Biogeoclimatic regions and land-use structure bacterial biodiversity and nitrogen cycling in headwater stream sediments

Biogeoclimatic regions and land-use structure bacterial biodiversity and nitrogen cycling in headwater stream sediments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biogeoclimatic regions and land-use structure bacterial biodiversity and nitrogen cycling in headwater stream sediments Lucía Cabello-Alemán, Victor Carpena-Istán, Encarnación Fenoy, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9556210/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Microbial communities in freshwater ecosystems are structured by both natural environmental variation and anthropogenic pressures, yet how these drivers interact to shape biodiversity and ecosystem functioning remains poorly understood. Here, we assessed how biogeoclimatic regions and land-use pressures jointly structure bacterial diversity and inferred nitrogen-cycling potential in stream sediments along a broad environmental gradient across the Iberian Peninsula. We used 16S rRNA metabarcoding and functional inference across samples spanning three biogeoclimatic regions and four land-use types. Bacterial community composition and diversity showed strong regional differentiation, with pronounced taxonomic turnover and distinct alpha-diversity patterns, whereas differences among land-use types were weaker, less spatially coherent, and primarily reflected shifts in specific community components. Despite this variation, a consistent core microbiome was observed across samples, suggesting the presence of taxa shared across contrasting conditions. Inferred nitrogen-cycling functional potential partially mirrored taxonomic patterns, with contrasting regional dominance of nitrification, denitrification, nitrogen fixation and dissimilatory nitrate reduction to ammonium, while land-use effects were subtle and nested within regional patterns. These results indicate that bacterial diversity and nitrogen-cycling potential are hierarchically structured, with biogeoclimatic conditions acting as primary ecological filters and land-use as a secondary, context-dependent driver. The relationship between taxonomic composition and inferred functional potential was more evident at the regional than at the local scale, where functional redundancy may buffer ecosystem processes. Our findings highlight the importance of incorporating regional baselines into biomonitoring frameworks and support the integration of taxonomic and functional approaches to improve ecological status assessments in freshwater ecosystems. Freshwater ecosystems Bacterial communities Anthropogenic pressure Nutrient cycling Environmental DNA (eDNA) Biomonitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction A central question in microbial ecology is whether the distribution and diversity of microorganisms across landscapes follow predictable, environmentally driven patterns or reflect the contingent outcome of dispersal limitation and stochastic processes [ 1 , 2 ]. This debate has gained renewed urgency in the context of global environmental change, as microbial communities underpin critical biogeochemical functions whose responses to shifting conditions remain poorly predictable [ 3 , 4 ]. In freshwater ecosystems, biodiversity is structured by gradients arising from spatial variation in climatic, geochemical, and landscape conditions that regulate habitat suitability and ecological interactions across scales [ 5 , 6 ]. Superimposed on these natural gradients, anthropogenic pressures, particularly land-use change, have become dominant drivers of ecological variation in fluvial networks, with the potential to modify or override underlying environmental filters [ 7 , 8 ]. However, the extent to which natural gradients and anthropogenic pressures interact to jointly structure microbial diversity and ecosystem functioning in freshwater systems remains insufficiently understood. In river networks, hydrological connectivity integrates heterogeneous environmental conditions across catchments, amplifying spatial gradients and making fluvial systems particularly responsive to both natural and anthropogenic drivers [ 9 ]. At broad spatial scales, biogeoclimatic factors define regional species pools and act as primary environmental filters, while local habitat conditions further refine community composition [ 6 ]. However, disentangling these mechanisms remains challenging in stream ecosystems, which comprise a mosaic of interconnected habitats experiencing strong temporal and spatial variability in hydrological regimes [ 10 , 11 ]. This complexity is further compounded by a highly dynamic dispersal pool, with microorganisms continuously exchanged among habitats and introduced from upstream and terrestrial sources [ 12 , 13 ], such that the relative importance of environmental filtering and dispersal varies with spatial scale and environmental context. Land-use intensification alters hydrological regimes, sediment dynamics, and nutrient inputs, leading to shifts in community composition and ecosystem functioning [ 14 , 15 ]. These pressures can promote biotic homogenisation by reducing regional distinctiveness and favouring the expansion of tolerant taxa [ 16 ], although evidence suggests that responses are often taxon-specific rather than community-wide [ 17 ]. Despite growing evidence that both natural gradients and anthropogenic pressures shape microbial communities, it remains unclear whether these drivers operate hierarchically, with regional environmental filters constraining local responses, or interactively, with land-use effects varying with the broader environmental context. Headwater streams draining diverse catchments across environmental gradients are particularly well suited to address this question, as their strong coupling with the surrounding landscape makes them sensitive to drivers operating at multiple spatial scales, and their disproportionate role in regulating nutrient fluxes gives their microbial communities particular biogeochemical relevance [ 18 , 19 , 20 , 21 ]. Stream sediments are key habitats for microbial communities and function as biogeochemical hotspots where nutrient cycling and organic matter processing are actively regulated [ 22 , 23 ]. They also accumulate environmental DNA from both local and upstream sources, providing a spatially and temporally integrated representation of catchment-scale ecological conditions and driving increasing interest in sedimentary DNA as a biomonitoring tool [ 24 , 25 ]. Within these habitats, microorganisms regulate major pathways of the nitrogen cycle, including nitrification, denitrification, nitrogen fixation, and dissimilatory nitrate reduction to ammonium (DNRA), collectively influencing nutrient availability, water quality, and greenhouse gas emissions [ 26 , 27 ]. However, the extent to which shifts in taxonomic composition translate into changes in functional potential, and how biogeoclimatic gradients and land-use pressures jointly influence both dimensions, remains unclear. Recent advances in high-throughput sequencing, including 16S rRNA metabarcoding coupled with functional inference approaches, now enable the simultaneous assessment of taxonomic diversity and metabolic potential [ 28 , 29 ]. However,integrating these dimensions across environmental gradients remains insufficiently explored, limiting our ability to interpret ecological responses to environmental change and to develop robust microbial indicators of ecosystem status [ 30 ]. In this study, we assessed how biogeoclimatic regions and land-use pressures jointly structure microbial biodiversity in headwater stream sediments and explored their implications for nitrogen-cycling potential. We hypothesized that these factors operate hierarchically, with biogeoclimatic conditions acting as primary ecological filters that define regional species pools, and land-use pressures operating as secondary filters that modulate community structure within that regional context. We further hypothesized that this hierarchical structuring extends to functional potential, with patterns of nitrogen-cycling capacity broadly corresponding to taxonomic composition across environmental gradients. Specifically, we analyzed (i) variation in bacterial alpha-diversity and compositional structure (beta-diversity) along biogeoclimatic and land-use contexts, (ii) the effect of these drivers on community structure, (iii) taxonomic diversity patterns and the functional potential associated with the nitrogen cycle, and (iv) the extent to which taxonomic diversity patterns are associated with shifts in inferred nitrogen-cycling functional potential across environmental conditions. 2. Material and methods 2.1. Study area We conducted a field-based comparative study using a factorial design across biogeoclimatic regions and land-use types in 12 headwater streams across the Iberian Peninsula. The study encompassed three bioclimatic regions: Mediterranean lowlands and the Sierra Nevada mountains in southern Spain, and the Cantabrian Mountains in northern Spain. In each region, four headwater streams were selected to represent dominant catchment land-use types: natural vegetation (reference condition), forestry, agriculture, and urban land-use. Streams were classified according to the dominant land-use within their catchments (Fig. 1 , Table S1 ). This study was conducted in parallel with other ongoing studies within the LANDCOMP project (see: https://biodiversity.umbc.edu/landcomp/ ) , which are currently unpublished. 2.2. Land-use characterization Land-use composition within each catchment was characterized using the CORINE Land Cover dataset (CLC 2018) from the Copernicus Land Monitoring Service, together with a Digital Elevation Model (DEM) from official national sources. Catchment boundaries were delineated through hydrological analysis using flow direction and flow accumulation algorithms implemented in QGIS (v. 3.40.5) with SAGA NextGen tools. Land-use composition was quantified by intersecting CLC classes with catchment polygons and calculating the area proportion for each class. Land cover was subsequently reclassified into four dominant categories –urban, agricultural, forestry, and natural vegetation–, and expressed as the percentage of each category within each catchment. 2.3. Field sampling Surface sediment samples (5–10 cm depth) were collected in each stream using a core sampler at three randomly distributed sites along a 50 m reach during the spring–summer period. At each site, three replicate cores were taken and pooled into a single composite sample. Sediments were sieved through a 2 mm steel mesh to retain the fine fraction, and a subsample was preserved in sterile 15 mL polyethylene tubes for molecular analyses. All samples were transported to the laboratory in dark coolers on ice. In total, 36 sediment samples were collected and stored at − 80°C until further processing. 2.4. DNA extraction Total bacterial genomic DNA was isolated from 250–500 mg of each sediment sample using a DNeasy PowerSoil kit (Qiagen, Germany) according to the manufacturer's instructions. The extracted DNA concentration and purity (260/280 and 260/230 ratios) were measured using a Qubit 4 Fluorometer (Invitrogen™, Thermo Fisher Scientific, Waltham, MA, USA) and a NanoDrop ND-1000 spectrophotometer (ThermoFisher Scientific Inc., USA), respectively. DNA samples were stored at − 20°C prior to sequencing or at − 80°C for long-term preservation. 