Abiotic factors and heavy metals defining eco-physiological niche in fish

Abstract

Ecosystem dynamics undergoing alterations in structure and function highlights the need to look into the relations between ecological parameters and organismal fitness and tolerance. Ecophysiological studies are used to understand how organisms adapt to and cope up with environmental stress. Current study uses a process-based approach to model physiochemical parameters regarding seven different fish species. Species respond to climatic variations via acclimation or adaptation through physiological plasticity. Four sites are differentiated into two types based on the water quality parameters and metal contamination. Seven fish species are clustered into two groups, each group depicting separate pattern of response in similar habitat. In this manner, biomarkers from three different physiological axes- stress, reproduction, and neurology were taken to determine the organism's ecological niche. Cortisol, Testosterone, Estradiol, and AChE are the signature molecules estimated for the said physiological axes. The ordination technique, nonmetric multidimensional scaling, has been utilized to visualize the differentiated physiological response to changing environmental conditions. Then, Bayesian Model Averaging (BMA) was used to identify the factors that play a key role in refining the stress physiology and determining the niche. Current study confirms different species belonging to similar habitats respond to various environmental and physiological factors in a different manner as various biomarkers respond in a species-specific pattern that induces the choice of habitat preference controlling its ecophysiological niche. In the present study, it is quite apparent that adaptive mechanism of fish to environmental stress is achieved through modification of physiological mechanisms through a panel of biochemical markers. These markers organize a cascade of physiological event at various levels including reproduction.

Keywords: Antioxidants; Bayesian Model Averaging; Ecological niche; Fish; Sundarbans; Systems ecology.