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Evaluating the Causal Role of Environmental Data in Shellfish Biotoxin Contamination on the Portuguese Coast

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Progress in Artificial Intelligence (EPIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14116))

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Abstract

Shellfish accumulation of marine biotoxins at levels unsafe for human consumption may severely impact their harvesting and farming, which has been grown worldwide in response to the growing demand for nutritious food and protein sources. In Southern European countries, diarrhetic shellfish poisoning (DSP) toxins are the most abundant and frequent toxins derived from algal blooms, affecting shellfish production yearly. Therefore, it is essential to understand the natural phenomenon of DSP toxins accumulation in shellfish and the meteorological and biological parameters that may regulate and influence its occurrence. In this work, we studied the relationship between the time series of several meteorological and biological variables and the time series of the concentration of DSP toxins in mussels on the Portuguese coast, using the Pearson’s correlation coefficient, time series regression modeling, Granger causality, and dynamic Bayesian networks using the MAESTRO tool. The results show that, for the models tested, the mean sea surface and air temperature time series with a one, two, or three-week lag can be valuable candidate predictors for forecasting the DSP concentration in mussels. Overall, this proof-of-concept study emphasizes the importance of statistical learning methodologies for analyzing time series environmental data and illustrates the importance of several variables in predicting DSP biotoxins concentration, which can help the shellfish production sector mitigate the negative impacts of DSP biotoxins accumulation.

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Acknowledgment

This work was funded by Fundação para a Ciência e a Tecnologia (FCT) through project MATISSE (DSAIPA/DS/0026/2019), and also UIDB/00297/2020, UIDP/00297/2020 (NOVA Math), UIDB/00667/2020, UIDP/00667/2020 (UNIDEMI), UIDB/50008/2020 (IT), UIDB/50021/2020 (INESC-ID), PTDC/CTM-REF/2679/2020, CEECINST/00042/2021. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 951970 - OLISSIPO project.

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Authors and Affiliations

  1. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029, Lisbon, Portugal

    Ana Rita Baião, Carolina Peixoto & Susana Vinga

  2. Center for Mathematics and Applications (NOVA Math), Department of Mathematics, NOVA FCT, 2829-516, Caparica, Portugal

    Marta B. Lopes

  3. UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA FCT, 2829-516, Caparica, Portugal

    Marta B. Lopes

  4. IPMA—Instituto Português do Mar e da Atmosfera, 1495-006, Lisbon, Portugal

    Pedro Reis Costa

  5. Instituto de Telecomunicações, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Alexandra M. Carvalho

  6. Instituto Superior Técnico, Av Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Alexandra M. Carvalho

Authors
  1. Ana Rita Baião
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  2. Carolina Peixoto
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  3. Marta B. Lopes
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  4. Pedro Reis Costa
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  5. Alexandra M. Carvalho
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  6. Susana Vinga
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Corresponding author

Correspondence to Susana Vinga .

Editor information

Editors and Affiliations

  1. Lucy Family Institute for Data and Society, Notre Dame, IN, USA

    Nuno Moniz

  2. GECAD, Polytechnic of Porto, Porto, Portugal

    Zita Vale

  3. GRIA—LIACC, University of Azores, Ponta Delgada, Portugal

    José Cascalho

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Catarina Silva

  5. IEETA, University of Aveiro, Aveiro, Portugal

    Raquel Sebastião

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Baião, A.R., Peixoto, C., Lopes, M.B., Costa, P.R., Carvalho, A.M., Vinga, S. (2023). Evaluating the Causal Role of Environmental Data in Shellfish Biotoxin Contamination on the Portuguese Coast. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_26

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  • DOI: https://doi.org/10.1007/978-3-031-49011-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49010-1

  • Online ISBN: 978-3-031-49011-8

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