{"paper_id":"215de67f-e5a7-4056-8406-71815b2ac6c0","body_text":"1 \n Title - Urbanisation Reshapes Freshwater Microbiomes: A Systematic Review of Ecological 1 \nPatterns and Functional Shifts 2 \n 3 \nAuthor- Khushi Thakur1, Raashi Jain1, Shreejit Panda2, Hiya Chakma1, Aditi Sudhir1, Ananya 4 \nMukherjee1 5 \nAuthors affiliations- 1School of Arts and Sciences, Azim Premji University, Bhopal, Madhya 6 \nPradesh, India 7 \n2 Indian Institute of Science Education and Research, Bhopal,Madhya Pradesh, India 8 \nCorresponding Author: Ananya Mukherjee 9 \nSchool of Arts and Sciences, Azim Premji University 10 \nBhopal, Madhya Pradesh, India 11 \nEmail: ananya.mukherjee@apu.edu.in 12 \nPhone: +91 8276826363 13 \n 14 \nKeywords: urbanisation, freshwater microbiome, systematic review, antimicrobial resistance, 15 \nurban waterbodies, lakes, rivers, functional traits 16 \n 17 \n 18 \nTable of Contents: 19 \nGraphical Abstract                                                                                                                        2 20 \nAbstract 21 \nIntroduction 22 \nObjectives                                                                                                                                        6 23 \nMethods 24 \nResult 25 \nNumber of Studies 26 \nGeographic Distribution of Studies 27 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n2 \nStudy Sites 28 \nMicrobial Functional Traits Studies 29 \nAnthropogenic Activities 30 \nMicrobial Diversity 31 \nMicrobial Diversity across Anthropogenic Activities 32 \nEffect of urbanisation across studies on microbial diversity 33 \nCorrelation of Microbial Diversity across Anthropogenic Activities 34 \nPrincipal Component Analysis 35 \nRelative Stressor Composition 36 \nLimitations 37 \nDiscussion 38 \nBibliography 39 \nBibliography of the papers used for data extraction: 40 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n3\n41 \nGraphical abstract 42 \n 43 \nWaterbodies in urban areas function as convergence platforms for anthropogenic and 44 \nenvironmental microbiomes. Runoffs, wastewater and effluents contain antimicrobial resistance 45 \ngenes and other pathogens that survive in water due to inadequate treatment. Disposal, use, and 46 \noverflow of wastewater cause restructuration of microbial communities, proliferation of 47 \nopportunistic microorganisms, and spread of antimicrobial resistance in aquatic ecosystems. 48 \n 49 \nAbstract 50 \nRapid urbanisation has profoundly shaped microbial diversity across different ecosystems. 51 \nFreshwater microbiomes are particularly affected by urbanisation activities, such as 52 \neutrophication, pollution, runoff, and sewage. This is of significant concern as marginalised 53 \ncommunities often depend on waterbodies for their livelihood. Freshwater bodies play a crucial 54 \nrole in maintaining both human and ecological health at population level. Currently, we lack a 55 \n3  \n \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n4 \nsystematic understanding of  the global impacts of urbanisation on freshwater microbiomes in 56 \nrelation to human health, ecosystem functioning, and sustainability. 57 \nWe identified 90 eligible papers from the last 25 years after screening based on the inclusion 58 \nexclusion criteria. We extracted data that examined changes in the functional traits such as 59 \nantimicrobial resistance  (AMR), nutrient cycling of the microbiome in urban waterbodies and 60 \nseveral other factors. Data were extracted by a thematic analysis followed by a narrative 61 \nsynthesis on specific functional traits. This systematic review presents a comprehensive analysis 62 \non the changes and challenges brought about by urbanisation on freshwater bodies.  63 \nOur results indicate that urbanisation leads to reduced bacterial diversity of urban waterbodies, 64 \nwith a striking increase in reporting of Proteobacteria, Cyanobacteria and Coliform bacteria. 65 \nThese insights will help inform public health strategies and sustainable urban planning. 66 \nIntroduction 67 \nCities are the fastest growing ecosystems globally with 81 per cent of the world’s population 68 \nalready residing in urban areas(United Nations, 2025). Urban ecosystems are also highly 69 \nheterogeneous with a complex mosaic of natural and built environments consisting of buildings, 70 \nparks, gardens, remnant forests, roads, pavements and waterbodies(Jones et al., 2022). It is no 71 \nsurprise that we live on a planet that is consistently shaped by human activity and cities, the hub 72 \nof human activity-are rarely designed keeping flora or fauna in mind. Consequently animals 73 \noften enter cities as their natural habitats are reformed or displaced(Schell et al., 2020). 74 \nAdditionally, microbes, which are fundamental to public health as human commensal microbiota 75 \nand invisible biodiversity(Matthews et al., 2024), rarely feature in our thinking of urban fauna. 76 \nThis is particularly interesting despite a long history of evidence showing their influence on 77 \nurban life. John Snow’s seminal work on cholera has shown how the urban dwellers' interaction 78 \nwith microbes can be very different from those of their rural counterparts(Tulchinsky et 79 \nal.,2018).  80 \nIn the last 100 years with the rising use of antibiotics, pollution and the urban rural divide the 81 \nrelationship between humans and microbes has become more complex if not diversified(Kabwe 82 \net al., 2020). We are now surrounded by more novel antibiotic resistant genes(ARG) than ever 83 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n5 \nbefore. Another growing risk is antimicrobial resistance(AMR) as a recent study predicted that in 84 \n2050 the global bacterial AMR burden will spell particularly high mortality rates for South Asia, 85 \nAfrica and the Caribbean(Naghavi et al., 2024).  86 \nMicrobial ecology depicts how ecosystems respond to human-driven climate change. One Health 87 \nproposes that the well-being of humans is linked to the health(World Health Organization of the 88 \necosystem and the thread that holds it all together are microbial contributions(WHO), 2025). 89 \nMany studies discuss microbe host associations such as plants, humans and animals, as an eco-90 \nholobiont or an extended second genome(Banerjee & Heijden, 2023). Even recently microbial 91 \nresearch would heavily focus on pathogens affecting microbial hosts and while understanding 92 \nzoonotic diseases remain vital, loss of microbial diversity can cause dysbiosis(Berg et al., 2020). 93 \nDysbiosis is caused when the composition of microbial diversity drops and leads to the 94 \nemergence of pathogens which affects the immune system of the host.  Thus any dynamic built 95 \nenvironment can face the threat of dysbiosis, especially urban landscapes which are constantly 96 \nshaped by anthropogenic activities. In fact metagenomic maps of urban microbiomes and 97 \nantimicrobial resistance markers revealed that most cities have a unique microbial 98 \nfingerprint(Danko et al., 2021). 99 \nIn this complex mosaic of microbial diversity in urban systems, one crucial and perhaps 100 \nunderstudied domain are urban freshwater systems that provide a juxtaposition of ecology and 101 \nsociety. Historically habitats and settlements have come up around water bodies whether built or 102 \nnatural such as rivers, lakes, reservoirs, ponds, coastal or inland regions(Cruz-Cano et al., 2025). 