. The 46 MAGs assigned to known species according to GTDB-Tk had an average nucleotide identity (ANI) ranging from 95.0 to 95.4% (mean 95.01±0.06). There was also substantial taxonomic novelty detected, with 169 (20.6%) of the recovered MAGs representing previously uncharacterised genera, and 774 MAGs (94.4%) representing previously uncharacterised species with no suitable GTDB-Tk reference (≤65% ANI across all species within the database; Supplementary Data 3) (Fig. 1C). Stringent assessment using only near-complete MAGs revealed that 306 (93%) were novel species-level genome bins, with no previously identified representatives. Across all river biofilms, Pseudomonadota was the most dominant phylum and comprised almost half of the total community, with a mean relative abundance of 48.49% (±14.8% SD) (Fig. 1D). Other abundant phyla included Cyanobacteriota with a mean relative abundance of 15.68% (±16.9%), Bacteroidota with a mean relative abundance of 14.77% (±9.5%), and Actinomycetota with a mean relative abundance of 6.27% (±5.1%). Other less abundant phyla each comprised less than 5% of the total community on average. This community composition aligns with previous studies on benthic biofilms collected from a variety of river types globally, including the groundwater and rain-fed River Thames (United Kingdom)30 and urban and rural rivers in China31, and glacier-fed streams in alpine regions such as the Southern Alps (New Zealand) and the Caucasus (Russia)32. These studies also reported the dominance of Pseudomonadota and Bacteroidota, which have been reported to play important roles in the degradation of organic matter, and Cyanobacteriota, which contributes to primary productivity33. In contrast, river surface waters are typically dominated by Pseudomonadota and Bacteroidota, but also by Actinomycetota and Verrucomicrobiota25,30, highlighting differences in community assembly between benthic biofilms and pelagic habitats.

Genomic traits of river biofilm bacteria

We found that genome size and GC content were correlated with phylogenetic background, delineating these traits across the bacterial tree of life (Fig. 1E). It was observed that many Myxococcota MAGs are large with a high GC content, Pseudomonadota MAGs are small to medium with a high GC content, and Cyanobacteriota and Bacteroidota MAGs are small to medium with a lower GC content. A similar phylogenetic relationship between GC content and genome size has been observed in pelagic and sediment bacteria from lakes and rivers34–36, suggesting that the patterns observed for many phyla such as Myxococcota, Pseudomonadota, Cyanobacteriota, and Bacteroidota are consistent with broader trends across freshwater environments. These traits may reflect ecological niche adaptation. For example, bacteria with larger genomes are typically copiotrophs, with numerous genes associated with complex metabolic processes, allowing them to thrive in fluctuating or nutrient-rich environments37. In contrast, those with smaller genomes often have specific nutrient requirements and occupy more stable or oligotrophic environments37, consistent with genome streamlining theory38. Furthermore, lower GC content reduces nitrogen requirements, which may be advantageous for bacteria in nitrogen-limited environments36. Genome size is likely affected by a complex interplay of environmental factors, in addition to trophic strategies39, and biotic interactions40,41.

Functional annotation of these MAGs revealed multiple adaptations for biofilm matrix colonisation and maintenance, including genes associated with biofilm formation, quorum sensing, chemotaxis, flagellar assembly, the two-component system, exopolysaccharide (EPS) biosynthesis and ATP synthase-binding cassette (ABC) transporters (Supplementary Fig. 2). Notably, many of these genes were enriched in larger MAGs, which is consistent with the functional and metabolic flexibility of copiotrophs and may reflect the greater genomic complexity associated with larger bacterial genomes36. These traits are broadly conserved across bacterial biofilms in other freshwater environments, including glacier-fed streams32 and other cryospheric systems42, where they have also been found across diverse taxonomic groups, highlighting the phylogenetically widespread contribution to the biofilm matrix through roles in biofilm formation, motility, sensing the environment, and nutrient acquisition.

Biogeography of river biofilm bacteria

River biofilms host complex communities composed of both generalist and specialist taxa, each fulfilling distinct ecological roles2. Generalist taxa are highly adaptable, thriving across a diversity of habitats, and tolerating a broad range of environmental conditions, whereas specialist taxa are adapted to specific habitats, thriving only under specific environmental conditions43. Despite the ecological significance of these communities, the biogeographic patterns of river biofilm bacteria are poorly understood and have not yet been addressed in the context of ecological niche space. Using biogeographic mapping and niche breadth analysis coupled with measures of abundance and occupancy, we investigated the spatial distribution and ecological niches of river biofilm bacteria.

The distribution of MAGs across the river network revealed national-scale biogeographic patterns (Fig. 2). Pseudomonadota and Bacteroidota exhibited high relative abundances throughout the country (>5% in more than 90% of sites), dominating biofilm communities. In contrast, Cyanobacteriota displayed regional preferences, with higher relative abundance in the north and southwest of England, and a lower occupancy overall (Fig. 3A), indicating that they occasionally dominate biofilm communities (>5% in only 63% of sites). Other taxa, such as Acidobacteriota, Myxococcota, and Nitrospirota, had a widespread presence but exhibited localised hotspots where their relative abundance was notably higher. Campylobacter and Chloroflexota had more restricted biogeographic distributions and were present at low relative abundances in only 13 and 79 samples, respectively. However, most MAGs were widespread rather than limited to specific locations. Each MAG occupied an average of 413 samples (92%) spatially distributed across the river network, with 176 MAGs (21%) present in all samples. Many of these 176 high-occupancy MAGs were the most abundant members of the community, with a mean relative abundance of up to 2.49% (Fig. 3A). A similar relationship between occupancy and mean relative abundance has been observed in surface water bacterial communities in rivers across North America25. However, while river networks comprise mostly high-occupancy taxa, studies of bacterial communities in spatially distributed lakes have found a greater proportion of low-occupancy taxa26,44. This difference likely reflects the underlying ecological factors that differ between rivers and lakes, such as habitat connectivity, dispersal dynamics, and environmental stability45.

Using niche breadth analysis, we further categorised the MAGs as generalists (niche breadth, BN > median BN) or specialists (BN < median BN). This metric leverages the relative abundance of MAGs to quantify how evenly they are distributed across the river network, providing a measure of their ecological breadth. Among the 176 high-occupancy MAGs, 88% were identified as generalists. Generalists comprised 58.58% of Pseudomonadota MAGs, and 58.62% of Verrucomicrobiota MAGs, while 51.19% of Cyanobacteriota MAGs, 60.00% of Acidobacteriota MAGs, 64.67% of Bacteroidota MAGs, and 64.71% of Myxococcota MAGs were categorised as specialists (Fig. 3B). Previous studies have also detected a high abundance of generalist bacteria in river and stream biofilms2,46,47. Furthermore, the distance-decay relationship of bacterial community dissimilarity and geographic distance was positive, but weak (R2=0.04, p<0.001) (Supplementary Fig. 3A, B), suggesting dispersal rates in bacterial biofilms are high, although other processes could also contribute, such as slow ecological or evolutionary turnover. Nevertheless, some degree of species sorting due to local environmental conditions likely occurs, consistent with the Baas Becking hypothesis, stating that everything is everywhere, but the environment selects48. Taken together, these results reveal a dominance of high-occupancy, generalist MAGs across the river network, emphasising the role of rivers as highly interconnected ecosystems with potential for widespread microbial dispersal. The ability of generalist bacteria to tolerate a diverse range of environmental conditions is likely an advantage in lotic ecosystems, where biofilm communities may become dislodged and recolonise further downstream46.

Metabolic potential of river biofilm communities

Biofilm communities play critical roles in ecological processes such as primary production, nutrient cycling, and the transformation and breakdown of organic material and pollutants3,49. Their contributions to biogeochemical cycling can strongly influence the health and productivity of river ecosystems by mediating the availability of nutrients and other compounds to higher trophic levels in the food web5. Previous studies have emphasised the importance of assessing the metabolic capabilities of biofilms to better understand their functional roles in river ecosystems6. Despite this, there are few examples of studies that have conducted genome-resolved assessments of microbial metabolic potential within river biofilms32, particularly across broad spatial scales. As a result, detailed insights into the distribution and prevalence of key metabolic functions, which are necessary to understand the role of biofilm microbes in ecosystem functioning, remain limited.

The majority of bacterial MAGs were found to have the capacity to contribute to carbon, nitrogen, and sulfur cycling in rivers. Among these, Pseudomonadota, Cyanobacteriota, and Bacteroidota MAGs accounted for more than 52.41%, 16.62% and 10.95%, respectively, of the nutrient cycling genes identified in the biofilms (Supplementary Fig. 4). Across the 820 MAGs, the majority of steps in carbon, nitrogen, and sulfur cycling pathways were represented (Fig. 4A–C). Nearly all MAGs encoded for genes involved in organic carbon oxidation (99.39%), and a substantial proportion of the MAGs included genes involved in fermentation (86.83%), acetate oxidation (73.66%), sulfur oxidation (47.93%), and nitrite ammonification (43.29%). Many of these pathways represented by the river biofilms, including organic carbon oxidation and sulfur oxidation, were also prevalent in mountain stream biofilms50.

Carbon cycling genes, such as sgdh (pentose phosphate pathway) and RuBisCO IV (carbon fixation), were abundant in members of Pseudomonadota and Cyanobacteriota (Fig. 4D, Supplementary Fig. 5A). Nitrogen cycling genes, including nirD and nirB (nitrite reductases) (Fig. 4E, Supplementary Fig. 5B), and sulfur cycling genes, including sdo (sulfur dioxygenase), fccB (sulfur dehydrogenase) and sat (sulfate reduction) (Fig. 4F, Supplementary Fig. 5C), were particularly abundant in members of Pseudomonadota, Cyanobacteriota, and Bacteroidota. Additionally, Pseudomonadota and Acidobacteriota MAGs encoded the most diverse range of genes involved in these pathways, with Pseudomonadota encoding 17, 16, and 10 genes and Acidobacteriota encoding 13, 15, and 9 genes involved in the carbon, nitrogen, and sulfur cycles, respectively. The biofilm MAGs also encoded a range of genes involved in oxygen and hydrogen cycling (Supplementary Fig. 5D, E, Supplementary Fig. 6, Supplementary Data 4). Many of these metabolic functions are associated with the production and attenuation of greenhouse gases such as carbon dioxide, methane, and nitrous oxide51. These results highlight the potential of biofilm bacteria to perform diverse biochemical roles, which may include contributing to the flux of greenhouse gases in river ecosystems.

Up to 91.10% of biofilm MAGs contained genes for aerobic respiration, and 21.59% of MAGs, which included members of Pseudomonadota, Actinomycetota, Acidobacteriota, Deinococcota, and Planctomycetota, also possessed genes associated with anaerobic respiration. Additionally, some MAGs, notably those belonging to Pseudomonadota and Cyanobacteriota, had genes for both core and accessory photosynthetic pigments (Fig. 5B). The coexistence of the trophic capabilities identified among biofilm bacteria is consistent with studies showing that biofilms often exist as stratified micro-environments, in which aerobic and photosynthetic bacteria dominate the outer layers exposed to light and oxygen, while anaerobes occupy deeper oxygen-depleted layers3. Such trophic diversity may contribute to the versatility of biofilm bacteria, enabling them to occupy multiple ecological niches within the complex biofilm matrix.

Biofilm formation is a key survival strategy for many freshwater microbes, allowing them to gain physical protection from the extracellular polymeric matrix, accumulate and readily access nutrients which may include biopolymers derived from the matrix itself, and interact with other taxa3. Further to the observed prevalence of biofilm-associated genes such as those involved in quorum sensing, chemotaxis, flagellar assembly, and EPS biosynthesis (Supplementary Fig. 2), genes associated with biofilm formation were also identified in 96.95% of MAGs encompassing nearly all bacterial phyla detected (Fig. 5C). Exceptions to this were Patescibacteria, which are known for their reduced genomes and symbiotic lifestyle52 and likely depend on interactions with biofilm-forming bacteria rather than contributing to biofilm formation directly, and Bacillota, which may play a less prominent role in biofilm formation, possibly by relying on an alternative survival strategy such as sporulation53. These findings highlight the widespread importance of biofilm formation as a strategy for microbial survival in dynamic river ecosystems, facilitating interactions among taxa, promoting community stability, and acting as a nutrient resource that may buffer potential effects from nutrient fluctuations and other environment distrubances3,54. Additionally, genes involved in a variety of other stress tolerance strategies were identified in the biofilm MAGs, such as compatible solutes accumulation, membrane stability, scavenging of reactive oxygen species, and repair and degradation of damaged proteins (Fig. 5C). Many of these stress tolerance strategies have also been identified in biofilm bacteria in glacier-fed streams32, highlighting that the widespread presence of stress tolerance genes may support the persistence of biofilm bacteria under fluctuating or unfavourable condition in freshwater environments.

Biofilm communities play an essential role in the uptake, transformation, and degradation of organic compounds, including those that are harmful pollutants in river ecosystems49. The vast majority of MAGs (92.20%) contained genes involved in organophosphorus transport (Fig. 5A), which is a common pollutant in rivers derived from agricultural pesticides55. A total of 20.49% of MAGs, which included members of Pseudomonadota, Actinomycetota, Deinococcota, Myxococcota, Cyanobacteriota, and Verrucomicrobia, had genes for the transport of aromatic acids. These compounds, while naturally present in ecosystems, can also include polycyclic aromatic hydrocarbons (PAHs) introduced into rivers from industrial activities56. Furthermore, 99.39% of MAGs, which encompassed all bacterial phyla detected, had genes involved in the transport of metals. These transportation genes are widespread in bacteria for acquiring essential metals, but they may also participate in the cycling of metals in rivers, which alongside natural sources, may enter rivers through industrial activities57. The widespread presence of these genes indicates that biofilm bacteria may be capable of transporting and therefore potentially transforming harmful organic pollutants that pose a significant risk to aquatic life in rivers. Biofilm bacteria may contribute to metal detoxification by immobilising metals and reducing their bioavailability in the water. This demonstrates the importance of biofilm bacteria in maintaining water quality4 and their valuable application as bioindicators for monitoring ecosystem health58, particularly as rivers are under increasing threat from pollution and contaminants worldwide7.

