# Biogeographical Distribution of River Microbial Communities in Atlantic Catchments

Alejandra Goldenberg-vilar, María Morán-luis, David R. Vieites, Jose Manuel

Álvarez-Martínez, Ana Silió, Cendrine Mony, Simone Varandas, Sandra

Mariza Monteiro, Diane Burgess, Edna Cabecinha, et al.

► To cite this version:

Alejandra Goldenberg-vilar, María Morán-luis, David R. Vieites, Jose Manuel Álvarez-Martínez, Ana Silió, et al. Biogeographical Distribution of River Microbial Communities in Atlantic Catchments. Environmental Microbiology Reports, 2025, 17 (1), pp.e70065. 10.1111/1758-2229.70065. hal-04920960

HAL Id: hal-04920960

https://hal.science/hal-04920960v1

Submitted on 30 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

OPEN ACCESS

# Biogeographical Distribution of River Microbial Communities in Atlantic Catchments

Alejandra Goldenberg-Vilar¹ | María Morán-Luis¹ | David R. Vieites² | José Manuel Álvarez-Martínez¹,³ | Ana Silió¹ | Cendrine Mony⁴ | Simone Varandas⁵,⁶,⁷,⁸ | Sandra Mariza Monteiro⁵ | Diane Burgess⁹ | Edna Cabecinha⁵,⁶,⁷,⁸ | José Barquín¹

¹IHCantabria—Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Universidad de Cantabria, Santander, Spain | ²Instituto de Investigaciones Marinas, Consejo Superior de Investigaciones Científicas, Vigo, Spain | ³Biodiversity Research Institute, IMIB (Univ. Oviedo-CSIC-Princ. Asturias), Mieres, Spain | ⁴University of Rennes, UMR CNRS Ecobio, Rennes Cedex, France | ⁵Centre for the Research and Technology of Agro-Environmental and Biological Sciences, CITAB/Inov4Agro, Universidade de Trás-Os-Montes e Alto Douro, UTAD, Vila Real, Portugal | ⁶BIOPOLIS Program in Genomics, Biodiversity and Ecosystems, Universidade do Porto, Vairão, Portugal | ⁷CIBIO Research Centre in Biodiversity and Genetic Resources, Universidade do Porto, Vairão, Portugal | ⁸InBIO Research Network in Biodiversity and Evolutionary Biology, Universidade do Porto, Vairão, Portugal | ⁹Agriculture and Food Bioscience Institute, Belfast, Northern Ireland, UK

**Correspondence:** Alejandra Goldenberg-Vilar (alejandra.goldenberg@unican.es)

**Received:** 11 June 2024 | **Revised:** 26 November 2024 | **Accepted:** 2 December 2024

**Funding:** This study was financed by the Interreg Atlantic Area program as part of the ALICE project (https://project-alice.com/) (EAPA_261/2016). The present study was also financed by FCT-Portuguese Foundation for Science and Technology, through the FCT/MCTES (PIDDAC), under the project UIDB/04033/2020 (CITAB).

**Keywords:** Atlantic landscapes | eDNA | eukaryotes | freshwater microbial communities | land use | prokaryotes

## ABSTRACT

Microbes inhabit virtually all river ecosystems, influencing energy flow and playing a key role in global sustainability and climate change. Yet, there is uncertainty about how various taxonomic groups respond to large-scale factors in river networks. We analysed microbial community richness and composition across six European Atlantic catchments using environmental DNA sequencing. Our findings reveal different drivers for diversity and composition: land use is pivotal for eukaryotes, while climate and geology are crucial for prokaryotes. A strong regional influence shapes these communities, with warmer, drier regions (Portugal and France) differing from cooler, wetter ones (Northern Spain, Ireland and the United Kingdom). These patterns suggest potential indicators for global change, such as taxa resistant to temperature increases and water scarcity, or those sensitive to land use changes.

## 1 | Introduction

Global biodiversity is significantly threatened by the combined effects of human-induced climate and land use changes which tend to potentially exacerbate both the pace and the amplitude of the current biodiversity crisis (IPBES 2019). Fluvial ecosystems are particularly vulnerable to climate change and anthropogenic impacts as they integrate all the effects of human activities across the landscapes and river networks (Markovic et al. 2017; David et al. 2021). Among all organisms inhabiting riverine ecosystems, microbes occupy almost all aquatic habitats across

environmental gradients (Mansour et al. 2018), encompassing a high diversity (Staley et al. 2015) including prokaryotes (bacteria and archaea), fungi, algae, protozoans and meiobenthos (Veach and Griffiths 2018). They can be free-living plankton communities inhabiting the water column (David et al. 2020) or biofilms formed in and inhabiting benthic and hyporheic sediments (Valentine and Mariotti 2020). This high diversity is related to major ecosystem functions as they are keystones for biogeochemical cycles (Cavicchioli et al. 2019), the principal decomposers in aquatic ecosystems (Tlili et al. 2017), pollutant degraders and they are also involved in pathogen control

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). *Environmental Microbiology Reports* published by John Wiley & Sons Ltd.</footnote>
(Delgado-Baquerizo et al. 2020). As a result, changes in micro-
bial biodiversity induced by global change may affect current
ecosystem functioning and resilience. Microbial changes also
strongly influence the response of other organisms to climate
change (Cavicchioli et al. 2019). For example, soil microbial
diversity influences plant diversity because of their role in nu-
trient cycling (Walker et al. 2018). Therefore, improving our
understanding of how these communities change in response
to climate variability and human activities is essential for de-
fining appropriate management actions to preserve ecosystem
multifunctionality despite environmental changes (Delgado-
Baquerizo et al. 2020).

Biodiversity patterns in riverine environments are mostly stud-
ied considering only macroorganisms (e.g., macroalgae, macro-
phytes, macroinvertebrates and vertebrate communities; Metz
et al. 2022; Seena et al. 2019), overlooking microorganisms,
and especially eukaryotes. Concerning microbial communi-
ties, the molecular diversity of microbial eukaryotes in aquatic
ecosystems is far less investigated than their prokaryotic coun-
terparts. Moreover, most of the studies dealing with river micro-
bial communities (RMC) focus on a single microbial taxonomic
domain (only prokaryotes or only eukaryotes), while only 1% of
the studies investigate prokaryotes and eukaryotes simultane-
ously (Li, Gao, et al. 2021, Li, Hu, et al. 2021). RMC have been
described in relationship to water physicochemical properties,
such as pH, conductivity, temperature, dissolved oxygen, as well
as nitrogen-related properties and phosphorus-related indices
(Li, Gao, et al. 2021, Li, Hu, et al. 2021), while their response
to large-scale environmental factors has been much less inves-
tigated. Large-scale factors in rivers include climatic, geologi-
cal, hydrological and topographical catchment characteristics
and land use and land cover (LULC) composition. Some of the
studies addressing these large-scale factors on RMC include the
effect of temperature in fungal communities (Seena et al. 2019),
the amount and timing of precipitation on microbial communi-
ties (Zeglin 2015) or the effect of land use change in prokary-
otes (Chen et al. 2018; Ji et al. 2022; Zhao et al. 2022; Hermans
et al. 2017; Griffiths et al. 2011). Moreover, it should be noted
that important changes on RMC have been reported at the river
network scale. For example, distinct bacterial composition were
found in the transition from tributaries to more downstream
reaches because of mass effects and species sorting mechanisms
(Crump, Amaral-Zettler, and Kling 2012; Besemer et al. 2013;
Read et al. 2015; Staley et al. 2015; Niño-García, Ruiz-González,
and Del Giorgio 2016). These changes have also been attributed
to changes in elevation, hydrological conditions and nutrient
concentrations (Besemer et al. 2013; Savio et al. 2015; Wang
et al. 2017; Henson et al. 2018).

This knowledge is crucial to increase our understanding on
how microbial diversity and activity that governs small-scale in-
teractions translate to large system fluxes (Delgado-Baquerizo
et al. 2020). Nevertheless, it is still unknown if general patterns
can be detected across multiple catchments along large-scale
gradients and how these environmental characteristics will in-
teract to shape RMC diversity and composition. To provide a sys-
tematic assessment of the forces driving microbial diversity and
composition across large-scale gradients, the present study aims
at examining the response of RMC to biogeographical gradients
over human-dominated catchments at the European scale. We

will analyse the effect of regional drivers (climate, geology and
hydrology), catchment characteristics (elevation, catchment
area) and LULC composition on RMC. The study includes pro-
karyotes, fungi, protists and algae from both water and biofilm
compartments. The inclusion of taxa from multiple kingdoms is
critical for supporting the development of robust indicators of
ecosystem health and resilience (Battin et al. 2016). More spe-
fically, we aimed at testing the following two main hypothe-
ses: (i) distinct RMC compositional and richness patterns can
be detected across catchments likely because climate, geological
and hydrological characteristics are different among them; (ii)
there is a strong effect of river network position within the entire
catchment (i.e., headwaters, middle and lower river reaches) due
to changes in physicochemical characteristics and LULC pat-
terns along the river continuum.

2 | Materials and Methods

2.1 | Study Sites

This study included six catchments distributed across five
European countries: Ireland, the United Kingdom, France,
Spain and Portugal (Figure 1). The Couesnon River, located in
northwestern France, has an 89 km length, and it drains the
Armorican massif (1128 km²), discharging into the Bay of Mont-
Saint-Michel (UNESCO World Heritage site). This river presents
a relatively smooth orography, with a moderate top elevation
(256 m) at the source. The Couesnon River catchment consists
mainly of agricultural land with mixed grazing pastures in the
upper stream and polders at the interface with the sea (79.6%),
followed by artificial areas (7.5%) and a very reduced forest cover
(3.6%) (Fonseca et al. 2022). The climate is maritime, with an
average monthly temperature ranging between 17.5°C in July
and 5°C in December, along with a mean annual precipitation of
787 mm, of which a third occurs from October to December. The
low soil permeability (grain and schist rocks) and the important
rainfalls cause a dense hydrographic network in the area.

The Carlingford Lough catchment is located on the East Coast
of Ireland, across the border between the Republic of Ireland
and the United Kingdom. The Lough is a coastal embayment
extending 16.5 km into the Irish Sea. The Mourne Mountains in
Northern Ireland delimit the catchment in its northern part and
the Cooley Mountains in the Republic of Ireland on its south-
ern side. The catchment's highest elevation is 600 m above the
Irish Sea. Inflowing catchments to the lough drain an area of
475 km², with the majority lying within the United Kingdom
(426 km²). The LULC is mainly comprised of meadows (58.8%)
and shrubland (15.4%), followed by wetlands (10%), artificial
land (5.6%) and forests (5.5%) (Fonseca et al. 2022). The cli-
mate in the Carlingford—Lough catchment is temperate, with
average monthly temperature ranging from 17°C to 7°C, and
average rainfall between 800 and 1000 mm. A combination of
metamorphosed shale, sandstones and igneous granites under-
lies the area.