103 \nThese continue to underpin urban livelihood, recreational activities, tourism and even play a role 104 \nin mitigating climate change. They also bear the burden of waste, pollution, anthropogenic 105 \nactivities, toxic waste, fecal contamination  and cultural activities. Marginalised communities 106 \ndepend on them for drinking water as well making them directly relevant to public health in the 107 \nsurrounding area(Dickin & Gabrielsson, 2023). This is vital at a time when nearly 4 billion 108 \npeople face water scarcity at least once a year(Mekonnen & Hoekstra, 2016). Thus water 109 \nmanagement practices become crucial for the health and well being of the population around 110 \nwaterbodies. Urban planning tends to look at waterbodies as passive infrastructure elements 111 \ninstead of thriving ecosystems. Determining the urban waterbody microbiome is not just an 112 \nexercise in curiosity but data that can assist urban governance and planning, stormwater design, 113 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n6 \nrestoration, control pollution and surveil public health.  114 \nWith the advent of sequencing methods there is no scarcity of microbiome data from specific 115 \nurban ecosystems, especially waterbodies surrounding a city(González et al., 2025). However, 116 \nwe have a fragmented view of the microbial make-up often in the form of case studies and 117 \ngeographically restricted datasets which prevent us from having a wider context of the ecological 118 \npatterns around the world. A broader view of the literature can help in looking at both short term 119 \nfluctuations and long term geographical or climatic trends arising from climate change, pollution 120 \nor anthropogenic activities. Evidence based urban water management and ecological theory can 121 \nbe strengthened by checking microbial trends beyond mere identification or pathogen dynamics. 122 \nFundamental processes such as nutrient cycling and AMR need coordinated comparative 123 \napproaches.  124 \nKeeping this in mind we synthesized insights from 90 studies across three different research 125 \nsearch engines to look at the effect urbanisation has on the microbiome of waterbodies in cities 126 \nacross the globe. We have taken a comprehensive approach and shed light on the different 127 \nhuman activities observed with respect to the microbial communities observed and nutrient 128 \ncycling types in global urban freshwater bodies. Our goal was to identify microbes beyond their 129 \npathogenic traits and also look at the trend of urbanisation versus the type of microbe identified 130 \nover time.  131 \n 132 \nObjectives 133 \nThe primary questions addressed in this paper are:- 134 \n●  What evidence exists for the effect of urbanisation on urban freshwater body 135 \nmicrobiomes in terms of microbial diversity and functional shifts? 136 \n●  What are the critical gaps in the available literature and where else can the focus lie?  137 \n●  How can we look at microbial ecological patterns across geography in urban freshwater 138 \nbodies? 139 \n●  Should policy makers focus on microbial make-up of wetlands beyond a few identifying 140 \norganisms while restoring waterbodies in cities? 141 \nThis review was conducted to address the objectives. It is following a systematic approach, 142 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n7 \nconforming to the PRISMA reporting standards to ensure comprehensive and unbiased coverage 143 \nof literature (Page et al., 2020).  144 \nMethods 145 \nThis study combines systematic review methodology with systematic mapping to identify 146 \npatterns in microbial taxa reporting, functional traits, and anthropogenic drivers across urban 147 \nfreshwater ecosystems. 148 \nWe used three different bibliographic databases for this literature review: Web of Science 149 \n(WOS), Google Scholar (GS) and SCOPUS. Each search string was adapted to suit the database. 150 \nThe search was conducted between January 2025 and March 2025. The number of records 151 \nretrieved with each string is shown in Table 1. Table 2 contains the rationale for each inclusion 152 \nand exclusion criteria. We only considered research articles and not reviews. 153 \n 154 \nWe used Google Scholar, Web of Science and SCOPUS to search for articles, develop the search 155 \nstring as well as the inclusion-exclusion criteria. The final search string used for each database 156 \ncan be found in Table 1. WOS, GS and SCOPUS were the databases used to obtain studies for 157 \nanalyses. In the search string, we used terms synonymous with lakes, waterbodies, urbanisation 158 \nand microbiome. We also used Boolean operators such as “AND” and “OR”. We identified more 159 \narticles by going through the bibliographies of the studies included after primary screening. For 160 \nWOS and SCOPUS we retrieved all the results of each search string, for GS we retrieved results 161 \nfrom the first 10 pages for each search string. These were collated and duplicates were removed. 162 \n 163 \nDatabases Search Strings Records retrieved \nWOS TS=( urban blue space* OR lake* OR \npond* OR river* OR stream* OR \ncanal* OR waterway* OR urban \nwetland* OR stormwater pond* OR \nreservoir*) AND TS=(microbiome* \nOR microbiota* OR bacteria* OR \nfungi OR prokaryote* OR eukaryote* \n1055 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n8 \nOR belowground biodivers* OR soil \nmicrobe* OR AMR AND TS=( \nurbanisation OR urbanisation OR \nurban development OR urban \nexpansion OR urban growth OR land \nuse change OR urban sprawl OR \nurban migration ) NOT TS=( marine \nOR ocean* OR hospital OR clinical \nOR human gut OR medical OR \nCOVID OR aquaculture OR coastal ) \nSCOPUS (TITLE-ABS-KEY((\"urban blue \nspace*\" OR lake* OR pond* OR \nriver* OR stream* OR canal* OR \nwaterway* OR \"urban wetland*\" OR \n\"stormwater pond*\" OR reservoir*) \nAND (microbiome* OR microbiota* \nOR bacteria OR fungi OR \nprokaryotes* OR eukaryote* OR \n\"belowground biodiversity*\" OR \n\"soil microbe*\" OR AMR) \nAND (urbanisation* OR \"urban \ndevelopment\" OR \"urban expansion\" \nOR \"urban growth\" OR \"land use \nchange\" OR \"urban sprawl\" OR \n\"urban migration\")) \nAND NOT TITLE-ABS-\nKEY(marine OR ocean* OR hospital \nOR clinical OR \"human gut\" OR \nmedical OR COVID* OR \naquaculture OR coastal)) \n100 \nGoogle Scholar (urban blue space OR lake OR pond \nOR river OR stream OR canal OR \nwaterway OR urban wetland OR \nstormwater pond OR reservoir) \n406 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n9 \nAND \n(microbiome OR microbiota OR \nbacteria OR fungi OR prokaryote OR \neukaryote OR belowground \nbiodiversity OR soil microbiome OR \nAMR) \nAND \n(urbanisation OR urbanisation OR \nurban development OR urban \nexpansion OR urban growth OR land \nuse change OR urban sprawl OR \nurban migration)  \nNOT \n(marine OR ocean OR hospital OR \nclinical OR human gut OR medical \nOR COVID OR aquaculture OR \ncoastal) \n \nTable 1: Search strings and number of records retrieved. 164 \n 165 \nThe scoping exercise revealed that studies revolving around the water microbiome and 166 \nurbanisation began around 2000 and so, we included research published from 2000 to 2025. We 167 \ndid not place any restriction on geographic locations but, we only considered published studies 168 \n(i.e., no grey literature) and studies published in English as it was difficult to obtain reliable 169 \ntranslations.  