Due to the complex terrain, sparse precipitation observation sites, and uneven distribution of precipitation in the northeastern slope of the Qinghai–Tibet Plateau, it is necessary to establish a precipitation estimation method with strong applicability. In this study, the precipitation observation data from meteorological stations in the northeast slope of the Qinghai–Tibet Plateau and 11 geographical and topographic factors related to precipitation distribution were used to analyze the main factors affecting precipitation distribution. Based on the above, a multivariate linear regression precipitation estimation model was established. The results revealed that precipitation is negatively related to latitude and elevation, but positively related to longitude and slope for stations with an elevation below 1700 m. Meanwhile, precipitation shows positive correlations with both latitude and longitude, and negative correlations with elevation for stations with elevations above 1700 m. The established multivariate regression precipitation estimating model performs better at estimating the mean annual precipitation in autumn, summer, and spring, and is less accurate in winter. In contrast, the multivariate regression mode combined with the residual error correction method can effectively improve the precipitation forecast ability. The model is applicable to the unique natural geographical features of the northeast slope of the Qinghai–Tibet Plateau. The research results are of great significance for analyzing the temporal and spatial distribution pattern of precipitation in complex terrain areas.

Topographic factors are critical for influencing vegetation distribution patterns, and studying the interactions between them can enhance our understanding of future vegetation dynamics. We used the Moderate-resolution Imaging Spectroradiometer Normalized Differential Vegetation Index (MODIS NDVI) image dataset (2000–2019), combined with the Digital Elevation Model (DEM), and vegetation type data for trend analysis, and explored NDVI variation and its relationship with topographic factors through an integrated geographically-weighted model in the Three Parallel Rivers Region (TPRR) of southeastern Qinghai-Tibet Plateau (QTP) in the past 20 years. Our results indicated that there was no significant increase of NDVI in the entire basin between 2000–2019, except for the Lancang River basin. In the year 2004, abrupt changes in NDVI were observed across the entire basin and each sub-basin. During 2000–2019, the mean NDVI value of the whole basin increased initially and then decreased with the increasing elevation. However, it changed marginally with variations in slope and aspect. We observed a distinct spatial heterogeneity in vegetation patterns with elevation, with higher NDVI in the southern regions NDVI than those in the north as a whole. Most of the vegetation cover was concentrated in the slope range of 8~35°, with no significant difference in distribution except flat land. Furthermore, from 2000 to 2019, the vegetation cover in the TPRR showed an improving trend with the changes of various topographic factors, with the largest improvement area (36.10%) in the slightly improved category. The improved region was mainly distributed in the source area of the Jinsha River basin and the southern part of the whole basin. Geographically weighted regression (GWR) analysis showed that elevation was negatively correlated with NDVI trends in most areas, especially in the middle reaches of Nujiang River basin and Jinsha River basin, where the influence of slope and aspect on NDVI change was considerably much smaller than elevation. Our results confirmed the importance of topographic factors on vegetation growth processes and have implications for understanding the sustainable development of mountain ecosystems.

Since the 1970s, great progress has been made in research on the spatio-temporal pattern of population ageing at multiple spatial scales by both domestic and foreign scholars. However, the analysis of the process mechanism of regional ageing is still insufficient. Based on the county-scale data in 2000 and 2010 censuses, the spatio-temporal pattern of population ageing is statistically analyzed. Moreover, the Structural Equation Model (or SEM) is developed to explore the path relationship between various variables. Consequently, the influencing mechanism of regional ageing process is thoroughly revealed. The main results are as follows. Firstly, the line from Baotou City of Inner Mongolia autonomous region to Tengchong County of Yunnan province (or BT Line) is a geographical demarcation of regional ageing disparities in China. Southeast of the BT Line witnesses contiguous areas of higher ageing counties which interspersed with lower ageing counties, whereas northwest of the BT Line is a continuous area of lower ageing counties with just one middle-level ageing belt located in the north margin. Secondly, the fitting results of SEM interpret the influencing mechanism of regional ageing disparities in China. Thirdly, life expectancy, fertility and migration are three endogenous factors of regional ageing process. Thereinto, life expectancy has significantly positive effect, while fertility and migration have significantly negative effects on regional ageing. Lastly, geographical environment has significant direct and indirect effects on regional ageing in China. In general, the income increase, social progress, and the improvement of living conditions and natural environmental quality would enhance regional ageing level, otherwise the improvement of urbanization level would decrease regional ageing level.

