Biofilm bacteria communities in oligotrophic headwater streams of Patagonia: 16S rRNA metabarcoding across climate, stream order and forest intervention

Biofilm bacteria communities in oligotrophic headwater streams of Patagonia: 16S rRNA metabarcoding across climate, stream order and forest intervention | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 April 2025 V1 Latest version Share on Biofilm bacteria communities in oligotrophic headwater streams of Patagonia: 16S rRNA metabarcoding across climate, stream order and forest intervention Authors : Ibeth Gonzalez 0009-0004-2175-5290 , Brian Reid 0000-0002-4274-3350 [email protected] , Delphine Vanhaecke , Paulo Moreno-Meynard 0000-0003-0289-8187 , Anna Astorga , and Roy MacKenzie Authors Info & Affiliations https://doi.org/10.22541/au.174411961.19476925/v1 349 views 141 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Patagonia headwater streams, distinguished by ultra-low levels of inorganic nutrients and intact forested watersheds, are a potential global reference for intact stream communities and ecosystems in the temperate zone. Understanding steady state and dynamics of these remote stream ecosystems is challenged by near absence of visual or physical evidence of stream biofilm or phytobenthos. In order to understand biofilm community patterns and potential controls of climate regime and watershed land use, we conducted seasonal 16S rRNA metabarcoding of epilithic biofilm of nested zero to 2nd order forested stream networks. PCR amplification of 16S rRNA V4 region using dual-barcoded primers 16Sv4_515F and 16Sv4_806R revealed over 73,000 unique ASVs. Proteobacteria (mean 40.9% and 48.2% for respective dry and humid watershed clusters), were followed by Cyanobacteria (33.5% and 25.3% respectively), the latter surprising given the presumed forested/shaded heterotrophic reaches. While the full prokaryote diversity showed strongest responses to stream order, peaking in smallest 0-order reaches, cyanobacteria diversity responded to other landscape drivers such as climate regime, season and watershed intervention, with string seasonal peaks in winter and summer in 2nd order humid zone reaches, where riparian canopy loss results in a release from light limitation. Given the oligotrophic context, patterns in Cyanobacteria and functional taxa related to nitrogen cycling (e.g. Nitrosomonas) are also discussed. General conclusions on the Patagonian aquatic microbial observatory are presented, together with interpretation of a stream continuum concept for prokaryote and cyanobacteria stream microbiome in Patagonia. Introduction Watersheds are an integrating unit of landscapes, linking a mosaic of terrestrial patches and freshwater networks. Streams draining small watersheds provide continuous information on landscape function (Freeman et al., 2007), via hydrologic signatures (McMillan et al., 2021) including vegetation (Gribovsky et al., 2010) and hydrologic intermittency (Datry et al., 2014), temperature signals (Horner et al., 2019), weathering (Buoman et al., 2013) and chemical signatures (Spier et al., 2023; Rock et al., 2023). Headwater streams of small watersheds contribute 70% of global river networks (Armatulli et al., 2022), have disproportionate effects on water quality (Alexander et al., 2007), and may be the most sensitive part of river networks to land cover and climate drivers (Lowe and Likens, 2005). These complex and often short-lived signals (McMillan 2021; Rock et al 2024) are most commonly observed via monitoring of small watersheds, typically via a combination of high frequency sensors (e.g. water level and temperature) together with manual water quality sampling of nutrients and other parameters (Harmel et al., 2006). However, short-lived signals, pulses and disturbance events may not be captured by the latter component, requiring other (e.g. biological) indicators of these processes. Communities of microorganisms in headwaters, including bacteria, archaea, algae, fungi, protozoa, and metazoans, persisting on the physical substrate while surviving on a continuously flowing and changing supply of resources from the water column (Battin et al., 2003), play a vital role in maintaining microbial diversity within fluvial networks (Besemer et al., 2013; Malazarte et al., 2022). Benthic microbial biofilms consist of a diverse array of unicellular organisms enmeshed in a permeable extracellular matrix (Battin, et al., 2016), and represent a critical zone of high biological activity and ecosystem processes in streams and rivers. Within the benthic zone, stream biofilms attached to stones or other stable substrate serve as crucial loci of enzymatic processes, regulate the retention, mineralization or release of nutrients and carbon (Anderson et al., 1997; Fazi et al., 2016; Guerrieri et al., 2022), provide degradation of organic matter and primary productivity (Allan et al., 2020; Bistarelli et al., 2024), and are the foundation of metazoan food webs. Conversely, climate and land cover changes may also be imprinted on attached microbial biofilm in headwaters (Meyer et al., 2007). DNA Metabarcoding is one of the most rapidly advancing methods for characterizing microbial diversity, offering advantages of automated detection of diverse taxa, minimizing human bias, and may be cost-effective compared to traditional biological evaluation methods (Ji et al., 2013; Taberlet et al., 2012, 2018; Pilgrim et al., 2022). High-throughput sequencing, aligned with reference taxonomic databases (Keck et al., 2017), allows for the detection of rare or low-abundance species (Zhan et al., 2013), and its resolution is invaluable for detecting assemblage shifts caused by environmental disturbances (Stein et al., 2014). Recent studies based on 16S metabarcoding in stream networks, for example, have shown higher alpha and beta diversity in forested headwaters, with significant reductions in downstream areas subject to land-use changes like agriculture and urbanization (Laperriere et al., 2020; Besemer et al., 2013). Despite advances in methodology, significant challenges include baseline representation from both a global/geographic and ecosystem perspective. Promising initiatives like the Latin American Aquatic Microbial Observatory (AMOL; Fermani et al., 2024) advance the coordination of methodologies for microbiome monitoring in lesser represented regions. However, new initiatives and emerging methods leave ample room for expansion and