Climatic change conditions and anthropogenic pressures have strong impact on the harmful bloom development along the coasts worldwide. The PSP toxin producing Alexandrium minutum causes harmful blooms in the Mediterranean coastal areas with its frequency increasing and capacity of dispersal due of resting stages production and settling in new areas. Therefore, there is an urgent need for new methods for managing and forecasting these harmful algal blooms. Early warning and forecasting systems for HABs are shown to be useful tools together with rapid and species-specific accurate detection of cells and toxins in the seawater, sediments and seafood chain. Mathematical models based on Machine Learning techniques and principally those based on Random Forests, ensemble techniques that combine many classification trees, are very promising both at regional and at wider scale. We trained a Random Forest based on environmental predictive data and A. minutum molecular abundance from the sampling area close to aquaculture farms. This technique proved to be effective and allowed developing a qualitative predictive model for the toxic A. minutum forecasting, although most relationships among variables are still unknown. The improved molecular methods of qPCR applied to species-specific quantification of harmful dinoflagellates based on sxt genes allowed to rapidly determine the STX–producing cell concentration of Alexandrium species, especially at early warning stage of a bloom, in coastal area highly exploited by maritime activities. The concomitant molecular quantification of A. minutum resting cysts in harbour sediments offered further information on the seed bed size and the germination potential. Toxin contamination of both seawater and shellfish samples was assessed by hydrophilic interaction liquid chromatography combined with high resolution mass spectrometry and correlation between toxicity data with molecular abundance of A. minutum were used to determine the likelihood of risk from PSP toxins in bivalve molluscs harvested in southern Mediterranean Bay. Monitoring and predicting strategies for HABs by combined different methodologies could play a fundamental role in preventing and control health and economic risks related to toxin-producing species blooms in coastal areas.

The implementation of different tools for understanding and managing harmful algal blooms: case studies in the Mediterranean Sea

Carmela Dell'Aversano;Luciana Tartaglione;
2018

Abstract

Climatic change conditions and anthropogenic pressures have strong impact on the harmful bloom development along the coasts worldwide. The PSP toxin producing Alexandrium minutum causes harmful blooms in the Mediterranean coastal areas with its frequency increasing and capacity of dispersal due of resting stages production and settling in new areas. Therefore, there is an urgent need for new methods for managing and forecasting these harmful algal blooms. Early warning and forecasting systems for HABs are shown to be useful tools together with rapid and species-specific accurate detection of cells and toxins in the seawater, sediments and seafood chain. Mathematical models based on Machine Learning techniques and principally those based on Random Forests, ensemble techniques that combine many classification trees, are very promising both at regional and at wider scale. We trained a Random Forest based on environmental predictive data and A. minutum molecular abundance from the sampling area close to aquaculture farms. This technique proved to be effective and allowed developing a qualitative predictive model for the toxic A. minutum forecasting, although most relationships among variables are still unknown. The improved molecular methods of qPCR applied to species-specific quantification of harmful dinoflagellates based on sxt genes allowed to rapidly determine the STX–producing cell concentration of Alexandrium species, especially at early warning stage of a bloom, in coastal area highly exploited by maritime activities. The concomitant molecular quantification of A. minutum resting cysts in harbour sediments offered further information on the seed bed size and the germination potential. Toxin contamination of both seawater and shellfish samples was assessed by hydrophilic interaction liquid chromatography combined with high resolution mass spectrometry and correlation between toxicity data with molecular abundance of A. minutum were used to determine the likelihood of risk from PSP toxins in bivalve molluscs harvested in southern Mediterranean Bay. Monitoring and predicting strategies for HABs by combined different methodologies could play a fundamental role in preventing and control health and economic risks related to toxin-producing species blooms in coastal areas.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/737908
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