A multitrophic model of a rice-fish agroecosystem: I. A tropical fishpond food web

A modular, object oriented, multitrophic model of the mass dynamics of a tropical pond is reported. The model includes the interactions of bacteria, blue–green algae, small and large phytoplankton in the first trophic level; heterotrophic nanoflagellates (HNF), ciliates rotifers and cladocerans in the second; phytoplankton grazing fish (Nile tilapia, Oreochromis niloticus L.) in the third, and for heuristic purposes the African catfish (Clarius gariepinus Burchell) in the fourth. The large number of species in the system precludes modeling individual species and hence simplifications were made to capture the ecological roles the organisms play in the pond ecosystem. The mass dynamics of twelve representative functional plankton species (hereafter called meta-species), based on the biology of known species, were included in the model. The same mass based population dynamics model coded in object oriented C2+ was used for all meta species allowing the same sub models to be accessed by all meta-species and the meta-species themselves to be easily added or removed from the food web. Only tilapia and Clarias were modeled as a distinct species having mass age-structure. The physiologically based Gutierrez and Baumgärtner (Can. Entomol. (1984) 116) model was used to model the behavioral and physiological biology of resource acquisition and allocation for all species. Chapraʹs (1997) model was used to describe nutrient and water dynamics. Among the other processes modeled were the dynamics of particulate non-living organic carbon, dissolved organic carbon, ammonium ion concentration, nitrification, and soluble reactive phosphorus as well as water balance and heat flux. These models were linked to the meta-species population dynamics models to capture the effects of nitrogen acquisition and recycling on system dynamics. Phosphorus was assumed not to be limiting in our model. Simulation experiments were used to explore the dynamics of the pond food web, the bottom-up effects of factors that block nutrient recycling, and the top–down role of tilapia in system dynamics and nutrient recycling. The model successfully predicted tilapia growth.