Abstract
Fish abundance is directly linked to species diversity, indicating the importance of maintaining rich fish communities for ecosystem stability and productivity. The aim of the study is to fit hierarchical models to modelled fish abundance through the following objectives: Evaluate fish abundance and occurrences using abundance formulae and their diversity index, fit hierarchical models, Investigate the variability of fish abundance and occurrences in different fishing locations and to identify the consequences of location specific management actions. Shannon weinner and Sampson diversity index reveals that Monai fishing location has the highest percentage of catch ranging to 30%. Cast net is found to be the most efficient method with highest count value of 1.9457, Poisson and negative binomial models reveal that, the locations have no significant difference and there is variability among fish catch over the years. Negative binomial reveals that Monai has the highest fish in abundance having the fish count value of 1.067 with a decrease in fish population by 7%. These results indicate significant variations in fish abundance and occurrence across the locations, years and methods. From the comparative regression and negative binomial model. Negative binomial model has the lowest log like hood of 7855874.07, with a deviance of 434.34. This infers that the negative binomial regression performs better than the Poisson regression in modelling fish abundance and occurrence. This study contributes valuable knowledge about dynamics of fish populations and basis for informed decision making in fisheries management and conservation.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call