Ecology

AbstractSugar beet is a vital sugar-producing crop, and continuous cropping poses a significant threat to its growth, leading to a decline in yield and quality. This study aimed to investigate the effects of two bacterial agents, Bacillus subtilis and Bacillus mucilaginosus, on the growth, soil physicochemical properties, and rhizosphere microbial community of sugar beet seedlings. We employed pot experiments and amplicon sequencing to analyze the impact of applying two different Bacillus agents on the microbial community structure in the rhizosphere soil of continuously cropped sugar beet and explore the microbial composition, environmental driving factors, and potential functions present within the microbial communities. The results showed that both Bacillus agents and their combination significantly promoted the growth of continuous cropping sugar beet seedlings, reaching or even surpassing the levels observed in crop rotation, improved soil pH, and enhanced soil environment. High-throughput sequencing analysis of the rhizosphere soil revealed that all Bacillus treatments induced changes in the diversity and structural composition of the rhizosphere microbial community, and significantly increased the relative abundance of Proteobacteria, thereby enriching beneficial microorganisms such as Pseudomonas, Novosphingobium, and Sphingomonas compared with that in the control group. Additionally, the application of Bacillus inoculants significantly enhanced the nitrate respiration, nitrogen respiration, and chitinolytic functions. These two bacterial agents optimized soil physicochemical properties and improved the rhizosphere soil microbial community structure, promoting sugar beet seedling growth and effectively mitigating the negative effects of continuous cropping.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the NCBI Sequence Read Archive (SRA) database under accession numbers PRJNA1310802 (ITS) and PRJNA1310801 (16S).
ReferencesSteven, Z., Zhang, R. H. & Stephen, K. In In integrated processing technologies for food and agricultural by-products (ed. Stephen, Z.) 331–351 (Academic Press, Cambridge, 2019).
Google Scholar 
Chhikara, N. et al. Bioactive compounds of beetroot and utilization in food processing industry: A critical review. Food Chem. 272, 192–200. https://doi.org/10.1016/j.foodchem.2018.08.022 (2018).
Google Scholar 
Mall, A. K. et al. Sugar beet cultivation in India: Prospects for bio-ethanol production and value-added co-products. Sugar Tech. 23(6), 1218–1234. https://doi.org/10.1007/s12355-021-01007-0 (2021).
Google Scholar 
Jbawi, E. et al. Genotype: Environment interaction study in sugar beet (Beta vulgaris L.). Int. J. Environ. 5(3), 74–86. https://doi.org/10.3126/ije.v5i3.15706 (2016).
Google Scholar 
Holmquist, L. et al. Major latex protein-like encoding genes contribute to Rhizoctonia solani defense responses in sugar beet. Mol. Genet. Genom. 296(1), 155–164. https://doi.org/10.1007/s00438-020-01735-0 (2020).
Google Scholar 
Wu, X. et al. The effect of long-term continuous cropping of black pepper on soil bacterial communities as determined by 454 pyrosequencing. PLoS ONE 10(8), e0136946–e0136946. https://doi.org/10.1371/journal.pone.0136946 (2015).
Google Scholar 
Zhu, B. et al. Diversity of rhizosphere and endophytic fungi in Atractylodes macrocephala during continuous cropping. PeerJ 8, e8905. https://doi.org/10.7717/peerj.8905 (2020).
Google Scholar 
Tang, J., Xue, Z. Q., Daroch, M. & Ma, J. Impact of continuous Salvia miltiorrhiza cropping onrhizosphere actinomycetes and fungi communities. Ann. Microbiol. 65(3), 1267–1275. https://doi.org/10.1007/s13213-014-0964-2 (2015).
Google Scholar 
Cui, R. F. et al. The response of sugar beet rhizosphere micro-ecological environment to continuous cropping. Front. Microbiol. 13, 956785. https://doi.org/10.3389/fmicb.2022.956785 (2022).
Google Scholar 
Kui, L. et al. Large-scale characterization of the soil microbiome in ancient tea plantations using high-throughput 16S rRNA and internal transcribed spacer amplicon sequencing. Front. Microbiol. 12, 745225. https://doi.org/10.3389/fmicb.2021.745225 (2021).
Google Scholar 
Pimentel, D. et al. Conserving biological diversity in agricultural/forestry systems. Bioscience 42(5), 354–362. https://doi.org/10.2307/1311782 (1992).
Google Scholar 
Tkacz, A. et al. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. ISME J. 9, 2349–2359 (2015).
Google Scholar 
Tong, J., Cao, M. & Wang, R. Effects of restoration time on microbial diversity in rhizosphere and non-rhizosphere soil of Bothriochloa ischaemum. Int. J. Environ. Res. Public Health. 15, 2155. https://doi.org/10.3390/ijerph15102155 (2018).
Google Scholar 
Zheng, X. et al. Effects of a microbial restoration substrate on plant growth and rhizo-sphere bacterial community in a continuous tomato crop-ping greenhouse. Sci. Rep. 10, 13729. https://doi.org/10.1038/s41598-020-70737-0 (2020).
Google Scholar 
Cao, B. et al. Insight into the variation of bacterial structure in atrazine-contaminated soil regulating by potential phytoremediator: Pennisetum americanum (L.) K. Schum.. Front Microbiol. 9, 864. https://doi.org/10.3389/fmicb.2018.00864 (2018).
Google Scholar 
Das, S. et al. Taxonomic and functional responses of soil microbial communities to slag-based fertilizer amendment in rice cropping systems. Environ. Int. 127, 531–539. https://doi.org/10.1016/j.envint.2019.04.012 (2019).
Google Scholar 
Kang, P. et al. A comparison of microbial composition under three tree ecosystems using the stochastic process and network complexity approaches. Front. Microbiol. 13, 1018077. https://doi.org/10.3389/fmicb.2022.1018077 (2022).
Google Scholar 
Zhang, X. et al. Plant growth and development of tropical seagrass deter-mined rhizodeposition and its related microbial community. Mar Pollut Bull. 199, 115940. https://doi.org/10.1016/j.marpolbul (2024).
Google Scholar 
Liu, J. et al. Development of a soil quality index for Camellia oleifera forestland yield under three different parent materials in Southern China. Soil Till Res. 176, 45–50. https://doi.org/10.1016/j.still.2017.09.013 (2018).
Google Scholar 
Qi, R. M. et al. Temperature effects on soil organic carbon, soil labile organic carbon fractions, and soil enzyme activities under long-term fertilization regimes. Appl. Soil Ecol. 102, 36–45. https://doi.org/10.1016/j.apsoil.2016.02.004 (2016).
Google Scholar 
Ahsan, T. et al. Effects of microbial agent and microbial fertilizer input on soil microbial community structure and diversity in a peanut continuous cropping system. J. Adv. Res. 64, 1–13. https://doi.org/10.1016/j.jare.2023.11.028 (2024).
Google Scholar 
Li, M. et al. Effects of continuous cropping of sugar beet (Beta vulgaris L.) on its endophytic and soil bacterial community by high-throughput sequencing. Ann. Microbiol. 70, 39. https://doi.org/10.1186/s13213-020-01583-8 (2020).
Google Scholar 
Sun, R. C. et al. Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw. Soil Biol. Biochem. 88, 9–18. https://doi.org/10.1016/j.soilbio.2015.05.007 (2015).
Google Scholar 
Zhang, S. N. et al. Cow manure application effectively regulates the soil bacterial community in tea plantation. BMC Microbiol. 20(1), 190. https://doi.org/10.1186/s12866-020-01871-y (2020).
Google Scholar 
Cai, X. Y. et al. Effects and mechanisms of symbiotic microbial combination agents to control tomato fusarium crown and root rot disease. Front. Microbiol. 12, 629793. https://doi.org/10.3389/fmicb.2021.629793 (2021).
Google Scholar 
Duan, Y. N. et al. The phlorizin-degrading Bacillus licheniformis XNRB-3 mediates soil microorganisms to alleviate apple replant disease. Front. Microbiol. 13, 839484. https://doi.org/10.3389/fmicb.2022.839484 (2022).
Google Scholar 
Olanrewaju, O. S., Ayilara, M. S., Ayangbenro, A. S. & Babalola, O. O. Genome mining of three plant growth-promoting bacillus species from maize rhizosphere. Appl. Biochem. Biotechnol. 193(12), 3949–3969. https://doi.org/10.1007/s12010-021-03660-3 (2021).
Google Scholar 
Solanki, M. K. et al. Diversity and antagonistic potential of Bacillusspp. associated to the rhizosphere of tomato for the management of Rhizoctonia solani. Biocontrol. Sci. Technol. 22(2), 203–217. https://doi.org/10.1080/09583157.2011.649713 (2012).
Google Scholar 
Chen, X. R. et al. Bacillus velezensis strain GUMT319 reshapes soil microbiome biodiversity and increases grape yields. Biology. 11(10), 1486–1486. https://doi.org/10.3390/biology11101486 (2022).
Google Scholar 
Shao, F. F., Tao, W. H., Yan, H. K. & Wang, Q. J. Effects of microbial organic fertilizer (MOF) application on desert soil enzyme activity and jujube yield and quality. Agronomy 13(9), 2427–2427. https://doi.org/10.3390/agronomy13092427 (2023).
Google Scholar 
Gajbhiye, A., Rai, A. R. & Meshram, S. U. Isolation, evaluation and characterization of Bacillus subtilis from cotton rhizospheric soil with biocontrol activity against Fusarium oxysporum. World. J. Microb. Biot. 26(7), 1187–1194. https://doi.org/10.1007/s11274-009-0287-9 (2009).
Google Scholar 
Govindasamy, V. et al. Bacillus and Paenibacillus spp.: Potential PGPR for sustainable agriculture. 18. https://doi.org/10.1007/978-3-642-13612-2_15 (2010).Qiao, J. Q. et al. Addition of plant-growth-promoting Bacillus subtilis PTS-394 on tomato rhizosphere has no durable impact on composition of root microbiome. BMC Microbiol. 17(1), 131. https://doi.org/10.1186/s12866-017-1039-x (2017).
Google Scholar 
Sui, J. K. et al. Effects of Bacillus subtilis T6–1 on the rhizosphere microbial community structure of continuous cropping poplar. Biology. 11(5), 791–791. https://doi.org/10.3390/biology11050791 (2022).
Google Scholar 
Wang, D. et al. Genomic insights and functional analysis reveal plant growth promotion traits of Paenibacillus mucilaginosus G78. Genes 14(2), 392–392. https://doi.org/10.3390/genes14020392 (2023).
Google Scholar 
Lu, R. K. Soil and agricultural chemistry analysis (ed. Lu, R. K.) pp. 248–254 (1999).Huang, W. J. et al. Effects of continuous sugar beet cropping on rhizospheric microbial communities. Genes 11(1), 13–13. https://doi.org/10.3390/genes11010013 (2019).
Google Scholar 
Kõljalg, U. et al. The taxon hypothesis paradigm: On the unambiguous detection and communication of taxa. Microorganisms. 8(12), 1910–1910. https://doi.org/10.3390/microorganisms8121910 (2020).
Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).
Google Scholar 
Li, Q., Huang, Y., Xin, S. & Li, Z. Y. Comparative analysis of bacterioplankton assemblages from two subtropical karst reservoirs of southwestern China with contrasting trophic status. Sci. Rep. 10(1), 22296. https://doi.org/10.1038/s41598-020-78459-z (2020).
Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75(23), 7537–7541. https://doi.org/10.1128/AEM.01541-09 (2009).
Google Scholar 
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genomebiology.com (London. Print) 12 (6): R60-R60. http://genomebiology.com/2011/11/6/R60 (2011).Yuan, M. M. et al. Climate warming enhances microbial network complexity and stability. Nat. Clim. Change. 11(4), 343–348. https://doi.org/10.1038/s41558-021-00989-9 (2021).
Google Scholar 
Xu, W. F. et al. Structure and ecological function of the soil microbiome associated with ‘Sanghuang’ mushrooms suffering from fungal diseases. BMC Microbiol. 23(1), 218. https://doi.org/10.1186/s12866-023-02965-z (2023).
Google Scholar 
Richard, M. C., David, W. H. & Joseph, W. K. Rhizobacterial colonization of bermudagrass by Bacillus spp. in a Marvyn loamy sand soil. Appl. Soil Ecol. 141, 10–17. https://doi.org/10.1016/j.apsoil.2019.04.018 (2019).
Google Scholar 
Zhang, Q. X. et al. Wheat rhizosphere colonization by Bacillus amyloliquefaciens W10 and Pseudomonas protegens FD6 suppress soil and in planta abundance of the sharp eyespot pathogen Rhizoctonia cerealis. J. Appl. Microbiol. 134, 1–11. https://doi.org/10.1093/jambio/lxad101 (2023).
Google Scholar 
Chen, W. M. et al. Biochar combined with Bacillus subtilis SL-44 as an eco-friendly strategy to improve soil fertility, reduce Fusarium wilt, and promote radish growth. Ecotoxicol. Environ. Saf. 251, 114509. https://doi.org/10.1016/j.ecoenv.2023.114509 (2023).
Google Scholar 
Han, S. C., Kim, K., Maung, C. E. H. & Kim, K. Y. Growth enhancement of tomato by a plant growth promoting bacterium, bacillus subtilis PE7. Korean J. Soil Sci. Fertil. 56(4), 398–406. https://doi.org/10.7745/kjssf.2023.56.4.398 (2023).
Google Scholar 
Torres, M. et al. Growth promotion on horticultural crops and antifungal activity of Bacillus velezensis XT1. Appl. Soil Ecol. 150, 103453. https://doi.org/10.1016/j.apsoil.2019.103453 (2020).
Google Scholar 
Shen, Z. Z. et al. Effect of biofertilizer for suppressing Fusarium wilt disease of banana as well as enhancing microbial and chemical properties of soil under greenhouse trial. Appl. Soil Ecol. 93, 111–119. https://doi.org/10.1016/j.apsoil.2015.04.013 (2015).
Google Scholar 
Wu, L. X., Wang, Y., Lyu, H. & Chen, X. D. Effects of a compound Trichoderma agent on Coptis chinensis growth, nutrients, enzyme activity, and microbial community of rhizosphere soil. PeerJ 11, e15652. https://doi.org/10.7717/peerj.15652 (2023).
Google Scholar 
Asaf, S., Numan, M., Khan, A. L. & Al-Harrasi, A. Sphingomonas: From diversity and genomics to functional role in environmental remediation and plant growth. Crit. Rev. Biotechnol. 40(2), 138–152. https://doi.org/10.1080/07388551.2019.1709793 (2020).
Google Scholar 
Li, Q. et al. Plant growth-promoting rhizobacterium Pseudomonas sp. CM11 specifically induces lateral roots. New Phytol. 235(4), 1575–1588. https://doi.org/10.1111/nph.18199 (2022).
Google Scholar 
Wang, Q. et al. The endophytic bacterium Sphingomonas SaMR12 alleviates Cd stress in oilseed rape through regulation of the GSH-AsA cycle and antioxidative enzymes. BMC Plant Biol. 20, 63. https://doi.org/10.1186/s12870.-020-2273-1 (2020).
Google Scholar 
Song, J. et al. Rhizosphere microbiomes of potato cultivated under bacillus subtilis treatment influence the quality of potato tubers. Int. J. Mol. Sci. 22(21), 12065–12065. https://doi.org/10.3390/ijms222112065 (2021).
Google Scholar 
Zhao, J. et al. The rhizosphere microbial community response to a bio-organic fertilizer: Finding the mechanisms behind the suppression of watermelon Fusarium wilt disease. Acta Physiol. Plant. 40(1), 17. https://doi.org/10.1007/s11738-017-2581-8 (2017).
Google Scholar 
Li, X. Y. et al. Microbiome analysis and biocontrol bacteria isolation from rhizosphere soils associated with different sugarcane root rot severity. Front. Bioeng. Biotechnol. 10, 1062351. https://doi.org/10.3389/fbioe.2022.1062351 (2022).
Google Scholar 
Download referencesAcknowledgementsThis research was made possible through the collaboration and support of the National Sugar Crop Improvement Centre of Heilongjiang University and Heilongjiang Provincial Key Laboratory of Ecological Restoration and Resource Utilization for Cold Region.FundingThis research was funded by the Heilongjiang Provincial Postdoctoral Research Funding Program (LBH-Z23255).Author informationAuthors and AffiliationsCollege of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, 150000, ChinaYanchun Sun, Qun Song, Zenghao Wang, Youkai Gao, Liuli Wei, Yuguang Wang, Rui Chen & Gui GengAuthorsYanchun SunView author publicationsSearch author on:PubMed Google ScholarQun SongView author publicationsSearch author on:PubMed Google ScholarZenghao WangView author publicationsSearch author on:PubMed Google ScholarYoukai GaoView author publicationsSearch author on:PubMed Google ScholarLiuli WeiView author publicationsSearch author on:PubMed Google ScholarYuguang WangView author publicationsSearch author on:PubMed Google ScholarRui ChenView author publicationsSearch author on:PubMed Google ScholarGui GengView author publicationsSearch author on:PubMed Google ScholarContributionsQun Song and Zenghao Wang: Conceptualization, Methodology, Writing-Original draft preparation; Youkai Gao, Liuli Wei, Rui Chen: Data curation, Sample analysis; Yanchun Sun: Conceptualization, Resources, Supervision, Writing-Reviewing; Gui Geng, Yuguang Wang: Methodology, Writing-Reviewing. All authors contributed to the article and approved the submitted version.Corresponding authorsCorrespondence to
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Reprints and permissionsAbout this articleCite this articleSun, Y., Song, Q., Wang, Z. et al. Effects of bacillus on continuous cropping of sugar beets and their rhizosphere microbial community.
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AbstractMarine fishery resource prediction is crucial for sustainable fishery management and ecosystem conservation, yet traditional statistical methods face limitations in capturing the complex non-linear relationships and multi-scale temporal dependencies inherent in marine environmental systems. This study proposes a novel CNN-XGBoost fusion model that integrates convolutional neural networks’ temporal pattern recognition capabilities with extreme gradient boosting’s ensemble learning strengths for enhanced marine fishery resource forecasting. The fusion architecture employs a hierarchical two-stage framework where CNN components extract high-level temporal features from multi-source marine environmental data, while XGBoost modules process both extracted features and engineered variables to generate final predictions. Comprehensive experiments demonstrate that the proposed fusion model achieves superior performance compared to standalone CNN, XGBoost, and traditional ARIMA approaches, with 19.1% improvement in RMSE and statistically significant enhancements across all evaluation metrics. The optimal fusion weight analysis reveals that CNN-extracted features and XGBoost-processed features are weighted at 40 and 60% respectively in the final prediction fusion, achieving RMSE of 2.847, MAE of 2.184, and R2 of 0.846. These percentages represent fusion weight allocation rather than prediction accuracy values. Time series analysis confirms robust performance across seasonal variations and exceptional capability in predicting extreme abundance events critical for adaptive fishery management. The results provide valuable insights for sustainable marine resource management and offer practical tools for fishery policymakers and resource managers.

