GAM模型和BRT模型在长江口鱼类群落多样性预测中的比较

doi:10.13287/j.1001-9332.201902.037

应用生态学报 ›› 2019, Vol. 30 ›› Issue (2): 644-652.doi: 10.13287/j.1001-9332.201902.037

GAM模型和BRT模型在长江口鱼类群落多样性预测中的比较

吴建辉^1,2,戴黎斌^1,3,4,戴小杰^1,3,4,5,田思泉^1,3,4,5*,刘健²,陈锦辉²,王学昉^1,3,4,5王家启^1,3,4

¹上海海洋大学海洋科学学院, 上海 201306;
²上海市长江口中华鲟自然保护区管理处, 上海 200092;
³中国远洋渔业数据中心, 上海 201306;
⁴大洋渔业资源可持续开发教育部重点实验室, 上海 201306;
⁵国家远洋渔业工程技术研究中心, 上海 201306

收稿日期:2018-05-29 修回日期:2018-12-04 出版日期:2019-02-20 发布日期:2019-02-20
通讯作者: E-mail:sqtian@shou.edu.cn
作者简介:吴建辉,男,1980年生,工程师.主要从事中华鲟保护与保护区管理研究.E-mail:WJH0618@163.com
基金资助:
本文由上海市科委地方能力建设项目(18050502000)和长江口中华鲟增殖放流跟踪监测和效果评估项目(S170062)资助

Comparison of generalized additive model and boosted regression tree in predicting fish community diversity in the Yangtze River Estuary, China.

WU Jian-hui^1,2, DAI Li-bin^1,3,4, DAI Xiao-jie^1,3,4,5, TIAN Si-quan^1,3,4,5*, LIU Jian², CHEN Jin-hui², WANG Xue-fang^1,3,4,5, WANG Jia-qi^1,3,4

¹College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
²Superintendence Department of Shanghai Yangtze Estuarine Nature Reserve for Chinese Sturgeon, Shanghai 200092, China;
³National Data Centre for Distant-Water Fisheries of China, Shanghai 201306, China;
⁴Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China;
⁵National Distant-water Fisheries Engineering Research Center, Shanghai 201306, China

Received:2018-05-29 Revised:2018-12-04 Online:2019-02-20 Published:2019-02-20
Supported by:
This work was supported by the Shanghai Municipal Science and Technology Commission Local Capacity Construction Project, China (18050502000) and the Tracking Monitoring and Evaluation of Chinese Sturgeon Proliferation and Release in the Yangtze River Estuary, China (S170062).

摘要/Abstract

摘要： 长江口为西太平洋最大的河口,评估其鱼类群落多样性分布能够为长江口生态系统的修复和管理提供科学依据.本研究基于2012—2014年长江口渔业监测数据,分别使用GAM模型和BRT模型建立各站点水域鱼类群落多样性指数与环境和时空因子之间的关系.结合线性回归方程,采用交叉验证的方式对模型的预测能力和拟合效果进行评价,并绘制了2014年长江口鱼类群落多样性指数和丰富度指数的空间分布图.结果表明: 盐度、pH和叶绿素a对多样性指数贡献最高,pH、溶解氧和叶绿素a是对丰富度指数贡献率最高的环境因子.BRT模型对于多样性指数和丰富度指数的拟合和预测结果均优于GAM模型.空间分布预测显示,相较于GAM模型,BRT模型能够对长江口小面积水域间的鱼类群落多样性作更好的区分,河口外侧水域的鱼类群落多样性明显高于河口内侧水域,而北支水域的多样性高于南支水域.

关键词: 长江口, 提升回归树(BRT)模型, 广义加性模型(GAM), 鱼类群落多样性

Abstract: Yangtze River Estuary is the biggest estuarine ecosystem in the western Pacific Ocean. Evaluating fish community in this ecosystem can provide scientific basis for its restoration and mana-gement. Generalized additive model (GAM) and boosted regression tree (BRT) were built to examine the relationship between fish community diversity and environmental and spatio-temporal variables based on data collected during 2012-2014. Combined with linear regression analysis, a cross validation was used to evaluate the fitness and predictive performance of both models. We plotted the spatial distribution of fish community diversity and richness in each station of the Yangtze River Estuary in 2014. The results showed that salinity, pH and chlorophyll-a had the most contribution on diversity, while pH, dissolved oxygen and chlorophyll-a were the most contributive variables on richness. BRT models showed better fitness and lower prediction error than GAM models. In contrast to GAM models, BRT models could distinguish the fish community index in each station area with respect to the spatial prediction. The diversity index in external water was obviously greater than that in internal water. Meanwhile, the station at higher latitude had a higher diversity index in both external and internal water.

Key words: generalized additive model, fish community diversity, Yangtze River Estuary, boosted regression tree model

吴建辉,戴黎斌,戴小杰,田思泉,刘健,陈锦辉,王学昉王家启. GAM模型和BRT模型在长江口鱼类群落多样性预测中的比较[J]. 应用生态学报, 2019, 30(2): 644-652.

WU Jian-hui, DAI Li-bin, DAI Xiao-jie, TIAN Si-quan, LIU Jian, CHEN Jin-hui, WANG Xue-fang, WANG Jia-qi. Comparison of generalized additive model and boosted regression tree in predicting fish community diversity in the Yangtze River Estuary, China.[J]. Chinese Journal of Applied Ecology, 2019, 30(2): 644-652.

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GAM模型和BRT模型在长江口鱼类群落多样性预测中的比较

Comparison of generalized additive model and boosted regression tree in predicting fish community diversity in the Yangtze River Estuary, China.

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