FISH WELFARE AND ANTIMICROBIAL USE IN AQUACULTURE: A TEXT MINING APPROACH TO IDENTIFY KEY TRENDS
Abstract
The aquaculture industry faces growing concerns regarding fish welfare and the overuse of antimicrobials, both of which are critical to ensuring the sustainability and ethical practices within the sector. This study applies text mining and topic modeling techniques to analyze a large corpus of research papers and reports on fish welfare and antimicrobial use in aquaculture. The research aims to identify prevailing themes, trends, and emerging issues regarding these critical topics. Using Latent Dirichlet Allocation (LDA) for topic modeling and natural language processing (NLP) techniques for data cleaning, this study identifies key concerns related to antimicrobial resistance (AMR), fish health management, sustainable practices, and regulatory frameworks in aquaculture. The results suggest that while antimicrobial resistance remains a central issue, there is an increasing focus on alternative therapeutic strategies, probiotic use, and improved welfare standards for farmed fish. The study highlights the importance of addressing these concerns through evidence-based policies and industry innovations to promote sustainable aquaculture practices.
Keywords
Text mining, topic modeling, fish welfareHow to Cite
References
Costa AP, Roubach R, Dallago BSL, Bueno GW, McManus C, Bernal FEM. Influence of stocking density on growth performance and welfare of juvenile tilapia (Oreochromis niloticus) in cages. Arq Bras Med Vet Zootec. 2017;69(1):243–51. https://doi.org/10.1590/1678-4162-8939.
Ferri G, Lauteri C, Vergara A. Antibiotic Resistance in the Finfish Aquaculture Industry: a review. Antibiotics. 2022;11(11):1574. https://doi.org/10.3390/antibiotics11111574.
Chen J, Sun R, Pan C, Sun Y, Mai B, Li QX. Antibiotics and Food Safety in Aquaculture. J Agric Food Chem. 2020;68(43):11908–19. https://doi.org/10.1021/acs.jafc.0c03996.
Salamanca N, Giráldez I, Morales E, De La Rosa I, Herrera M. Phenylalanine and tyrosine as feed additives for reducing stress and Enhancing Welfare in Gilthead Seabream and Meagre. Animals. 2020;11(1):45. https://doi.org/10.3390/ani11010045.
Jani K, Srivastava V, Sharma P, Vir A, Sharma A. Easy Access to Antibiotics; spread of Antimicrobial Resistance and implementation of one Health Approach in India. J Epidemiol Glob Health. 2021;11(4):444–52. https://doi.org/10.1007/s44197-021-00008-2.
Bizzaro G, Vatland AK, Pampanin DM. The one-health approach in seaweed food production. Environ Int. 2022;158:106948. https://doi.org/10.1016/j.envint.2021.106948.
Miao L, Li H, Ding W, Lu S, Pan S, Guo X, Zhou XN, Wang D. Research priorities on One Health: a bibliometric analysis. Front Public Health. 2022;10:889854. https://doi.org/10.3389/fpubh.2022.889854.
Rømer Villumsen K, Bojesen AM. Addressing a rule of Thumb: modeling the effects of Meteorological conditions on prescription of antimicrobials in Aquaculture. Microbiol Spectr. 2022;10(5):e0175222. https://doi.org/10.1128/spectrum.01752-22.
Hossain A, Habibullah-Al-Mamun M, Nagano I, Masunaga S, Kitazawa D, Matsuda H. Antibiotics, antibiotic-resistant bacteria, and resistance genes in aquaculture: risks, current concern, and future thinking. Environ Sci Pollut Res Int. 2022;29(8):11054–75. https://doi.org/10.1007/s11356-021-17825-4.
Larsson DGJ, Flach CF. Antibiotic resistance in the environment. Nat Rev Microbiol. 2022;20:257–69. https://doi.org/10.1038/s41579-021-00649-x.
