The relationship between Rastrelliger kanagurta and its environmental parameters in exclusive economic zone (EEZ) of Malaysia using geographic information system (GIS) and statistical modeling approaches

Recent approaches in statistical modeling on fisheries research have focused on addressing the relationship between fish catch per unit effort (CPUE) data and remotely sensed oceanographic data. This study used multiple statistical modeling approaches to ensure a satisfied result in determining the most important parameter that influences the CPUE distribution of Rastrelliger kanagurta. Oceanographic data including water depth (WD), chlorophyll-a (CHL), sea surface temperature (SST), sea surface height (SSH) and surface wind (SW) are the environmental parameters which were extracted from 570 locations within EEZ of Malaysia by using GIS techniques. The combination of CPUE and oceanographic data was analyzed and then fitted simultaneously in each performance of Generalized Additive Model (GAM), Generalized Linear Model (GLM), Boosted Regression Tree (BRT) and Multivariate Adaptive Regression Splines (MARS). Each statistical modeling approach resulted difference ways to describe the relationship between fish and its environment. Results showed the non-linear behavior fish CPUE having the strongest overall relationship with SSH as SSH was always at the top. This indicated that SSH was the most important parameter affecting CPUE based on the highest percentage of relative influence and variable importance with great significant relationship (p<0.001) compared to other parameters. This study showed the capability of GIS and statistical modeling to understand the R. kanagurta distribution and its environment parameters in South China Sea.