representation. In the AMOL example, fluvial ecosystems are represented by mid-catchment systems (headwaters not yet considered), water column methods are favored over potentially more stable and representative benthic communities (Lear et al., 2013), and a design incorporating reference systems is needed in order to distinguish local vs. global stressors, as they are for other biological indicators (Whittier et al., 2007). Biofilms on fixed substrate (e.g. epilithic) serve as early indicators of environmental degradation land-use and climate changes, (Laperriere et al., 2020). Metabarcoding of the stream microbiome is therefore a non-exclusive advance and complement to traditional monitoring based on manual quantification of stream benthic primary producers (Biggs and Kilroy, 2000), together with high frequency sensors and water quality observations. Headwater streams in Patagonia, with near-reference condition forested watersheds in proximity to early stages of intervention (Astorga et al., 2018; Astorga et al., 2022), provide an ideal context for understanding the effects of local (land use) and large scale (climate/global change) drivers on stream biota, biofilm and ecosystem function. Perhaps the most prominent study focused on the region, Perakis and Hedin’s (2002) observations from forested headwater streams (presumed 0-order) showed extremely low levels of inorganic nitrogen, together with globally unusual chemical speciation (e.g. total dissolved N>>NH4>NO3). Although there have been no complementary studies to date on biological composition or ecosystem function of these unique Austral systems, taken as baseline this suggests a similarly unusual microbial biofilm, in terms of composition and function/adaptation to low nutrient concentrations, proportions and chemical speciation (Diaz et al., 2007). Despite near-reference conditions, Patagonia headwaters may nevertheless be subject to global/climate-driven shifts in snow and vegetation (Choler et al., 2024), atmospheric deposition (Dentener et al., 2006), weathering rates, carbon mineralization, elevation shifts in soils (Matus et al 2024; Sa et al 2018), shift in upstream/downstream flow generation (Feng and Gleason 2024), and direct effects on stream substrate and attached biofilm (Timoner et al., 2012). These remote locations may also nevertheless be vulnerable to land use change, with expected increase in forest loss (Armenteras et al., 2021; Hernandez et al., 2023), and wildfire (Leon-Muñoz et al., 2021). Key questions in the context of microbial observatory include: what is the sensitivity of headwater stream biofilm communities to climate change effects on stream hydrology and temperature, how will it respond to global change effects on nutrient deposition regime, and what are the local responses to changes in nutrients, organic matter and sediment inputs, light and other physical drivers linked to land use change (Dohet et al., 2015). The primary objective of this study is a molecular baseline for aquatic biofilm communities in southwestern Patagonia headwater streams, focused on prokaryote composition via 16S rRNA metabarcoding. Given the unusual biogeochemistry of nitrogen in Patagonian streams, understanding general community composition, relative abundance of cyanobacteria, together with other taxa potentially relevant procaryotes (e.g. Nitrosomonas, Nitrobacter ), are a starting point for identifying critical areas for biogeochemical activity in headwater streams ( sensu McClain et al., 2003). We characterized biofilm composition and evaluated seasonal variation within Patagonian headwater stream networks (0 - 2 order), representing two climates/biomes (humid-evergreen vs. dry/deciduous) and also reference (primary forest) vs. impacted watersheds affected by recent land use or land cover change ( i.e. evidence of fire, forest management, grazing). Compared to previous study designs (Fermani et al., 2024; Malazarte et al., 2023) this study emphasizes epilithic biofilm as a fixed-substrate biological indicator of watershed drivers, with potentially robust response within hierarchical stream networks, specifically in terms of initial zones of stream generation (0-order streams). Methods Study sites: Sites are based on a pre-existing foundation of long-term watershed monitoring stations (2016-present) in two climate regimes: a 3-watershed cluster in humid temperate rainforest zone (Hu; precip. 1400 - 1600 mm/yr.) and a corresponding 3-watershed cluster in dry zone transition to cold steppe (Dr; precip. 500-700 mm/yr.) starting in 2016. Each cluster consists of one reference site (absence of fire, roads, logging or grazing activities) and two impacted sites (variable impacts, from moderate forest management pressure for firewood, to nearly complete basin-scale forest conversion to pasture. Additional nested basins were added in 2021 as a nested hierarchy complement for each cluster, consisting of three zero-order stream basins and two second order streams (Fig. 1; Table 1). Dominant vegetation at Hu sites is the evergreen Nothofagus betuloides mixed with deciduous N. pumilo, with shading ranging from 80-95% in 0-1 order reaches (Hu3 and HU3a1 reaches retaining remnant riparian vegetation despite catchment-wide forest loss). Seasonal variation in canopy shading at Hu sites was minimal, variation driven more by spatial heterogeneity. Shading at 2 nd order Hu4 and Hu5 sites was less than 5% for all seasons, a result of loss of riparian canopy and increased stream width. Vegetation at Dr sites was dominated by the deciduous N. pumilo in 0-1 order reaches (80-90% shading), shifting to lower-stature N. antarctica in lower elevation 2 nd order reaches Dr4 and Dr5 (60% and 84% shading respectively). Seasonally increased light penetration following leaf shedding in Dr watersheds was generally less than 10%, largely due to overhanging trunks and high branch density. Nested watersheds also differ in mean and reach-scale elevation (Table 1), which affect thermal regime by default ( i.e . accumulated degree days are expected to be significantly higher in humid/evergreen sites and higher-order streams). All stations were instrumented with water level dataloggers (HOBO U20-001; Onset Inc.) located in stilling wells, and sampled monthly for water chemistry and physical parameters. Further information on land use history, reference conditions and criteria, and conceptual models of headwater longitudinal stream gradients for