The Paiva River catchment (796 km²), located in northern
Portugal, is a medium-sized watercourse (115 km length) draining into the highly regulated Douro River. The catchment orography is complex, with the river source located approximately
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** | Map of river networks, study area boundaries and sampling sites along the Atlantic catchments: (a) Carlingford Lough (Ireland and the United Kingdom), (b) Couesnon (France), (c) Pas, Miera and Asón (Spain) and (d) Paiva (Portugal).</figcaption></figure>

1000 m high and the river junction to the Douro at 10 m high above sea level. This catchment comprises both gentle and very steep slopes, mainly covered by forest, which are mainly euca-lyptus plantations (41%) and pasture (38%), followed by agricul-tural fields (13%) (Fonseca et al. 2022). Most of its catchment is subject to a temperate climate; however, the estuary has a Mediterranean climate with an average monthly temperature ranging from 28°C in August to 11°C in January, and a mean annual precipitation of 977 mm with a marked dry season last-ing almost 4 months (from June to September).

The Spanish site includes three neighbouring catchments (Pas, Miera and Asón rivers) draining into the Cantabrian Sea with a total area of 2047 km² and a river length of 223.9 km. The orogra-phy is complex, with relatively soft slopes in coastal areas, very mountainous middle internal valleys (elevations from 100 to 1200 m) and upper mountainous ranges with altitudes exceed-ing 1700 m and very pronounced slopes. Vegetation comprises shrublands (28.2%), meadows (26%), natural forests (21%) and estuarine wetlands (16.7%), with a small proportion of artificial areas (Fonseca et al. 2022). There are also very contrasted LULC patterns across the three catchments, with a historical degrada-tion of natural forests in Pas and Miera while Asón preserves extensive areas of natural forest stands (Belmar et al. 2018). The climate is temperate hyper-oceanic, with mild temperatures and high humidity due to regular precipitation and fog. Average

temperature ranges between 25°C in August and 10°C in February, along with a mean annual precipitation of 1042 mm, but with a high spatial gradient (e.g., over 2000 mm in the moun-tain tops and below 1000 mm in some coastal areas).

## 2.2 | Field Survey

Water and biofilm samples were taken for 103 sites located along the river network to capture within-catchment variability (15 for Carlingford Lough, Couesnon and Miera; 19 for Asón and Paiva; and 20 for the Pas river). Samples were collected from July to September 2018. Water samples were collected on sterile bottles at intervals of 12 min over 1 h (five bottles of 1 L each). After the collection of the five bottles, one composite sample was created combining 200 mL of each water bottle to a final volume of 1 L, in this way, we created an integrated sample to capture stochas-tic temporal variability in each river sampling site. The litre was filtered using a sterile filter column with a cellulose nitrate fil-ter (Whatman plc, Maidstone, UK; ø 25 mm, pore size 0.45 µm). While we are aware that some bacterial taxa may be lost using a fil-ter of 0.45, other phyla such as Cyanobacteria, Desulfobacterota, Firmicutes, Gemmatimonadota, Myxococcota and Nitrospirota might be underrepresented, as Byappanahalli et al. 2021 stated. Due this lack of consensus, we chose one of the most standard filter pore size (0.45 µm) for our study in order to capture most of
the targeted organisms (prokaryotes and eukaryotes). To charac-
terise benthic biofilms, we selected six cobbles from riffle areas,
concentrating on the river's main flow. Zones of slow current
(approx. ≤20 cm s⁻¹) were avoided as they allow the build-up
of loosely attached diatoms, silt and other debris. Cobbles were
collected at a depth of ca. 10 cm to ensure that they were not
exposed to air in the previous 4 weeks. Areas of heavy shade and
those close to the bank were avoided. Stones were thoroughly
scrubbed with a sterile dishwasher brush. Around 10 mL of the
dislodged material was filtered using a filter with the same char-
acteristics as the one used for water samples. Water and biofilm
samples were preserved in absolute ethanol and stored at -22°C
right after being surveyed.

## 2.3 | DNA Extraction, PCR Amplification, Sequencing and Bioinformatics Analysis

DNA extraction and amplification were carried out under phys-
ically separated and specifically dedicated laminar flow hoods
to minimise contamination risk. Extractions were done using
a QIAGEN DNeasy Blood & Tissue kit (Qiagen) following the
manufacturer's instructions. The quality and quantity of puri-
fied amplicon were checked using the 2200 TapeStation (Agilent
Technologies, Santa Clara, USA). We amplified the V4 region
of the prokaryotic 16S rRNA gene (254 bp; Caporaso et al. 2011)
and the V7 region of the eukaryotic 18S rRNA gene (123 bp; Gast,
Dennett, and Caron 2004) using well-established primer pairs.
We selected these general primers as they amplify across all
microbial eukaryote diversity. We are aware that this sequence
length has limited resolution at the species level for fungi, pro-
tists and other algal groups. Still, it is nonetheless well-suited
to explore genus-level diversity (De Vargas et al. 2015). Besides,
the 18S allows more reliable quantification of fungi than the ITS
region (Lepère et al. 2019). DNA amplification was carried out
in single reactions, as recommended by Marotz et al. (2019), in
two sequential PCRs using the Q5 High-Fidelity polymerase 2x
Master Mix (New England Biolabs). After each PCR, the ampli-
fied products were purified with HighPrep PCR beads (Ampure
XP). Primers for the second PCR included unique sequencing
barcodes as well as Illumina adapters. Following purification,
DNA concentrations were measured, and amplification specific-
ity was checked for all samples using gel electrophoresis. Three
negative controls of sterile H₂O were included during both ex-
traction and amplification that in no case yielded detectable
DNA concentrations (based on gel electrophoresis and measured
DNA concentrations). The purified PCR products were pooled in
equimolar concentrations and sequenced on the Illumina HiSeq
for 16S gene at Centro Nacional de Análisis Genómico (CNAG-
CRG, Barcelona, Spain) and MiSeq platforms for the 18S gene at
Macrogen (Macrogen Inc. Seoul, Republic of Korea).

The sequencing platform performed demultiplexing and pro-
vided a fastq file for each of the 206 libraries (103 sample li-
braries for water and 103 for biofilm samples). A first quality
filtering step excluded DNA reads below 330 and 150 bp read
length (for prokaryotes and eukaryotes, respectively), with a
Phred quality score below 23 over a moving window of 25 bp,
with more than one mismatch in the primer sequence and ho-
mopolymer over 8 bp, or with ambiguous base. All the fastq files
were then treated together following the bioinformatics process

described in Vasselon et al. (2017) using the Mothur software
(Schloss et al. 2009). Reads that were not fully aligned with the
SILVA (v132) database were removed. The 206 resulting files
were analysed together. Chimera removal was done using the
Uchime algorithm (Edgar et al. 2011). Taxonomic classifications
of amplicon sequence variants (ASV) were performed with a
similarity cut-off of 97% (for prokaryotes) and 90% (for eukary-
otes) against the SILVA (v132) database using the UCLUST al-
gorithm. Singleton sequences were removed to reduce potential
inflation of diversity due to sequencing errors. DNA reads as-
signed to ‘Eukaryota unclassified’ or ‘unclassified’ without in-
formation in any taxonomic rank were also removed for further
analysis (22% of reads). Samples with several reads below 10.000
were discarded from the analysis (Aylagas et al. 2021; Grosser
et al. 2019). We explored biodiversity patterns from ASV because
ASV outperforms OTU method for estimating richness in pro-
karyotes and eukaryotes (e.g., fungi; Joos et al. 2020).

## 2.4 | Environmental Variables

We analysed environmental variables in three groups: (i) cli-
matic, hydrological and geological variables related to the catch-
ment regional characteristics, (ii) topographical variables related
to the river network and (iii) LULC variables. All environmen-
tal variables were integrated into a synthetic river network for
each of the identified river reaches by running NetMap's virtual
watershed algorithms (Barquín et al. 2015). Netmap is an inte-
grated suite of numerical models and analysis tools that devel-
ops geospatial solutions for a wide range of applications for river
ecosystems, such as hazard mitigation, watershed restoration or
conservation (Barquín et al. 2015). The river networks were de-
lineated using flow directions inferred from the available DEMs
(Figure 1), using the algorithm described in Clarke, Burnett, and
Miller (2008). Each river network was divided into reaches of
length between 100 and 500 m and always divided into conflu-
ences, as these can produce important morphological changes
in the channel and flood zones (Benda et al. 2004). All environ-
mental variables were derived from each of the reaches of each
river network.

For the regional variables, we measured climate, geology and X.
Climatic variables for all case studies were derived from differ-
ent data sources (Fonseca et al. 2022) and included mean annual
precipitation (MNDOCUMENTS) and mean maximum, minimum, average
temperature and mean temperature during summer (MN_Tx,
Mn_tn and MN_tg, TMESU, respectively). For the Portuguese
case study, a previously developed high-resolution climate dataset,
PT.HRES was used (Fonseca et al. 2022). For the remaining
case studies, climatic data were retrieved from the E-OBS v20e
database at a ~10 km spatial resolution (Cornes et al. 2018) and
resampled to produce the targeted, refined spatial resolution
(~1 km spatial resolution).

Topographic variables were derived from National Digital
Elevation models (DEM) for each country, with a 5 m spatial
resolution. Global ASTER GDEM V2 (90 m) was used when no
other information was available (see Table S1). See Table S2 to
find data sources per country. From DEM, we derived the fol-
lowing topographic variables by using Netmap tools: the average
elevation of the river reach (ELEV_M), the distance from the
basin outlet (OUT_DIST), stream order (STRM_ORDER) and catchment area (AREASQKM). Lithological variables were derived from digital cartography based on regional national maps provided by local stakeholders and SOILGRIDS database (de Sousa et al. 2020) as reference information and included four rock types: igneous (MN_IG), limestone (M_LM), sedimentary (MN_SD) and schists (MN_SQ) (see Table S1). Regarding hydrology, we considered two main variables: mean flow during summer in each reach (FLOW) and number of high flow events per year using an upper threshold of three times the median flow over all years (FRE3; Table S1). They were derived from the Spatial Processes in Hydrology (SPHY) model, a hydrological modelling tool developed by Terink et al. (2015), and applied to the different case studies using the national gauge stations network and local river monitoring systems in each country. The mean generic erosion potential (MN_gepdelm and BF_gepdelm) was determined to identify potential sediment production, transport and deposition areas (Hernández-Romero et al. 2022). NetMap's shallow landslide potential index can be used to consider the potential for processes that range from shallow landslide, and debris flow to gullying and sheet washing in convergent areas on hillsides.