利用2000年和2010年分县人口普查数据,分析中国县域人口老龄化的空间格局及变化,采用结构方程模型拟合人口老龄化影响因子之间的路径关系,探讨中国县域人口老龄化的影响机制。结果表明:内蒙古包头至云南腾冲是中国县域人口老龄化较为清晰的空间分界线,其东南部老龄化高值县区连片集中,低值县区夹杂其间;西北全域老龄化率普遍较低,仅在北缘横亘一条中值县区连绵带。结构方程模型的拟合结果很好地解释了中国县域人口老龄化空间差异的影响机制。迁移率、预期寿命和生育率是人口老龄化进程的内生因素,其中,预期寿命对县域人口老龄化有正向效应,迁移率和生育率则对县域人口老龄化有负向效应。社会经济、家庭状况和自然环境等因素是人口老龄化进程的外生因素,对中国县域人口老龄化有显著的直接和间接效应。收入增加、社会发展、居住条件改善以及环境舒适性提高等推高县域人口老龄化水平,城镇化水平提高则降低县域人口老龄化水平。

The Qinling Mountains, located at the junction of warm temperate and subtropical zones, serves as the boundary between North and South China. Exploring the sensitivity of vegetation response to hydrothermal dynamics can be conducive to understanding the pattern and dynamics of main vegetation types and the mechanism of their response to changes in temperature and moisture. Importance should be attached to the laws of vegetation change in different climate zones. To reveal the sensitivity and areal differentiation of vegetation responses to hydrothermal dynamics, the spatial and temporal variation characteristics of NDVI and SPEI on the southern and northern slopes of the Qinling Mountains from 2000 to 2018 are explored using the meteorological data from 32 meteorological stations and the MODIS NDVI datasets. The results show that: (1) The overall vegetation coverage of the Qinling Mountains improved significantly from 2000 to 2018. The NDVI rise rate and area ratio on the southern slope were higher than those on the northern slope, and the vegetation on the southern slope exhibited better improvement than that on the northern slope. The Qinling Mountains showed an insignificant humidification trend. The humidification rate and humidification area of the northern slope were greater than those on the southern slope. (2) Vegetation on the northern slope of the Qinling Mountains was more sensitive to hydrothermal dynamics than that on the southern slope. Vegetation was most sensitive to hydrothermal dynamics from March to June on the northern slope, and from March to May (spring) on the southern slope. The vegetation on the northern and southern slopes was mainly affected by hydrothermal dynamics on a scale of 3-7 months, and it responds weakly to hydrothermal dynamics on a scale of 11-12 months. (3) 90.34% of NDVI and SPEI were positively correlated in the Qinling Mountains. Spring humidification in most parts of the study area could promote the growth of vegetation all the year round. The sensitivity of vegetation responses to hydrothermal dynamics with increasing altitude increased first and then decreased. The altitude of 800 to 1200 m was the most sensitive altitude for vegetation response to hydrothermal dynamics. The sensitivity of vegetation response at the elevation of 1200-3000 m decreased with the increasing altitude. The grass was the most sensitive vegetation type to hydrothermal dynamics on the northern and southern slopes of the Qinling Mountains, but most of other vegetation types on the northern slope were more sensitive to hydrothermal dynamics than those on the southern slope.

秦岭位于暖温带与亚热带交界处,也是中国南北地理分界线,秦岭南北坡植被对干湿变化响应敏感性,可以折射出暖温带、亚热带地区主要植被类型对干湿变化的响应规律和机制特征,对深入理解不同气候带植被变化规律具有重要意义。本文利用秦岭山地32个气象站点的气象数据和MODIS NDVI时间序列数据集,探讨了2000—2018年秦岭南北坡NDVI和SPEI时空变化特征,揭示了南北坡植被对干湿变化响应敏感性及其空间差异。结果表明：① 2000—2018年秦岭植被覆盖情况整体显著改善,但秦岭南坡NDVI上升幅度和面积占比均高于北坡,南坡植被比北坡改善情况好。秦岭湿润化趋势不显著,但秦岭北坡湿润化速率和面积占比均大于南坡。② 秦岭北坡植被比南坡植被更易受干湿变化影响,秦岭北坡植被对3—6月总体干湿变化最为敏感,南坡植被对3—5月（春季）干湿变化最为敏感。秦岭南北坡植被主要受3~7个月尺度干湿变化影响,对11~12个月尺度的干湿变化响应较弱。③ 秦岭有90.34%的区域NDVI与SPEI呈正相关,大部分地区春季湿润化能促进全年植被生长;随海拔上升,植被对干湿变化响应敏感性先上升再下降,海拔800~1200 m是植被响应最敏感的海拔段,海拔1200~3000 m随海拔上升植被响应敏感性下降;南北坡草丛均是对干湿变化响应最为敏感的植被类型,但秦岭北坡多数植被类型对干湿变化响应比南坡敏感。

