Analysis For Spatial-temporal Variation And Stock Assessment Of Albacore(Thunnus Alalunga) In The Indian Ocean

1 Study on the Temp-Spatial Distribution of Albacore Resources in the Indian OceanThe temp-spatial distribution of albacore resources for longline from 2005 to 2014 in the Indian Ocean is analyzed by using the methods of ArcGIS?Fishing ground gravity and Grey Relational Analysis.The results are shown as:(1)the annual change and monthly change of CPUE of albacore both generally highlight the rising trend and the boundary of average CPUE appears between 2009 and 2010.(2)the spatial output distribution in ten years can be classified into three kinds,the year for 2005,2006 and 2007 are belonged to the first kind with the correlation coefficient value of above 0.85 and its catch mainly come from the western areas.The year for 2008 and 2009 are belonged to the second kind with the correlation coefficient value of above 0.75 and its catch is mainly located at the western and eastern areas with little in middle area.The year for 2010,2011,2012,2013 and 2014 are belonged to the third kind with the correlation coefficient value of above 0.71,whose catch mainly distributed in western area and the eastwards 10-20 degrees of longitude compared with 2005-2009..(3)the distribution of high CPUE is similar with the catch.(4)the results from grey relationship degree indicated that the highest level for albacore are in 2005,2010,2012,2013 and 2014,the middle level are in 2006,2008,2009 and 2011,and the lowest level is in 2007.2 Stock Assessment of Albacore(Thunnus alalunga)in the Indian Ocean using Biomass Dynamics ModelBiomass dynamics model is the most widely used method in fishery stock assessment,because of its simple structure and light need of data.This type of model has been long used in tuna stock assessments in the Atlantic and Indian Oceans.However,the impacts of the shape of surplus production model and fitting criteria were not often investigated in these applications.In this study,we assessed the Indian Ocean albacore(Thunnus alalunga)using ASPIC(A Surplus Production Model Incorporating Covariates),the typical biomass dynamics model using catch and abundance index as the main data.We focused on comparing the results of selecting different surplus production models(FOX and LOGISTIC)and model fitting criteria(Least Sum of Squared Errors or SSE and Least Sum of Absolute Errors or LAV).The results showed that the selections of surplus production models and model fitting criteria obviously impacted the estimates of biological reference points(MSY,FMSY,and BMSY).Overall,the former influenced more greatly than the later.However,the results did not tell much difference in judging the stock status in terms of overfishing or overfished.This study highlights the need of serious consideration of surplus production model and fitting criteria in the application of biomass dynamics model,based on the fishery and biological characteristic of stock.3.The retrospective analysis in surplus production model using albacore(Thunnus alalunga)data in the Indian OceanWith the increasing diversity of fishery stock assessment models and parameter settings,the selection of assessment models and data quality have a key impact on resource evaluation results and management development,which leads to the inaccuracy of the assessment result.A retrospective pattern(RP)is a systematic inconsistency among a series of estimates of population size,or related assessment variables,based on increasing periods of data(Mohn 1999).This paper makes a retrospective analysis of albacore in the Indian Ocean using the non-equilibrium surplus production model-ASPIC.The MOHN ? was used mainly as the metric of RP and the cause of RP was reduced to errors in data or in assumption of models or both.The result indicates that the estimates in seven years data shows a negative value(?=-0.108),which is in accordance with the analysis figure tendency.RP is one of hot and hard topics in fishery stock assessment at present.The retrospective patterns are an indication something is inconsistent in the data or model assumptions.However,retrospective patterns are just one diagnostic for stock assessments and lack of a retrospective pattern does not necessarily imply that all is well.