LULC parameters were derived from regional LULC maps obtained from remote sensing and local occurrence data, using CORINE Land Cover 2018 map (CLC) from the Copernicus Land Monitoring Service (CLMS) as a baseline to produce coherent information at the European scale (Álvarez-Martínez, Silió-Calzada, and Barquín 2018). By running NetMap tools over LULC patterns, we have included four land cover categories in the assessments: mean broadleaf forest area (MN_blf) and (BF_blf); mean area of planted coniferous forest, *Eucalyptus* spp. or other plantations (sylviculture areas and labelled as MN_syl and BF_syl); mean agricultural area (MN_agr and BF_agr) and mean urban area (MN_uhd and BF_uhd). LULC variables were expressed as the percentage area that is covered by each class at two spatial scales: (a) MN—the drainage basin, catchment scale, and (b) BF—reach scale measured as 40 m buffer polygons to the reach that represented each river section (Pérez-Silos, Álvarez-Martínez, and Barquín 2019). Averages of environmental variables per catchment are available in Table S3.

## 2.5 | Data Analysis

Water and biofilm taxa inventories were merged after bioinformatic processing and prior to any statistical analyses into a single sample for each surveyed river reach. All analyses were evenly performed for prokaryotes, fungi, heterotrophic protists and algae (autotrophs belonging to the Protists and Plantae kingdom).

Before the analysis leading to testing the hypothesis, we performed species accumulation curves and compositional tree-maps, checked estimated diversity across catchments and tested if differences in RMC diversity existed between catchments to have an overview of the prokaryote and eukaryote dataset in the Atlantic catchments. To test differences in microbial diversity between catchments, we used one-way ANOVAs and Tukey post hoc tests once we checked that assumptions of normality and homogeneity of variances were met. Tests were done for

each taxonomic group. Because ASV richness in next-generation sequencing is highly sensitive to differences in the number of sequences obtained across samples, we used rarefaction analysis to the smallest sequencing depth within each group to obtain a robust comparison of relative richness between samples using the function 'rarefy' in vegan (Oksanen et al. 2013). This function determines the species detected in random subsamples of identical sequence counts. Rarefaction curves were performed with the function ggrare from the ranacapa package (Kandlikar et al. 2018). A non-parametric richness index (Abundance-based Coverage Estimator; ACE) was calculated to estimate the true richness of all groups at the catchment scale. ACE is the most appropriate richness index in this study because it estimates the proportion of all individuals in rare species that are not singletons (Magurran and McGill 2010). Correlation matrices of richness and environmental variables were created to test the effect of large and catchment scale variables on RMC diversity (Hypotheses i and ii), using rcorr R function and corrplot package. A full multiple regression model has been done with all environmental variables and variable selection was performed using a stepwise model based on AIC criteria. Finally, variation partitioning of environmental variables and richness of RMC was done using the function varpart from the vegan package.

For compositional analysis, ASV matrix was Hellinger transformed. ASV with very low abundances were discarded for compositional analyses while retaining 98% of cumulative read abundance (ASV with abundances > 0.005 for prokaryotes and > 0.001 for eukaryotes). To test if distinct RMC can be detected among catchments (Hypothesis i) we used a permutational multivariate analysis of variance (PERMANOVA) with the function 'adonis' based on Bray Curtis distance using vegan package (Oksanen et al. 2013), and using catchment type as the explanatory variable. To further perform multilevel pairwise comparisons, the function 'pairwise.adonis' (Martinez Arbizu 2020) was applied. Correlation of compositional matrices was done using Mantel test based on Bray Curtis distance as applied in the vegan package.

The effect of environmental variables on microbial composition was tested with multivariate analysis (leading to test Hypotheses i and ii). Environmental variables were log₁₀ converted. Some variables were strongly correlated with others ($R^2$ > 0.7) and were removed from the redundancy analysis (RDA). These variables were: river width (WIDTH_m) correlated with catchment area (AREA_SQKM), mean minimum temperature (Mn_tn), mean maximum temperature (MN_tx) and mean temperature during summer (TMESU), correlated with mean average temperature (MN_tg), mean flow during summer (FLOW) correlated with catchment area (AREA_SQKM). Environmental variables were divided into three groups: (1) variables related to the regional identity of the catchment (climate, geology and hydrological variables); (2) topographical variables (related to the downstream gradient or network position); and (3) LULC variables.

For testing the hypothesis related to disentangling which group of environmental variables was the most important in shaping ASV composition, we performed RDA to explore environment-community relationships for both prokaryotes and eukaryotes at the highest level of taxonomic resolution possible (genus
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | Overview of the prokaryote (V4 16S rDNA) and eukaryote (V7 18S rDNA) datasets in the Atlantic catchments. Sequence size rarefaction curve for each of the analysed groups (a–d) and treemaps representing the diversity (size of rectangles) of the most abundant phyla per taxonomical group ((e) prokaryotes, (f) fungi, (g) heterotrophic protists and (h) algae). Panel e—A: Acidobacteria; C: Cyanobacteria; Ch: Chloroflexi; E: Euryarchaeota; P: Planctomycetes; V: Verrucomicrobia. Panel f—M: Mucoromycota; P: Peronosporomycetes; Zoopag: Zoopagomycota. Panel g—Labyrinthulo: Labyrinthulomycetes; M: Mast-3. Panel h—Cryptopag: Cryptophyta.</figcaption></figure>

for richness patterns showed that the most relevant fractions shaping RMC diversity were the individual fractions as the interactive effects were very low. The drivers for diversity in prokaryotes were, in order of importance, geological (23% explained individually), topographical (12% explained individually) and LULC (3.6% explained individually; Table S6). Eukaryote diversity, in contrast, were driven mostly by LULC variables (with 13.8%, 12.8% and 14.7% of explained variance by LULC fraction individually for fungi, protists and algae; Figure 5 and Table S6) and by climatic-geological variables in the second place (7.4%; 7.5% and 12.6% for fungi, protist and algae, respectively). Topographical variables were only significant for prokaryote and fungal diversity (AREASKQM for both, and elevation was only significant for prokaryote diversity; Table S9).

**3.4 | Environmental Drivers of RMC Community Composition**

Regarding community composition, all three groups of environmental variables (climatic and hydrogeological, topographical and LULC) were significant. The total explained variation of the RDA analysis was highest for prokaryotes (35%) followed by algae (28.3%), fungi (24.5%) and protists (18%; Figure 4; Table S7). The analysis of variation partitioning revealed that the highest percentage of unique explained variation was for climatic-geological variables (24.6% for prokaryotes; 22.7% for fungi; 34.8% for protists and 35.4% for algae), being average temperature and precipitation most important for bacteria and fungi, while the percentage of limestone was the most important for protists and algae (Figure 5 and Table S7). The next
<table><caption>**TABLE 1** | ASV richness for the six catchments, showing rarefied richness, observed richness, output of ACE richness estimators and percentage of richness retrieved per catchment in each of the microbial groups (prokaryotes, fungi, heterotrophic protists and algae).</caption><thead><tr><th></th><th></th><th>Rarefied richness</th><th>Observed richness</th><th>ACE</th><th>Richness retrieved (%)</th></tr></thead><tbody><tr><td rowspan="5">Prokaryotes</td><td>Ason</td><td>158,015.00</td><td>158,015</td><td>311,055 ± 325</td><td>70</td></tr><tr><td>Miera</td><td>171,053.00</td><td>171,053</td><td>288,492 ± 347</td><td>59</td></tr><tr><td>Pas</td><td>173,272.10</td><td>214,540</td><td>304,491 ± 323</td><td>70</td></tr><tr><td>Carlingford</td><td>149,144.10</td><td>178,471</td><td>253,854 ± 295</td><td>70</td></tr><tr><td>Paiva</td><td>142,982.70</td><td>194,422</td><td>241,879 ± 247</td><td>80</td></tr><tr><td rowspan="5">Fungi</td><td>Couesnon</td><td>155,346.40</td><td>185,082</td><td>267,268 ± 306</td><td>69</td></tr><tr><td>Ason</td><td>18,368.82</td><td>20,318</td><td>27,231 ± 80</td><td>75</td></tr><tr><td>Miera</td><td>21,325.91</td><td>24,127</td><td>30,333 ± 88</td><td>80</td></tr><tr><td>Pas</td><td>25,556.36</td><td>31,404</td><td>36,426 ± 89</td><td>86</td></tr><tr><td>Carlingford</td><td>17,227.20</td><td>18,212</td><td>23,701 ± 81</td><td>77</td></tr><tr><td rowspan="5">Protist</td><td>Paiva</td><td>22,168.94</td><td>22,168</td><td>26,406 ± 78</td><td>84</td></tr><tr><td>Couesnon</td><td>16,089.02</td><td>16,267</td><td>21,028 ± 75</td><td>77</td></tr><tr><td>Ason</td><td>10,920.56</td><td>13,625</td><td>17,015 ± 65</td><td>80</td></tr><tr><td>Miera</td><td>13,366.57</td><td>16,302</td><td>19,794 ± 69</td><td>82</td></tr><tr><td>Pas</td><td>13,363.82</td><td>14,747</td><td>18,095 ± 66</td><td>81</td></tr><tr><td rowspan="5">Algae</td><td>Carlingford</td><td>12,601.00</td><td>12,601</td><td>16,607 ± 66</td><td>76</td></tr><tr><td>Paiva</td><td>13,606.94</td><td>14,944</td><td>17,404 ± 61</td><td>86</td></tr><tr><td>Couesnon</td><td>10,792.06</td><td>13,168</td><td>16,262 ± 62</td><td>81</td></tr><tr><td>Ason</td><td>29,651.66</td><td>35,423</td><td>48,853 ± 122</td><td>72</td></tr><tr><td>Miera</td><td>41,217.52</td><td>53,526</td><td>65,487 ± 127</td><td>81</td></tr><tr><td rowspan="5">Algae</td><td>Pas</td><td>33,608.00</td><td>33,608</td><td>43,473 ± 108</td><td>77</td></tr><tr><td>Carlingford</td><td>39,398.07</td><td>39,648</td><td>55,020 ± 129</td><td>72</td></tr><tr><td>Paiva</td><td>43,523.30</td><td>46,275</td><td>57,829 ± 122</td><td>80</td></tr><tr><td>Couesnon</td><td>30,918.90</td><td>32,184</td><td>40,670 ± 103</td><td>79</td></tr></tbody></table>

group in order of importance was the group of topographical variables (outlet distance, catchment area, stream order and elevation; 16% of the unique explained variation for prokaryotes, 17% for fungi, 14% for protists and 13% for algae, Figure 5 and Table S7). The last environmental variable group was related to LULC, and the percentage of unique explained variation was 8.4% for prokaryotes, 3.8% for fungi 2.5% for protists and 8.4% for algae (Figure 5 and Table S7). The variable with the highest significant contribution for all the biological groups was the percentage of agricultural area in the catchment (MN_agr), although the mean generic erosion potential (MN_gepdelm) and the mean percentage of urban area (MN_uhd) in the catchment was also significant for most biological groups (all of them except bacteria for mean generic erosion potential; Table S8). To a lesser extent, the mean percentage of sylviculture was significant for all the groups, and broadleaf forest areas were significant for all except protists (Table S8). The variation partitioning analysis also revealed that an important part of the variance explained was due to the interaction effects. This was especially relevant for climatic-geological and

LULC for all the groups (between 17.5% and 25% of explained variation, Table S7) and also between climatic-geological and topographical for bacteria and fungi (between 13% and 15%, Table S7).