2.5. Library preparation and sequencing Amplicon libraries were prepared from genomic DNA targeting the V3–V4 region of the bacterial 16S rRNA gene using the primers Bakt341F (5′-CCTACGGGNGGCWGCAG-3′) and Bakt805R (5′-GACTACHVGGGTATCTAATCC-3′). PCR amplifications were performed following standard protocols, including negative controls without template DNA to monitor potential contamination. PCR products were quantified using the Qubit dsDNA HS Assay (Thermo Fisher Scientific) and pooled in equimolar concentrations. The pooled library was sequenced on an Illumina NovaSeq platform using paired-end 250 bp reads. 2.6. Bioinformatic processing, taxonomic diversity and functional analyses Amplicon sequences were processed using Cutadapt [ 31 ] and DADA2 [ 32 ] within QIIME2 (v. 2023.7) [ 33 ] to remove primers, denoise reads, and generate amplicon sequence variants (ASVs). Taxonomic assignment was performed using a naïve Bayes classifier trained on the SILVA database (v. 132) [ 34 , 35 , 36 ]. Subsequent analyses, including diversity estimation, ordination, statistical testing, and data visualization, were conducted in R (v. 4.4) using the microeco package [ 37 ]. The dataset was rarefied to the minimum sequencing depth to ensure comparability across samples. Alpha diversity was estimated using Shannon diversity, Chao 1 richness and observed ASV richness. Differences among biogeoclimatic regions and land-use types were assessed using analysis of variance (ANOVA). When significant effects were detected, pairwise comparisons were performed using Tukey´s HSD test. Beta diversity was quantified using Bray-Curtis dissimilarities, and visualized through Principal Coordinates Analysis (PCoA). Differences in community composition were tested using permutational multivariate analysis of variance (PERMANOVA; [ 38 ]), with biogeoclimatic region and land-use type as explanatory variables. We evaluated the effects of region and land-use type using mixed-effects models (when applicable), including region and land-use type as fixed factors and stream as a random effect. Analyses were performed using the adonis2 function in the vegan R package with 999 permutations, and the effects of each factor were evaluated using marginal tests. Differential taxonomic composition was assessed using linear discriminant analysis effect size (LEfSe) [ 39 ], applying a significance threshold of p 2. Core and shared taxa were identified based on ASV occurrence patterns and visualized using Venn diagrams. Functional potential was inferred from 16S rRNA gene sequences using PICRUSt2 [ 28 ], based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs [ 40 ] in Python (v. 3.13). Predicted genes were grouped into major nitrogen-cycle pathways (e.g., nitrification, denitrification, nitrogen fixation, nitrate respiration, nitrate/nitrite assimilation, ammonium assimilation, and Dissimilatory Nitrate Reduction to Ammonium [DNRA]) according to KEGG annotations. Differential abundance of functional pathways was assessed using LinDA [ 41 ], with false discovery rate (FDR)-corrected p-values (q < 0.05) used to control for multiple testing using “ggpicrust2” package [ 42 ]. 3. Results 3.1. Sequencing results and relative abundance of the dominant bacterial families A total of 36 samples were initially analysed, but a small subset failed to yield sequencing data of sufficient quality and was excluded from downstream analyses. The remaining samples generated 5,994 amplicon sequence variants (ASVs), with sequencing depth ranging from 1,083 to 9,128 reads per sample. After filtering low-abundance and low-frequency taxa, 1,453 ASVs were retained for subsequent analyses. Taxonomic assignment success decreased progressively as taxonomic resolution increased. All 1,453 ASVs were assigned at the kingdom and phylum levels, 1,452 at the class level, 1,437 at the order level, and 1,378 at the family level, declining to 1,055 ASVs at the genus level. Relative abundance of the dominant families showed marked regional variation, whereas differences among land-use categories were comparatively less pronounced (Fig. 2 ). Several families were consistently detected across most samples, particularly Pirellulaceae and Flavobacteriaceae. At the family level, regional differences in dominant taxa were apparent. Mediterranean lowlands samples showed high relative abundances of Pirellulaceae, along with an increased representation of Flavobacteriaceae in streams with natural vegetation and an enrichment of Coleofasciculaceae in forested sites. Sierra Nevada mountains samples were dominated by Clostridiaceae and Xanthobacteraceae, along with greater variability in Pirellulaceae. In the Cantabrian mountains, Flavobacteriaceae were particularly abundant in agricultural streams, with relatively higher contributions of Chitinophagaceae and Sphingomonadaceae. 3.2. Alpha-diversity patterns across regions and land-use types Variation in alpha-diversity was more strongly associated with biogeoclimatic region than with land-use type. Alpha-diversity differed significantly among regions for all metrics analysed (Fig. 3 a). Shannon diversity showed a clear regional pattern (p < 0.001), with the lowest values in the Mediterranean lowlands, intermediate values in the Sierra Nevada, and the highest values in the Cantabrian Mountains. Richness-based indices followed a similar pattern, with both Chao1 (p < 0.001) and observed ASV richness (p < 0.001) being lowest in the Mediterranean lowlands and highest in the Cantabrian Mountains, while no significant differences were detected between the Sierra Nevada mountains and the Mediterranean lowlands. In contrast, the effect of land-use on alpha-diversity was weaker and less consistent (Fig. 3 b). Shannon diversity did not differ significantly among land-use categories (p = 0.056), although agricultural streams tended to show slightly higher values. Richness-based metrics indicated modest but significant differences, with higher values in agricultural streams (Chao1: p = 0.027; observed ASVs: p = 0.024), whereas forest and urban streams showed intermediate values. 3.3. Community structure and exclusive microbiomes across regions and land-uses Community structure differed clearly across regions, whereas differentiation among land-use categories was weaker and less consistent (Fig. 4 ). Principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarities revealed compositional differences in sediment bacterial communities, with the first two axes explaining 35.4% of the total variation (PC1 = 21.4%, PC2 = 14%). Samples were primarily structured along the biogeoclimatic axis, with clear separation between the Mediterranean lowlands and the Cantabrian Mountains, while Sierra Nevada samples occupied intermediate positions. Land-use categories showed substantial overlap in the ordination space, with urban samples tending to occupy a more restricted portion, particularly in the Mediterranean lowlands and Sierra Nevada, whereas agricultural streams showed greater dispersion and overlapped with both natural and forested streams. PERMANOVA confirmed that both region (R² = 0.22, F = 3.47, p = 0.001) and land-use (R² = 0.25, F = 2.65, p = 0.001) significantly influenced bacterial community composition. However, while the statistical effect of land-use was similar in magnitude to that of region, the greater overlap among land-use categories in the ordination space indicates that land-use-associated differences were less spatially coherent and more context-dependent than those associated with the biogeoclimatic pattern. Interestingly, while land-use explained a slightly higher proportion of the total variance, the biogeoclimatic region exhibited a higher F-statistic. This suggests that the regional signal is a more robust and efficient predictor of community assembly per degree of freedom. Tests for homogeneity of multivariate dispersion were not significant (p = 0.595), indicating that results were not evidently driven by differences in within-group variability. While land-use significantly influenced community composition (Table S2), these effects should be interpreted with caution given the inherent site-specific variability of the individual streams selected to represent each land-use category. Analysis of shared and exclusive ASVs further supported this pattern (Fig. 5 ). Across regions (Fig. 5 a), 547 ASVs (37.6%) were shared among all three regions, while each region also harboured a distinct fraction of unique taxa, with the Mediterranean lowlands showing the highest number of exclusive ASVs (131; 9%), followed by the Sierra Nevada (50; 3.4%) and the Cantabrian Mountains (43; 3%). At the family level, the shared regional microbiome comprised 105 families dominated by Pirellulaceae, Flavobacteriaceae, Chitinophagaceae, and Sphingomonadaceae, while exclusive families also varied among regions, with the Mediterranean lowlands showing the highest number of region-specific families (56), followed by the Sierra Nevada (34) and the Cantabrian Mountains (29). Across land-use types (Fig. 5 b), 421 ASVs (29%) were shared among all categories, with additional overlap particularly between forested and agricultural streams. In contrast to regional patterns, exclusive ASVs were scarce across most land-use categories. At the family level, the shared microbiome comprised 92 families dominated by Pirellulaceae, Flavobacteriaceae, and Chitinophagaceae. Exclusive families were scarce across most land-use types, with only three in streams with natural vegetation and slightly higher numbers in forested and agricultural streams. Urban streams exhibited a markedly higher number of exclusive families (68) compared to other land-use types, including a relatively large proportion of unclassified taxa, with Bacteroidetes vadinHA17 and Pirellulaceae as the most abundant exclusive family. 3.4. Differentially enriched taxa across regions and land-uses LEfSe analysis revealed clear taxonomic differentiation of bacterial communities, with multiple lineages contributing to the observed patterns rather than isolated taxa (Fig. 6 ). Across regions, distinct taxonomic signatures were evident (Fig. 6 a). The Mediterranean lowlands were mainly characterized by Proteobacteria (particularly Rhodobacterales) and Planctomycetota (e.g., Pirellulales), with additional contributions from uncultured Bacteroidetes lineages. In Sierra Nevada, discriminant clades were primarily concentrated within Firmicutes (especially Clostridia and Bacilli), Acidobacteriota, and Desulfobacterota. The Cantabrian Mountains exhibited a broader signal, with enrichment of Bacteroidota (e.g., Flavobacteriales, Chitinophagales), Verrucomicrobiota, Cyanobacteria, and Alphaproteobacteria (e.g., Sphingomonadales). Overall, regional differences involved multiple phyla and taxonomic levels, indicating strong differentiation among biogeoclimatic contexts. Patterns associated with land-use types were less consistent and more diffuse (Fig. 6 b). Urban streams showed enrichment across several taxonomic groups, including Firmicutes (notably Clostridia), Proteobacteria (e.g., Gammaproteobacteria and Rhodobacterales), Campylobacterota, and Bacteroidota (e.g., Bacteroidales and Sphingobacteriales). Forested streams also exhibited a relatively broad signal spanning multiple lineages, including Planctomycetota, Acidobacteriota, Alphaproteobacteria (e.g., Rhizobiales, Sphingomonadales), Bacteroidota (Chitinophagales), and Cyanobacteria. Streams with natural vegetation showed fewer discriminant taxa, indicating weaker differentiation. Agricultural streams were associated with fewer and less distinct taxonomic signatures, including Verrucomicrobiota, Campylobacterota, and Cytophagales. 