Time-Lagged Correlation Analysis of Shellfish Toxicity Reveals Predictive Links to Adjacent Areas, Species, and Environmental Conditions

Toxins (Basel). 2022 Sep 30;14(10):679. doi: 10.3390/toxins14100679.

Abstract

Diarrhetic Shellfish Poisoning (DSP) is an acute intoxication caused by the consumption of contaminated shellfish, which is common in many regions of the world. To safeguard human health, most countries implement programs focused on the surveillance of toxic phytoplankton abundance and shellfish toxicity levels, an effort that can be complemented by a deeper understanding of the underlying phenomena. In this work, we identify patterns of seasonality in shellfish toxicity across the Portuguese coast and analyse time-lagged correlations between this toxicity and various potential risk factors. We extend the understanding of these relations through the introduction of temporal lags, allowing the analysis of time series at different points in time and the study of the predictive power of the tested variables. This study confirms previous findings about toxicity seasonality patterns on the Portuguese coast and provides further quantitative data about the relations between shellfish toxicity and geographical location, shellfish species, toxic phytoplankton abundances, and environmental conditions. Furthermore, multiple pairs of areas and shellfish species are identified as having correlations high enough to allow for a predictive analysis. These results represent the first step towards understanding the dynamics of DSP toxicity in Portuguese shellfish producing areas, such as temporal and spatial variability, and towards the development of a shellfish safety forecasting system.

Keywords: DSP; Portuguese coast; correlation analysis; shellfish; toxicity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Marine Toxins / analysis
  • Marine Toxins / toxicity
  • Phytoplankton
  • Shellfish / analysis
  • Shellfish Poisoning*

Substances

  • Marine Toxins

Grants and funding

This work was funded by the project “MATISSE: A machine learning-based forecasting system for shellfish safety” (DSAIPA/DS/0026/2019). The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, CEECIND/01399/2017, UIDB/04326/2020, UIDP/04326/2020 and LA/P/0101/2020 (CCMAR), UIDB/04516/2020 (NOVA LINCS), UIDB/00297/2020 (NovaMath), and UIDB/50021/2020 (INESC-ID). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 951970 (OLISSIPO project).