Pearson correlations of environmental variables and communities highlighted two main groups for variables (column dendrograms) and taxa (row dendrograms; Figure 6a-d), separating warmer catchments RMC (Paiva and Couesnon—red rectangles; Figure 6) from the RMC more prevalent in the Spanish and Carlingford Lough catchments with lower average temperatures and regular rainfall (blue rectangles; Figure 6). For prokaryotes, we have detected consistent responses of taxa and environmental variables at the order level. For example, we have found taxa from orders Sphingomonadales, Aeromonadales and Betaproteobacteriales (Burkholderiaceae) positively related to increases on temperature. Taxa from Chitinophagales (Sediminibacterium sp.), Micrococcales, Frankiales (Sporichthyaceae) or Cytophagales (Pseudarcicella sp.) were correlated to both temperature and agricultural
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | (a–d) Variation in rarefied richness (expressed as number of ASVs) across catchments. Letters denote statistically homogeneous groups based on ANOVA. (e–j) Rarefied richness correlations (Pearson) between RMC groups.</figcaption></figure>

areas. Some taxa were more abundant under higher elevations for example Betaproteobacteriales (Ellin6067) or Sphingomonadales. In relation to fungi, some generalisations were possible at the phylum level. For example, we found the phylum LKM15 positively related to temperature, while Chytridiomycota and Blastocladiomycota were positively related to both temperature and agricultural area in the catchment. It was also noticeable that these taxa were found predominately in lower reaches. From other phyla, taxa from classes Dothideomycetes, Peronosporomycetes (*Phytopythium* sp.) or Pucciniomycetes (order Pucciniales) were also significantly related to temperature and, at the same time, more abundant at stream reaches with higher altitude. The protistan taxa favoured by higher temperatures were *Lembadion* sp. (Ciliophora) and Ancyromonadida class. In addition, affected positively by both, temperature and agricultural areas were mostly ciliates (Haptoria, Choreotrichia and *Strombidium* sp.). In general, heterotrophic protists were more abundant at the

lower part of the river networks, especially ciliates (*Peritrichia* sp., *Salpingoeca* sp., *Carchesium* sp.).

The algae in the warmer catchments (Paiva and Coueson) were split into two groups: (1) related to higher temperatures and larger agricultural areas, mainly from Cryptophyceae class (*Synura* sp. or *Cryptomonas* sp.), and (2) significantly correlated to temperature but also igneous rocks (dominant at the Paiva catchment) for example Dinoflagellata and Zygnematophyceae classes and some diatoms that increased with elevation (*Achnanthidium* sp., *Ulnaria* sp.). On the other hand, taxa dominant in the lower reaches were centric diatoms (*Cyclotella* sp.) or taxa from the Chrysophyceae class.

In the opposite group (Spanish catchments and Carlingford Lough marked with blue rectangles in Figure 6), we found taxa positively related to the most humid conditions and overall lower average temperatures. Prokaryotes from orders Rhizobiales, Enterobacteriales and Deinococcales were positively related with
<figure><img src="image_5.png" /><figcaption>**FIGURE 4** | (a–d) Redundancy analysis (RDA) between microbial communities (prokaryotes; fungi; heterotrophic protists and algae) at highest level of taxonomic resolution possible and environmental variables. For each of the analysed communities, environmental variables were selected via redundancy analysis followed by forward selection separately per group of variables: (1) climatic-geological (red), (2) topographical (black) and (3) LULC (grey). Then, the selected variables were used for final redundancy analysis. See Table S1 for environmental variable codes. (e–j) Correlations of community composition (Mantel tests based on Bray Curtis similarity).</figcaption></figure>

precipitation but also to elevation (Figure 6a), while Bacteroidales and Flavobacteriales were more prominent in lower reaches in areas with high average precipitation (Figure 6a). In relation to Fungi, the majority of taxa from phyla Ascomycota and Basidiomycota and all of taxa from Zoopagomycota were also positively related to precipitation, being some of them positively correlated to elevation (Peronosporomycetes, Agaricomycetes; Figure 6b). Protist taxa related to precipitation and limestone rocks were mainly included in the Cercozoa phylum (Figure 6c), being more prevalent in higher altitudes. Other protist phyla (Ciliophora, Labyrinthulomycetes or MAST-2) were more prevalent in the lower parts of the river network (Figure 6c). Regarding algal groups, the ones positively related to precipitation and limestone rocks belong mostly to Chrysophyceae (*Hydrurus* sp., *Spumella* sp.). Phaeophyceae (Ectocarpales), Diatomea and Florideophyceae (red algae such as *Audouinella* sp.). Positively correlated to elevation stood out *Hydrurus* sp. and *Cocconeis* sp., while in lower reaches the abundance of *Poteriospumella* sp. or the diatom *Surirella* sp. (Figure 6d) increased markedly.

4 | Discussion

The extent of the eDNA ALICE project dataset allows an unprecedented examination of the structure of RMC across biogeographical gradients over Atlantic landscapes. Here, we found evidence of divergent drivers for RMC diversity and composition. Environmental correlates of species richness were not common across taxa and were predominantly related to topographical and geological variables for bacterial diversity and LULC for eukaryote diversity. This suggests that large-scale factors are not the most important drivers of eukaryote diversity but factors such as land cover (forests) and land use (extension of agricultural or urban areas). Species composition, in contrast, was strongly correlated across taxa, with a number of the same environmental drivers being significant for most microbial communities. Spatial scale structured the observed compositional patterns of RMC communities, denoting the importance of environmental filters related to climatic and geological characteristics shaping RMC
at European and regional scale, while the most important variables at the catchment scale were river network position (i.e., downstream gradient) and LULC.

The lack of correlation between prokaryote and eukaryote diversity has been found in other studies of freshwater organisms, as well as a high correlation between the richness of fungi and protist (Wood et al. 2017). Recent studies have revealed that a large fraction of protists also feed differentially on fungi

<table><caption>**TABLE 2** | Richness model parameters per group (prokaryotes, fungi, heterotrophic protists and algae) on best model selected according to AIC criteria. F-statistic, adjusted R² and p values are also shown.</caption><thead><tr><th>Group</th><th>Model parameters for richness</th><th>F</th><th>Adj R<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td>Prokaryotes</td><td>AREASQKM, FRE3, MN_uhd, MN_IG, MN_SD, MN_SQ, MN_syl, MN_tg, ELEV_M</td><td>7.216</td><td>0.37</td><td>&lt;0.0001</td></tr><tr><td>Fungi</td><td>AREASQKM, MN_blf, BF_blf, BF_agr, BF_gepdelm, MN_IG, MN_LM, MN_SD, MN_SQ</td><td>3.261</td><td>0.175</td><td>0.0018</td></tr><tr><td>Protists</td><td>BF_blf, MN_uhd, BF_gepdelm, MN_SQ, MN_SD, MN RR</td><td>4.316</td><td>0.172</td><td>0.0007</td></tr><tr><td>Algae</td><td>BF_blf, MN_uhd, BF_gepdelm, MN_SQ, MN_syl</td><td>6.41</td><td>0.22</td><td>&lt;0.0001</td></tr></tbody></table>

(Geisen 2016) and that protist diversity, and community composition explained more variation in the beta diversity of certain saprotrophic fungi than abiotic factors (Huang et al. 2021). The high correlation between fungal and protistan richness thus, can be attributed to the modulating role that protists might have on fungal communities.

Species composition, in contrast, was strongly correlated between RMC groups. The observed co-occurring patterns of all the groups analysed with environmental variables have been related to a Clementsian view of communities responding as coherent units (Wood et al. 2017). The correlation in community composition across all these disparate taxa implies an interconnected network of species that responds to the same environmental conditions according to the spatial scale and influence of each other's presence and abundance due to trophic interactions, mutualistic and competitive relationships.

## 4.1 | 16S and 18S rRNA Gene Sequences and Taxonomic Coverage

Our dataset, covered around ~9 and ~45 million reads and ca. 200.000 ASVs and 75.000 ASVs for prokaryotes and eukaryotes per catchment. As with many other biodiversity datasets built with metabarcoding techniques over large scales (Debroas et al. 2017; Malviya et al. 2016), the rarefaction analysis of prokaryotes and eukaryotes indicated that communities did not reach saturation. However, our extensive sampling effort (in terms of spatial extent and sequencing depth) gathered the majority (ca. 75%) of the expected diversity of prokaryotic and eukaryotic ribosomal diversity. Microbial diversity in terms of ASVs was higher in the prokaryote community than in the eukaryote community, as found in other studies on river biofilms (Sun et al. 2014; Guo et al. 2021). Regarding eukaryotes, the Atlantic rivers have shown a prevalence of protist rDNA biodiversity compared to classical multicellular eukaryotes, that is, animals, plants and fungi. Protists accounted for ca. 60% of

<figure><img src="image_6.png" /><figcaption>**FIGURE 5** | (a–d) Variation partitioning results denoting the percentage of unique explained variation per group (single effects) of environmental variables (climatic-geological, topographical and LULC) for RMC richness and composition. (e–h) Percentage of shared variation (interaction effects) of the three groups of variables for RMC richness and composition.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**FIGURE 6** | Correlation heatmaps showing the Pearson correlation between the significant environmental variables based on RDAs and the top 30 most abundant taxa per taxonomical group: (a) prokaryotes, (b) fungi, (c) heterotrophic protist and (d) algae. Column dendrograms denote correlation between environmental variables and row dendrograms denote taxa correlations. Taxa prevalence (Prev.) and relative abundance (Abun.) is also shown per taxonomical group. The first division of row dendrograms split the taxa in two groups: red rectangles denote communities more abundant in warmer catchments (Couesnon and Paiva) while blue rectangles denote communities more abundant in catchments with lower average temperature and regular year rainfall (Spanish catchments and Carlingford Lough). See Table S1 for environmental variable codes.</figcaption></figure>

total eukaryotic freshwater ribosomal diversity, which may indicate a lower proportional richness of protists in river ecosystems concerning marine environments (>85%; De Vargas et al. 2015). Fungi, conversely, comprised ca. 30% of eukaryote richness, somewhat higher than the previously reported contribution of fungi to overall eukaryotic richness in freshwater environments (ca. of 17%; Lepère et al. 2019).

