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Entropy Approach of Processing for Fish Acoustic Telemetry Data to Detect Atypical Behavior During Welfare Evaluation

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Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13920))

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Abstract

Fish telemetry is an important tool for studying fish behavior, allowing to monitor fish movements in real-time. Analyzing telemetry data and translating it into meaningful indicators of fish welfare remains a challenge. This is where entropy approaches can provide valuable insights. Methods based on information theory can quantify the complexity and unpredictability of animal behavior distribution, providing a comprehensive understanding of the animal state. Entropy-based techniques can analyze telemetry data and detect changes in fish behavior, or irregularity. By analyzing the accelerometer data, using entropy approach, it is possible to identify atypical behavior that may be indicative of compromised welfare

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References

  1. Prapti, D.R., Mohamed Shariff, A.R., Che Man, H., Ramli, N.M., Perumal, T., Shariff, M.: Internet of things (IoT)-based aquaculture: an overview of IoT application on water quality monitoring. Rev. Aquac. 14(2), 979–992 (2022)

    Article  Google Scholar 

  2. Rowan, N.J.: The role of digital technologies in supporting and improving fishery and aquaculture across the supply chain-Quo Vadis? Aquaculture and Fisheries (2022)

    Google Scholar 

  3. Zhang, H., Gui, F.: The application and research of new digital technology in marine aquaculture. J. Mar. Sci. Eng. 11(2), 401 (2023)

    Article  Google Scholar 

  4. O’Donncha, F., Grant, J.: Precision aquaculture. IEEE Internet Things Mag. 2(4), 26–30 (2019)

    Article  Google Scholar 

  5. Bárta, A., Souček, P., Bozhynov, V., Urbanová, P., Bekkozhayeova, D.: Trends in online biomonitoring. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2018. LNCS, vol. 10813, pp. 3–14. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78723-7_1

    Chapter  Google Scholar 

  6. Mustapha, U.F., Alhassan, A.W., Jiang, D.N., Li, G.L.: Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA). Rev. Aquac. 13(4), 2076–2091 (2021)

    Article  Google Scholar 

  7. Yadav, A., Noori, M.T., Biswas, A., Min, B.: A concise review on the recent developments in the internet of things (IoT)-based smart aquaculture practices. Rev. Fish. Sci. Aquac. 31(1), 103–118 (2023)

    Article  Google Scholar 

  8. Abinaya, T., Ishwarya, J., Maheswari, M.: A novel methodology for monitoring and controlling of water quality in aquaculture using internet of things (IoT). In: 2019 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–4. IEEE (2019)

    Google Scholar 

  9. Rastegari, H., et al.: Internet of things in aquaculture: a review of the challenges and potential solutions based on current and future trends. Smart Agric. Technol. 4, 100187 (2023)

    Article  Google Scholar 

  10. Sun, M., Yang, X., Xie, Y.: Deep learning in aquaculture: a review. J. Comput. 31(1), 294–319 (2020)

    Google Scholar 

  11. Gladju, J., Kamalam, B.S., Kanagaraj, A.: Applications of data mining and machine learning framework in aquaculture and fisheries: a review. Smart Agric. Technol. 4, 100061 (2022)

    Article  Google Scholar 

  12. Antonucci, F., Costa, C.: Precision aquaculture: a short review on engineering innovations. Aquac. Int. 28(1), 41–57 (2020)

    Article  Google Scholar 

  13. Hu, Z., Li, R., Xia, X., Yu, C., Fan, X., Zhao, Y.: A method overview in smart aquaculture. Environ. Monit. Assess. 192(8), 1–25 (2020). https://doi.org/10.1007/s10661-020-08409-9

    Article  Google Scholar 

  14. Saberioon, M., Gholizadeh, A., Cisar, P., Pautsina, A., Urban, J.: Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Rev. Aquac. 9(4), 369–387 (2017)

    Article  Google Scholar 

  15. Brijs, J., Føre, M., Gräns, A., Clark, T., Axelsson, M., Johansen, J.: Bio-sensing technologies in aquaculture: how remote monitoring can bring us closer to our farm animals. Philos. Trans. R. Soc. B 376(1830), 20200218 (2021)

    Article  CAS  Google Scholar 

  16. Pramana, R., Suprapto, B.Y., Nawawi, Z.: Remote water quality monitoring with early-warning system for marine aquaculture. In: E3S Web of Conferences. vol. 324, p. 05007. EDP Sciences (2021)

    Google Scholar 

  17. Davidson, K., et al.: HABreports: online early warning of harmful algal and biotoxin risk for the Scottish shellfish and finfish aquaculture industries. Front. Mar. Sci. 8, 631732 (2021)

    Article  Google Scholar 

  18. Zhabitskii, M., Andryenko, Y., Malyshev, V., Chuykova, S., Zhosanov, A.: Digital transformation model based on the digital twin concept for intensive aquaculture production using closed water circulation technology. In: IOP Conference Series: Earth and Environmental Science, vol. 723, p. 032064. IOP Publishing (2021)

