Abstract:
Fisheries sector plays important role in addressing the national, regional, and
global need for food security of human, and the sector supports nearly one
million fishers, workers, and their family members in Sri Lanka. Yellowfin
tuna is an important contributor to export revenue of Sri Lanka. The highly
migratory and widely dispersed yellowfin tuna landings are influenced by
seasonal and geographic fluctuations. Abrupt changes in climatic conditions
disrupt the estimation of stocks. Thus, predicting spatial distribution of the
species is important in reducing the fishing cost. The Sea Surface Temperature
(SST), Sea Surface Salinity (SSS), and Sea Surface Chlorophyll (CHL),
obtained from remote sensing satellites were used in predicting yellowfin tuna
caught by longline vessels in the Indian Ocean near Sri Lanka. In this context,
SST, SSS, CHL, and CPUE (Catch per unit effort), computed as the number
of fish caught per hook per trip collected from the logbooks of the Department
of Fisheries, Sri Lanka, were evaluated. The relationship between sea surface
temperature, sea surface salinity, sea surface chlorophyll, and CPUE has been
determined using the Generalized Additive Model (GAM). The present
findings on the CPUE data throughout 2019 indicate that SST (280C - 28.50C),
SSS (33.5ppt - 34 ppt), and CHL (0.15mg/dm-3 – 0.17mg/dm-3) have the
optimum CPUE values. Results showed a good match between predicted and
actual catch. The present study affirms that developed model could estimate
yellowfin tuna stock at given SST, SSS, and CHL data.