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Data availability

The marine fishery resource datasets used in this study were obtained from multiple sources, with access information provided below to facilitate reproducibility and follow-up research. Fishery catch records were obtained from the National Fisheries Database of China (http://www.cnfm.gov.cn/) operated by the Ministry of Agriculture and Rural Affairs, available upon reasonable request with appropriate data sharing agreements that comply with commercial confidentiality requirements. Researchers interested in accessing these data should contact the Fisheries Bureau (email: [email protected]) with a formal data request describing the research purpose, intended use, and data protection measures.Satellite-derived oceanographic data are publicly accessible through the following sources: (1) MODIS sea surface temperature and chlorophyll-a concentration data were downloaded from NASA’s Ocean Color Web portal (https://oceancolor.gsfc.nasa.gov/), specifically utilizing MODIS Aqua Level-3 mapped products (dataset identifiers: AQUA_MODIS.20080101_20231231.L3m.MO.SST.sst.4 km and AQUA_MODIS.20080101_20231231.L3m.8D.CHL.chlor_a.4 km); (2) SeaWiFS chlorophyll-a concentration data for the earlier period (1997-2010) were obtained from the same NASA Ocean Color portal (https://oceancolor.gsfc.nasa.gov/data/seawifs/). All satellite data are freely available without registration and can be accessed through the portal’s data browser or bulk download protocols.Meteorological data including wind speed, wind direction, and precipitation measurements were provided by the China Meteorological Administration through their National Meteorological Information Center data portal (http://data.cma.cn/). Access requires registration (free for research purposes) and adherence to the CMA data policy (http://data.cma.cn/en/site/index.html). Station-level daily observations can be requested through the portal’s data ordering system, with typical processing time of 3-5 business days for historical data requests. Ocean current velocity data were obtained from the China High-Frequency Radar Ocean Observation Network operated by the State Oceanic Administration, available through collaborative research agreements. Researchers should contact the National Marine Data and Information Service (email: [email protected], website: http://www.nmdis.org.cn/) to inquire about data access procedures.The processed datasets supporting the conclusions of this article, including the preprocessed and harmonized multi-source data, engineered features, and model predictions, are available from the corresponding author (Mingqi Zhang, email: [email protected]) upon reasonable request, subject to privacy and confidentiality restrictions imposed by original data providers. The Python code implementing the CNN-XGBoost fusion model, including data preprocessing scripts, model architecture definitions, training procedures, and evaluation metrics, will be made publicly available on GitHub (https://github.com/[username]/CNN-XGBoost-Marine-Fishery) upon publication acceptance, licensed under MIT License to facilitate reproducibility and encourage further methodological development by the research community.
ReferencesVianna, G. M. S., Zeller, D. & Pauly, D. Rethinking sustainability of marine fisheries for a fast-changing planet. Npj Ocean. Sustain. 1, 78. https://doi.org/10.1038/s44183-024-00078-2 (2024).
Google Scholar 
Zhang, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0 (2003).
Google Scholar 
Stergiou, K. I. & Browman, H. I. Bridging the gap between aquatic and terrestrial ecology. Mar. Ecol. Prog. Ser. 304, 271–307. https://doi.org/10.3354/meps304271 (2005).
Google Scholar 
Torres, M., Lim, B., Arık, S., Loeff, N. & Pfister, T. Time-series forecasting with deep learning: a survey. Philosophical Trans. Royal Soc. A. 379, 20200209. https://doi.org/10.1098/rsta.2020.0209 (2021).
Google Scholar 
Shi, X. et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Adv. Neural. Inf. Process. Syst. 28, 802–810 (2015).
Google Scholar 
Zeng, A., Chen, M., Zhang, L. & Xu, Q. Are Transformers effective for time series forecasting? Proc. AAAI Conf. Artif. Intell. 37, 11121–11128. https://doi.org/10.1609/aaai.v37i9.26317 (2023).
Google Scholar 
Wu, N., Green, B., Ben, X. & O’Banion, S. Deep transformer models for time series forecasting: the influenza prevalence case. arXiv (2020).Zhang, Y., Sun, X., Chen, L. & Yan, J. Deep learning for ocean temperature forecasting: a survey. Intell. Mar. Technol. Syst. 2, 42. https://doi.org/10.1007/s44295-024-00042-3 (2024).
Google Scholar 
Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785 (2016).Bentéjac, C., Csörgő, A. & Martínez-Muñoz, G. A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev. 54, 1937–1967. https://doi.org/10.1007/s10462-020-09896-5 (2021).
Google Scholar 
Kim, T. & Cho, S. Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182, 72–81. https://doi.org/10.1016/j.energy.2019.05.230 (2019).
Google Scholar 
Vaswani, A. et al. Attention is all you need. Adv. Neural. Inf. Process. Syst. 30, 5998–6008 (2017).
Google Scholar 
Zhang, Y. & Yan, B. A systematic review for transformer-based long-term series forecasting. Artif. Intell. Rev. 57, 186. https://doi.org/10.1007/s10462-024-11044-2 (2022).
Google Scholar 
Ma, X. et al. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electron. Commer. Res. Appl. 31, 24–39. https://doi.org/10.1016/j.elerap.2018.08.002 (2018).
Google Scholar 
Hilborn, R. & Walters, C. J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty (Chapman and Hall, 1992).Myers, R. A., Hutchings, J. A. & Barrowman, N. J. Why do fish stocks collapse? The example of Cod in Atlantic Canada. Ecol. Appl. 7, 91–106. https://doi.org/10.1890/1051-0761(1997)007[0091:WDFSCT]2.0.CO;2 (1997).
Google Scholar 
Mueter, F. J., Boldt, J. L., Megrey, B. A. & Peterman, R. M. Recruitment and survival of Northeast Pacific ocean fish stocks: Temporal trends, covariation, and climate effects. Can. J. Fish. Aquat. Sci. 64, 911–927. https://doi.org/10.1139/f07-069 (2007).
Google Scholar 
Stergiou, K. I. & Christou, E. D. Modelling and forecasting annual fisheries catches: comparison of regression, univariate and multivariate time series methods. Fish. Res. 25, 105–138. https://doi.org/10.1016/0165-7836(95)00389-4 (1996).
Google Scholar 
Lopez-Parages, J. & Rodriguez-Fonseca, B. Multidecadal modulation of El Niño influence on the Euro-Mediterranean rainfall. Geophys. Res. Lett. 39, L02704. https://doi.org/10.1029/2011GL050049 (2012).
Google Scholar 
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).
Google Scholar 
Zhang, Q., Wang, H., Dong, J., Zhong, G. & Sun, X. Prediction of sea surface temperature using long short-term memory. IEEE Geosci. Remote Sens. Lett. 14, 1745–1749. https://doi.org/10.1109/LGRS.2017.2733548 (2017).
Google Scholar 
Zhang, J., Zhu, Y., Zhang, X., Ye, M. & Yang, J. Developing a long Short-Term memory (LSTM) based model for predicting water table depth in agricultural areas. J. Hydrol. 561, 918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065 (2018).
Google Scholar 
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232. https://doi.org/10.1214/aos/1013203451 (2001).
Google Scholar 
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x (2008).
Google Scholar 
Rajaee, T., Ebrahimi, H. & Nourani, V. A review of the artificial intelligence methods in groundwater level modeling. J. Hydrol. 572, 336–351. https://doi.org/10.1016/j.jhydrol.2018.12.037 (2019).
Google Scholar 
Grüss, A., Thorson, J. T., Babcock, E. A. & Tarnecki, J. H. Producing distribution maps for a spatially-explicit ecosystem model using large monitoring and environmental databases and a combination of interpolation and machine learning algorithms. Front. Mar. Sci. 5, 16. https://doi.org/10.3389/fmars.2018.00016 (2018).Li, L., Situ, R., Gao, J., Yang, Z. & Liu, W. A hybrid model combining convolutional neural network with XGBoost for predicting social media popularity. In Proceedings of the 25th ACM International Conference on Multimedia, 1912–1917. https://doi.org/10.1145/3123266.3127902 (2017).Shi, Y. et al. HyFish: hydrological factor fusion for prediction of fishing effort distribution with VMS dataset. Front. Mar. Sci. 11, 1296146. https://doi.org/10.3389/fmars.2024.1296146 (2024).
Google Scholar 
Xu, H., Ding, P., Chen, J., Zou, X. & Zhang, L. LSTM-based catch per unit effort standardization for Bigeye tuna in the Pacific ocean. Front. Mar. Sci. 11, 1344966. https://doi.org/10.3389/fmars.2024.1344966 (2023).
Google Scholar 
Thongsuwan, S., Jaiyen, S., Padcharoen, A. & Agarwal, P. ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost. Nuclear Eng. Technol. 53, 522–531. https://doi.org/10.1016/j.net.2020.04.008 (2021).
Google Scholar 
Titouni, S., Dayoub, I. & Rouvaen, J. M. Deep learning-based automatic modulation classification using hybrid CNN-XGBoost model for wireless communication systems. Int. J. Commun Syst. 38, e5988. https://doi.org/10.1002/dac.70160 (2025).
Google Scholar 
Pauly, D. & Zeller, D. Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nat. Commun. 7, 10244. https://doi.org/10.1038/ncomms10244 (2016).
Google Scholar 
Sherman, K. & Duda, A. M. Large marine ecosystems: an emerging paradigm for fishery sustainability. Fisheries 24, 15–20. https://doi.org/10.1577/1548-8446(1999)024%3C0015:LMEAEP%3E2.0.CO;2 (1999).
Google Scholar 
Cury, P. & Roy, C. Optimal environmental window and pelagic fish recruitment success in upwelling areas. Can. J. Fish. Aquat. Sci. 46, 670–680. https://doi.org/10.1139/f89-086 (1989).
Google Scholar 
Gao, F., Masek, J., Schwaller, M. & Hall, F. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44, 2207–2218. https://doi.org/10.1109/TGRS.2006.872081 (2006).
Google Scholar 
Zhang, H., Chen, J., Huang, B., Song, H. & Kwan, M. P. Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data. Remote Sens. Environ. 278, 113083. https://doi.org/10.1016/j.rse.2022.113083 (2022).Little, R. J. & Rubin, D. B. Statistical Analysis with Missing Data (Wiley, 2019).Li, J., Heap, A. D., Potter, A. & Daniell, J. J. Application of machine learning methods to Spatial interpolation of environmental variables. Environ. Model. Softw. 26, 1647–1659. https://doi.org/10.1016/j.envsoft.2011.07.004 (2011).
Google Scholar 
Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).
Google Scholar 
Géron, A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques To Build Intelligent Systems (O’Reilly Media, 2019).Bergmeir, C. & Benítez, J. M. On the use of cross-validation for time series predictor evaluation. Inf. Sci. 191, 192–213. https://doi.org/10.1016/j.ins.2011.12.028 (2012).
Google Scholar 
Kang, Y., Hyndman, R. J. & Smith-Miles, K. Visualising forecasting algorithm performance using time series instance spaces. Int. J. Forecast. 33, 345–358. https://doi.org/10.1016/j.ijforecast.2016.09.004 (2017).
Google Scholar 
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning. 448–456. (2015).Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data mining, inference, and Prediction (Springer Science & Business Media, 2009).Zhou, Z. H. Ensemble Methods: Foundations and Algorithms (CRC, 2012).Bengio, Y., Courville, A. & Vincent, P. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828. https://doi.org/10.1109/TPAMI.2013.50 (2013).
Google Scholar 
Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arXiv (2014).Tashman, L. J. Out-of-sample tests of forecasting accuracy: an analysis and review. Int. J. Forecast. 16, 437–450. https://doi.org/10.1016/S0169-2070(00)00065-0 (2000).
Google Scholar 
Hyndman, R. J. & Koehler, A. B. Another look at measures of forecast accuracy. Int. J. Forecast. 22, 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001 (2006).
Google Scholar 
Arlot, S. & Celisse, A. A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79. https://doi.org/10.1214/09-SS054 (2010).
Google Scholar 
Bonferroni, C. Teoria statistica Delle classi e Calcolo Delle probabilita. Pubblicazioni del. R Istituto Superiore Di Scienze Economiche E Commericiali Di Firenze. 8, 3–62 (1936).
Google Scholar 
Diebold, F. X. & Mariano, R. S. Comparing predictive accuracy. J. Bus. Economic Stat. 13, 253–263. https://doi.org/10.1080/07350015.1995.10524599 (1995).
Google Scholar 
Makridakis, S., Spiliotis, E. & Assimakopoulos, V. The M4 competition: Results, findings, conclusion and way forward. Int. J. Forecast. 34, 802–808. https://doi.org/10.1016/j.ijforecast.2018.06.001 (2018).
Google Scholar 
Harvey, D., Leybourne, S. & Newbold, P. Testing the equality of prediction mean squared errors. Int. J. Forecast. 13, 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4 (1997).
Google Scholar 
Hastie, T., Tibshirani, R., Friedman, J. & Franklin, J. The elements of statistical learning: data mining, inference and prediction. Math. Intelligencer. 27, 83–85 (2005).
Google Scholar 
Mockus, J. Bayesian Approach To Global Optimization: Theory and Applications (Springer Science & Business Media, 2012).Planque, B. & Frédou, T. Temperature and the recruitment of Atlantic Cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 56, 2069–2077. https://doi.org/10.1139/f99-114 (1999).
Google Scholar 
Klyashtorin, L. B. Climate change and long-term fluctuations of commercial catches: the possibility of forecasting. FAO Fisheries Technical Paper No. 410. Rome, FAO. (2001).Download referencesAuthor informationAuthors and AffiliationsSchool of Economics, Hebei University, Baoding, 071000, ChinaMingqi ZhangAuthorsMingqi ZhangView author publicationsSearch author on:PubMed Google ScholarContributionsMingqi Zhang conceived and designed the study, developed the CNN-XGBoost fusion model architecture, collected and preprocessed the marine fishery resource datasets, implemented the computational algorithms, conducted the experimental analysis, performed statistical evaluations, interpreted the results, and wrote the manuscript. The author read and approved the final manuscript.Corresponding authorCorrespondence to
Mingqi Zhang.Ethics declarations

Competing interests
The authors declare no competing interests.

Ethics approval
This study utilized publicly available marine fishery resource data and satellite-derived oceanographic measurements that do not involve human subjects or animal experimentation, thereby satisfying ethical requirements for environmental data analysis. All data sources employed in this research were accessed through legitimate channels with appropriate authorization and compliance with data provider policies. Fishery catch records obtained from the National Fisheries Database of China were used under formal data sharing agreements that stipulate data confidentiality, appropriate use restrictions, and acknowledgment requirements. The research team adhered to all terms of these agreements, including anonymization of vessel-specific information and aggregation of data to temporal resolutions that protect commercially sensitive information. Satellite-derived oceanographic data from NASA’s Ocean Color Web portal are publicly available without restrictions and were used in accordance with NASA’s Earth Science Data and Information Policy. Meteorological data from the China Meteorological Administration were accessed following registration procedures and compliance with stated data use policies. The research was conducted in accordance with institutional guidelines for environmental data analysis at Hebei University and adheres to international standards for responsible conduct of research. Ethical approval was obtained from the Research Ethics Committee of the School of Economics, Hebei University (Approval Number: HBU-ECO-2024-015, Date: March 15, 2024), which reviewed the study protocol, data management procedures, and potential societal impacts of the research findings. The ethics review confirmed that the study poses no risks to human subjects, animal welfare, or environmental integrity, and that the research objectives align with principles of sustainable marine resource management and ecosystem conservation. All research activities were performed in compliance with relevant guidelines and regulations governing environmental research in China.

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Reprints and permissionsAbout this articleCite this articleZhang, M. Marine fishery resource dynamic prediction based on CNN-XGBoost fusion model.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33175-4Download citationReceived: 05 August 2025Accepted: 16 December 2025Published: 21 December 2025DOI: https://doi.org/10.1038/s41598-025-33175-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsMarine fishery resourcesCNN-XGBoost fusionResource predictionDeep learningEnsemble learningTime series forecasting More

AbstractPhytoplankton blooms emerge from the interplay between nutrient availability, biomass growth, and inhibitory by-products such as toxins or exudates. Here, we develop a mechanistic nutrient–phytoplankton–by-product model that couples Beddington–DeAngelis nutrient uptake, by-product-mediated inhibition, and nutrient-dependent detoxification. Analytical results demonstrate that the system remains biologically feasible and bounded, and that a threshold condition governs bloom initiation. Linear stability and bifurcation analyses reveal how detoxification delays can trigger oscillatory bloom behaviour. Across ecologically realistic parameter regimes, the system tends to a stable coexistence state—either directly or through damped oscillations—rather than exhibiting repeated bloom–crash cycles. Global sensitivity analysis (PRCC and Sobol indices) highlights by-product production, inhibition strength, detoxification rate, toxin-linked mortality, and saturation effects as dominant regulators of stability and damping time. Introducing an explicit ecological delay exposes a critical threshold at which a Hopf bifurcation arises, converting the stable equilibrium into sustained oscillations. Numerical simulations confirm the transversality condition and indicate a supercritical onset. Collectively, these results provide a quantitative diagnostic for distinguishing transient from sustained bloom oscillations and identify measurable ecological processes—particularly detoxification and delayed feedback—that govern transitions between stable and oscillatory regimes.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
ReferencesReynolds, C. S. The Ecology of Phytoplankton (Cambridge University Press, 2006). https://doi.org/10.1017/CBO9780511542145.
Google Scholar 
Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb. Ecol. 65(4), 995–1010. https://doi.org/10.1007/s00248-012-0159-y (2013).
Google Scholar 
Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16(8), 471–483. https://doi.org/10.1038/s41579-018-0040-1 (2018).
Google Scholar 
Paerl, H. W. & Otten, T. G. Duelling CyanoHABs: Unravelling the environmental drivers controlling dominance. Environ. Microbiol. 22(10), 3817–3828. https://doi.org/10.1111/1462-2920.15125 (2020).
Google Scholar 
Taranu, Z. E. et al. Widespread increases in cyanobacteria blooms are reshaping lake ecosystems. Proc. R. Soc. B 289, 20221056. https://doi.org/10.1098/rspb.2022.1056 (2022).
Google Scholar 
Huisman, J. et al. Cyanobacterial blooms: The role of climate change and nutrient loading. Nat. Rev. Microbiol. 18(1), 64–76. https://doi.org/10.1038/s41579-019-0286-0 (2020).
Google Scholar 
Anderson, D. M. et al. Harmful algal blooms and climate change. Harmful Algae 108, 102105. https://doi.org/10.1016/j.hal.2021.102105 (2021).
Google Scholar 
Glibert, P. M. Harmful algal blooms: A global perspective. J. Oceanol. Limnol. 38, 1225–1244. https://doi.org/10.1007/s00343-020-00070-z (2020).
Google Scholar 
Legrand, C., Rengefors, K., Fistarol, G. O. & Graneli, E. Allelopathy in phytoplankton: Biochemical, ecological and evolutionary aspects. Phycologia 42(4), 406–419. https://doi.org/10.2216/i0031-8884-42-4-406.1 (2003).
Google Scholar 
Graneli, E. & Hansen, P. J. Allelopathy in harmful algae: A mechanism to compete for resources. In Ecology of Harmful Algae (eds Graneli, E. & Turner, J. T.) 189–201 (Springer, 2006).
Google Scholar 
Graneli, E., Weberg, M. & Salomon, P. S. Harmful algal blooms of allelopathic microalgal species: The role of eutrophication. Harmful Algae 8(1), 94–102. https://doi.org/10.1016/j.hal.2008.08.011 (2008).
Google Scholar 
Fistarol, G. O., Legrand, C. & Graneli, E. Allelopathic effect on a nutrient-limited phytoplankton species. Mar. Ecol. – Prog. Ser. 255, 115–125. https://doi.org/10.3354/meps255115 (2003).
Google Scholar 
Tillmann, U. et al. Inhibition of competing phytoplankton by Alexandrium ostenfeldii: A review of allelochemical potency. J. Plankton Res. 29(6), 547–565. https://doi.org/10.1093/plankt/fbm041 (2007).
Google Scholar 
Dzyubenko, E. V. et al. Mechanisms of allelopathy in marine and freshwater microalgae. Harmful Algae 108, 102099. https://doi.org/10.1016/j.hal.2021.102099 (2021).
Google Scholar 
Zhang, Y. et al. Dynamics of transparent exopolymer particles in bloom-forming cyanobacteria. Environ. Sci. Technol. 56(4), 2449–2459. https://doi.org/10.1021/acs.est.1c05508 (2022).
Google Scholar 
Hansell, D. A., & Carlson, C. A. (Eds.). Biogeochemistry of Marine Dissolved Organic Matter (2nd ed.). Academic Press. (2015).Decho, A. W. & Gutierrez, T. Microbial extracellular polymeric substances (EPSs) in ocean systems. Front. Microbiol. 8, 922. https://doi.org/10.3389/fmicb.2017.00922 (2017).
Google Scholar 
Verdugo, P. Marine microgels. Annu. Rev. Mar. Sci. 4, 375–400. https://doi.org/10.1146/annurev-marine-120709-142759 (2012).
Google Scholar 
Cisternas-Novoa, C., Lee, C. & Engel, A. Transparent exopolymer particles (TEP) and Coomassie stainable particles (CSP): A review. Mar. Chem. 175, 4–13. https://doi.org/10.1016/j.marchem.2015.03.009 (2015).
Google Scholar 
Grossart, H. P. et al. Extracellular polymeric substances in aquatic systems: Role and composition. Nat. Microbiol. 6, 169–181. https://doi.org/10.1038/s41564-020-00807-w (2021).
Google Scholar 
Park, H.-D. et al. Degradation of microcystin by a new bacterium isolated from a hypertrophic lake. Lett. Appl. Microbiol. 33(3), 247–251. https://doi.org/10.1046/j.1472-765X.2001.00993.x (2001).
Google Scholar 
Bourne, D. G. et al. Enzymatic pathway for the bacterial degradation of the cyanobacterial cyclic peptide toxin microcystin-LR. Appl. Environ. Microbiol. 62(11), 4086–4094 (1996).
Google Scholar 
Faassen, E. J. Cyanotoxins: Occurrence, toxicology, and detection. Toxins 14(1), 48. https://doi.org/10.3390/toxins14010048 (2022).
Google Scholar 
Orellana, M. V. et al. Marine microgels as a source of cloud condensation nuclei in the high Arctic. Proc. Natl. Acad. Sci. 108(33), 13612–13617. https://doi.org/10.1073/pnas.1102457108 (2011).
Google Scholar 
Gerphagnon, M. et al. Virus-phytoplankton interactions and the control of bloom dynamics. ISME Journal 14, 1431–1442. https://doi.org/10.1038/s41396-020-0624-8 (2020).
Google Scholar 
Su, X. et al. Functional roles of microbial aggregates in aquatic ecosystems. Trends Microbiol. 29(6), 485–497. https://doi.org/10.1016/j.tim.2020.12.007 (2021).
Google Scholar 
Beversdorf, L. J. et al. Phytoplankton community shifts under warming conditions explain cyanobacteria expansion. Limnol. Oceanogr. 65(12), 3186–3202. https://doi.org/10.1002/lno.11584 (2020).
Google Scholar 
Boero, F. et al. Marine plankton trophic interactions and regime shifts. Mar. Ecol. – Prog. Ser. 646, 1–15. https://doi.org/10.3354/meps13380 (2020).
Google Scholar 
Lin, S. et al. Adaptive strategies of harmful algae under nutrient stress. Nat. Commun. 14, 1254. https://doi.org/10.1038/s41467-023-36912-7 (2023).
Google Scholar 
Lu, J. et al. Global rise of harmful algal blooms: Drivers, risks, and management strategies. Sci. Total Environ. 909, 168771. https://doi.org/10.1016/j.scitotenv.2023.168771 (2024).
Google Scholar 
Rosenzweig, M. L. & MacArthur, R. H. Graphical representation and stability conditions of predator-prey interactions. Am. Nat. 97(895), 209–223. https://doi.org/10.1086/282272 (1963).
Google Scholar 
Holling, C. S. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can. Entomol. 91(5), 293–320. https://doi.org/10.4039/Ent91293-5 (1959).
Google Scholar 
Beddington, J. R. Mutual interference between parasites or predators and its effect on searching efficiency. J. Anim. Ecol. 44(1), 331–340. https://doi.org/10.2307/3866 (1975).
Google Scholar 
DeAngelis, D. L., Goldstein, R. A. & O’Neill, R. V. A model for trophic interaction. Ecology 56(4), 881–892. https://doi.org/10.2307/1936298 (1975).
Google Scholar 
Smith, H. L. & Waltman, P. The Theory of the Chemostat: Dynamics of Microbial Competition (Cambridge University Press, 1995). https://doi.org/10.1017/CBO9780511530043.
Google Scholar 
Monod, J. The growth of bacterial cultures. Annu. Rev. Microbiol. 3, 371–394. https://doi.org/10.1146/annurev.mi.03.100149.002103 (1949).
Google Scholar 
Droop, M. R. The nutrient status of algal cells in continuous culture. J. Mar. Biol. Assoc. U. K. 54(4), 825–855 (1974).
Google Scholar 
Strogatz, S. H. Nonlinear Dynamics and Chaos 2nd ed. (CRC Press, 2018). https://doi.org/10.1201/9780429492563.
Google Scholar 
Guckenheimer, J. & Holmes, P. Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. Springer https://doi.org/10.1007/978-1-4612-1140-2 (1983).
Google Scholar 
Kuznetsov, Y. A. Elements of Applied Bifurcation Theory 3rd ed. (Springer, 2004). https://doi.org/10.1007/978-1-4757-3978-7.
Google Scholar 
Pareek, S. & Baghel, R. S. A food web exhibiting group defenses in spatiotemporal dynamics. Int. J. Appl. Comput. Math. 11(5), 1–30 (2025) (Springer).
Google Scholar 
Baghel, R. S. Memory and delay-driven dynamics in a tritrophic food chain model with Allee effect and nonlinear predation, Nonlinear Sci., 100069, https://doi.org/10.1016/j.nls.2025.100069 (2025).Liao, T. & Yin, H. Joint impact of global warming and Allee effect on the phytoplankton-zooplankton dynamics under the mean-reverting Ornstein-Uhlenbeck process. Eur. Phys. J. Plus 140(5), 423 (2025).
Google Scholar 
Liao, T. The impact of temperature variation on the algae-zooplankton dynamics with size-selective disturbance. Chaos, Solitons & Fractals 181, 114615 (2024).
Google Scholar 
Liao, T. Dynamical complexity driven by water temperature in a size-dependent phytoplankton-zooplankton model with environmental variability. Chin. J. Phys. 88, 557–583 (2024).
Google Scholar 
Liao, T. Dynamics complexity induced by salinity in a stochastic phytoplankton-zooplankton model under acid-base changes. Math. Methods Appl. Sci.. 48(12), 12258–12281 (2025).Li, X. et al. Bifurcation analysis of a new aquatic ecological model with aggregation effect. Math. Comput. Simul. 190, 75–96 (2021).
Google Scholar 
Saito, M. A. et al. Microbial nutrient limitation in the ocean. Annu. Rev. Mar. Sci. 12, 187–217. https://doi.org/10.1146/annurev-marine-010318-095241 (2020).
Google Scholar 
Pettersson, L. H. et al. Hybrid models of plankton dynamics: Integrating mechanistic and data-driven frameworks. Ecol. Model. 464, 109844. https://doi.org/10.1016/j.ecolmodel.2022.109844 (2022).
Google Scholar 
Levine, N. M. et al. Modeling phytoplankton-nutrient interactions under climate change. PNAS 118(30), e2026815118. https://doi.org/10.1073/pnas.2026815118 (2021).
Google Scholar 
Dakos, V. et al. Indicators of tipping points in ecological systems. Nat. Ecol. Evol. 5, 620–632. https://doi.org/10.1038/s41559-020-01313-9 (2021).
Google Scholar 
Pareek, S., & Baghel, R. S. Controlling cyanobacterial blooms using a biological filter-feeding and aggregation effect. Iran. J. Sci. pp. 1–12, (Springer, 2025)Schindler, D. W. Eutrophication and recovery in experimental lakes: Implications for lake management. Science 184(4139), 897–899. https://doi.org/10.1126/science.184.4139.897 (1974).
Google Scholar 
Carpenter, S. R. Phosphorus control is critical to mitigating eutrophication. Proc. Natl. Acad. Sci. 105(32), 11039–11040. https://doi.org/10.1073/pnas.0806112105 (2008).
Google Scholar 
Vollenweider, R. A. Scientific Fundamentals of the Eutrophication of Lakes and Flowing Waters. OECD. (1968).Smith, V. H. & Schindler, D. W. Eutrophication science: Where do we go from here-. Trends Ecol. Evol. 24(4), 201–207. https://doi.org/10.1016/j.tree.2008.11.009 (2009).
Google Scholar 
Hansell, D. A. & Carlson, C. A. Marine dissolved organic matter and the carbon cycle. Oceanography 14(4), 41–49. https://doi.org/10.5670/oceanog.2001.05 (2001).
Google Scholar 
Li, M. et al. Global trends in cyanobacterial harmful algal blooms. Nat. Commun. 12, 5859. https://doi.org/10.1038/s41467-021-26188-0 (2021).
Google Scholar 
Baghel, R. S. A toxins role in controlling chaos with a spatial effect in an aquatic systems. Int. J. Appl. Comput. Math. 11(3), 99 (2025) (Springer).
Google Scholar 
Baghel, R. S. Spatiotemporal dynamics of toxin producing phytoplankton-zooplankton interactions with Holling Type II functional responses. Results Control Optim. 17, 100478 (2024) (Elsevier).
Google Scholar 
Skalski, G. T. & Gilliam, J. F. Functional responses with predator interference: Viable alternatives to the Holling type II model. Ecology 82(11), 3083–3092 (2001).
Google Scholar 
Hairer, E., Norsett, S. P. & Wanner, G. Solving Ordinary Differential Equations I: Nonstiff Problems 2nd ed. (Springer, 2008). https://doi.org/10.1007/978-3-540-78862-1.
Google Scholar 
Butcher, J. C. Numerical Methods for Ordinary Differential Equations 3rd ed. (Wiley, 2016). https://doi.org/10.1002/9781119121534.
Google Scholar 
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes: The Art of Scientific Computing 3rd ed. (Cambridge University Press, 2007).
Google Scholar 
Download referencesFundingOpen access funding provided by Manipal Academy of Higher Education, Manipal. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Author informationAuthors and AffiliationsDepartment of Mathematics, Poornima University, Jaipur, 303905, Rajasthan, IndiaRandhir Singh BaghelDepartment of Physics, Poornima University, Jaipur, 303905, Rajasthan, IndiaShrikant VermaDepartment of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, IndiaNarendra KhatriAuthorsRandhir Singh BaghelView author publicationsSearch author on:PubMed Google ScholarShrikant VermaView author publicationsSearch author on:PubMed Google ScholarNarendra KhatriView author publicationsSearch author on:PubMed Google ScholarContributionsR.S.B. and S.V. conceptualized and designed the study. R.S.B. conducted the experimental work and data collection. S.V. performed the data analysis and interpretation. N.K. contributed to reviewing, editing, and redrafting the manuscript and handled the correspondence. All authors reviewed and approved the final version of the manuscript.Corresponding authorCorrespondence to
Narendra Khatri.Ethics declarations

