No abstract

The Pacific oyster, Crassostrea gigas, plays a significant role in the aquaculture industry in Ireland. Episodes of increased mortality in C. gigas have been described in many countries, and in Ireland since 2008. The cause of mortality events in C. gigas spat and larvae is suspected to be multifactorial, with ostreid herpesvirus 1 (OsHV-1, in particular OsHV-1μvar) considered a necessary, but not sufficient, cause. The objectives of the current study were to describe mortality events that occurred in C. gigas in Ireland during the summer of 2011 and to identify any associated environmental, husbandry and oyster endogenous factors. A prospective cohort study was conducted during 2010-2012, involving 80 study batches, located at 24 sites within 17 bays. All 17 bays had previously tested positive for OsHV-1μvar. All study farmers were initially surveyed to gather relevant data on each study batch, which was then tracked from placement in the bay to first grading. The outcome of interest was cumulative batch-level mortality (%). Environmental data at high and low mortality sites were compared, and a risk factor analysis, using a multiple linear regression mixed effects model, was conducted. Cumulative batch mortality ranged from 2% to 100% (median=16%, interquartile range: 10-34%). The final multivariable risk factor model indicated that batches imported from French hatcheries had significantly lower mortalities than non-French hatcheries; sites which tested negative for OsHV-1μvar during the study had significantly lower mortalities than sites which tested positive and mortalities increased with temperature until a peak was reached. There were several differences between the seed stocks from French and non-French hatcheries, including prior OsHV-1μvar exposure and ploidy. A range of risk factors relating to farm management were also considered, but were not found significant. The relative importance of prior OsHV-1μvar infection and ploidy will become clearer with ongoing selection towards OsHV-1μvar resistant oysters. Work is currently underway in Ireland to investigate these factors further, by tracking seed from various hatchery sources which were put to sea in 2012 under similar husbandry and environmental conditions.

This paper describes details of an oil spill model, OILTRANS, developed by the authors. The model is an off-line particle-transport model coupled to the most up to date operational met-ocean model

One of the key needs of the aquaculture industry is the implementation of effective management methods to ensure the sustainability, economic viability and minimization of negative impacts on

No abstract

This paper presents a high resolution operational biogeochemical model of the North-East Atlantic that encompasses part of the continental shelf and adjacent deep sea and includes all of Ireland's territorial waters. The setup of the model is described, followed by its skill assessment in reproducing chlorophyll

An operational model for an area of the northeast Atlantic that encompasses all of Ireland’s territorial waters has been developed. The model is an implementation of the Regional Ocean Modelling System (ROMS) and uses operationally available atmospheric and boundary forcing, and a global tide solution for tidal forcing. River forcing is provided by climatological daily discharge rates for 29 rivers across Ireland, west Britain, and west France. It is run in an operational framework to produce 7-day hindcasts once a week, and daily 3-day forecasts which are published in a number of formats. We evaluated the model skill by comparing with measured data and calculating statistics such as mean error, root mean square error (RMSE), and correlation coefficient. The observations consist of satellite Sea Surface Temperature (SST), total surface velocity fields from satellite, water level time series from around the Irish coast, and temperature and salinity data from Array for Real-Time Geostrophic Oceanography (ARGO) and Conductivity Temperature Depth (CTD) profiles. The validation period is from 1 January 2016 until 31 December 2019. The correlation coefficient between the model and satellite SST is 0.97 and recorded in March and April 2018. The model error is about 5% of the total M2 amplitude in the Celtic Sea recorded at Dunmore East tide gauge station. The maximum RMSE between the model and the CTD temperature profiles is 0.8 °C while it is 0.17 PSU for salinity. The model correctly defines the shelf water masses around Ireland. In 2019 the Irish Coastal Current (ICC) was very strong and well defined along most of the western Irish coast. The model results have well reproduced the ICC front for the whole simulation period.

Bivalve shellfish such as oysters and mussels can concentrate human pathogens when grown in areas impacted by municipal wastewater. Under EU regulation this risk to consumers is controlled by determining the sanitary quality of bivalve shellfish production areas based on the concentration of E. coli present in shellfish flesh. The authors present a modelling approach to simulate an uptake of E. coli from seawater and subsequent depuration by M. edulis. The model that dynamically predicts E. coli concentration in the mussel tissue is embedded within a 3-D numerical modelling system comprising of a hydrodynamic, biogeochemical, shellfish ecophysiological and the newly proposed microbial modules. The microbial module has two state variables, namely, the concentrations of E. coli in water and in the mussel tissue.Novel formulations to calculate the filtration rates by mussels and the resulting uptake of bacteria are proposed; these rates are updated at every computational time step.Concentrations of E. coli in seawater are also updated accordingly taking into account the amounts ingested by mussels. The model has been applied to Bantry Bay in the south-west of Ireland. The results indicate that the model is capable of reproducing the official classification of shellfish waters in the bay based on monthly sampling at several stations. The predicted filtration rates and ratios of E. coli in water and mussels also compare well with the literature. The model thus forms a tool that may be used to assist in the classification of shellfish waters at much greater spatial and temporal detail than that offered by a field monitoring programme. Moreover, it can also aid in designing an efficient monitoring programme. The model can also be utilised to determine the contribution of individual point sources of pollution on the microbial loading in mussels and, when incorporated into an operational framework, it can provide a short-term forecasting of microbial contamination in a shellfishery. Also, the model can be easily extended to include other shellfish and pathogen species.