dry/wet zones can be found in Astorga et al. (2018; 2022; 2023). Field sampling procedures Three sampling stations separated by 10 to 20 meters were systematically chosen from a relatively uniform and characteristic reach near the downstream monitoring location of the 16 sites (Table 1; Fig.1). Although morphology ranged from riffle/run to step-pool, the principal sampling substrate of stones were frequent or dominant at all sites (60-100% cover). Proceeding from downstream to upstream direction, three stones were randomly selected within a 2 to 3-meter radius from flowing water and near thalweg (10-40 cm depth), the latter to minimize the potential for rewetted substrate. Stones were deposited in sterile Whirl-pak ® sample bags, and scrubbed in the field with a brush for biofilm removal (Eco-Alps Water project protocol; Rimet et al., 2020). Scraped stones were washed with 20 ml of nuclease-free, sterile water, followed by collection of 10 ml of the resulting mixture in a sterile 50 ml falcon tube and pre-filled with 40 ml of absolute ethanol. The samples were kept in a cooler at 4°C until they reached the laboratory, where they were stored at -20°C until DNA extraction. All sampling materials, including brushes and nitrile gloves were previously sterilized and sealed until use. A total of 9 stones per site (3 pooled per station) was chosen based on the optimization curve of ≈ 10+ stones suggested by Biggs and Kilroy (2000). Sampling was conducted seasonally starting in January/February 2023, followed by April/May 2023, July 2023 and October/November 2023, corresponding with the austral summer, fall (post-leaf loss), winter and spring (post leaf emergence), respectively, culminating in the collection of 181 biofilm samples and 16 field blanks, the latter processed prior to biofilm sampling for each sampling period. Laboratory extraction protocol Genomic DNA was extracted from biofilm samples, negative field controls and extraction blanks (total 216). DNA extraction was performed using the Purelink DNA Microbiome Purification Kit (Life Technologies, Thermo Scientific, CA, USA) according to the manufacturer’s instructions. To obtain a homogeneous solution, the 50 mL Falcon tube containing the biofilm/ethanol mixture was shaken by inverting. A 2 mL sample of this solution was pipetted (2 x 1 mL) into a 2 mL sterile microcentrifuge tube, which was then sealed. Centrifugation was conducted at 18,000 x g, 4°C, for 30 minutes. The supernatant was discarded, and the resulting pellet was processed per the manufacturer’s instructions, with an additional mechanical bead-beating tissue cell lysis step using the FASTPrep-24 TM disruptor (MP Biomedical). DNA concentration was quantified using a Qubit 2.0 fluorometer with a Qubit dsDNA high-sensitivity assay kit (Life Technologies, Thermo Fisher Scientific, Carlsbad, CA, USA) prior to PCR amplification. After extraction and isolation, a molecular library was prepared following a modified version of the Earth Microbiome Project (EMP) 16S Illumina Amplicon Protocol. PCR amplification targeted the V4 region of the 16S rRNA gene, using dual-barcoded primers: the forward primer 16Sv4_515F (5’-GTGCCAGCMGCCGCGGTAA) and the reverse primer 16Sv4_806R (5’-GGACTACHVGGGTWTCTAAT) (Caporaso et al., 2011), linked to Illumina adapters (MiSeq overhangs) and barcodes as per Kozich et al., 2013. The 16S-V4 PCR reaction mixture consisted of 2.5 µM forward and reverse primer, 1.25 U/25 µl of ampliTAQ Gold DNNA Polymerase (Life Technologies, Thermo Fisher), 6.25 mM MgCl 2 and 10 ng/µl ddH2O). Eight negative PCR controls were added to the total number of samples. DNA amplification was carried out under the following thermocycler conditions: 3 min at 94°C, followed by 30 cycles of 94°C for 45 s, 50°C for 60 s, and 72°C for 90 s, ending with 10 min at 72°C (Caporaso et al., 2012). PCR products were visualized by gel electrophoresis to confirm amplification, with a target amplicon size of fluorometer (Life Technologies, Thermo-Fisher). Sequencing was conducted on an Illumina MiSeq with paired-end 150 bp reads at the AUSTRAL-Omics Genomics Sequencing Lab, UACh, Valdivia. In total, DNA was sequenced from 100 samples and field blanks from humid zone watersheds, and 97 samples from dry zone watersheds. Bioinformatic pipeline Raw sequences were demultiplexed based on barcodes using Cutadapt v. 4.7 (Martin et al., 2011). DNA sequence analysis was conducted with the Quantitative Insights Into Microbial Ecology (QIIME) pipeline, version 2023.9.1 (Caporaso et al., 2012). MiSeq overhangs, primers, and barcodes were removed, and sequences were quality-checked. Sequences were dereplicated and denoised using DADA2 (Callahan et al., 2016) implemented in QIIME2 (Bolyen et al., 2019). Illumina forward and reverse reads were assembled using the CASAVA 1.8 paired-end fastq format. DADA2 was used to trim primers, remove low-quality sequence regions based on PHRED33 scores, join paired-end reads, filter out low-quality sequences, and identify amplicon sequence variants (ASVs) in a feature table with read counts for ASVs (qiime dada2 denoise-paired). The average quality score of sequences declined at 290 bp, so forward reads were trimmed to 270 bp and reverse reads to 240 bp using the DADA2 plugin, with a minimum overlap of 12 bp (default) to prepare an abundance table of unique ASVs. Prior to taxonomic assignment, a naive bayes classifier algorithm (Wang et al., 2007) was trained on the SILVA 16S rRNA gene reference database v.138.1 by extracting reads with the 16Sv4 _ 515F and 16Sv4_806R primer sequences, using subsets of ASVs clustered at 97% identity. Sequences were taxonomically classified, and ASVs were removed if they were classified as chloroplasts or mitochondria, or if they were not classified to the domains Bacteria and Archaea. To approximate even sampling depth, the sequence dataset was then rarefied to 2,900 sequences per sample prior to calculation of biodiversity metrics. We selected a rarefaction threshold of 2,900 sequences because it represented the largest tolerable loss of data while retaining as many samples as possible. Finally, rarefaction removed 43 of 224 samples (field blanks, extraction and PCR negative controls) that either did not PCR amplify or produced fewer than 2,900 sequences. Water Chemistry Sampling and Analysis Water quality characterization is based on a two-year baseline of nearly monthly sampling, and where coinciding with microbial biofilm sampling, water