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为实现南极磷虾(Euphausia superba)渔场的有效预测和高效开发。本研究基于"龙腾"号拖网渔船在南设得兰群岛和象岛周围海域2015—2019年渔捞日志的数据,应用随机森林模型(Random forest, RF)和广义可加模型(Genera-lized additive model, GAM)分析了南极磷虾单位捕捞努力量渔获量(Catch per unit effort, CPUE)与环境因子(经纬度、表层水温、水深、离岸距离)之间的关系。RF模型分析显示:经度和纬度与南极磷虾CPUE整体上呈负相关关系;随着表层水温的升高南极磷虾的CPUE呈逐渐下降趋势;当水深小于1 000 m时,南极磷虾CPUE较高,当水深大于1 000 m时,南极磷虾CPUE与水深呈负相关关系,这表明水深对南极磷虾的分布影响显著。GAM模型分析显示:南极磷虾CPUE与经度在整体上呈极显著非线性负相关关系(P<0.01);与表层水温呈极显著线性负相关关系(P<0.01);与纬度、水深和离岸距离呈极显著非线性相关关系(P<0.01)。综合这两种模型结果表明,南极磷虾适宜栖息于较浅海域且在不同海域对水温的适宜性不同,CPUE高值相对集中于62.5°S—63.5°S、58°W—60°W范围。相较于GAM模型,RF模型拟合效果更优(R2=0.331 4±0.000 7,RMSE=0.248 1±0.000 2)。RF模型通过计算变量重要性并排序,选择对南极磷虾的CPUE产生影响的主导因子,在研究海洋生物资源分布中有广阔的应用前景。
Abstract：In order to provide fundamental knowledge for fishing ground prediction and fishery resource efficient exploitation, we analyzed the relationship between Antarctic krill(Euphausia superba) catch per unit effort(CPUE) and environmental factors(latitude and longitude, water temperature, water depth, offshore distance) in the marine areas around the South Shetlands and Elephant Island based on the fishing log data collected with "Long-teng" boat from 2015 to 2019 using the random forest(RF) and generalized additive model(GAM). Results from the RF indicated that longitude and latitude negatively correlate with CPUE on the whole, CPUE of Antarctic krill decreases with the increase of water tempera-ture sharply, implying its significant influence on the distribution of Antarctic krill, and CPUE is relative high and steady when water depth below 1 000 m and decreases with the increase of water depth when its value greater than 1 000 m.GAM results indicated significant nonlinear relationships between CPUE and longitude latitude, water depth and offshore distance(P<0.01) and a significant linear negative relationship between CPUE and water temperature(P<0.01).Result from the two models indicated that the Antarctic krill was suitable to inhabit in shallow sea and had different suitability to water temperature in different sea areas. The higher CPUE were relatively concentrated in the range of 62.5°S—63.5°S and 58°W—60°W. Compared with GAM, RF model has better fitting effect; R2 and RMSE were higher. RF sorted the importance of variables in the input data set and then chose the dominant factors that affect the CPUE. RF can better deal with multivariate cases and intera-ctive effects among the variables. Therefore, RF has a broad application prospect in the fishery data analysis.
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基本信息:
DOI：10.16441/j.cnki.hdxb.20200243
中图分类号:S931.1
引用信息:
[1]刘俊超,贾明秀,冯卫东,等.基于RF和GAM模型的南极磷虾资源分布与环境因子关系研究[J],2021,51(08):20-29.DOI:10.16441/j.cnki.hdxb.20200243.
基金信息:
国家重点研究发展计划项目(YS2020YFD090016);; 南极磷虾高效捕捞技术研发项目(20150256)资助~~