    Google Scholar 

  19. Lima, A.C., Royer, E., Bolzonella, M., Pastres, R.: Digital twin prototypes in flow-through systems for finfish. Aquaculture 2021 (2021)

    Google Scholar 

  20. Lan, H.Y., Ubina, N.A., Cheng, S.C., Lin, S.S., Huang, C.T.: Digital twin architecture evaluation for intelligent fish farm management using modified analytic hierarchy process. Appl. Sci. 13(1), 141 (2022)

    Article  Google Scholar 

  21. Ahmed, A., Zulfiqar, S., Ghandar, A., Chen, Y., Hanai, M., Theodoropoulos, G.: Digital twin technology for aquaponics: towards optimizing food production with dynamic data driven application systems. In: Tan, G., Lehmann, A., Teo, Y.M., Cai, W. (eds.) AsiaSim 2019. CCIS, vol. 1094, pp. 3–14. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1078-6_1

    Chapter  Google Scholar 

  22. Muñoz, L., Aspillaga, E., Palmer, M., Saraiva, J.L., Arechavala-Lopez, P.: Acoustic telemetry: a tool to monitor fish swimming behavior in sea-cage aquaculture. Front. Mar. Sci. 7, 645 (2020)

    Article  Google Scholar 

  23. Føre, M.: Using acoustic telemetry to monitor the effects of crowding and delousing procedures on farmed Atlantic salmon (Salmo salar). Aquaculture 495, 757–765 (2018)

    Article  Google Scholar 

  24. Brownscombe, J.W., Griffin, L.P., Brooks, J.L., Danylchuk, A.J., Cooke, S.J., Midwood, J.D.: Applications of telemetry to fish habitat science and management. Can. J. Fish. Aquat. Sci. 79(8), 1347–1359 (2022)

    Article  Google Scholar 

  25. Brownscombe, J.W., et al.: Conducting and interpreting fish telemetry studies: considerations for researchers and resource managers. Rev. Fish Biol. Fish. 29, 369–400 (2019)

    Article  Google Scholar 

  26. Gesto, M., Zupa, W., Alfonso, S., Spedicato, M.T., Lembo, G., Carbonara, P.: Using acoustic telemetry to assess behavioral responses to acute hypoxia and ammonia exposure in farmed rainbow trout of different competitive ability. Appl. Anim. Behav. Sci. 230, 105084 (2020)

    Article  Google Scholar 

  27. Hassan, W., Føre, M., Urke, H.A., Ulvund, J.B., Bendiksen, E., Alfredsen, J.A.: New concept for measuring swimming speed of free-ranging fish using acoustic telemetry and doppler analysis. Biosys. Eng. 220, 103–113 (2022)

    Article  CAS  Google Scholar 

  28. Alfonso, S., Zupa, W., Spedicato, M.T., Lembo, G., Carbonara, P.: Use of telemetry sensors as a tool for health/welfare monitoring of European sea bass (Dicentrarchus labrax) in aquaculture. In: 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), pp. 262–267. IEEE (2021)

    Google Scholar 

  29. Azevedo, J., Bartolomeu, T., Teixeira, S., Teixeira, J.: Design concept of a non-invasive tagging device for blue sharks. In: Innovations in Mechanical Engineering II. icieng 2022. Lecture Notes in Mechanical Engineering, pp. 80–90. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09382-1_8

  30. Nguyen, V.M., Young, N., Brownscombe, J.W., Cooke, S.J.: Collaboration and engagement produce more actionable science: quantitatively analyzing uptake of fish tracking studies. Ecol. Appl. 29(6), e01943 (2019)

    Article  PubMed  Google Scholar 

  31. Williamson, M.J.: Analysing detection gaps in acoustic telemetry data to infer differential movement patterns in fish. Ecol. Evol. 11(6), 2717–2730 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bohaboy, E.C., Guttridge, T.L., Hammerschlag, N., Van Zinnicq Bergmann, M.P., Patterson III, W.F.: Application of three-dimensional acoustic telemetry to assess the effects of rapid recompression on reef fish discard mortality. ICES J. Mar. Sci. 77(1), 83–96 (2020)

    Google Scholar 

  33. Matley, J.K., et al.: Global trends in aquatic animal tracking with acoustic telemetry. Trends Ecol. Evol. 37(1), 79–94 (2022)

    Article  PubMed  Google Scholar 

  34. Lees, K.J., MacNeil, M.A., Hedges, K.J., Hussey, N.E.: Estimating survival in a remote community-based fishery using acoustic telemetry. Can. J. Fish. Aquat. Sci. 79(11), 1830–1842 (2022)

    Article  Google Scholar 

  35. Bassing, S.B., et al.: Are we telling the same story? comparing inferences made from camera trap and telemetry data for wildlife monitoring. Ecol. Appl. 33(1), e2745 (2023)

    Article  PubMed  Google Scholar 

  36. Hvas, M., Folkedal, O., Oppedal, F.: Fish welfare in offshore salmon aquaculture. Rev. Aquac. 13(2), 836–852 (2021)

    Article  Google Scholar 

  37. Arechavala-Lopez, P., Cabrera-Álvarez, M.J., Maia, C.M., Saraiva, J.L.: Environmental enrichment in fish aquaculture: a review of fundamental and practical aspects. Rev. Aquac. 14(2), 704–728 (2022)