Environmental drivers of river biofilm communities

Our findings demonstrate the taxonomic diversity of biofilm bacteria and their extensive metabolic and functional roles which are essential for riverine ecosystem functioning. However, while previous studies have explored the environmental drivers of river benthic biofilm communities at the individual catchment scale10,13,59, large-scale patterns and drivers remain poorly understood27, particularly in comparison to surface water communities25. This knowledge gap limits our ability to understand and predict how biofilms and wider river ecosystems respond to environmental change across diverse landscapes. By incorporating detailed spatial information of the upstream catchment alongside high-resolution monitoring of water chemistry, this study represents a novel and comprehensive effort to assess the environmental drivers of river biofilm bacteria across large spatial scales.

Variance partitioning analysis was used to identify the environmental factors shaping river biofilm community composition across England. Environmental variables were categorised as catchment land cover, catchment geology, water chemistry and watershed characteristics. The total variance in the relative abundance of each MAG explained by the full set of measured environmental variables averaged 70.63% (±19.5%). When grouped by phylum, the total variance explained by all environmental variables ranged from 46.55% (Bacillota) to 97.36% (Gemmatimonadota) (Fig. 6A). Catchment land cover has previously been shown to significantly influence bacterial community composition in New Zealand stream biofilms16,27, and in North American river surface water25. Consistent with these findings, upstream catchment land cover was also identified as an important driver of river biofilm communities in England, accounting for up to 24.81% of observed variance at the phylum level, with a mean of 12.78% (±7.3%), although variance explained by land cover was particularly low for some phyla such as Gemmatimonadota (0.66%). The most dominant land cover types included acid grassland, improved grassland, and arable and horticulture, although the proportion of variance explained by these factors varied among bacterial phyla (Fig. 6C).

Upstream catchment geology was found to account for the largest proportion of variance in biofilm bacterial community composition, explaining between 10.94 to 93.52% across bacterial phyla, with a mean of 45.68% (±18.7%). The proportion of the upstream catchment represented by calcareous and siliceous geologies were identified as the dominant geological drivers, explaining up to 37.46% (mean 18.38%±8.4%) and 27.76% (mean 13.55%±5.3%) of variation, respectively (Fig. 6D). To further evaluate the influence of these geological types, non-metric multidimensional scaling (NMDS) was performed, which revealed clear clustering of river biofilms along a gradient of calcareous to siliceous geology, suggesting that calcareous and siliceous geology structure bacterial communities in river biofilms differently (Supplementary Fig. 8, Supplementary Data 5). Furthermore, calcareous geology correlated positively with surface water pH (r=0.36, p<0.001), alkalinity (r=0.33, p<0.001), and conductivity (r=0.29, p<0.001), while siliceous geology correlated negatively with these variables (r=−0.32, r=−0.53, and r=−0.31, respectively, p<0.001) (Supplementary Fig. 9, Supplementary Data 5). These findings are consistent with established relationships between geology and river water chemistry, particularly regarding CaCO3-based buffering capacity (i.e., alkalinity), which is higher in catchments underlain by calcareous geology60,61. The influence of geology on biofilm communities therefore likely operates through its effects on pH, alkalinity, and conductivity47,62.

Despite these associations, water chemistry variables explained a relatively small proportion of variance in biofilm community composition (2.41 to 7.45%, with a mean of 4.51% ±1.6% per phylum). However, individual variables such as alkalinity, conductivity, pH, dissolved oxygen, and water temperature were still influential, each accounting for up to 1.6% of the observed variation (Fig. 6B). While water chemistry was measured and averaged over a three-month period to capture temporal variability, these measurements may still underrepresent the influence of catchment geology, which acts as a persistent landscape-level driver influencing river systems and therefore biofilm development over much longer timescales. Additionally, the biofilm matrix may act as a buffer against physiochemical fluctuations in the water column3, reducing the influence of water chemistry on biofilm communities.

In addition to geological and chemical drivers, orthophosphate, nitrate-N, and nitrite-N emerged as key nutrients shaping biofilm community composition, with orthophosphate most notably influencing the relative abundance of Planctomycetota (2.55%) and Bdellovibrionota (2.03%), nitrate-N influencing the relative abundance of Planctomycetota (0.95%) and Nitrospirota (0.90%), and nitrite-N influencing the relative abundance of Bacillota (2.21%) and Bdellovibrionota (1.24%). Wastewater treatment plant (WWTP) load, which correlated positively with orthophosphate (r=0.66, p<0.001), nitrate-N (r=0.48, p<0.001) and nitrite-N (r=0.56, p<0.001) (Supplementary Fig. 9), was also identified as a key driver of Bdellovibrionota, Nitrospirota, and Planctomycetota relative abundance, accounting for 4.89, 4.51, and 1.33% of variance, respectively (Fig. 6E). These results suggest that WWTP effluent, which introduces organic nutrients into the watercourse, may impact the dynamics of biofilm bacterial communities, with possible implications for microbial nutrient cycling and water quality14.

The River Continuum Concept (RCC)63 describes how continuous physical gradients along a river’s course, from headwaters to mouth, shape its biological and chemical properties. Low-order streams are shallow, narrow and strongly influenced by shading and allochthonous input from vegetation. As stream order, and therefore river width and depth, increase towards the mouth, autochthonous inputs play a progressively larger role63. Successional shifts in surface water bacterial communities along the river continuum have been well-documented within individual catchments64–66. This trend extends to the national-scale, where stream order was identified as the most significant environmental driver shaping bacterial communities in the surface water of rivers across North America25, ranging from stream order 1 to 12. In contrast, stream order within the RSN, which ranged from 1 to 8, was not a dominant driver of bacterial community composition in river biofilms across England, explaining less than 1% of variance for each bacterial phylum (Fig. 6e). Associated physical factors such as river depth and width explained up to 6.31 and 5.81%, respectively, but shading accounted for less than 1.32% of bacterial community composition. Bacterial communities within stream biofilms from a pre-alpine catchment in Austria (stream order 1–5) were also found to show patterns contradictory to the RCC67. These contrasting findings between river surface water and biofilm bacterial communities suggest that successional changes along the river continuum may affect these distinct microbial communities differently. While variation in surface water bacterial communities can be primarily explained by the RCC, local factors such as geology, land cover, nutrients and WWTP load appear to be more influential in shaping river biofilm communities. However, the smaller range of stream orders in the English RSN, compared with those investigated in North America, may partially contribute to the weaker relationship observed between stream order and biofilm community composition. Further studies of biofilm bacterial communities in larger rivers and comparisons between co-located water column and biofilm samples are required to fully understand the influence of the RCC on freshwater bacteria. Seasonal fluctuations could also influence river biofilm community composition, functional potential, and the relative importance of environmental drivers, though seasonal effects have yet to be comprehensively explored at a large-scale. Furthermore, cross-domain biotic interactions may also be an important driver of biofilm bacterial community dynamics and require further study54,67.

Methods
Sample collection
A total of 450 river biofilm samples were collected over a three-year period (2021–2023) from 146 sites within the EA’s RSN programme. The sampling sites were selected using a randomised, spatially balanced design to ensure representative and unbiased spatial coverage across England68. A total of 174 samples were collected in 2021, 178 in 2022, and 98 in 2023, with 62 sites being sampled in all three years. Biofilms were collected from the river benthos by scraping biofilm-covered stones from the river bed at each site with a sterile toothbrush and deionised water69. At sites where stones were not accessible, biofilms were collected by macrophyte scrape. The suspended biofilm was transferred to a 15 ml tube and preserved in the field in 5 ml nucleic acid preservation buffer (3.5M ammonium sulphate, 17mM sodium citrate and 13mM EDTA)70. Samples were immediately transported to the EA’s National Laboratory at Starcross, Exeter where they were concentrated by centrifugation and frozen. Samples were then transported to the UK Centre for Ecology & Hydrology (UKCEH), Wallingford on dry ice and stored at −20°C.

Water chemistry
Water chemistry variables, including water temperature (°C), pH, alkalinity to pH 4.5 as CaCO3 (mg L−1), conductivity, and the concentration of chloride ion (mg L−1), dissolved oxygen (DO, mg L−1), dissolved oxygen saturated (mg L−1), dissolved organic carbon (DOC, mg L−1), total phosphorus (TP, mg L−1), orthophosphate (mg L−1), total nitrogen (TN, mg L−1), total oxidised nitrogen (TON, mg L−1), nitrate-nitrogen (nitrate-N, mg L−1), nitrite-nitrogen (nitrite-N, mg L−1), ammoniacal nitrogen (ammoniacal-N, mg L−1), ammonia-N unionised (mg L−1), and reactive SiO2 (mg L−1), were measured using surface water samples collected from each sampling site. All water chemistry data is available at: https://environment.data.gov.uk/water-quality/view/landing. For each variable, a mean was calculated from up to 5 independent measurements taken on separate occasions at each site across a 3-month period up to and including the day of biofilm sampling and is provided in Supplementary Data 1.

Upstream catchments
The upstream catchment of each RSN site was determined using 10m flow direction and accumulation grids and the sampling points which were snapped to the maximum flow accumulation cell within 50 m of its location. The digital elevation model has a 10m resolution and was based on NEXTMap which was derived from airborne Interferometric Synthetic Aperture Radar (IFSAR) and is available on the UKCEH Catchment Management Modelling Platform (https://catalogue.ceh.ac.uk/cmp/documents). The area of the derived shapefile was then used as the area of the upstream catchment (km2). The upstream catchment shapefiles were intersected with the 10m scale 2020 and 2021 UKCEH land cover maps available at: https://www.ceh.ac.uk/data/ukceh-land-cover-maps, and the British Geological Survey (BGS) geology map available at: https://www.bgs.ac.uk/datasets/bgs-geology-250k. The percentage of the upstream catchment area covered by each land cover type or geology was calculated for each sampling site.

Watershed characteristics
Strahler numbers for the branches of the rivers where sampling sites are located were determined by spatially joining the GRTS points with the 2021 OS Open Rivers Network layer (https://www.ordnancesurvey.co.uk/products/os-open-rivers). To provide an estimate of river depth and width at sampling sites, sampling points were intersected with the gridded 1km2 physical river characteristics for the UK v2 dataset (https://doi.org/10.5285/8df65124-68e9-4c68-8659-1c6b82c735e9). To determine the number of wastewater treatment plants (WWTPs) in the upstream catchments of each site and the total population equivalent loads, a spatial intersect was performed between the upstream catchments and a dataset of WWTPs in England and Wales with their population equivalent loads mapped to discharge location (https://www.data.gov.uk/dataset/0f76a1c3-1368-476b-a4df-7ef32bfd9a8b/urban-waste-water-treatment-directive-treatment-plants). Count and sum functions were used to provide the number of WWTPs and their total population equivalent loads respectively. Average shading from landforms and surface model objects was determined for each sampling by spatially joining the sampling points with a 25m buffer zone to the Environment Agency Keeping Rivers Cool relative shading map available at: https://data.catchmentbasedapproach.org/maps/theriverstrust::riparian-shade-england.

DNA extraction
Each biofilm sample was defrosted and briefly vortexed to homogenise the sample. The Quick-DNA fecal/soil microbe kit (Zymo Research, California, U.S.) was used to extract DNA from 100 µL of sample with the following amendments to the manufacturer’s protocol to maximise DNA yield: Zymo DNA/RNA shield was used as the lysis buffer, samples were lysed at 20 Hz for 20 min using the TissueLyser II (Qiagen, Germany), and 20 µL of proteinase K was added to the lysate prior to incubation at 65°C for 20 min71. Purified DNA was eluted in 100 µL of elution buffer. The purity of extracted DNA was checked using the NanoDrop 8000 spectrophotometer (Thermo Fisher Scientific, MA, U.S.). The concentration of DNA was measured using the QuantiFluor ONE dsDNA kit (Promega, Madison, WI, U.S.) with a BioTek Cytation 5 imaging reader (Agilent Technologies, California, U.S.) according to the manufacturer’s protocol. The DNA was stored frozen at −70°C for long-term archiving at UKCEH, Wallingford.

Metagenomic sequencing
Extracted DNA was sent to Novogene UK for library preparation and 2×150 bp shotgun metagenomic sequencing on an Illumina NovaSeq 6000 with an S4 flow cell to achieve a sequencing depth of at least 10 Gb raw data per sample. Between 2.85 million and 188.26 million raw reads were generated per sample, with a median of 72.16 million raw reads per sample.

Metagenomic data processing
Illumina adaptor sequences were trimmed, and metagenomic reads were filtered to a minimum quality score of 25 using TrimGalore v0.6.5 (https://github.com/FelixKrueger/TrimGalore). Reads mapping to the human reference genome (GRCh38; downloaded on 2023-11-16 from https://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/GRCh38_latest/refseq_identifiers/GRCh38_latest_genomic.fna.gz) were removed. After quality filtering and trimming, between 4.28 million and 90.17 million reads were obtained per sample, with a median of 36.37 million reads per sample. To determine the overall profile and percentage of archaea, bacteria, and eukaryotes in the dataset, SingleM v0.16.072 was run on the pre-processed reads. MultiQC v1.1773 was used to process all filtered reads to obtain basic statistics per sample. Megahit v1.2.974 was used to assemble the reads into contigs, which were subsequently used for binning, functional annotation, and downstream analyses. For functional analysis, open-reading frames (ORFs) were predicted using Prodigal v2.6.375, which were fed into EggNOG-mapper v2.1.976 for functional annotations via the built-in v6.0 database. The ORFs were used in featureCounts77 to generate per-gene coverage, while Kraken2 v2.1.278 and Bracken v2.6.079 were used to taxonomically annotate the contigs, using the PlusPFP (https://benlangmead.github.io/aws-indexes/k2) database, which includes RefSeq protozoa, fungi, plant, archaea, bacteria, virus, plasmid, and human sequences. For stringency, a 0.7 confidence threshold was used in Kraken2. The contigs were also used to obtain metagenome-assembled genomes (MAGs), by dereplicating bins obtained via MetaBAT v2.1580, MetaBinner v1.4.381 and CONCOCT v1.1.082 using dRep v3.2.283. The completion and contamination of the dereplicated bins were estimated with CheckM2 v1.2.284, alongside taxonomic annotation using the comprehensive and phylogenetically consistent prokaryotic genome taxonomy database, GTDB-Tk v2.3.285. Species were delineated using standard GTDB-Tk thresholds of ≥95% average nucleotide identity (ANI) across ≥65% of the genome alignment fraction.