3.5. Inferred nitrogen-cycling potential across regions and land-uses The Mediterranean lowlands showed a greater representation of genes linked to nitrification and denitrification, along with higher abundances of genes involved in nitrate/nitrite and ammonium assimilation. Sierra Nevada streams exhibited elevated relative abundances of genes associated with nitrogen fixation, DNRA and selected nitrate respiration pathways. The Cantabrian Mountains displayed a more balanced distribution across modules, without strong enrichment of any single pathway. Variation among streams within regions was moderate, with some modules showing site-specific peaks. Across land-use types, no consistent differences in inferred nitrogen-cycling functional potential were detected across categories (Fig. 7 ). Where differences were observed, they were subtle, site-specific, and largely nested within regional patterns. Urban streams in the Mediterranean lowlands and Sierra Nevada tended to show greater representation of the most abundant pathways in each region, whereas forested streams in the Mediterranean lowlands exhibited higher nitrate/nitrite assimilation. In Sierra Nevada, streams with natural vegetation showed greater inferred potential related to nitrification, whereas in the Cantabrian Mountains, agricultural streams were associated with higher levels of inferred nitrogen-cycling functional potential related to nitrate/nitrite assimilation, denitrification, and ammonium assimilation. It is important to note that the functional profiles described here represent predicted genomic potential inferred from 16S rRNA sequences rather than direct measurements of metabolic activity. While PICRUSt2 provides a robust framework for generating hypotheses on inferred nitrogen-cycling functional potential, its accuracy relies on the proximity of available reference genomes in databases like KEGG, and it may not fully capture the metabolic plasticity or the activity of uncultured lineages present in stream sediments. Therefore, these patterns of inferred nitrogen-cycling functional modules should be interpreted as hypotheses of functional capacity that warrant further validation through metagenomics, metatranscriptomics, or direct measurements of enzymatic process rates to confirm actual biogeochemical flux. 4. Discussion Our results show that microbial biodiversity and inferred nitrogen-cycling functional potential in headwater stream sediments are structured by the interaction between biogeoclimatic regions and land-use pressures, with regional environmental conditions acting as primary ecological filters and anthropogenic disturbances operating as secondary, context-dependent modifiers. This hierarchical organization is consistent with metacommunity theory, which predicts that large-scale environmental filters delimit the regional species pool while local processes shape communities at finer scales [ 43 , 6 ]. This pattern extends beyond taxonomic composition to encompass functional potential, with stronger coupling between community structure and nitrogen-cycling capacity at broad spatial scales than at local scales. These findings indicate that the relationship between microbial diversity and ecosystem functioning in headwater streams is scale-dependent, with implications for how biomonitoring frameworks should account for natural environmental variation when interpreting anthropogenic impacts. 4.1. The biogeoclimatic context as a primary determinant of microbial biodiversity Biogeoclimatic regions emerged as the main drivers of microbial biodiversity across headwater streams, as reflected by the clear spatial clustering, and their superior statistical potency compared to land-use. This suggests that the biogeoclimatic signal is a more efficient and parsimonious predictor of community assembly, even though land-use captured a slightly higher absolute proportion of the total variance. This dominance is further evidenced by the pronounced taxonomic turnover among regions (Mediterranean lowlands, Sierra Nevada mountains and Cantabrian mountains). This turnover, largely characterised by lineage replacement rather than shifts in relative abundance, extended across multiple taxonomic levels, indicating broad phylogenetic reorganisation consistent with evidence that microbial biogeography is shaped by environmental and historical processes operating at large spatial scales [ 1 ]. The high beta-diversity observed across regions, together with the presence of region-specific taxonomic signatures, supports the role of environmental filtering and aligns with metacommunity theory, which predicts a strong influence of environmental conditions in structuring aquatic microbial communities [ 44 , 45 ]. Alpha-diversity patterns provide additional insight into these regional differences. Higher richness in the Cantabrian mountains likely reflects greater water availability and thermal stability [ 46 ], whereas semi-arid systems support more specialised and less diverse communities adapted to environmental stress [ 47 ]. Differences in the identity of dominant taxa further illustrate these contrasts, with lineages typically associated with dry and oligotrophic conditions (e.g., Pirellulaceae, Coleofasciculaceae) prevailing in the Mediterranean lowlands, and taxa linked to more humid and productive environments (e.g., Chitinophagaceae, Sphingomonadaceae) dominating in the Cantabrian Mountains [ 48 , 49 , 50 ]. 4.2. A stable core microbiome with selective responses to environmental variation Microbial communities across headwater streams were characterised by a substantial core microbiome, with nearly a third of all ASVs shared across samples. This widespread core persisted despite pronounced regional differentiation and was also evident across contrasting land-use types, indicating that a consistent set of taxa is maintained across a wide range of environmental conditions. The coexistence of this stable core with context-dependent taxa suggests that environmental variation, including land-use pressures, drives selective reorganisation of microbial communities rather than complete biotic homogenisation [ 51 , 52 ]. The persistence of generalist taxa such as Pirellulaceae, Flavobacteriaceae, and Chitinophagaceae across contrasting conditions indicates a degree of community stability, suggesting that core ecosystem functions mediated by these taxa may be maintained under anthropogenic pressure [ 53 , 54 ]. At the land-use scale, differences were expressed primarily through shifts in specific community components rather than wholesale compositional change. Streams with natural vegetation showed the lowest compositional exclusivity and greater homogeneity, consistent with more stable environmental conditions. Forested systems displayed moderate differentiation, with higher abundances of taxa associated with organic matter decomposition and oligotrophic conditions (e.g., Planctomycetota, Acidobacteriota), groups that have been reported to decline under increasing anthropogenic influence [ 15 ]. Agricultural systems were associated with taxa involved in the degradation of complex organic compounds and in disturbed environments (e.g., Verrucomicrobiaceae, Campylobacterota [ 55 , 15 ]. Urban systems showed comparatively greater differentiation, with enrichment of taxa commonly associated with eutrophic and anthropogenic conditions (e.g., Firmicutes, Campylobacterota, Gammaproteobacteria) [ 56 , 57 , 58 ], as well as a higher number of exclusive taxa, including a large proportion of unclassified lineages. The recurrent enrichment of disturbance-associated taxa in urban systems, together with the more limited differentiation observed in less-disturbed land-use types, suggests that anthropogenic pressures act as selective filters that restructure specific components of the microbial community within the constraints imposed by the regional environmental context. 4.3. Taxonomic patterns and inferred nitrogen cycling potential Regional variation in taxonomic composition was broadly paralleled by shifts in inferred nitrogen-cycling functional potential, consistent with a correspondence between community structure and metabolic capacity that is more apparent at broad spatial scales than at local scales. Although a formal quantitative assessment of this relationship was beyond the scope of this study, the parallel structuring of taxonomic and inferred functional patterns across regions suggests that biodiversity reorganisation along biogeoclimatic regions is unlikely to be functionally neutral, with both the identity and diversity of taxa likely contributing to nitrogen transformation processes [ 59 ]. In the Mediterranean lowlands, the higher relative abundance of genes associated with nitrification and denitrification was consistent with adaptation to fluctuating redox conditions and nutrient pulses typical of semi-arid, intermittent systems. In Sierra Nevada, higher abundances of genes associated with nitrogen fixation and DNRA suggested a greater reliance on internal nitrogen cycling and nitrogen conservation, potentially under oligotrophic conditions or in organic-rich microenvironments where DNRA can outcompete denitrification [ 36 ]. The prevalence of Firmicutes in this region is consistent with this interpretation, given that some members of this phylum exhibit metabolic versatility, including capacities for N₂ fixation [ 57 ]. The Cantabrian Mountains exhibited a more even distribution across functional modules, without strong enrichment of any single pathway, possibly reflecting greater hydrological stability and more continuous nutrient inputs. In contrast, land-use effects on inferred nitrogen-cycling functional potential were subtle and inconsistent across categories, suggesting that functional redundancy may buffer ecosystem processes against local disturbances [ 60 ]. Where land-use-associated differences were observed, they were largely nested within regional patterns rather than representing independent functional responses. These patterns indicate that the relationship between microbial diversity and inferred nitrogen-cycling potential is scale-dependent, being more apparent at the regional biogeoclimatic scale than at the local land-use scale. 