Short-Term Warming Induces Cyanobacterial Blooms and Antibiotic Resistance in Freshwater Lake, as Revealed by Metagenomics Analysis
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Abstract
Climate change threatens freshwater ecosystems, potentially intensifying cyanobacterial blooms and antibiotic resistance. We investigated these risks in Cosseys Reservoir, New Zealand, using short-term warming simulations (22 °C, 24 °C, and 27 °C) with additional oxidative stress treatments. A metagenomic analysis revealed significant community shifts under warming. The cyanobacterial abundance increased from 6.11% to 20.53% at 24 °C, with Microcystaceae and Nostocaceae proliferating considerably. The microcystin synthesis gene (mcy) cluster showed a strong association with cyanobacterial abundance. Cyanobacteria exhibited enhanced nutrient acquisition (pstS gene) and an upregulated nitrogen metabolism under warming. Concurrently, antibiotic resistance genes (ARGs) increased, particularly multidrug resistance genes (50.82% of total ARGs). A co-association network analysis identified the key antibiotic-resistant bacteria (e.g., Streptococcus pneumoniae and Acinetobacter baylyi) and ARGs (e.g., acrB, MexK, rpoB2, and bacA) central to resistance dissemination under warming conditions. Oxidative stress exacerbated both cyanobacterial growth and ARGs’ proliferation, especially efflux pump genes (e.g., acrB, adeJ, ceoB, emrB, MexK, and muxB). This study demonstrated that even modest warming (2–5 °C) could promote both toxic cyanobacteria and antibiotic resistance. These findings underscore the synergistic effects of temperature and oxidative stress posed by climate change on water quality and public health, emphasizing the need for targeted management strategies in freshwater ecosystems. Future research should focus on long-term impacts and potential mitigation measures.
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Cites
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
Prokka: rapid prokaryotic genome annotation
Seemann, T. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu153 · Cited by 18,512 · see citing works
TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data
Chen, C., Chen, H., Zhang, Y., Thomas, H. R., Frank, M. H., He, Y., & Xia, R. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.06.009 · Cited by 14,600 · see citing works
Fast and sensitive protein alignment using DIAMOND
Buchfink, B., Xie, C., & Huson, D. H. (2015) · Nature Methods · doi:10.1038/nmeth.3176 · Cited by 14,201 · see citing works
Prodigal: prokaryotic gene recognition and translation initiation site identification
Hyatt, D., Chen, G.-L., LoCascio, P. F., Land, M., Larimer, F. W., & Hauser, L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-119 · Cited by 12,426 · see citing works
Gephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675 · see citing works
Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 9,781 · see citing works
Quality control and preprocessing of metagenomic datasets
Schmieder, R., & Edwards, R. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr026 · Cited by 4,989 · see citing works
Wickham, H. (2011) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.147 · Cited by 4,340 · see citing works
Controlling Eutrophication: Nitrogen and Phosphorus
Conley, D. J., Paerl, H. W., Howarth, R. W., Boesch, D. F., Seitzinger, S. P., Havens, K. E., Lancelot, C., & Likens, G. E. (2009) · Science · doi:10.1126/science.1167755 · Cited by 3,926 · see citing works
CARD 2020: Antibiotic Resistome Surveillance with the Comprehensive Antibiotic Resistance Database
Alcock, B. P., Raphenya, A. R., Lau, T. T. Y., Tsang, K. K., Bouchard, M., Edalatmand, A., Huynh, W., Nguyen, A.-L. V., Cheng, A. A., Liu, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz935 · Cited by 3,202 · see citing works
Inferring Correlation Networks from Genomic Survey Data
Friedman, J., & Alm, E. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002687 · Cited by 2,688 · see citing works
