Integrating remotely-sensed environmental data to investigate their value in understanding the spatio-temporal distribution of sea lice in BC

Principal Investigator: Krishna Thakur
Co-Investigators: Crawford Revie, Raphael Vanderstichel & Thitiwan Patanasatienkul

Sea surface temperature (SST) and salinity (SSS) are essential variables at the interface of the ocean and atmosphere when considering risk factors for disease in farmed and wild fish stocks. Ecological research has seen a recent increase in the use of digital and satellite technologies, including remote-sensing tools. The near real-time availability of these data allows for use in forecast models, surveillance of pathogens, and the construction of risk maps.

We compared remote-sensed SST and SSS data from several tools with local measurements of water temperatures and salinity, and explored ways to minimize the differences between local and remote-sensed data. The local data were from 24 salmon farms and 208 wild fish surveillance sites in coastal British Columbia, Canada, from 2003 to 2016. We compared 20,513 and 20,038 local records for water temperature and salinity, respectively, with SST and SSS values from several different remote-sensing tools, for each of the sites and days.

Among the SST tools evaluated, UKMO SST (UK Meteorological Office) had the highest agreement with local water temperature measurements. The other SST products had poor spatial coverage for the study area and did not provide data for any of the study sites. None of the SSS products evaluated provided appropriate corresponding values for local records, suggesting that spatial coverage for the study area is currently lacking. This study demonstrates that, among SST products, UKMO SST is, currently, best suited for aquaculture studies in coastal BC, while no similar SSS products are yet available for coastal BC.