4.4. Implications for biomonitoring Our findings highlight the potential of sediment metabarcoding as a biomonitoring tool, as sediment-associated microbial communities integrate environmental signals across spatial scales and are sensitive to broad-scale environmental contexts and, to a lesser extent, local anthropogenic pressures [ 25 , 30 , 24 ]. The clear differentiation observed across biogeoclimatic regions, together with more limited differentiation among land-use types, suggests the potential to incorporate microbial assemblages into ecological assessment frameworks. The strong influence of biogeoclimatic context underscores the need to incorporate regional baselines into biomonitoring frameworks, as natural variation in community composition can be substantial and may otherwise be misattributed to anthropogenic impacts [ 6 ]. This is particularly relevant in regions spanning pronounced environmental gradients, such as the Iberian Peninsula, where biogeoclimatic variation contributes substantially to community turnover independently of land-use effects. At the same time, the contrasting responses between overall community structure and taxon-specific changes suggest that effective biomonitoring should combine community-level diversity metrics with the identification of indicator taxa [ 61 , 62 ]. In this context, the consistent enrichment of groups such as Campylobacterota and Clostridia in urban streams highlights their potential as candidate indicators of anthropogenic disturbance in headwater systems [ 15 , 58 ]. Finally, integrating taxonomic composition with inferred functional potential provides a promising avenue for developing more informative bioindicators that explicitly link biodiversity patterns to ecosystem functioning [ 60 , 63 ]. However, as functional profiles were inferred from taxonomic data, further validation using direct measurements of functional processes would strengthen their application in biomonitoring contexts. Such approaches may improve the capacity of biomonitoring programmes to detect not only compositional changes but also shifts in key ecosystem processes, ultimately contributing to more comprehensive assessments of ecological status in freshwater ecosystems [ 64 ]. Although our study highlights a dominant regional signal over land-use impacts, we acknowledge that our design, which prioritized a broad biogeoclimatic gradient across 12 streams, may have limited power to detect subtle land-use effects. The high within-stream variability and the absence of multiple stream replicates per land-use category mean that land-use signals might be partially masked by local environmental filters not captured in this study. 5. Conclusions Our results show that microbial biodiversity and inferred nitrogen-cycling potential in stream sediments are primarily structured by biogeoclimatic conditions, which act as dominant ecological filters shaping both community composition and functional profiles. In contrast, land-use exerts a secondary, context-dependent effect, modifying specific components of microbial assemblages without overriding the regional signal. Despite this variability, a widespread core microbiome dominated by generalist taxa was consistently observed, suggesting that key ecosystem functions may be maintained under anthropogenic pressure. In this context, the link between community structure and function appears to be scale-dependent, with regional differences driving functional contrasts, while local functional redundancy may buffer ecosystem processes against land-use impacts. These findings highlight the need to incorporate regional baselines into biomonitoring frameworks, as strong natural variability may otherwise obscure anthropogenic effects. Integrating diversity metrics with indicator taxa and functional information can therefore provide a more robust assessment of freshwater ecosystem status. However, further research with broader spatial replication is needed to determine whether land-use effects are consistently subordinate to regional filters across spatial scales. Declarations Competing interests The authors declare no competing interests. Funding This work was supported by a grant from the Spanish Ministry of Universities (FPU22/00899) and by funds of the consolidated Research Group BIO-175 from the Government of the Junta de Andalucía (Andalusian Research Plan, Junta de Andalucía, Spain). JP was supported by the PPIT-UAL and 2021–2027 FEDER Operative Program Andalusia (program 54.A, ref. HIPATIA2023_05) Artificial intelligence tools were used to assist in the preparation and language editing of the manuscript. Author Contribution L.C.A., L.B., J.J.C. and M.J.L. conceived the study. L.C.A., V.C.I., E.F., J.P., R.H.M. and M.J.L. developed the methodology. L.C.A. and V.C.I. performed the formal analyses and investigation. L.C.A. wrote the original draft. J.J.C. and M.J.L. acquired funding. L.B., J.P., J.C.C. and M.J.L. provided resources. J.J.C. and M.J.L. supervised the work. All authors contributed to reviewing and editing the manuscript. 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Ecol Evol 9:12000–12016. https://doi.org/10.1002/ece3.5707 Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformationlandcomp.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 May, 2026 Reviewers agreed at journal 19 May, 2026 Reviewers agreed at journal 19 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 06 May, 2026 Submission checks completed at journal 06 May, 2026 First submitted to journal 28 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9556210","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641654061,"identity":"8d49c35e-7ae5-45c2-bbef-4310b35fd850","order_by":0,"name":"Lucía Cabello-Alemán","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACPgYGxgcSFTYMDOzEamFjYGA2sDiTBqRI0MImUdlymBQt7IcfG9xsOJ/Y38zA+PAHUVp40gwfztxxO3HGYQZmYx6itEgwGBtLnrmd2HCYgU2aOIdJsH+T/tt2LnH+YQb2n8Q5TILHTEKy7UDiBqAtDMQ5jCen2EDiTLLxxsOMzdJEaeFnP74RGJV2svOONx/8SJTDkABjA4kaRsEoGAWjYBTgBAC8Ri6K+ATOXwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Almería","correspondingAuthor":true,"prefix":"","firstName":"Lucía","middleName":"","lastName":"Cabello-Alemán","suffix":""},{"id":641654062,"identity":"62c0597d-64d7-43d4-b10d-3e9a8b39f25c","order_by":1,"name":"Victor Carpena-Istán","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Carpena-Istán","suffix":""},{"id":641654063,"identity":"592c00be-f470-4ac5-8329-460a152ac726","order_by":2,"name":"Encarnación Fenoy","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"Encarnación","middleName":"","lastName":"Fenoy","suffix":""},{"id":641654065,"identity":"cb33cc7a-f2fb-4fdc-8814-a2e5dc2ea1d6","order_by":3,"name":"Javier Pérez","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Pérez","suffix":""},{"id":641654066,"identity":"b3c754af-e642-469d-8cb7-e981f5ff567c","order_by":4,"name":"Rafael Hernández-Maqueda","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Hernández-Maqueda","suffix":""},{"id":641654069,"identity":"cb24699a-eaf2-465d-85bf-7f7a0ac1a051","order_by":5,"name":"Luz Boyero","email":"","orcid":"","institution":"University of the Basque Country (UPV/EHU)","correspondingAuthor":false,"prefix":"","firstName":"Luz","middleName":"","lastName":"Boyero","suffix":""},{"id":641654075,"identity":"b523a438-efa1-45f0-b491-aa2cd1bd9f5f","order_by":6,"name":"José Jesús Casas","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Jesús","lastName":"Casas","suffix":""},{"id":641654081,"identity":"ca916553-7797-486d-bb2c-2c5958380690","order_by":7,"name":"María José López","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"José","lastName":"López","suffix":""}],"badges":[],"createdAt":"2026-04-28 15:39:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9556210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9556210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109449559,"identity":"97622c25-6c39-4913-9385-e76f77be3612","added_by":"auto","created_at":"2026-05-18 08:45:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":845734,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the 12 headwater streams studied across three bioclimatic regions of the Iberian Peninsula (Cantabrian Mountains, Sierra Nevada, and Mediterranean lowlands). In each region, four streams represent the dominant catchment land-use types: natural vegetation, forestry, agricultural, and urban land use. Bar charts show catchment land-use composition, and the map is overlaid with a water balance (P − ET) gradient.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/6d9fbb8961063278b04d7f41.jpg"},{"id":109760038,"identity":"5895e4c8-f9d3-421e-9d6e-eb39ad021191","added_by":"auto","created_at":"2026-05-22 07:28:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":564053,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the relative abundance of the ten most abundant bacterial families across sediment samples. Columns represent samples grouped by region and land-use type, and rows correspond to bacterial families. Color intensity indicates relative abundance.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/4c7d8549fddbf85b49dc1faa.jpg"},{"id":109761062,"identity":"3c033b41-0a53-49dd-8c5d-2d1c8608fb57","added_by":"auto","created_at":"2026-05-22 07:29:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":626049,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity of sediment bacterial communities across (a) biogeoclimatic regions and (b) land-use types. Metrics include Shannon diversity, Chao1 richness, and observed ASVs. Letters indicate significant differences among groups (p \u0026lt; 0.05). ML, Mediterranean lowlands; SN, Sierra Nevada; CM, Cantabrian Mountains.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/24cc4e43b1fa4fa6fd9d76f9.jpg"},{"id":109761064,"identity":"9b0c436a-2087-4831-8ed8-e8b431a37851","added_by":"auto","created_at":"2026-05-22 07:29:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":529691,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinates analysis (PCoA) based on Bray–Curtis dissimilarities showing differences in sediment bacterial community composition across regions and catchment land-use types. Colours represent land-use categories and shapes connect streams within each region. Polygons indicate the convex hulls encompassing samples from each region.\u003c/p\u003e","description":"","filename":"Fig4..jpg","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/59d28c06a34fe61022ea2057.jpg"},{"id":109760315,"identity":"3ed880e6-b656-4674-9680-f7f440963ffa","added_by":"auto","created_at":"2026-05-22 07:28:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1008901,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of shared and exclusive bacterial taxa across (a) biogeoclimatic regions and (b) land-use types. Venn diagrams show the number of ASVs shared and unique among groups. Pie charts illustrate the family-level composition of the core microbiome (taxa shared across all groups) and of taxa exclusive to each region or land-use category. Percentages in the Venn diagrams indicate the proportion of total ASVs.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/d155e708848d4be1d3399fad.jpg"},{"id":109449562,"identity":"d9a42d3c-81d4-430f-b6ac-3771a5d187f8","added_by":"auto","created_at":"2026-05-18 08:45:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1285478,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially enriched bacterial taxa. (a) Cladogram showing taxa associated with biogeoclimatic regions. (b) Cladogram showing taxa associated with land-use types. Circle size reflects relative abundance, and colors indicate enrichment. Letters preceding taxon names indicate taxonomic rank (p, phylum; c, class; o, order; f, family; g, genus).\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/fbbc0f7afcf451afaa53c86e.jpg"},{"id":109799559,"identity":"463e9fa6-3637-4309-9cef-7f1817fad56e","added_by":"auto","created_at":"2026-05-22 15:31:21","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":740183,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of inferred nitrogen-cycling functional potential across sampling sites. Rows represent genes associated with major nitrogen transformation pathways, and columns represent sites grouped by region and land-use. Colors indicate scaled gene abundance (z-scores). Functional annotations of the genes are provided in Table S3.