Application of GLM and GAM for Studying Relationship between Environmental Factors and Resource Density for Mantis Shrimp Oratosquilla oratoria in Bohai Bay
XU Hailong1,2, LIU Zhuoying1, WANG Rui3, XUE Wei1, MA Ting1, GU Dexian4
1. Tianjin Key Laboratory of Aqua-Ecology and Aquaculture, Tianjin 300384, China; 2. National Demonstration Center for Experimental Aqua-Ecology and Aquaculture Education, Tianjin Agriculture University, Tianjin 300384, China; 3. Third Institute of Oceanography, Ministry of National Resource, Xiamen 361005, China; 4. Tianjin Fishery Institute, Tianjin 300171, China
Abstract：Mantis shrimp Oratosquilla oratoria is an important fishery resources in the Bohai Bay. Understanding of the distribution of mantis shrimp, based on the relationship between environmental factors and resource density, is useful for the future research in assessment and management for mantis shrimp. Therefore, the relationship between resource density and environment factors was established based on generalized linear model (GLM) and generalized additive model (GAM). Data including resource density (abundance and biomass) of mantis shrimp and environment conditions of fishing ground were surveyed in Tianjin Bohai Bay from 2014 to 2015, which environment factors included longitude (LON), latitude (LAT), sea surface temperature (SST), sea surface salinity (SSS), chlorophyll a (Chla) concentration, dissolved oxygen (DO) concentration, pH and depth. The results showed that based on the GLM model, the primary factors affecting the abundance of mantis shrimp were DO, LON×Depth, Depth and SST, and the principle factors affecting the biomass of mantis shrimp were pH, LAT and LON×LAT in descending order of effect when the combined effects of factors were considered. The GAM results indicated that the ranges of deviances explained of every environment factor for abundance and biomass of mantis shrimp were varied from 35.12% to 64.35% and from 37.18% to 67.94%, respectively. And the deviances explained the combined effects GAMs between environment factors and abundance as well as biomass equal to 99.957% and 99.911%, respectively.
徐海龙, 刘卓莹, 王芮, 薛薇, 马婷, 谷德贤. 基于两种模型的渤海湾口虾蛄资源与环境关系研究[J]. 水产科学, 2022, 41(2): 183-191.
XU Hailong, LIU Zhuoying, WANG Rui, XUE Wei, MA Ting, GU Dexian. Application of GLM and GAM for Studying Relationship between Environmental Factors and Resource Density for Mantis Shrimp Oratosquilla oratoria in Bohai Bay. Fisheries Science, 2022, 41(2): 183-191.
[1] 徐海龙,韩颖,谷德贤,等.渔业资源自然死亡估算方法研究进展[J].水产科技情报,2019,46(3):160-164. [2] 刘海映,徐海龙,林月娇.盐度对口虾蛄存活和生长的影响[J].大连水产学院学报,2006,21(2):180-183. [3] 王文宇,邵全琴,薛允传,等.西北太平洋柔鱼资源与海洋环境的GIS空间分析[J].地球信息科学,2003,5(1):39-44. [4] 郑波,陈新军,李纲.GLM和GAM模型研究东黄海鲐资源渔场与环境因子的关系[J].水产学报,2008,32(3):379-386. [5] 赵静,梁金玲,周曦杰,等.基于GAM模型的马鞍列岛海域优势甲壳类与环境因子的关系研究[J].南方水产科学,2017,13(3):26-35. [6] 徐海龙,张桂芬,乔秀亭,等.黄海北部口虾蛄体长及体质量关系研究[J].水产科学,2010,29(8):451-454. [7] 徐海龙,刘海映,林月娇.温度和盐度对口虾蛄呼吸的影响[J].水产科学,2008,27(9):443-446. [8] 宁加佳,杜飞雁,王雪辉,等.基于稳定同位素的口虾蛄食性分析[J].水产学报,2016,40(6):903-910. [9] 安继宗,徐海龙,王彦怀,等.口虾蛄幽门胃、中肠、后肠及中肠腺形态组织学观察[J].河北渔业,2018(8):14-16. [10] 王娜,徐海龙,武子山,等.塘沽上岸渔获物组成变化趋势分析[J].河北渔业,2011(11):32-34. [11] 谷德贤,刘国山,王晓宇,等.基于GAM模型的天津海域鱼类资源和环境因子关系的初步研究[J].天津农学院学报,2017,24(1):38-43. [12] 谷德贤,刘茂利.天津海域口虾蛄群体结构及资源量分析[J].河北渔业,2011(8):24-26. [13] 潘国良,张龙,朱增军,等.浙江南部近岸海域春季口虾蛄(Oratosquilla oratoria)生物量的时空分布[J].海洋与湖沼,2013,44(2):366-370. [14] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 12763.4—2007,海洋调查规范 第4部分 海水化学要素调查[S].北京:中国标准出版社,2007. [15] 谷德贤,王婷,王娜,等.渤海湾口虾蛄假蚤状幼体的密度分布及影响因素研究[J].大连海洋大学学报,2018,33(1):65-71. [16] 谷德贤,王婷,徐海龙,等.天津海域鱼卵、仔稚鱼资源动态研究[J].大连海洋大学学报,2020,35(4):557-563. [17] HASTIE T, TIBSHIRANI R. Generalized additive models:some applications[J].Journal of the American Statistical Association,1987,82(398):371-386. [18] 刘修泽,郭栋,王爱勇,等.辽东湾海域口虾蛄的资源特征及变化[J].水生生物学报,2014,38(3):602-608. [19] 吴强,陈瑞盛,黄经献,等.莱州湾口虾蛄的生物学特征与时空分布[J].水产学报,2015,39(8):1166-1177. [20] 许莉莉,薛莹,焦燕,等.海州湾及邻近海域口虾蛄群体结构及资源分布特征[J].中国海洋大学学报(自然科学版),2017,47(4):28-36. [21] 徐海龙,陈勇,陈新军,等.引入不确定性对Von Bertalanffy生长方程关系参数估算的影响[J].水产科学,2016,35(2):169-173. [22] DOBSON A. An introduction to generalized linear models [M].2nd ed. London:Chapman and Hall/CRC,2001. [23] VENABLES W N, DICHMONT C M. GLMs, GAMs and GLMMs:an overview of theory for applications in fisheries research[J].Fisheries Research,2004,70(2/3):319-337. [24] 朱源,康慕谊.排序和广义线性模型与广义可加模型在植物种与环境关系研究中的应用[J].生态学杂志,2005,24(7):807-811. [25] 武胜男,陈新军.基于GLM和GAM的日本鲭太平洋群体补充量与产卵场影响因子关系分析[J].水产学报,2020,44(1):61-70. [26] BELLIDO J M, PIERCE G J, WANG J. Modelling intra-annual variation in abundance of squid Loligo forbesi in Scottish waters using generalised additive models[J].Fisheries Research,2001,52(1/2):23-39. [27] GUISAN A, WEISS S B, WEISS A D. GLM versus CCA spatial modeling of plant species distribution[J].Plant Ecology,1999,143(1):107-122. [28] GUISAN A, EDWARDS T C Jr, HASTIE T Jr. Generalized linear and generalized additive models in studies of species distributions:setting the scene[J].Ecological Modelling,2002,157(2/3):89-100.
2022, Vol. 41 