MicroTrait v1.0.086 was used to extract fitness traits from the MAGs, based on protein family sequence similarities and Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KOs). The metabolic and biogeochemical traits and pathways of the MAGs were also analysed using METABOLIC v4.0, which integrates numerous annotation databases, including KEGG and curated hidden Markov model (HMM) databases87. The metabolic pathways were further assessed via metabolisHMM v2.22, which uses curated HMMs for functional annotations of metabolic characteristics spanning carbon, nitrogen, sulfur, and hydrogen cycling pathways88. MicrobeAnnotator v2.0.5 was used to annotate the MAGs using the -light option against the Kofam and Swissprot databases89. Biofilm associated KOs were then identified according to the BBSdb of bacterial biofilm-associated proteins90 supplemented with additional KOs found in KEGG pathways such as quorum sensing (map02024), flagellar assembly (map02040), chemotaxis (map02030), and biofilm formation (map02025, map02026, map05111). The workflow was implemented using the Snakemake workflow management system v7.8.291, and is available at: https://github.com/amycthorpe/metag_analysis_EA for preprocessing the metagenomic reads to MAG assembly. The analysis workflow, including dereplication, taxonomy assignment and the implementation of microTrait, METABOLIC, and metabolisHMM, is available at: https://github.com/amycthorpe/EA_metag_post_analysis.

Data analysis
Using contamination and completeness statistics estimated with CheckM2, the MAGs were categorised as near-complete (>90% complete and <5% contamination) or medium-quality (>70% complete and <10% contamination)92, with remaining MAGs that did not meet the thresholds categorised as low-quality. Medium and high-quality MAGs were retained for downstream analysis. A phylogenetic tree was constructed using the maximum likelihood method with the ggtree R package v3.12.093 using the multiple sequence alignment from the genome taxonomy database toolkit analysis (GTDB-Tk). For biogeographic mapping, relative abundance was determined for each MAG, summed at the phylum level and plotted according to site latitude and longitude using rnaturalearth R package v1.10.900094.

A Bray–Curtis beta diversity dissimilarity matrix was generated based on MAG relative abundance using the vegan R package v2.6.1095. The relationship between the dissimilarity matrix and geographic distance between samples was investigated with a linear regression. Non-metric multidimensional scaling was performed based on the dissimilarity matrix. Environmental variables were fitted to the ordination space using the vegan envfit() function with 999 permutations. To investigate sample clustering within the ordination space by dominant geological drivers, the log ratio of the proportion of the upstream catchment represented by calcareous to siliceous geologies was calculated.

For occupancy and relative abundance analyses, mean relative abundance was calculated for each MAG across all samples, and the number of samples each MAG was present in was used as a measure of occupancy. Levins’ niche breadth (BN) was calculated for each MAG using the MicroNiche R package v1.0.096. This metric quantifies the proportional similarity of resource use, where a BN closer to one is indicative of a more uniform distribution and therefore a broad niche and generalist lifestyle, while a BN closer to zero indicates a narrower niche with a preference for specific conditions96. MAGs with a BN greater than the median BN (0.03) were categorised as generalists, and those with a BN less than the median were categorised as specialists. All MAGs were above the recommended limit of quantification threshold of 1.65. The percentage of generalists and specialist MAGs was calculated for each bacterial phylum. The presence of metabolic and functional traits identified for each MAG using METABOLIC, metabolisHMM, and microTrait were weighted by mean relative abundance and summed at the phylum level and genus level. Biofilm-associated KOs identified with MicrobeAnnotator were normalised by coding density per MAG.