samples were upstream of and following benthic sampling. Samples were filtered in the field, stored on ice, followed by refrigeration (alkalinity, NH 4 ion) or freezing (nutrients) for later laboratory analysis. Field measurement of water temperature, dissolved oxygen, and specific conductivity were recorded with hand-held instruments (ProDSS, YSI Inc.). pH was analyzed in the laboratory shortly after sampling, while alkalinity titration to colorimetric endpoint (Metrohm Inc. Dosimat with bromocresol green–methyl red indicator) is reported as mg/L carbonate (APHA, 2005). Inorganic nitrogen (NOx) and soluble reactive phosphorous were also analyzed from 0.45 µm filtered samples via colorimetric cadmium reduction or ascorbic acid methods, respectively (APHA, 2005). Dissolved organic nitrogen (DON) was analyzed for NO 3 following digestion with potassium persulfate. NH 4 was analyzed by ion chromatography (Dionex™ ICS-5000 with eluent generation). Statistical Analysis Hill numbers (Hill, 1973) were calculated at the level of Family for the full community and phylum cyanobacteria. Hill numbers provide a unified mathematical framework encompassing a broad range of biodiversity indices (Kang et al., 2016). These numbers are parameterized by the order q, which determines the sensitivity of the index to species (un)evenness in abundances (Jost, 2006). Specifically, q=0 represents species richness, q=1 corresponds to the exponential of Shannon entropy, and q=2 reflects the inverse Simpson index, which gives greater weight to dominant species. By varying q, the importance of rare or abundant species in the analysis can be modulated. Rarefaction curves were also generated for each analysis group. Hill numbers were calculated by R/hilldiv package. Analyses of variance using generalized linear models (GLM-ANOVA) were conducted to assess differences in richness (Hill number q = 0), Shannon entropy (q = 1) and Simpson-Gini (q = 2) at the taxonomic level of family. Poisson or negative binomial GLM models with a logarithmic link function were fitted for richness, while Gamma GLM models with an inverse link function were applied for Shannon entropy. These models were constructed for two databases, the Full Base (archaea and bacteria) and phylum cyanobacteria, using season, climate, stream order, and watershed impact as independent predictors. Interaction effects were also evaluated, with significance determined through model selection using likelihood ratio tests. For models with significant predictors ( p -value < 0.05), post hoc analyses were performed using Tukey’s all-pair comparisons. Model fit was assessed by examining standardized residuals with the R/DHARMa package (Hartig, 2020). Principal Component Analysis (PCA) was employed to reduce data dimensionality for the analysis groups described above and to explore correlations between sites and organisms at the family taxonomic level. PCA variables were standardized (centered and scaled). To assess differences in community composition related to stream order, a Permutational Multivariate Analysis of Variance (PERMANOVA) was conducted using the vegdist and adonis2 functions, based on Bray-Curtis distances of ASV abundance. Statistical significance was determined using 999 permutations. When the factor ”Order” was significant in the PERMANOVA results, a Mann-Whitney U test was performed to compare abundance values across the levels of ”Order” (0, 1, and 2). Based on previous local experience with trace levels of inorganic nutrients as independent variables (Astorga et al 2021), analysis of water chemistry was oriented towards base-line characterization and general (subtle) distinction between dry and humid watershed sites (e.g. paired t-test of means across watershed clusters). All statistical analyses were conducted in R version 4.3.1 (R Development Core Team, 2022). Results Water Chemistry: All sites were characterized by very low concentrations of inorganic N, on average 4.7 µg/l (±4.0) N-NO x for dry sites and 3.5 µg/l (±4.0), while proportion of N-NH 4 was consistently greater than NO x (Table 1), with NO x /NH 4 fractionation ratios of 0.54 and 0.32 for Dr and Hu sites, respectively. The organic N fraction was on average 4.6 and 2.9 times the inorganic fractions (NO 3 + NH 4 ) for Dr and Hu sites, respectively. No consistent pattern was evident across stream order or intervention. Inorganic P of 32.3 µg/l (± 16.9) P-PO 4, for dry sites was on average eight times higher than humid sites, the latter with a mean of 4.1 µg/l (± 1.1). However, the difference was characterized by dry zone pulse, peaking on the 14 April sampling (62.7 – 85.2 µg/l P-PO 4 ) and sustained between March and May, rather than steady concentration (typically less than 20 µg/l P; not shown). As a consequence, inorganic N:P molar ratio of 1.0 for dry sites was 10 times lower than 10.6 ratio at humid sites. Dry sites had higher pH and alkalinity, compared to the mildly acidic and poorly buffered humid sites (Table 1). Microbial community The post-filtering/denoising algorithm retained 28,114,790 sequence reads, divided into four sequencing libraries, and assigned to 73,725 unique ASVs. Assigned taxonomic levels identified 25 phyla, 43 classes, 110 orders, 163 families and 211 genera. At lower taxonomic levels, a considerable number of unassigned taxa (e.g. no result, “ambiguous,” Incertae Sedis etc.) were classified, also varying by taxonomic level (Suppl. Fig. 1). For example, unassigned order following assigned class resulted in a median loss of 15% of ASVs per sample, with binomial distribution ranging to 65%. Median information loss was 25% at the level of family, while at the level of genus the mode shifts significantly to 60% of unclassified ASVs per sample. Based on these patterns, analysis at the level of taxonomic family was used henceforth, in or to maximize taxonomic resolution while minimizing information loss. Three dominant bacteria phyla accounted cumulatively for 55-90% of taxa (Fig. 2): Proteobacteria (mean 40.9% and 48.2% for respective dry and humid watershed clusters), were followed by Cyanobacteria (33.5% and 25.3% respectively) and Bacteroidetes (12.1% and 10.6 %). Archaea were present, 0.012% and 0.005% of dry and humid zone stream microbial communities, principally Methanobacteriaceae and Woesearchaeia in 0-order humid zone streams, but otherwise limited to extremely low abundance Community composition at the family-level was distinguished by