Spatiotemporal patterns of paralytic shellfish toxins and their relationships with environmental variables in British Columbia, Canada from 2002 to 2012

Stephen Finnis, Nikolas Krstic, Lorraine McIntyre, Trisalyn Nelson, Sarah B. Henderson

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Background Harmful algal blooms produce paralytic shellfish toxins that accumulate in the tissues of filter feeding shellfish. Ingestion of these toxic shellfish can cause a serious and potentially fatal condition known as paralytic shellfish poisoning (PSP). The coast of British Columbia is routinely monitored for shellfish toxicity, and this study uses data from the monitoring program to identify spatiotemporal patterns in shellfish toxicity events and their relationships with environmental variables. Methods The dinoflagellate genus Alexandrium produces the most potent paralytic shellfish toxin, saxitoxin (STX). Data on all STX measurements were obtained from 49 different shellfish monitoring sites along the coast of British Columbia for 2002–2012, and monthly toxicity events were identified. We performed hierarchical cluster analysis to group sites that had events in similar areas with similar timing. Machine learning techniques were used to model the complex relationships between toxicity events and environmental variables in each group. Results The Strait of Georgia and the west coast of Vancouver Island had unique toxicity regimes. Out of the seven environmental variables used, toxicity in each cluster could be described by multivariable models including monthly sea surface temperature, air temperature, sea surface salinity, freshwater discharge, upwelling, and photosynthetically active radiation. The sea surface salinity and freshwater discharge variables produced the strongest univariate models for both geographic areas. Conclusions Applying these methods in coastal regions could allow for the prediction of shellfish toxicity events by environmental conditions. This has the potential to optimize biotoxin monitoring, improve public health surveillance, and engage the shellfish industry in helping to reduce the risk of PSP.

Original languageEnglish (US)
Pages (from-to)190-200
Number of pages11
JournalEnvironmental Research
Volume156
DOIs
StatePublished - Jul 1 2017
Externally publishedYes

Keywords

  • Biotoxin monitoring
  • Machine learning
  • Paralytic shellfish poisoning
  • Public health
  • Spatiotemporal pattern analysis

ASJC Scopus subject areas

  • Biochemistry
  • Environmental Science(all)

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