## 4.2 | Drivers of RMC at the Regional Scale: Climate and Geology

Large scale geological variables turned out to be significant in explaining the diversity of RMC, especially for bacteria, while climatological variables did not impact significantly the diversity of RMC. In contrast to metazoans, a substantial portion of bacterial aquatic diversity within surface freshwaters may originate in soil environments (Crump, Amaral-Zettler, and Kling 2012). In this regard, headwaters have been suggested to collect microorganisms from terrestrial sources (Crump, Amaral-Zettler, and Kling 2012) which can contribute to community assembly of benthic bacterial biofilms (Besemer et al. 2013). Headwaters are

intimately connected to the terrestrial environment (England and Rosemond 2004) and are characterised by a large ratio of benthic surface area to water volume, relative to larger fluvial ecosystems downstream. This might explain why geological variables such as igneous, sedimentary and schist explain a substantial part of the bacterial richness model.

While RMC diversity varied significantly only between some of the groups between catchments (algae and bacteria), the dissimilarity of community structure varied significantly between the Atlantic catchments for all organism groups considered. This low compositional dissimilarity for all groups suggests a convergence of communities driven by climatic characteristics, specifically lower average annual temperatures and regular rainfall throughout the year. This consistent driver for all organism groups highlights the importance of the environmental filter comprising large-scale climatic factors in determining the regional species pool. In particular, climate was more determinant than geology for the composition of bacteria and fungi, while geological characteristics (% of limestone) were more relevant for the composition of protists and algae.
<figure><img src="image_8.png" /><figcaption>FIGURE 6 | (Continued)</figcaption></figure>

Temperature is one of the primary factors driving biological com-
munity composition worldwide (Zeglin 2015; Liu et al. 2019; Chen
et al. 2018; Yang et al. 2019). Specifically, temperature was the sec-
ond most important variable after metals for determining the com-
position of stream bacteria according to the review of Zeglin (2015)
and one of the most important in the study of Liu et al. (2019) and
Ruiz-González et al. (2013). Similarly, the effect of temperature in
other microorganisms such as fungi, protists or algae is also deter-
minant in shaping the distribution of these communities at large
scales (Zhang et al. 2018; Xiao et al. 2018). Geology, on the other
hand, determined the potential range of proximate physicochem-
ical variables such as nutrients, conductivity and pH, which are
widely known to be key variables shaping the distribution of many
microbes such as phagotrophic protists and other algae (e.g., dia-
toms and green algae; Potapova and Charles 2002). For example,
limestone turned out to be one of the most important geological
variables, may be due the relatively high export of dissolved ions in
limestone dominated catchments.

4.2.1 | Potential Indicators of Temperature Changes

We highlighted in our study taxa whose presence and abundance
were related to warmer temperatures in all taxonomic groups.
For instance, temperature had a positive influence on the abun-
dance of several bacterial taxa from the orders Chitinophagales
(e.g., *Sediminibacterium* spp.) and Frankiales (family

Sporichthyaceae). These taxonomical orders dominate streams
during the warmest months (Engloner et al. 2023) and can sur-
vive under very high temperatures (Rincón-Molina et al. 2019;
Chaudhary and Kim 2016). The abundance of other bacterial
taxa such as Betaproteobacteriales and some Bacteroidetes has
been reported to increase with global warming (von Scheibner
et al. 2014), which were also correlated with temperature in
our study. We found that temperature has also a positive in-
fluence on fungi from phylum Ascomycota (Bipolaris spp. or
Acremonium spp.), Aphelidiomycota or Chytridiomycota, het-
erotrophic protists from the order Litostomatea (Haptoria) and
phototroph protists such as dinoflagellates. Fungi from phylum
Chytridiomycota has been reported to be the most dominant
group in streams with water temperature above 8.6°C (Seena
et al. 2019). Dinoflagellates were especially favoured by higher
temperatures while lower temperature generally favoured some
species of diatoms (Xiao et al. 2018). A warming effect and a
trend of increasing eutrophication might promote dinoflagel-
lates over diatoms (Xiao et al. 2018). High water temperature
(20°C–27°C) was also positively affecting the occurrence of
Cryptomonas spp. (Phylum Cryptophyta), as it has been found
that is favourable for its reproduction (Zhu, Bi, and Hu 2013).
In addition, we found that temperature rise can be detrimental
for some other taxa, for example, the brown algae *Hydrurus* sp.,
which is ecologically important as a food source for macroin-
vertebrates and does not tolerate temperatures above 10°C–12°C
(Stanković and Leitner 2016).
<figure><img src="image_9.png" /><figcaption>FIGURE 6 | (Continued)</figcaption></figure>

## 4.3 | Drivers of RMC at the Catchment Scale: Topographical Variables and LULC

Once the environmental filter of climatic and geological constraints has been passed, RMC responded primarily to topographical variables related to river network position (i.e., downstream gradient) and land use variables which in many cases are intimately related, demonstrating the occurrence of biogeographical patterns at the river network level. In relation to this, it is worth noting that drainage area upstream of the site was the best predictive spatial feature for bacterioplankton richness compared to other physical and chemical variables (Read et al. 2015; Besemer et al. 2013). In agreement with our study, numerous studies on rivers have found that upstream river reaches amassed high bacterial species richness, before richness decreased further downstream (Crump, Amaral-Zettler, and Kling 2012; Savio et al. 2015; Niño-García, Ruiz-González, and Del Giorgio 2016). This pattern contrasts with the theoretical predictions of the RCC and other patterns reported from studies on invertebrate and fish assemblages (Besemer et al. 2013). Microbial communities from headwater reaches are heavily influenced by a large input of allochthonous organisms from soils and groundwater into the river network, as it was mentioned in the previous section. As the rivers progresses, such mass effects likely heeded to species sorting, a process requiring an increased water residence time to allow for the selection of species based on local environmental conditions (Crump, Amaral-Zettler, and

Kling 2012; Savio et al. 2015; Niño-García, Ruiz-González, and Del Giorgio 2016). Moreover, changes in microbial community composition from upstream to downstream sections were also significant and can be linked to the important downstream hydrological and biogeochemical changes along the river continuum but at the same time, to the intensity of disturbances due to human activities (e.g., changes in land use or vegetation; Leira and Sabater 2005; Henson et al. 2018).

In relation to eukaryotic community composition, the highest percentage of unique explained variation in relation to the longitudinal river gradient was found for fungal taxa, although their effect was also noticeable in heterotrophic protists and algae, in agreement with several studies that have detected a similar effect of river network position on eukaryotic communities (Wu and Liu 2018; Leira and Sabater 2005; Ortiz-Vera et al. 2018; Guo et al. 2021; Zhang et al. 2023). Interestingly, this directional pattern was much less explored for several groups as fungi (Miura and Urabe 2015).

In relation to community diversity, LULC variables (forest area and extension of urban areas) explained a significant part of eukaryote diversity. It was noticeable that variables related to forest coverage (such as the area of broadleaf forests and silviculture plantations) had different effects reducing bacterial diversity while increasing the diversity of fungal, protistan and algal communities. Some authors reported lower bacterial richness
<figure><img src="image_10.png" /><figcaption>FIGURE 6 | (Continued)</figcaption></figure>

in forested areas (Wood et al. 2017; Labouyrie et al. 2023) and argued that woodlands might be a highly competitive environment with reduced niche opportunities where fewer microbial taxa can persist. For instance, the Paiva catchment displayed low species richness of bacteria but the highest of algae and high dissimilarity compared to the other catchments. From all study cases, the Paiva catchment is the one with the highest coverage of eucalyptus plantations (sylviculture plantations), although the ecological status in most of the sampled stations is of excellent quality (Fonseca et al. 2020). With eucalyptus plantations dominating Paiva catchment, the lower bacterial diversity at Paiva could be due to the more recalcitrant character of eucalyptus leaves for decomposition in relation to native broadleaf species (Fabian et al. 2017).

Regarding community composition, it is worth mentioning that land use variables had a twofold and even threefold effect for bacteria and algae in comparison to fungi and protists. This effect consisted mainly on higher abundances for bacteria and algae involved in nitrogen cycling pathways. In our study, the catchments' water quality and quantity were impacted by agricultural practices that presumably affected nutrient loads, especially in the Couesnon River (Thomas et al. 2022). This effect also involved agricultural and urban areas in other catchments, such as the lower parts of the Miera river, Paiva or Carlingford Lough.

Finally, the changes observed in community composition in relation to river network position and land use types were also dependent on the higher scale environmental filters (climatic and geological characteristics). This is backed by the strong interactions found in this study between climatic-geological variables and topographical and LULC in explaining community composition. These interactions show that global change effects on Atlantic RMC might be asymmetrical in relation to the region's climatic and geological characteristics.

### 4.3.1 | Potential Indicators of River Network Position and LULC

Our study shed evidence on RMC compositional changes along the river network and in relation to LULC characteristics.

In the middle or lower parts of the river network with a high percentage of limestone and lower average temperature during the year (Spanish and Carlingford Lough), we found an increasing abundance of Bacteroidetes such as *Fluviicola* spp. or *Flavobacterium* spp. These bacteria are common inhabitants of detrital aggregates, linked to algal bloom and the degradation of algal sulfated polysaccharides (Kosek et al. 2019). On the other hand, in the catchments characterised by higher average temperatures and a marked dry season (Paiva and Couesnon
catchments), other Bacteroidetes dominate downstream (e.g., *Pseudarcicella* spp., NS11-12_marine_group) as well as other bacteria from the Microbacteriaceae family. The changes in Bacteroidetes distribution found in this study agree with Niño-García, Ruiz-González, and Del Giorgio (2016), but they contrast with other studies which propose a Bacteroidetes decrease downstream (e.g., Read et al. 2015). Bacteroidetes are believed to degrade high-molecular-weight organic compounds, and therefore, we argue that downstream patterns could depend no only on river network position but also on the effect that land uses might have on the concentration of these organic compounds (Wang et al. 2017; Zhao et al. 2022).