170 \nWe estimated the comprehensiveness of the search string by checking for overlap between the 171 \nthree databases. We found a significant overlap between the three databases, suggesting that all 172 \nthree databases covered important studies published during the established time frame. The 173 \ncollated list of papers was divided into 5 groups such that every section was screened by two 174 \nauthors. This screening was based on the title and abstract. The agreement coefficient for each 175 \nsection was calculated. The agreement coefficient is a statistical measure used to quantify the 176 \nreliability between two raters using different methods. In this study, the method used is Cohen’s 177 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n10 \nkappa. The kappa value was calculated for each section and overall value (0.85) was obtained by 178 \naveraging those values. This was followed by full-text screening.  179 \nInclusion Rationale \nUrban blue spaces Studies conducted in freshwater bodies, lakes, rivers, ponds \nWater body \nmicrobe/microbiome/microbiota/bact\neria/fungi/prokaryotes/eukaryotes \nStudies involving microbes present in the water hence, can include \nbacteria, fungi, protists, some types algae, individual taxa and \ngroups/community \nRelevant ecosystem Only aquatic freshwater \nAnalytical modelling \nCorrelative studies are included but analytical techniques used are also \nnoted to understand the cause-effect relationship depicted \nTime Period 2000-2025 \nLanguage Studies published in English language or a reliable translation to English \nFunctional traits \nStudies depicting functional traits such as antibiotic resistance, nitrogen \ncycling genes are also considered \n  \nExclusion Rationale \nUrban blue spaces Excluding spaces like coastal areas, bays, harbors, stormwater \nHeavily industrialized areas, areas \nthat have been left behind (like \nlandfills and so on) \nExcluding spaces that involved extensive industrialisation, because those \nsites will be far away from urban spaces and consist of heavy metals \nwhich may drastically alter the community. \nNon-urban  \nExcluding studies involving non urban blue spaces present in rural areas \nor pristine areas. \nEcosystems \nExcluding air-borne microbiomes, microbiomes on surfaces other than \nsoil, endosphere, phyllosphere and wetlands. \nSoil microbiome \nExcluding studies involves the microfauna, meiofauna, macrofauna \nmegafauna present in the soil. \nReviews/speculative studies/Human \nhealth \nExcluding Article/literature reviews/meta-analysis and studies which \nfocus on human health \n 180 \nTable 2:  Different inclusion and exclusion criteria that were used to select the studies meant for mapping. The criteria includes 181 \nsynonyms and the rationale behind each criteria. 182 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n11  \n 183 \nThe data extracted (Table 3) was: microbial communities, microbial indices, microbial functional 184 \ntraits, sample information, site information, study site details, climatic conditions, techniques 185 \nused for microbial identification, type of urban water body, type of human activity observed or 186 \nstudied, relationship between urbanisation and microbes, types of statistical tests, electronic 187 \npublishing date, journal the study is published in, authors origins and funding agencies involved. 188 \nThe results were through graphs and the graphs were made using R and QGIS.  189 \n 190 \n 191 \nData Points Sub data points Rationale \n \n \nMicrobial data \nMicrobial communities It is collected to obtain a thorough \nunderstanding of the microbial \ncommunity present in the water \nsample.  \nMicrobial indices \nMicrobial functional traits \n \nSample information \nNumber of samples It is collected to understand how \nwell the study captures the natural \nvariations.  Number of replicates \nType of Sample Water or sediment sample It reconfirms if the samples \nobserved were water only.  \n \n \nSite Information \nGeographical region It is collected to make note of the \ncountry, the type of water body \nstudied in the paper.  \nType of Site \nAge of Site \nStudy site details Month(s) It is collected to understand the \nclimatic conditions during which \nsampling took place.  Year(s) \nClimatic conditions \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n12 \nSeasons \nTechniques for microbial \nidentification \n It is collected to understand how \nmicrobial identification took place.  \nType of urban space   \nType of human activity observed or \nstudied \n It is collected as a proxy for \nurbanisation.  \nRelationship  between \nurbanisation/human activity and \nmicrobes \n It is collected to check if the paper \nderives a relationship between \nurbanisation and microbes if yes, \nwhich direction.  \nTypes of Statistical tests  It is collected to check the \nmethodological rigour of the paper \nand compare analytical approaches \nbetween studies.  \nElectronic Publishing date Month   \nYear  \nJournal Paper is published in   \nElectronic journal   \nAuthor origins Number of institutions  \nCountries involved  \nFunding agencies involved  It was collected to understand if \nthese studies were backed by \ngovernmental agencies or private \nagencies. \n 192 \nTable 3: Each data point that was extracted along with subcategories for each. It takes into account the variability 193 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n1 3\nobserved while scoping.  194 \nResult 195 \nNumber of Studies  196 \nThe search string resulted in 1,561 papers from three unique databases. After removing 197 \nduplicates, 1,059 papers were screened. 940 papers were excluded after title and abstract 198 \nscreening from the review based on the eligibility criteria. Out of 123 papers, 17 papers could not199 \nbe retrieved. 106 papers were screened for full text screening, out of which 16 rejected further 200 \nbased on the exclusion criteria. Thus, 90 papers were included in the review. Fig 1 shows the 201 \nPRISMA (Page et al., 2020) chart for details of the screening process.  202 \n 203 \n 204 \n 205 \n 206 \n 207 \n 208 \n 209 \n 210 \n 211 \n 212 \n 213 \n 214 \n 215 \n 216 \n 217 \n 218 \n 219 \n 220 \n 221 \n 222 \n3  \not \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n1 4\n 223 \n 224 \n 225 \n 226 \n 227 \n 228 \n 229 \n 230 \n 231 \n 232 \n 233 \nFig. 1 -PRISMA flow chart for systematic literature reviews. The flow chart shows the systematic approach taken to select the 234 \nstudies for the review. It shows the number of studies selected, removed and reason of exclusion at each step. The agreement 235 \ncoefficient was calculated after the primary screening (Page et al., 2012). 236 \nGeographic Distribution of Studies  237 \nThere is an uneven geographic distribution of papers across the globe depicted in Fig 2. 238 \nDeveloped countries such as China, United States of America and Canada are over represented 239 \nin water microbial research. China contributed the highest number of studies (55.5%, n = 50) 240 \nfollowed by the United States of America (12.22%, n = 11, Fig 3). South Asia and Africa had 241 \nfewer studies on urban freshwater microbiomes, despite facing more water stress due to rapid 242 \nurbanisation and industrialization.  243 \n 244 \n 245 \n4  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n1 5\n246 \nFig 2 - The global distribution of studies on urban freshwater microbiomes. More than 50 studies have been conducted in the 247 \nPeople's Republic of China followed by the USA with 11 studies.  248 \n 249 \nStudy Sites  250 \nAmong the selected papers, lakes and rivers are the most commonly studied sites (n=27 and 37) 251 \nrespectively, showing proportionally higher attention. In contrast, the other types of sites such as 252 \nchannels, estuary, catchment areas as depicted in Fig. 3 are studied much less frequently, 253 \nappearing in only one or two papers. Few studies among the examined publications include 254 \nwater samples from nearby construction sites that show the direct impact of anthropogenic 255 \nactivities on waterbodies and the microbiome inside it.  256 \n 257 \n5  \n \nas \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n16  \n 258 \nFig 3: This figure shows the number of studies conducted in different types of sites. Freshwater sites also include Freshwater 259 \nsprings. NA in the graph, is for the studies that did not mention the type of sites just written as water samples taken from surface 260 \nwaters. Waste water includes influents and effluents of the wastewater system. And the Rivers also include the riverine system.  