Pathview: an R/Bioconductor package for pathway-based data integration and visualization
Luo, W., & Brouwer, C. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt285 · Cited by 2,297 · see citing works
Climate Change 2021—The Physical Science Basis
(2021) · Chemistry International · doi:10.1515/ci-2021-0407 · Cited by 2,268 · see citing works
The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change
O’Neil, J. M., Davis, T. W., Burford, M. A., & Gobler, C. J. (2012) · Harmful Algae · doi:10.1016/j.hal.2011.10.027 · Cited by 2,225 · see citing works
Climate change: Links to global expansion of harmful cyanobacteria
Paerl, H. W., & Paul, V. J. (2011) · Water Research · doi:10.1016/j.watres.2011.08.002 · Cited by 1,728 · see citing works
Clark, K., Karsch‐Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1276 · Cited by 1,471 · see citing works
OpenFluor– an online spectral library of auto-fluorescence by organic compounds in the environment
Murphy, K. R., Stedmon, C. A., Wenig, P., & Bro, R. (2013) · Analytical Methods · doi:10.1039/c3ay41935e · Cited by 1,118 · see citing works
State of knowledge and concerns on cyanobacterial blooms and cyanotoxins
Merel, S., Walker, D., Chicana, R., Snyder, S. A., Baurès, E., & Thomas, O. P. (2013) · Environment International · doi:10.1016/j.envint.2013.06.013 · Cited by 980 · see citing works
Scientists’ warning to humanity on the freshwater biodiversity crisis
Albert, J. S., Destouni, G., Duke-Sylvester, S. M., Magurran, A. E., Oberdorff, T., Reis, R. E., Winemiller, K. O., & Ripple, W. J. (2021) · Ambio · doi:10.1007/s13280-020-01318-8 · Cited by 943 · see citing works
Oxidative stress in cyanobacteria
Latifi, A., Ruiz, M., & Zhang, C. (2008) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2008.00134.x · Cited by 792 · see citing works
Bacterial community dynamics are linked to patterns of coral heat tolerance
Ziegler, M., Seneca, F., Yum, L. K., Palumbi, S. R., & Voolstra, C. R. (2017) · Nature Communications · doi:10.1038/ncomms14213 · Cited by 613 · see citing works
NetCoMi: network construction and comparison for microbiome data in R
Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A., & Depner, M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa290 · Cited by 602 · see citing works
MyTaxa: an advanced taxonomic classifier for genomic and metagenomic sequences
Luo, C., Rodriguez‐R, L. M., & Konstantinidis, K. T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku169 · Cited by 574 · see citing works
Antibiotic resistance increases with local temperature
MacFadden, D. R., McGough, S. F., Fisman, D. N., Santillana, M., & Brownstein, J. S. (2018) · Nature Climate Change · doi:10.1038/s41558-018-0161-6 · Cited by 567 · see citing works
SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline
Tamames, J., & Puente‐Sánchez, F. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.03349 · Cited by 494 · see citing works
Tomitani, A., Knoll, A. H., Cavanaugh, C. M., & Ohno, T. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600999103 · Cited by 468 · see citing works
Sun, Q., Miao, C., Hanel, M., Borthwick, A. G. L., Duan, Q., Ji, D., & Li, H. (2019) · Environment International · doi:10.1016/j.envint.2019.04.025 · Cited by 386 · see citing works
CyanoMetDB, a comprehensive public database of secondary metabolites from cyanobacteria
Jones, M. R., Pinto, E., Torres, M. D. A., Dörr, F., Mazur‐Marzec, H., Szubert, K., Tartaglione, L., Dell’Aversano, C., Miles, C. O., Beach, D. G., et al. (2021) · Water Research · doi:10.1016/j.watres.2021.117017 · Cited by 378 · see citing works
Climate Change Impacts on Harmful Algal Blooms in U.S. Freshwaters: A Screening-Level Assessment
Chapra, S. C., Boehlert, B. B., Fant, C., Bierman, V. J., Henderson, J., Mills, D., Mas, D. M. L., Rennels, L., Jantarasami, L., Martinich, J. A., et al. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.7b01498 · Cited by 357 · see citing works
Lyu, F., Han, F., Ge, C., Mao, W., Chen, L., Hu, H., Chen, G., Lang, Q., & Fang, C. (2023) · iMeta · doi:10.1002/imt2.85 · Cited by 348 · see citing works
