Trophic Modelling of the Cross River Estuary Ecosystem, Nigeria

A trophic modelling of the Cross River estuary ecosystem was undertaken using Ecopath with Ecosim (EwE). Data were obtained from peer reviewed journals, monographs and stock assessment reports and used to construct the Ecopath model used to investigate the structure, function and system throughput of the estuary. These data included biological data such as biomass, production/ biomass, consumption/biomass, fish catch and diet composition of each species or functional group included in the model. The network analysis routine was used to estimate trophic levels and trophic interactions of functional groups, health condition and developmental stage, and food web features. Results of Ecopath model revealed three apical predators: Shark, Senegalese tonguesole, and Sea catfish. The ratio of total primary production to total respiration value of 4.182 revealed that the Cross River estuary is an immature ecosystem void of biological and ecosystem pollution and is at its early developmental stage. The mean trophic level of the catch of 2.186 for the system and the high gross food conversion efficiency (GE) of 0.300 for Macrobrachium spp and 0.292 for Penaeid shrimp, revealed high fishing pressure on the herbivores. The estuary ecosystem has a short food chain and low connectance index of 0.1879 which has phytoplankton being grazed upon by zooplankton which in turn are taken up by bonga. Model results and physico-chemical parameters showed that the Cross River estuary is still relatively pristine and immature ecosystem, void of organic pollution and with relatively low primary productivity as evident in relatively low nutrient concentration. The high value of ecotrophic efficiency (EE) of 0.993 for bonga and 0.991 for Macrobrachium spp indicate that the population of these animals are heavily exploited and predated upon in the estuary. Despite that this is a preliminary trophic model, it is clear that fish populations in the estuary are experiencing excessive fishing pressure; thus necessitating management measures to reduce pressure, while relevant data especially on fish diet composition and fish landing should be collected in order to get improved model parameterization in the future.