| Chile jack mackerel(Trachurus murphyi)(CJM) occupied a very important position in the world’s economy marine fishes. It is a typical oceanic pelagic Highly Migratory Fish Stocks, distributed across the entire South Pacific. It is differences in population structure and spatial distribution of Chile jack mackerel, the fishing ground has a certain extent migrate with the change of sea conditions every year, also with the increased of fishing intensity and effects of climate change, its resources has been destroyed. Therefore, further study for the spatial distribution, spatial patterns and temporal dynamics of Chilean jack mackerel resource, understand the migration of fishing grounds changes of Chilean jack mackerel were very important for scientific managing and sustainable developing Chilean jack mackerel resources.In this paper, based on the fishing activity data of Shanghai Kaichuang Deep Sea Co. Ltd trawlers in southeast pacific from 2005 to 2014, geostatistics method and ESDA spatial analysis were used in variogram analysis, spatial interpolation or estimates, hot spot analysis and spatial correlation regionalized variables to understand the spatial variability of stock density for C hilean jack mackerel. The main conclusions were as follows:(1) The spatial variability analysis of stock density for C hilean jack mackerel. The geostatistics were used to analyze the spatial distribution characteristics of stock density for C hilean jack mackerel to analysis the spatial variability of it. The result showed that the Spherical Model in 2007, 2008, 2011 and 2012 are moderate degree of spatial autocorrelation, Exponential Model in 2007, 2008 and 2011 are moderate degree of spatial auto correlation, Gaussian Model only in 2013 and 2014 showed a weak degree of spatial auto correlation, rest of the year are moderate degree of spatial auto correlation. O verall with Gaussian model the space auto correlation of CPUE fit the best, followed by spherical model, the exponential model is the weakest, and Gaussian Model is the optimal model. In the isotropic and anisotropic condition, the overall space self-related trends of the three models are basically the same, there is a big difference in spatial variability of CPUE.(2) Multi-angle and multi- method analysis the annual variation of stock density spatial distribution for C hilean jack mackerel, explore its variation. Use ordinary kriging interpolation method of geostatistical simulation and prediction the fishing grounds of Chilean jack mackerel, The results show that the high value areas of stock density are concentrated in the northeast waters of 78o-85 oW from 2005 to 2007, gradually decreasing from northeast to southwest, but the gradient is more moderate, and there is a tendency to move towards the western waters. The distribution of high-value areas for stock density is scattered on the whole than before in 2008 and 2009, it is distribution is not obvious, and is distributed in the deep waters of 80 oW west. The stock density showing the trend of high in southwest and low in northeast in 2010. The high value area of stock density moved northward in 2011 and 2012, and west to the east by decreasing. The high value area of stock density moved southward in 2013 and 2014, the distribution is narrow and relatively focus. The distributed are most widely in 2007 and 2009, the distributed are minimum distribution in 2013 and 2014. Comparing with actual distribution and CPUE, it appears that there has no big difference between actual and predicted CPUE with the error less than 0.1t/h each year, only difference between 2013 and 2014 is relatively large, the predicted mean CPUE are from 3.64 to 7.02t/h. The results of hot spot analysis and geostatistics analysis are basically same, the result of the two analysis methods can support each other. The center of stock density is no big change from 2005 to 2007 and description the fishing grounds is more stable of this period, after moving toward the southwest and description there are also distributed fishing grounds in western waters, moved to eastward and northward after 2009 and show the new changes of stock distribution for C hilean jack mackerel. Overall, the annual variation of spatial distribution for C hilean jack mackerel stock is significant.(3) The Spatial pattern of stock density for C hilean jack mackerel. Use ESDA method and GIS technology to explore the spatial distribution and spatial distribution pattern of stock density for C hilean jack mackerel. The results show that the distribution of Chilean jack mackerel was mostly spatial concentration distributio n from 2005 to 2014, the agglomeration characteristics and trends were difference in different year, the interannual differences in the distribution pattern were significant. The relative stock density of Chilean jack mackerel had a characteristic of ―high in northeast and low in southwest‖ in 2005, high- high type and low- low type are centralized distribution. The station numbers of high-high type had increase in 2006. The proportion of station numbers of low- low type had increase in 2007, and the distribution area moved westward. The station of high-high type and low- low type were interspersed distribution in 2008. The spatial distribution of high-high type and low- low type moved southward in 2009. The distributed more concentrated in 2010 and 2011, the enclave of high- high type located in the western waters, the low- low type mainly in the eastern part of the fishing ground. The distribution had a trend moved eastward in 2012. The station numbers reduce significantly and distributed in the eastern sea in 2013 and 2014. The space distribution pattern of C hilean jack mackerel changed significantly in different year, the distribution of high- high type and low- low type had certain rules but less obvious, May be related to the different fishing waters each year.(4) Geostatistics method and ESDA spatial analysis used in analysis the spatial heterogeneity and temporal dynamics of stock density for C hilean jack mackerel had a better result, showed that geostatistics method and spatial analysis combined with ESDA used in marine fisheries research in feasible, can greatly improve the accuracy of analysis results, showed a new research perspective and methods for future research the fishing ground move of Chilean jack mackerel and its relationship with the marine environment. |
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