261 \n 262 \nMicrobial Functional Traits Studies 263 \nNutrient cycling was the most frequently reported functional trait, appearing in 30.8% of 264 \nincluded studies(Fig. 4), contributing 30.80%, followed by AMR/pathogen-related traits at 265 \n20.09%. Stress-related traits, such as seasonal variation, biofilm formation, and temperature 266 \ntolerance, accounted for 14.73%. In contrast, physical traits and mobile genetic 267 \nelements/horizontal gene transfer were underrepresented. 268 \nThe temporal look at the functional trait, Fig 4b,  shows that studies on microbiome were limited 269 \nuntil 2010 but have grown exponentially since 2016, and all traits have been studied relatively 270 \nevenly across the years since.  271 \n 272 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n1 7\n273 \n274 \nFig 4 a.The above figure shows the frequency of various microbial functional traits occurring among the selected papers. b. The 275 \nfrequency of various functional traits being studied each year. 276 \n7  \n \n \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n18  \nAnthropogenic Activities  277 \nThe most studied anthropogenic activity across the studies were tourism and tourist residence 278 \n(21.6%, n = 106, Fig 5), followed by land use impact (20%, n = 100), sewage (16.2%, n = 81) 279 \nand discharge ( 12.8%, n = 64) as shown in Fig 5. Because studies often report multiple drivers, 280 \npercentages represent frequency of mention rather than mutually exclusive categories.Certain 281 \ndrivers such as stormwater runoff and cultural practices appear underrepresented, suggesting 282 \npotential gaps in current research focus. 283 \n 284 \n 285 \nFig 5.The above figure shows the frequency of various anthropogenic activities studied in the selected papers. Tourism and 286 \nresidential land use were the most frequently studied anthropogenic drivers, followed by land-use change, sewage discharge, and 287 \nwastewater inputs.  288 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n19  \nMicrobial Diversity 289 \nMany studies reported the presence of bacteria without further classification. The most 290 \ncommonly reported bacterial groups were Proteobacteria, followed by Cyanobacteria and 291 \nActinobacteria(Fig 6). Other frequently reported groups included Firmicutes, Chloroflexi, and 292 \nBacteroidetes. Some of the least reported groups were Deferribacteres, Frankiales, Parcubacteria, 293 \nand Thaumarchaeota, as depicted in Fig. 6. 294 \nIt represents the frequency of reporting across studies and does not necessarily reflect relative 295 \nabundance changes under urbanisation. The most frequently reported microbes were microbes 296 \nfrom the human gut, indicating a sewage contamination in the waterbodies. Further, many of the 297 \nreported microbes have proven to have harmful impacts on human health, such as Proteobacteria, 298 \nhaving evidence in metabolic disorders and inflammatory bowel disease and playing a role also 299 \nin lung diseases, such as asthma and chronic obstructive pulmonary disease (Rizzatti et al, 2017). 300 \nCyanobacterial blooms result in the bioaccumulation of cyanotoxins that are associated with 301 \nrenal failure, decreased platelets, increased leukocytes, and the development of hepatotoxicosis 302 \n(Villalobos et al, 2025). 303 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n20 \n 304 \nFig 6. The top 50 microbes classes found in the papers. Proteobacteria is a broad generalist bacterial phylum that is present in 305 \nalmost all the papers studied. It was followed by Cyanobacteria, Actinobacteria, Bacteriodetes, Firmicutes and Chloroflexi. 306 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n21 \nMicrobial Diversity across Anthropogenic Activities 307 \nThe heatmap reflects co-reporting patterns of microbial taxa across studies examining different 308 \nanthropogenic drivers. Fig 7  does not represent quantitative abundance measurements within 309 \nindividual waterbodies. Tourism, Discharge, Pollution, Wastewater, Land Use, and 310 \nSewage)shows significantly higher microbial abundance and diversity. Proteobacteria and 311 \nActinobacteria, generalists, show positive association with sewage, tourism and land use impact, 312 \nsuggesting they play an important role in the formation of the particular microbial signature of a 313 \nwater body. Agriculture, Deforestation, and Drainage promote the establishment of a few more 314 \nniche or specialist species due to the conditions provided. Activities like Sewage, Land Use, and 315 \nPollution are clustered together because they all show high microbial abundance. On the other 316 \nhand, Agriculture and Deforestation are branched off alone because their microbial communities 317 \nare different. This could be due to the uneven distribution of papers across all anthropogenic 318 \nactivities, but this still highlights how different anthropogenic stressors mold the microbial 319 \nsignatures of various ecosystems. 320 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n22  321 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n23 \nFig 7. The above heat map shows the presence of microbial communities between various anthropogenic activities. The bacterial 322 \ncount is taken in logarithmic scale to show the variation between the count.Generalist taxa such as Proteobacteria, Cyanobacteria, 323 \nand Actinobacteria were consistently reported across studies examining multiple anthropogenic activities.The groupings show the 324 \nanthropogenic activities as well as microbial community composition that are the most identical to one another. 325 \n 326 \n 327 \nEffect of urbanisation across studies on microbial diversity 328 \nUrbanisation was consistently found to be a driver of microbial community restructuring, and 329 \nthis was evident both taxonomically and functionally (Table 4 and 5). Alpha diversity (28 330 \nstudies) was found to be inconsistent and therefore not a reliable measure of urbanisation. 331 \nDiversity was found to be maintained or even enhanced under nutrient enrichment, even where 332 \nchanges to the microbial community were evident (26 studies). These changes were found to be 333 \nmore indicative of the effects of urbanisation. Proteobacteria was dominant (33 studies), and this 334 \nwas enhanced, especially in wastewater and nutrient-rich environments. This was due to the 335 \nability of this group to thrive under disturbed conditions. Conversely, coliforms and E. coli (18 336 \nstudies) were found to increase significantly in urbanized environments, especially where there 337 \nwas a sewage input. This was a reliable indicator of urbanisation. Pathogens and virulence 338 \nfactors (11 studies) were also found to be enriched.The functional response was found to be 339 \nclosely associated with the urban drivers. The antibiotic resistance genes (ARGs) were found to 340 \nbe widespread and enriched, especially in wastewater-influenced systems, suggesting that urban 341 \nwaters harbor antimicrobial resistance. The nitrogen cycling genes and associated 342 \nmicroorganisms were found to be consistently altered, suggesting the enhanced nutrient 343 \ntransformation potential under eutrophic conditions. Similarly, the carbon metabolism was found 344 \nto be enriched in response to organic pollution and DOM, suggesting enhanced microbial carbon 345 \ntransformation potential. Overall, the functional potential was found to be enriched in response 346 \nto urban drivers, including pathways associated with respiration, nutrient cycling, and stress 347 \nresponse.  