Variance partitioning was used to identify the environmental drivers of bacterial community composition at the MAG level. A linear mixed model using maximum likelihood estimation was fitted to the matrix of MAG relative abundance to estimate the contribution of each environmental variable to community variation using the variancePartition R package v1.34.097. Due to low variance for some MAGs, variance partitioning was performed based on a subset of 340 MAGs. Samples (n=450) were filtered to complete observations according to environmental data availability (n=401). Pearson correlations (two-sided) were computed using the cor() function in R to investigate correlation among the environmental variables and with bacterial phyla on the subset of 401 samples with complete observations. To optimise variance partitioning model performance, some measured water chemistry parameters, including total phosphorus, were not included in the analysis due to significant co-correlation (r>0.80, p<0.001) with other, more biologically available measures (e.g., orthophosphate, Supplementary Data 5). Variance explained per phylum was calculated as a mean of variance explained per MAG. Data analysis was performed in R v4.4.098, and R scripts for data analysis and visualisation are available at: https://github.com/amycthorpe/biofilm_MAG_analysis.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors present an ambitious study that investigates microbial taxonomic and function diversity of river biofilms at a broad geographic scale, highlighting their ecological importance and response to environmental change. This is timely given the increasing pressures of climate and land use change. Genome size, GC content, bacterial community composition, and predicted functional traits were assessed as a function of different environmental drivers, spatial distribution, watershed characteristics, and niche breadth.The authors found biofilms were dominated by generalists, including many novel taxa, with geology, land cover, and nutrients explaining ~90% of community variation. Given the complexity of river biofilms, and the factors that regulate them, the general conclusions from this work support the importance of river biofilms and their ecological roles.\n\nThe scope of this study is impressive, and the authors clearly demonstrate their expertise in managing and interpreting complex environmental and genomic datasets. The use of variancePartition for modeling environmental drivers of microbial composition, niche breadth estimation, and the integration of trait-based approaches across geographic space adds further depth to the study. This integrative approach provides valuable insight into the ecological roles and resilience of microbial biofilms in river systems.\n\nHowever, despite the study’s broad geographic scope, the results are largely descriptive and fall short of advancing understanding of the genes, metabolic pathways, or specific environmental pressures that regulate river biofilm composition and function. While novel in scale, the results offer few new mechanistic insights or conceptual frameworks, providing limited advancement to the field of microbial ecology.\n\nMajor comments:\n\nThe dataset used in this study is extensive and presents a valuable opportunity to advance our understanding of biofilm community structure and function across river systems. However, the manuscript would benefit from a more cohesive narrative and a clearer articulation of the hypotheses and research questions. Currently, the results are largely descriptive, and although some broad patterns are reported (e.g., gene presence, taxonomic composition at the phylum level), the manuscript does not fully capitalize on the richness of the dataset. Moreover, it lacks structural cohesion and a clear narrative to appeal to a broad readership.\n\nSeveral areas could be strengthened. First, only a subset of available environmental variables (e.g., orthophosphate only rather than including total phosphorus) are analyzed, limiting the interpretability of nutrient-driven patterns. Moreover, since geology accounts for the largest proportion of variance, including metrics like CaCO3 (i.e., alkalinity) would also be relevant —does alkalinity in rivers depend on surrounding geology? While the authors highlight geology as a strong driver (e.g., calcareous vs. siliceous substrates), it’s unclear whether rivers were clustered or compared along those axes to support this conclusion more directly. Similarly, while geology and land cover are identified as primary drivers of biofilm communities, their influence likely occurs through changes in water chemistry. However, water chemistry explained little variation in community composition, raising the question of whether this relationship is adequately captured, or if other factors are more directly influencing biofilm composition. This could use more clarification and discussion.\n\nSecond, analyses are restricted to phylum-level, which may obscure ecologically meaningful variation at finer taxonomic levels. Incorporating genus-level trends could yield new insights, especially when paired with genome-resolved functional traits. Although the authors emphasize functional potential (e.g., genes for carbon and nitrogen utilization), variance partitioning shows only modest explanatory power for environmental variables. More discussion on microbe-microbe interactions within the biofilm matrix or other potential variables responsible for the presence of these genes is warranted.\n\nIn short, the manuscript gestures at the importance of river biofilms and their functional capacity, but the analysis and interpretation stop short of delivering a clear conceptual advance. Many of the discussion points are overly broad and lack direct relevance to the results, which weakens their impact and clarity in relation to the study's findings. With stronger framing, more targeted use of the data, and a sharper interpretation of key findings, this study could make a more compelling contribution.\n\nMinor comments:\n\nL1: The title should reflect the main findings of the paper. Currently, it reads too broad. Please refine.\nL26: The sum total variance here is misleading since nutrients accounted for a smaller contribution compared to geology and land cover.\nL31: This is mentioned in the provided citation, the relevance/importance of fungi in rivers systems should be noted, see https://pmc.ncbi.nlm.nih.gov/articles/PMC4601154/pdf/emss-63879.pdf\nL35: Aquatic plants? Litter from riparian vegetation? Please clarify.\nL36: Microbe-microbe interactions (quorum sensing, cooperation/competition) also occur within the biofilm matrix and contribute to composition and function.\nL41: How could these results help inform management? No mention of application other than it could, rather than how.\nL43: Microbe-microbe interactions? Macroinvertebrate grazers?\nL47: Good to include a mention of biotic interactions.\nL49: How is this limited? In what ways?\nL51: What obstacles make this challenging? Moreover, https://www.nature.com/articles/s41522-021-00251-2 discusses the advantage of forming biofilms in mitigating against environmental stressors as well as antagonistic microbe-microbe interactions—could biofilms be more resistant to change compared to water column, sediment or other sink locations in rivers?\nL71: Did you control for interannual variability? Seasonality likely plays a big role\nsee https://journals.asm.org/doi/10.1128/AEM.72.1.713-722.2006\nhttps://www.nature.com/articles/s41598-020-73293-9\nhttps://www.nature.com/articles/s41586-024-08240-z\nhttps://journals.asm.org/doi/10.1128/aem.72.1.713-722.2006\nL72: Why were these sites chosen relative to others? Were the same sites sampled over the 3 year period?\nL78: Do you have total Phosphorus? This is a limiting nutrient important for microbial metabolism.\nL85: This header should be tailored to the content of this section—primarily methods and results for GC association with env. variables. should be divided accordingly.\nL88: Why? How is it different?\nL95: Can this be specified at a higher taxonomic resolution? Fungi, algae, etc.\nAlgae are a large carbon source for heterotrophic microbes in biofilms see\nhttps://www.sciencedirect.com/science/article/pii/S0923250815000819?via=ihub\nThe role of algae in biofilm function might explain some of the variation observed in this study.\nL100: It is surprising that on average only 2 distinct taxa per sample were found (~1000 taxa across ~400 samples). As sequencing depth was impressive at nearly 70M reads per sample, inadequate depth does not seem to explain this. The authors should discuss this in the context of their argument that these biofilms harbor mostly generalists. However, this also highlights whether dereplication was appropriate for this dataset. This step prevents the authors from detecting population level variance that may have corresponded more strongly with the measurement of environmental parameters.\nL108: Many microbial studies refrain from assigning bacterial taxa down to the species level. It is therefore unclear whether this finding that 95.1% of recovered MAGs represented previously uncharacterized species is truly a discovery of novel, undescribed taxa. This seems like it could have been caused by the choice of database or analysis pipeline.\nL111: Please highlight why Phylum-level was chosen as opposed to other taxonomic levels. Family- or order-level might yield new insights, especially in conjunction with predictive function see Shah et al. 2024 https://enviromicro-journals.onLlibrary.wiley.com/doi/10.1111/1462-2920.16634\nL117: What type of rivers? Similar rivers to those in this study, globally? Would also be good to mention if these rivers are rain or spring-fed since you also mention glacial-fed rivers.\nL121: This statement relates to my concern about dereplication. Demonstrating that GC content or genome size varies across broad phylogenetic groups is not new. However, it is conceivable that further analysis of this dataset at finer taxonomic levels might reveal exciting patterns between environmental parameters and GC content and/or genome size. This paragraph as a whole seems problematic because of the coarse taxonomic scale used for this analysis.\nL129: Is there a citation for this? If the citation is in the following sentence then please merge since it reads as a stand-alone.\nL130: Can you clarify the statement of stability? This reads as if nutrient-rich environments are less stable?\nL132: What hypothesis? That genome-size may confer an advantage in nutrient-specific environments? Please state more clearly.\nL136: Have you investigated other genomic parameters beyond gene size and %GC? see Shah et al. 2024 for work on ABC transporters and other adaptations across a large number of freshwater lakes\nhttps://enviromicro-journals.onLlibrary.wiley.com/doi/10.1111/1462-2920.16634\nGrazers have been found to influence biofilms, the idea of aquatic macroinverts as a source or dispersal mechanism of biofilms could be highlighted. Moreover, temperature, light, as well as surrounding riparian vegetation could also explain variation based on certain genes/metabolic pathways. If you have the data, this would be good to include. see Borton et al.\nhttps://doi.org/10.1038/s41586-024-08240-z\nL159: Would be good to bring in the Baas Becking hypothesis here for context.\nL181: This finding suggests that dispersal is high within river systems, but are you seeing distinct compositions across rivers? Within rivers between upstream and downstream?\nL189: What is known so far? The authors mention that \"X is limited, or unknown\" but do not provide much commentary or mention on the knowledge gaps themselves.\nL192: Semantics, but “contributed” implies activity but this study only addresses functional capacity. Descriptions should reflect this (e.g., “contribute to X based on genes encoding for X.”).\nL194: A few more words are needed here to explain methodology. Where did these cycles come from? KEGG pathway database? Further, many of the findings in this paragraph are descriptive and do not differ from expectations based on previously described freshwater bacteria.\nL212: With no association with the environmental drivers of your study, how does this relate? This seems to be a list of all annotated genes without a framework of the broader relevance of these genes. See https://www.nature.com/articles/s41522-021-00251-2 which also mentions that the biomatrix itself can serve as a resource for bacterial communities. It would be good to include more discussion on how dissolved biopolymers from the matrix could perhaps buffer potential effects from nutrient fluctuations or disturbance.\nL213: predicted functional capabilities?\nL225: Penesyan et al 2020 https://www.nature.com/articles/s41522-021-00251-2 explains that the stratification of river biofilms is dependent on location in the water column \"shallow aquatic environments, microbial species are organised according to their metabolic and energetic properties\" since you have depth data this could be useful to include. Moreover, could you clarify if this is related more to the env. variables of interest, or from the biofilm matrix?\nL240: Cooperative and antagonistic interactions? Selective pressure of microbe-microbe interactions are also at play and should be mentioned. Did you notice community turnover across sampling years? How consistent are the communities over time?\nL249: How precise are these genes for anthropogenic pollutants? It would be useful to clarify that these polycylic aromatic compounds differ from naturally found aromatics, such as those comprising plant secondary metabolites.\nL245: Do the rivers in this study have a history of pollution? If so, from what type (mining, agriculture, sewage, etc.)?\nL251: Do you have a table of these genes?\nL252: Similarly, can we be sure ‘transport of metals’ is related to industrial discharge rather than the transport of metals in natural systems without pollutants? (i.e. siderophores for Iron)\nL266: Planktonic? Please clarify.\nL274: Please provide more detail for the other characteristics.\nL275: Please state which factors.\nL281: Please identify which land cover types contributed most to composition.\nL291: Have you explored if these metrics differ between rivers grouped by geology type? The dataset also contains CaCO3, so did you find if concentrations vary among your sites?\nL293: Perhaps a more precise phrase than ‘water chemistry’ is needed. The prior sentence discusses that pH and alkalinity do have important effects on biofilm communities. Water chemistry traditionally includes pH and alkalinity.\nL296: The Methods state that biofilms were scrapped from stones and macrophytes. Please rework this statement given the locations from which biofilms were sampled (not only stones)—it does not seem probable that microbes would directly rely on stone-derived nutrients. It would be interesting, however, to see if P water chemistry varies by geology since soil and bank erosion also contribute to P in river systems. See this paper on geologic N inputs\nhttps://www.nature.com/articles/27410\nL296: A bit of a stretch to include this unless you have chemistry data on the biofilm itself, otherwise should be stated with caution.\nL320: Were there relevant methodological differences between the two studies? For example, it is unclear through this point in the manuscript whether the current study sampled biofilms of both organic and non-organic substrates.\nL314: Do you find that cyanobacteria are more abundant in higher order streams? Or other photosynthetic Eukaryotes?\nFigure 1: Presents a detailed overview of MAG quality, taxonomic novelty, and phylogenetic diversity, which is useful for assessing the scope of the dataset. However, it functions more as a summary of dataset characteristics rather than a figure that advances the narrative or supports a central hypothesis. Consider moving this figure to the supplement and using main-text figures to focus more directly on ecological or functional insights derived from the MAGs.\nFigure 3: Legend needs to be more detailed.\nFigure 4: Where did these pathways in a - c come from? These pathways seem to be overly simplified.\nFigure 5: This is a nice summary of biofilm function, but the major conclusions to be drawn from this are unclear.\nL577: Was spatial sampling across and within years randomized? Certain biases could have arisen in the dataset if certain regions/landscapes were sampled in a single year whereas a different landscape was sampled in a different year. If randomization was not done, then showing limited year-to-year variation in important parameters would be useful (i.e. were any of these years significantly lower in flow rate/drought?).\nL581: Were any statistical comparisons made between biofilms collected from river stone versus those collected from macrophytes?\nL582: This looks like a commonly used mixture to preserve RNA, not DNA. This should be corrected to avoid confusion and a citation should be included to justify this mixture works well as an alternative to premade solutions (i.e. RNALater).\nL595: Measurements were taken during a three month period, which was prior to biofilm collection, but were these measurements taken exactly three months prior to sampling? If not, what range of timespans elapsed between nutrient measures and biofilm sampling? How well do these nutrient measurements correspond with the conditions that occurred at these sites during biofilm collection?\nL649 and 658: Given this range of sample depths, median depth would be more informative than mean.\nL689: These are well accepted standards, but should still be cited: https://www.nature.com/articles/nbt.3893\nL695: Please provide a more detailed (brief) explanation of this. Helpful for a broader audience\nSee Finn et al 2020 https://academic.oup.com/femsec/article/96/8/fiaa131/5863182\nL704: Please identify the LMM adjustment used.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The study “National-scale biogeography and function of river and stream bacterial biofilm communities” by Thorpe et al. provide a more comprehensive understanding of the ecological dynamics of freshwater microbes and their drivers at a global scale. More specifically they highlight (i) the microbial community composition of the river biofilm across the 450 samples in 126 National River Surveillance Network sites, (ii) metabolic potential of recovered 1014 MAGs in C, N and S cycling were illustrated, and (iii) role of environmental drivers on variation in community composition. The concept of the study was quite good and findings are interesting.\n\nHowever, out of the total MAGs obtained, authors included the low-quality MAGs with more than 10% contamination in metabolic analysis (line 195). Recent studies are generally considering the MAGs with less than 10% contamination for analysis (Borton et al. 2024. A functional microbiome catalogue crowdsourced from North American rivers. Nature; Garner et al. 2023. A genome catalogue of lake bacterial diversity and its drivers at continental scale. Nature Microbiology; Bowers et al. 2017. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature Biotechnology; Singleton et al. 2024. Microflora Danica: the atlas of Danish environmental microbiomes. bioRxiv). I suggest to re-do the analysis without low-quality MAGs. It can misinterpret the results in terms of metabolic potential due to contamination. It is quite confusing as a reader in some places authors used only high-quality MAGs (line 109), in some places all MAGs were included. Along with this author did not discuss about the genes related to biofilm formations in MAGs as well the overall discuss was not well written. There are few more questions need to be addressed to improve the quality of the results and their interpretations.\n\n1- Authors need to remove the citation from the abstract.\n2- Line 20: Are these 1014 MAGs high quality ? and classified up to species or strain level\n3- Line 25: Environmental drivers, most notably geology, land cover, and nutrients, explained up to 90% of the variation in community composition. The value is quite high than previously reported average variation. Authors need to clarify this? Is it for overall community composition or for individual taxa?\n4- There is no result and discussion heading in the Manuscript. Please add to separate the section from the introduction.\n5- Line 78: Unit of measurement is not provided in “Extended Data Table 2”. Summary of water chemistry data.\n6- Line 671: Which tools authors had used for dereplication of bins?\n7- In the section “Metabolic and functional potential of river biofilm communities”, genes related to biofilm are not described. Freshwater system can be a source of antimicrobial resistance genes, it would be great if authors can find the MAGs carrying these genes and how their abundance vary across the river network. Authors could have described more about the bioremediation potential of these microbes.\n8- Freshwater is considered as one of the source of GHG emission, therefore author can find out the genomic potential of these MAGs in GHG attenuation and how these biofilms can play an important role in GHG mitigation, if possible.\n9- Authors can also perform the microbial source tracking of MAGs across the riverine network to understand the microbial movement from source to the sink and which are the seeding microbial members of the biofilm, if possible\n10- Authors can perform differential abundant analysis for MAGs across the different sampling sites.\n11- Authors need to improve the discussion in the whole manuscript as it is not up to the standard of the journal."}, "author_response": "Response to Reviewers\nManuscript ID: NCOMMS-25-17856A\nTitle: National-scale biogeography and function of river and stream bacterial biofilm\ncommunities\nOur responses to each individual comment are given below in blue text. Tracked\nchanges have been made in the revised manuscript, and quoted line numbers\ncorrespond to the clean ‘no mark-up’ version of the revised manuscript.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #1 (Remarks to the Author):\nThe authors present an ambitious study that investigates microbial taxonomic and\nfunction diversity of river biofilms at a broad geographic scale, highlighting their\necological importance and response to environmental change. This is timely given the\nincreasing pressures of climate and land use change. Genome size, GC content,\nbacterial community composition, and predicted functional traits were assessed as a\nfunction of different environmental drivers, spatial distribution, watershed\ncharacteristics, and niche breadth. The authors found biofilms were dominated by\ngeneralists, including many novel taxa, with geology, land cover, and nutrients\nexplaining ~90% of community variation. Given the complexity of river biofilms, and\nthe factors that regulate them, the general conclusions from this work support the\nimportance of river biofilms and their ecological roles.\nThe scope of this study is impressive, and the authors clearly demonstrate their\nexpertise in managing and interpreting complex environmental and genomic datasets.\nThe use of variancePartition for modeling environmental drivers of microbial\ncomposition, niche breadth estimation, and the integration of trait-based approaches\nacross geographic space adds further depth to the study. This integrative approach\nprovides valuable insight into the ecological roles and resilience of microbial biofilms\nin river systems.\nHowever, despite the study’s broad geographic scope, the results are largely\ndescriptive and fall short of advancing understanding of the genes, metabolic\npathways, or specific environmental pressures that regulate river biofilm composition\nand function. While novel in scale, the results offer few new mechanistic insights or\nconceptual frameworks, providing limited advancement to the field of microbial\necology.\nWe would like to thank the reviewers for taking the time to read and review our\nmanuscript, and for their positive comments and insightful suggestions, which we have\nincorporated into our revised manuscript or clarified as necessary.\nMajor comments:\n1.1. The dataset used in this study is extensive and presents a valuable opportunity to\nadvance our understanding of biofilm community structure and function across river\nsystems. However, the manuscript would benefit from a more cohesive narrative and\na clearer articulation of the hypotheses and research questions. Currently, the results\nare largely descriptive, and although some broad patterns are reported (e.g., gene\npresence, taxonomic composition at the phylum level), the manuscript does not fully\ncapitalize on the richness of the dataset. Moreover, it lacks structural cohesion and a\nclear narrative to appeal to a broad readership.\nWe thank the reviewer for this comment and as suggested have included research\nquestions to better capture the narrative of our research in lines 93-106. Clearly stating\nthe research questions that align with the structure of the results and discussion\nprovides a stronger framework and emphasises the key novel insights gained from the\ndataset and their wider relevance. The revisions made throughout the manuscript in\nresponse to the suggestions below have expanded and improved the depth of our\nresults, discussion, and conclusions.\n1.2. Several areas could be strengthened. First, only a subset of available\nenvironmental variables (e.g., orthophosphate only rather than including total\nphosphorus) are analyzed, limiting the interpretability of nutrient-driven patterns.\nMetadata variables were selected based on data completeness, co-correlation and\necological relevance to ensure robust analysis. The variance partitioning model fails if\nmany strongly co-correlating variables are included. Total phosphorus was not\nincluded in this analysis as it significantly co-correlated with orthophosphate (r = 0.98,\np < 0.001), and orthophosphate was selected because it is the most bioavailable form\nin rivers, and therefore most ecologically relevant to the microbial community.\nOther variables that were not selected for this analysis include total nitrogen which\nwas available for <50% samples (nitrite-N and nitrate-N were more complete and\nincluded), chloride which co-correlated with conductivity (r = 0.84, p < 0.001) and had\nfewer available measurements (403 vs 444 for conductivity), and ammonia-N which\nco-correlated with both conductivity (r = 0.89, p < 0.001) and ammoniacal-N (r= 0.86,\np < 0.001). We have emphasised our selection choices in the Methods (lines 854-858),\nand we have now included all available environmental variables, including total\nphosphorus, in both the correlation analysis (Extended Data Fig. 9) and NMDS\nanalysis (Extended Data Fig. 8, Supplementary Data 5) to improve interpretability of\nthe patterns, and provided the raw data in Supplementary Data 1.\n1.3. Moreover, since geology accounts for the largest proportion of variance, including\nmetrics like CaCO3 (i.e., alkalinity) would also be relevant—does alkalinity in rivers\ndepend on surrounding geology? While the authors highlight geology as a strong\ndriver (e.g., calcareous vs. siliceous substrates), it’s unclear whether rivers were\nclustered or compared along those axes to support this conclusion more directly.\nSimilarly, while geology and land cover are identified as primary drivers of biofilm\ncommunities, their influence likely occurs through changes in water chemistry.\nHowever, water chemistry explained little variation in community composition, raising\nthe question of whether this relationship is adequately captured, or if other factors are\nmore directly influencing biofilm composition. This could use more clarification and\ndiscussion.\nWe would like to emphasise that alkalinity was analysed in the original manuscript,\nand the dependence of alkalinity (and other water chemistry variables) on surrounding\ngeology and land cover were analysed and discussed (lines 352-355). However, we\nthank the reviewer for prompting us to explore this relationship further, and we have\nnow included NMDS analysis to demonstrate clustering of samples along the geology\ngradient and the relationships with water chemistry (Extended Data Fig. 8). We have\nfurther elaborated on the relative influence of geology and water chemistry as\nsuggested (lines 348-360), in addition to the influence of other factors that were not\nmeasured in the present study e.g., the biofilm matrix, which may buffer against\nconditions in the water column (lines 365-370).\n1.4. Second, analyses are restricted to phylum-level, which may obscure ecologically\nmeaningful variation at finer taxonomic levels. Incorporating genus-level trends could\nyield new insights, especially when paired with genome-resolved functional traits.\nWe thank the reviewers for their suggestion regarding genus-level trends. We want to\nreiterate that all the analysis was done at the metagenome-assembled genome (MAG)\nlevel, which are annotated down to the species resolution, as mentioned throughout\nthe data analysis methods section. Clarifications have been made throughout to\nhighlight this further (e.g., lines 832-834, 841-845, 846-849). Given the large set of\nMAGs recovered within this study, presenting and discussing the metabolic and\nvariance partitioning information for all MAGs or even all genera individually would be\nchallenging and lack clarity for readers. However, as suggested, we have now included\nfigures presented at the genus level (Extended Data Fig. 5 and 7), highlighting the top\n30 genera selected for based on their prevalence and abundance within the dataset,\nwhile MAG level gene abundances were provided in Supplementary Data 4 in the\noriginal manuscript.\n1.5. Although the authors emphasize functional potential (e.g., genes for carbon and\nnitrogen utilization), variance partitioning shows only modest explanatory power for\nenvironmental variables. More discussion on microbe-microbe interactions within the\nbiofilm matrix or other potential variables responsible for the presence of these genes\nis warranted.\nWe have discussed the relatively low explanatory power for certain environmental\nvariables in more detail (lines 361-370) and further highlighted the role of microbe-\nmicrobe interactions within the biofilms and the possibility of this being another factor\ncontributing to the variation observed (lines 402-406).\n1.6. In short, the manuscript gestures at the importance of river biofilms and their\nfunctional capacity, but the analysis and interpretation stop short of delivering a clear\nconceptual advance. Many of the discussion points are overly broad and lack direct\nrelevance to the results, which weakens their impact and clarity in relation to the\nstudy's findings. With stronger framing, more targeted use of the data, and a sharper\ninterpretation of key findings, this study could make a more compelling contribution.\nWe thank the reviewers for prompting us to further the depth of our analysis and\ndiscussion. After making the suggested revisions, we believe the narrative, analysis\nand ecological advancements better represent the depth and complexity of the\ndataset. We also want to emphasise that this dataset is the first of its kind and serves\nas a critical baseline study for understanding the community assemblage and\nfunctions within river biofilms at a national scale.\nMinor comments:\n1.7. L1: The title should reflect the main findings of the paper. Currently, it reads too\nbroad. Please refine.\nWe thank the reviewer for this comment and are happy to consider any suggestions\nthat they and the Editor may have with respect to this.\n1.8. L26: The sum total variance here is misleading since nutrients accounted for a\nsmaller contribution compared to geology and land cover.\nWe have removed nutrients from the list to focus on only the most dominant\nenvironmental variables in the abstract and reworded for clarity (lines 27-29).\n1.9. L31: This is mentioned in the provided citation, the relevance/importance of fungi\nin rivers systems should be noted,\nsee https://pmc.ncbi.nlm.nih.gov/articles/PMC4601154/pdf/emss-63879.pdf\nWe thank the reviewers for highlighting this and have now updated the Introduction in\nline 36 to include all major phylogenetic clades, including fungi, that are relevant to\nbiofilms, as reported in the paper by Besemer et al.\n1.10. L35: Aquatic plants? Litter from riparian vegetation? Please clarify.\nWe have clarified that we are referring to aquatic plants on line 37.\n1.11. L36: Microbe-microbe interactions (quorum sensing, cooperation/competition)\nalso occur within the biofilm matrix and contribute to composition and function.\nWe highlighted that biofilms host interacting assemblages of microbes in this\nsentence, and as suggested, we have further elaborated on this important point in line\n52-53. Further to this, we have also elaborated on interactions in line 62-63 following\ncomments 1.13 and 1.14 below, highlighting interactions as a potential factor\ncontributing to composition and function.\n1.12. L41: How could these results help inform management? No mention of\napplication other than it could, rather than how.\nWe have expanded the introduction with specific examples where understanding the\ndiversity and functions may be applied to ecosystem management. See lines 42-47 in\nrevised manuscript.\n1.13. L43: Microbe-microbe interactions? Macroinvertebrate grazers?\nSee response to comments 1.11 (above) and 1.14 (below).\n1.14. L47: Good to include a mention of biotic interactions.\nAs suggested, we have now highlighted biotic interactions (competition, cooperation,\ngrazing) in our summary of factors influencing microbial community composition and\nfunction in lines 52-53 and lines 62-63.\n1.15. L49: How is this limited? In what ways?\nWe have discussed the limitations to our knowledge of microbial biofilm diversity and\nfunction, many of which are the same as the limitations to identifying environmental\ndrivers in comment 1.16 below, including the complex and heterogenous nature of\nbiofilms, the relatively small scale of previous studies (lines 59-68) and lack of\nmetagenomic-based approaches (lines 76-78).\n1.16. L51: What obstacles make this challenging?\nMoreover, https://www.nature.com/articles/s41522-021-00251-2 discusses the\nadvantage of forming biofilms in mitigating against environmental stressors as well as\nantagonistic microbe-microbe interactions—could biofilms be more resistant to\nchange compared to water column, sediment or other sink locations in rivers?\nFurther to comment 1.15 above, we have updated the Introduction to highlight the\nspecific obstacles that make identifying environmental drivers challenging, notably the\nmultitude of environmental variables that may act on biofilms in different ways, the lack\nof studies that explore a full suite of environmental variables, and as suggested, the\nfact that the biofilms themselves could further complicate relationships due to buffering\nagainst fluctuations and complex microbe-microbe interactions (lines 61-64).\n1.17. L71: Did you control for interannual variability? Seasonality likely plays a big role\nsee https://journals.asm.org/doi/10.1128/AEM.72.1.713-722.2006\nhttps://www.nature.com/articles/s41598-020-73293-9\nhttps://www.nature.com/articles/s41586-024-08240-z\nhttps://journals.asm.org/doi/10.1128/aem.72.1.713-722.2006\nWe agree that seasonality may have an effect. This analysis is part of a different\nhypothesis that is currently being tested for a submission in a future manuscript with\nincreased power with respect to additional samples and sites for a more balanced\ndesign, owing to using a more cost-effective analysis (metabarcoding). We have\ntherefore highlighted that seasonality may be a factor influencing biofilm community\ndynamics that requires further study on lines 391-395.\n1.18. L72: Why were these sites chosen relative to others? Were the same sites\nsampled over the 3 year period?\nThese sites were selected by the Environment Agency (UK) using a randomised\nsampling design to create a network of sites with good spatial coverage of England,\nand capture all major land cover types, geologies and physicochemical conditions as\nexplained and elaborated on in the revised manuscript in lines 86-92 and lines 692-\n694 in the Methods. Further to this, we have included a breakdown of how many\nsamples were collected per year and how many sites were sampled in every year on\nlines 694-695.\n1.19. L78: Do you have total Phosphorus? This is a limiting nutrient important for\nmicrobial metabolism.\nTotal phosphorus was measured but not included within the variance partitioning\nanalysis for reasons explained in the response to comment 1.3 and lines 854-858 in\nthe Methods. However, in response to the suggestions, we have now included total\nphosphorus in the correlation analysis (Extended Data Fig. 9, Supplementary Data 5)\nand NMDS analysis (Extended Data Fig. 8) to improve interpretation of the\nenvironmental relationships.\n1.20. L85: This header should be tailored to the content of this section—primarily\nmethods and results for GC association with env. variables. should be divided\naccordingly.\nWe have added a new section heading: ‘Genomic traits of river biofilm bacteria’ (line\n149).\n1.21. L88: Why? How is it different?\nFor reasons detailed in responses to comments 1.15 and 1.16, and there are few\nstudies on the complexity of river biofilms, particularly at a large scale, compared to\nthe others focused on the water column (for example, Borton et al. 2024, which we\nhave also cited for clarity on lines 111-113.\n1.22. L95: Can this be specified at a higher taxonomic resolution? Fungi, algae, etc.\nAlgae are a large carbon source for heterotrophic microbes in biofilms\nsee https://www.sciencedirect.com/science/article/pii/S0923250815000819?via%3Di\nhub The role of algae in biofilm function might explain some of the variation observed\nin this study.