A generic approach for the development of short-term predictions of Escherichia coli and biotoxins in shellfish.

W Schmidt,Pi Miller,Jd Shutler,K Davidson,Cja Campos,Db Jones,Hl Evers-King

doi:10.3354/aei00265

Abstract

Microbiological contamination or elevated marine biotoxin concentrations within shellfish can result in temporary closure of shellfish aquaculture harvesting, leading to financial loss for the aquaculture business and a potential reduction in consumer confidence in shellfish products. We present a method for predicting short-term variations in shellfish concentrations of Escherichia coli and biotoxin (okadaic acid and its derivates dinophysistoxins and pectenotoxins). The approach was evaluated for 2 contrasting shellfish harvesting areas. Through a meta-data analysis and using environmental data (in situ, satellite observations and meteorological nowcasts and forecasts), key environmental drivers were identified and used to develop models to predict E. coli and biotoxin concentrations within shellfish. Models were trained and evaluated using independent datasets, and the best models were identified based on the model exhibiting the lowest root mean square error. The best biotoxin model was able to provide 1 wk forecasts with an accuracy of 86%, a 0% false positive rate and a 0% false discovery rate (n = 78 observations) when used to predict the closure of shellfish beds due to biotoxin. The best E. coli models were used to predict the European hygiene classification of the shellfish beds to an accuracy of 99% (n = 107 observations) and 98% (n = 63 observations) for a bay (St Austell Bay) and an estuary (Turnaware Bar), respectively. This generic approach enables high accuracy short-term farm-specific forecasts, based on readily accessible environmental data and observations.

Highlights

Aquaculture plays a major role in meeting the demand in seafood production, and with the decline of wild fish stocks (FAO 2005), production is expected to grow further (Kobayashi et al 2015)
The E. coli data were collated for St Austell Bay and Turnaware Bar, for the peri- flesh (EU 2004b,c), and so values within the dataset ods 2008−2016 and 2011−2016, respectively (Table 1). below this reporting limit were adjusted to 15 μg okadaic acid (OA)
The accuracy of each model was evaluated using their respective evaluation datasets (Table 1) by calculating the root mean square error (RMSE) and bias between the predicted and the actual observed E. coli and biotoxin concentrations

Summary

INTRODUCTION

Aquaculture plays a major role in meeting the demand in seafood production, and with the decline of wild fish stocks (FAO 2005), production is expected to grow further (Kobayashi et al 2015). One important phytoplankton genus known to produce the biotoxin okadaic acid (OA) and its derivates dinophysistoxins (DTX) and pectenotoxins (PTX) is Dinophysis (Reguera et al 2012) This group of toxins can cause gastrointestinal illness (diarrhetic shellfish poisoning) in humans even when the density of causative organisms is low (Reguera et al 2012). Leaving the shellfish living in the water allows the contaminants to depurate and dissipate naturally (Egmond et al 2004, Davidson et al 2011) Once this has occurred, the shellfish farm and harvesting is re-opened and all stock can be safely sold and consumed. We identified key environmental drivers of E. coli and biotoxin concentrations within the shellfish in 2 different shellfish harvesting areas This information was used to create short-term (1 wk) forecast models, with the intention that the forecast could be used to inform and support farm management decisions

MATERIALS AND METHODS

Evaluation

RESULTS AND DISCUSSION

CONCLUSIONS