摘要
陆地生态系统净初级生产力(Net primary productivity,NPP)是衡量植被固碳能力的重要指标。秦岭陕西段作为陕西省及周边地区的重要生态屏障,其NPP时空格局变化及驱动机制对区域碳循环研究具有重要意义。基于GEE获取秦岭陕西段2001—2023年NPP并分析其时空变化规律,结合最优参数的地理探测器和偏最小二乘法结构方程模型,量化了多元驱动力对NPP时空分异格局的影响。结果表明:研究时段内秦岭陕西段NPP多年均值为602.20 g C m^(-2)a^(-1),年际变化速率为5.57 g C m^(-2)a^(-1)(P<0.01),93.11%的区域NPP呈极显著与显著增加趋势;蒸散、降水和核归一化植被指数是影响NPP空间格局的主要因素,其中蒸散和降水交互作用对NPP的影响最强,其次是蒸散与气温;气候、地形和人类活动可以直接影响NPP,也可以通过植被指数间接影响NPP(例如海拔→人类足迹→蒸散→核归一化植被指数→NPP)。揭示了多元驱动力耦合对秦岭陕西段山地生态系统NPP时空分异格局的作用机制与影响路径,不仅为秦岭陕西段碳循环研究提供了科学依据,还将为其他山地陆地生态系统NPP时空分异格局复杂机制探索提供有益参考。
Net primary productivity(NPP)of terrestrial ecosystems served as a key indicator of vegetation carbon sequestration capacity.The Shaanxi section of the Qinling Mountains as a vital ecological barrier in Shaanxi Province and its surrounding areas,investigating the spatiotemporal patterns of NPP and their driving mechanisms was crucial for understanding regional carbon cycling.This study utilized Google Earth Engine(GEE)to derive NPP data(2001—2023)for the Shaanxi section of the Qinling Mountains and analyzed its spatiotemporal patterns.The Theil⁃Sen median trend analysis along with the Mann⁃Kendall significance test revealed the trend features of NPP changes in the Shaanxi section of the Qinling Mountains from 2001 to 2023.By integrating the optimal parameters⁃based geodetector(OPGD)and partial least squares structural equation modeling(PLS⁃SEM),we quantified the impacts of multiple drivers on NPP spatiotemporal divergence patterns.The results showed that during the study period,the mean annual NPP in the Shaanxi section of the Qinling Mountains was 602.20 g C m^(-2)a^(-1),and the spatial distribution pattern was characterised by a high in the north⁃west versus a low in the south⁃east,and the rate of change of the interannual variation was 5.57 g C m^(-2)a^(-1)(P<0.01),of which 81.62%of the area exhibits a highly significant increasing trend,11.48%of the area shows a significant increase trend and the area of insignificant change is mainly located in the southeastern part of the study area as well as in the gully areas.The primary factors influencing the spatial pattern of NPP are evapotranspiration,precipitation,and the kernel normalized difference vegetation index,with a total explanatory power of over 0.67.The strongest influence on NPP is the interaction between evapotranspiration and precipitation,following by the interaction between evapotranspiration and temperature,which had an explanatory power of 0.502 and 0.460,respectively.Climate,topography,and human activities directly affected NPP and indirectly influenced NPP through vegetation indices(e.g.,altitude→human footprint→evapotranspiration→kernel normalized difference vegetation index→NPP).This research revealed the mechanisms and impact pathways of multiple driving forces coupling on the temporal and spatial NPP divergence patterns within the mountain ecosystems of the Shaanxi section of the Qinling Mountains.This study provided a scientific basis for the ecological protection and management of the Shaanxi section of the Qinling Mountains and offered valuable insights for understanding the complex mechanisms driving spatial and temporal divergence patterns of NPP in other mountainous terrestrial ecosystems.
作者
古斌斌
徐国保
王子昂
李群
张扬
陈安安
GU Binbin;XU Guobao;WANG Ziang;LI Qun;ZHANG Yang;CHEN Anan(College of Urban and Environmental Sciences,Northwest University,Xi′an 710127,China;Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity,Xi′an 710127,China)
出处
《生态学报》
北大核心
2025年第22期11105-11116,共12页
Acta Ecologica Sinica
基金
陕西省自然科学基金项目(2023⁃JC⁃QN⁃0300,2025JC⁃JCQN⁃008)
陕西省秦创原项目(QCYRCXM⁃2023⁃091)
国家级大学生创新训练项目(202410697033)。
关键词
净初级生产力
GEE
基于最优参数的地理探测器
偏最小二乘法结构方程模型
时空分异
Net primary productivity(NPP)
Google Earth Engine(GEE)
optimal parameters-based geographical detector
partial least squares structural equation modeling
spatiotemporal variation