Proteobacteria families Burkholderiaceae and Sphingomonadaceae, followed by Cyanobacteria families Nostocaceae and unidentified Oxyphotobacteria (BLAST of unique ASVs UF1, UF2, UF3 indicated Chamaesiphonaceae at 97% identity match for all three). Family level composition showed differences in dominant taxa across climate zones (Fig. 2), specifically the exclusive humid zone dominance of Xanthobacteraceae, Solibacteraceae (sub3) and Pedosphaeraceae (0-order streams), and Beijerinckiaceae (2 nd order; Fig. 2a). Meanwhile, exclusive dominant taxa to dry zone streams include Nocardiodaceae and Blastocatellaceae (0-order), Sulfuricellaceae (2 nd order) with Rhodobacteriaceae, Gleobacteriaceae and Flavobacteriaceae also distinct to dry zone, but more generally distributed across stream orders (Fig. 2b). Note that “exclusive” taxa here refer to a threshold of Dry zone sites had consistently high proportion of Cyanobacteria and increased richness (Gleobacteriaceae), while humid zone Cyanobacteria showed strong seasonal shifts evident only in 2 nd order streams, occupying a high proportion of overall community composition in winter and summer periods, reduced to < 10% during fall and spring (e.g. Shannon index, climate x order interaction; Table 2). Dominant taxa showing trajectories of change, i.e. consistent non-fluctuating shifts along 0 to 2 nd order streams, include dry zone increases in Burkholderiaceae, Leptolyngbyaceae and Saprospiraceae, together with decrease in Sphingomonadaceae, among others (summarized in Fig. 3; Mann-Whitney test P < 0.001). Burkholderiaceae and Saprospiraceae in humid zone streams also showed significant positive stream order trajectories, together with significant declining trend for Rhizobiales and Hyphomicrobiacea (Fig. 3; Mann-Whitney test P < 0.001). Lastly, although relatively less frequent, Nitrosomonadaceae also showed significant decline trajectory with stream order in humid zone streams (Fig. 3). Note that, although visible signs of sample processing in the field included organic matter and fine sediment, physical evidence of biofilm was limited, while macroscopic growth (i.e. filamentous or colonial) was not apparent at any sampling site or season. Microbial diversity in terms of Hill Numbers (Fig. 4), plotted as a gradient along orders q = 0,1,2 (richness, Shannon and Simpson-Gini, respectively), also indicate stream order a principal control on stream microbial communities, evident in the seasonal shift in Hill curve shape. Humid zone sites showed seasonal shifts at the lower end of the spectrum (q < 1.5; Fig. 4f), conversely the dry zone sites had seasonal shifts across the range of q (0 2.0; Fig. 4c). Compared to overall prokaryote ASV diversity in this study, the cyanobacteria diversity showed greater and more variable seasonal shifts in Hill curves, for both dry and humid sites (Fig. 4g-l), especially for spring cyanobacteria communities in the humid zone 2 nd order streams (Fig. 4l). The overall spectrum of Hill curves is consistent (Suppl. Fig. 3), showing the highest full base variation across lower Hill orders (q = 0, 1), while variation in the cyanobacteria curves was evident across the whole spectrum (q = 0, 1, 2). Hence these parameters were used as the basis for subsequent analysis of variance of microbial community diversity. Analysis of the effects of independent variables of climate zone, stream order, season and intervention on the full base microbial community diversity (ANOVA; Table 2) showed strongest and most significant effects of stream order, consistent across all indices, and with a consistent decline in diversity indices across 0 to 1 st and 2 nd stream orders for all seasons in Hu sites, but only winter and spring samples Dr sites (Suppl. Table 1). General seasonal richness patterns were also significant, mainly via interactive effects with climate, stream order and intervention, especially evident for Shannon and Simpson indices (Table 2; Suppl. Table 1). Although the diversity indices showed weakest response to watershed intervention (Table 2), the cyanobacteria ANOVA results for Shannon and Simpson indices indicated strongest individual drivers of watershed impact and also climate regime ( Chi p < 0.001), together with interactive effects of climate x order, and climate x season (Table 2). At fine scales of within-reach sampling ( i.e. stations separated by 10 - 20 meters within respective stream orders), richness accumulation curves based on three sample stations showed consistent increases in richness, effectively at the same rate for 0,1 st and 2 nd stream orders (Suppl. Fig. 2), with minor variations with season and climate zone (inset table; Suppl. Fig. 2). Although the observed species accumulation, evident from repeated within-reach sampling, does not sufficiently indicate where taxonomic richness may plateau for any given reach-scale sampling event ( i.e. within respective stream orders), when plotted in terms of global species accumulation ( i.e. results across all independent variables of climate, impact, order and season; secondary Y-axis; Suppl. Fig. 2), within-reach sampling captures on average 87% of the global microbial diversity (range 74-94%; Inset Table, Suppl. Fig. 2). PCA analysis based on the two classes of watershed impact (Suppl. Fig. 4) revealed distinct clusters for cyanobacterial taxa for both Hu (PC1 35.5%; PC2 11.4%; 44.9% overall) and Dr zone streams (PC1 19.8%; PC2 13.7%; 33.5% overall). Nostocaceae showed significantly higher abundance in reference sites (Mann-Whitney U-test; p < 0.01), while impacted sites were characterized by significant contributions from Oscillatoriaceae (both climate regimes) and Leptolyngbiaceae (dry zone streams; Suppl. Fig. 4). The only other PCA results producinng results > 30%, (not shown) were Cyanobaceria on 2 classes of climate (overall 35% PC1 & PC2) and the Full Base on 3 classes of stream order (overall 33.8% PC1 & PC2) only the latter with clustering distinguishing the smallest 0-order streams. Discussion Based on this first report of microbial 16s rRNA community composition from headwater streams in Patagonia, these results advance a global reference baseline for humid/evergreen and dry/deciduous biomes in the temperate zone, together with early stages of intervention from land use changes (Astorga et al., 2022) and also early stages of climate change impacts (Falvey and Garreaud, 2007). The two climate “extremes” (noting that the full Patagonia gradient