Abstract
Multiple regression models were used to predict aquaculture production in Pelorus Sound, a 50 km long estuary supporting 68% of New Zealand's greenshell mussel Perna canali - culus aquaculture industry (worth NZ$ 204 million per annum). Mussel meat yield was modelled using both biological predictors, including seston (indexed by particulate nitrogen, PN), phyto- plankton and nutrients collected over 9 yr (July 1997 to November 2005) by the mussel industry, and physical, climatic predictors, including Southern Oscillation Index (SOI), along-shelf winds, sea surface temperature (SST) and Pelorus River flow, held in New Zealand national databases. Yield was best predicted using biological predictors collected locally at the farms inside the sound, but it was also predictable using only physical predictors collected distant from the farming region. Seston (mussel food) was also predictable using the physical predictors. Optimal predictor sets for yield and seston differed between summer and winter half-years. In summer, deep water (which enters the sound through the estuarine circulation) at the sound entrance was nitrate (NO3 � )-rich during upwelling conditions (negative SOI, NNW wind stress and cool SST). The increased NO3 � levels, in turn, triggered increased PN within the sound. In the winter half-year, PN was unrelated to upwelling and NO3 � effects at the entrance and was instead related to river flow. Remotely-sensed SST data showed that in summer, upwelling affected the entrance waters of the sound under negative SOI and upwelling-favourable wind stress, patterns which dissipated in winter. Overall, these results show that time series of physical drivers can be useful for explain- ing production variation of farmed bivalves and indicate the prospects for using data routinely col- lected in national databases for predicting mussel yield.
Highlights
Predicting the biological yield of aquaculture is important to the industry carrying out the farming
We examined teleconnections of macro-scale forcing with mesoscale upwelling dynamics affecting NO3− supply at the sound entrance, using spatial correlations of sea surface temperature (SST), wind and Southern Oscillation Index (SOI) data, to describe linkages of central New Zealand oceanography with seston biomass variation within the sound
With the addition of multiple regression modeling and new ocean and remote sensing time series, the present study has elucidated propositions made in earlier work (Zeldis et al 2008) about forcing of Pelorus Sound bivalve aquaculture yield
Summary
Predicting the biological yield of aquaculture is important to the industry carrying out the farming. The teleconnections of large-scale climatic drivers, for example between basin-scale El Niño Southern Oscillation (ENSO) dynamics (McPhaden et al 2006) and local atmospheric and oceanic effects, can be subtle The way they combine to drive conditions conducive to primary and secondary production within coastal marine ecosystems can be complex and variable Large sets of appropriate environmental data (atmospheric, hydrometric and oceanic) are needed, along with aquaculture production information, set against a background of local ecosystem understanding (e.g. Alvarez-Salgado et al 2008, Barbosa et al 2010). Such requirements appear to have made predictive models of bivalve aquaculture yield relatively rare
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