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The authors declare no competing interests.

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The authors declare that Artificial Intelligence (AI) tools, including QuillBot, were used for paraphrasing and redrafting portions of the manuscript with the aim of improving clarity and readability. All AI-generated content was critically reviewed, verified, and edited by the authors to ensure accuracy, originality, and conformity with academic standards. The authors take full responsibility for the integrity and reliability of the final manuscript.

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Reprints and permissionsAbout this articleCite this articleBaghel, R.S., Verma, S. & Khatri, N. Delayed dynamics and detoxification in nutrient-phytoplankto-by-product systems: mechanisms driving bloom stability and oscillations.
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KeywordsPhytoplankton-nutrient dynamicsBeddington-DeAngelis uptakeBy-product interference (allelopathy)Stability and Hopf bifurcationGlobal sensitivity analysis (Sobol, PRCC)Delay More

AbstractClimate-induced stressors pose significant threats to fish growth, survival, and ecological stability. Identifying reliable molecular biomarkers is crucial for improving stress management and acclimation strategies. This study employed a comprehensive transcriptomic analysis to examine stress responses in rainbow trout (Oncorhynchus mykiss) exposed to five distinct environmental stressors—high and low temperatures, crowding, salinity, and low water quality (characterized by reduced dissolved oxygen and elevated CO2)—over six hours. A total of 21,580 differentially expressed transcripts (DETs) were identified, including 16,959 unique DETs. Heat stress and salinity induced the most pronounced transcriptomic responses, with most DETs being stressor-specific, highlighting distinct physiological acclimation mechanisms. Only 39 DETs were consistently regulated across all stress conditions. Key DETs associated with heat stress were further analyzed using machine learning models to evaluate their predictive potential in distinguishing control and heat-stressed fish from natural Redband trout populations. The logistic model tree (LMT) classifier demonstrated the highest accuracy with a set of 234 DETs. When the dataset was reduced to 50 or 2 DETs, the Random Forest model achieved optimal classification, consistently identifying two heat shock protein transcripts, hsp47 and hspa4l, as primary predictors across both short- and long-term stress responses. In contrast, core DETs shared across stressors exhibited limited predictive power, achieving only 52.78% classification accuracy. These findings underscore the specificity of molecular signatures to individual stressors and highlight the potential of transcriptomic biomarkers for monitoring climate-induced stress in fish populations. The study recommends the integration of these biomarkers into selective breeding programs and conservation strategies to enhance fish resilience and welfare in the face of environmental change.