    Article  Google Scholar 

  38. Jones, N.A., Webster, M.M., Salvanes, A.G.V.: Physical enrichment research for captive fish: time to focus on the details. J. Fish Biol. 99(3), 704–725 (2021)

    Article  PubMed  Google Scholar 

  39. Macaulay, G., Bui, S., Oppedal, F., Dempster, T.: Challenges and benefits of applying fish behaviour to improve production and welfare in industrial aquaculture. Rev. Aquac. 13(2), 934–948 (2021)

    Article  Google Scholar 

  40. Sloman, K.A., Bouyoucos, I.A., Brooks, E.J., Sneddon, L.U.: Ethical considerations in fish research. J. Fish Biol. 94(4), 556–577 (2019)

    Article  PubMed  Google Scholar 

  41. Runde, B.J., Michelot, T., Bacheler, N.M., Shertzer, K.W., Buckel, J.A.: Assigning fates in telemetry studies using hidden Markov models: an application to deepwater groupers released with descender devices. North Am. J. Fish. Manag. 40(6), 1417–1434 (2020)

    Article  Google Scholar 

  42. Elliott, C.W., Ridgway, M.S., Blanchfield, P.J., Tufts, B.L.: Novel insights gained from tagging walleye (Sander vitreus) with pop-off data storage tags and acoustic transmitters in Lake Ontario. J. Great Lakes Res. 49, 51–530 (2023)

    Article  Google Scholar 

  43. Smirnov, N.: Ob uklonenijah empiriceskoi krivoi raspredelenija. Recl. Math.(Matematiceskii Sb.) NS 6(48), 3–26 (1939)

    Google Scholar 

  44. Kolmogorov, A.: On determination of empirical low of distribution. J. Ital. Inst. Actuaries 4, 83–91 (1933)

    Google Scholar 

  45. Lee, S., Kim, M.: On entropy-based goodness-of-fit test for asymmetric student-t and exponential power distributions. J. Stat. Comput. Simul. 87(1), 187–197 (2017)

    Article  Google Scholar 

  46. Evren, A., Tuna, E.: On some properties of goodness of fit measures based on statistical entropy. Int. J. Res. Rev. Appl. Sci. 13, 192–205 (2012)

    Google Scholar 

  47. Shoaib, M., Siddiqui, I., Rehman, S., ur Rehman, S., Khan, S.: Speed distribution analysis based on maximum entropy principle and Weibull distribution function. Environ. Prog. Sustain. Energy 36(5), 1480–1489 (2017)

    Google Scholar 

  48. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)

    Article  Google Scholar 

  49. Wiener, N.: Cybernetics or Control and Communication in the Animal and the Machine. MIT Press, Cambridge (2019)

    Google Scholar 

  50. Tsallis, C.: Possible generalization of Boltzmann-Gibbs statistics. J. Stat. Phys. 52, 479–487 (1988)

    Article  Google Scholar 

  51. Havrda, J., Charvát, F.: Quantification method of classification processes. concept of structural \( a \)-entropy. Kybernetika 3(1), 30–35 (1967)

    Google Scholar 

  52. Jizba, P., Korbel, J., Zatloukal, V.: Tsallis thermostatics as a statistical physics of random chains. Phys. Rev. E 95(2), 022103 (2017)

    Article  PubMed  Google Scholar 

  53. Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)

    Article  Google Scholar 

  54. Urban, J., Vanek, J., Stys, D.: Preprocessing of microscopy images via Shannon’s entropy (2009)

    Google Scholar 

  55. Urban, J.: Information Entropy. Applications from Engineering with MATLAB Concepts, p. 43 (2016)

    Google Scholar 

  56. Martins, C.I., et al.: Behavioural indicators of welfare in farmed fish. Fish Physiol. Biochem. 38, 17–41 (2012)

    Article  CAS  PubMed  Google Scholar 

  57. Sánchez-Suárez, W., Franks, B., Torgerson-White, L.: From land to water: taking fish welfare seriously. Animals 10(9), 1585 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  58. Fife-Cook, I., Franks, B.: Positive welfare for fishes: rationale and areas for future study. Fishes 4(2), 31 (2019)

    Article  Google Scholar 

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Acknowledgments

The study was financially supported by the Ministry of Education, Youth and Sports of the Czech Republic - project CENAKVA (LM2018099) and the European Union’s research and innovation program under grant agreement No. 652831 (AQUAEXCEL3.0). Author thanks to M.Føre for data access and discussion.

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Authors and Affiliations

  1. Laboratory of Signal and Image Processing, Institute of Complex Systems, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, University of South Bohemia in Ceske Budejovice, Zamek 136, 37 333, Nove Hrady, Czech Republic

    Jan Urban

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Correspondence to Jan Urban .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas Ruiz

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Urban, J. (2023). Entropy Approach of Processing for Fish Acoustic Telemetry Data to Detect Atypical Behavior During Welfare Evaluation. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_2

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