348 \n 349 \nUrban Driver Microbial Response Evidence From Studies Ecological Implication \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n24 \nWastewater discharge \nIncreased coliforms, \npathogens, and ARGs \nMany urban rivers and \ncanals show elevated \nfecal indicators \ndownstream \nIndicates direct human \ncontamination and public \nhealth risk \nNutrient enrichment \nIncreased nitrogen and \nphosphorus cycling \nmicrobes \nDenitrification genes \nand nutrient-\ntransforming taxa \nenriched \nDrives eutrophication and \naltered biogeochemical \ncycling \nOrganic pollution / DOM inputs \nIncreased respiration \nand carbon metabolism \npathways \nUrban sites show higher \norganic carbon \noxidation and DOM \nbioavailability \nAccelerates microbial \ncarbon processing \nLand-use change and impervious \nsurfaces \nAltered microbial \ncomposition and \nsometimes reduced \ndiversity \nUrban streams show \nshifts toward \ndisturbance-tolerant taxa \nIndicates environmental \nfiltering and habitat \nmodification \nAntibiotics and pollutants \nEnrichment of ARGs \nand defense pathways \nARGs and antibiotic \nresistance phenotypes \ncommon in urban waters\n \nPromotes antimicrobial \nresistance reservoirs \nHydrological alteration \nCommunity \nhomogenization and \nbloom-associated \nmicrobes \nUrban wetlands and \nlakes show bloom-\nspecialized microbial \ncommunities \nAlters ecosystem stability \nand microbial food webs \nTable 4. Different types of urban drivers explored across the 90 studies that alter microbial communities, increasing pathogens, 350 \nARGs, and nutrient-cycling taxa, with implications for ecosystem function and public health. 351 \n 352 \nIndicator \nDirection of \nChange \nNumber \nof Studies \nReporting \nKey Observations Across \nStudies \nEcological \nInterpretation \nAlpha diversity (Shannon, \nChao1, richness) Mixed 28\n \nSome urban waters show \nlower diversity due to \nstress and pollution, while \nDiversity alone is not a \nconsistent indicator of \nurban impact; \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n25 \nnutrient enrichment and \nrunoff can increase \ndiversity. Many studies \nshow stable diversity but \nmajor compositional shifts. \ncommunity \ncomposition is more \nsensitive. Proteobacteria abundance Frequently dominant  33 \nProteobacteria commonly \nremain dominant across \nsites and often increase in \ndisturbed waters, \nwastewater-influenced \nsystems, and nutrient-rich \nenvironments. \nHighly adaptable taxa \nassociated with organic \npollution, nutrient \nenrichment, and \ndisturbance tolerance. \nAntibiotic resistance genes \n(ARGs) \nIncreased or detected \nfrequently 19  \nARGs and resistance \nphenotypes detected in \nsediments, water, and \nbiofilms; some studies \nshow strong ARG–mobile \ngenetic element \ncorrelations. \nUrban waters may act \nas reservoirs and \ntransmission pathways \nfor antimicrobial \nresistance. \nColiforms / E. coli / fecal \nindicators \nStrong increase in \nurban sites 18\n \nHighest levels downstream \nof sewage outflows, \nwastewater discharge, and \nhighly urbanized \ncatchments. \nReliable indicators of \nanthropogenic \ncontamination and \nsewage influence. \nPathogens / virulence \nfactors Often enriched 11\n \nOpportunistic pathogens \nand virulence genes \nfrequently detected in \nurban rivers, ponds, and \ncanals. \nUrban aquatic systems \nmay act as pathogen \nreservoirs with \npotential public health \nimplications. \nNitrogen cycling genes \nAltered (often more \ndenitrification or N-\nprocessing) 15\n \nChanges in genes such as \nnarG, nirS, amoA, and \nenrichment of nitrogen-\ntransforming bacteria \nobserved along urban \nNutrient enrichment \nand runoff alter \nbiogeochemical cycling \nand ecosystem \nfunctioning. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n26 \ngradients. \nCarbon metabolism / DOM \nprocessing Altered 13  \nurbanisation changes DOM \ncomposition, respiration \nrates, and microbial carbon \nmetabolism pathways. \nUrban runoff and \norganic inputs reshape \ncarbon processing and \nmicrobial energy \npathways. \nCommunity composition \nStrong shifts or \nhomogenization 26\n \nUrban gradients drive \nmajor restructuring of \nmicrobial assemblages, \neven where alpha diversity \nremains stable. \nEnvironmental filtering \nand pollution select for \ndisturbance-tolerant \nmicrobial taxa. \nFunctional potential / \nmetabolic pathways Frequently altered 17\n \nEnrichment of metabolic \npathways related to \nrespiration, nutrient \nprocessing, and stress \nresponse. \nIndicates functional \nreorganization of \nmicrobial ecosystems \nunder urban pressure. \nTable 5 Urbanisation alters microbial diversity, composition, and function, with consistent increases in fecal indicators, ARGs, 353 \nand disturbance-tolerant taxa, while community composition and functional potential shift more reliably than diversity alone. 354 \nCorrelation of Microbial Diversity across anthropogenic activities  355 \nFig 8 highlights how different anthropogenic stressors mold the microbial signatures of various 356 \necosystems. The correlation matrix shows the statistical relationship between the microbiome of 357 \nvarious anthropogenic stressors. Spearman's Correlation was used to calculate the correlation 358 \ncoefficients.  359 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n27 \nThe strongest positive correlation was found between Heavy Metal and Human Disturbance. 360 \nThis suggests that patterns of human distribution are strongly correlated with levels of heavy 361 \nmetal contamination in urban waterbodies, and are associated with shifts in the native 362 \nmicrobiome. Agriculture and wastewater with its high positive correlation indicate that the 363 \nfarming structure shares the same basin for wastewater discharge and wastewater. Drainage has 364 \nthe most independent microbiome composition, not dependent on the other anthropogenic 365 \nactivities listed. The correlation matrix has more positive correlations due to the higher 366 \nabundance of generalists such as Proteobacteria and Actinobacteria, which drives homogeneity 367 \nacross sites. The correlation analysis reflects similarities in reported microbial taxa across studies 368 \ninvestigating different anthropogenic drivers and should not be interpreted as within-site 369 \necological interactions. 370 \n 371 \nFig 8. The correlation plot between the microbial community found across various anthropogenic activities. The Spearman’s 372 \ncoefficients are given between the microbial communities most reported in presence of anthropogenic activity. The strongest 373 \ncorrelation was found in agriculture with wastewater and population, heavy metal with human disturbance, wastewater, pollution 374 \nstressor and population.Higher positive correlation indicates presence of similar microbial communities in both the anthropogenic 375 \nactivity sites. The correlation coefficients between all the activities show higher positive correlation due to presence of generalist 376 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n28 \nspecies such as Proteobacteria.This reflects similarity in taxa reported across studies rather than direct ecological interactions 377 \nwithin individual waterbodies. 