Predicting potential fishing zones of Japanese common squid (Todarodes pacificus) using remotely sensed images in coastal waters of south-western Hokkaido, Japan

The present study used nighttime visible satellite images to identify the daily presence and absence of the fishing vessel aggregations, targeting the Japanese common squid Todarodes pacificus in the coastal waters of south-western Hokkaido, Japan. Here, statistical generalized additive model GAM and generalized linear model GLM and machine learning models boosted regression tree BRT were developed using a 3-year 2000–2002 presence/absence information from squid fishing aggregations and environmental variables night-time sea surface temperature SST, chlorophyll-a Chl-a concentration, Kd490 diffuse attenuation coefficients of downwelling irradiance at 490 nm, and bathymetry. Our findings showed that BRT outperformed the regression-based models in predicting the potential squid fishing zones during the validation period 2003. Results from BRT indicated that potential fishing zones were closely associated with water depth. Both SST and Chl-a concentration were also found highly influential to squid occurrence, while Kd490, which is related to the water transparency, showed relatively less impact on the squid distribution. The spatial predictions using daily data from 2000 to 2003 revealed the gradual eastward movement of potential fishing zones between June and December, consistent with the pattern of squid fishing activities. Four experimental fishing surveys were further conducted to validate and improve our model predictions against experience-based fishing surveys. The results showed that the squid catches using our model predictions in 2012 substantially exceeded the average catches of experience-based fishing in 2011.