Regarding eukaryotes, we found a higher relative abundance of the phylum Ascomycota in the midstream and downstream river reaches, which may be due to its critical role in degrading organic substrates (Chen et al. 2020) and also because of increased levels of nutrients, especially carbon and phosphorus (Chen et al. 2020; Guo et al. 2019). Ciliated protists and sessile ones whose part of their life cycle are free-swimmers were also more prominent in downstream river reaches. In lower reaches we also found higher abundance of centric (*Cyclotella* sp., *Cyclostephanos* sp., or *Discostella* sp.) and motile diatoms (*Navicula* sp., or *Nitzschia* sp.). Centric diatoms have a planktonic life form and can dominate in eutrophic, turbid and slow-flowing waters (Goldenberg-Vilar et al. 2014; Muylaert and Sabbe 1999; Ha, Jang, and Joo 2003).

In the upper river reaches, the order Rhizobiales indicated for-
ested streams although, interestingly, different taxa were char-
acteristic of the warmer (Paiva and Couesnon) or the cooler and
more humid catchments (northern Spanish and Carlingford
Lough). Taxa from the phylum Basidiomycota (Agaricomycetes
and Microbotryomycetes), which are mostly wood decayers
(Hartmann et al. 2017), were also abundant in the upstream
river reaches. Similarly, heterotrophic protists and algae were
also found predominantly in upstream river reaches. These
taxa involved the phylum Cercozoa (different taxa regarding
the catchment) and algae from class Chrysophyceae (*Hydrurus*
sp.) in the more humid catchments (Spanish and Carlingford
Lough), while desmids (family Zygnematophyceae) were more
abundant in the upstream sites of Paiva catchments.

Finally, the effect of agricultural and urban land uses was re-
lated to higher abundances of Gammaproteobacteria, such as
*Malikia* spp. or *Rickettsiella* spp. *Malikia* spp. are associated
to globally important nitrogen cycling pathways, including de-
nitrification and nitrogen fixation, while *Rickettsiella* spp. is
an animal pathogen (Yang et al. 2020). In addition, *Nitrospira*
spp. was also positively associated to increases on agricultural
and urban areas (e.g., Couesnon and Miera catchments). This
bacteria is also involved in the nitrogen cycle (oxidising nitrite
to nitrate; Daims and Wagner 2018) and is an indicator of high
nitrogen concentration. In this regard, the Miera catchment is
highly deforested with high nutrient levels in summer in its
lower reaches due to reduced base flows (Belmar et al. 2018) and
the effects of sewage effluents in the lower part of the catchment
(Rodríguez-Castillo et al. 2017). Another clear imprint of land
use was the high occurrence of faecal indicators in the middle
and lower reaches of the catchments with high pressure of live-
stock farming, especially the Spanish and Carlingford Lough

catchments. The impacts of livestock farming were evident
with a higher number of bacteria from the phylum Firmicutes,
Bacteroidetes, Clostridia class and members from the family
Ruminococcaceae, taxa prevalent in livestock-affected and
densely-populated regions (Gao et al. 2022; Teixeira et al. 2020;
Kosek et al. 2019).

5 | Conclusions

This study has reported a massive invisible diversity of RMC
across the Atlantic Europe, providing evidence of the role of
different drivers of RMC diversity and composition. Diversity of
prokaryotes were mostly affected by topographical and geologi-
cal variables in comparison to the diversity of eukaryotic groups
driven mainly by LULC.

On the other hand, species composition was highly consistent
across taxa, with several of the same environmental factors
being important for most groups. RMC compositional patterns
denoted a strong regional footprint likely related to the environ-
mental filter of climate and geology and secondly, to changes
driven by variables operating at the catchment scale (topography
and LULC). This delineates distinctions between communities
in warmer catchments with dry seasons (Paiva and Couesnon
catchments in France and Portugal) compared to those with
cooler temperatures and more consistent year-round rainfall
(Spanish catchments and Carlingford Lough in the United
Kingdom). Our research highlights varying sensitivities among
the community composition of bacteria, fungi, heterotrophic
protists and algae. Bacteria and fungal community composi-
tion exhibited more robust responses to climatic variables, con-
trasting with the higher sensitivity of heterotrophic protists and
algae to geological characteristics (e.g., limestone or igneous
rocks). Furthermore, the impact of land use variables was dis-
tinct, notably affecting the composition of bacteria and algae the
most, especially regarding the percentage of agricultural areas,
emphasising their involvement in nitrogen cycling pathways.
Finally, the large extent of this study, together with a sampling
scheme covering inter and intra-catchment variability, has un-
veiled potential indicators of global change, including taxa fa-
vouring increasing temperatures, as well as indicators resilient
to elevated nutrient levels in urban and more polluted agricul-
tural areas. Microbes are at the basis of the food web and lead
many key ecological functions, this study contributes to unmask
RMC patterns in Atlantic catchments what could be paramount
to address future actions to mitigate and adapt to the effects of
global change.

This article directly contributes to several Sustainable
Development Goals (SDGs), especially SDG 13 (Climate Action)
and SDG 15 (Life on Land). By examining microbial biodiver-
sity in river ecosystems and how these communities respond to
climate change and land use alterations, the research addresses
key aspects of climate resilience and ecosystem sustainability.
Understanding microbial diversity's role in biogeochemical cy-
cles and ecosystem functions is crucial for developing models
to predict ecosystem responses to environmental changes which
aligns with the goals of mitigating climate change and preserv-
ing biodiversity. Additionally, the study's focus on land use pat-
terns, particularly agriculture and urbanisation, underscores its
relevance to SDG 6 (Clean Water and Sanitation), as it enhances
knowledge of water quality and ecosystem health, both essential
for sustainable water management practices.

Author Contributions

**Alejandra Goldenberg-Vilar:** conceptualization, investigation, writing – original draft, methodology, visualization, writing – review and editing, software, formal analysis, data curation. **María Morán-Luis:** conceptualization, investigation, writing – original draft, methodology, visualization, software, formal analysis, data curation. **David R. Vieites:** investigation, writing – review and editing, methodology. **José Manuel Álvarez-Martínez:** investigation, writing – review and editing, software, methodology. **Ana Silió:** investigation, methodology, writing – review and editing, software. **Cendrine Mony:** investigation, writing – review and editing. **Simone Varandas:** investigation. **Sandra Mariza Monteiro:** investigation. **Diane Burgess:** investigation. **Edna Cabecinha:** investigation, writing – review and editing, funding acquisition. **José Barquín:** conceptualization, investigation, funding acquisition, writing – review and editing, project administration, supervision.

Acknowledgements

This study was financed by the Interreg Atlantic Area program as part
of the ALICE project (https://project-alice.com/) (EAPA_261/2016).
The present study was also financed by FCT-Portuguese Foundation
for Science and Technology, through the FCT/MCTES (PIDDAC),
under the project UIDB/04033/2020 (CITAB). We thank Marta Sainz
Bariain, Alexia González Ferreras, Edurne Estévez Cano and André
Fonseca for the help provided during fieldwork campaigns. We thank
SAGE Couesnon for helping with the site selection for the Couesnon
case study in France. Special thanks to Valentin Vasselon (SCIMABIO)
for assistance with the bioinformatic analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available on request
from the corresponding author.

References

Álvarez-Martínez, J. M., A. Silió-Calzada, and J. Barquín. 2018. "Can Training Data Counteract Topographic Effects in Supervised Image Classification? A Sensitivity Analysis in the Cantabrian Mountains (Spain)." *International Journal of Remote Sensing* 39, no. 23: 8646-8669.

Aylagas, E., J. Atalah, P. Sánchez-Jerez, et al. 2021. "A Step Towards the Validation of Bacteria Biotic Indices Using DNA Metabarcoding for Benthic Monitoring." *Molecular Ecology Resources* 21, no. 6: 1889-1903.

Barquín, J., L. E. Benda, F. Villa, et al. 2015. "Coupling Virtual Watersheds With Ecosystem Services Assessment: A 21st Century Platform to Support River Research and Management." *Wiley Interdisciplinary Reviews: Water* 2, no. 6: 609–621.

Battin, T. J., K. Besemer, M. M. Bengtsson, A. M. Romani, and A. I. Packmann. 2016. "The Ecology and Biogeochemistry of Stream Biofilms." *Nature Reviews Microbiology* 14: 251–263.

Belmar, O., J. Barquín, J. M. Álvarez-Martínez, F. J. Peñas, and M. Del Jesus. 2018. "The Role of Forest Maturity in Extreme Hydrological Events." *Ecohydrology* 11: e1947.

Benda, L. E. E., K. Andras, D. Miller, and P. Bigelow. 2004. "Confluence Effects in Rivers: Interactions of Basin Scale, Network Geometry, and Disturbance Regimes." *Water Resources Research* 40, no. 5: 1–15.

Besemer, K., G. Singer, C. Quince, E. Bertuzzo, W. Sloan, and T. J. Battin. 2013. "Headwaters Are Critical Reservoirs of Microbial Diversity for Fluvial Networks." *Proceedings of the Royal Society B: Biological Sciences* 280, no. 1771: 20131760.

Byappanahalli, M. N., M. B. Nevers, D. Shively, C. H. Nakatsu, J. L. Kinzelman, and M. S. Phanikumar. 2021. "Influence of Filter Pore Size on Composition and Relative Abundance of Bacterial Communities and Select Host-Specific MST Markers in Coastal Waters of Southern Lake Michigan." *Frontiers in Microbiology* 12: 665664.

Caporaso, J. G., C. L. Lauber, W. A. Walters, et al. 2011. "Global Patterns of 16S rRNA Diversity at a Depth of Millions of Sequences per Sample." *Proceedings of the National Academy of Sciences* 108: 4516-4522.

Cavicchioli, R., W. J. Ripple, K. N. Timmis, et al. 2019. "Scientists' Warning to Humanity: Microorganisms and Climate Change." *Nature Reviews Microbiology* 17: 569-586.

Chaudhary, D. K., and J. Kim. 2016. "Novosphingobium naphthae Sp. Nov., From Oil-Contaminated Soil." *International Journal of Systematic and Evolutionary Microbiology* 66: 3170-3176.

Chen, J., P. Wang, C. Wang, et al. 2020. "Fungal Community Demonstrates Stronger Dispersal Limitation and Less Network Connectivity Than Bacterial Community in Sediments Along a Large River." *Environmental Microbiology* 22, no. 3: 832-849.

Chen, W., G. Wilkes, I. U. Khan, et al. 2018. "Aquatic Bacterial Communities Associated With Land Use and Environmental Factors in Agricultural Landscapes Using a Metabarcoding Approach." *Frontiers in Microbiology* 9: 2301.

Clarke, S. E., K. M. Burnett, and D. J. Miller. 2008. "Modeling Streams and Hydrogeomorphic Attributes in Oregon From Digital and Field Data 1." *JAWRA Journal of the American Water Resources Association* 44, no. 2: 459-477.