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/b0be25b776979f7d8a491d7e.jpg"},{"id":109760276,"identity":"93d82153-0d1a-4519-a571-2fe31557514a","added_by":"auto","created_at":"2026-05-22 07:28:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2905058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/d79270ba-c516-4197-a253-da6c4c99a0a2.pdf"},{"id":109449557,"identity":"2fa646c6-45bc-4b26-94b5-405fc826f0e3","added_by":"auto","created_at":"2026-05-18 08:45:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":84354,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformationlandcomp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9556210/v1/9d32659df55c6916bcd19f63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biogeoclimatic regions and land-use structure bacterial biodiversity and nitrogen cycling in headwater stream sediments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA central question in microbial ecology is whether the distribution and diversity of microorganisms across landscapes follow predictable, environmentally driven patterns or reflect the contingent outcome of dispersal limitation and stochastic processes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This debate has gained renewed urgency in the context of global environmental change, as microbial communities underpin critical biogeochemical functions whose responses to shifting conditions remain poorly predictable [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In freshwater ecosystems, biodiversity is structured by gradients arising from spatial variation in climatic, geochemical, and landscape conditions that regulate habitat suitability and ecological interactions across scales [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Superimposed on these natural gradients, anthropogenic pressures, particularly land-use change, have become dominant drivers of ecological variation in fluvial networks, with the potential to modify or override underlying environmental filters [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the extent to which natural gradients and anthropogenic pressures interact to jointly structure microbial diversity and ecosystem functioning in freshwater systems remains insufficiently understood.\u003c/p\u003e \u003cp\u003eIn river networks, hydrological connectivity integrates heterogeneous environmental conditions across catchments, amplifying spatial gradients and making fluvial systems particularly responsive to both natural and anthropogenic drivers [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At broad spatial scales, biogeoclimatic factors define regional species pools and act as primary environmental filters, while local habitat conditions further refine community composition [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, disentangling these mechanisms remains challenging in stream ecosystems, which comprise a mosaic of interconnected habitats experiencing strong temporal and spatial variability in hydrological regimes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This complexity is further compounded by a highly dynamic dispersal pool, with microorganisms continuously exchanged among habitats and introduced from upstream and terrestrial sources [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], such that the relative importance of environmental filtering and dispersal varies with spatial scale and environmental context.\u003c/p\u003e \u003cp\u003eLand-use intensification alters hydrological regimes, sediment dynamics, and nutrient inputs, leading to shifts in community composition and ecosystem functioning [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These pressures can promote biotic homogenisation by reducing regional distinctiveness and favouring the expansion of tolerant taxa [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], although evidence suggests that responses are often taxon-specific rather than community-wide [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite growing evidence that both natural gradients and anthropogenic pressures shape microbial communities, it remains unclear whether these drivers operate hierarchically, with regional environmental filters constraining local responses, or interactively, with land-use effects varying with the broader environmental context. Headwater streams draining diverse catchments across environmental gradients are particularly well suited to address this question, as their strong coupling with the surrounding landscape makes them sensitive to drivers operating at multiple spatial scales, and their disproportionate role in regulating nutrient fluxes gives their microbial communities particular biogeochemical relevance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStream sediments are key habitats for microbial communities and function as biogeochemical hotspots where nutrient cycling and organic matter processing are actively regulated [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. They also accumulate environmental DNA from both local and upstream sources, providing a spatially and temporally integrated representation of catchment-scale ecological conditions and driving increasing interest in sedimentary DNA as a biomonitoring tool [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Within these habitats, microorganisms regulate major pathways of the nitrogen cycle, including nitrification, denitrification, nitrogen fixation, and dissimilatory nitrate reduction to ammonium (DNRA), collectively influencing nutrient availability, water quality, and greenhouse gas emissions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the extent to which shifts in taxonomic composition translate into changes in functional potential, and how biogeoclimatic gradients and land-use pressures jointly influence both dimensions, remains unclear. Recent advances in high-throughput sequencing, including 16S rRNA metabarcoding coupled with functional inference approaches, now enable the simultaneous assessment of taxonomic diversity and metabolic potential [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However,integrating these dimensions across environmental gradients remains insufficiently explored, limiting our ability to interpret ecological responses to environmental change and to develop robust microbial indicators of ecosystem status [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we assessed how biogeoclimatic regions and land-use pressures jointly structure microbial biodiversity in headwater stream sediments and explored their implications for nitrogen-cycling potential. We hypothesized that these factors operate hierarchically, with biogeoclimatic conditions acting as primary ecological filters that define regional species pools, and land-use pressures operating as secondary filters that modulate community structure within that regional context. We further hypothesized that this hierarchical structuring extends to functional potential, with patterns of nitrogen-cycling capacity broadly corresponding to taxonomic composition across environmental gradients. Specifically, we analyzed (i) variation in bacterial alpha-diversity and compositional structure (beta-diversity) along biogeoclimatic and land-use contexts, (ii) the effect of these drivers on community structure, (iii) taxonomic diversity patterns and the functional potential associated with the nitrogen cycle, and (iv) the extent to which taxonomic diversity patterns are associated with shifts in inferred nitrogen-cycling functional potential across environmental conditions.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eWe conducted a field-based comparative study using a factorial design across biogeoclimatic regions and land-use types in 12 headwater streams across the Iberian Peninsula. The study encompassed three bioclimatic regions: Mediterranean lowlands and the Sierra Nevada mountains in southern Spain, and the Cantabrian Mountains in northern Spain. In each region, four headwater streams were selected to represent dominant catchment land-use types: natural vegetation (reference condition), forestry, agriculture, and urban land-use. Streams were classified according to the dominant land-use within their catchments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This study was conducted in parallel with other ongoing studies within the LANDCOMP project (see: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biodiversity.umbc.edu/landcomp/\u003c/span\u003e\u003cspan address=\"https://biodiversity.umbc.edu/landcomp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which are currently unpublished.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Land-use characterization\u003c/h2\u003e \u003cp\u003eLand-use composition within each catchment was characterized using the CORINE Land Cover dataset (CLC 2018) from the Copernicus Land Monitoring Service, together with a Digital Elevation Model (DEM) from official national sources. Catchment boundaries were delineated through hydrological analysis using flow direction and flow accumulation algorithms implemented in QGIS (v. 3.40.5) with SAGA NextGen tools. Land-use composition was quantified by intersecting CLC classes with catchment polygons and calculating the area proportion for each class. Land cover was subsequently reclassified into four dominant categories \u0026ndash;urban, agricultural, forestry, and natural vegetation\u0026ndash;, and expressed as the percentage of each category within each catchment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Field sampling\u003c/h2\u003e \u003cp\u003eSurface sediment samples (5\u0026ndash;10 cm depth) were collected in each stream using a core sampler at three randomly distributed sites along a 50 m reach during the spring\u0026ndash;summer period. At each site, three replicate cores were taken and pooled into a single composite sample. Sediments were sieved through a 2 mm steel mesh to retain the fine fraction, and a subsample was preserved in sterile 15 mL polyethylene tubes for molecular analyses. All samples were transported to the laboratory in dark coolers on ice. In total, 36 sediment samples were collected and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. DNA extraction\u003c/h2\u003e \u003cp\u003eTotal bacterial genomic DNA was isolated from 250\u0026ndash;500 mg of each sediment sample using a DNeasy PowerSoil kit (Qiagen, Germany) according to the manufacturer's instructions. The extracted DNA concentration and purity (260/280 and 260/230 ratios) were measured using a Qubit 4 Fluorometer (Invitrogen\u0026trade;, Thermo Fisher Scientific, Waltham, MA, USA) and a NanoDrop ND-1000 spectrophotometer (ThermoFisher Scientific Inc., USA), respectively. DNA samples were stored at \u0026minus;\u0026thinsp;20\u0026deg;C prior to sequencing or at \u0026minus;\u0026thinsp;80\u0026deg;C for long-term preservation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Library preparation and sequencing\u003c/h2\u003e \u003cp\u003eAmplicon libraries were prepared from genomic DNA targeting the V3\u0026ndash;V4 region of the bacterial 16S rRNA gene using the primers Bakt341F (5\u0026prime;-CCTACGGGNGGCWGCAG-3\u0026prime;) and Bakt805R (5\u0026prime;-GACTACHVGGGTATCTAATCC-3\u0026prime;). PCR amplifications were performed following standard protocols, including negative controls without template DNA to monitor potential contamination. PCR products were quantified using the Qubit dsDNA HS Assay (Thermo Fisher Scientific) and pooled in equimolar concentrations. The pooled library was sequenced on an Illumina NovaSeq platform using paired-end 250 bp reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Bioinformatic processing, taxonomic diversity and functional analyses\u003c/h2\u003e \u003cp\u003eAmplicon sequences were processed using Cutadapt [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and DADA2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] within QIIME2 (v. 2023.7) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] to remove primers, denoise reads, and generate amplicon sequence variants (ASVs). Taxonomic assignment was performed using a na\u0026iuml;ve Bayes classifier trained on the SILVA database (v. 132) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Subsequent analyses, including diversity estimation, ordination, statistical testing, and data visualization, were conducted in R (v. 4.4) using the microeco package [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The dataset was rarefied to the minimum sequencing depth to ensure comparability across samples.