The cyanotoxin-microcystins: current overview
Rastogi, R. P., Sinha, R. P., & Incharoensakdi, A. (2014) · Reviews in Environmental Science and Bio/Technology · doi:10.1007/s11157-014-9334-6 · Cited by 336 · see citing works
Impact of climate change and anthropogenic activities on aquatic ecosystem – A review
Priya, A. K., Muruganandam, M., Sivarethinamohan, R., Sujatha, S., Gaddam, M. K. R., Velusamy, P., Gomathi, R., Ravindiran, G., Gurugubelli, T. R., & Muniasamy, S. K. (2023) · Environmental Research · doi:10.1016/j.envres.2023.117233 · Cited by 323 · see citing works
Rastogi, R. P., Madamwar, D., & Incharoensakdi, A. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.01254 · Cited by 316 · see citing works
Oxidative stress generation by microcystins in aquatic animals: Why and how
Amado, L. L., & Monserrat, J. M. (2009) · Environment International · doi:10.1016/j.envint.2009.10.010 · Cited by 315 · see citing works
staRdom: Versatile Software for Analyzing Spectroscopic Data of Dissolved Organic Matter in R
Pucher, M., Wünsch, U., Weigelhofer, G., Murphy, K. R., Hein, T. I. A., & Graeber, D. (2019) · Water · doi:10.3390/w11112366 · Cited by 310 · see citing works
Li, H., Zhou, X., Huang, Y., Liao, B., Cheng, L., & Ren, B. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.622534 · Cited by 284 · see citing works
Self-citationAcute animal and human poisonings from cyanotoxin exposure — A review of the literature
Wood, R. (2016) · Environment International · doi:10.1016/j.envint.2016.02.026 · Cited by 240 · see citing works
Rising CO2 Levels Will Intensify Phytoplankton Blooms in Eutrophic and Hypertrophic Lakes
Verspagen, J. M. H., de Waal, D. B. V., Finke, J. F., Visser, P., van Donk, E., & Huisman, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104325 · Cited by 237 · see citing works
Berry, J., Gantar, M., Perez, M., Berry, G., & Noriega, F. (2008) · Marine Drugs · doi:10.3390/md6020117 · Cited by 231 · see citing works
Lu, J., Jin, M., Nguyen, S. H., Mao, L., Li, J., Coin, L., Yuan, Z., & Guo, J. (2018) · Environment International · doi:10.1016/j.envint.2018.06.004 · Cited by 220 · see citing works
Jin, M., Lu, J., Chen, Z., Nguyen, S. H., Mao, L., Li, J., Yuan, Z., & Guo, J. (2018) · Environment International · doi:10.1016/j.envint.2018.07.046 · Cited by 182 · see citing works
Guo, Y., Liu, M., Liu, L., Liu, X., Chen, H., & Yang, J. (2018) · Environment International · doi:10.1016/j.envint.2018.04.045 · Cited by 137 · see citing works
Fernández‐González, C., Tarran, G. A., Schuback, N., Woodward, E. M. S., Arı́stegui, J., & Emilio Marañón, C. (2022) · Communications Biology · doi:10.1038/s42003-022-03971-z · Cited by 122 · see citing works
Frías, J. E., Flores, E., & Herrero, A. (1997) · Journal of Bacteriology · doi:10.1128/jb.179.2.477-486.1997 · Cited by 114 · see citing works
Wang, J., Zhu, J., Liu, S., Liu, B., Gao, Y., & Wu, Z. (2011) · Chemosphere · doi:10.1016/j.chemosphere.2011.06.076 · Cited by 110 · see citing works
Mechanisms of Stress Tolerance in Cyanobacteria under Extreme Conditions
Yadav, P., Singh, R. P., Rana, S., Joshi, D., Kumar, D., Bhardwaj, N., Gupta, R. K., & Kumar, A. (2022) · Stresses · doi:10.3390/stresses2040036 · Cited by 109 · see citing works
Omata, T. (1995) · Plant and Cell Physiology · doi:10.1093/oxfordjournals.pcp.a078751 · Cited by 103 · see citing works
SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o
Puente‐Sánchez, F., García-García, N., & Tamames, J. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03703-2 · Cited by 102 · see citing works
Metagenomic analysis of an urban resistome before and after wastewater treatment
Lira, F., Vaz‐Moreira, I., Tamames, J., Manaia, C. M., & Martínez, J. L. (2020) · Scientific Reports · doi:10.1038/s41598-020-65031-y · Cited by 96 · see citing works
Zhao, Y., Yan, Y., Xie, L., Wang, L., He, Y., Wan, X., & Xue, Q. (2020) · Environment International · doi:10.1016/j.envint.2020.105648 · Cited by 95 · see citing works