is 3-4 times greater than the precipitation range included here) produced moderately distinct patterns in dominant microbial communities at the family taxonomic level. However, patterns in richness indices (Table 2; Suppl. Table 1), together with rates of species accumulation through reach-scale sampling (Suppl. Fig. 2), suggest that local reach scales (10s of meters) may represent a large proportion of “regional” diversity – here represented by a nearly 4-fold increase in precipitation and ≈ 150 km linear distance (Fig. 1). The classic sense that “everything is everywhere…” but not only “the environment selects” (Baas Becking 1934, sensu DeWitt and Bouvier 2006) but so does the sampling design. This potentially illustrates one of the challenges of newly developed and ascendant metabarcoding methods: the need to critically evaluate sampling design elements of replication vs. spatial scale, or a reworking through the ontogeny of traditional stream sampling methods applied to smaller and essentially invisible biota (e.g. compared to macroinvertebrates, organic matter studies; Minshall et al., 1983; Heino et al., 2005). A related observation on limits of taxonomic resolution and existing databases (Suppl. Fig. 1) also highlights the methodological challenges, specifically the second mode ( i.e. shift in statistical distribution) of taxonomic information at the level of genus. It remains to be demonstrated how much the outcome in microbial composition would change as a function of post-processing procedures, such as libraries based on local sequencing and depth and sequencing coverage, but also field procedures, including within-reach sampling intensity (e.g. # pooled stones/surface, spatial separation; Suppl. Fig. 2) and microhabitat representation such as detritus pools (Findlay et al., 2002) and organic matter inputs (Hayer et al., 2021). Standard and robust methods remain a central challenge to metabarcoding as a component of stream and watershed monitoring. Nutrients play a central part in defining reference ecosystems highlighted in the previous section. The results here reaffirm the globally significant N patterns reported by Perakis and Hedin (2002): NOx consistently over 10 times lower than the global average (also shown for Patagonia lakes; Diaz et al 2007), with extremely low levels of inorganic nitrogen, and inverted ratios of N-speciation (DON>NH 4 >NOx; Table 1). The results here also go beyond the presumed 0-order streams studied by Perakis and Hedin (2002), the pattern sustaining over the transition to larger 1 st and 2 nd order systems, although occasional increase in NO x with stream order in humid climate streams was evident. Phosphorous, which was not treated in the work by Perakis and Hedin (2002), shows a more dramatic difference of elevated P in the dry climate regime. The higher inorganic P in the dry zone community, corresponds with a more consistent dominance of cyanobacteria families within the stream microbiome (Fig. 2). Higher dry zone P levels compared to humid sites support the suggestion by Diaz et al., (2007), who, reviewing nutrient limitation patterns in ultra-oligotrophic Patagonian lakes, concluded that overall N-limitation of near-pristine Patagonian aquatic ecosystems is further regulated by a threshold of P limitation on N-fixing cyanobacteria of 8 µg/l P-PO4. Mean inorganic P values for dry and humid sites (Table 1), are respectively above and below this threshold. Although other taxa potentially related to N cycling, such as denitrification by Nitrosomonas , were a relatively minor component of the microbial community (1.4% Nitrosomonidaceae in Hu sites), it is worth comparing the significant decline in with stream order (Fig. 3, HU sites, lower right panel) with corresponding water quality (Table 2). This comparison is challenging given natural temporal variability streams at concentration ranges near laboratory detection limits, however the decline in Nitrosomonas does not apparently corresponding with inorganic N or NO 3 /NH 4 ratios (Table 1). While the latter ratio may be potentially useful as representation of the proportion of substrate/product (although information on the NO 2 intermediate is lacking), the metabarcoding results suggest an interesting possibility of potentially being a more sensitive indicator (or integrator) of water quality gradients. These findings, together with the shifts in cyanobacteria taxa corresponding with intervention (Suppl. Fig. 4; Table 2) suggest a corresponding cyanobacteria response to decreased inorganic nitrogen availability, together with increase in light penetration at downstream sites from loss of riparian tree canopy. Additionally, a potentially positive effect of temperature in unshaded streams may relate to cyanobacteria dominance in summer, while winter dominance at the same sites may follow nutrient pulses from leaf fall at higher elevations. However empirical evidence of drivers for these potential patterns requires further experimental study, especially where plausible explanations are lacking. Stream order was the most consistent correlate of diversity of microbial communities, which is consistent with the few comparable studies (Malazarte et al., 2022), suggesting a more universal control of stream size and functional distance from origin (e.g. stream continuum; Vannote et al., 1980; Doretto et al., 2020). The results presented here reinforce this generalization across climate and levels of intervention. But it also extends the observed range upstream to zero-order streams, characterized by extremes of intermittency (drying) and constancy (stable groundwater input). While Malazarte and others (2022) sampled under the presumption that stream microbial diversity in headwaters is driven by soils, an alternative explanation based on (largely) unmapped and under-represented zero order systems, such as those studied here, requires further consideration. In terms of land use effects, it may be generally true that urban or agricultural catchments exhibit different functional gene compositions compared to those draining native forests (Dopheide et al., 2015) especially regarding taxa that thrive in nutrient-rich conditions (Qu et al., 2017). However, land use changes in Patagonian streams studied here appeared to be a lesser factor in explaining microbial community composition and diversity (Table 2). This may be that the impacts are too recent, land use impacts related to colonization trends