Abstract:
Marine capture fisheries are subjected to anthropogenic pressures and impact from climate change. These two factors result in the inter-annual variability of marine fish species. In this study, we have tried to nullify the anthropogenic effect in the form of fishing effort by subjecting the fish catch data to a standardization procedure which provided us with the standardized catch per unit effort (SCPUE). The candidate species selected is Indian Oil Sardine which is the dominant and most commercially harvested species. SCPUE of Indian Oil Sardine data and six major climate variables viz., sea surface temperature, precipitation, chlorophyll-a concentration, upwelling, sea-level anomaly and wind speed were subjected to a simulation analysis using regression model with Autoregressive integrated moving average (ARIMA) noise. The lead/lag duration for different climate-related variables used in forecasting the fish biomass was estimated using a Cross-correlation function (CCF) and the model enabled forecast was made for the year 2014 and 2015. The suitable model for the best Akaike Information Criterion (394.13) enabled with a good fit for the output was used for the prediction. On the basis of AIC values and best fit obtained during the study we have arrived at the best combination of variables among the 31 models tested. The variables used in this study was predicted using the approved IPCC models and RCP 4.5 and 6.0 scenarios. Thus we could achieve (i) standardization of fishing effort which segregated the variability in inter-annual fluctuations in Indian Oil Sardine due to effort changes (ii) Scenarios generated for the variables relevant to model studies and predicting them for different RCPs and (iii) Model for forecasting the Indian Oil Sardine biomass using the predicted variables. The study calls upon the need for integrating climatic variables into biomass forecasting with more refined protocols to ascertain the future of Indian marine fisheries in a climate change scenario.
Keywords: ARIMA, simulation, climate variables, inter-annual variability, fisheries, Indian Oil sardine
