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

引用

Frontiers of Environmental Science & Engineering 2023年第6期17卷 153-163页

作者： Zhongyao Liang Yaoyang Xu Gang Zhao Wentao Lu Zhenghui Fu Shuhang Wang Tyler Wagner National Engineering Laboratory for Lake Pollution Control and Ecological Restoration State Environment Protection Key Laboratory for Lake Pollution ControlChinese Research Academy of Environmental SciencesBeijing 100012China Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies Xiamen UniversityXiamen 361102China College of the Environment&Ecology Xiamen UniversityXiamen 361102China Key Laboratory of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Department of Global Ecology Carnegie Institution for ScienceStanfordCA 94305USA Institute of Strategic Planning Chinese Academy of Environmental PlanningBeijing 100012China The Center for Beautiful China Chinese Academy of Environmental PlanningBeijing 100012China U.S.Geological Survey Pennsylvania Cooperative Fish and Wildlife Research UnitPennsylvania State UniversityUniversity ParkPA 16802USA

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“potentials”of 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.

关键词： Driver-response upper boundary of relationship Interpretable machine learning Quantile regression Total phosphorus Chlorophyll a

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