characterized by historic events (e.g. wildfire) are not consistent with disturbance pressures such as limited sustained grazing (see Astorga et al 2018 for a regional review). The most severely altered sites are the higher order humid-zone streams, where forest conversion to pasture and grazing pressures are predominant in lower watersheds (Hu4, 5) or are nearly complete (Hu3, 3a1). At these sites, the effect of intervention may be a cofactor in the previously discussed seasonal cyanobacterial dominance in second order humid zone streams. Biofilms in larger streams have been generally observed to have reduced local diversity and a more homogenized community composition compared to the more diverse biofilms found in headwaters (Besemer et al., 2015). While elsewhere it remains unclear whether this is a product of natural ecological gradients, or increased human effect of homogenization in higher order streams ( i.e. lower elevation, more accessible), here the limited effects of intervention suggest natural gradients, although with the possible exception of alteration of light regime mentioned above. Collectively, this underscores the critical role of headwaters as reservoirs of microbial diversity within fluvial networks, exerting a profound influence on downstream ecosystems. Finally, in the most general sense, although there is limited comparable information in the literature that might guide expectations regarding the contribution of cyanobacteria, their high relative abundance and strong seasonal dynamics (Fig.2), together with divergent patterns of richness (Table 2; Fig. 4; Suppl. Fig. 3) and response to intervention (Suppl. Fig. 4) were altogether unexpected. This is especially considering that 14 of 16 sample reaches were essentially shaded, and presumable heterotrophic systems. These findings together with the strong winter/summer seasonal peaks in cyanobacteria contribution in the two sites with light exposure (Hu4 and Hu5) suggest that 16S rRNA of Cyanobacteria have significant potential for research in headwater streams, especially where direct physical cues of biofilm are nearly absent. We propose deeper exploratory (geography, watershed attributes), empirical (dynamics over finer time and spatial scales) and experimental research (combined studies with nutrient diffusion substrates – NDS and laboratory experimentation; e.g. Hagy et al 2020). Additionally, a dual molecular marker approach (e.g. high-throughput sequencing of V4–V5 region of the procaryote 16S rRNA and the V1–V2 region of eucaryote 18S rRNA; Zancarini et al., 2017) would potentially highlight prokaryotic-eukaryotic interactions (especially relevant fungal microbiome in heterotrophic reaches; Zhao et al., 2024), together with environmental factors shaping the full microbial phototroph functional community. Conclusions and Reccomendations • This study provides the first report of microbial 16S rRNA community composition from headwater streams in Patagonia, establishing a global reference baseline for temperate humid/evergreen and dry/deciduous biomes, and contributing to our understanding of microbial ecology and nutrient dynamics in pristine temperate streams. Contribute to global representation of reference material, especially extreme or remote regions with reference state and/or limited intervention. • The study reaffirms globally significant patterns of extremely low inorganic nitrogen levels in Patagonian streams, consistent with previous findings by Perakis and Hedin (2002). • This research extends beyond previous metabarcoding studies by considering 0-order streams as sites of stream generation, and previous biogeochemistry studies by simultaneously considering inorganic phosphorous. The Patagonian climate gradient representing two biomes produced moderately distinct patterns in dominant microbial communities at the family taxonomic level, a general effect of reduced diversity with increased stream order (larger streams), while the cyanobacteria community was more responsive to seasonal shifts, climate regime and differences in watershed land use. • The study highlights methodological challenges in metabarcoding, including the need for improved reference sequence databases and critical evaluation of sampling design. Local reach scales (tens of meters) may represent a large proportion of regional microbial diversity upstream, highlighting the importance of sampling design in metabarcoding studies, the need to build up regional reference libraries, and address taxonomic database limitations. • We recommend experimental approaches such as diffusion substrate combined with metabarcoding for understanding biogeochemical controls on stream microbiome, and also extension of network sampling downstream to medium and major rivers and a wide range of light regime and elements of the river continuum. Figures and Tables Table 1 . Site locations and summary information for the 16 study watersheds, representing 8 basin nest/hierarchy in both dry/deciduous forest (Dr) and humid/evergreen forest (Hu) biomes. Stream order follows Strahler (1957). Watershed condition I classed as Reference (R: Intact/Primary Forest) and Intervention (I: Wildfire/Roads/Forest Management/Ranching). Precipitation regime is based o WorldClim 2.0 global gridded climate (Hijmans et al., 2017), watershed area modeled via ArcGis 10.8 ArcHydro 10.8 package and ALOS World 3D-30m digital elevation model v.3.1 (Japan Aerospace Exploration Agency JAXA; 2022). Stream order was assigned based on Chilean topographic maps (IGM 6918 hydrography, 1969). Table 2 . Results of four-factor ANOVA for 16s rRNA microbial diversity (dependent variables Richness and Shannon diversity indices at the family taxonomic level) as a function of independent variable for season ( n = 4), climate ( n = 2), stream order ( n = 3) and impact ( n = 2), together with respective interaction (latter showing significant cases only). Significance indicated by * ( p > 0.05), ** ( p ≥ 0.001) and *** ( p < 0.001), respectively. Fig. 1. Study site. Regional map of southwestern Chilean Patagonia from 40 – 53 ˚S (a), with locations of watershed clusters (triangles) within major Pacific basins (red polygons): Aysén River Basin (Dr: dry/deciduous site), Baker River Basin (Hu: humid/evergreen site). Close-up map of nested-hierarchical study headwater basins Hu (b) and Dr (c) sites. Locations of long-term monitoring, flow-gauging and biofilm sampling stations shown as triangles. Inset