378 \n 379 \nPrincipal Component Analysis 380 \nIn Fig 9 Dim1 represents the \"total human impact or gradient of urban pollution\". Samples on the 381 \nfar right are highly contaminated; samples on the left are relatively \"clean”. Dim1 has a high 382 \nexplanatory power of 55.5% tells us that microbial diversity across waterbodies is driven by the 383 \nHuman Impact gradient. Dim2 represents the secondary variation distinguishing between urban 384 \nand population-related impacts and industrial impacts. Sewage, Pollution, Wastewater, and 385 \nTourism/Residence are packaged together and directed towards the right, which indicates that 386 \nthey are correlated and drive similar microbial changes in waterbodies. Heavy Metal and Human 387 \nDisturbance point downward, suggesting they create a distinct environmental niche that differs 388 \nfrom “Sewage”. Drainage and Deforestation point in opposing directions from the main cluster 389 \nindication; they have a unique effect on the aquatic environment that is not completely 390 \nexhaustive with the urban pollution.  The longer the arrow represents more importance of the 391 \nstressors in shaping the microbial communities of the waterbodies. Proteobacteria, 392 \nCyanobacteria, and Actinobacteria are highly \"enriched\" by urban waste. They likely thrive in 393 \nhigh-nutrient (eutrophic) conditions provided by sewage and tourism-related runoff. The 394 \nmicrobes in the central area are not strongly associated with these specific stressors.  395 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n2 9\n 396 \nFig 9. - PCA analysis revealed clustering of anthropogenic drivers such as sewage discharge, land-use change, and pollution 397 \nstressors, indicating similarity in microbial taxa reported across these disturbance categories.PC1 = disturbance intensity, PC2 = 398 \nfunctional variation. 399 \n 400 \n9  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n30 \nRelative Stressor Composition 401 \nThe Relative Stressor Composition, Fig 10,  reveals that the most dominant taxa are heavily 402 \npartitioned by anthropogenic pressures. Proteobacteria, Cyanobacteria, and Actinobacteria show 403 \nlarger sections for sewage, tourism and wastewater. High percentages of these blocks suggest 404 \nthese microbes are robust indicators of urban runoff. This shows the microbial community 405 \ncomposition in urban waterbodies is not random but is specifically composed by the type of 406 \nenvironmental stressors present. 407 \n 408 \nFig 10. Relative Stressor composition of top 20 microbes.Proteobacteria, Cyanobacteria and Actinobacteria are comprised of 409 \nmultiple stressors with various relative weightage. But some like Ciliophora, Armatimonadetes and Nitrospirae have fewer 410 \nstressors indicating they are present only in certain ecosystem conditions. 411 \nLimitations 412 \nOne major limitation of this study is the lack of rural urban comparisons in the studies that we 413 \npicked up based on our search strings, which would have made the study stronger and more 414 \ncomprehensive. We were also forced to rely on reporting frequency of different microbes which 415 \nmay have resulted in over dominance of certain species across studies; while useful for detecting 416 \nmajor trends, this method does not take into account effect size, magnitude of change, or 417 \nsignificance, and therefore certain taxa may be emphasized while others, though showing small 418 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n31  \nchanges, may not receive adequate representation. With the timeline we selected for this review 419 \nthere is considerable heterogeneity between sequencing platforms, depth of sequencing, and 420 \nbioinformatic pipelines, such as different regions of 16S rRNA, and these differences make 421 \nstudies less comparable to each other, which could lead to biases in community composition, 422 \ndiversity, and taxonomic resolution. Additionally, with most of the studies reported from China 423 \nand North America our data gets skewed due to limited representation from the Global South, 424 \nand this bias constrains the generalizability of findings, particularly given that urbanisation 425 \npatterns, infrastructure, and environmental conditions differ substantially across regions. Finally, 426 \nwe chose papers which were written in English, which may have resulted in language bias, as 427 \nstudies conducted in other languages, especially in countries experiencing rapid urbanisation, 428 \nmay have been excluded, leading to an incomplete global overview due to the lack of local 429 \nstudies. 430 \nDiscussion  431 \nThis is a comprehensive systematic review of the current global literature available on the 432 \ninfluence of urbanisation on microbial communities in freshwater bodies. From 90 selected 433 \npapers we identified critical gaps and biases that hinder a holistic understanding of these 434 \ninteractions. Notably, there is a lack of research from economically developing regions, Africa, 435 \nand Southeast Asia (Fig. 2), limiting our ability to generalise global findings or look for 436 \ngeographical patterns in these areas. This is particularly crucial given that the predicted AMR 437 \nburden in these regions is expected to be highest by 2050 especially affecting Africa and South 438 \nAsian countries(Naghavi et al., 2024). In this context, the functional and taxonomic patterns 439 \nidentified in previous research  become a key factor in understanding the ecological aspects of 440 \nthis risk. 441 \nA high prevalence of functional traits related to nutrient cycling  and AMR/pathogens were 442 \nidentified, the co-occurrence of which suggests that urban freshwater systems are shaped by 443 \nresource enrichment and may serve as petri dishes for resistance proliferation behaviour.  The 444 \nreporting frequency of Proteobacteria,Cyanobacteria, Actinobacteria, and Firmicutes adapt well 445 \nto  nutrient-rich and organically loaded environments. This also mirrors well to what research 446 \nstudies have reported over the last 25 years. Additionally, these phyla are ubiquitous in areas of 447 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n32 \nhigh anthropogenic pressure where freshwater are often at the receiving end of sewage, untreated 448 \nwastewater, runoff, and pollutants thus selecting for generalist microbial groups capable of rapid 449 \ngrowth and metabolic flexibility. This is consistent with studies from Germany that show 450 \n(Daga/i2 Quisbert et al., 2021) how urbanisation poses long-term health risks in waterbodies.  451 \nSimilarly, in Numberger et al. (2022), Actinobacteriota and Gammaproteobacteria had ASVs 452 \nunique to urban waters in freshwater bodies, including Acidovorax (Burkholderiales), 453 \nFlavobacterium (Bacteroidota), and Pseudomonas (Gammaproteobacteria). Mohanta and Goel 454 \n(2014) found that multiple drug-resistant bacteria were highest in samples from rapidly 455 \nurbanising areas. Urbanisation thus acts as an ecological filter, with similar microbial patterns 456 \nemerging globally in climatically distinct regions, selecting for phyla that rapidly grow in areas 457 \naffected by sewage and tolerant of disturbances such as pollution thereby favouring opportunistic 458 \npathogens. 