An argo‐based experiment providing near‐real‐time subsurface oceanic environmental information for fishery data

Abstract

AbstractA better understanding of the relationships between oceanic environments and fishing conditions could make the utilization of fish more efficient, profitable, and sustainable. The current lack of high‐precision subsurface seawater information has long been a constraint on fishery research. Using near‐real‐time Argo observations, this paper presents a new approach called gradient‐dependent optimal interpolation. This approach provides daily subsurface oceanic environmental information according to fishery dates and locations. An experiment was conducted in the western and central Pacific Ocean using yellowfin tuna (YFT) catch data in August 2017. The results of seawater temperature and salinity represented differences of less than ±0.5°C and ±0.05, respectively, according to verification of error analysis and truth‐finding comparisons. After applying the constructed temperature and salinity profiles, we described the relationship between subsurface information and yellowfin tuna catch distribution. Statistical analysis revealed that yellowfin tuna were more adapted to warmer and saltier seawater. At the near‐surface (<5 m), the most suitable temperature was 28–29°C, although yellowfin tuna can endure a temperature range from 11 to 12°C at a depth of 300 m. The corresponding upper boundary of the thermocline was approximately 75 m, with a mean strength of 0.074°C/m, and the most suitable salinity for yellowfin tuna was 34.5–36.0 at depths shallower than 300 m. These results indicated that the constructed subsurface information was very close to the true values and they had high spatial and temporal accuracy.