Mathematical modelling of the seasonal variability of plankton and forage fish in the Gulf of Kachchh

NPZfc: An ecological relation based fish catch prediction model using Artificial Neural Network

“…Plankton dynamics of the ecosystem also has distinct spatio-temporal variations (Srichandan et al, 2015; Mukherjee et al, 2018). These variations have a considerable effect on the forage fish population, which in turn draws the attention of ecologists to develop compartment models (Kumar and Kumari, 2015; Franks, 2002). The present study finds the scope of establishing a link between plankton dynamics and feeding ecology studies to develop a fish catch model.…”

“…The relation between these 9 four compartments provided a platform to build a model that can be predictive in nature. The four compartments here were developed into an NPZf c model, through the artificial neural network, that can be very useful over the traditional mathematical NPZ models (Franks, 2002) or recently developed NPZF models (Kumar and Kumari, 2015). The mathematical NPZ models are an effective approach to predict the dynamics in aquatic systems that are hard to measure for (Franks, 2002).…”

Diagnosis of planktonic trophic network dynamics with sharp qualitative changes

“…In a context of an emerging biological oceanography discipline and considering the limited computing resources, the first planktonic TN (or food web) models simply consisted of mass fluxes between nutrients, phytoplankton, and zooplankton nodes (Steele 1958(Steele , 1974. These so-called NPZ models (NPZD, NPZDB, or even NPZF when detritus, bacteria, or fishes are comprised, respectively) coupled to observed or simulated physical conditions have demonstrated their predictive ability to capture bulk system properties (biomass and primary production) at both regional and global scales (Mitra et al 2007, Kriest et al 2010, Oke et al 2013, Hernández-Carrasco et al 2014, Turner et al 2014, Kumar and Kumari 2015.…”