State Key Laboratory of Grassland Agro-ecosystems, School of Life Sciences, Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University, No. 222, South Tianshui Road, Lanzhou 730000, China.
State Key Laboratory of Grassland Agro-ecosystems, School of Life Sciences, Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University, No. 222, South Tianshui Road, Lanzhou 730000, China.
Sci Total Environ. 2018 Mar;616-617:1174-1180. doi: 10.1016/j.scitotenv.2017.10.203. Epub 2017 Oct 27.
Understanding under which climate and soil conditions the plant productivity-precipitation relationship is linear or nonlinear is useful for accurately predicting the response of ecosystem function to global environmental change. Using long-term (2000-2016) net primary productivity (NPP)-precipitation datasets derived from satellite observations, we identify >5600pixels in the North Hemisphere landmass that fit either linear or nonlinear temporal NPP-precipitation relationships. Differences in climate (precipitation, radiation, ratio of actual to potential evapotranspiration, temperature) and soil factors (nitrogen, phosphorous, organic carbon, field capacity) between the linear and nonlinear types are evaluated. Our analysis shows that both linear and nonlinear types exhibit similar interannual precipitation variabilities and occurrences of extreme precipitation. Permutational multivariate analysis of variance suggests that linear and nonlinear types differ significantly regarding to radiation, ratio of actual to potential evapotranspiration, and soil factors. The nonlinear type possesses lower radiation and/or less soil nutrients than the linear type, thereby suggesting that nonlinear type features higher degree of limitation from resources other than precipitation. This study suggests several factors limiting the responses of plant productivity to changes in precipitation, thus causing nonlinear NPP-precipitation pattern. Precipitation manipulation and modeling experiments should combine with changes in other climate and soil factors to better predict the response of plant productivity under future climate.
了解植物生产力与降水之间的关系呈线性还是非线性，有助于准确预测生态系统功能对全球环境变化的响应。本研究利用卫星观测得到的长期（2000-2016 年）净初级生产力（NPP）-降水数据集，确定了北半球陆地超过 5600 个符合线性或非线性时间 NPP-降水关系的像素。评估了线性和非线性类型之间气候（降水、辐射、实际蒸散与潜在蒸散的比值、温度）和土壤因子（氮、磷、有机碳、田间持水量）的差异。分析表明，线性和非线性类型均表现出相似的年际降水变异性和极端降水的发生。置换多元方差分析表明，辐射、实际蒸散与潜在蒸散的比值和土壤因子在线性和非线性类型之间存在显著差异。非线性类型的辐射较低和/或土壤养分较少，表明其受到降水以外资源的限制程度较高。本研究提出了限制植物生产力对降水变化响应的几个因素，从而导致非线性 NPP-降水模式。降水操纵和建模实验应结合其他气候和土壤因素的变化，以更好地预测未来气候下植物生产力的响应。
