Influence of climate on Pelorus Sound mussel aquaculture yields: predictive models and underlying mechanisms

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 canaliculus aquaculture industry (worth NZ$ 204 million per annum). Mussel meat yield was modelled using both biological predictors, including seston (indexed by particulate nitrogen, PN), phytoplankton 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 (NO 3 − )-rich during upwelling conditions (negative SOI, NNW wind stress and cool SST). The increased NO 3 − levels, in turn, triggered increased PN within the sound. In the winter half-year, PN was unrelated to upwelling and NO 3 − 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 explaining production variation of farmed bivalves and indicate the prospects for using data routinely collected in national databases for predicting mussel yield.

A modelling synthesis of the volume flux through Cook Strait

“…The drag coefficient was therefore multiplied by a factor that was adjusted to optimise agreement: the final value chosen was 1.2. A similar adjustment has been found to be necessary for previous coastal modelling exercises around New Zealand (Hadfield and Zeldis 2012;Zeldis et al 2013). The need for this adjustment may indicate that the NZLAM wind speeds are biased low and/or that the Smith (1988) drag coefficient formula gives results that are too low for the wind and wave conditions in coastal areas.…”

“…Oceanographically, it is perhaps best known for its strong tidal currents (Vennell 1998) and the consequent high levels of turbulence (Stevens 2018). However, regionally at least, the subtidal fluxes of heat, salt and nutrients are of significant importance to natural and built ecosystems (Zeldis et al 2013;Chiswell et al 2017). Here we describe hydrodynamic model estimates of the volume flux through the Strait.…”

Physical oceanography of New Zealand/Aotearoa shelf seas – a review

“…The freshwater flux from the Pelorus River generates relatively strong stratification after large rainfall events (Heath 1976b;Proctor and Hadfield 1998;Stevens and Smith 2016). Recently Stevens (2014) suggested that the oceanic source waters for the sounds might at times come from the east rather than from the west (Zeldis et al 2013). Long-term transport in the Sounds is primarily controlled by stratification (Sutton and Hadfield 1997).…”

Significant impact from blue mussel Mytilus galloprovincialis biofouling on aquaculture production of green-lipped mussels in New Zealand

“…phytoplankton and other seston) to cultured Perna. In Pelorus Sound, temporal changes in Perna yield have been linked to changes in seston availability determined by oceanic upwelling at the entrance to the Sound and riverine nutrient inputs at the head (Zeldis et al 2008(Zeldis et al , 2013. Depending on location, farm-scale seston depletion may also occur (Keeley et al 2009, Stenton-Dozey 2013, with the extent depending on factors relating to environmental drivers, bay-scale farming intensity and farm-scale management (e.g.…”