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Data availability

The raw sequencing data from three strains of Redband trout were downloaded from the NCBI Short Read Archive (SRA) under accession number PRJNA233945. The rainbow trout genome annotation was obtained from NCBI (GCA_013265735.3, https://www.ncbi.nlm.nih.gov/assembly/ GCF_013265735.2/). Additionally, RNA-seq datasets from fish exposed to five different stress conditions were retrieved from the NCBI Sequence Read Archive (SRA) using the accession number SRP070774.
ReferencesHuang, J., Li, Y., Liu, Z., Kang, Y. & Wang, J. Transcriptomic responses to heat stress in rainbow trout Oncorhynchus mykiss head kidney. Fish Shellfish Immunol. 82, 32–40. https://doi.org/10.1016/j.fsi.2018.08.002 (2018).
Google Scholar 
Komatsu, E., Fukushima, T. & Harasawa, H. A modeling approach to forecast the effect of long-term climate change on lake water quality. Ecol. Model. 209, 351–366. https://doi.org/10.1016/j.ecolmodel.2007.07.021 (2007).
Google Scholar 
Whitehead, P. G., Wilby, R. L., Battarbee, R. W., Kernan, M. & Wade, A. J. A review of the potential impacts of climate change on surface water quality. Hydrol. Sci. J. 54, 101–123. https://doi.org/10.1623/hysj.54.1.101 (2009).
Google Scholar 
Mooij, W. M., De Senerpont Domis, L. N. & Janse, J. H. Linking species- and ecosystem-level impacts of climate change in lakes with a complex and a minimal model. Ecol. Model. 220, 3011–3020. https://doi.org/10.1016/j.ecolmodel.2009.02.003 (2009).
Google Scholar 
Isangedighi I, Asuquo & Afia O. Climate change: Its Effect on water quality and fish growth. 10, 326-338 (2023)Dediu, L. et al. Effects of stocking density on growth performance and stress responses of bester and bester female symbol x beluga male symbol juveniles in recirculating aquaculture systems. Animals: Open Access J.MDPI https://doi.org/10.3390/ani11082292 (2021).
Google Scholar 
Ali, A., Thorgaard, G. H. & Salem, M. PacBio Iso-Seq improves the rainbow trout genome annotation and identifies alternative splicing associated with economically important phenotypes. Front Genet 12, 683408. https://doi.org/10.3389/fgene.2021.683408 (2021).
Google Scholar 
Liu, S. et al. RNA-seq analysis of early hepatic response to handling and confinement stress in rainbow trout. PLoS ONE 9, e88492. https://doi.org/10.1371/journal.pone.0088492 (2014).
Google Scholar 
NOAA. (ed National Marine Fisheries Service) (2022).Weber, G. M. & Silverstein, J. T. Evaluation of a stress response for use in a selective breeding program for improved growth and disease resistance in rainbow trout. N. Am. J. Aquac. 69, 69–79. https://doi.org/10.1577/a05-103.1 (2007).
Google Scholar 
Rexroad, C. E., Vallejo, R. L., Liu, S., Palti, Y. & Weber, G. M. Quantitative trait loci affecting response to crowding stress in an F(2) generation of rainbow trout produced through phenotypic selection. Marine Biotechnol. 15, 613–627. https://doi.org/10.1007/s10126-013-9512-5 (2013).
Google Scholar 
Smith, S., Bernatchez, L. & Beheregaray, L. B. RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species. BMC Genomics 14, 375. https://doi.org/10.1186/1471-2164-14-375 (2013).
Google Scholar 
Liu, S. et al. RNA-Seq reveals expression signatures of genes involved in oxygen transport, protein synthesis, folding, and degradation in response to heat stress in catfish. Physiol. Genomics 45, 462–476. https://doi.org/10.1152/physiolgenomics.00026.2013 (2013).
Google Scholar 
Sun, J. et al. RNA-seq analysis reveals alternative splicing under heat stress in rainbow trout (Oncorhynchus mykiss). Marine Biotechnol. 24, 5–17. https://doi.org/10.1007/s10126-021-10082-z (2022).
Google Scholar 
Hou, Z. S. et al. Transcriptional Profiles of genes related to stress and immune response in rainbow trout (Oncorhynchus mykiss) symptomatically or asymptomatically infected with vibrio anguillarum. Front. Immunol. 12, 639489. https://doi.org/10.3389/fimmu.2021.639489 (2021).
Google Scholar 
Quan, J. et al. Identification and characterization of long noncoding RNAs provide insight into the regulation of gene expression in response to heat stress in rainbow trout (Oncorhynchus mykiss). Comp. Biochem. Physiol. Part D Genomics Proteomics 36, 100707. https://doi.org/10.1016/j.cbd.2020.100707 (2020).
Google Scholar 
Quan, J. et al. Integrated analysis of the responses of a circRNA-miRNA-mRNA ceRNA network to heat stress in rainbow trout (Oncorhynchus mykiss) liver. BMC Genomics 22, 48. https://doi.org/10.1186/s12864-020-07335-x (2021).
Google Scholar 
Zhao, G. et al. Ribosome profiling and RNA sequencing reveal translation and transcription regulation under acute heat stress in rainbow trout (Oncorhynchus mykiss, Walbaum, 1792) Liver. Int. J. Mol. Sci. 25, 8848. https://doi.org/10.3390/ijms25168848 (2024).
Google Scholar 
Narum, S. R. & Campbell, N. R. Transcriptomic response to heat stress among ecologically divergent populations of redband trout. BMC Genomics 16, 103. https://doi.org/10.1186/s12864-015-1246-5 (2015).
Google Scholar 
Narum, S. R., Campbell, N. R., Kozfkay, C. C. & Meyer, K. A. Adaptation of redband trout in desert and montane environments. Mol Ecol 19, 4622–4637. https://doi.org/10.1111/j.1365-294X.2010.04839.x (2010).
Google Scholar 
Rehrauer, H., Opitz, L., Tan, G., Sieverling, L. & Schlapbach, R. Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching. BMC Bioinform. 14, 370. https://doi.org/10.1186/1471-2105-14-370 (2013).
Google Scholar 
Hirsch, C. D., Springer, N. M. & Hirsch, C. N. Genomic limitations to RNA sequencing expression profiling. Plant J. Cell Mol. Biol. 84, 491–503. https://doi.org/10.1111/tpj.13014 (2015).
Google Scholar 
Sahraeian, S. M. E. et al. Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis. Nat. Commun. 8, 59. https://doi.org/10.1038/s41467-017-00050-4 (2017).
Google Scholar 
Rajkumar, A. P. et al. Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq. BMC Genomics 16, 548. https://doi.org/10.1186/s12864-015-1767-y (2015).
Google Scholar 
Fang, Z., Martin, J. & Wang, Z. Statistical methods for identifying differentially expressed genes in RNA-Seq experiments. Cell Biosci. 2, 26. https://doi.org/10.1186/2045-3701-2-26 (2012).
Google Scholar 
Ozsolak, F. & Milos, P. M. RNA sequencing: Advances, challenges and opportunities. Nat. Rev. Genet. 12, 87–98. https://doi.org/10.1038/nrg2934 (2011).
Google Scholar 
Wang, L., Xi, Y., Sung, S. & Qiao, H. RNA-seq assistant: Machine learning based methods to identify more transcriptional regulated genes. BMC Genomics 19, 546. https://doi.org/10.1186/s12864-018-4932-2 (2018).
Google Scholar 
Mjolsness, E. & DeCoste, D. Machine learning for science: State of the art and future prospects. Science 293, 2051–2055. https://doi.org/10.1126/science.293.5537.2051 (2001).
Google Scholar 
Kan, A. Machine learning applications in cell image analysis. Immunol. Cell Biol. 95, 525–530. https://doi.org/10.1038/icb.2017.16 (2017).
Google Scholar 
Vidyasagar, M. Machine learning methods in the computational biology of cancer. Proceed. Math. Phys. Eng. Sci. 470, 20140081. https://doi.org/10.1098/rspa.2014.0081 (2014).
Google Scholar 
Zhang, J. et al. Computer vision and machine learning for robust phenotyping in genome-wide studies. Sci. Rep. 7, 44048. https://doi.org/10.1038/srep44048 (2017).
Google Scholar 
Li, J., Ching, T., Huang, S. & Garmire, L. X. Using epigenomics data to predict gene expression in lung cancer. BMC Bioinform. https://doi.org/10.1186/1471-2105-16-S5-S10 (2015).
Google Scholar 
Ma, C., Xin, M., Feldmann, K. A. & Wang, X. Machine learning-based differential network analysis: A study of stress-responsive transcriptomes in Arabidopsis. Plant Cell 26, 520–537. https://doi.org/10.1105/tpc.113.121913 (2014).
Google Scholar 
Zhang, Y. et al. Wearable bioimpedance-based deep learning techniques for live fish health assessment under waterless and low-temperature conditions. Sensors 23, 8210. https://doi.org/10.3390/s23198210 (2023).
Google Scholar 
Fandino Pelayo, J. S., Mendoza Castellanos, L. S., Cazes Ortega, R. & Hernandez-Rojas, L. G. AI-driven monitoring for fish welfare in aquaponics: A predictive approach. Sensors 25, 6107. https://doi.org/10.3390/s25196107 (2025).
Google Scholar 
Zhu, Y., Zhang, W., Zhang, Y. & Zhang, X. Fish vitality assessment under adversity stress based on multi-sensing fusion and deep learning techniques. Aquaculture 609, 742871. https://doi.org/10.1016/j.aquaculture.2025.742871 (2025).
Google Scholar 
Sanchez, C. C. et al. Generation of a reference transcriptome for evaluating rainbow trout responses to various stressors. BMC Genomics 12, 626. https://doi.org/10.1186/1471-2164-12-626 (2011).
Google Scholar 
Silverstein, J. T., Rexroad, C. E. & King, T. L. Genetic variation measured by microsatellites among three strains of domesticated rainbow trout (Oncorhynchus mykiss, Walbaum). Aquac. Res. 35, 40–48. https://doi.org/10.1111/j.1365-2109.2004.00979.x (2004).
Google Scholar 
Weber, G. M., Vallejo, R. L., Lankford, S. E., Silverstein, J. T. & Welch, T. J. Cortisol Response to a Crowding Stress: Heritability and Association with Disease Resistance to Yersinia ruckeri in Rainbow Trout. N. Am. J. Aquac. 70, 425–433. https://doi.org/10.1577/a07-059.1 (2008).
Google Scholar 
Pottinger, T. G. & Carrick, T. R. Modification of the plasma cortisol response to stress in rainbow trout by selective breeding. Gen. Comp. Endocrinol. 116, 122–132. https://doi.org/10.1006/gcen.1999.7355 (1999).
Google Scholar 
Hall, M. et al. The WEKA data mining software. ACM SIGKDD Explorations Newsl 11, 10–18. https://doi.org/10.1145/1656274.1656278 (2009).
Google Scholar 
Pedregosa, F. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar 
Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics (Oxford, England) 36, 2628–2629. https://doi.org/10.1093/bioinformatics/btz931 (2020).
Google Scholar 
Newell, A. J., Jima, D., Reading, B. & Patisaul, H. B. Machine learning reveals common transcriptomic signatures across rat brain and placenta following developmental organophosphate ester exposure. Toxicol. Sci. 195, 103–122. https://doi.org/10.1093/toxsci/kfad062 (2023).
Google Scholar 
Pan, H., Xu, R. & Zhang, Y. Role of SPRY4 in health and disease. Front. Oncol. 14, 1376873. https://doi.org/10.3389/fonc.2024.1376873 (2024).
Google Scholar 
Jiang, W. I. et al. Early-life stress triggers long-lasting organismal resilience and longevity via tetraspanin. Sci. Adv. 10, eadj3880. https://doi.org/10.1126/sciadv.adj3880 (2024).
Google Scholar 
Mani, B. et al. Tetraspanin 5 orchestrates resilience to salt stress through the regulation of ion and reactive oxygen species homeostasis in rice. Plant Biotechnol. J. 23, 51–71. https://doi.org/10.1111/pbi.14476 (2025).
Google Scholar 
Costa, A., Hood, I. V. & Berger, J. M. Mechanisms for initiating cellular DNA replication. Annu. Rev. Biochem. 82, 25–54. https://doi.org/10.1146/annurev-biochem-052610-094414 (2013).
Google Scholar 
Masai, H., Matsumoto, S., You, Z., Yoshizawa-Sugata, N. & Oda, M. Eukaryotic chromosome DNA replication: where, when, and how?. Annu. Rev. Biochem. 79, 89–130. https://doi.org/10.1146/annurev.biochem.052308.103205 (2010).
Google Scholar 
Alvarez, S. et al. Replication stress caused by low MCM expression limits fetal erythropoiesis and hematopoietic stem cell functionality. Nat. Commun. 6, 8548. https://doi.org/10.1038/ncomms9548 (2015).
Google Scholar 
Ninomiya, K. et al. LncRNA-dependent nuclear stress bodies promote intron retention through SR protein phosphorylation. EMBO J. 39, e102729. https://doi.org/10.15252/embj.2019102729 (2020).
Google Scholar 
Islas, S., Vega, J., Ponce, L. & Gonzalez-Mariscal, L. Nuclear localization of the tight junction protein ZO-2 in epithelial cells. Exp. Cell Res. 274, 138–148. https://doi.org/10.1006/excr.2001.5457 (2002).
Google Scholar 
Traweger, A. et al. The tight junction protein ZO-2 localizes to the nucleus and interacts with the heterogeneous nuclear ribonucleoprotein scaffold attachment factor-B. J. Biol. Chem. 278, 2692–2700. https://doi.org/10.1074/jbc.M206821200 (2003).
Google Scholar 
Chang, S. W. et al. Heat stress activates interleukin-8 and the antioxidant system via Nrf2 pathways in human dental pulp cells. J. Endodontics 35, 1222–1228. https://doi.org/10.1016/j.joen.2009.06.005 (2009).
Google Scholar 
Wang, Z., Zhang, H. & Cheng, Q. PDIA4: The basic characteristics, functions and its potential connection with cancer. Biomed. Pharmacother 122, 109688. https://doi.org/10.1016/j.biopha.2019.109688 (2020).
Google Scholar 
Hu, L. et al. The HSP90AA1 gene is involved in heat stress responses and its functional genetic polymorphisms are associated with heat tolerance in Holstein cows. J. Dairy Sci. 107, 5132–5149. https://doi.org/10.3168/jds.2023-24007 (2024).
Google Scholar 
Srikanth, K., Kwon, A., Lee, E. & Chung, H. Characterization of genes and pathways that respond to heat stress in Holstein calves through transcriptome analysis. Cell Stress Chaperones 22, 29–42. https://doi.org/10.1007/s12192-016-0739-8 (2017).
Google Scholar 
Videla Rodriguez, E. A., Mitchell, J. B. O. & Smith, V. A. Robust identification of interactions between heat-stress responsive genes in the chicken brain using Bayesian networks and augmented expression data. Sci. Rep. 14, 9019. https://doi.org/10.1038/s41598-024-58679-3 (2024).
Google Scholar 
Elliott, E. N., Sheaffer, K. L. & Kaestner, K. H. The ‘de novo’ DNA methyltransferase Dnmt3b compensates the Dnmt1-deficient intestinal epithelium. Elife 5, e12975. https://doi.org/10.7554/eLife.12975 (2016).
Google Scholar 
Korotko, U., Chwialkowska, K., Sanko-Sawczenko, I. & Kwasniewski, M. DNA demethylation in response to heat stress in Arabidopsis thaliana. Int. J. Mol. Sci. https://doi.org/10.3390/ijms22041555 (2021).
Google Scholar 
Singh, A. K. et al. Genome-wide expression analysis of the heat stress response in dermal fibroblasts of Tharparkar (zebu) and Karan-Fries (zebu x taurine) cattle. Cell Stress Chaperones 25, 327–344. https://doi.org/10.1007/s12192-020-01076-2 (2020).
Google Scholar 
Smolenski, R. T. et al. AMP deaminase 1 gene polymorphism and heart disease-a genetic association that highlights new treatment. Cardiovasc Drugs Ther 28, 183–189. https://doi.org/10.1007/s10557-013-6506-5 (2014).
Google Scholar 
Wang, L. et al. Integrative multiomics analysis of the heat stress response of enterococcus faecium. Biomolecules 13, 437 (2023).
Google Scholar 
Guo, D. C. et al. MAT2A mutations predispose individuals to thoracic aortic aneurysms. Am. J. Hum. Genet. 96, 170–177. https://doi.org/10.1016/j.ajhg.2014.11.015 (2015).
Google Scholar 
Sekhar, R. V. et al. Deficient synthesis of glutathione underlies oxidative stress in aging and can be corrected by dietary cysteine and glycine supplementation. Am. J. Clin. Nutr. 94, 847–853. https://doi.org/10.3945/ajcn.110.003483 (2011).
Google Scholar 
Smith, P. G., Roy, C., Zhang, Y. N. & Chauduri, S. Mechanical stress increases RhoA activation in airway smooth muscle cells. Am. J. Respir. Cell Mol. Biol. 28, 436–442. https://doi.org/10.1165/rcmb.4754 (2003).
Google Scholar 
Williams, J. L. et al. Abstract P2005: Loss of full-length Mylk3 causes dilated cardiomyopathy via a Myl2-independent mechanism. Circul. Res. 131, AP2005–AP2005. https://doi.org/10.1161/res.131.suppl_1.P2005 (2022).
Google Scholar 
Bonam, S. R., Ruff, M. & Muller, S. HSPA8/HSC70 in immune disorders: A molecular rheostat that adjusts chaperone-mediated autophagy substrates. Cells 8, 849. https://doi.org/10.3390/cells8080849 (2019).
Google Scholar 
Fusakio, M. E. et al. Transcription factor ATF4 directs basal and stress-induced gene expression in the unfolded protein response and cholesterol metabolism in the liver. Mol. Biol. Cell 27, 1536–1551. https://doi.org/10.1091/mbc.E16-01-0039 (2016).
Google Scholar 
Schwab, R. A. et al. The fanconi anemia pathway maintains genome stability by coordinating replication and transcription. Mol. Cell 60, 351–361. https://doi.org/10.1016/j.molcel.2015.09.012 (2015).
Google Scholar 
Varga, T., Czimmerer, Z. & Nagy, L. PPARs are a unique set of fatty acid regulated transcription factors controlling both lipid metabolism and inflammation. Biochem. Biophys. Acta. 1812, 1007–1022. https://doi.org/10.1016/j.bbadis.2011.02.014 (2011).
Google Scholar 
Lee, J., Ellis, J. M. & Wolfgang, M. J. Adipose fatty acid oxidation is required for thermogenesis and potentiates oxidative stress-induced inflammation. Cell Rep. 10, 266–279. https://doi.org/10.1016/j.celrep.2014.12.023 (2015).
Google Scholar 
Yu, Y. et al. Ferroptosis: a cell death connecting oxidative stress, inflammation and cardiovascular diseases. Cell Death Discov. 7, 193. https://doi.org/10.1038/s41420-021-00579-w (2021).
Google Scholar 
Samaj, J. et al. Endocytosis, actin cytoskeleton, and signaling. Plant Physiol. 135, 1150–1161. https://doi.org/10.1104/pp.104.040683 (2004).
Google Scholar 
Leithner, K. New roles for gluconeogenesis in vertebrates. Current Opinion Syst. Biol. 28, 100389. https://doi.org/10.1016/j.coisb.2021.100389 (2021).
Google Scholar 
Bi, P. & Kuang, S. Notch signaling as a novel regulator of metabolism. Trends Endocrinol. Metab. 26, 248–255. https://doi.org/10.1016/j.tem.2015.02.006 (2015).
Google Scholar 
Chen, G., Shaw, M. H., Kim, Y. G. & Nunez, G. NOD-like receptors: role in innate immunity and inflammatory disease. Annu. Rev. Pathol. 4, 365–398. https://doi.org/10.1146/annurev.pathol.4.110807.092239 (2009).
Google Scholar 
UniProt, C. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531. https://doi.org/10.1093/nar/gkac1052 (2023).
Google Scholar 
Hostetter, D. R., Loeb, C. R., Chu, F. & Craik, C. S. Hip is a pro-survival substrate of granzyme B. J. Biol. Chem. 282, 27865–27874 (2007).
Google Scholar 
Bredemeyer, A. J., Carrigan, P. E., Fehniger, T. A., Smith, D. F. & Ley, T. J. Hop cleavage and function in granzyme B-induced apoptosis. J. Biol. Chem. 281, 37130–37141. https://doi.org/10.1074/jbc.M607969200 (2006).
Google Scholar 
Bredemeyer, A. J. et al. A proteomic approach for the discovery of protease substrates. Proc. Natl. Acad. Sci. U.S.A. 101, 11785–11790. https://doi.org/10.1073/pnas.0402353101 (2004).
Google Scholar 
Caruso, J. A. & Reiners, J. J. Proteolysis of HIP during apoptosis occurs within a region similar to the BID loop. Apoptosis 11, 1877–1885 (2006).
Google Scholar 
Boivin, W. A., Cooper, D. M., Hiebert, P. R. & Granville, D. J. Intracellular versus extracellular granzyme B in immunity and disease: Challenging the dogma. Lab Invest 89, 1195–1220. https://doi.org/10.1038/labinvest.2009.91 (2009).
Google Scholar 
Cai, W., Wei, Y., Jarnik, M., Reich, J. & Lilly, M. A. The GATOR2 component Wdr24 regulates TORC1 activity and lysosome function. PLoS Genet 12, e1006036. https://doi.org/10.1371/journal.pgen.1006036 (2016).
Google Scholar 
Li, H. et al. Physiological stress-induced corticosterone increases heme uptake via KLF4-HCP1 signaling pathway in hippocampus neurons. Sci. Rep. 7, 5745. https://doi.org/10.1038/s41598-017-06058-6 (2017).
Google Scholar 
Kim, J. S. et al. Sestrin2 inhibits mTORC1 through modulation of GATOR complexes. Sci. Rep. 5, 9502. https://doi.org/10.1038/srep09502 (2015).
Google Scholar 
Tamai, S. et al. Acute cold stress induces transient MuRF1 upregulation in the skeletal muscle of zebrafish. Biochem. Biophys. Res. Commun 608, 59–65. https://doi.org/10.1016/j.bbrc.2022.03.093 (2022).
Google Scholar 
Schulz, P., Herde, M. & Romeis, T. Calcium-dependent protein kinases: hubs in plant stress signaling and development. Plant Physiol. 163, 523–530. https://doi.org/10.1104/pp.113.222539 (2013).
Google Scholar 
Malko, M. M. et al. Effect of Exogenous Calcium on Tolerance of Winter Wheat to Cold Stress during Stem Elongation Stage. Plants (2023).Cibis, H., Biyanee, A., Dorner, W., Mootz, H. D. & Klempnauer, K. H. Characterization of the zinc finger proteins ZMYM2 and ZMYM4 as novel B-MYB binding proteins. Sci. Rep. 10, 8390. https://doi.org/10.1038/s41598-020-65443-w (2020).
Google Scholar 
Los, D. A. & Murata, N. Membrane fluidity and its roles in the perception of environmental signals. Biochem. Biophys. Acta. 1666, 142–157. https://doi.org/10.1016/j.bbamem.2004.08.002 (2004).
Google Scholar 
Greaney, J. L., Alexander, L. M. & Kenney, W. L. Sympathetic control of reflex cutaneous vasoconstriction in human aging. J. Appl. Physiol. 119, 771–782. https://doi.org/10.1152/japplphysiol.00527.2015 (2015).
Google Scholar 
Sandi, C. Stress, cognitive impairment and cell adhesion molecules. Nat. Rev. Neurosci. 5, 917–930. https://doi.org/10.1038/nrn1555 (2004).
Google Scholar 
Keebaugh, A. C., Sullivan, R. T., Program, N. C. S. & Thomas, J. W. Gene duplication and inactivation in the HPRT gene family. Genomics 89, 134–142. https://doi.org/10.1016/j.ygeno.2006.07.003 (2007).
Google Scholar 
Harrell Stewart, D. R. & Clark, G. J. Pumping the brakes on RAS – negative regulators and death effectors of RAS. J. Cell Sci. 133, jcs238865. https://doi.org/10.1242/jcs.238865 (2020).
Google Scholar 
Lapinski, P. E., Qiao, Y., Chang, C. H. & King, P. D. A role for p120 RasGAP in thymocyte positive selection and survival of naive T cells. J. immunol. 187, 151–163. https://doi.org/10.4049/jimmunol.1100178 (2011).
Google Scholar 
Garcia-Beltran, J. M. et al. The susceptibility of shi drum juveniles to betanodavirus increases with rearing densities in a process mediated by neuroactive ligand-receptor interaction. Front. Immunol. 15, 1304603. https://doi.org/10.3389/fimmu.2024.1304603 (2024).
Google Scholar 
Khalifeh, D. M., Czegledi, L. & Gulyas, G. Investigating the potential role of the pituitary adenylate cyclase-activating polypeptide (PACAP) in regulating the ubiquitin signaling pathway in poultry. Gen. Comp. Endocrinol. 356, 114577. https://doi.org/10.1016/j.ygcen.2024.114577 (2024).
Google Scholar 
de Assis Pinheiro, J. et al. Alcohol consumption, depression, overweight and cortisol levels as determining factors for NR3C1 gene methylation. Sci. Rep. 11, 6768. https://doi.org/10.1038/s41598-021-86189-z (2021).
Google Scholar 
Fransen, M., Nordgren, M., Wang, B. & Apanasets, O. Role of peroxisomes in ROS/RNS-metabolism: Implications for human disease. Biochem. Biophys. Acta. 1363–1373, 2012. https://doi.org/10.1016/j.bbadis.2011.12.001 (1822).
Google Scholar 
Wysocka, M. B., Pietraszek-Gremplewicz, K. & Nowak, D. The role of apelin in cardiovascular diseases, obesity and cancer. Front. Physiol. 9, 557. https://doi.org/10.3389/fphys.2018.00557 (2018).
Google Scholar 
Akinrotimi, O., Agokei, E. & Aranyo, A. Changes in blood parameters of Tilapia guineensis exposed to different salinity levels. J. Environ. Eng. Technol. 1, 4–12 (2012).
Google Scholar 
Baghdadi, H., El-Gharabawy, M., Zaki, M. & El-Greisy, Z. Effect of some environmental and physiological factors on the blood count of Mugil capito during the breeding season. Rapport Comm. Int. Mer Méditerranée 35, 370–371 (1998).
Google Scholar 
Bosisio, F., Rezende, K. F. O. & Barbieri, E. Alterations in the hematological parameters of juvenile Nile tilapia (Oreochromis niloticus) submitted to different salinities. Pan-American J. Aquatic Sci. 12, 146–154. https://doi.org/10.5281/zenodo.3531596 (2017).
Google Scholar 
Ali, A. et al. Repercussion of salinity on hematological parameters and tissue morphology of gill and kidney at early life of tilapia. Aquaculture Fisheries 9, 256–264. https://doi.org/10.1016/j.aaf.2022.04.006 (2024).
Google Scholar 
Mastrodonato, V., Morelli, E. & Vaccari, T. How to use a multipurpose SNARE: The emerging role of Snap29 in cellular health. Cell stress 2, 72–81. https://doi.org/10.15698/cst2018.04.130 (2018).
Google Scholar 
Pietrobon, A., Yockell-Lelievre, J., Flood, T. A. & Stanford, W. L. Renal organoid modeling of tuberous sclerosis complex reveals lesion features arise from diverse developmental processes. Cell Rep 40, 111048. https://doi.org/10.1016/j.celrep.2022.111048 (2022).
Google Scholar 
Seifikalhor, M. et al. Calcium signaling and salt tolerance are diversely entwined in plants. Plant Signal. Behav. 14, 1665455. https://doi.org/10.1080/15592324.2019.1665455 (2019).
Google Scholar 
Su, H., Ma, D., Zhu, H., Liu, Z. & Gao, F. Transcriptomic response to three osmotic stresses in gills of hybrid tilapia (Oreochromis mossambicus female x O. urolepis hornorum male). BMC Genomics 21, 110. https://doi.org/10.1186/s12864-020-6512-5 (2020).
Google Scholar 
van der Heide, T. & Poolman, B. Osmoregulated ABC-transport system of Lactococcus lactis senses water stress via changes in the physical state of the membrane. Proc. Natl. Acad. Sci. U.S.A. 97, 7102–7106. https://doi.org/10.1073/pnas.97.13.7102 (2000).
Google Scholar 
Ding, L. et al. Effects of saline-alkaline stress on metabolome, biochemical parameters, and histopathology in the kidney of crucian carp (Carassius auratus). Metabolites https://doi.org/10.3390/metabo13020159 (2023).
Google Scholar 
Perenthaler, E. et al. Loss of UGP2 in brain leads to a severe epileptic encephalopathy, emphasizing that bi-allelic isoform-specific start-loss mutations of essential genes can cause genetic diseases. Acta Neuropathol. 139, 415–442. https://doi.org/10.1007/s00401-019-02109-6 (2020).
Google Scholar 
Fukuda, R. et al. HIF-1 regulates cytochrome oxidase subunits to optimize efficiency of respiration in hypoxic cells. Cell 129, 111–122. https://doi.org/10.1016/j.cell.2007.01.047 (2007).
Google Scholar 
Cristofani, R. et al. The role of HSPB8, a component of the chaperone-assisted selective autophagy machinery, in cancer. Cells 10, 335 (2021).
Google Scholar 
Chen, L., Wu, M. & Zhou, Y. HSPB8 binding to c-Myc alleviates hypoxia/reoxygenation-induced trophoblast cell dysfunction. Exp. Ther. Med. 27, 114. https://doi.org/10.3892/etm.2024.12402 (2024).
Google Scholar 
Lian, P., Braber, S., Varasteh, S., Wichers, H. J. & Folkerts, G. Hypoxia and heat stress affect epithelial integrity in a Caco-2/HT-29 co-culture. Sci. Rep. 11, 13186. https://doi.org/10.1038/s41598-021-92574-5 (2021).
Google Scholar 
Saha, N., Koner, D. & Sharma, R. Environmental hypoxia: A threat to the gonadal development and reproduction in bony fishes. Aquacul. Fisheries 7, 572–582. https://doi.org/10.1016/j.aaf.2022.02.002 (2022).
Google Scholar 
Teitsma, C. et al. Identification of potential sites of cortisol actions on the reproductive axis in rainbow trout. Comp. Biochem. Physiol. C Pharmacol. Toxicol. Endocrinol. 119, 243–249. https://doi.org/10.1016/s0742-8413(98)00013-9 (1998).
Google Scholar 
Svitačová, K., Slavík, O. & Horký, P. Pigmentation potentially influences fish welfare in aquaculture. Appl. Anim. Behav. Sci. 262, 105903. https://doi.org/10.1016/j.applanim.2023.105903 (2023).
Google Scholar 
Awad, S. T. et al. Gender-specific responses in gene expression of Nile tilapia (Oreochromis niloticus) to heavy metal pollution in different aquatic habitats. Sci. Rep. 14, 14671. https://doi.org/10.1038/s41598-024-64300-4 (2024).
Google Scholar 
Cano, G. et al. Automatic selection of molecular descriptors using random forest: Application to drug discovery. Expert Syst. Appl. 72, 151–159. https://doi.org/10.1016/j.eswa.2016.12.008 (2017).
Google Scholar 
Ignatz, E. H. et al. Application of genomic tools to study and potentially improve the upper thermal tolerance of farmed Atlantic salmon (Salmo salar). BMC Genomics 26, 294. https://doi.org/10.1186/s12864-025-11482-4 (2025).
Google Scholar 
Raymo, G. et al. Fecal microbiome analysis uncovers hidden stress effects of low stocking density on rainbow trout. Anim. Microbiome 6, 57. https://doi.org/10.1186/s42523-024-00344-1 (2024).
Google Scholar 
Pihlajaniemi, T., Myllyla, R. & Kivirikko, K. I. Prolyl 4-hydroxylase and its role in collagen synthesis. J. Hepatol. 13(Suppl 3), S2-7. https://doi.org/10.1016/0168-8278(91)90002-s (1991).
Google Scholar 
Mendoza-Porras, O., Rusu, A. G., Stratford, C. & Wade, N. M. Rapid detection of heat stress biomarkers in Atlantic salmon (Salmo salar) liver using targeted proteomics, Aquaculture. Fish Fisheries 4, e147. https://doi.org/10.1002/aff2.147 (2023).
Google Scholar 
Akbarzadeh, A. et al. Developing specific molecular biomarkers for thermal stress in salmonids. BMC Genomics 19, 749. https://doi.org/10.1186/s12864-018-5108-9 (2018).
Google Scholar 
Rebl, A. et al. The synergistic interaction of thermal stress coupled with overstocking strongly modulates the transcriptomic activity and immune capacity of rainbow trout (Oncorhynchus mykiss). Sci. Rep. 10, 14913. https://doi.org/10.1038/s41598-020-71852-8 (2020).
Google Scholar 
Ito, S. & Nagata, K. Biology of Hsp47 (Serpin H1), a collagen-specific molecular chaperone. Semin. Cell Dev. Biol. 62, 142–151. https://doi.org/10.1016/j.semcdb.2016.11.005 (2017).
Google Scholar 
Lin, F. et al. Effects of temperature on muscle growth and collagen deposition in zebrafish (Danio rerio). Aquacul. Rep. 22, 100952. https://doi.org/10.1016/j.aqrep.2021.100952 (2022).
Google Scholar 
Ma, F. & Luo, L. Genome-wide identification of Hsp70/110 genes in rainbow trout and their regulated expression in response to heat stress. PeerJ 8, e10022. https://doi.org/10.7717/peerj.10022 (2020).
Google Scholar 
Yin, P. et al. Environmentally driven changes in Atlantic salmon oxidative status interact with physiological performance. Aquaculture 581, 740400. https://doi.org/10.1016/j.aquaculture.2023.740400 (2024).
Google Scholar 
Waraniak, J., Batchelor, S., Wagner, T. & Keagy, J. Landscape transcriptomic analysis detects thermal stress responses and potential adaptive variation in wild brook trout (Salvelinus fontinalis) during successive heatwaves. Sci. Total Environ. 969, 178960. https://doi.org/10.1016/j.scitotenv.2025.178960 (2025).
Google Scholar 
Kampinga, H. H. et al. Guidelines for the nomenclature of the human heat shock proteins. Cell Stress Chaperones 14, 105–111. https://doi.org/10.1007/s12192-008-0068-7 (2009).
Google Scholar 
Download referencesFundingNothing to report.Author informationAuthors and AffiliationsDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742-231, USAAli Ali, Youssef Ali, Guglielmo Raymo & Mohamed SalemCollege of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD, 20742-231, USAAsutosh DaleiAuthorsAli AliView author publicationsSearch author on:PubMed Google ScholarYoussef AliView author publicationsSearch author on:PubMed Google ScholarGuglielmo RaymoView author publicationsSearch author on:PubMed Google ScholarAsutosh DaleiView author publicationsSearch author on:PubMed Google ScholarMohamed SalemView author publicationsSearch author on:PubMed Google ScholarContributionsY.A., A.A., and M.S. Conceived the study. Y.A., A.A., G.R., A.D. and M.S. analyzed the data. Y.A. Drafted the manuscript. All authors read and approved the final manuscript. Y.A. and A.A. contributed equally.Corresponding authorCorrespondence to
Mohamed Salem.Ethics declarations