459 \nAbove mentioned biases aside, due to the limited number of studies from the Global South, the 460 \nmethodology of different studies is also varied. Some studies have relied on single-season, short-461 \nterm sampling or screening, as compared to systematic sampling over different seasons (Wang et 462 \nal 2018, Zhao et al. 2022). It is difficult to infer the effect of seasonal variations on microbiome 463 \ndiversity. Certain studies have also derived their data from 16s amplicon sequencing, while few 464 \nothers from metagenomic analysis. There is a significant gap in the availability of transcriptomic, 465 \nproteomic, and metabolomic data. Although some research has examined samples across urban 466 \ngradients, most studies are restricted to a limited number of sampling sites, making broader 467 \necological inferences challenging. 468 \nThe consistent reporting of similar bacterial phyla across diverse geographic regions and 469 \nanthropogenic disturbances suggests increasing microbial homogenisation in urban freshwater 470 \necosystems globally.China, which has the most number of studies on urban water microbiomes, 471 \nis suffering from waterbody loss (Xiao et al., 2022). This is mainly due to urbanisation, where 472 \nwaterbodies play an important role in providing drinking water. China being one of the world’s 473 \nleading producers and consumers for antibiotics, is a potential centre for development and 474 \ndissemination of ARGs (Zhang et al., 2023). As a precautionary measure, they have invested a 475 \nlot of funding towards harmonious development between resources and environments, in the 476 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n33 \nform of ‘Sponge city programs’ and ‘The Administrative Measures of the Urban Blue 477 \nLine’(Xiao et al., 2022).  478 \nSimilarly studies on freshwater microbiomes need to be expanded to the economically 479 \ndeveloping countries as these studies reveal the presence of human health risks, impacts of 480 \npollution and help in the long term monitoring of the ecosystem health (McLellan et al., 2015). 481 \nThe global understanding of urban freshwater microbiomes is currently geographically skewed, 482 \nlimiting predictive power and policy relevance in regions expected to face the highest AMR 483 \nburden by 2050. 484 \nAs a finite resource it is vital to maintain the microbial biodiversity of urban waterbodies to 485 \nsustain people. This also brings up questions around equity as marginalised communities depend 486 \nmore on freshwater bodies for survival. In a (Satterthwaite et al. 2022) Bengaluru case study, the 487 \nmost rapidly urbanising city in India, it was discovered that lower income communities closer to 488 \nthe lake faced higher risk of water related fecal contamination. Community distribution of 489 \nmicrobes, if conducted in countries of South America and South Asia, can help us estimate the 490 \ndisease vulnerability of communities that depend on such wetlands for survival. Public health 491 \nwelfare programs and education campaigns around the  microbial make-up of an ecosystem like 492 \nwaterbodies especially with AMR on the rise can create some much needed awareness around 493 \nthese sites.  494 \nAs  Maria Magdalena Warter, points out “Just like our gut, freshwater ecosystems need a 495 \nfunctioning microbiome. Bacteria and other microorganisms form the basis of food chains and 496 \nmetabolic processes, as well as the self-purification capacity of waterbodies\"(Leibniz Institute of 497 \nFreshwater Ecology and Inland Fisheries (IGB), 2025). Thus there is certainly a need for a better 498 \nwastewater management system that protects the natural urban waterbodies and prevents further 499 \nentry of AMR. Across Table 4 and 5 the findings were consistent with the expected effects of 500 \nspecific urban drivers. Antibiotics and pollutants are associated with enriched resistance traits, 501 \nand hydrological changes were associated with homogenization and bloom-forming 502 \nmicroorganisms. 503 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n34  \n Urban freshwater systems increasingly reflect wastewater-associated microbial signatures, 504 \nindicating that anthropogenic inputs contribute to a partial convergence between natural and 505 \nhuman-associated microbiomes(Numberger et al., 2022). One can infer from this study that 506 \nARGs and fecal indicators may serve as more reliable monitoring targets than diversity metrics 507 \nalone. These patterns reinforce a One Health perspective, where environmental microbial 508 \ndynamics, ecosystem functioning, and human health risks are closely interconnected through 509 \nshared exposure pathways. Policies around better wastewater and runoff management along with 510 \npathogen indicators can help mitigate AMR and associated risks. Future research should focus on 511 \nusing expansive research methods and a multi-omics approach, aside from developing standard 512 \nmethodologies and geographic representation, especially in rapidly urbanising areas. These 513 \nresearch methods are going to be crucial in developing futuristic frameworks that link microbial 514 \necology with urban planning and management. 515 \nBibliography 516 \n●  UN DESA, 2018. 68% of the world population is projected to live in urban areas by 517 \n2050. UN DESA Voice. https://www.un.org/development/desa/en/news/population/2018-518 \nrevision-of-world-urbanisation-prospects.html. 519 \n●  Banerjee, S., Van der Heijden, M.G.A., 2023. Soil microbiomes and one health. Nat. 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The microbiome of a polluted urban lake 769 \nharbors pathogens with diverse antimicrobial resistance and virulence genes. Environ. 770 \nPollut. 273, 116488. https://doi.org/10.1016/j.envpol.2021.116488 771 \n●  Roberto, A.A., Van Gray, J.B., Engohang-Ndong, J., Leff, L.G., 2019. Distribution and 772 \nco-occurrence of antibiotic and metal resistance genes in biofilms of an 773 \nanthropogenically impacted stream. Sci. Total Environ. 688, 437–449. 774 \nhttps://doi.org/10.1016/j.scitotenv.2019.06.053 775 \n●  Wheeler, S.J., 2024. The effect of urbanisation on fungal microbial communities in the 776 \nTatnuck Brook waterway in Worcester, Massachusetts. Thesis. Clark University, 777 \nWorcester, MA. https://doi.org/10.1101/2025.04.01.646624 778 \n●  Schäfer, C., Ho, J., Lotz, B., Armbruster, J., Putz, A., Zou, H., Li, C., Ye, C., Zheng, B., 779 \nHügler, M., Tiehm, A., 2019. Evaluation and application of molecular denitrification 780 \nmonitoring methods in the northern Lake Tai, China. Sci. Total Environ. 663, 686–695. 781 \nhttps://doi.org/10.1016/j.scitotenv.2019.01.359 782 \n●  Sharma, N., Das, B.K., Bhattacharjya, B.K., Chaudhari, A., Behera, B.K., Kumar, A.P., 783 \nChakraborty, H.J., 2024. Metagenomic insights into microbial community, functional 784 \nannotation, and antibiotic resistance genes in Himalayan Brahmaputra River sediment, 785 \nIndia. Front. Microbiol. 15, 1426463. https://doi.org/10.3389/fmicb.2024.1426463 786 \n●  She, Y., Wang, P., Wen, J., Ding, M., Zhang, H., Nie, M., Huang, G., 2024. Riverine 787 \nbacterial communities are more shaped by species sorting in intensive urban and 788 \nagricultural watersheds. Front. Microbiol. 15, 1463549. 789 \nhttps://doi.org/10.3389/fmicb.2024.1463549 790 \n●  Shen, H., Dong, W., Zeng, Y., Li, X., Zhang, Y., Zhou, C., Peng, N., Zhao, S., 2024. 791 \nGeographic variation in microbes and water quality in urban lakes restored with 792 \nsubmerged plants: migration of microbial communities across habitats. Preprint. 793 \nResearch Square.  https://doi.org/10.21203/rs.3.rs-4885478/v1  794 \n●  Shu, W., Wang, P., Zhang, H., Ding, M., Wu, B., 2020. Seasonal and spatial distribution 795 \nand assembly processes of bacterioplankton communities in a subtropical urban river. 796 \nFEMS Microbiol. Ecol. 96, fiaa154. https://doi.org/10.1093/femsec/fiaa154 797 \n●  Song, Y., Cao, X., Li, S.-A., Li, Z., Grossart, H.