A Boosted Regression Tree Approach to Modeling Coilia dussumieri Distribution in Relation to Environmental Variables of Mumbai, India

Abstract

India is a major player in global fisheries, with Coilia dussumieri being a significant species. This study investigates the spatiotemporal distribution of C. dussumieri along the Mumbai coast, focusing on the influence of environmental factors. Data on fish catch and environmental variables (Sea surface temperature (SST), salinity, pH, Sea surface height (SSH), current speed (CS), and chlorophyll-a were collected during fortnightly surveys. The association between these factors and Catch Per Unit Effort (CPUE) was modelled using Boosted Regression Trees (BRT). Results revealed that salinity, chlorophyll-a, and SST were the most influential factors, with optimal ranges observed for each. The model demonstrated a non-linear and interactive effect of salinity and chlorophyll-a on CPUE. A greater percentage of juveniles were found in the catch, according to an analysis of length-frequency data, underscoring the need of taking juvenile populations into account when developing management plans. This work provides significant insight on the ecological needs of C. dussumieri and highlights the necessity of managing fisheries using an ecosystem-based strategy that takes environmental factors and juvenile population dynamics into account.