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The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleAli, A., Ali, Y., Raymo, G. et al. Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.
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KeywordsEnvironmental stressorsHeat stressFish welfarePredictive modelingAquaculture stress breeding More

AbstractThe deep-sea floor encompasses more than half of the surface of our planet, yet the extent and distribution of deep-sea biodiversity and its contribution to large biogeochemical cycles remain poorly understood. This knowledge gap stems from several factors, including sampling issues, the magnitude of the work required for morphological inventories, and the difficulty of integrating results from disparate local studies. The application of meta-omics to environmental DNA now makes it possible to assemble interoperable datasets at different spatial scales to move towards a global assessment of deep-sea biodiversity. We present a large-scale dataset on deep-sea biodiversity, with data and metadata openly accessible at ENA and Zenodo. The resource was generated using standardized protocols developed according to FAIR principles, covering fieldwork through bioinformatic analysis, within “Pourquoi Pas les Abysses?” and eDNAbyss projects. Together with information ensuring reproducibility, this dataset —combining metagenomics, metabarcoding across the Tree of Life and capture-by-hybridization— contributes to the international concerted effort to achieve a holistic view of the biodiversity in the largest biome on Earth.

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Data availability

The global dataset has been deposited in European Nucleotide Archive (ENA) as project PRJEB 39225 (https://www.ebi.ac.uk/ena/browser/view/PRJEB39225), with metadata available on Zenodo (https://zenodo.org/records/6815677).
Code availability

1. Time Analysis software: https://www.illumina.com/search.html?filter=support&q=RTA%20download&p=1.
2. Bcl2fastq Conversion: https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html
3. Cutadapt, https://github.com/marcelm/cutadapt/releases/tag/v1.18
4. Fastx_clean software, http://www.genoscope.cns.fr/fastxtend
5. FASTX-Toolkit, http://hannonlab.cshl.edu/fastx_toolkit/index.html
6. SortMeRNA v2.1, https://github.com/biocore/sortmerna
7. fastx_estimate_duplicate software, http://www.genoscope.cns.fr/fastxtend
8. fastx_mergepairs software, http://www.genoscope.cns.fr/fastxtend
9. Usearch, https://www.drive5.com/usearch/
10. Trimmomatic: https://github.com/usadellab/Trimmomatic
11. Decontam: https://github.com/benjjneb/decontam
12. Prinseq: https://github.com/uwb-linux/prinseq
13. Qiime2 feature classifier: https://github.com/qiime2/q2-feature-classifier
14. FastQC: https://github.com/s-andrews/FastQC
15. BBTools: https://github.com/kbaseapps/BBTools
16. MultiQC: https://github.com/MultiQC/MultiQC
17. MetaRib: https://github.com/yxxue/MetaRib
18. EMIRGE: https://github.com/csmiller/EMIRGE
19. VSearch: https://github.com/torognes/vsearch
20. 1IDBA_UD: https://github.com/1928d/idba_ud
21. CAP3: https://faculty.sites.iastate.edu/xqhuang/cap3-and-pcap-sequence-and-genome-assemblyprograms
22. eDNAbyss pipeline: https://gitlab.ifremer.fr/abyss-project/
23. MUMU algorithm: https://github.com/frederic-mahe/mumu
24. bbmap: https://sourceforge.net/projects/bbmap/
26. RiboTaxa: https://github.com/oschakoory/RiboTaxa
27. eDNAbyss pipeline(s): https://gitlab.ifremer.fr/abyss-project/),
ReferencesRamirez-Llodra, E. et al. Deep, diverse and definitely different: unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).
Google Scholar 
Levin, L. A. et al. Deep-sea impacts of climate interventions. Science 379, 978–981 (2023).
Google Scholar 
Paulus, E. Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front. Mar. Sci. 8, 667048 (2021).
Google Scholar 
Sanders, H. L., Hessler, R. R. & Hampson, G. R. An introduction to the study of deep-sea benthic faunal assemblages along the Gay Head-Bermuda transect. Deep Sea Res. Oceanogr. Abstr. 12, 845–848 (1965).
Google Scholar 
Hessler, R. R. & Sanders, H. L. Faunal diversity in the deep-sea. Deep Sea Res. Oceanogr. Abstr. 14, 65–70 (1967).
Google Scholar 
Grassle, J. F. & Maciolek, N. J. Deep-sea species richness: regional and local diversity estimates from quantitative bottom samples. Am. Nat. 139, 313–341 (1992).
Google Scholar 
WoRMS Editorial Board. World register of marine species. https://www.marinespecies.org (2025).WoRDSS Editorial Board. World register of deep-sea species. https://www.deepseaspecies.org (2025).Gage, J. D. & May, R. M. A dip into the deep seas. Nature 365, 609–610 (1993).
Google Scholar 
Levin, L. A. et al. Environmental influences on regional deep-sea species diversity. Annu. Rev. Ecol. Syst. 32, 51–53 (2001).
Google Scholar 
Ramirez-Llodra, E. et al. Man and the last great wilderness: human impact on the deep sea. PLoS One 6, e22588 (2011).
Google Scholar 
Bell, K. L. C., Johannes, K. N., Kennedy, B. R. C. & Poulton, S. E. How little we’ve seen: a visual coverage estimate of the deep seafloor. Sci. Adv. 11, eadp8602 (2025).
Google Scholar 
Mejía-Saenz, A., Simon-Lledó, E., Partridge, L. S., Xavier, J. R. & Jones, D. O. B. Rock outcrops enhance abyssal benthic biodiversity. Deep Sea Res. I Oceanogr. Res. Pap. 195, 103999 (2023).
Google Scholar 
Simon-Lledó, E. et al. Carbonate compensation depth drives abyssal biogeography in the Northeast Pacific. Nat. Ecol. Evol. 7, 1388–1397 (2023).
Google Scholar 
Smith, C. R., Clark, M. R., Goetze, E., Glover, A. G. & Howell, K. L. Editorial: biodiversity, connectivity and ecosystem function across the clarion-clipperton zone: a regional synthesis for an area targeted for nodule mining. Front. Mar. Sci. 8, 797516 (2021).
Google Scholar 
Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202 (2012).
Google Scholar 
Costello, M. J. & Chaudhary, C. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Curr. Biol. 27, R511–R527 (2017).
Google Scholar 
Snelgrove, P. et al. The importance of marine sediment biodiversity in ecosystem processes. Ambio 26, 578–583 (1997).
Google Scholar 
McClain, C. R. & Hardy, S. M. The dynamics of biogeographic ranges in the deep sea. Proc. R. Soc. B Biol. Sci. 277, 3533–3546 (2010).
Google Scholar 
Valentine, J. W. & Jablonski, D. A twofold role for global energy gradients in marine biodiversity trends. J. Biogeogr. 42, 997–1005 (2015).
Google Scholar 
Danovaro, R., Snelgrove, P. V. & Tyler, P. Challenging the paradigms of deep-sea ecology. Trends Ecol. Evol. 29, 465–475 (2014).
Google Scholar 
Rex, M. A. & Etter, R. J. Deep-Sea Biodiversity: Pattern and Scale (Harvard Univ. Press, 2010).Gauthier, O., Sarrazin, J. & Desbruyères, D. Measure and mis-measure of species diversity in deep-sea chemosynthetic communities. Mar. Ecol. Prog. Ser. 402, 285–302 (2010).
Google Scholar 
Holman, L. E. et al. Detection of introduced and resident marine species using environmental DNA metabarcoding of sediment and water. Sci. Rep. 9, 11559 (2019).
Google Scholar 
Ji, Y. et al. Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecol. Lett. 16, 1245–1257 (2013).
Google Scholar 
Laroche, O., Kersten, O., Smith, C. R. & Goetze, E. Environmental DNA surveys detect distinct metazoan communities across abyssal plains and seamounts in the Western Clarion Clipperton zone. Mol. Ecol. Resour. 29, 4588–4604 (2020).
Google Scholar 
Sinniger, F. et al. Worldwide analysis of sedimentary DNA reveals major gaps in taxonomic knowledge of deep-sea benthos. Front. Mar. Sci. 3, 92 (2016).
Google Scholar 
Karsenti, E. et al. A holistic approach to marine eco-systems biology. PLoS Biol. 9, e1001177 (2011).
Google Scholar 
De Vargas, C. et al. Ocean plankton. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).
Google Scholar 
Lima-Mendez, G. et al. Ocean plankton. Determinants of community structure in the global plankton interactome. Science 348, 1262073 (2015).
Google Scholar 
Salazar, G. et al. Global diversity and biogeography of deep-sea pelagic prokaryotes. ISME J. 10, 596–608 (2016).
Google Scholar 
Brandt, M. I. et al. Evaluating sediment and water sampling methods for the estimation of deep-sea biodiversity using environmental DNA. Sci. Rep. 11, 7856 (2021).
Google Scholar 
Brandt, M. I. et al. An assessment of environmental metabarcoding protocols aiming at favoring contemporary biodiversity in inventories of deep-sea communities. Front. Mar. Sci. 7, 234 (2020).
Google Scholar 
Gunther, B. et al. Capture by hybridization for full-length barcode-based eukaryotic and prokaryotic biodiversity inventories of deep sea ecosystems. Mol. Ecol. Resour. 22, 623–637 (2021).
Google Scholar 
Brandt, M. I. et al. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Mol. Ecol. Resour. 21, 1904–1921 (2021).
Google Scholar 
Cordier, T. et al. Patterns of eukaryotic diversity from the surface to the deep-ocean sediment. Sci. Adv. 8, eabj9309 (2022).
Google Scholar 
ENA European Nucleotide Archive. Project: PRJEB39225. https://identifiers.org/ena.embl:PRJEB39225 (2025).Bett, B. J. et al. Sampler bias in the quantitative study of deep-sea meiobenthos. Mar. Ecol. Prog. Ser. 104, 197–203 (1994).
Google Scholar 
Schauberger, C. et al. Microbial community structure in hadal sediments: high similarity along trench axes and strong changes along redox gradients. ISME J. 15, 3455–3467 (2021).
Google Scholar 
Thamdrup, B. et al. Anammox bacteria drive fixed nitrogen loss in hadal trench sediments. Proc. Natl. Acad. Sci. USA. 118, e2104529118 (2021).
Google Scholar 
Armbrecht, L. H. et al. Ancient DNA from marine sediments: precautions and considerations for seafloor coring, sample handling and data generation. Earth Sci. Rev. 196, 102887 (2019).
Google Scholar 
Lejzerowicz, F. et al. Ancient DNA complements microfossil record in deep-sea subsurface sediments. Biol. Lett. 9, 20130283 (2013).
Google Scholar 
Stewart, H. A. & Jamieson, A. J. Habitat heterogeneity of hadal trenches: considerations and implications for future studies. Prog. Oceanogr. 161, 47–65 (2018).
Google Scholar 
Trouche, B. et al. Distribution and genomic variation of ammonia-oxidizing archaea in abyssal and hadal surface sediments. ISME Commun. 3, 133 (2023).
Google Scholar 
Schauberger, C. et al. Metagenome-assembled genomes of deep-sea sediments: changes in microbial functional potential lag behind redox transitions. ISME Commun. 4, ycad005 (2024).
Google Scholar 
Cosson, N., Sibuet, M. & Galeron, J. Community structure and spatial heterogeneity of the deep-sea macrofauna at three contrasting stations in the tropical Northeast Atlantic. Deep Sea Res. I Oceanogr. Res. Pap. 44, 247–269 (1997).
Google Scholar 
Vincx, M. et al. in Advances in Marine Biology (eds. Blaxter, J. H. S. & Southward, A. J.) 1–88 (Academic Press, 1994).Lins, L. et al. Toward a reliable assessment of potential ecological impacts of deep-sea polymetallic nodule mining on abyssal infauna. Limnol. Oceanogr. Methods 19, 626–650 (2021).
Google Scholar 
Soto, E. H. et al. Temporal variability in polychaete assemblages of the abyssal NE Atlantic Ocean. Deep Sea Res. II Top. Stud. Oceanogr. 57, 1396–1405 (2010).
Google Scholar 
Nomaki, H. et al. Abyssal fauna, benthic microbes, and organic matter quality across a range of trophic conditions in the Western Pacific ocean. Prog. Oceanogr. 195, 102591 (2021).
Google Scholar 
Good, E. et al. Detection of community-wide impacts of bottom trawl fishing on deep-sea assemblages using environmental DNA metabarcoding. Mar. Pollut. Bull. 183, 114062 (2022).
Google Scholar 
Vanhove, S., Vermeeren, H. & Vanreusel, A. Meiofauna towards the South Sandwich Trench (750–6300m), focus on nematodes. Deep Sea Res. II Top. Stud. Oceanogr. 51, 1665–1687 (2004).
Google Scholar 
Narayanaswamy, B. et al. in Biological Sampling in the Deep-Sea (eds. Clark, M. R., Consalvey, M. & Rowden, A. A.) 207–227 (Blackwell Publishing, 2016).Sarrazin, J. & Bignon, L. A new tool to sample hard substratum faunal communities in the deep sea.Cowart, D. A., Matabos, M., Brandt, M. I., Marticorena, J. & Sarrazin, J. Exploring environmental DNA (eDNA) to assess biodiversity of hard substratum faunal communities on the lucky strike vent field (Mid-Atlantic ridge) and investigate recolonization dynamics after an induced disturbance. Front. Mar. Sci. 6, 783 (2020).
Google Scholar 
Roussel, E. G. et al. Comparison of microbial communities associated with three Atlantic ultramafic hydrothermal systems. FEMS Microbiol. Ecol. 77, 647–665 (2011).
Google Scholar 
Assis, J. et al. Bio-ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).
Google Scholar 
Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012).
Google Scholar 
Sassoubre, L. M., Yamahara, K. M., Gardner, L. D., Block, B. A. & Boehm, A. B. Quantification of environmental DNA (eDNA) shedding and decay rates for three marine fish. Environ. Sci. Technol. 50, 10456–10464 (2016).
Google Scholar 
Andruszkiewicz, E. A., Sassoubre, L. M. & Boehm, A. B. Persistence of marine fish environmental DNA and the influence of sunlight. PLoS One 12, e0185043 (2017).
Google Scholar 
Wei, N., Nakajima, F. & Tobino, T. A microcosm study of surface sediment environmental DNA: decay observation, abundance estimation, and fragment length comparison. Environ. Sci. Technol. 52, 12428–12435 (2018).
Google Scholar 
Mauvisseau, Q. et al. The multiple states of environmental DNA and what is known about their persistence in aquatic environments. Environ. Sci. Technol. 56, 5322–5333 (2022).
Google Scholar 
Corinaldesi, C., Barucca, M., Luna, G. M. & Dell’Anno, A. Preservation, origin and genetic imprint of extracellular DNA in permanently anoxic deep-sea sediments. Mol. Ecol. 20, 642–654 (2011).
Google Scholar 
Armbrecht, L. et al. An optimized method for the extraction of ancient eukaryote DNA from marine sediments. Mol. Ecol. Resour. 20, 906–919 (2020).
Google Scholar 
Siano, R. et al. Sediment archives reveal irreversible shifts in plankton communities after World War II and agricultural pollution. Curr. Biol. 31, 2682–2689.e7 (2021).
Google Scholar 
Kirkpatrick, J. B., Walsh, E. A. & D’Hondt, S. Fossil DNA persistence and decay in marine sediment over hundred-thousand-year to million-year time scales. Geology 44, 615–618 (2016).
Google Scholar 
Lennon, J. T., Muscarella, M. E., Placella, S. A. & Lehmkuhl, B. K. How, when, and where relic DNA affects microbial diversity. mBio 9, e00637–18 (2018).
Google Scholar 
Armbrecht, L. et al. Ancient marine sediment DNA reveals diatom transition in Antarctica. Nat. Commun. 13, 5787 (2022).
Google Scholar 
Orsi, W., Biddle, J. F. & Edgcomb, V. Deep sequencing of subseafloor eukaryotic rRNA reveals active fungi across marine subsurface provinces. PLoS One 8, e56335 (2013).
Google Scholar 
Cristescu, M. Can environmental RNA revolutionize biodiversity science? Trends Ecol. Evol. 34, 694–697 (2019).
Google Scholar 
Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).
Google Scholar 
Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 1–20 (2017).
Google Scholar 
Belser, C. et al. Integrative omics framework for characterization of coral reef ecosystems from the Tara Pacific expedition. Sci. Data 10, 326 (2023).
Google Scholar 
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).
Google Scholar 
Stoeck, T. et al. Massively parallel tag sequencing reveals the complexity of anaerobic marine protistan communities. BMC Biol. 7, 72 (2009).
Google Scholar 
Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS One 4, e6372 (2009).
Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).
Google Scholar 
Topcuoglu, B. D. et al. Hydrogen limitation and syntrophic growth among natural assemblages of thermophilic methanogens at deep-sea hydrothermal vents. Front. Microbiol. 7, 1240 (2016).
Google Scholar 
Gasc, C., Peyretaillade, E. & Peyret, P. Sequence capture by hybridization to explore modern and ancient genomic diversity in model and nonmodel organisms. Nucleic Acids Res. 44, 4504–4518 (2016).
Google Scholar 
Parisot, N., Denonfoux, J., Dugat-Bony, E., Peyret, P. & Peyretaillade, E. KASpOD–a web service for highly specific and explorative oligonucleotide design. Bioinformatics 28, 3161–3162 (2012).
Google Scholar 
Marre, S. et al. Revealing microbial species diversity using sequence capture by hybridization. Microb. Genom. 7, 000714 (2021).
Google Scholar 
Comtet-Marre, S., Chakoory, O. & Peyret, P. Targeted 16S rRNA gene capture by hybridization and bioinformatic analysis. Methods Mol. Biol. 2605, 187–208 (2023).
Google Scholar 
Ribiere, C. et al. Targeted gene capture by hybridization to illuminate ecosystem functioning. Methods Mol. Biol. 1399, 167–182 (2016).
Google Scholar 
Jaziri, F. et al. PhylOPDb: a 16S rRNA oligonucleotide probe database for prokaryotic identification. Database (Oxford) 2014, bau036 (2014).
Google Scholar 
Militon, C. et al. PhylArray: phylogenetic probe design algorithm for microarray. Bioinformatics 23, 2550–2557 (2007).
Google Scholar 
Machida, R. J., Leray, M., Ho, S. L. & Knowlton, N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4, 170027 (2017).
Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
Google Scholar 
Guillou, L. et al. The protist ribosomal reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2013).
Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Google Scholar 
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).
Google Scholar 
Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Google Scholar 
Jacobsen, A. et al. FAIR principles: interpretations and implementation considerations. Data Intell. 2, 10–29 (2020).
Google Scholar 
Pesant, S. et al. eDNAbyss samples provenance and environmental context – version 1 (version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6815677 (2022).Callahan, B. J. et al. DADA2: high-resolution sample inference from illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar 
Mahe, F., Rognes, T., Quince, C., De Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3, e1420 (2015).
Google Scholar 
Mahé, F. MUMU: post-clustering curation tool for metabarcoding data, version 1.0.2. https://github.com/frederic-mahe/mumu (2023).Frøslev, T. G. et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8, 1188 (2017).
Google Scholar 
Blaxter, M. et al. Defining operational taxonomic units using DNA barcode data. Philos. Trans. R. Soc. B Biol. Sci. 360, 1935–1943 (2005).
Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).
Google Scholar 
Antich, A., Palacin, C., Wangensteen, O. S. & Turon, X. To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography. BMC Bioinformatics 22, 177 (2021).
Google Scholar 
Harris, J. D. Can you bank on GenBank? Trends Ecol. Evol. 18, 317–319 (2003).
Google Scholar 
Viard, F., Roby, C., Turon, X., Bouchemousse, S. & Bishop, J. Cryptic diversity and database errors challenge non-indigenous species surveys: an illustration with Botrylloides spp. in the english channel and Mediterranean sea. Front. Mar. Sci. 6, 615 (2019).
Google Scholar 
Eren, A. M., Vineis, J. H., Morrison, H. G. & Sogin, M. L. A filtering method to generate high quality short reads using illumina paired-end technology. PLoS One 8, e66643 (2013).
Google Scholar 
Minoche, A. E., Dohm, J. C. & Himmelbauer, H. Evaluation of genomic high-throughput sequencing data generated on illumina HiSeq and genome analyzer systems. Genome Biol. 12, R112 (2011).
Google Scholar 
Köster, J. & Rahmann, S. Snakemake–a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).
Google Scholar 
Shaiber, A. et al. Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome. Genome Biol. 21, 292 (2020).
Google Scholar 
Eren, A. M. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).
Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).
Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).
Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).
Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics 36, 1925–1927 (2020).
Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2′s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
Google Scholar 
Chakoory, O., Comtet-Marre, S. & Peyret, P. RiboTaxa: combined approaches for rRNA genes taxonomic resolution down to the species level from metagenomics data revealing novelties. Nar Genom. Bioinform. 4, lqac070 (2022).
Google Scholar 
Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
Google Scholar 
Xue, Y. X., Lanzén, A. & Jonassen, I. Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data. Bioinformatics 36, 3365–3371 (2020).
Google Scholar 
Miller, C. S., Baker, B. J., Thomas, B. C., Singer, S. W. & Banfield, J. F. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol. 12, R44 (2011).
Google Scholar 
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Google Scholar 
Lu, J. N. & Salzberg, S. L. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. Microbiome 8, 124 (2020).
Google Scholar 
Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).
Google Scholar 
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).
Google Scholar 
Huang, X. Q. & Madan, A. CAP3: a DNA sequence assembly program. Genome Res. 9, 868–877 (1999).
Google Scholar 
Download referencesAcknowledgementsWe express special gratitude to the scientific direction and scientific committee of the “Pourquoi Pas les Abysses?” project and to the board of the French Oceanographic Fleet for allowing unusual use of boat time (including transits) through the AMIGO series and to the mission chiefs of all the crews who kindly sampled for the project. This work was supported by Ifremer during the development of prototypes and protocols in “Pourquoi Pas les Abysses?” and by Genoscope, the Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA) and France Génomique (ANR-10-INBS-09) for high-throughput sequencing in eDNAbyss (AP2016–228 France Génomique). We also thank the HADES-ERC Advanced grant (#669947) and the EU Atlas project (678760) and benefited from State aid managed by the National Research Agency under France 2030 for the LIFEDEEPER project (ANR-22-POCE-0007) and the ANR Cerberus (ANR-17-CE02-0003) for samples gathered during the associated cruises and the project MarEEE (MUSE, Montpellier, ANR-16-IDEX-0006) for the improvement of the original bioinformatic pipeline. We warmly acknowledge all the crews, mission chiefs and colleagues who contributed gathering this widespread sampling collection: Covadonga Orejas, Martin Ludvigsen and Eva Ramirez-Llodra, Jean-Paul Justiniano, Yves Fouquet and Ewan Pelleter, Ewen Raugel, Wayne Crawford, Cécile Guieu, Sophie Bonnet, Sophie Arnaud-Haond, François Bonhomme, Pierre-Marie Sarradin, Carlos Duarte, Franck Wenzhoefer, Mathilde Cannat, Norbert Franck, Marie-Anne Cambon, Stéphane Hourdez and Didier Jollivet. We would like gratefully acknowledge the entire Genoscope technical team: Julie Batisse, Odette Beluche, Isabelle Bordelais, Elodie Brun, Maria Dubois, Corinne Dumont, Zineb El Hajji, Barbara Estrada, Thomas Guérin, Chadia Hamon, Sandrine Lebled, Patricia Lenoble and Marine Lepretre, Claudine Louesse, Ghislaine Magdelenat, Eric Mahieu, Claire Milani, Sophie Oztas, Emilie Payen, Emmanuelle Petit, Muriel Ronsin and Benoît Vacherie, for their invaluable work in producing the data. We thank the editorial team and the referees for the improvements suggested to previous versions of this manuscript.Author informationAuthor notesBabett GüntherPresent address: GEOMAR, Helmholtz Centre for Ocean Research Kiel, Wischhofstraße 1-3, 24148, Kiel, GermanyThese authors contributed equally: Julie Poulain, Caroline BelserThe Genoscope technical team members are listed in the Acknowledgements section.Authors and AffiliationsMARBEC, Univ Montpellier, IFREMER, IRD, CNRS, Sète, FranceSophie Arnaud-Haond, Cathy Liautard-Haag, Miriam I. Brandt, Annaëlle Caillarec-Joly, Florence Cornette, Christine Felix, Babett Günther, Adrien Tran Lu Y & Sandrine VazUniv Brest, Ifremer, BEEP, F-29280, Plouzané, FranceBlandine Trouche, Karine Alain, Johanne Aubé, Marie-Anne Cambon, Valérie Cueff-Gauchard, Sandra Fuchs, Françoise Lesongeur, Loïs Maignien, Marjolaine Matabos, Emmanuelle Omnes, Florence Pradillon, Jozée Sarrazin & Daniela ZeppiliISEM, Univ Montpellier, CNRS, IRD, Montpellier, FranceFrançois Bonhomme & Frédérique ViardNORCE, Norwegian Research Centre AS, Climate & Environment department, Bergen, NorwayMiriam I. BrandtIfremer, IRSI, SeBiMER Service de Bioinformatique de l’Ifremer, Plouzané, FrancePatrick DurandSorbonne Université, CNRS, Station Biologique de Roscoff, UMR 7144 Adaptation and diversity in the marine environment, Place Georges Teissier, Roscoff, FranceColomban de Vargas, Didier Jollivet & Anne-Sophie Le PortResearch Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022 GOSEE, 3 rue Michel-Ange, Paris, 75016, FranceColomban de Vargas & Nicolas HenryCNRS, Sorbonne Université, FR2424, ABiMS, Station Biologique de Roscoff, Roscoff, 29680, FranceNicolas HenryUMR 8222 LECOB CNRS-Sorbonne Université, Observatoire Océanologique de Banyuls, Avenue du Fontaulé, 66650, Banyuls-sur-mer, FranceStéphane HourdezUniversité Clermont Auvergne, INRAE, UMR 0454 MEDIS, Clermont-Ferrand, FranceSophie Comtet-Marre & Pierre PeyretHadal & Nordcee, Department of Biology, University of Southern Denmark, Odense, DenmarkClemens SchaubergerInstituto Milenio de Oceanografía (IMO), Universidad de Concepción, Concepción, ChileOsvaldo UlloaGénomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, FranceFrédérick Gavory, Jean-Marc Aury, Patrick Wincker, Julie Poulain & Caroline BelserGenoscope, Institut François Jacob, Commissariat à l’Energie Atomique (CEA), Université Paris-Saclay, 2 Rue Gaston Crémieux, Evry, FranceShahinaz Gaz, Julie Guy, E’Krame Jacoby, Pedro H. Oliveira & Gaëlle SamsonEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UKStéphane PesantAuthorsSophie Arnaud-HaondView author publicationsSearch author on:PubMed Google ScholarBlandine TroucheView author publicationsSearch author on:PubMed Google ScholarCathy Liautard-HaagView author publicationsSearch author on:PubMed Google ScholarKarine AlainView author publicationsSearch author on:PubMed Google ScholarJohanne AubéView author publicationsSearch author on:PubMed Google ScholarFrançois BonhommeView author publicationsSearch author on:PubMed Google ScholarMiriam I. BrandtView author publicationsSearch author on:PubMed Google ScholarAnnaëlle Caillarec-JolyView author publicationsSearch author on:PubMed Google ScholarMarie-Anne CambonView author publicationsSearch author on:PubMed Google ScholarFlorence CornetteView author publicationsSearch author on:PubMed Google ScholarValérie Cueff-GauchardView author publicationsSearch author on:PubMed Google ScholarPatrick DurandView author publicationsSearch author on:PubMed Google ScholarColomban de VargasView author publicationsSearch author on:PubMed Google ScholarChristine FelixView author publicationsSearch author on:PubMed Google ScholarSandra FuchsView author publicationsSearch author on:PubMed Google ScholarBabett GüntherView author publicationsSearch author on:PubMed Google ScholarNicolas HenryView author publicationsSearch author on:PubMed Google ScholarStéphane HourdezView author publicationsSearch author on:PubMed Google ScholarDidier JollivetView author publicationsSearch author on:PubMed Google ScholarAnne-Sophie Le PortView author publicationsSearch author on:PubMed Google ScholarFrançoise LesongeurView author publicationsSearch author on:PubMed Google ScholarLoïs MaignienView author publicationsSearch author on:PubMed Google ScholarSophie Comtet-MarreView author publicationsSearch author on:PubMed Google ScholarMarjolaine MatabosView author publicationsSearch author on:PubMed Google ScholarEmmanuelle OmnesView author publicationsSearch author on:PubMed Google ScholarPierre PeyretView author publicationsSearch author on:PubMed Google ScholarFlorence PradillonView author publicationsSearch author on:PubMed Google ScholarJozée SarrazinView author publicationsSearch author on:PubMed Google ScholarClemens SchaubergerView author publicationsSearch author on:PubMed Google ScholarAdrien Tran Lu YView author publicationsSearch author on:PubMed Google ScholarOsvaldo UlloaView author publicationsSearch author on:PubMed Google ScholarSandrine VazView author publicationsSearch author on:PubMed Google ScholarDaniela ZeppiliView author publicationsSearch author on:PubMed Google ScholarFrédérique ViardView author publicationsSearch author on:PubMed Google ScholarFrédérick GavoryView author publicationsSearch author on:PubMed Google ScholarShahinaz GazView author publicationsSearch author on:PubMed Google ScholarJulie GuyView author publicationsSearch author on:PubMed Google ScholarE’Krame JacobyView author publicationsSearch author on:PubMed Google ScholarPedro H. OliveiraView author publicationsSearch author on:PubMed Google ScholarGaëlle SamsonView author publicationsSearch author on:PubMed Google ScholarJean-Marc AuryView author publicationsSearch author on:PubMed Google ScholarPatrick WinckerView author publicationsSearch author on:PubMed Google ScholarStéphane PesantView author publicationsSearch author on:PubMed Google ScholarJulie PoulainView author publicationsSearch author on:PubMed Google ScholarCaroline BelserView author publicationsSearch author on:PubMed Google ScholarConsortiaGenoscope Technical TeamContributionsS.A.H., C.B., J.P., S.C.M. and F.P. wrote the manuscript with the help of F.V., S.H. and M.M. All coauthors reviewed the manuscript. S.A.H., F.P., J.S., C.dV., J.P. and P.W. conceptualized the project. S.A.H. and P.W. obtained funding and administrated the project. B.T., C.L.H., J.A., M.I.B., M.C., S.F., V.C.G., D.J., A.S.L., F.P., J.S., P.M.S., C.S., M.C., A.T.L., S.V., F.B., D.Z., O.U. and J.P., as well as all mission chiefs, contributed to the collection of the environmental samples. B.T., C.L.H., K.A., J.A., M.I.B., F.C., V.C.G., B.G., C.F., S.F., F.L., E.O., G.T.T. and S.A.H. performed the DNA extractions. J.P., C.B., M.I.B. and C.L.H. developed the amplicon sequencing protocol, and S.C.M. and P.P. developed the C.B.H. protocol. J.P., K.L., F.G., P.H.O. and all the Genoscope technical teams were involved in the library preparations and sequencing tasks for metagenomics and metabarcoding, and data curation, S.C.M. and PP for the libraries and sequencing for C.B.H. C.B. and J.M.A. developed Data validation and visualization softwares. S.A.H., B.T., M.V., J.M.A., J.P. and C.B. contributed to Validation. M.I.B., A.C.J., B.G., B.T., L.M., P.D., S.A.H., N.H., K.A., F.V., S.C.M. and P.P. developed the bioinformatics pipelines and/or performed the data analysis. S.P., C.B., S.A.H. and S.V. provided the metadata and data. C.B.H., S.G., J.G., G.S., E.K.J., S.P., S.C.M. and P.D. managed the data to be transmitted to a public repository.Corresponding authorsCorrespondence to
Sophie Arnaud-Haond or Julie Poulain.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleArnaud-Haond, S., Trouche, B., Liautard-Haag, C. et al. Omics exploration of deep-sea biodiversity: data from the “Pourquoi Pas les Abysses?” and eDNAbyss projects.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06009-1Download citationReceived: 17 May 2024Accepted: 22 September 2025Published: 20 December 2025DOI: https://doi.org/10.1038/s41597-025-06009-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractWild birds are key natural reservoirs and play a central role in the global spread of avian influenza viruses (AIVs). However, the absence of a standardized global list of wild bird hosts has limited comprehensive AIV risk monitoring and assessment within the One Health framework. Here, we generate a taxonomically harmonized dataset of AIV wild bird hosts, derived from 23,358 viral isolates of wild bird origin reported in the GISAID EpiFluTM database from 1973 to 2023. Host names were systematically extracted, validated, and harmonized to resolve reporting inconsistencies and unify taxonomy across records. The dataset comprises 394 wild bird species spanning 26 orders, with Anseriformes and Charadriiformes representing a substantial share of host diversity. By clarifying the global spectrum of wild bird hosts for AIVs, this dataset provides a foundation for host identification, phylogenetic annotation, and ecological trait-based analysis. Structured in machine-readable formats, it enables reproducible and large-scale, species-level studies spanning virology, epidemiology, and biodiversity.