\u003c/p\u003e \u003cp\u003eAlpha diversity was estimated using Shannon diversity, Chao 1 richness and observed ASV richness. Differences among biogeoclimatic regions and land-use types were assessed using analysis of variance (ANOVA). When significant effects were detected, pairwise comparisons were performed using Tukey\u0026acute;s HSD test. Beta diversity was quantified using Bray-Curtis dissimilarities, and visualized through Principal Coordinates Analysis (PCoA). Differences in community composition were tested using permutational multivariate analysis of variance (PERMANOVA; [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]), with biogeoclimatic region and land-use type as explanatory variables. We evaluated the effects of region and land-use type using mixed-effects models (when applicable), including region and land-use type as fixed factors and stream as a random effect. Analyses were performed using the adonis2 function in the vegan R package with 999 permutations, and the effects of each factor were evaluated using marginal tests. Differential taxonomic composition was assessed using linear discriminant analysis effect size (LEfSe) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], applying a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an LDA score cutoff of \u0026gt;\u0026thinsp;2. Core and shared taxa were identified based on ASV occurrence patterns and visualized using Venn diagrams.\u003c/p\u003e \u003cp\u003eFunctional potential was inferred from 16S rRNA gene sequences using PICRUSt2 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in Python (v. 3.13). Predicted genes were grouped into major nitrogen-cycle pathways (e.g., nitrification, denitrification, nitrogen fixation, nitrate respiration, nitrate/nitrite assimilation, ammonium assimilation, and Dissimilatory Nitrate Reduction to Ammonium [DNRA]) according to KEGG annotations. Differential abundance of functional pathways was assessed using LinDA [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], with false discovery rate (FDR)-corrected p-values (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05) used to control for multiple testing using \u0026ldquo;ggpicrust2\u0026rdquo; package [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sequencing results and relative abundance of the dominant bacterial families\u003c/h2\u003e \u003cp\u003eA total of 36 samples were initially analysed, but a small subset failed to yield sequencing data of sufficient quality and was excluded from downstream analyses. The remaining samples generated 5,994 amplicon sequence variants (ASVs), with sequencing depth ranging from 1,083 to 9,128 reads per sample. After filtering low-abundance and low-frequency taxa, 1,453 ASVs were retained for subsequent analyses. Taxonomic assignment success decreased progressively as taxonomic resolution increased. All 1,453 ASVs were assigned at the kingdom and phylum levels, 1,452 at the class level, 1,437 at the order level, and 1,378 at the family level, declining to 1,055 ASVs at the genus level.\u003c/p\u003e \u003cp\u003eRelative abundance of the dominant families showed marked regional variation, whereas differences among land-use categories were comparatively less pronounced (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Several families were consistently detected across most samples, particularly Pirellulaceae and Flavobacteriaceae. At the family level, regional differences in dominant taxa were apparent. Mediterranean lowlands samples showed high relative abundances of Pirellulaceae, along with an increased representation of Flavobacteriaceae in streams with natural vegetation and an enrichment of Coleofasciculaceae in forested sites. Sierra Nevada mountains samples were dominated by Clostridiaceae and Xanthobacteraceae, along with greater variability in Pirellulaceae. In the Cantabrian mountains, Flavobacteriaceae were particularly abundant in agricultural streams, with relatively higher contributions of Chitinophagaceae and Sphingomonadaceae.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Alpha-diversity patterns across regions and land-use types\u003c/h2\u003e \u003cp\u003eVariation in alpha-diversity was more strongly associated with biogeoclimatic region than with land-use type. Alpha-diversity differed significantly among regions for all metrics analysed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Shannon diversity showed a clear regional pattern (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the lowest values in the Mediterranean lowlands, intermediate values in the Sierra Nevada, and the highest values in the Cantabrian Mountains. Richness-based indices followed a similar pattern, with both Chao1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and observed ASV richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) being lowest in the Mediterranean lowlands and highest in the Cantabrian Mountains, while no significant differences were detected between the Sierra Nevada mountains and the Mediterranean lowlands. In contrast, the effect of land-use on alpha-diversity was weaker and less consistent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Shannon diversity did not differ significantly among land-use categories (p\u0026thinsp;=\u0026thinsp;0.056), although agricultural streams tended to show slightly higher values. Richness-based metrics indicated modest but significant differences, with higher values in agricultural streams (Chao1: p\u0026thinsp;=\u0026thinsp;0.027; observed ASVs: p\u0026thinsp;=\u0026thinsp;0.024), whereas forest and urban streams showed intermediate values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Community structure and exclusive microbiomes across regions and land-uses\u003c/h2\u003e \u003cp\u003eCommunity structure differed clearly across regions, whereas differentiation among land-use categories was weaker and less consistent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Principal coordinates analysis (PCoA) based on Bray\u0026ndash;Curtis dissimilarities revealed compositional differences in sediment bacterial communities, with the first two axes explaining 35.4% of the total variation (PC1\u0026thinsp;=\u0026thinsp;21.4%, PC2\u0026thinsp;=\u0026thinsp;14%). Samples were primarily structured along the biogeoclimatic axis, with clear separation between the Mediterranean lowlands and the Cantabrian Mountains, while Sierra Nevada samples occupied intermediate positions. Land-use categories showed substantial overlap in the ordination space, with urban samples tending to occupy a more restricted portion, particularly in the Mediterranean lowlands and Sierra Nevada, whereas agricultural streams showed greater dispersion and overlapped with both natural and forested streams.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePERMANOVA confirmed that both region (R\u0026sup2; = 0.22, F\u0026thinsp;=\u0026thinsp;3.47, p\u0026thinsp;=\u0026thinsp;0.001) and land-use (R\u0026sup2; = 0.25, F\u0026thinsp;=\u0026thinsp;2.65, p\u0026thinsp;=\u0026thinsp;0.001) significantly influenced bacterial community composition. However, while the statistical effect of land-use was similar in magnitude to that of region, the greater overlap among land-use categories in the ordination space indicates that land-use-associated differences were less spatially coherent and more context-dependent than those associated with the biogeoclimatic pattern. Interestingly, while land-use explained a slightly higher proportion of the total variance, the biogeoclimatic region exhibited a higher F-statistic. This suggests that the regional signal is a more robust and efficient predictor of community assembly per degree of freedom. Tests for homogeneity of multivariate dispersion were not significant (p\u0026thinsp;=\u0026thinsp;0.595), indicating that results were not evidently driven by differences in within-group variability. While land-use significantly influenced community composition (Table S2), these effects should be interpreted with caution given the inherent site-specific variability of the individual streams selected to represent each land-use category.\u003c/p\u003e \u003cp\u003eAnalysis of shared and exclusive ASVs further supported this pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Across regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), 547 ASVs (37.6%) were shared among all three regions, while each region also harboured a distinct fraction of unique taxa, with the Mediterranean lowlands showing the highest number of exclusive ASVs (131; 9%), followed by the Sierra Nevada (50; 3.4%) and the Cantabrian Mountains (43; 3%). At the family level, the shared regional microbiome comprised 105 families dominated by Pirellulaceae, Flavobacteriaceae, Chitinophagaceae, and Sphingomonadaceae, while exclusive families also varied among regions, with the Mediterranean lowlands showing the highest number of region-specific families (56), followed by the Sierra Nevada (34) and the Cantabrian Mountains (29).\u003c/p\u003e \u003cp\u003eAcross land-use types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), 421 ASVs (29%) were shared among all categories, with additional overlap particularly between forested and agricultural streams. In contrast to regional patterns, exclusive ASVs were scarce across most land-use categories. At the family level, the shared microbiome comprised 92 families dominated by Pirellulaceae, Flavobacteriaceae, and Chitinophagaceae. Exclusive families were scarce across most land-use types, with only three in streams with natural vegetation and slightly higher numbers in forested and agricultural streams. Urban streams exhibited a markedly higher number of exclusive families (68) compared to other land-use types, including a relatively large proportion of unclassified taxa, with Bacteroidetes vadinHA17 and Pirellulaceae as the most abundant exclusive family.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Differentially enriched taxa across regions and land-uses\u003c/h2\u003e \u003cp\u003eLEfSe analysis revealed clear taxonomic differentiation of bacterial communities, with multiple lineages contributing to the observed patterns rather than isolated taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Across regions, distinct taxonomic signatures were evident (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The Mediterranean lowlands were mainly characterized by Proteobacteria (particularly Rhodobacterales) and Planctomycetota (e.g., Pirellulales), with additional contributions from uncultured Bacteroidetes lineages. In Sierra Nevada, discriminant clades were primarily concentrated within Firmicutes (especially Clostridia and Bacilli), Acidobacteriota, and Desulfobacterota. The Cantabrian Mountains exhibited a broader signal, with enrichment of Bacteroidota (e.g., Flavobacteriales, Chitinophagales), Verrucomicrobiota, Cyanobacteria, and Alphaproteobacteria (e.g., Sphingomonadales). Overall, regional differences involved multiple phyla and taxonomic levels, indicating strong differentiation among biogeoclimatic contexts.