Okacha RC, Olatoye IO, Adedeji OB. Food safety impacts of antimicrobial use and their residues in aquaculture. Public Health Rev. 2018;39(21):1–22. https://doi.org/10.1186/s40985-018-0099-2.
De Souza GLE, De Brito KCT, Nakazato G, Cavalli LS, Otutumi LK, De Brito BG. Antimicrobials and resistant bacteria in global fish farming and the possible risk for public health. Arq Inst Biol. 2020;87:1–11. https://doi.org/10.1590/1808-1657000362019.
Sneddon LU, Braithwaite VA, Gentle MJ. Do fishes have nociceptors? Evidence for the evolution of a vertebrate sensory system. Proc. R. Soc. London Ser. B Biol. Sci. 2003;270:1115–1121.
Ashley PJ, Sneddon LU, McCrohan CR. Nociception in fish: stimulus-response properties of receptors on the head of trout Oncorhynchus mykiss. Brain Res. 2007;1166:47–54. https://doi.org/10.1016/j.brainres.2007.07.011.
De Pasquale C, Sturgill J, Braithwaite VA. A standardized protocol for preference testing to assess fish welfare. J Vis Exp. 2020;156:e60674. https://doi.org/10.3791/60674.
Fernö A, Folkedal O, Nilsson J, Kristiansen TS. Inside the fish brain: cognition, learning consciousness. In: Kristiansen T, Fernö A, Pavlidis M, van de Vis H, editors. The Welfare of Fish. Animal Welfare Cham. Springer, 2020;20.
Eroglu Y. Text Mining Approach for Trend Tracking in Scientific Research: a Case Study on Forest Fire. Fire. 2023;6:33. https://doi.org/10.3390/fire6010033.
Mongeon P, Paul-Hus A. The journal coverage of web of science and Scopus: a comparative analysis. Scientometrics. 2016;106(1):213–28.
Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097.
Sebastiani F. Machine learning in automated text categorization. ACM-CSUR. 2002;34(1):1–47.
Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Inf Process Manage. 1988;24(5):513–23. https://doi.org/10.1016/0306-4573(88)90021-0.
Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res. 2003;3:993–1022.
Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, Zhao L. Latent dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019;78(11):15169–211. https://doi.org/10.1007/s11042-018-6894-4.
Grün B, Hornik K. Topicmodels: an R Package for Fitting Topic models. J Stat Softw. 2011;40(13):1–30. https://doi.org/10.18637/jss.v040.i13.
Nalon E, Contiero B, Gottardo F, Cozzi G. The Welfare of Beef Cattle in the scientific literature from 1990 to 2019: a text Mining Approach. Front Vet Sci. 2021;7:588749. https://doi.org/10.3389/fvets.2020.588749.
Verschuere L, Rombaut G, Sorgeloos P, Verstraete W. Probiotic bacteria as biological control agents in aquaculture. Microbiol Mol Biol Rev. 2000;64(4):655–71. https://doi.org/10.1128/MMBR.64.4.655-671.2000.
Hong HA, Duc LH, Cutting SM. The use of bacterial spore formers as probiotics. FEMS Microbiol Rev. 2005;29(4):813–35. https://doi.org/10.1016/j.femsre.2004.12.001.
Seiler C, Berendonk TU. Heavy metal driven co-selection of antibiotic resistance in soil and water bodies impacted by agriculture and aquaculture. Front Microbiol. 2012;3:399. https://doi.org/10.3389/fmicb.2012.00399.
Huntingford FA, Adams C, Braithwaite VA, Kadri S, Pottinger TG, Sandøe P, Turnbull JF. Current issues in fish welfare. J Fish Biol. 2006;68(2):332–72. https://doi.org/10.1111/j.0022-1112.2006.001046.
Cabello FC, Godfrey HP, Tomova A, Ivanova L, Dölz H, Millanao A, Buschmann AH. Antimicrobial use in aquaculture re-examined: its relevance to antimicrobial resistance and to animal and human health. Environ Microbiol. 2013;15(7):191742. https://doi.org/10.1111/1462-2920.12134.
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