\nFor reasons outlined in line 120, bacterial diversity and function was the focus of this\nmanuscript, and breaking down the proportion of reads assigned deeper than\neukaryotes is therefore out of the scope of this manuscript. The eukaryotic\ncontributions of biofilm communities are part of another ongoing manuscript with a\ntargeted metabarcoding approach. However, we acknowledge the importance of\neukaryotes and that interactions between domains could explain some variation, as\nwe have now highlighted in response to comments 1.11, 1.13, and 1.14.\n1.23. L100: It is surprising that on average only 2 distinct taxa per sample were found\n(~1000 taxa across ~400 samples). As sequencing depth was impressive at nearly\n70M reads per sample, inadequate depth does not seem to explain this. The authors\nshould discuss this in the context of their argument that these biofilms harbor mostly\ngeneralists. However, this also highlights whether dereplication was appropriate for\nthis dataset. This step prevents the authors from detecting population level variance\nthat may have corresponded more strongly with the measurement of environmental\nparameters.\nWe thank the reviewer for this comment. However, we must reiterate that the number\nof recovered MAGs does not represent 2 distinct taxa per sample. In fact, what we\nreport in the study is the number of “non-redundant” taxa across the samples. As\nhighlighted in line 122, the total number of MAGs recovered was 4,027 which were\ndereplicated using drep (line 792) to avoid overinflation of the functional gene\ndetection and annotation, whilst mapping the reads per sample against the\ndereplicated set to obtain sample-specific abundances for taxa and genes. Since this\nstudy is focussed on the overview of the MAGs, their biogeography and their\nrespective functions, the need for population-level variance is out of the scope. While\nwe agree with the reviewer that this would be interesting, the analyses of\nenvironmental drivers at a population and pangenome-scale will be presented in future\nmanuscripts focussing on key indicator taxa of the river ecosystem.\n1.24. L108: Many microbial studies refrain from assigning bacterial taxa down to the\nspecies level. It is therefore unclear whether this finding that 95.1% of recovered\nMAGs represented previously uncharacterized species is truly a discovery of novel,\nundescribed taxa. This seems like it could have been caused by the choice of\ndatabase or analysis pipeline.\nAs reported in the methods in lines 794-795, we used the GTDB database, which is a\ncomprehensive collection of all the known and recorded archaeal and bacterial\ngenomes, including reference genomes\n(https://doi.org/10.1093/bioinformatics/btac672). At the time of analysis, this collection\ncomprised over 400,000 bacterial genomes and is curated. Therefore, we must\nrespectfully disagree with the reviewer that the database was insufficient, reiterating\nour findings to be novel, or previously undescribed MAGs. Moreover, our analysis\npipeline was built using the standard tools used for MAG identification, which includes\ncomprehensive cutoffs for completion and contamination. To improve clarity of this\naspect, we have updated the Methods in lines 794-785 to highlight the choice of\ndatabase.\n1.25. L111: Please highlight why Phylum-level was chosen as opposed to other\ntaxonomic levels. Family- or order-level might yield new insights, especially in\nconjunction with predictive function see Shah et al. 2024 https://enviromicro-\njournals.onLlibrary.wiley.com/doi/10.1111/1462-2920.16634\nSee response to comment 1.4. All our analysis were performed at the MAG level and\nsubsequently grouped for visualisation at the phylum level to provide a more concise\nand clearer overview. We would like to highlight that all data and analysis is provided\nat the MAG level along with taxonomic assignment in the supplementary data files 2,\n3, 4 and 5 in the original submission.\n1.26. L117: What type of rivers? Similar rivers to those in this study, globally? Would\nalso be good to mention if these rivers are rain or spring-fed since you also mention\nglacial-fed rivers.\nWe have added more detail and specified the type of rivers in the cited studies on lines\n142-144.\n1.27. L121: This statement relates to my concern about dereplication. Demonstrating\nthat GC content or genome size varies across broad phylogenetic groups is not new.\nHowever, it is conceivable that further analysis of this dataset at finer taxonomic levels\nmight reveal exciting patterns between environmental parameters and GC content\nand/or genome size. This paragraph as a whole seems problematic because of the\ncoarse taxonomic scale used for this analysis.\nAs highlighted in our response to the comment 1.4 and 1.25, all the analyses were\nperformed at MAG-level, while visual presentations are presented at the phylum-level\nfor clarity. The relationship between GC and genome size is presented at the MAG\nlevel (Fig. 1E). In line with our response to 1.23 and 1.25, dereplication is necessitated\nfor the scope of this study, where for the first time, we are reporting these genomic\ntraits being driven by environmental variables in river biofilms.\n1.28. L129: Is there a citation for this? If the citation is in the following sentence then\nplease merge since it reads as a stand-alone.\nWe have added the citation to both sentences on lines 156-160 to be clear these are\nfindings from the same study.\n1.29. L130: Can you clarify the statement of stability? This reads as if nutrient-rich\nenvironments are less stable?\nWe have adjusted the wording of this statement on lines 159-160 to clarify that those\nwith smaller genomes may be better suited to stable or oligotrophic environments.\n1.30. L132: What hypothesis? That genome-size may confer an advantage in nutrient-\nspecific environments? Please state more clearly.\nWe have revised this to directly state the hypothesis we are referring to on lines 161-\n164.\n1.31. L136: Have you investigated other genomic parameters beyond gene size and\n%GC? see Shah et al. 2024 for work on ABC transporters and other adaptations\nacross a large number of freshwater lakes\nhttps://enviromicro-journals.onLlibrary.wiley.com/doi/10.1111/1462-2920.16634\nGrazers have been found to influence biofilms, the idea of aquatic macroinverts as a\nsource or dispersal mechanism of biofilms could be highlighted. Moreover,\ntemperature, light, as well as surrounding riparian vegetation could also explain\nvariation based on certain genes/metabolic pathways. If you have the data, this would\nbe good to include. see Borton et al. https://doi.org/10.1038/s41586-024-08240-z\nWe thank the reviewer for this interesting comment. We have included several\nmetadata variables in our analysis, including temperature, shade, other\nphysicochemical parameters (see Supplementary Data 1 and 5), but we do not have\ndata on riparian vegetation or macroinvertebrates, so are unable to perform that\nanalysis.\nWe tested observations such as genome size and GC content, similar to the work by\nShah et al. (Fig. 1E), and have now expanded on this as suggested with additional\nbiofilm adaptations including ABC transporters, but also chemotaxis, flagellar\nassembly, EPS biosynthesis, quorum sensing, and the two-component system, see\nExtended Data Fig. 2 and lines 167-176.\n1.32. L159: Would be good to bring in the Baas Becking hypothesis here for context.\nWe have related our findings to the Baas Becking hypothesis, as suggested, see lines\n217-218.\n1.33. L181: This finding suggests that dispersal is high within river systems, but are\nyou seeing distinct compositions across rivers? Within rivers between upstream and\ndownstream?\nWe tested the composition of the bacterial communities across rivers upstream vs\ndownstream by exploring their relationship in the context of stream order but did not\nfind significant associations (lines 392-394, Fig. 6E, Extended Data Fig. 9).\n1.34. L189: What is known so far? The authors mention that \"X is limited, or unknown\"\nbut do not provide much commentary or mention on the knowledge gaps themselves.\nWe have revised this and provide examples of the specific knowledge gaps, see lines\n230-236.\n1.35. L192: Semantics, but “contributed” implies activity but this study only addresses\nfunctional capacity. Descriptions should reflect this (e.g., “contribute to X based on\ngenes encoding for X.”).\nWe have reworded the sentence and others throughout this section to reflect this. See\nlines 237-261.\n1.36. L194: A few more words are needed here to explain methodology. Where did\nthese cycles come from? KEGG pathway database? Further, many of the findings in\nthis paragraph are descriptive and do not differ from expectations based on previously\ndescribed freshwater bacteria.\nAs suggested, we have added more detail to the methods outlining the METABOLIC\ntool used to identify metabolic pathways, which are identified using several databases\nintegrated within the tool, including KEGG, see lines 798-800 and cited paper\n(https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01213-\n8). Similar to this, we have also provided more detail outlining the metabolisHMM tool,\nsee lines 800-802 and cited paper\n(https://www.biorxiv.org/content/10.1101/2019.12.20.884627v2). We reiterate that this\nstudy is the first of its kind, necessitating a broad overview of the various functions\nfound in microbial biofilms in freshwaters. A study to this scale has not been\nundertaken, except for the one by Borton et al., who did not include biofilms in their\nanalysis. We therefore respectfully suggest that a description of the identified functions\nis well within the scope of this manuscript.\n1.37. L212: With no association with the environmental drivers of your study, how does\nthis relate? This seems to be a list of all annotated genes without a framework of the\nbroader relevance of these genes. See https://www.nature.com/articles/s41522-021-\n00251-2 which also mentions that the biomatrix itself can serve as a resource for\nbacterial communities. It would be good to include more discussion on how dissolved\nbiopolymers from the matrix could perhaps buffer potential effects from nutrient\nfluctuations or disturbance.\nWe thank the reviewer for this comment and have updated the discussion to further\nelaborate on how matrix biopolymers may serve as a nutrient resource and buffer the\neffects of nutrient fluctuations and other disturbances. See lines 283, 291-295.\n1.38. L213: predicted functional capabilities?\nAs stated in our response to comment 1.35, we have reworded the section throughout\nto reflect these results are predicted capabilities or capacity based on identified genes\nlines 264-315.\n1.39. L225: Penesyan et al 2020 https://www.nature.com/articles/s41522-021-00251-\n2 explains that the stratification of river biofilms is dependent on location in the water\ncolumn \"shallow aquatic environments, microbial species are organised according to\ntheir metabolic and energetic properties\" since you have depth data this could be\nuseful to include. Moreover, could you clarify if this is related more to the env. variables\nof interest, or from the biofilm matrix?\nThe river depth data is an estimate of river depth from a physical characteristic map\n(lines 735-738) and was not measured in situ at the time of biofilm sampling. Although\ninteresting, it would therefore not be appropriate to include the suggested analysis.\nWe have clarified that we are referring to the microenvironment within the biofilm\nmatrix itself on line 276.\n1.40. L240: Cooperative and antagonistic interactions? Selective pressure of microbe-\nmicrobe interactions are also at play and should be mentioned. Did you notice\ncommunity turnover across sampling years? How consistent are the communities over\ntime?\nIn line with our responses to previous comments on including interactions as a factor,\nwe have included the discussion of microbe-microbe interactions in lines 52-53, 62-\n63, 165-166 and 405-406. in the revised manuscript. We agree with the reviewer that\nannual turnover is an interesting question. However, as stated on line 695, only a small\nproportion of the sites were sampled in all three years and temporal analysis of the\ncurrent dataset would therefore lack statistical power. This is out of the scope of the\npresent manuscript and will be explored in a future manuscript when more temporal\nsamples have been collected as part of routine monitoring.\n1.41. L249: How precise are these genes for anthropogenic pollutants? It would be\nuseful to clarify that these polycylic aromatic compounds differ from naturally found\naromatics, such as those comprising plant secondary metabolites.\nThis is a good point to highlight, and we have reworded the sentence to reflect that\nthe identified genes are also relevant to naturally occurring compounds on line 305.\n1.42. L245: Do the rivers in this study have a history of pollution? If so, from what type\n(mining, agriculture, sewage, etc.)?\nThe accurate historical data of pollution for the significant number of sites/rivers\nsampled across England that would be required to provide this information is not\navailable, however, we do provide WWTP load, which is included within the variance\npartitioning analysis, discussed in lines 376-382, and provided in Supplementary Data\n1.\n1.43. L251: Do you have a table of these genes?\nYes, provided as Supplementary Data 4: metabolism_and_functions.xlsx along with\nthe original submission.\n1.44. L252: Similarly, can we be sure ‘transport of metals’ is related to industrial\ndischarge rather than the transport of metals in natural systems without pollutants?\n(i.e. siderophores for Iron)\nAs above, we have reworded this paragraph to be clear the identified genes may be\nrelated to naturally occurring compounds/metals in addition to those from\nanthropogenic sources on line 308.\n1.45. L266: Planktonic? Please clarify.\nThe cited studies all looked at benthic biofilms on stones, we have reworded this\nsentence for clarity on line 320.\n1.46. L274: Please provide more detail for the other characteristics.\nDetails of the other characteristics are discussed in detail in the following paragraphs\n(WWTP load, line 376-382, stream order, line 392-393, river width and depth, line 393-\n395, and shading, line 395-396).\n1.47. L275: Please state which factors.\nWe have reworded this to be clear that we are referring to the collective variance\nexplained by all measured factors here on lines 331-332.\n1.48. L281: Please identify which land cover types contributed most to composition.\nWe have identified the dominant land cover types as suggested on lines 340-342.\n1.49. L291: Have you explored if these metrics differ between rivers grouped by\ngeology type? The dataset also contains CaCO3, so did you find if concentrations vary\namong your sites?\nWe have explored the relationships with water chemistry parameters, including\nalkalinity across the geology gradient further with NMDS analysis (Extended Data Fig.\n8, lines 348-352.\n1.50. L293: Perhaps a more precise phrase than ‘water chemistry’ is needed. The prior\nsentence discusses that pH and alkalinity do have important effects on biofilm\ncommunities. Water chemistry traditionally includes pH and alkalinity.\npH and alkalinity are included within the water chemistry category. We have reworded\nthis section to avoid confusion, see lines 361-365.\n1.51. L296: The Methods state that biofilms were scrapped from stones and\nmacrophytes. Please rework this statement given the locations from which biofilms\nwere sampled (not only stones)—it does not seem probable that microbes would\ndirectly rely on stone-derived nutrients. It would be interesting, however, to see if P\nwater chemistry varies by geology since soil and bank erosion also contribute to P in\nriver systems. See this paper on geologic N\ninputs https://www.nature.com/articles/27410\nAs explained in lines 695-698, biofilms were predominately sampled from stones with\noccasional exceptions from macrophytes, however, this information was not recorded\ndue to the nature of the routine monitoring program (see response to comments 1.53\nand 1.60). As explained in our response to comment 1.2, TP and TN were not analysed\nfurther due to data availability and co-correlation with other biologically relevant\nmeasures. However, we have removed the sentence (see response to comment 1.52\nbelow), and we agree with the reviewers that this could be an interesting relationship,\nand as such have included TP in our NMDS analysis to show the relationship with\ngeology (Extended Data Fig. 8).\n1.52. L296: A bit of a stretch to include this unless you have chemistry data on the\nbiofilm itself, otherwise should be stated with caution.\nConsidering we do not have chemistry data on the biofilm microenvironment itself, we\nhave removed this statement as suggested and instead highlight that the relatively\nsmall variance explained by water chemistry may in part be due to the biofilm matrix\nbuffering communities against fluctuations in the water (lines 365-370), as suggested\nby the reviewer in comment 1.37.\n1.53. L320: Were there relevant methodological differences between the two studies?\nFor example, it is unclear through this point in the manuscript whether the current\nstudy sampled biofilms of both organic and non-organic substrates.\nMain methodological difference between the two studies (present study and Borton et\nal., 2024) which we highlight is surface water vs biofilm bacterial communities on line\n391. See response to comment 1.51 and 1.60 – biofilms in the present study were\npredominately sampled from stones with occasional exceptions from macrophytes\nwhere stones were not accessible, however due to the large-scale routine monitoring\nprogram, it was not recorded for every site whether samples were stone or macrophyte\nbiofilms.\n1.54. L314: Do you find that cyanobacteria are more abundant in higher order\nstreams? Or other photosynthetic Eukaryotes?\nThe focus of this manuscript is on bacteria, and eukaryotes are therefore beyond the\nscope of the research questions. However, we found that stream order accounted for\n<1% variation in any bacterial phylum, including cyanobacteria, as discussed in lines\n392-394. Furthermore, cyanobacteria negatively correlated with stream order,\nindicating a lower abundance in higher order streams, although this correlation was\nnot strong (r=-0.16, p <0.001, Extended Data Fig. 9, Supplementary Data 5). We have\ndiscussed this in relation to other studies on biofilms, which also found similar\nrelationships with stream order and the river continuum concept (RCC) in contrast to\nplanktonic studies such as Borton et al. (lines 396-400).