\u003c/p\u003e \u003cp\u003ePatterns associated with land-use types were less consistent and more diffuse (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Urban streams showed enrichment across several taxonomic groups, including Firmicutes (notably Clostridia), Proteobacteria (e.g., Gammaproteobacteria and Rhodobacterales), Campylobacterota, and Bacteroidota (e.g., Bacteroidales and Sphingobacteriales). Forested streams also exhibited a relatively broad signal spanning multiple lineages, including Planctomycetota, Acidobacteriota, Alphaproteobacteria (e.g., Rhizobiales, Sphingomonadales), Bacteroidota (Chitinophagales), and Cyanobacteria. Streams with natural vegetation showed fewer discriminant taxa, indicating weaker differentiation. Agricultural streams were associated with fewer and less distinct taxonomic signatures, including Verrucomicrobiota, Campylobacterota, and Cytophagales.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Inferred nitrogen-cycling potential across regions and land-uses\u003c/h2\u003e \u003cp\u003eThe Mediterranean lowlands showed a greater representation of genes linked to nitrification and denitrification, along with higher abundances of genes involved in nitrate/nitrite and ammonium assimilation. Sierra Nevada streams exhibited elevated relative abundances of genes associated with nitrogen fixation, DNRA and selected nitrate respiration pathways. The Cantabrian Mountains displayed a more balanced distribution across modules, without strong enrichment of any single pathway. Variation among streams within regions was moderate, with some modules showing site-specific peaks.\u003c/p\u003e \u003cp\u003eAcross land-use types, no consistent differences in inferred nitrogen-cycling functional potential were detected across categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Where differences were observed, they were subtle, site-specific, and largely nested within regional patterns. Urban streams in the Mediterranean lowlands and Sierra Nevada tended to show greater representation of the most abundant pathways in each region, whereas forested streams in the Mediterranean lowlands exhibited higher nitrate/nitrite assimilation. In Sierra Nevada, streams with natural vegetation showed greater inferred potential related to nitrification, whereas in the Cantabrian Mountains, agricultural streams were associated with higher levels of inferred nitrogen-cycling functional potential related to nitrate/nitrite assimilation, denitrification, and ammonium assimilation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is important to note that the functional profiles described here represent predicted genomic potential inferred from 16S rRNA sequences rather than direct measurements of metabolic activity. While PICRUSt2 provides a robust framework for generating hypotheses on inferred nitrogen-cycling functional potential, its accuracy relies on the proximity of available reference genomes in databases like KEGG, and it may not fully capture the metabolic plasticity or the activity of uncultured lineages present in stream sediments. Therefore, these patterns of inferred nitrogen-cycling functional modules should be interpreted as hypotheses of functional capacity that warrant further validation through metagenomics, metatranscriptomics, or direct measurements of enzymatic process rates to confirm actual biogeochemical flux.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur results show that microbial biodiversity and inferred nitrogen-cycling functional potential in headwater stream sediments are structured by the interaction between biogeoclimatic regions and land-use pressures, with regional environmental conditions acting as primary ecological filters and anthropogenic disturbances operating as secondary, context-dependent modifiers. This hierarchical organization is consistent with metacommunity theory, which predicts that large-scale environmental filters delimit the regional species pool while local processes shape communities at finer scales [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This pattern extends beyond taxonomic composition to encompass functional potential, with stronger coupling between community structure and nitrogen-cycling capacity at broad spatial scales than at local scales. These findings indicate that the relationship between microbial diversity and ecosystem functioning in headwater streams is scale-dependent, with implications for how biomonitoring frameworks should account for natural environmental variation when interpreting anthropogenic impacts.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The biogeoclimatic context as a primary determinant of microbial biodiversity\u003c/h2\u003e \u003cp\u003eBiogeoclimatic regions emerged as the main drivers of microbial biodiversity across headwater streams, as reflected by the clear spatial clustering, and their superior statistical potency compared to land-use. This suggests that the biogeoclimatic signal is a more efficient and parsimonious predictor of community assembly, even though land-use captured a slightly higher absolute proportion of the total variance. This dominance is further evidenced by the pronounced taxonomic turnover among regions (Mediterranean lowlands, Sierra Nevada mountains and Cantabrian mountains). This turnover, largely characterised by lineage replacement rather than shifts in relative abundance, extended across multiple taxonomic levels, indicating broad phylogenetic reorganisation consistent with evidence that microbial biogeography is shaped by environmental and historical processes operating at large spatial scales [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The high beta-diversity observed across regions, together with the presence of region-specific taxonomic signatures, supports the role of environmental filtering and aligns with metacommunity theory, which predicts a strong influence of environmental conditions in structuring aquatic microbial communities [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlpha-diversity patterns provide additional insight into these regional differences. Higher richness in the Cantabrian mountains likely reflects greater water availability and thermal stability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], whereas semi-arid systems support more specialised and less diverse communities adapted to environmental stress [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Differences in the identity of dominant taxa further illustrate these contrasts, with lineages typically associated with dry and oligotrophic conditions (e.g., Pirellulaceae, Coleofasciculaceae) prevailing in the Mediterranean lowlands, and taxa linked to more humid and productive environments (e.g., Chitinophagaceae, Sphingomonadaceae) dominating in the Cantabrian Mountains [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. A stable core microbiome with selective responses to environmental variation\u003c/h2\u003e \u003cp\u003eMicrobial communities across headwater streams were characterised by a substantial core microbiome, with nearly a third of all ASVs shared across samples. This widespread core persisted despite pronounced regional differentiation and was also evident across contrasting land-use types, indicating that a consistent set of taxa is maintained across a wide range of environmental conditions. The coexistence of this stable core with context-dependent taxa suggests that environmental variation, including land-use pressures, drives selective reorganisation of microbial communities rather than complete biotic homogenisation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The persistence of generalist taxa such as Pirellulaceae, Flavobacteriaceae, and Chitinophagaceae across contrasting conditions indicates a degree of community stability, suggesting that core ecosystem functions mediated by these taxa may be maintained under anthropogenic pressure [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the land-use scale, differences were expressed primarily through shifts in specific community components rather than wholesale compositional change. Streams with natural vegetation showed the lowest compositional exclusivity and greater homogeneity, consistent with more stable environmental conditions. Forested systems displayed moderate differentiation, with higher abundances of taxa associated with organic matter decomposition and oligotrophic conditions (e.g., Planctomycetota, Acidobacteriota), groups that have been reported to decline under increasing anthropogenic influence [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Agricultural systems were associated with taxa involved in the degradation of complex organic compounds and in disturbed environments (e.g., Verrucomicrobiaceae, Campylobacterota [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Urban systems showed comparatively greater differentiation, with enrichment of taxa commonly associated with eutrophic and anthropogenic conditions (e.g., Firmicutes, Campylobacterota, Gammaproteobacteria) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], as well as a higher number of exclusive taxa, including a large proportion of unclassified lineages.\u003c/p\u003e \u003cp\u003eThe recurrent enrichment of disturbance-associated taxa in urban systems, together with the more limited differentiation observed in less-disturbed land-use types, suggests that anthropogenic pressures act as selective filters that restructure specific components of the microbial community within the constraints imposed by the regional environmental context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Taxonomic patterns and inferred nitrogen cycling potential\u003c/h2\u003e \u003cp\u003eRegional variation in taxonomic composition was broadly paralleled by shifts in inferred nitrogen-cycling functional potential, consistent with a correspondence between community structure and metabolic capacity that is more apparent at broad spatial scales than at local scales. Although a formal quantitative assessment of this relationship was beyond the scope of this study, the parallel structuring of taxonomic and inferred functional patterns across regions suggests that biodiversity reorganisation along biogeoclimatic regions is unlikely to be functionally neutral, with both the identity and diversity of taxa likely contributing to nitrogen transformation processes [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the Mediterranean lowlands, the higher relative abundance of genes associated with nitrification and denitrification was consistent with adaptation to fluctuating redox conditions and nutrient pulses typical of semi-arid, intermittent systems. In Sierra Nevada, higher abundances of genes associated with nitrogen fixation and DNRA suggested a greater reliance on internal nitrogen cycling and nitrogen conservation, potentially under oligotrophic conditions or in organic-rich microenvironments where DNRA can outcompete denitrification [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The prevalence of Firmicutes in this region is consistent with this interpretation, given that some members of this phylum exhibit metabolic versatility, including capacities for N₂ fixation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The Cantabrian Mountains exhibited a more even distribution across functional modules, without strong enrichment of any single pathway, possibly reflecting greater hydrological stability and more continuous nutrient inputs.