A regional algorithm to model mesozooplankton biomass along the southwestern Bay of Bengal

A three-dimensional regression analysis attempted to model mesozooplankton (MSP) biomass using sea surface temperature (SST) and chlorophyll-a (Chl-a). The study was carried out from January 2014 to July 2015 in the southwestern Bay of Bengal (BoB) and sampling was carried out on board Sagar Manjusha and Sagar Purvi. SST ranged from 26.2 to 33.1 °C while Chl-a varied from 0.04 to 6.09 μg L^-1. During the course of the study period, there was a weak correlation (r = 0.32) between SST and Chl-a statistically. MSP biomass varied from 0.42 to 9.63 mg C m^-3 and inversely related with SST. Two kinds of approaches were adopted to develop the model by grouping seasonal datasets (four seasonal algorithms) and comprising all datasets (one annual algorithm). Among the four functions used (linear, paraboloid, the Lorentzian and the Gaussian functions), paraboloid model was best suited. The best seasonal and annual algorithms were applied in the synchronous MODIS-derived SST and Chl-a data to estimate the MSP biomass in the southwestern BoB. The modelled MSP biomass was validated with field MSP biomass and the result was statistically significant, showing maximum regression coefficient for the seasonal algorithms (R² = 0.60; p = 0.627; α = 0.05), than the annual algorithm (R² = 0.52; p = 0.015, α = 0.05).