\n1.55. Figure 1: Presents a detailed overview of MAG quality, taxonomic novelty, and\nphylogenetic diversity, which is useful for assessing the scope of the dataset. However,\nit functions more as a summary of dataset characteristics rather than a figure that\nadvances the narrative or supports a central hypothesis. Consider moving this figure\nto the supplement and using main-text figures to focus more directly on ecological or\nfunctional insights derived from the MAGs.\nWe agree with the reviewers that figures illustrating the ecological or functional insights\nare important, and Figure 1 does provide a clear summary of dataset characteristics\nwhich is integral to the study and introduces the reader to the scope of the dataset.\nFurthermore, the phylogenetic diversity and taxonomic novelty in particular are key\noutcomes of the study which need to be highlighted in the main text. We have therefore\ndecided to keep Figure 1 in the main text.\n1.56. Figure 3: Legend needs to be more detailed.\nWe have added more detail in the legend about occupancy and niche breadth as\nrequested, see lines 668-671.\n1.57. Figure 4: Where did these pathways in a - c come from? These pathways seem\nto be overly simplified.\nAs outlined in our response to comment 1.36, we have now provided further detail to\nthe METABOLIC tool which was used to generate these pathways using KEGG\nmodules (see lines 798-800 and cited paper). In response to this comment, we have\nalso included this information in the legend of Fig. 4, lines 672-677, and similarly for\nFig. 5, lines 678-682.\n1.58. Figure 5: This is a nice summary of biofilm function, but the major conclusions\nto be drawn from this are unclear.\nWe thank the reviewer for this comment and would like to highlight that the findings\nfrom microtrait (Fig. 5) are discussed in detail from line 264 to line 315 and Fig. 5 is\ncited throughout this section. Major conclusions are the prevalence of metabolic traits\ne.g., breakdown of organic compounds (lines 265-269 and lines 299-315), diversity of\ntrophic capabilities (lines 270-280), and range of biofilm and stress-related genes\n(lines 281-298). Together these highlight the exceptional functional versatility of the\nbiofilm bacteria (lines 418-424 in the conclusion). We have added a subheading for\nthis section focusing on function and Fig. 5 for clarity and distinction (line 263).\n1.59. L577: Was spatial sampling across and within years randomized? Certain biases\ncould have arisen in the dataset if certain regions/landscapes were sampled in a single\nyear whereas a different landscape was sampled in a different year. If randomization\nwas not done, then showing limited year-to-year variation in important parameters\nwould be useful (i.e. were any of these years significantly lower in flow rate/drought?).\nYes, the RSN was designed using an unbiased, randomised and spatially balanced\napproach to reduce pressure-based sampling (Brown et al., 2015 -\nhttps://www.sciencedirect.com/science/article/pii/S1878029615003205), and we have\nadded more detail about the sampling design on lines 692-694.\n1.60. L581: Were any statistical comparisons made between biofilms collected from\nriver stone versus those collected from macrophytes?\nThis is not possible as samples were not recorded as being stone or macrophyte\nbiofilms. The samples were collected as part of routine monitoring for the Water\nFramework Directive (WFD) assessment, and this information is not required for the\ntrophic diatom index calculations that the monitoring program focusses on, and as\nsuch is not recorded. However, samples were predominantly stone scrapes,\nmacrophyte scrapes were only performed at occasional sites where no stones were\naccessible as explained on lines 697-698 and comments 1.51 and 1.53.\n1.61. L582: This looks like a commonly used mixture to preserve RNA, not DNA. This\nshould be corrected to avoid confusion and a citation should be included to justify this\nmixture works well as an alternative to premade solutions (i.e. RNALater).\nThe buffer is an RNALater-like solution for nucleic acid preservation, and DNA\nextracted from biofilms stored in this buffer have been shown to be stable for >3 years\n(Warren et al., 2024). We have added this supporting reference to the methods on line\n700.\n1.62. L595: Measurements were taken during a three month period, which was prior\nto biofilm collection, but were these measurements taken exactly three months prior\nto sampling? If not, what range of timespans elapsed between nutrient measures and\nbiofilm sampling? How well do these nutrient measurements correspond with the\nconditions that occurred at these sites during biofilm collection?\nMeasurements were taken throughout the 3-month period up to and including time of\nbiofilm collection. This therefore included up to five independent measurements taken\non separate occasions across a timespan of 3 months and averaged. This was done\nto capture longer term conditions, rather than being limited to potentially transient\nconditions at the time of sampling due to the variability of grab sampled water\nchemistry. We have expanded this to clarify on lines 714-716.\n1.63. L649 and 658: Given this range of sample depths, median depth would be more\ninformative than mean.\nThis is a good point, we have now included median raw (line 769) and filtered (778)\nreads per sample.\n1.64. L689: These are well accepted standards, but should still be\ncited: https://www.nature.com/articles/nbt.3893\nWe have now included the citation for MAG standards on line 817.\n1.65. L695: Please provide a more detailed (brief) explanation of this. Helpful for a\nbroader audience See Finn et al\n2020 https://academic.oup.com/femsec/article/96/8/fiaa131/5863182\nWe thank the reviewers’ for prompting us to provide more information on Levins’ niche\nbreadth, which we have now expanded on in lines 832-840.\n1.66. L704: Please identify the LMM adjustment used.\nMaximum likelihood estimation was used, and we have amended the sentence on\nlines 847-848 to clarify.\nReviewer #3 (Remarks to the Author):\nThe study “National-scale biogeography and function of river and stream bacterial\nbiofilm communities” by Thorpe et al. provide a more comprehensive understanding\nof the ecological dynamics of freshwater microbes and their drivers at a global scale.\nMore specifically they highlight (i) the microbial community composition of the river\nbiofilm across the 450 samples in 126 National River Surveillance Network sites, (ii)\nmetabolic potential of recovered 1014 MAGs in C, N and S cycling were illustrated,\nand (iii) role of environmental drivers on variation in community composition. The\nconcept of the study was quite good and findings are interesting.\nHowever, out of the total MAGs obtained, authors included the low-quality MAGs with\nmore than 10% contamination in metabolic analysis (line 195). Recent studies are\ngenerally considering the MAGs with less than 10% contamination for analysis (Borton\net al. 2024. A functional microbiome catalogue crowdsourced from North American\nrivers. Nature; Garner et al. 2023. A genome catalogue of lake bacterial diversity and\nits drivers at continental scale. Nature Microbiology; Bowers et al. 2017. Minimum\ninformation about a single amplified genome (MISAG) and a metagenome-assembled\ngenome (MIMAG) of bacteria and archaea. Nature Biotechnology; Singleton et al.\n2024. Microflora Danica: the atlas of Danish environmental microbiomes. bioRxiv). I\nsuggest to re-do the analysis without low-quality MAGs. It can misinterpret the results\nin terms of metabolic potential due to contamination. It is quite confusing as a reader\nin some places authors used only high-quality MAGs (line 109), in some places all\nMAGs were included. Along with this author did not discuss about the genes related\nto biofilm formations in MAGs as well the overall discuss was not well written. There\nare few more questions need to be addressed to improve the quality of the results and\ntheir interpretations.\nWe would like to thank Reviewer 3 for reviewing our manuscript, and for their helpful\nrecommendations, which we have incorporated into our revised manuscript or clarified\nas necessary.\n2.1. Authors need to remove the citation from the abstract.\nCitations have been removed from the abstract.\n2.2. Line 20: Are these 1014 MAGs high quality? and classified up to species or strain\nlevel\nThe 1,014 MAGs encompassed all dereplicated MAGs, however, in response to the\nreviewer’s suggestions above we have filtered out low-quality MAGs to ensure robust\nanalysis and accurate annotation (lines 817-819). We now report results based on 820\nmedium and high-quality MAGs (see line 129). The 820 MAGs are classified to the\nspecies level where possible using GTDB (lines 794-795 and lines 130-131), and the\nproportion identified as novel genera and species remains robust with only minor\nchanges to the percentage compared to when low-quality MAGs were included (i.e.,\n21.6 to 20.6% novel genera). We have amended our results, figures, and discussion\naccordingly throughout, and we would like to highlight that this quality filtering has not\nsignificantly changed any of our findings or conclusions.\n2.3. Line 25: Environmental drivers, most notably geology, land cover, and nutrients,\nexplained up to 90% of the variation in community composition. The value is quite high\nthan previously reported average variation. Authors need to clarify this? Is it for overall\ncommunity composition or for individual taxa?\nWe originally reported total variance explained by the full suite of analysed\nenvironmental factors at the phylum level (highest total explained by phylum – ‘up to\n90%’). However, for clarity and improved resolution, we have now reported the\nvariance explained by all analysed environmental factors as an average across\nindividual MAGs, see line 27-29.\n2.4. There is no result and discussion heading in the Manuscript. Please add to\nseparate the section from the introduction.\nWe have added this section heading line 108.\n2.5. Line 78: Unit of measurement is not provided in “Extended Data Table 2”.\nSummary of water chemistry data.\nThank you for pointing this out, we have now included units for each measurement in\nthe table legend.\n2.6. Line 671: Which tools authors had used for dereplication of bins?\nWe thank the reviewer for highlighting this, and we have now added that we used\ndRep on line 792.\n2.7. In the section “Metabolic and functional potential of river biofilm communities”,\ngenes related to biofilm are not described. Freshwater system can be a source of\nantimicrobial resistance genes, it would be great if authors can find the MAGs carrying\nthese genes and how their abundance vary across the river network. Authors could\nhave described more about the bioremediation potential of these microbes.\nGenes related to biofilms e.g., biofilm formation, are discussed on lines 281-295. We\nagree with the reviewer that reporting AMR genes would be an interesting application.\nHowever, this is beyond the scope of the present manuscript and would require in-\ndepth analysis and therefore a separate manuscript which is currently in prep.\nSimilarly, the bioremediation potential of these MAGs is currently being analysed for\na future manuscript with in-depth analysis associated with particular phylogenetic\ngroups\n(https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.76405\n8/full). However, we have explored other biofilm related genes as suggested, including\nABC transporters, chemotaxis, flagellar assembly, EPS biosynthesis, quorum sensing,\nand the two-component system in Extended Data Fig. 2, lines 284-285 and 167-176.\n2.8. Freshwater is considered as one of the source of GHG emission, therefore author\ncan find out the genomic potential of these MAGs in GHG attenuation and how these\nbiofilms can play an important role in GHG mitigation, if possible.\nThe metabolic pathways identified using the METABOLIC tool are presented in Fig. 4,\nthis includes pathways and genes which are associated with greenhouse gases, which\nwe have now emphasised (lines 258-261).\n2.9. Authors can also perform the microbial source tracking of MAGs across the\nriverine network to understand the microbial movement from source to the sink and\nwhich are the seeding microbial members of the biofilm, if possible\nWe acknowledge that this is an interesting point, however we do not have the required\nsource and sink samples to perform source tracking.\n2.10. Authors can perform differential abundant analysis for MAGs across the different\nsampling sites.\nDue to the national scale survey nature of our dataset, differential abundance testing\nwould not be appropriate as we do not have two discrete groupings.\n2.11. Authors need to improve the discussion in the whole manuscript as it is not up\nto the standard of the journal.\nWe thank the reviewer for this comment and as suggested, following the many\nrevisions made throughout, we have improved the Discussion to improve clarity and\nscientific reasoning."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thorpe et al have made a nice effort to address many of our minor concerns from the prior round of review. Unfortunately, we feel that several of our major concerns have not been adequately addressed by this revision. Nevertheless, we appreciate that the geographic scope of this dataset will without question provide excellent baseline data for many future studies. However, we think that many of the findings are overstated and unsupported, often missing key comparisons to controls and benchmarks, and so this manuscript would still require significant revision prior to consideration for publication.\n\nA thorough description is provided of the biofilm community and genes within these taxa. However, with no quantitative comparisons of these results to the microbes inhabiting the water column, or other biofilm communities inhabiting other environments, there is no context for evaluating what is unique about these biofilm communities in terms of taxonomy, genomic characteristics or functional potential. Thus, the majority of results reported in this manuscript are descriptive and fall short of advancing understanding of the field of microbial ecology. For example, a sizable portion of the manuscript is dedicated to comparing GC content and genome size of different bacterial phyla. However, it is well appreciated in the field that such traits vary across the tree of life and the authors have not directly responded to this concern. How different are these features in their samples vs. those from a broad range of samples? Further, the authors argue that given the presence of many genes within the C, N and S cycling pathways and that these communities are vital to the cycling of these nutrients in rivers. We may be misinterpreting their statements, but in essence our understanding is that they are arguing that the presence of C, N, and S cycling genes is a surprising finding, when of course many microbes sequenced from many sources would have genes involved in C/N/S cycling given the fundamental importance of these genes to life. How does the occurrence of genes within these pathways differ from any other complex environmental community of bacteria? Are genes in these cycles over-represented in riverine biofilms compared to the null expectation (such as in contrast to the occurrence of these genes in surface water communities)? These controls are essential in order to make any substantive conclusion about the role of riverine biofilms in C/N/S cycling, and the authors should be able to make these comparisons using publicly available data sets. The same holds for claiming the value of riverine biofilms in metal transport, as iron transport, for example, is essential for most life.\n\nThe manuscript makes large claims about the critical role of riverine biofilms in ecosystem function and “modulating fluxes of greenhouse gases in river ecosystems” (L261). It is not possible to conclude from metagenomic data alone that these communities have a sizable effect on greenhouse gas fluxes. Doing so requires in-situ measurements or mesocosm studies in which water quality changes are measured between treatments with and without biofilm communities, or contrasting measures when biofilm communities vary in taxonomic composition. At a minimum, measures of gene expression are needed.\n\nOverall, we think this manuscript is still of high potential interest, because the results presented in Figure 6 are very interesting. Our suggestion was to focus on these results and perhaps pare down the other data presented unless these key control analyses are added. A stronger manuscript could be developed that focuses specifically on these results of how various environmental parameters predict biofilm composition and function. This is an outstanding dataset, but a sizable amount more needs to be done to elevate the interpretability and impact of these results.\n\nAdditional Specific Concerns\n\nTitle: there is no reference to a quite interesting aspect of this paper--- environmental parameters are predictive of biofilm biogeography\n\nL121. Not following how a dataset with 311 genera has only 47 species?\n\nL133. Based on the information currently provided, we are still not fully convinced of their argument that 94.4% of recovered MAGs represented previously uncharacterized species. Could you provide mean ANIs between your MAGs and the closest hit to the GTDBK database? As bacterial ‘species’ are an area of continued debate, more clarity of exactly how species are being defined is needed.\n\nL152. Lack of context. If one were to look at Myxococcota MAGs in a range of environments, would they all have a large genome with high GC content? Unclear if this is an advance in understanding about the Myxococcota in general, or those specific Myxococcota that happen to inhabit riverine biofilms.\n\nL160. Would be useful to add reviews about genome streamlining theory (Giovannoni et al. 2014).\n\nL167-176. The manuscript is strengthened by this addition of genes involved in biofilm formation but again lacks sufficient context. Are these same genes universally found in other biofilms? In other biofilms, has it been found that “these traits were distributed across MAGs spanning diverse taxonomic groups?” Or is this a unique finding to riverine biofilms?\n\nL210. The extra information regarding niche breadth is appreciated, but I am concerned this may be circular logic. If certain taxa are found in nearly all samples (i.e. high occupancy) in a sample set collected across a wide range of habitat types, would these taxa not by definition need to tolerate a wide range of habitat types found in the sample set and thereby be considered generalists? More discussion on how to interpret these results would be helpful.\n\nL214. While a weak relationship between community dissimilarity and geographic distance could indicate high dispersal, is this the only possibility? If dispersal rates were low but evolutionary change was also low, or pressures on community composition/species sorting were uniform across space, would we not also see a weak relationship?