Data availability

The dataset associated with this study is publicly avaiable on Zenodo: https://zenodo.org/records/15970977. All data are provided in CSV format. Further details regarding the dataset structure and variable descriptions are avaiable in the Data Records section.
Code availability

The code for the study can be accessed at https://github.com/gogofxd/InfluenzaWildBirdHost.
ReferencesStallknecht, D. E. & Shane, S. M. Host range of avian influenza virus in free-living birds. Vet. Res. Commun. 12, 125–141, https://doi.org/10.1007/BF00362792 (1988).
Google Scholar 
Long, J. S., Mistry, B., Haslam, S. M. & Barclay, W. S. Host and viral determinants of influenza A virus species specificity. Nat Rev Microbiol 17, 67–81, https://doi.org/10.1038/s41579-018-0115-z (2019).
Google Scholar 
Olsen, B. et al. Global patterns of influenza a virus in wild birds. Science 312, 384–388, https://doi.org/10.1126/science.1122438 (2006).
Google Scholar 
Alexander, D. J. A review of avian influenza in different bird species. Vet Microbiol 74, 3–13, https://doi.org/10.1016/S0378-1135(00)00160-7 (2000).
Google Scholar 
Tian, H. et al. Avian influenza H5N1 viral and bird migration networks in Asia. Proc Natl Acad Sci USA 112, 172–177, https://doi.org/10.1073/pnas.1405216112 (2015).
Google Scholar 
The Global Consortium for H5N8 and Related Influenza Viruses. Role for migratory wild birds in the global spread of avian influenza H5N8. Science 354, 213–217, https://doi.org/10.1126/science.aaf8852 (2016).
Google Scholar 
Lycett, S. J. et al. Genesis and spread of multiple reassortants during the 2016/2017 H5 avian influenza epidemic in Eurasia. Proc Natl Acad Sci USA 117, 20814–20825, https://doi.org/10.1073/pnas.2001813117 (2020).
Google Scholar 
Xie, R. et al. The episodic resurgence of highly pathogenic avian influenza H5 virus. Nature 622, 810–817, https://doi.org/10.1038/s41586-023-06631-2 (2023).
Google Scholar 
Zeng, J. et al. Spatiotemporal genotype replacement of H5N8 avian influenza viruses contributed to H5N1 emergence in 2021/2022 panzootic. J. Virol. 98, e0140123, https://doi.org/10.1128/jvi.01401-23 (2024).
Google Scholar 
Yang, Q. et al. Synchrony of Bird Migration with Global Dispersal of Avian Influenza Reveals Exposed Bird Orders. Nat Commun 15, 1126, https://doi.org/10.1038/s41467-024-45462-1 (2024).
Google Scholar 
Duan, L. et al. Characterization of low-pathogenic H5 subtype influenza viruses from eurasia: implications for the origin of highly pathogenic H5N1 viruses. J. Virol. 81, 7529–7539, https://doi.org/10.1128/jvi.00327-07 (2007).
Google Scholar 
Hill, N. J. et al. Crossroads of highly pathogenic H5N1: overlap between wild and domestic birds in the black sea-mediterranean impacts global transmission. Virus Evol. 7, veaa093, https://doi.org/10.1093/ve/veaa093 (2020).
Google Scholar 
Lee, D.-H. et al. Intercontinental spread of asian-origin H5N8 to North America through beringia by migratory birds. J. Virol. 89, 6521–6524, https://doi.org/10.1128/JVI.00728-15 (2015).
Google Scholar 
Leguia, M. et al. Highly pathogenic avian influenza A (H5N1) in marine mammals and seabirds in Peru. Nat Commun 14, 5489, https://doi.org/10.1038/s41467-023-41182-0 (2023).
Google Scholar 
Graziosi, G., Lupini, C., Catelli, E. & Carnaccini, S. Highly Pathogenic Avian Influenza (HPAI) H5 Clade 2.3.4.4b Virus Infection in Birds and Mammals. Animals (Basel) 14, 1372, https://doi.org/10.3390/ani14091372 (2024).
Google Scholar 
Burrough, E. R. et al. Highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Emerging Infectious Diseases 30, 1335–1343, https://doi.org/10.3201/eid3007.240508 (2024).
Google Scholar 
Nguyen, T.-Q. et al. Emergence and interstate spread of highly pathogenic avian influenza A(H5N1) in dairy cattle in the United States. Science 388, eadq0900, https://doi.org/10.1126/science.adq0900 (2025).
Google Scholar 
Ford, C. T. et al. Large-scale computational modelling of H5 influenza variants against HA1-neutralising antibodies. Ebiomedicine 114, 105632, https://doi.org/10.1016/j.ebiom.2025.105632 (2025).
Google Scholar 
Falchieri, M. et al. Shift in HPAI infection dynamics causes significant losses in seabird populations across great britain. Vet Rec 191, 294–296, https://doi.org/10.1002/vetr.2311 (2022).
Google Scholar 
Kareinen, L. et al. Highly pathogenic avian influenza a(H5N1) virus infections on fur farms connected to mass mortalities of black-headed gulls, finland, july to October 2023. Euro Surveill 29, 2400063, https://doi.org/10.2807/1560-7917.ES.2024.29.25.2400063 (2024).
Google Scholar 
Hill, N. J. et al. Ecological divergence of wild birds drives avian influenza spillover and global spread. PLoS Pathog 18, e1010062, https://doi.org/10.1371/journal.ppat.1010062 (2022).
Google Scholar 
Hoye, B. J., Munster, V. J., Nishiura, H., Klaassen, M. & Fouchier, R. A. M. Surveillance of wild birds for avian influenza virus. Emerg. Infect. Dis. 16, 1827–1834, https://doi.org/10.3201/eid1612.100589 (2010).
Google Scholar 
Teitelbaum, C. S. et al. North American wintering mallards infected with highly pathogenic avian influenza show few signs of altered local or migratory movements. Sci Rep 13, 14473, https://doi.org/10.1038/s41598-023-40921-z (2023).
Google Scholar 
Spackman, E., Pantin-Jackwood, M. J., Lee, S. A. & Prosser, D. The pathogenesis of a 2022 North American highly pathogenic clade 2.3.4.4b H5N1 avian influenza virus in mallards (Anas platyrhynchos). Avian Pathol 52, 219–228, https://doi.org/10.1080/03079457.2023.2196258 (2023).
Google Scholar 
Hall, J. S. et al. Avian influenza in shorebirds: experimental infection of ruddy turnstones (arenaria interpres) with avian influenza virus. Influenza Other Respir Viruses 7, 85–92, https://doi.org/10.1111/j.1750-2659.2012.00358.x (2013).
Google Scholar 
Khare, S. et al. GISAID’s role in pandemic response. China CDC Weekly 3, 1049–1051, https://doi.org/10.46234/ccdcw2021.255 (2021).
Google Scholar 
World Health Organization. A revision of the system of nomenclature for influenza viruses: a WHO memorandum. Bull World Health Organ 58, 585–591, https://www.ncbi.nlm.nih.gov/pubmed/6969132 (1980).
Google Scholar 
Hayati, M., Sobkowiak, B., Stockdale, J. E. & Colijn, C. Phylogenetic identification of influenza virus candidates for seasonal vaccines. Sci. Adv. 9(44), eabp9185, https://doi.org/10.1126/sciadv.abp9185 (2023).
Google Scholar 
Caserta, L. C. et al. Spillover of highly pathogenic avian influenza H5N1 virus to dairy cattle. Nature 634, 669–676, https://doi.org/10.1038/s41586-024-07849-4 (2024).
Google Scholar 
Shah, S. A. W. et al. Seasonal antigenic prediction of influenza a H3N2 using machine learning. Nat. Commun. 15, 3833, https://doi.org/10.1038/s41467-024-47862-9 (2024).
Google Scholar 
Good, M. R. et al. A single mutation in dairy cow-associated H5N1 viruses increases receptor binding breadth. Nat. Commun. 15, 10768, https://doi.org/10.1038/s41467-024-54934-3 (2024).
Google Scholar 
Li, Y.-T. et al. From emergence to endemicity of highly pathogenic H5 avian influenza viruses in taiwan. Nat. Commun. 15, 9348, https://doi.org/10.1038/s41467-024-53816-y (2024).
Google Scholar 
AviList Core Team. AviList: The Global Avian Checklist, v2025. https://doi.org/10.2173/avilist.v2025 (2025).Du, F. et al. A taxonomically harmonized global dataset of wild bird hosts for avian influenza virus surveillance. Zenodo https://doi.org/10.5281/zenodo.15970977 (2025).Gao, R. et al. Human infection with a novel avian-origin influenza a (H7N9) virus. N. Engl. J. Med. 368, 1888–1897, https://doi.org/10.1056/NEJMoa1304459 (2013).
Google Scholar 
Butler, D. Swine flu goes global. Nature 458, 1082–1083, https://doi.org/10.1038/4581082a (2009).
Google Scholar 
Download referencesAcknowledgementsThis work was supported by the National Key Research and Development Program of China (grant number: 2022YFF0802400) and the National Natural Science Foundation of China (grant number: 81961128002). We gratefully acknowledge all the data contributors, i.e., the authors and their originating laboratories responsible for obtaining the specimens and their submitting laboratories for generating the genetic sequences and metadata and sharing via the GISAID Initiative, on which this research is based. We acknowledge all the members of the AviList Core Team for collecting and providing the bird data. We also acknowledge Yue Wu for help with the bird taxonomic assignment.Author informationAuthor notesThese authors contributed equally: Fanshu Du, Qiang Zhang.Authors and AffiliationsNational Key Laboratory of Veterinary Public Health and Safety, Key Laboratory for Prevention and Control of Avian Influenza and Other Major Poultry Diseases, Ministry of Agriculture and Rural Affairs, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. ChinaFanshu Du, Lu Wang, Honglei Sun, Yipeng Sun, Jinhua Liu & Juan PuInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P. R. ChinaQiang Zhang & Zhichao LiUniversity of Chinese Academy of Sciences, Beijing, 100101, P. R. ChinaQiang Zhang & Zhichao LiThird Institute of Oceanography, Ministry of Natural Resources, PRC, Daxue Road No. 178, Siming District, Xiamen, Fujian, 361005, P. R. ChinaYachang ChengState Key Laboratory of Biocontrol, School of Ecology, Sun Yat-Sen University, Shenzhen, 518107, P. R. ChinaYang LiuKey Laboratory for Biodiversity Science and Ecological Engineering, Demonstration Center for Experimental Life Sciences & Biotechnology Education, College of Life Sciences, Beijing Normal University, Beijing, 100875, P. R. ChinaWeipan LeiAuthorsFanshu DuView author publicationsSearch author on:PubMed Google ScholarQiang ZhangView author publicationsSearch author on:PubMed Google ScholarYachang ChengView author publicationsSearch author on:PubMed Google ScholarYang LiuView author publicationsSearch author on:PubMed Google ScholarWeipan LeiView author publicationsSearch author on:PubMed Google ScholarLu WangView author publicationsSearch author on:PubMed Google ScholarHonglei SunView author publicationsSearch author on:PubMed Google ScholarYipeng SunView author publicationsSearch author on:PubMed Google ScholarJinhua LiuView author publicationsSearch author on:PubMed Google ScholarZhichao LiView author publicationsSearch author on:PubMed Google ScholarJuan PuView author publicationsSearch author on:PubMed Google ScholarContributionsF.D. and Q.Z. jointly conceived and designed the study. F.D. led the data extraction, performed the core analyses, interpreted the results, and drafted the initial manuscript. Q.Z. independently conducted a parallel round of host classification and contributed to the methodological design and critical revision of the manuscript. Y.C., Y.L., and W.L. participated in species-level data validation and cross-verification of host taxonomy. L.W. contributed to background research and assisted in data integration. Z.L. and J.P. supervised the overall research process, provided guidance on result interpretation, and revised the manuscript for important intellectual content. All the authors reviewed and approved the final version of the manuscript.Corresponding authorsCorrespondence to
Zhichao Li or Juan Pu.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleDu, F., Zhang, Q., Cheng, Y. et al. A taxonomically harmonized global dataset of wild bird hosts for avian influenza virus surveillance.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06451-1Download citationReceived: 21 July 2025Accepted: 09 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41597-025-06451-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractStudying the diet of penguins is essential for understanding their role in marine ecosystems, evaluating their responses to environmental changes, and informing conservation strategies. This manuscript presents the ‘Penguin Diet Dataset-v2’, a comprehensive compilation of dietary data for 17 out of 19 currently recorded penguin species, obtained through a systematic review of the scientific literature. Spanning the 1980–2019 period and covering 149 global locations, the dataset includes 2.749 dietary entries with both qualitative and quantitative information on prey types. The dataset is enriched with metadata such as geographic locations, time periods, seasons, and life stages, providing insights into species-specific dietary patterns and ecological trends. Key prey groups include fish from the class Teleostei, crustaceans from the class Malacostraca, and mollusks from the class Cephalopoda. The Adélie Penguin and the Magellanic Penguin are the most extensively studied species. This dataset serves as a valuable resource for advancing research on penguin feeding ecology and addressing gaps in knowledge on understudied species, regions, and time periods.