\u003c/p\u003e \u003cp\u003eIn contrast, land-use effects on inferred nitrogen-cycling functional potential were subtle and inconsistent across categories, suggesting that functional redundancy may buffer ecosystem processes against local disturbances [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Where land-use-associated differences were observed, they were largely nested within regional patterns rather than representing independent functional responses. These patterns indicate that the relationship between microbial diversity and inferred nitrogen-cycling potential is scale-dependent, being more apparent at the regional biogeoclimatic scale than at the local land-use scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Implications for biomonitoring\u003c/h2\u003e \u003cp\u003eOur findings highlight the potential of sediment metabarcoding as a biomonitoring tool, as sediment-associated microbial communities integrate environmental signals across spatial scales and are sensitive to broad-scale environmental contexts and, to a lesser extent, local anthropogenic pressures [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The clear differentiation observed across biogeoclimatic regions, together with more limited differentiation among land-use types, suggests the potential to incorporate microbial assemblages into ecological assessment frameworks.\u003c/p\u003e \u003cp\u003eThe strong influence of biogeoclimatic context underscores the need to incorporate regional baselines into biomonitoring frameworks, as natural variation in community composition can be substantial and may otherwise be misattributed to anthropogenic impacts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This is particularly relevant in regions spanning pronounced environmental gradients, such as the Iberian Peninsula, where biogeoclimatic variation contributes substantially to community turnover independently of land-use effects. At the same time, the contrasting responses between overall community structure and taxon-specific changes suggest that effective biomonitoring should combine community-level diversity metrics with the identification of indicator taxa [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In this context, the consistent enrichment of groups such as Campylobacterota and Clostridia in urban streams highlights their potential as candidate indicators of anthropogenic disturbance in headwater systems [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, integrating taxonomic composition with inferred functional potential provides a promising avenue for developing more informative bioindicators that explicitly link biodiversity patterns to ecosystem functioning [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. However, as functional profiles were inferred from taxonomic data, further validation using direct measurements of functional processes would strengthen their application in biomonitoring contexts. Such approaches may improve the capacity of biomonitoring programmes to detect not only compositional changes but also shifts in key ecosystem processes, ultimately contributing to more comprehensive assessments of ecological status in freshwater ecosystems [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Although our study highlights a dominant regional signal over land-use impacts, we acknowledge that our design, which prioritized a broad biogeoclimatic gradient across 12 streams, may have limited power to detect subtle land-use effects. The high within-stream variability and the absence of multiple stream replicates per land-use category mean that land-use signals might be partially masked by local environmental filters not captured in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur results show that microbial biodiversity and inferred nitrogen-cycling potential in stream sediments are primarily structured by biogeoclimatic conditions, which act as dominant ecological filters shaping both community composition and functional profiles. In contrast, land-use exerts a secondary, context-dependent effect, modifying specific components of microbial assemblages without overriding the regional signal.\u003c/p\u003e \u003cp\u003eDespite this variability, a widespread core microbiome dominated by generalist taxa was consistently observed, suggesting that key ecosystem functions may be maintained under anthropogenic pressure. In this context, the link between community structure and function appears to be scale-dependent, with regional differences driving functional contrasts, while local functional redundancy may buffer ecosystem processes against land-use impacts.\u003c/p\u003e \u003cp\u003eThese findings highlight the need to incorporate regional baselines into biomonitoring frameworks, as strong natural variability may otherwise obscure anthropogenic effects. Integrating diversity metrics with indicator taxa and functional information can therefore provide a more robust assessment of freshwater ecosystem status. However, further research with broader spatial replication is needed to determine whether land-use effects are consistently subordinate to regional filters across spatial scales.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by a grant from the Spanish Ministry of Universities (FPU22/00899) and by funds of the consolidated Research Group BIO-175 from the Government of the Junta de Andaluc\u0026iacute;a (Andalusian Research Plan, Junta de Andaluc\u0026iacute;a, Spain).\u003c/p\u003e \u003cp\u003eJP was supported by the PPIT-UAL and 2021\u0026ndash;2027 FEDER Operative Program Andalusia (program 54.A, ref. HIPATIA2023_05)\u003c/p\u003e \u003cp\u003eArtificial intelligence tools were used to assist in the preparation and language editing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.C.A., L.B., J.J.C. and M.J.L. conceived the study. L.C.A., V.C.I., E.F., J.P., R.H.M. and M.J.L. developed the methodology. L.C.A. and V.C.I. performed the formal analyses and investigation. L.C.A. wrote the original draft. J.J.C. and M.J.L. acquired funding. L.B., J.P., J.C.C. and M.J.L. provided resources. J.J.C. and M.J.L. supervised the work. All authors contributed to reviewing and editing the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRaw sequencing data are available in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1457075.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartiny JBH, Bohannan BJM, Brown JH et al (2006) Microbial biogeography: Putting microorganisms on the map. Nat Rev Microbiol 4:102\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrmicro1341\u003c/span\u003e\u003cspan address=\"10.1038/nrmicro1341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanson CA, Fuhrman JA, Horner-Devine MC et al (2012) Beyond biogeographic patterns: Processes shaping the microbial landscape. 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Ecol Evol 9:12000\u0026ndash;12016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.5707\u003c/span\u003e\u003cspan address=\"10.1002/ece3.5707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Freshwater ecosystems, Bacterial communities, Anthropogenic pressure, Nutrient cycling, Environmental DNA (eDNA), Biomonitoring","lastPublishedDoi":"10.21203/rs.3.rs-9556210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9556210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicrobial communities in freshwater ecosystems are structured by both natural environmental variation and anthropogenic pressures, yet how these drivers interact to shape biodiversity and ecosystem functioning remains poorly understood. Here, we assessed how biogeoclimatic regions and land-use pressures jointly structure bacterial diversity and inferred nitrogen-cycling potential in stream sediments along a broad environmental gradient across the Iberian Peninsula. We used 16S rRNA metabarcoding and functional inference across samples spanning three biogeoclimatic regions and four land-use types. Bacterial community composition and diversity showed strong regional differentiation, with pronounced taxonomic turnover and distinct alpha-diversity patterns, whereas differences among land-use types were weaker, less spatially coherent, and primarily reflected shifts in specific community components. Despite this variation, a consistent core microbiome was observed across samples, suggesting the presence of taxa shared across contrasting conditions. Inferred nitrogen-cycling functional potential partially mirrored taxonomic patterns, with contrasting regional dominance of nitrification, denitrification, nitrogen fixation and dissimilatory nitrate reduction to ammonium, while land-use effects were subtle and nested within regional patterns. These results indicate that bacterial diversity and nitrogen-cycling potential are hierarchically structured, with biogeoclimatic conditions acting as primary ecological filters and land-use as a secondary, context-dependent driver. The relationship between taxonomic composition and inferred functional potential was more evident at the regional than at the local scale, where functional redundancy may buffer ecosystem processes. Our findings highlight the importance of incorporating regional baselines into biomonitoring frameworks and support the integration of taxonomic and functional approaches to improve ecological status assessments in freshwater ecosystems.\u003c/p\u003e","manuscriptTitle":"Biogeoclimatic regions and land-use structure bacterial biodiversity and nitrogen cycling in headwater stream sediments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 08:45:01","doi":"10.21203/rs.3.rs-9556210/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"284691808436273739596914605819574221709","date":"2026-05-20T22:14:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192902850062603855932174788844075806123","date":"2026-05-19T16:18:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20321036654476076370220513081036026788","date":"2026-05-19T08:36:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27343400653566383222195417956512334362","date":"2026-05-08T19:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T22:37:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T02:38:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-07T02:38:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2026-04-28T15:26:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"52011f6a-b547-4138-ae42-311773f1761c","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"284691808436273739596914605819574221709","date":"2026-05-20T22:14:43+00:00","index":51,"fulltext":""},{"type":"reviewerAgreed","content":"192902850062603855932174788844075806123","date":"2026-05-19T16:18:32+00:00","index":50,"fulltext":""},{"type":"reviewerAgreed","content":"20321036654476076370220513081036026788","date":"2026-05-19T08:36:13+00:00","index":49,"fulltext":""},{"type":"reviewerAgreed","content":"27343400653566383222195417956512334362","date":"2026-05-08T19:10:12+00:00","index":26,"fulltext":""},{"type":"reviewersInvited","content":"31","date":"2026-05-07T22:37:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T02:38:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-07T02:38:35+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T08:45:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 08:45:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9556210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9556210","identity":"rs-9556210","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}