\n\nL277-280. These inferences are not supported by results in this study. There is no microspatial component to the sampling of these biofilms, at least as reported. So, it seems that the authors assume their diversity measures are a consequence of stratification. Authors should reduce causal language and remove statements that are not drawn from the presented results. Making such inferences could be acceptable if it is made clear that future studies are needed to prove causality.\n\nL296-298. It is challenging to see how these claims are supported with no reference samples to compare to. Are these genes associated with stress tolerance strategies found in all bacteria? Are these genes significantly more prevalent in riverine biofilms, than for example the surface water communities?\n\nL310-311. Certain metals, such as Iron, are essential for cellular growth. As a consequence, most bacteria have means of transporting such metals, such as siderophores. To make a claim that this high prevalence of metal transporters is due to transporting and transforming harmful pollutants, the authors must provide adequate context. Is this “99.39% of MAGs” significantly greater than the proportion of MAGs found in other systems to have these same genes involved in transport of metals?\n\nL382. This statement needs to be moderated. This study does not measure how biofilms with certain community compositions versus others change nutrient cycling or impacts water quality.\n\nL399-400. Comparison to the North American dataset is certainly warranted, but caution is needed when drawing interpretations between these two studies because there are multiple variables that differ. For example, the NA study includes much larger rivers, which could have driven the results in this study that stream order was predictive. So, we do not think it is a valid argument that stream order shapes surface water bacterial communities but does not shape riverine biofilm communities. There are confounding variables between the two studies that would need to be addressed before such a claim could be made.\n\nL418-421. It is difficult for a reader to appreciate how this is a ‘diverse array of genes’ and functions without any reference point. How does this compare to complex environmental bacterial communities in surface waters? Or other biofilms sampled in other environments? Again, here is where a contrast to bacterial communities in the surface waters could go a long way towards demonstrating why these riverine biofilms are potentially unique in their contributions to riverine function.\n\nL433-435. Seems like an overreach. No measurements were taken for how biofilms alter water quality or public health. The link to biodiversity conservation is unclear. Making such claims necessitates functional measurements of how biofilms alter water quality.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors have comprehensively responded to all the comments, resulting in substantial improvements to the manuscript."}, "author_response": "Response to Reviewers\nManuscript ID: NCOMMS-25-17856A\nTitle: National-scale biogeography and function of river and stream bacterial biofilm\ncommunities\n\nWe would like to thank the reviewers for taking the time to review our revised\nmanuscript, and we appreciate their recognition of the geographic scope and value of\nour dataset in “providing excellent baseline data for many future studies,” which\nreflects the overarching goal of our study.\n\nWe have carefully revised our manuscript to ensure that interpretations are balanced,\nprecise, and fully supported by our data by adjusting our language throughout. For\nexample, we have clarified our approach to species delineation (L131-136 and L829-\n831), highlighted that a prevalence of C, N and S cycling genes suggests a capacity\nto perform diverse biochemical roles which may include contribution to greenhouse\ngas fluxes in river ecosystems (L272-275), emphasised that genes such as metal\ntransport genes are also involved in essential metabolism pathways (L325-327).\nThese modifications improve the clarity and interpretability of our results and avoid\nimplying causality beyond what our data support.\n\nWe appreciate the reviewer’s view that comparisons with biofilms or other sample\ntypes in different ecosystems could provide interesting insights. We have therefore\nincorporated several additional comparisons. For example, we have identified\nsimilarities and differences in the dominant phyla observed with other river biofilms\nand surface waters (L144-153), explored the relationship between genome size and\nGC content observed for specific taxa across biofilm, pelagic and sediment\ncommunities from other rivers and lakes (L160-164), highlighted that the prevalence\nof biofilm-associated (L182-186) and stress tolerance genes (L312-315) is conserved\nacross biofilms from a range of freshwater environments, and more comprehensively\naddressed the key differences between our study and Borton et al. (2024), highlighting\nwhere further research is required (L409-427). These additions better place our\nfindings in a broader ecological context.\n\nHowever, we respectfully note that our study was not designed to test hypotheses\nregarding differences across environments or to identify features unique to river\nbiofilms. The primary aims of this work, as stated in the introduction (L98-106), were\nto characterise the biogeography, functional potential and environmental drivers of\nriver biofilm bacterial communities at a national scale. While we acknowledge that\nmore extensive comparative analyses, such as “quantitative comparisons of these\nresults to the microbes inhabiting the water column,” could be valuable, such analyses\ncurrently have substantial limitations due to methodological differences and the wide\ngeographic spread of publicly available metagenomes. For example, the suggested\nadditional analysis would require comparison with surface water metagenomes from\nNorth American rivers in GROWdb (Borton et al., 2024). However, as pointed out by\nthe reviewers’ themselves in comment 12, there are confounding variables between\nthe datasets, including methodological differences and the 5,000-9,000 km distance\nbetween the studies. It would therefore be difficult to determine whether observed\ndifferences reflect habitat type or spatial variation. We believe such a comparison\nwould have limited interpretive value, and the suggested analysis would therefore\nrequire substantial additional data collection which is beyond the scope of this\nmanuscript. We highlight this and the need for further comparative research utilising\nco-located biofilm and water column samples in our revised manuscript (L423-427).\n\nOverall, these revisions have significantly strengthened the manuscript, clearly\nsituating our work within the existing literature, maintaining alignment with the study’s\nscope and aims, and establishing a national-scale genomic resource that will serve as\nan essential foundation for future comparative and experimental studies.\n\nOur detailed responses to each individual comment are given below in blue text.\nTracked changes have been made in the revised manuscript, and quoted line numbers\ncorrespond to the clean ‘no mark-up’ version of the revised manuscript.\n\nAdditional Specific Concerns\n\nTitle: there is no reference to a quite interesting aspect of this paper--- environmental\nparameters are predictive of biofilm biogeography\n\nWhile we agree that this is an important aspect of our study, we believe our current\ntitle accurately reflects the central focus of the study which is the national scale\nbiogeography and functional potential of river biofilm bacteria.\n\n1. L121. Not following how a dataset with 311 genera has only 47 species?\n\nWe thank the reviewer for highlighting this. The statement in our manuscript,\n“…including 20 known phyla, 35 classes, 91 orders, 160 families, 311 genera and 46\nspecies” (lines 130-131 in the revised version), refers to the MAGs that could be\nassigned a taxonomy at each rank, and how many of these taxonomies were unique\nphyla, classes, etc. Out of the 820 MAGs, 46 were assigned to species-level (all\ndifferent species), and the remaining 774 did not have any species-level assignment,\nas discussed in the following sentence and presented in Fig. 1C. These 774 MAGs do\nnot have representative genomes in the GTDB-Tk database and represent unknown\nor unnamed species. We also corrected a small typo in the manuscript, changing 47\nto 46 species.\n\n2. L133. Based on the information currently provided, we are still not fully convinced\nof their argument that 94.4% of recovered MAGs represented previously\nuncharacterized species. Could you provide mean ANIs between your MAGs and the\nclosest hit to the GTDBK database? As bacterial ‘species’ are an area of continued\ndebate, more clarity of exactly how species are being defined is needed.\n\nSpecies were defined according to average nucleotide identify (ANI) with reference\ngenomes in GTDB-Tk. GTDB-Tk follows the widely accepted standard that a genome\nis considered the same species as a reference genome if it shares >95% ANI across\n>65% of the aligned genome, as the developers have explained here:\nhttps://github.com/Ecogenomics/GTDBTk/issues/190#issuecomment-542961623.\nUsing these thresholds, 774 of the 820 MAGs (94.4%) lacked a suitable reference in\nthe database and are therefore considered potential novel species. We have revised\nlines 131-136 in the Results and Discussion and lines 829-831 in the Methods to clarify\nthis, and the full GTDB-Tk output with ANI values is now provided in Supplementary\nData 3.\n\n3. L152. Lack of context. If one were to look at Myxococcota MAGs in a range of\nenvironments, would they all have a large genome with high GC content? Unclear if\nthis is an advance in understanding about the Myxococcota in general, or those\nspecific Myxococcota that happen to inhabit riverine biofilms.\n\nWe provided context in the original manuscript by noting that “A similar phylogenetic\nrelationship between GC content and genome size has been observed in lake bacteria\n(Cabello-Yeves et al., 2019; Shah et al., 2024)”. However, we have now provided an\nadditional reference further supporting this finding in river systems (Chiriac et al.,\n2023) and included more detail to highlight that the patterns observed in genome size\nand GC content for Myxococcota, Pseudomonadota, Cyanobacteriota, and\nBacteroidota specifically are consistent with those found in sediment and pelagic\nsamples from lakes and other rivers in lines 160-164.\n\n4. L160. Would be useful to add reviews about genome streamlining theory\n(Giovannoni et al. 2014).\n\nWe have included the suggested review about genome streamlining theory in line 168.\n\n5. L167-176. The manuscript is strengthened by this addition of genes involved in\nbiofilm formation but again lacks sufficient context. Are these same genes universally\nfound in other biofilms? In other biofilms, has it been found that “these traits were\ndistributed across MAGs spanning diverse taxonomic groups?” Or is this a unique\nfinding to riverine biofilms?\n\nWe agree that additional context is useful, and genes associated with biofilm formation\nhave been reported across diverse taxonomic groups in other freshwater biofilms. We\nhave revised the text in lines 182-186 to reflect this and that our findings extend this\nobservation to river biofilms.\n\n6. L210. The extra information regarding niche breadth is appreciated, but I am\nconcerned this may be circular logic. If certain taxa are found in nearly all samples (i.e.\nhigh occupancy) in a sample set collected across a wide range of habitat types, would\nthese taxa not by definition need to tolerate a wide range of habitat types found in the\nsample set and thereby be considered generalists? More discussion on how to\ninterpret these results would be helpful.\n\nWe thank the reviewer for this thoughtful comment. As the reviewer highlights,\noccupancy and niche breadth are related, as taxa occurring in many samples are more\nlikely to be generalists. However, the two metrics are not identical. Occupancy\nmeasures the frequency of occurrence across samples, while niche breadth\ncomplements this by quantifying the evenness of relative abundance across samples.\nTaxa that are both widespread and evenly distributed would have a high occupancy\nand high niche breadth and therefore be classified as generalists. In contrast, taxa that\nare widespread but concentrated in particular habitat types would show high\noccupancy but low niche breadth and would therefore be classified as specialists. This\napproach allowed us to move beyond occupancy alone as a proxy and to provide a\nmore mechanistic measure of ecological strategies. We have clarified this distinction\nin lines 219-221.\n\n7. L214. While a weak relationship between community dissimilarity and geographic\ndistance could indicate high dispersal, is this the only possibility? If dispersal rates\nwere low but evolutionary change was also low, or pressures on community\ncomposition/species sorting were uniform across space, would we not also see a weak\nrelationship?\n\nWe agree with the reviewer that this is important to highlight, and we have added the\nother possibilities in lines 228-230.\n\n8. L277-280. These inferences are not supported by results in this study. There is no\nmicrospatial component to the sampling of these biofilms, at least as reported. So, it\nseems that the authors assume their diversity measures are a consequence of\nstratification. Authors should reduce causal language and remove statements that are\nnot drawn from the presented results. Making such inferences could be acceptable if\nit is made clear that future studies are needed to prove causality.\n\nThe sentence was intended to provide context for the range of trophic capabilities\nobserved, highlighting that biofilms are known from previous studies (Penesyan et al.,\n2021) to have a stratified structure. We have reworded this to remove causal\ninterpretation as suggested, emphasising this as ecological context in lines 289-294.\n\n9. L296-298. It is challenging to see how these claims are supported with no reference\nsamples to compare to. Are these genes associated with stress tolerance strategies\nfound in all bacteria? Are these genes significantly more prevalent in riverine biofilms,\nthan for example the surface water communities?\n\nWe thank the reviewer for this thoughtful comment. Our aim was not to explore\nenrichment relative to other environments, but to highlight that a broad set of stress\nrelated genes were present in the biofilm MAGs. However, we have compared our\nresults with other environments in lines 312-315, highlighting that stress tolerance\ngenes are widespread in biofilm bacteria across different freshwater environments.\n\n10. L310-311. Certain metals, such as Iron, are essential for cellular growth. As a\nconsequence, most bacteria have means of transporting such metals, such as\nsiderophores. To make a claim that this high prevalence of metal transporters is due\nto transporting and transforming harmful pollutants, the authors must provide\nadequate context. Is this “99.39% of MAGs” significantly greater than the proportion\nof MAGs found in other systems to have these same genes involved in transport of\nmetals?\n\nWe do not make any claims as indicated by the reviewer that the observed “high\nprevalence of metal transporters is due to transporting and transforming harmful\npollutants”. Rather, the presence of these genes suggests the possible capability to\ntransport metal pollutants which may include natural or anthropogenic sources.\nHowever, we have now modified the statement to include that these genes are also\nwidespread in bacteria for acquiring essential metals, in addition to their possible\ncontribution to metal cycling in rivers in lines 325-327.\n\n11. L382. This statement needs to be moderated. This study does not measure how\nbiofilms with certain community compositions versus others change nutrient cycling or\nimpacts water quality.\n\nThis sentence has been modified as suggested in lines 399-402.\n\n12. L399-400. Comparison to the North American dataset is certainly warranted, but\ncaution is needed when drawing interpretations between these two studies because\nthere are multiple variables that differ. For example, the NA study includes much larger\nrivers, which could have driven the results in this study that stream order was\npredictive. So, we do not think it is a valid argument that stream order shapes surface\nwater bacterial communities but does not shape riverine biofilm communities. There\nare confounding variables between the two studies that would need to be addressed\nbefore such a claim could be made.\n\nWe would like to emphasise that the finding that the RCC is not a strong driver of\nbacterial community composition in river biofilms is supported by Besemer et al. (2013)\n(lines 416-418. Although the difference in the stream order range between our study\nand the North American study was highlighted, we agree with the reviewer that this\nrequires more discussion. We have therefore expanded on the differences between\nthe studies in lines 423-425. We also highlight that further study, particularly utilising\nco-located surface water and biofilm samples, is required to fully understand the\ninfluence of the RCC on freshwater bacteria (425-237).\n\n13. L418-421. It is difficult for a reader to appreciate how this is a ‘diverse array of\ngenes’ and functions without any reference point. How does this compare to complex\nenvironmental bacterial communities in surface waters? Or other biofilms sampled in\nother environments? Again, here is where a contrast to bacterial communities in the\nsurface waters could go a long way towards demonstrating why these riverine biofilms\nare potentially unique in their contributions to riverine function.\n\nIn this concluding paragraph, we highlight the diverse range of metabolic functions\nand genes detected within our national river biofilm dataset. Our study focuses\nspecifically on characterising river biofilms, and direct comparisons to surface waters\nor other environments have limited value due to the reasons outlined by the reviewers\nin comment 12 and are beyond the scope of this work.\n\n14. L433-435. Seems like an overreach. No measurements were taken for how\nbiofilms alter water quality or public health. The link to biodiversity conservation is\nunclear. Making such claims necessitates functional measurements of how biofilms\nalter water quality.\n\nWe have revised the text in lines 457-460 to clarify that our findings provide baseline\ndata that may support monitoring and management, rather than being interpreted as\nimplying direct effects. We removed the less clear link to biodiversity conservation and\npublic health to avoid overinterpretation."}]

Decision letter
The authors have comprehensively responded to all the comments, resulting in substantial improvements to the manuscript.