Approaching the upper boundary of driver-response relationships:identifying factors using a novel framework integrating quantile regression with interpretable machine learning

作     者：Zhongyao Liang Yaoyang Xu Gang Zhao Wentao Lu Zhenghui Fu Shuhang Wang Tyler Wagner Zhongyao Liang;Yaoyang Xu;Gang Zhao;Wentao Lu;Zhenghui Fu;Shuhang Wang;Tyler Wagner

作者机构：National Engineering Laboratory for Lake Pollution Control and Ecological RestorationState Environment Protection Key Laboratory for Lake Pollution ControlChinese Research Academy of Environmental SciencesBeijing 100012China Fujian Provincial Key Laboratory for Coastal Ecology and Environmental StudiesXiamen UniversityXiamen 361102China College of the Environment&EcologyXiamen UniversityXiamen 361102China Key Laboratory of Urban Environment and HealthInstitute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Department of Global EcologyCarnegie Institution for ScienceStanfordCA 94305USA Institute of Strategic PlanningChinese Academy of Environmental PlanningBeijing 100012China The Center for Beautiful ChinaChinese Academy of Environmental PlanningBeijing 100012China U.S.Geological SurveyPennsylvania Cooperative Fish and Wildlife Research UnitPennsylvania State UniversityUniversity ParkPA 16802USA 

出 版 物：《Frontiers of Environmental Science & Engineering》 (环境科学与工程前沿（英文）)

年 卷 期：2023年第17卷第6期

页      面：153-163页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by the National Natural Science Foundation of China(Nos.71761147001 and 42030707) the International Partnership Program by the Chinese Academy of Sciences(No.121311KYSB20190029) the Fundamental Research Fund for the Central Universities(No.20720210083) the National Science Foundation(Nos.EF-1638679,EF-1638554,EF-1638539,and EF-1638550) Any use of trade,firm,or product names is for descriptive purposes only and does not imply endorsement by the US Government 

主　　题：Driver-response Upper boundary of relationship Interpretable machine learning Quantile regression Total phosphorus Chlorophyll a 

摘      要：The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships,and can help inform ecosystem management,but has rarely been *** this study,we propose a novel framework integrating quantile regression with interpretable machine *** the first stage of the framework,we estimate the upper boundary of a driver-response relationship using quantile ***,we calculate“potentialsof the response variable depending on the driver,which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter ***,we identify key factors impacting the potential using a machine learning *** illustrate the necessary steps to implement the framework using the total phosphorus(TP)-Chlorophyll a(CHL)relationship in lakes across the continental *** found that the nitrogen to phosphorus ratio(N:P),annual average precipitation,total nitrogen(TN),and summer average air temperature were key factors impacting the potential of CHL depending on *** further revealed important implications of our findings for lake eutrophication *** important role of N:P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient *** wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication *** novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.