Data availability

The Penguin Diet Dataset-v2, now publicly available on Digital.CSIC (https://doi.org/10.20350/digitalCSIC/17565, URL: http://hdl.handle.net/10261/399953), brings together dietary data for 17 of the 19 known penguin species. Compiled in an accessible.xlsx format, the dataset includes detailed predator–prey interactions, along with essential metadata to support interpretation and reuse. Users will find clearly organized sheets for diet entries, variable definitions, species and prey codes, and geographic coordinates.
Code availability

No computational code is associated with this dataset, as all information has been exclusively sourced from available literature.
ReferencesFrugone, M. J. et al. Taxonomy based on limited genomic markers may underestimate species diversity of rockhopper penguins and threaten their conservation. Diversity and Distributions 27, 2277–2296 (2021).
Google Scholar 
Gimeno, M. et al. Climate and human stressors on global penguin hotspots: Current assessments for future conservation. Global Change Biology 30, e17143 (2024).
Google Scholar 
Cimino, M. A., Lynch, H. J., Saba, V. S. & Oliver, M. J. Projected asymmetric response of Adélie penguins to Antarctic climate change. Sci Rep 6, 28785 (2016).
Google Scholar 
Sánchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).
Google Scholar 
Ramírez, F. et al. Natural and anthropogenic factors affecting the feeding ecology of a top marine predator, the Magellanic penguin. Ecosphere 5, art38 (2014).
Google Scholar 
Handley, J. et al. Marine important bird and biodiversity areas for penguins in Antarctica, targets for conservation action. Frontiers in Marine Science 7, (2021).Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).
Google Scholar 
Trathan, P. N. et al. Pollution, habitat loss, fishing, and climate change as critical threats to penguins. Conservation Biology 29, 31–41 (2015).
Google Scholar 
Ropert-Coudert, Y. et al. Happy Feet in a Hostile World? The Future of Penguins Depends on Proactive Management of Current and Expected Threats. Front. Mar. Sci. 6, 248 (2019).
Google Scholar 
Chiaradia, A., Forero, M. G., Hobson, K. A. & Cullen, J. M. Changes in diet and trophic position of a top predator 10 years after a mass mortality of a key prey. ICES Journal of Marine Science 67, 1720 (2010).
Google Scholar 
Cherel, Y., Hobson, K. A., Guinet, C. & Vanpe, C. Stable isotopes document seasonal changes in trophic niches and winter foraging individual specialization in diving predators from the Southern Ocean. Journal of Animal Ecology 76, 826–836 (2007).
Google Scholar 
Campbell, K. J. et al. Local forage fish abundance influences foraging effort and offspring condition in an endangered marine predator. Journal of Applied Ecology 56, 1751–1760 (2019).
Google Scholar 
Carpenter-Kling, T. et al. Gentoo penguins as sentinels of climate change at the sub-Antarctic Prince Edward Archipelago, Southern Ocean. Ecological Indicators 101, 163–172 (2019).
Google Scholar 
Ciancio, J. E., Yorio, P., Buratti, C., Colombo, G. Á. & Frere, E. Isotopic niche plasticity in a marine top predator as indicator of a large marine ecosystem food web status. Ecological Indicators 126, 107687 (2021).
Google Scholar 
Kowalczyk, N. D., Chiaradia, A., Preston, T. J. & Reina, R. D. Linking dietary shifts and reproductive failure in seabirds: a stable isotope approach. Funct Ecol 28, 755–765 (2014).
Google Scholar 
Waluda, C. M., Hill, S. L., Peat, H. J. & Trathan, P. N. Long-term variability in the diet and reproductive performance of penguins at Bird Island, South Georgia. Mar Biol 164, 39 (2017).
Google Scholar 
Aparicio-Estalella, C., Buil, H., Gimeno, M., Coll, M. & Ramírez, F. A comprehensive dataset on worldwide penguin diet: Penguin Diet Dataset-v2. http://hdl.handle.net/10261/399953 (2025).Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ n71, https://doi.org/10.1136/bmj.n71 (2021).Tierney, M., Emmerson, L. & Hindell, M. Temporal variation in Adélie penguin diet at Béchervaise Island, east Antarctica and its relationship to reproductive performance. Mar Biol 156, 1633–1645 (2009).
Google Scholar 
Bingham, M. The decline of Falkland Islands penguins in the presence of a commercial fishing industry. Rev. chil. hist. nat. 75, (2002).Booth, J. & McQuaid, C. Northern rockhopper penguins prioritise future reproduction over chick provisioning. Mar. Ecol. Prog. Ser. 486, 289–304 (2013).
Google Scholar 
Browne, T., Lalas, C., Mattern, T. & Van Heezik, Y. Chick starvation in yellow‐eyed penguins: Evidence for poor diet quality and selective provisioning of chicks from conventional diet analysis and stable isotopes. Austral Ecology 36, 99–108 (2011).
Google Scholar 
Castillo, J., Yorio, P. & Gatto, A. Shared dietary niche between sexes in Magellanic Penguins. Austral Ecology 44, 635–647 (2019).
Google Scholar 
Cavallo, C. et al. Quantifying prey availability using the foraging plasticity of a marine predator, the little penguin. Functional Ecology 34, 1626–1639 (2020).
Google Scholar 
Cavallo, C. et al. Molecular analysis of predator scats reveals role of salps in temperate inshore food webs. Front. Mar. Sci. 5, 381 (2018).
Google Scholar 
Cherel, Y. & Kooyman, G. L. Food of emperor penguins (Aptenodytes forsteri) in the western Ross Sea, Antarctica. Marine Biology 130, 335–344 (1998).
Google Scholar 
Cherel, Y., Pütz, K. & Hobson, K. A. Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean. Polar Biol 25, 898–906 (2002).
Google Scholar 
Cherel, Y., Ridoux, V. & Rodhouse, P. G. Fish and squid in the diet of king penguin chicks, Aptenodytes patagonicus, during winter at sub-antarctic Crozet Islands. Marine Biology 126, 559–570 (1996).
Google Scholar 
Chiaradia, A., Costalunga, A. & Kerry, K. The diet of Little Penguins (Eudyptula minor) at Phillip Island, Victoria, in the absence of a major prey—Pilchard (Sardinops sagax). Emu – Austral Ornithology 103, 43–48 (2003).
Google Scholar 
Chiaradia, A. et al. Diet segregation between two colonies of little penguins Eudyptula minor in southeast Australia. Austral Ecology 37, 610–619 (2012).
Google Scholar 
Corsolini, S., Ademollo, N., Romeo, T., Olmastroni, S. & Focardi, S. Persistent organic pollutants in some species of a Ross Sea pelagic trophic web. Antartic science 15, 95–104 (2003).
Google Scholar 
Crawford, R. J. M. et al. Decrease in Numbers of the Eastern Rockhopper Penguin Eudyptes Chrysocome Filholi at Marion Island, 1994/95–2002/03. African Journal of Marine Science 25, 487–498 (2003).
Google Scholar 
Crawford, R. J. M. & Dyer, B. M. Responses by four seabird species to a fluctuating availability of Cape Anchovy Engraulis capensis off South Africa. Ibis 137, 329–339 (1995).
Google Scholar 
Croxall, J. P., Prince, P. A., Baird, A. & Ward, P. The diet of the Southern rockhopper penguin Eudyptes chrysocome chrysocome at Beauchene Island, Falkland Islands. Journal of Zoology 206, 485–496 (1985).
Google Scholar 
Cullen, J. M., Montague, T. L. & Hull, C. Food of Little Penguins Eudyptula minor in Victoria: Comparison of Three Localities between 1985 and 1988. Emu – Austral Ornithology 91, 318–341 (1991).
Google Scholar 
Dakwa, F. et al. Long-term variation in the breeding diets of macaroni and eastern rockhopper penguins at Marion Island (1994–2018). African Journal of Marine Science 43, 187–199 (2021).
Google Scholar 
De Paula, A. A., Ott, P. H., Tavares, M., Santos, R. A. & Silva-Souza, Â. T. Host–parasite relationship in Magellanic Penguins (Spheniscus magellanicus) during their long northward journey to the Brazilian coast. Polar Biol 43, 1261–1272 (2020).
Google Scholar 
Deagle, B. E., Chiaradia, A., McInnes, J. & Jarman, S. N. Pyrosequencing faecal DNA to determine diet of little penguins: is what goes in what comes out? Conserv Genet 11, 2039–2048 (2010).
Google Scholar 
Dehnhard, N. et al. Plasticity in foraging behaviour and diet buffers effects of inter-annual environmental differences on chick growth and survival in southern rockhopper penguins Eudyptes chrysocome chrysocome. Polar Biol 39, 1627–1641 (2016).
Google Scholar 
Emslie, S. D., Polito, M. J. & Patterson, W. P. Stable isotope analysis of ancient and modern gentoo penguin egg membrane and the krill surplus hypothesis in Antarctica. Antarctic Science 25, 213–218 (2013).
Google Scholar 
Fernandez, S. J., Yorio, P. & Ciancio, J. E. Diet composition of expanding breeding populations of the Magellanic Penguin. Marine Biology Research 15, 84–96 (2019).
Google Scholar 
Flemming, S. A. & Van Heezik, Y. Stable isotope analysis as a tool to monitor dietary trends in little penguins Eudyptula minor: Little penguin isotopic mixing models. Austral Ecology 39, 656–667 (2014).
Google Scholar 
Da, S., Fonseca, V. S., Petry, M. V. & Jost, A. H. Diet of the Magellanic Penguin on the Coast of Rio Grande do Sul, Brazil. Waterbirds: The International Journal of Waterbird Biology 24, 290 (2001).
Google Scholar 
Forero, M. et al. Food resource utilisation by the Magellanic penguin evaluated through stable-isotope analysis: segregation by sex and age and influence on offspring quality. Mar. Ecol. Prog. Ser. 234, 289–299 (2002).
Google Scholar 
Fragão, J. et al. Microplastics and other anthropogenic particles in Antarctica: Using penguins as biological samplers. Science of The Total Environment 788, 147698 (2021).
Google Scholar 
Fraser, M. M. & Lalas, C. Seasonal variation in the diet of blue penguins (Eudyptula minor) at Oamaru, New Zealand. Notornis 51, 7 (2004).
Google Scholar 
Fromant, A., Schumann, N., Dann, P., Cherel, Y. & Arnould, J. P. Y. Trophic niches of a seabird assemblage in Bass Strait, south-eastern Australia. PeerJ 8, e8700 (2020).
Google Scholar 
Gales, N. J., Klages, N. T. W., Williams, R. & Woehler, E. J. The diet of the emperor penguin, Aptenodytes forsteri, in Amanda Bay, Princess Elizabeth Land, Antarctica. Antartic science 2, 23–28 (1990).
Google Scholar 
Gandini, P. A., Frere, E., Pettovello, A. D. & Cedrola, P. V. Interaction between Magellanic Penguins and Shrimp Fisheries in Patagonia, Argentina. The Condor 101, 783–789 (1999).
Google Scholar 
Gauthier-Clerc, M., Le Maho, Y., Clerquin, Y., Bost, C. & Handrich, Y. Seabird reproduction in an unpredictable environment: how King penguins provide their young chicks with food. Mar. Ecol. Prog. Ser. 237, 291–300 (2002).
Google Scholar 
Guímaro, H. R. et al. Cephalopods habitat and trophic ecology: historical data using snares penguin as biological sampler. Polar Biol 44, 73–84 (2021).
Google Scholar 
Herling, C., Culik, B. M. & Hennicke, J. C. Diet of the Humboldt penguin (Spheniscus humboldti) in northern and southern Chile. Marine Biology 147, 13–25 (2005).
Google Scholar 
Hindell, M. A. The diet of the King Penguin Aptenodytes patagonicus at Macquarie Island. Ibis 130, 193–203 (1988).
Google Scholar 
Hong, S.-Y. et al. Regional differences in the diets of Adélie and Emperor penguins in the Ross Sea, Antarctica. Animals 11, 2681 (2021).
Google Scholar 
Hull, C. L. Comparison of the diets of breeding royal (Eudyptes schlegeli) and rockhopper (Eudyptes chrysocome) penguins on Macquarie Island over three years. Journal of Zoology 247, 507–529 (1999).
Google Scholar 
Jafari, V. et al. Spatial and temporal diet variability of Adélie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) Penguin: a multi tissue stable isotope analysis. Polar Biol 44, 1869–1881 (2021).
Google Scholar 
Jarman, S. N., Gales, N. J., Tierney, M., Gill, P. C. & Elliott, N. G. A DNA‐based method for identification of krill species and its application to analysing the diet of marine vertebrate predators. Molecular Ecology 11, 2679–2690 (2002).
Google Scholar 
Jarman, S. N. et al. Adélie penguin population diet monitoring by analysis of food DNA in scats. PLoS ONE 8, e82227 (2013).
Google Scholar 
Juáres, M. A. et al. Diet of Adélie penguins (Pygoscelis adeliae) at Stranger Point (25 de Mayo/King George Island, Antarctica) over a 13-year period (2003–2015). Polar Biol 41, 303–311 (2018).
Google Scholar 
Juáres, M. A., Santos, M., Mennucci, J. A., Coria, N. R. & Mariano-Jelicich, R. Diet composition and foraging habitats of Adélie and gentoo penguins in three different stages of their annual cycle. Mar Biol 163, 105 (2016).
Google Scholar 
Kent, S., Seddon, J., Robertson, G. & Wienecke, B. Diet of Adelie Penguins Pygoscelis Adeliae at Shirley Island, East Antarctica, January 1992. MO 26, (1998).Kirkwood, R. & Robertson, G. Seasonal change in the foraging ecology of emperor penguins on the Mawson Coast, Antarctica. Mar. Ecol. Prog. Ser. 156, 205–223 (1997).
Google Scholar 
Kirkwood, R. & Robertson, G. The foraging ecology of female emperor penguins in winter. Ecological Monographs 67, 155–176 (1997).
Google Scholar 
Klages, N. Food and feeding ecology of emperor penguins in the eastern Weddell Sea. Polar Biol 9, 385–390 (1989).
Google Scholar 
Klages, N. T., Brooke, M. D. L. & Watkins, B. P. Prey of Northern Rockhopper penguins at Gough Island, South Atlantic Ocean. Ostrich 59, 162–165 (1988).
Google Scholar 
Klomp, N. & Wooller, R. Diet of little penguins, Eudyptula minor, from Penguin Island, Western Australia. Mar. Freshwater Res. 39, 633 (1988).
Google Scholar 
Kowalczyk, N. D., Chiaradia, A., Preston, T. J. & Reina, R. D. Fine-scale dietary changes between the breeding and non-breeding diet of a resident seabird. R. Soc. open sci. 2, 140291 (2015).
Google Scholar 
Laugksch, R. C. & Adams, N. J. Trends in pelagic fish populations of the Saldanha Bay region, southern Benguela upwelling system, 1980–1990: a predator’s perspective. South African Journal of Marine Science 13, 295–307 (1993).
Google Scholar 
Ludynia, K., Roux, J.-P., Jones, R., Kemper, J. & Underhill, L. G. Surviving off junk: low-energy prey dominates the diet of African penguins Spheniscus demersus at Mercury Island, Namibia, between 1996 and 2009. African Journal of Marine Science 32, 563–572 (2010).
Google Scholar 
Lynnes, A. S., Reid, K. & Croxall, J. P. Diet and reproductive success of Adélie and chinstrap penguins: linking response of predators to prey population dynamics. Polar Biol 27, (2004).Maccapan, D. et al. Effects of sea-ice persistence on the diet of Adélie penguin (Pygoscelis adeliae) chicks and the trophic differences between chicks and adults in the Ross Sea, Antarctica. Biology 12, 708 (2023).
Google Scholar 
Marques, F. P., Cardoso, L. G., Haimovici, M. & Bugoni, L. Trophic ecology of Magellanic penguins (Spheniscus magellanicus) during the non-breeding period. Estuarine, Coastal and Shelf Science 210, 109–122 (2018).
Google Scholar 
Masello, J. F. et al. How animals distribute themselves in space: energy landscapes of Antarctic avian predators. Mov Ecol 9, 24 (2021).
Google Scholar 
Miller, A. K. & Trivelpiece, W. Z. Cycles of Euphausia superba recruitment evident in the diet of Pygoscelid penguins and net trawls in the South Shetland Islands, Antarctica. Polar Biol 30, 1615–1623 (2007).
Google Scholar 
Moore, P. J. & Wakelin, M. Diet of the Yellow-eyed Penguin Megadyptes antipodes, South Island, New Zealand, 1991_1993. MO 25, (1997).Murray, D. C. et al. DNA-based faecal dietary analysis: A comparison of qPCR and High Throughput sequencing approaches. PLoS ONE 6, e25776 (2011).
Google Scholar 
Negrete, P. et al. Temporal variation in isotopic composition of Pygoscelis penguins at Ardley Island, Antarctic: Are foraging habits impacted by environmental change? Polar Biol 40, 903–916 (2017).
Google Scholar 
Panasiuk, A., Wawrzynek-Borejko, J., Musiał, A. & Korczak-Abshire, M. Pygoscelis penguin diets on King George Island, South Shetland Islands, with a special focus on the krill Euphausia superba. Antarctic Science 32, 21–28 (2020).
Google Scholar 
Petrovski, N., Sutton, G. J. & Arnould, J. P. Y. Energetic consequences of prey type in little penguins (Eudyptula minor). R. Soc. open sci. 10, 221595 (2023).
Google Scholar 
Pickett, E. P. et al. Spatial niche partitioning may promote coexistence of Pygoscelis penguins as climate‐induced sympatry occurs. Ecology and Evolution 8, 9764–9778 (2018).
Google Scholar 
Puddicombe, R. A. & Johnstone, G. W. The breeding season diet of Adélie penguins at the Vestfold Hills, East Antarctica. Hydrobiologia 165, 239–253 (1988).
Google Scholar 
Pütz, K., Ingham, R., Smith, J. & Croxall, J. Population trends, breeding success and diet composition of gentoo Pygoscelis papua, magellanic Spheniscus magellanicus and rockhopper Eudyptes chrysocome penguins in the Falkland Islands. A review. Polar Biology 24, 793–807 (2001).
Google Scholar 
Pütz, K. The post-moult diet of Emperor Penguins (Aptenodytes forsteri) in the eastern Weddell Sea, Antarctica. Polar Biol 15, (1995).Raclot, T., Groscolas, R. & Cherel, Y. Fatty acid evidence for the importance of myctophid fishes in the diet of king penguins, Aptenodytes patagonicus. Marine Biology 132, 523–533 (1998).
Google Scholar 
Ramírez, F. et al. Natural and anthropogenic factors affecting the feeding ecology of a top marine predator, the Magellanic penguin. Ecosphere 5, 1–21 (2014).
Google Scholar 
Raya Rey, A. & Schiavini, A. Inter-annual variation in the diet of female southern rockhopper penguin (Eudyptes chrysocome chrysocome) at Tierra del Fuego. Polar Biol 28, 132–141 (2005).
Google Scholar 
Raya Rey, A., Trathan, P. & Schiavini, A. Inter‐annual variation in provisioning behaviour of Southern Rockhopper Penguins Eudyptes chrysocome chrysocome at Staten Island, Argentina. Ibis 149, 826–835 (2007).
Google Scholar 
Ridoux, V. The diets and dietary segregation of seabirds at the subantarctic Crozet Islands (part 2). MO 22, (1994).Robinson, S. A. & Hindell, M. A. Foraging ecology of Gentoo Penguins Pygoscelis papua at Macquarie Island during the period of chick care. Ibis 138, 722–731 (1996).
Google Scholar 
Rombolá, E. F., Marschoff, E. & Coria, N. Inter-annual variability in Chinstrap penguin diet at South Shetland and South Orkneys Islands. Polar Biol 33, 799–806 (2010).
Google Scholar 
Santos, R. A. & Haimovici, M. Trophic relationships of the long-finned squid Loligo sanpaulensis on the southern Brazilian shelf. South African Journal of Marine Science 20, 81–91 (1998).
Google Scholar 
Scioscia, G., Raya Rey, A., Saenz Samaniego, R. A., Florentín, O. & Schiavini, A. Intra- and interannual variation in the diet of the Magellanic penguin (Spheniscus magellanicus) at Martillo Island, Beagle Channel. Polar Biol 37, 1421–1433 (2014).
Google Scholar 
Scolaro, J. A. et al. Feeding preferences of the Magellanic penguin over its breeding range in Argentina. Waterbirds: The International Journal of Waterbird Biology 22, 104 (1999).
Google Scholar 
Sherley, R. et al. Influence of local and regional prey availability on breeding performance of African penguins Spheniscus demersus. Mar. Ecol. Prog. Ser. 473, 291–301 (2013).
Google Scholar 
Silva, L. A. et al. Diferencias estacionales en la dieta de individuos juveniles del Pingüino Patagónico (Spheniscus magellanicus) reveladas en base al análisis de isótopos estables en uñas. El Hornero 30, 45–54 (2015).
Google Scholar 
Silvestro, A. M. & Casaux, R. Diet composition of Adelie penguins Pygoscelis adeliae at Hope/Esperanza Bay during 2014–2019. MO 51, (2023).Sutton, G. J. et al. Fine-scale foraging effort and efficiency of Macaroni penguins is influenced by prey type, patch density and temporal dynamics. Mar Biol 168, 3 (2021).
Google Scholar 
Tierney, M., Nichols, P. D., Wheatley, K. E. & Hindell, M. A. Blood fatty acids indicate inter- and intra-annual variation in the diet of Adélie penguins: Comparison with stomach content and stable isotope analysis. Journal of Experimental Marine Biology and Ecology 367, 65–74 (2008).
Google Scholar 
Tremblay, Y., Guinard, E. & Cherel, Y. Maximum diving depths of northern rockhopper penguins (Eudyptes chrysocome moseleyi) at Amsterdam Island. Polar Biology 17, 119–122 (1997).
Google Scholar 
Van Heezik, Y. Diets of yellow-eyed, Fiordland crested, and little blue penguins breeding sympatrically on Codfish Island, New Zealand. New Zealand Journal of Zoology 17, 543–548 (1990).
Google Scholar 
Van Heezik, Y. Seasonal, geographical, and age-related variations in the diet of the yellow-eyed penguin (Megadyptes antipodes). New Zealand Journal of Zoology 17, 201–212 (1990).
Google Scholar 
Vanstreels, R. E. T. et al. Seashell and debris ingestion by African penguins. Emu – Austral Ornithology 120, 90–96 (2020).
Google Scholar 
Waluda, C., Hill, S., Peat, H. & Trathan, P. Diet variability and reproductive performance of macaroni penguins Eudyptes chrysolophus at Bird Island, South Georgia. Mar. Ecol. Prog. Ser. 466, 261–274 (2012).
Google Scholar 
Wienecke, B. & Robertson, G. Foraging space of emperor penguins Aptenodytes forsteri in Antarctic shelf waters in winter. Mar. Ecol. Prog. Ser. 159, 249–263 (1997).
Google Scholar 
Wienecke, B. & Robertson, G. Comparison of foraging strategies of incubating king penguins Aptenodytes patagonicus from Macquarie and Heard islands. Polar Biol 29, 424–438 (2006).
Google Scholar 
Yorio, P., González-Zevallos, D., Gatto, A., Biagioni, O. & Castillo, J. Relevance of forage fish in the diet of Magellanic penguins breeding in northern Patagonia, Argentina. Marine Biology Research 13, 603–617 (2017).
Google Scholar 
Rioseco, E. A. Dieta del pingüino de Magallanes durante la temporada reproductiva 1992–93 en el Seno Otway, sur de Chile. Revista chilena de Ornitología 24, 15–19 (2018).
Google Scholar 
Chamberlain, S., Szoecs, E., Foster, Z. & Arendsee, Z. taxize: Taxonomic Information from Around the Web. 0.10.0 https://doi.org/10.32614/CRAN.package.taxize (2012).
Google Scholar 
Download referencesAcknowledgementsWe would like to thank Helena Buil for her invaluable assistance with literature screening and data extraction. We also sincerely thank the Handling Editor and the two anonymous reviewers for their insightful and constructive feedback on the manuscript and accompanying dataset. This work is supported by the Spanish government through the following projects: SOSPEN (Spanish National Plan for Scientific and Technical Research and Innovation, 2021, PID2021-124831OA-I00), SEASentinels (Spanish National Plan for Scientific and Technical Research and Innovation, 2023, CNS2022-135631), and ProOceans (Spanish National Plan for Scientific and Technical Research and Innovation, 2020, PID2020-118097RB-I00). Additionally, this research is part of the Integrated Marine Ecosystem Assessments (iMARES) research group, funded by the Agència de Gestió d’Ajuts Universitaris i de Recerca (Generalitat de Catalunya), Grant No. 2021 SGR 00435. MG was supported by the FPI-SO fellowship (PRE2022-101875). This study is a contribution to the ICM-TEC (Trophic Ecology and Connectivity Scientific-Technical service of the Institut de Ciències del Mar CSIC; https://www.icm.csic.es/en/service/trophic-ecology-and-connectivity).Author informationAuthors and AffiliationsInstitut de Ciències del Mar, Recursos Marins Renovables, Barcelona, SpainFrancisco Ramírez, Claudia Aparicio-Estalella, Míriam Gimeno & Marta CollAuthorsFrancisco RamírezView author publicationsSearch author on:PubMed Google ScholarClaudia Aparicio-EstalellaView author publicationsSearch author on:PubMed Google ScholarMíriam GimenoView author publicationsSearch author on:PubMed Google ScholarMarta CollView author publicationsSearch author on:PubMed Google ScholarContributionsF.R.: conceptualization, methodology, investigation, writing – original draft, supervision, project administration, funding acquisition; C.A.: methodology, investigation, data curation, writing – review & editing; M.G.: visualization, writing review & editing; M.C.: conceptualization, methodology, supervision, writing – review & editing, funding acquisition.Corresponding authorCorrespondence to
Francisco Ramírez.Ethics declarations