Cornes, R. C., G. van der Schrier, E. J. M. van den Besselaar, and P. D. Jones. 2018. "An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets." *Journal of Geophysical Research: Atmospheres* 123: 9391-9409.

Crump, B. C., L. A. Amaral-Zettler, and G. W. Kling. 2012. "Microbial Diversity in Arctic Freshwaters Is Structured by Inoculation of Microbes From Soils." *ISME Journal* 6, no. 9: 1629-1639.

Daimis, H., and M. Wagner. 2018. "Nitrospira." *Trends in Microbiology* 26, no. 5: 462-463.

David, G. M., D. Moreira, G. Reboul, et al. 2021. "Environmental Drivers of Plankton Protist Communities Along Latitudinal and Vertical Gradients in the Oldest and Deepest Freshwater Lake." *Environmental Microbiology* 23: 1436-1451.

David, V., S. Tortajada, N. Savoye, et al. 2020. "Impact of Human Activities on the Spatio-Seasonal Dynamics of Plankton Diversity in Drained Marshes and Consequences on Eutrophication." *Water Research* 170: 115287.

de Sousa, L. M., L. Poggio, N. H. Batjes, et al. 2020. "SoilGrids 2.0: Producing Quality-Assessed Soil Information for the Globe." *Soil Discussions* 2020: 1-37.

De Vargas, C., S. Audic, N. Henry, et al. 2015. "Eukaryotic Plankton Diversity in the Sunlit Ocean." *Science* 348: 1261605.

Debroas, D., I. Domaizon, J. Humbert, et al. 2017. "Overview of Freshwater Microbial Eukaryotes Diversity: A First Analysis of Publicly Available Metabarcoding Data." *FEMS Microbiology Ecology* 93: fix023.

Delgado-Baquerizo, M., P. B. Reich, C. Trivedi, et al. 2020. "Multiple Elements of Soil Biodiversity Drive Ecosystem Functions Across Biomes." *Nature Ecology & Evolution* 4, no. 2: 210–220.
Edgar, R. C., B. J. Haas, J. C. Clemente, C. Quince, and R. Knight. 2011. "UCHIME Improves Sensitivity and Speed of Chimera Detection." *Bioinformatics* 27: 2194-2200.

England, L. E., and A. D. Rosemond. 2004. "Small Reductions in Forest Cover Weaken Terrestrial-Aquatic Linkages in Headwater Streams." *Freshwater Biology* 49: 721-734. https://doi.org/10.1111/j.1365-2427.2004.01219.x.

Engloner, A. I., M. Vargha, P. Kós, and A. K. Borsodi. 2023. "Planktonic and Epilithic Prokaryota Community Compositions in a Large Temperate River Reflect Climate Change Related Seasonal Shifts." *PLoS One* 18, no. 9: e0292057.

Fabian, J., S. Zlatanovic, M. Mutz, and K. Premke. 2017. "Fungal-Bacterial Dynamics and Their Contribution to Terrigenous Carbon Turnover in Relation to Organic Matter Quality." *ISME Journal* 11, no. 2: 415-425.

Fonseca, A. R., J. A. Santos, S. G. Varandas, et al. 2020. "Current and Future Ecological Status Assessment: A New Holistic Approach for Watershed Management." *Water* 12: 2839.

Fonseca, A., J. A. Santos, S. Mariza, et al. 2022. "Tackling Climate Change Impacts on Biodiversity Towards Integrative Conservation in Atlantic Landscapes." *Global Ecology and Conservation* 38: e02216.

Gao, F. Z., L. Y. He, L. X. Hu, et al. 2022. "Anthropogenic Activities and Seasonal Properties Jointly Drive the Assemblage of Bacterial Communities in Subtropical River Basins." *Science of the Total Environment* 806: 151476.

Gast, R. J., M. R. Dennett, and D. A. Caron. 2004. "Characterization of Protistan Assemblages in the Ross Sea, Antarctica, by Denaturing Gradient Gel Electrophoresis." *Applied and Environmental Microbiology* 70: 2028-2037.

Geisen, S. 2016. "The Bacterial-Fungal Energy Channel Concept Challenged by Enormous Functional Versatility of Soil Protists." *Soil Biology and Biochemistry* 102: 22-25.

Gilmore, R. A., S. Hutchins, X. Zhang, and E. Vallender. 2019. "MicrobiomeR: An R Package for Simplified and Standardized Microbiome Analysis Workflows." *Journal of Open Source Software* 4: 1299.

Goldenberg-Vilar, A., H. van Dam, J. A. Vonk, H. G. van der Geest, and W. Admiraal. 2014. "Typology of Diatom Communities in the Dutch Delta: Recognizing Patterns of Environmental Drivers in Nutrient Rich Ditches." *Ecological Indicators* 45: 561-569.

Griffiths, R. I., B. C. Thomson, P. James, T. Bell, M. Bailey, and A. S. Whiteley. 2011. "The Bacterial Biogeography of British Soils." *Environmental Microbiology* 13, no. 6: 1642-1654.

Grosser, S., J. Sauer, A. J. Paijmans, et al. 2019. "Fur Seal Microbiota Are Shaped by the Social and Physical Environment, Show Mother-Offspring Similarities and Are Associated With Host Genetic Quality." *Molecular Ecology* 28, no. 9: 2406-2422.

Guo, K., N. Wu, W. Li, A. Baattrup-Pedersen, and T. Riis. 2021. "Microbial Biofilm Community Dynamics in Five Lowland Streams." *Science of the Total Environment* 798: 149169.

Guo, Q., L. Yan, H. Korpelainen, Ü. Niinemets, and C. Li. 2019. "Plant-Plant Interactions and N Fertilization Shape Soil Bacterial and Fungal Communities." *Soil Biology and Biochemistry* 128: 127-138.

Ha, K., M. H. Jang, and G. J. Joo. 2003. "Winter *Stephanodiscus* Bloom Development in the Nakdong River Regulated by an Estuary Dam and Tributaries." *Hydrobiologia* 506: 221-227.

Hartmann, M., I. Brunner, F. Hagedorn, et al. 2017. "A Decade of Irrigation Transforms the Soil Microbiome of a Semi-Arid Pine Forest." *Molecular Ecology* 26, no. 4: 1190-1206.

Henson, M. W., J. Hanssen, G. Spooner, et al. 2018. "Nutrient Dynamics and Stream Order Influence Microbial Community Patterns Along

a 2914 Kilometer Transect of the Mississippi River." *Limnology and Oceanography* 63, no. 5: 1837-1855.

Hermans, S. M., H. L. Buckley, B. S. Case, F. Curran-Cournane, M. Taylor, and G. Lear. 2017. "Bacteria as Emerging Indicators of Soil Condition." *Applied and Environmental Microbiology* 83, no. 1: e02826.

Hernández-Romero, G., J. M. Álvarez-Martínez, I. Pérez-Silos, A. Silió-Calzada, D. R. Vieites, and J. Barquín. 2022. "From Forest Dynamics to Wetland Siltation in Mountainous Landscapes: A RS-Based Framework for Enhancing Erosion Control." *Remote Sensing* 14, no. 8: 1864.

Huang, X., J. Wang, K. Dumack, et al. 2021. "Protists Modulate Fungal Community Assembly in Paddy Soils Across Climatic Zones at the Continental Scale." *Soil Biology and Biochemistry* 160: 108358.

IPBES. 2019. *Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services*, edited by E. S. Brondízio, J. Settele, S. Díaz, and H. T. Ngo, 1144. Bonn, Germany: IPBES Secretariat.

Ji, H., H. Wei, R. Wang, et al. 2022. "Heterogeneity and Its Drivers of Microbial Communities and Diversity in Six Typical Soils Under Two Different Land Uses in Tropical and Subtropical Southern China." *Applied Soil Ecology* 179: 104555.

Joos, L., S. Beirinckx, A. Haegeman, et al. 2020. "Daring to Be Differential: Metabarcoding Analysis of Soil and Plant-Related Microbial Communities Using Amplicon Sequence Variants and Operational Taxonomical Units." *BMC Genomics* 21: 1-17.

Kandlikar, G. S., Z. J. Gold, M. C. Cowen, et al. 2018. "Ranacapa: An R Package and Shiny Web App to Explore Environmental DNA Data With Exploratory Statistics and Interactive Visualizations." *F1000Res* 7: 1734.

Kosek, K., A. Luczkiewicz, K. Kozioł, K. Jankowska, M. Ruman, and Ż. Polkowska. 2019. "Environmental Characteristics of a Tundra River System in Svalbard. Part 1: Bacterial Abundance, Community Structure and Nutrient Levels." *Science of the Total Environment* 653: 1571-1584.

Labouyrie, M., C. Ballabio, F. Romero, et al. 2023. "Patterns in Soil Microbial Diversity Across Europe." *Nature Communications* 14, no. 1: 3311.

Leira, M., and S. Sabater. 2005. "Diatom Assemblages Distribution in Catalan Rivers, NE Spain, in Relation to Chemical and Physiographical Factors." *Water Research* 39, no. 1: 73-82.

Lepère, C., I. Domaizon, J. Humbert, L. Jardillier, M. Hugoni, and D. Debroas. 2019. "Diversity, Spatial Distribution and Activity of Fungi in Freshwater Ecosystems." *PeerJ* 7: e6247.

Li, K., J. Hu, T. Li, et al. 2021. "Microbial Abundance and Diversity Investigations Along Rivers: Current Knowledge and Future Directions." *Wiley Interdisciplinary Reviews: Water* 8, no. 5: e1547.

Li, Y., L. Gao, L. Niu, et al. 2021. "Developing a Statistical-Weighted Index of Biotic Integrity for Large-River Ecological Evaluations." *Journal of Environmental Management* 277: 111382.

Liu, J., T. Tu, G. Gao, M. Bartlam, and Y. Wang. 2019. "Biogeography and Diversity of Freshwater Bacteria on a River Catchment Scale." *Microbial Ecology* 78: 324-335.

Magurran, A. E., and B. J. McGill. 2010. *Biological Diversity: Frontiers in Measurement and Assessment*. New York: Oxford University Press Inc.

Malviya, S., E. Scalco, S. Audic, et al. 2016. "Insights Into Global Diatom Distribution and Diversity in the World's Ocean." *Proceedings of the National Academy of Sciences* 113: E1516-E1525.

Mansour, I., C. M. Heppell, M. Ryo, and M. C. Rillig. 2018. "Application of the Microbial Community Coalescence Concept to Riverine Networks." *Biological Reviews* 93, no. 4: 1832-1845.

