2024-03-29T12:02:22Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/6222022-11-17T02:08:08Zhdl_2115_20049hdl_2115_141Estimation of Gillnet Selectivity Curve by Maximum最尤法による刺網の網目選択性曲線の推定法1000040261341Fujimori, Yasuzumi藤森, 康澄Tokai, Tadashiopen accessCopyright (c) 2001 the Japanese Society of Fisheries Science(日本水産学会)bi-normalcatch effortgillnetsmaximum likelihood methodSELECT modelselectivity curve665A maximum likelihood method of estimating gillnet selectivity when the data are obtained by gillnet fleets consisting of several nets of differing mesh size is presented in this paper. The SELECT model is expanded by application of the relative length (i.e. the ratio of fish length to mesh size) to obtain a master curve of gillnet selectivity. Four kinds of functional model, normal, lognormal, skew-normal and bi-normal are fitted to the data. In addition, two cases where the relative fishing intensity is either estimated or fixed by catch effort are compared. The bi-normal model has the lower model deviance regardless of whether the relative fishing intensity is estimated or not. The estimation of the relative fishing intensity by catch effort is also examined where the estimates of the parameter of the SELECT model are compared with the catch effort as determined by the number of nets of each mesh size used. For the bi-normal model these quantities compare well. Thus, it is concluded that this method gives reliable estimates even if the data for each mesh size is obtained with different catch efforts.Blackwell Publishing2001-08engjournal articleAMhttp://hdl.handle.net/2115/622https://doi.org/10.1046/j.1444-2906.2001.00301.x09199268Fisheries Science674644654https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/622/3/Estimation.zipapplication/zip205.7 KB2001-08https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/622/1/Manuscript.pdfapplication/pdf85.09 KB2001-08https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/622/2/TableAndFigure.pdfapplication/pdf136.24 KB2001-08