Yu, Q., Han, Q., Shi, S., Sun, X., Wang, X., Wang, S., Yang, J., Su, W., Nan, Z., & Li, H. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.160324 · Cited by 85 · see citing works
Li, Z., Sun, A., Liu, X., Chen, Q., Bi, L. M., Ren, P., Shen, J., Jin, S., He, J., Hu, H., et al. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.128442 · Cited by 80 · see citing works
Jang, M., Ha, K., & Takamura, N. (2006) · Toxicon · doi:10.1016/j.toxicon.2006.11.017 · Cited by 76 · see citing works
Physiology and molecular biology of aquatic cyanobacteria
Bullerjahn, G. S., & Post, A. F. (2014) · Frontiers in Microbiology · doi:10.3389/fmicb.2014.00359 · Cited by 61 · see citing works
Fang, M., Lei, Z., Ma, R., Jing, W., & Du, L. (2023) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2023.115607 · Cited by 61 · see citing works
Climate change amplifies the risk of potentially toxigenic cyanobacteria
Erratt, K. J., Creed, I. F., Lobb, D. A., Smol, J. P., & Trick, C. G. (2023) · Global Change Biology · doi:10.1111/gcb.16838 · Cited by 52 · see citing works
Marshall, I. P. G., Starnawski, P., Cupit, C., Caceres, E., Ettema, T. J. G., Schramm, A., & Kjeldsen, K. U. (2017) · Environmental Microbiology Reports · doi:10.1111/1758-2229.12544 · Cited by 51 · see citing works
Kublanov, I. V., Sigalova, O. M., Gavrilov, S. N., Lebedinsky, A. V., Rinke, C., Kovaleva, O. L., Chernyh, N. A., Ivanova, N., Daum, C. G., Reddy, T. B. K., et al. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00195 · Cited by 50 · see citing works
Raes, E. J., Karsh, K. L., Sow, S. L. S., Ostrowski, M. D., Brown, M. V., van de Kamp, J., Franco-Santos, R. M., Bodrossy, L., & Waite, A. M. (2021) · Nature Communications · doi:10.1038/s41467-021-22409-4 · Cited by 50 · see citing works
Ma, X., Zhang, X., Xia, J., Sun, H., Zhang, X., & Ye, L. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.149549 · Cited by 47 · see citing works
Cole, R., Hajat, S., Murage, P., Heaviside, C., Macintyre, H. L., Davies, M., & Wilkinson, P. (2023) · Environment International · doi:10.1016/j.envint.2023.107836 · Cited by 46 · see citing works
Tignat‐Perrier, R., van de Water, J. A. J. M., Guillemain, D., Aurelle, D., Allemand, D., & Ferrier‐Pagès, C. (2022) · Applied and Environmental Microbiology · doi:10.1128/aem.02340-21 · Cited by 42 · see citing works
Duan, X., Zhang, C., Struewing, I. T., Li, X., Allen, J., & Lu, J. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154568 · Cited by 40 · see citing works
Hassan, A. H. A., Hozzein, W. N., Mousa, A. S., Abdelghany, W. R., Alkhalifah, D. H. M., Selim, S., & AbdElgawad, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-72054-y · Cited by 40 · see citing works
Analysis, levels and seasonal variation of cyanotoxins in freshwater ecosystems
Filatova, D., Picardo, M., Núñez, Ó., & Farré, M. (2020) · Trends in Environmental Analytical Chemistry · doi:10.1016/j.teac.2020.e00091 · Cited by 38 · see citing works
Wood, S. A., Maier, M. Y., Puddick, J., Pochon, X., Zaiko, A., Dietrich, D. R., & Hamilton, D. P. (2016) · FEMS Microbiology Ecology · doi:10.1093/femsec/fiw234 · Cited by 37 · see citing works
Qi, W., Jonker, M. J., Teichmann, L., Wortel, M. T., & ter Kuile, B. H. (2023) · BMC Microbiology · doi:10.1186/s12866-023-03031-4 · Cited by 34 · see citing works
Oral exposure to environmental cyanobacteria toxins: Implications for cancer risk
Hernandez, B. Y., Zhu, X., Sotto, P. P., & Paulino, Y. C. (2021) · Environment International · doi:10.1016/j.envint.2021.106381 · Cited by 30 · see citing works
Linz, D. M., Sienkiewicz, N., Struewing, I. T., Stelzer, E. A., Graham, J. L., & Lu, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-29037-6 · Cited by 26 · see citing works
Yang, J., Yu, Q., Su, W., Wang, S., Wang, X., Han, Q., & Li, H. (2022) · Environmental Pollution · doi:10.1016/j.envpol.2022.120793 · Cited by 22 · see citing works
Pisaniello, A., Handley, K. M., White, W. L., Angert, E. R., Boey, J. S., & Clements, K. D. (2023) · BMC Microbiology · doi:10.1186/s12866-023-03025-2 · Cited by 19 · see citing works