-P., Ma, H., 2024. Human activities-798 \nimpacted lake dissolved organic matter affects phycosphere microbial diversity and DOM 799 \ndiversification via carbon metabolism. J. Environ. 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Coexistence of Synechococcus and Microcystis blooms in a 807 \ntropical urban reservoir and their links with microbiomes. Environ. Sci. Technol. 57, 808 \n1613–1624. https://doi.org/10.1021/acs.est.2c04943 809 \n●  Tran, N.H., Hoang, L., Nghiem, L.D., Nguyen, N.M.H., Ngo, H.H., Guo, W., Trinh, 810 \nQ.T., Mai, N.H., Chen, H., Nguyen, D.D., Ta, T.T., Gin, K.Y.-H., 2019. Occurrence and 811 \nrisk assessment of multiple classes of antibiotics in urban canals and lakes in Hanoi, 812 \nVietnam. Sci. Total Environ. 692, 157–174. 813 \nhttps://doi.org/10.1016/j.scitotenv.2019.07.092 814 \n●  Vaccaro, M., Pilat, A.M., Gusmano, L., Pham, M.T.N., Barich, D., Gibson, A., Epalle, 815 \nM., Frost, D.J., Volin, E., Slimak, Z.C., Menke, C.C., Fennessy, M.S., Slonczewski, J.L., 816 \n2025. Pond water microbiome antibiotic resistance genes vary seasonally with 817 \nenvironmental pH and tannins. Microbiol. Spectr. 13, e03034-24. 818 \nhttps://doi.org/10.1128/spectrum.03034-24 819 \n●  Odhiambo, K.A., Ogola, H.J.O., Onyango, B., Tekere, M., Ijoma, G.N., 2022. 820 \nContribution of pollution gradient to the sediment microbiome and potential pathogens in 821 \nurban streams draining into Lake Victoria (Kenya). Environ. Sci. Pollut. Res. 30, 36450–822 \n36471. https://doi.org/10.1007/s11356-022-24517-0 823 \n●  Onana, V.E., Beisner, B.E., Walsh, D.A., 2025. Water quality and land use shape 824 \nbacterial communities across 621 Canadian lakes. Environ. Microbiol. 27, e70037. 825 \nhttps://doi.org/10.1111/1462-2920.70037 826 \n●  Ooi, Q.E., Nguyen, C.T.T., Laloo, A.E., Koh, Y.Z., Swarup, S., 2024. Soil–sediment 827 \nconnectivity through Bayesian source tracking in an urban naturalised waterway via 828 \nmicrobial and isotopic markers. Sci. Total Environ. 949, 175152. 829 \nhttps://doi.org/10.1016/j.scitotenv.2024.175152 830 \n●  Opitz-Ríos, C., Burgos-Pacheco, A., Paredes-Cárcamo, F., Campanini-Salinas, J., 831 \nMedina, D.A., 2024. Metagenomics insight into veterinary and zoonotic pathogens 832 \nidentified in urban wetlands of Los Lagos, Chile. Pathogens 13, 788. 833 \nhttps://doi.org/10.3390/pathogens13090788 834 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n44  \n●  Pakoksung, K., Inseeyong, N., Chawaloesphonsiya, N., Punyapalakul, P., 835 \nChaiwiwatworakul, P., Xu, M., Chuenchum, P., 2025. Seasonal dynamics of water 836 \nquality in response to land use changes in the Chi and Mun River basins, Thailand. Sci. 837 \nRep. 15, 7101. https://doi.org/10.1038/s41598-025-91820-4 838 \n●  Pant, M., Singhal, N., Singh, J., 2023. Spatio-temporal variations in water quality of 839 \nRispana River in Dehradun, India. Sustain. Water Resour. Manag. 9, 123. 840 \nhttps://doi.org/10.1007/s40899-023-00906-2 841 \n●  Peng, C., Li, P., 2023. Abiotic and biotic effects on microbial diversity of small 842 \nwaterbodies in and around towns. Sustainability 15, 8151. 843 \nhttps://doi.org/10.3390/su15108151 844 \n●  Qin, H., Cao, X., Cui, L., Lv, Q., Chen, T., 2020. The influence of human interference on 845 \nzooplankton and fungal diversity in Poyang Lake watershed in China. Diversity 12, 296. 846 \nhttps://doi.org/10.3390/d12080296 847 \n●  Quillaguamán, J., Guzmán, D., Campero, M., Hoepfner, C., Relos, L., Mendieta, D., 848 \nHigdon, S.M., Eid, D., Fernández, C.E., 2021. The microbiome of a polluted urban lake 849 \nharbors pathogens with diverse antimicrobial resistance and virulence genes. Environ. 850 \nPollut. 273, 116488. https://doi.org/10.1016/j.envpol.2021.116488 851 \n●  Roberto, A.A., Van Gray, J.B., Engohang-Ndong, J., Leff, L.G., 2019. Distribution and 852 \nco-occurrence of antibiotic and metal resistance genes in biofilms of an 853 \nanthropogenically impacted stream. Sci. Total Environ. 688, 437–449. 854 \nhttps://doi.org/10.1016/j.scitotenv.2019.06.053 855 \n●  Wheeler, S.J., 2024. The effect of urbanisation on fungal microbial communities in the 856 \nTatnuck Brook waterway in Worcester, Massachusetts. Thesis. Clark University, 857 \nWorcester, MA. 858 \n●  Schäfer, C., Ho, J., Lotz, B., Armbruster, J., Putz, A., Zou, H., Li, C., Ye, C., Zheng, B., 859 \nHügler, M., Tiehm, A., 2019. Evaluation and application of molecular denitrification 860 \nmonitoring methods in the northern Lake Tai, China. Sci. Total Environ. 663, 686–695. 861 \nhttps://doi.org/10.1016/j.scitotenv.2019.01.359 862 \n●  Sharma, N., Das, B.K., Bhattacharjya, B.K., Chaudhari, A., Behera, B.K., Kumar, A.P., 863 \nChakraborty, H.J., 2024. Metagenomic insights into microbial community, functional 864 \nannotation, and antibiotic resistance genes in Himalayan Brahmaputra River sediment, 865 \nIndia. Front. Microbiol. 15, 1426463. https://doi.org/10.3389/fmicb.2024.1426463 866 \n●  She, Y., Wang, P., Wen, J., Ding, M., Zhang, H., Nie, M., Huang, G., 2024. Riverine 867 \nbacterial communities are more shaped by species sorting in intensive urban and 868 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint \n\n \n \n \n45 \nagricultural watersheds. Front. Microbiol. 15, 1463549. 869 \nhttps://doi.org/10.3389/fmicb.2024.1463549 870 \n●  Shen, H., Dong, W., Zeng, Y., Li, X., Zhang, Y., Zhou, C., Peng, N., Zhao, S., 2024. 871 \nGeographic variation in microbes and water quality in urban lakes restored with 872 \nsubmerged plants: migration of microbial communities across habitats. Preprint. 873 \nhttps://doi.org/10.21203/rs.3.rs-4885478/v1 874 \n●  Shu, W., Wang, P., Zhang, H., Ding, M., Wu, B., 2020. Seasonal and spatial distribution 875 \nand assembly processes of bacterioplankton communities in a subtropical urban river. 876 \nFEMS Microbiol. Ecol. 96, fiaa154. https://doi.org/10.1093/femsec/fiaa154 877 \n●  Song, Y., Cao, X., Li, S.-A., Li, Z., Grossart, H.-P., Ma, H., 2024. Human activities-878 \nimpacted lake dissolved organic matter affects phycosphere microbial diversity and DOM 879 \ndiversification via carbon metabolism. J. Environ. Manag. 367, 122011. 880 \nhttps://doi.org/10.1016/j.jenvman.2024.122011 881 \n●  Staley, C., Unno, T., Gould, T.J., Jarvis, B., Phillips, J., Cotner, J.B., Sadowsky, M.J., 882 \n2013. Application of Illumina next-generation sequencing to characterize the bacterial 883 \ncommunity of the Upper Mississippi River. J. Appl. Microbiol. 115, 1147–1158. 884 \nhttps://doi.org/10.1111/jam.12323 885 \n●  Te, S.H., Kok, J.W.K., Luo, R., You, L., Sukarji, N.H., Goh, K.C., Sim, Z.Y., Zhang, D., 886 \nHe, Y., Gin, K.Y.-H., 2023. Coexistence of Synechococcus and Microcystis blooms in a 887 \ntropical urban reservoir and their links with microbiomes. Environ. Sci. Technol. 57, 888 \n1613–1624. https://doi.org/10.1021/acs.est.2c04943 889 \n●  Tran, N.H., Hoang, L., Nghiem, L.D., Nguyen, N.M.H., Ngo, H.H., Guo, W., Trinh, 890 \nQ.T., Mai, N.H., Chen, H., Nguyen, D.D., Ta, T.T., Gin, K.Y.-H., 2019. Occurrence and 891 \nrisk assessment of multiple classes of antibiotics in urban canals and lakes in Hanoi, 892 \nVietnam. Sci. Total Environ. 692, 157–174. 893 \nhttps://doi.org/10.1016/j.scitotenv.2019.07.092 894 \n●  Vaccaro, M., Pilat, A.M., Gusmano, L., Pham, M.T.N., Barich, D., Gibson, A., Epalle, 895 \nM., Frost, D.J., Volin, E., Slimak, Z.C., Menke, C.C., Fennessy, M.S., Slonczewski, J.L., 896 \n2025. Pond water microbiome antibiotic resistance genes vary seasonally with 897 \nenvironmental pH and tannins. Microbiol. Spectr. 13, e03034-24. 898 \nhttps://doi.org/10.1128/spectrum.03034-24 899 \n 900 \n 901 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 1, 2026. ; https://doi.org/10.64898/2026.03.31.715732doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}