shows corresponding network structure of nested 0,1 and 2nd order basins. Fig. 2 . Seasonal variation in composition shifts of 16s rRNA microbial biofilm along 0 to 2 nd stream order gradients, shown for Hu (a) and Dr (b) sites. Dominant families are shown for respective climate regimes, within color-coded Phyla. All families >1.5% at any given period are shown, while distinct dominant taxa for respective climate zones are indicated by *). Scale shows general cumulative composition (note 2% offset separating Phyla, or 10% overall). Fig. 3. Violin plots illustrating representative shifts across stream orders for common or dominant taxa (family level) for dry and humid zone streams respectively. Significant increasing trends (blue shade) and decreasing trends (green shade) are indicated by letters (Mann-Whitney test, p < 0.001), while Average % ASVs indicated upper right for each panel. Fig 4 . Microbial diversity Hill Numbers (x-axis) at the taxonomic level of family, shown as gradient along orders q = 0,1,2 (alpha, beta and gamma diversity, respectively; y-axis). Left columns (a-f) are based on full database, right columns (g-l) for phyla cyanobacteria. Seasonal shifts shown by color code, while gray shading indicates footprint of corresponding dry (e.g.: a-c, g-i) vs. humid (d-f, j-l) sites. stream order represented by panel rows one (o – order), two (1st – order) and three (2nd – order). Suppl. Table 1. Seasonal variation in Richness/Shannon indices for humid (upper) and dry (lower) stream biofilm. General seasonal patterns also shown via columns, corresponding with 0 (red), 1 st (orange) and 2 nd order streams (blue) respectively. Humid sites exhibit richness and Shannon peak in diversity during fall for 1 st order, and fall/spring peaks for o and 2 nd order streams at the level of Genus. Meanwhile dry site diversity is less consistent across stream order and taxonomic level. Suppl. Fig. 1. Loss of taxonomic information as a function of taxonomic level. Histogram in 5% bins showing % of ASV lost per taxonomic increment, e.g. Phylum to Class (P-C), Class to Order (C-O), Order to Family (O-F) and Family to Genus (F-G). At the specific level loss is > 95% (not shown). Results suggest that the highest taxonomic Family level optimizes taxonomic resolution while maximizing conservation of information, and also suggesting potentially distinct microbiome of Patagonia headwater streams compared to existing databases. Suppl. Fig 2. Taxonomic richness accumulation curves for reach-level sampling (example shown is for Hu sites during winter season). Each sample, consisting of 3 pooled stones, is treated as one of three replicates at the reach level (e.g. ≈ 10 meter spacing) taxa accumulation for classified by stream order (colored symbols), show generally similar trajectories (slightly higher diversity with initial replicate for 0-0rder show here). Meanwhile, as a function of global diversity across all sites (grey symbols), replicates capture on average 60% (R1), 73% (R2) and 84% (R3), or between74-94% of global diversity. Inset table shows seasonal variation in accumulation across dry and humid zone watershed clusters. Suppl. Fig. 3 . Microbial diversity Hill curves at the taxonomic level of family, shown as gradient along orders q = 0,1,2 (x-axis; richness, Shannon and Simpson-Gini indices respectively) and corresponding ASVs (y-axis). All sites and seasons combined for full 16S Base (left panel) and phylum Cyanobacteria (Right panel). Suppl. Fig. 4. Cyanobacteria community across all samples and sites, classified as a function of watershed intervention. Clustering based on principal components analysis (left) is evident for reference (green) versus various stages of impairment (yellow). Boxplots (right) show ASV frequency for four of the most frequent taxa, showing dominance of Nostocaceae and Gleobacteriaceae under reference conditions, while shifts to Leptolyngbyaceae and Synechococcale ins. sed. are associated with and potential indicators of intervention. Author Contributions: BR and DV designed the research, IG and DV developed the metabarcoding procedures, BR, IG and AA performed the research, IG, PM and BR analyzed data, and all authors contributed to writing and production of figures and tables. Acknowledgements BR, DV and IG were supported by ANID FONDECYT 1221049 “Climate and land use controls on nutrient limitation and resource export from Patagonia headwater streams.” This work is a contribution to ANID R20F0002: “Long Term Socio-Ecological Research in Patagonia” (PATSER), which provided the baseline log-term monitoring framework of headwater watersheds. Data Availability : 16 rRNA amplicon sequences will be deposited in the NCBI Sequence Read Archive prior to publication. References Alexander, R. et al. 2007. The Role of Headwater Streams in Downstream Water Quality. J. Am. Wat. Res. Agency 43: 41-59. https://doi.org/10.1111/j.1752-1688.2007.00005.x Allan., J., David, Maria, M., Castillo., Krista, A., Capps. 2020. Stream Microbial Ecology. 225-245. doi: 10.1007/978-3-030-61286-3_8 Amatulli, G. et al. 2022. Hydrography90m: a new high-resolution global hydrographic dataset. Earth Sys. Sci. 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Keywords 16s rrna metabarcoding amplicon sequencing aquatic microbial observatory cyanobacteria epilithic biofilm headwater streams Authors Affiliations Ibeth Gonzalez 0009-0004-2175-5290 Universidad de Aysen View all articles by this author Brian Reid 0000-0002-4274-3350 [email protected] Centro de Investigación en Ecosistemas de la Patagonia View all articles by this author Delphine Vanhaecke Universidad de Aysen View all articles by this author Paulo Moreno-Meynard 0000-0003-0289-8187 Centro de Investigación en Ecosistemas de la Patagonia View all articles by this author Anna Astorga Centro de Investigación en Ecosistemas de la Patagonia View all articles by this author Roy MacKenzie Universidad de Magallanes View all articles by this author Metrics & Citations Metrics Article Usage 349 views 141 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ibeth Gonzalez, Brian Reid, Delphine Vanhaecke, et al. Biofilm bacteria communities in oligotrophic headwater streams of Patagonia: 16S rRNA metabarcoding across climate, stream order and forest intervention. Authorea . 08 April 2025. DOI: https://doi.org/10.22541/au.174411961.19476925/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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