Bayesian State-space Implementation of Schaefer Production Model forAssessment of Stock Status for Multi-gear Fishery

Knowing the status of marine fish stock is of utmost importance to develop management strategies for sustainable harvest of marine fishery resources. A widely accepted approach towards this is to derive sustainable harvest levels using time series data on fish catch and fishing effort based on fish stock assessment models like Schaefer’s model that describe the biomass dynamics. In India, the marine fishery is of complex multi-species nature where in different species are caught by a number of fishing gears and each gear harvests a number of species making it difficult to obtain the fishing effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum of efforts expended by different fishing gears. Hence, it demands the importance of effort standardisation for making use in stock assessment models. This paper describes a methodology for the standardization of fishing efforts and assessing fish stock status using Bayesian state-space implementation of the Schaefer production model (BSM). A Monte Carlo based method namely Catch-Maximum Sustainable Yield (CMSY), has also been used for estimating fisheries reference points from landings and a proxy for biomass using resilience of the species. The procedure has been illustrated with data on Indian mackerel (Rastrelliger Kanagurta) collected from the coastal state of Andhra Pradesh, India during 1997-2018. Maximum Sustainable Yield (MSY) of Indian mackerel for Andhra Pradesh has been estimated. A comparison between both CMSY and BSM methods have been made and found that the estimates are in close agreements.