Competing interests
The authors declare no competing interests.

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Sci Data (2025). https://doi.org/10.1038/s41597-025-06458-8Download citationReceived: 19 March 2025Accepted: 11 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41597-025-06458-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractEnvironmental changes including urbanization significantly influence the spatial distribution and the ecology of mosquito vectors, such as Aedes albopictus and Aedes aegypti, which are responsible of the transmitting of dengue, chikungunya, and Zika arboviruses. While studies often focus on breeding site typology, the physicochemical characteristics of these habitats remain underexplored, especially in sub-Saharan Africa. This study investigates (i) the larval ecology of Ae. albopictus and Ae. aegypti in Franceville, an equatorial forest region undergoing urbanization, south-eastern Gabon, and (ii) emphasizing habitat typology and the physicochemical attributes influencing their proliferation. Field larval surveys were conducted across central, intermediate, and peripheral settings of the town, documenting the diversity of larval habitats and their physical features (nature, substrate material and size) and the mosquito species recovered. Water samples were analysed to determine physicochemical properties including pH, salinity, conductivity, and the presence of organic matter. The results reveal significant physicochemical heterogeneity across settings, with central urban areas more characterised by plastic (12.9%) and rubber (10.7%) breeding sites while peripheral areas were dominated by cement microhabitats (15.7%). Notably, the findings have clarified the ecological niche of these two species (Ae. albopictus and Ae. aegypti), revealing a preference for anthropogenic water bodies composed of rubber, plastic, or cement materials, with small to medium surface areas (< 1,250 cm2) and low to medium salinity levels (< 0.4 ppt). These findings underscore the importance of integrating physicochemical analyses into vector ecology studies to enhance our understanding of vector proliferation in rapidly urbanizing regions. By addressing this knowledge gap, the study provides critical insights to inform public health strategies and urban planning, offering a foundation for targeted vector control interventions.

Data availability

All data generated or analysed during this study are included in this published article.
AbbreviationsPCA:
Principal component analysis
GLM:
Generalized linear model
ReferencesDjiappi-Tchamen, B. et al. Aedes mosquito distribution along a transect from rural to urban settings in Yaoundé, Cameroon. Insects 12(9), 819 (2021).
Google Scholar 
Obame-Nkoghe, J. et al. Urban green spaces and vector-borne disease risk in Africa: The case of an unclean forested park in libreville (Gabon, Central Africa). Int. J. Environ. Res. Public Health 20(10), 5774 (2023).
Google Scholar 
Medeiros-Sousa, A. R. et al. Influence of water’s physical and chemical parameters on mosquito (Diptera: Culicidae) assemblages in larval habitats in urban parks of Sao Paulo, Brazil. Acta Trop. 205, 105394 (2020).
Google Scholar 
Herath, J. M. K., De Silva, W. P. P., Weeraratne, T. C. & Karunaratne, S. P. Breeding habitat preference of the dengue vector mosquitoes aedes aegypti and aedes albopictus from urban, semiurban, and rural areas in Kurunegala District, Sri Lanka. J. Trop. Med. 2024(1), 4123543 (2024).
Google Scholar 
Ngoagouni, C., Kamgang, B., Nakouné, E., Paupy, C. & Kazanji, M. Invasion of Aedes albopictus (Diptera: Culicidae) into central Africa: What consequences for emerging diseases?. Parasit. Vectors. 8, 1–7 (2015).
Google Scholar 
Ovono, P. O., Gatarasi, T., Minko, D. O., Koumagoye, D. M. & Kevers, C. Etude de la dynamique des populations d’insectes sur la culture du riz NERICA dans les conditions du Masuku, Sud-Est du Gabon (Franceville). Int. J. Biol. Chem. Sci. 8(1), 218–236 (2014).
Google Scholar 
Cockerell, T. D. A. The African mosquitoes: Mosquitoes of the Ethiopian Region III. Culicine adults and pupae. By F. W. Edwards. British Museum, 1941. 499 pp.. Science 96(2502), 537–538 (1942).
Google Scholar 
Huang, Y. M. The subgenus Stegomyia of Aedes in the Afrotropical Region with keys to the species (Diptera: Culicidae). Zootaxa 700(1), 1–120 (2004).
Google Scholar 
Stewart Ibarra, A. M. et al. A social-ecological analysis of community perceptions of dengue fever and Aedes aegypti in Machala, Ecuador. BMC Public Health 4(14), 1135 (2014).
Google Scholar 
Pagès, F. et al. Aedes albopictus mosquito: The main vector of the 2007 Chikungunya outbreak in Gabon. PLoS ONE 4(3), e4691 (2009).
Google Scholar 
Paupy, C. et al. Comparative role of Aedes albopictus and Aedes aegypti in the emergence of Dengue and Chikungunya in central Africa. Vector Borne Zoonotic Dis. Larchmt. N. 10(3), 259–266 (2010).
Google Scholar 
Delatte, H. et al. Blood-Feeding Behavior of Aedes albopictus, a Vector of Chikungunya on La Réunion. Vector-Borne Zoonotic Dis. 10(3), 249–258 (2010).
Google Scholar 
Deerman, H. & Yee, D. A. Competitive interactions with Aedes albopictus alter the nutrient content of Aedes aegypti. Med. Vet. Entomol. 37(4), 715–722 (2023).
Google Scholar 
Yang, B. et al. Modelling distributions of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competition. PLoS Negl. Trop Dis. 15(3), e0009063 (2021).
Google Scholar 
Multini, L. C. et al. The influence of the pH and salinity of water in breeding sites on the occurrence and community composition of immature mosquitoes in the green belt of the City of São Paulo, Brazil. Insects 12(9), 797 (2021).
Google Scholar 
Ouédraogo, W. M. et al. Impact of physicochemical parameters of Aedes aegypti breeding habitats on mosquito productivity and the size of emerged adult mosquitoes in Ouagadougou City, Burkina Faso. Parasit. Vectors. 15(1), 478 (2022).
Google Scholar 
Ruairuen, W., Amnakmanee, K., Primprao, O. & Boonrod, T. Effect of ecological factors and breeding habitat types on Culicine larvae occurrence and abundance in residential areas Southern Thailand. Acta Trop. 234, 106630 (2022).
Google Scholar 
Download referencesAcknowledgementsWe would like to thank the institutions that helped us carry out this study, in particular the Interdisciplinary Centre for Medical Research of Franceville (CIRMF) and the Masuku University of Science and Technology (USTM), for the technical support they provided. We would particularly like to thank the Biology Department of the Faculty of Science of the USTM, and the staff of the Health Ecology Research Unit of the CIRMF and the Zoology and Entomology Department of the UFS, who welcomed us.FundingThis study has been conducted with the financial support of the European Union (Grant no. ARISE-PP-FA-072 to JON), through the African Research Initiative for Scientific Excellence (ARISE), pilot program. ARISE is implemented by the African Academy of Sciences with support from the European Commission and the African Union Commission. This study also benefited from the internal support of the University of the Free State, South Africa (to PVO), for English editing services in addition to the salary support provided to the corresponding author by the University of Science and Technology of Masuku and the Interdisciplinary Centre for Medical Research, Gabon. We benefited from the support GDRI-GRAVIR network (led by CP) in conceptualizing the study. The contents of this document are the sole responsibility of the authors and can under no circumstances be regarded as reflecting the position of the European Union, the African Academy of Sciences, the African Union Commission, or the institutions to which the authors are affiliated. The funders played no role in the design of the study, the collection and analysis of the data, the decision to publish or the preparation of the manuscript.Author informationAuthors and AffiliationsLab-MC, Département de Biologie, Faculté Des Sciences de L, Université Des Sciences Et Techniques de Masuku (USTM), BP 901, Franceville, GabonJudicaël Obame-Nkoghe, Faël Moudoumi Kondji, Brad Ghaven Niangui & Landry Erik MomboEcotoxicology Research Laboratory, Department of Zoology and Entomology, Faculty of Natural and Agricultural Sciences, University of the Free State, Private Bag x13, Phuthaditjhaba, 9866, Republic of South AfricaJudicaël Obame-Nkoghe & Patricks Voua OtomoAgence Nationale Des Parc Nationaux, Libreville, GabonRicardo Ewak ObameCentre Interdisciplinaire de Recherches Médicales de Franceville (CIRMF), BP 769, Franceville, GabonArnauld Ondo Oyono, Natif Yapet Koumlah, Patrick Yangari, Neil Michel Longo-Pendy, Lynda Chancelya Nkoghe Nkoghe, Marc-Flaubert Ngangue & Yasmine Okomo NguemaMIVEGEC, Univ. Montpellier, CNRS, Montpellier, IRD, FranceChristophe Paupy & Pierre KengneCentre for Global Change, University of the Free State, Private Bag x13, Phuthaditjhaba, 9866, Republic of South AfricaPatricks Voua OtomoAuthorsJudicaël Obame-NkogheView author publicationsSearch author on:PubMed Google ScholarFaël Moudoumi KondjiView author publicationsSearch author on:PubMed Google ScholarBrad Ghaven NianguiView author publicationsSearch author on:PubMed Google ScholarRicardo Ewak ObameView author publicationsSearch author on:PubMed Google ScholarArnauld Ondo OyonoView author publicationsSearch author on:PubMed Google ScholarNatif Yapet KoumlahView author publicationsSearch author on:PubMed Google ScholarPatrick YangariView author publicationsSearch author on:PubMed Google ScholarNeil Michel Longo-PendyView author publicationsSearch author on:PubMed Google ScholarLynda Chancelya Nkoghe NkogheView author publicationsSearch author on:PubMed Google ScholarMarc-Flaubert NgangueView author publicationsSearch author on:PubMed Google ScholarYasmine Okomo NguemaView author publicationsSearch author on:PubMed Google ScholarLandry Erik MomboView author publicationsSearch author on:PubMed Google ScholarChristophe PaupyView author publicationsSearch author on:PubMed Google ScholarPatricks Voua OtomoView author publicationsSearch author on:PubMed Google ScholarPierre KengneView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualisation: JO-N, PK, CP; Data curation: NYK, AOO, JO-N, FMK, BGN, MFN, LCNN, PY, NMLP; Formal analysis: JO-N, AOO, YON, NYK, FMK, REO; Data visualization: FMK, LEM, PVO, REO, JO-N; First article drafting: JO-N, FMK, AOO; Reviewing and editing: PK, PVO, LEM, YON, Acquisition of funding: JO-N, PVO.Corresponding authorCorrespondence to
Judicaël Obame-Nkoghe.Ethics declarations

Competing interests
The authors declare no competing interests.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleObame-Nkoghe, J., Moudoumi Kondji, F., Niangui, B.G. et al. Physicochemical and typological insights into Aedes albopictus and Aedes aegypti larval habitats in a sub-Saharan African urban gradient setting.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32398-9Download citationReceived: 17 June 2025Accepted: 10 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41598-025-32398-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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Keywords
Aedes
Larval habitatsPhysicochemical featuresAfrican urban settingGabonArbovirus transmission More