Microcystins in New Zealand: a review of occurrence, congener diversity and cell quotas
Puddick, J., Thomson‐Laing, G., & Wood, S. A. (2019) · New Zealand Journal of Botany · doi:10.1080/0028825x.2019.1573742 · Cited by 18 · see citing works
Impact of Climate Change on Antimicrobial Resistance Dynamics: An Emerging One Health Challenge
Qamar, M. U., & Aatika, A. (2023) · Future Microbiology · doi:10.2217/fmb-2023-0022 · Cited by 11 · see citing works
Padovan, A. C., Kennedy, K., & Gibb, K. S. (2023) · Harmful Algae · doi:10.1016/j.hal.2023.102476 · Cited by 9 · see citing works
Liu, D., Song, H., Li, Y., Huang, R., Liu, H., Tang, K., Jiao, N., & Liu, J. (2023) · Antioxidants · doi:10.3390/antiox12020423 · Cited by 8 · see citing works
Flagged first, then by citations
2 of the 80 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Manna, B., Zhou, X., & Singhal, N. (2025) · Water Research · doi:10.1016/j.watres.2025.123366 · Cited by 37 · see citing works
Self-citationAnalysis and diagnostic of reverse osmosis membrane fouling using industrial plant data
Elyaakouby, Y., El–Kordy, A., & Tilioua, A. (2026) · Cleaner Water · doi:10.1016/j.clwat.2026.100247 · Cited by 7 · see citing works
Du, Y., Zhao, S., Gao, Y., Yan, Y., Kong, Y., Meng, W., Lu, X., Zheng, S., Mu, H., Chen, X., et al. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.129178 · Cited by 4 · see citing works
Phycosphere as a hotspot of antibiotic resistomes in aquatic environments
Jia, J., Xue, X., Wang, Z., Xiong, X., Hu, H., & Wu, C. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.140513 · Cited by 2 · see citing works
García, G., Villalobos, S. D. L. S., Gutiérrez-Moreno, P., & Broce, K. (2026) · Water · doi:10.3390/w18040510 · Cited by 2 · see citing works
Gou, E., Hui, X., Liu, H., Guan, J., Yao, J., & Dai, X. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.147563 · Cited by 1 · see citing works
Miura, Y., Touge, Y., Tanaka, S., Masago, Y., IMAMOTO, H., 浅田, 安., Akiba, M., Nishimura, O., & Sano, D. (2025) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2025.101672
Yu, Y., Niu, Z., Zhao, Y., Li, S., ZHANG, Y., & Wang, C. (2026) · Biocontaminant · doi:10.48130/biocontam-0026-0001
Yang, X., Wu, P., Li, C., Zheng, Q., Shi, X., 苏浩昌, Wang, T., Xiong, X., Liu, Y., Xiao, Y., et al. (2026) · Marine Environmental Research · doi:10.1016/j.marenvres.2026.108197
Gao, Q., Lu, J., Hou, J., Ding, W., Xu, D., Zhou, C., & You, G. (2026) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2026.2741931
A cross-system mechanistic framework for phytoplankton bloom life cycles across aquatic environments
Tassadaq, W., Ma, J., Jeppesen, E., Zhang, W., Zhao, X., Mao, Y., Paerl, H. W., Brookes, J. D., Ma, X., Hu, W., et al. (2026) · The Innovation Water · doi:10.59717/j.tiw.2026.100029
Self-citation
- Authors
- Manna, B., Jay, E., Zhang, W., Zhou, X., Lyu, B., Thomas, G. M., & Singhal, N.
- Affiliations
- University of Auckland (NZ)
- Corresponding author
- Singhal, Naresh
- Venue
- Water
- Publisher
- MDPI AG
- Published
- 2024-09-18
- Volume
- 16
- Issue
- 18
- Pages
- 2655
- ISSN
- 2073-4441 (ISSN-L)
- OpenAlex
- W4402650217
- Cited by
- 11
- References
- 80
- Citations by year
- 2026: 8 · 2025: 3
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Water (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Royal Society of New Zealand (MFP-UOA2018, 9485/3729416)
- Faculty of Engineering, The University of Auckland to NS (MFP-UOA2018, 9485/3729416)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Environmental Science › Pollution › Pharmaceutical and Antibiotic Environmental Impacts
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- cyanobacterial blooms, antibiotic resistance genes, climate change, oxidative stress, freshwater lakes, Nostocaceae, multidrug resistance genes, efflux pump genes, nitrogen metabolism, microbial community shifts, antibiotic-resistant bacteria, water quality
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