基于GAM的海阳近海三疣梭子蟹资源丰度与环境因子的关系OA
Relationship between Abundance of Portunus trituberculatus in Haiyang Offshore Area and Environmental Factors Based on GAM
基于2010-2024 年海阳近海8 月三疣梭子蟹(Portunus trituberculatus)调查数据与当年5-8 月的海表温度(SST)、盐度(Sal)、溶解氧浓度(DO)和叶绿素a浓度(Chl-a)等环境数据,采用广义可加模型(GAM)分析了三疣梭子蟹资源丰度与这 4 项关键环境因子的关系.结果显示,在资源丰度与环境因子的关系研究中,5、6、7、8 月模型总偏差解释率为62.8%~80.4%,5 月模型解释率最高为80.4%,说明 5 月环境因子对当年 8 月三疣梭子蟹资源丰度的影响最为显著,其中 5 月SST对三疣梭子蟹资源丰度的解释率最大为44.8%.本研究推断5 月环境因子特别是SST对于8 月三疣梭子蟹资源丰度具有重要影响,为根据环境因子预报8 月三疣梭子蟹资源丰度提供科学依据.
Based on survey data of Portunus trituberculatus in the Haiyang offshore area from 2010 to 2024 and corresponding environmental data—including sea surface temperature(SST),salinity(Sal),dissolved oxygen concentration(DO),and chlorophyll-a concentration(Chl-a)from May to August each year—we employed a Generalized Additive Model(GAM)to analyze the relationship between P.trituberculatus abundance and these four key environmental factors.The results revealed that the total deviance explained by the GAMs ranged from 62.8%to 80.4%across the months,with the highest explanatory power(80.4%)in May,indicating that environ-mental conditions in May had the strongest influence in August P.trituberculatus abundance.Among the environ-mental variables in May,SST contributed the most(44.8%)to the model's explanatory power.The study suggests that environmental factors of the May,particularly SST,are critical in predicting August P.trituberculatus abun-dance,providing a scientific basis for forecasting crab populations using early-season environmental indicators.
GONG Yuanting;ZHAO Guoqing;ZHANG Yuqin;TANG Yongzheng;WANG Zhiyang
School of Ocean,Yantai University,Yantai 264006,ChinaEast China Sea Fisheries Research Institute,Shanghai 200090,ChinaYantai CIMC Blue Ocean Technology Co.,LTD,Yantai 264003,ChinaSchool of Ocean,Yantai University,Yantai 264006,ChinaSchool of Ocean,Yantai University,Yantai 264006,China
农业科技
三疣梭子蟹资源丰度海洋环境因子GAM海阳近海
Portunus trituberculatusresource abundancemarine environmental factorGAMHaiyang offshore area
《烟台大学学报(自然科学与工程版)》 2026 (1)
63-71,9
山东省渔业资源调查与监测项目(37000022P11000111802T).
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