Markovic, D., S. F. Carrizo, O. Kärcher, A. Walz, and J. N. David. 2017. "Vulnerability of European Freshwater Catchments to Climate Change." *Global Change Biology* 23: 3567-3580.
Marotz, C., A. Sharma, G. Humphrey, et al. 2019. "Triplicate PCR Reactions for 16S rRNA Gene Amplicon Sequencing Are Unnecessary." *BioTechniques* 67: 29-32.

Martinez Arbizu, P. 2020. "pairwiseAdonis: Pairwise Multilevel Comparison Using Adonis. R Package Version 0.4."

McMurdie, P. J., and S. Holmes. 2013. "Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data." *PLoS One* 8: e61217.

Metz, S., P. Huber, V. Accattatis, et al. 2022. "Freshwater Protists: Unveiling the Unexplored in a Large Floodplain System." *Environmental Microbiology* 24: 1731-1745.

Miura, A., and J. Urabe. 2015. "Spatial and Seasonal Changes in Species Diversity of Epilithic Fungi Along Environmental Gradients of a River." *Freshwater Biology* 60, no. 4: 673-685.

Muylaert, K., and K. Sabbe. 1999. "Spring Phytoplankton Assemblages in and Around the Maximum Turbidity Zone of the Estuaries of the Elbe (Germany), the Schelde (Belgium/The Netherlands) and the Gironde (France)." *Journal of Marine Systems* 22: 133-149.

Niño-García, J. P., C. Ruiz-González, and P. A. Del Giorgio. 2016. "Interactions Between Hydrology and Water Chemistry Shape Bacterioplankton Biogeography Across Boreal Freshwater Networks." *ISME Journal* 10, no. 7: 1755-1766.

Oksanen, J., F. G. Blanchet, R. Kindt, et al. 2013. "Package 'vegan'. Community Ecology Package, Version 2."

Ortiz-Vera, M. P., L. R. Olchanheski, E. G. da Silva, et al. 2018. "Influence of Water Quality on Diversity and Composition of Fungal Communities in a Tropical River." *Scientific Reports* 8, no. 1: 14799.

Pérez-Silos, I., J. M. Álvarez-Martínez, and J. Barquín. 2019. "Modelling Riparian Forest Distribution and Composition to Entire River Networks." *Applied Vegetation Science* 22, no. 4: 508-521.

Potapova, M. G., and D. F. Charles. 2002. "Benthic Diatoms in USA Rivers: Distributions Along Spatial and Environmental Gradients." *Journal of Biogeography* 29: 167-187.

R Core Team. 2021. *R: A Language and Environment for Statistical Computing*. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Read, D. S., H. S. Gweon, M. J. Bowes, et al. 2015. "Catchment-Scale Biogeography of Riverine Bacterioplankton." *ISME Journal* 9: 516-526.

Rincón-Molina, C. I., J. A. Hernández-García, R. Rincón-Rosales, et al. 2019. "Structure and Diversity of the Bacterial Communities in the Acid and Thermophilic Crater-Lake of the Volcano "El Chichón," Mexico." *Geomicrobiology Journal* 36: 97-109.

Rodríguez-Castillo, T., J. Barquín, M. Álvarez-Cabria, F. J. Peñas, and C. Álvarez. 2017. "Effects of Sewage Effluents and Seasonal Changes on the Metabolism of Three Atlantic Rivers." *Science of the Total Environment* 599: 1108-1118.

Ruiz-González, C., L. Proia, I. Ferrera, J. M. Gasol, and S. Sabater. 2013. "Effects of Large River Dam Regulation on Bacterioplankton Community Structure." *FEMS Microbiology Ecology* 84: 316-331.

Sarkar, D., M. D. Sarkar, and S. KernSmooth. 2015. "Package 'Lattice'. Version 0.20-33."

Savio, D., L. Sinclair, U. Z. Ijaz, et al. 2015. "Bacterial Diversity Along a 2600 km River Continuum." *Environmental Microbiology* 17: 4994-5007.

Schloss, P. D., S. L. Westcott, T. Ryabin, et al. 2009. "Introducing Mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities." *Applied and Environmental Microbiology* 75: 7537-7541.

Seena, S., F. Bärlocher, O. Sobral, et al. 2019. "Biodiversity of Leaf Litter Fungi in Streams Along a Latitudinal Gradient." *Science of the Total Environment* 661: 306-315.

Slowikowski, K., A. Schep, S. Hughes, et al. 2018. "Package ggrepel. Automatically Position Non-Overlapping Text Labels With ggplot2."

Staley, C., T. J. Gould, P. Wang, J. Phillips, J. B. Cotner, and M. J. Sadowsky. 2015. "Sediments and Soils Act as Reservoirs for Taxonomic and Functional Bacterial Diversity in the Upper Mississippi River." *Microbial Ecology* 71: 814-824.

Stanković, I., and P. Leitner. 2016. "The First Record of *Hydrurus foetidus* (Villars) Trevisan (Ochrophyta: Chrysophyceae) in Croatia With Ecological Notes." *Natura Croatica* 25, no. 2: 223-231.

Sun, Z., G. Li, C. Wang, et al. 2014. "Community Dynamics of Prokaryotic and Eukaryotic Microbes in an Estuary Reservoir." *Scientific Reports* 4: 1-8.

Teixeira, P., D. Dias, S. Costa, B. Brown, S. Silva, and E. Valério. 2020. "Bacteroides Spp. and Traditional Fecal Indicator Bacteria in Water Quality Assessment—An Integrated Approach for Hydric Resources Management in Urban Centers." *Journal of Environmental Management* 271: 110989.

ter Braak, C. J., and P. Šmilauer. 2012. "Canoco Reference Manual and User's Guide: Software for Ordination, Version 5.0."

Terink, W., A. F. Lutz, G. W. H. Simons, W. W. Immerzeel, and P. Droogers. 2015. "SPHY v2.0: Spatial Processes in Hydrology." *Geoscientific Model Development* 8, no. 7: 2009-2034.

Thomas, H., P. Gaetan, R. Roberta, et al. 2022. "European Blue and Green Infrastructure Network Strategy vs. the Common Agricultural Policy. Insights From an Integrated Case Study (Couesnon, Brittany)." *Land Use Policy* 120: 106277.

Tlili, A., J. Jabiol, R. Behra, C. Gil-Allue, and M. O. Gessner. 2017. "Chronic Exposure Effects of Silver Nanoparticles on Stream Microbial Decomposer Communities and Ecosystem Functions." *Environmental Science & Technology* 51, no. 4: 2447-2455.

Valentine, K., and G. Mariotti. 2020. "Does Eutrophication Affect the Ability of Biofilms to Stabilize Muddy Sediments?" *Estuarine, Coastal and Shelf Science* 232: 106490.

Vasselon, V., I. Domaizon, F. Rimet, M. Kahlert, and A. Bouchez. 2017. "Application of High-Throughput Sequencing (HTS) Metabarcoding to Diatom Biomonitoring: Do DNA Extraction Methods Matter?" *Freshwater Science* 36: 162-177.

Veach, A. M., and N. A. Griffiths. 2018. "Testing the Light: Nutrient Hypothesis: Insights Into Biofilm Structure and Function Using Metatranscriptomics." *Molecular Ecology* 27: 2909-2912.

von Scheibner, M., P. Dörge, A. Biermann, U. Sommer, H. G. Hoppe, and K. Jürgens. 2014. "Impact of Warming on Phyto-Bacterioplankton Coupling and Bacterial Community Composition in Experimental Mesocosms." *Environmental Microbiology* 16, no. 3: 718-733.

Walker, T. W., C. Kaiser, F. Strasser, et al. 2018. "Microbial Temperature Sensitivity and Biomass Change Explain Soil Carbon Loss With Warming." *Nature Climate Change* 8, no. 10: 885-889.

Wang, P., X. Wang, C. Wang, L. Miao, J. Hou, and Q. Yuan. 2017. "Shift in Bacterioplankton Diversity and Structure: Influence of Anthropogenic Disturbances Along the Yarlung Tsangpo River on the Tibetan Plateau, China." *Scientific Reports* 7, no. 1: 12529.

Wei, T., V. Simko, M. Levy, Y. Xie, Y. Jin, and J. Zemla. 2017. "Package 'Corrplot'." *Stat* 56: e24.

Wickham, H. 2011. "ggplot2." *Wiley Interdisciplinary Reviews: Computational Statistics* 3: 180-185.

Wood, J. R., R. J. Holdaway, K. H. Orwin, et al. 2017. "No Single Driver of Biodiversity: Divergent Responses of Multiple Taxa Across Land Use Types." *Ecosphere* 8: e01997.
Wu, W., and H. Liu. 2018. "Disentangling Protist Communities Identified From DNA and RNA Surveys in the Pearl River–South China Sea Continuum During the Wet and Dry Seasons." *Molecular Ecology* 27, no. 22: 4627–4640.

Xiao, W., X. Liu, A. J. Irwin, et al. 2018. "Warming and Eutrophication Combine to Restructure Diatoms and Dinoflagellates." *Water Research* 128: 206–216.

Yang, Y., S. Li, Y. Gao, Y. Chen, and A. Zhan. 2019. "Environment-Driven Geographical Distribution of Bacterial Communities and Identification of Indicator Taxa in Songhua River." *Ecological Indicators* 101: 62–70.

Yang, Y., Y. Hou, M. Ma, and A. Zhan. 2020. "Potential Pathogen Communities in Highly Polluted River Ecosystems: Geographical Distribution and Environmental Influence." *Ambio* 49, no. 1: 197–207.

Zeglin, L. H. 2015. "Stream Microbial Diversity in Response to Environmental Changes: Review and Synthesis of Existing Research." *Frontiers in Microbiology* 6: 454.

Zhang, B. G., J. Zhang, Y. Liu, P. Shi, and G. H. Wei. 2018. "Co-Occurrence Patterns of Soybean Rhizosphere Microbiome at a Continental Scale." *Soil Biology and Biochemistry* 118: 178–186.

Zhang, H., E. Mächler, F. Morsdorf, P. A. Niklaus, M. E. Schaepman, and F. Altermatt. 2023. "A Spatial Fingerprint of Land-Water Linkage of Biodiversity Uncovered by Remote Sensing and Environmental DNA." *Science of the Total Environment* 867: 161365.

Zhao, Z., R. Zhao, X. Qiu, Y. Wan, and L. Lee. 2022. "Structural Diversity of Bacterial Communities and Its Relation to Environmental Factors in the Surface Sediments From Main Stream of Qingshui River." *Water* 14, no. 21: 3356.

Zhu, K., Y. Bi, and Z. Hu. 2013. "Responses of Phytoplankton Functional Groups to the Hydrologic Regime in the Daning River, a Tributary of Three Gorges Reservoir, China." *Science of the Total Environment* 450: 169–177.

## Supporting Information

Additional supporting information can be found online in the Supporting Information section.