Extreme climate events affect sustainable development in many parts of the world. Although several studies of local extreme climate events exist, such events need more thorough investigation. Detailed studies of climatic variations that assess changes on both spatial and temporal scales are becoming increasingly necessary. In this study, 14 indices for extreme temperature and 12 indices for extreme precipitation were used to analyze the spatial distribution and temporal trends of extreme climate events in the three‐rivers headwater region (TRHR), China, over the past five decades. We found that the frequency of high temperature extremes has increased, whereas the frequency of dry extremes has decreased. Statistically significant upward trends emerged for the number of warm nights (TN90p) and the number of warm days (TX90p) in the TRHR, which increased at a rate of 11.75 and 6.79 days/10 years (p < 0.05), respectively. We also found a highly significant downward trend in the number of consecutive dry days with a rate of −4.05 days/10 years across the whole region. Overall, the TRHR has become warmer and wetter over the past 50 years and the frequency of extreme events has increased. Wavelet analysis indicated that there are significant relationships between extreme climate events in the TRHR and large‐scale ocean‐atmosphere circulation patterns, for example, the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. Our findings will help local water managers and stakeholders ensure that there are effective measures in place to adapt to and mitigate the effects of potential future climate change in the TRHR.

Anthropogenically elevated CO2(eCO2) concentrations have been suggested to increase woody cover within tropical ecosystems through fertilization. The effect of eCO2is built into Earth system models, although testing the relationship over long periods remains challenging. Here, we explore the relative importance of six drivers of vegetation change in western Africa over the past ~500,000 years (moisture availability, fire activity, mammalian herbivore density, temperature, temperature seasonality, CO2) by coupling past environmental change data from Lake Bosumtwi (Ghana) with global data. We found that moisture availability and fire activity were the most important factors in determining woody cover, whereas the effect of CO2was small. Our findings suggest that the role of eCO2effects on tropical vegetation in predictive models must be reconsidered.

This paper describes how to test, and potentially falsify, a multivariate causal hypothesis involving only observed variables (i.e., a path analysis) when the data have a hierarchical or multilevel structure, when different variables are potentially defined at different levels of such a hierarchy, and when different variables have different sampling distributions. The test is a generalization of Shipley's d-sep test and can be conducted using standard statistical programs capable of fitting generalized mixed models.

Natural systems are essentially complex. In most cases, fully understanding natural systems requires the capacity to examine simultaneous influences and responses among multiple interacting factors. Compared with traditional multivariate methods, structural equation model (SEM) could specify the causal or dependent relationships among variables using the prior knowledge of researchers before conducting relevant experiments, i.e. initial models. SEM could not only identify the individual path coefficient for each relationship, but also estimate the whole model fit to determine whether to revise the initial models. We attempt to introduce SEM from the following aspects: definition and types of variables in SEM, detailed procedures for how to analyze data through SEM, some applications of SEM in ecology and recommended software. We encourage more researchers to apply SEM in ecological data analyses in order to improve understanding of natural systems and advance the field of ecology.

基于多变量统计方法同时研究自然系统内多个因子之间的相互关系, 是阐释复杂的自然系统的一个重要手段。相比传统的多变量统计法, 结构方程模型基于研究者的先验知识预先设定系统内因子间的依赖关系, 不仅能够判别各因子之间的关系强度(路径系数), 还能对整体模型进行拟合和判断, 从而能更全面地了解自然系统。由于结构方程模型只在近年才被应用到生态学的数据分析中, 因此该文试图对其作一简略介绍, 包括结构方程模型的定义和变量类型, 结合事例研究展现结构方程模型分析的一般步骤、在生态学中的应用以及相关软件的介绍等。望能为相关研究人员提供直观的认识, 加强结构方程模型在生态学数据分析中的应用。

The three-rivers headwater region (THRHR) of Qinghai province, China plays a key role as source of fresh water and ecosystem services for central and eastern China. Global warming and human activities in the THRHR have threatened the ecosystem since the 1980s. Therefore, the Chinese government has included managing of the THRHR in the national strategy since 2003. The State Integrated Test and Demonstration Region of the THRHR highlights the connection with social engineering (focus on improving people's livelihood and well-being) in managing nature reserves. Based on this program, this perspective attempts a holistic analysis of the strategic role of the THRHR, requirements for change, indices of change, and approaches to change. Long-term success of managing nature reserves requires effective combination of ecological conservation, economic development, and social progress. Thus, the philosophy of social engineering should be employed as a strategy to manage the THRHR.

Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with consequences for the functioning of the Earth system and provision of ecosystem services(1,2). Yet how global vegetation is responding to the changing environment is not well established. Here we use three long-term satellite leaf area index (LAI) records and ten global ecosystem models to investigate four key drivers of LAI trends during 1982-2009. We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area, whereas less than 4% of the globe shows decreasing LAI (browning). Factorial simulations with multiple global ecosystem models suggest that CO2 fertilization effects explain 70% of the observed greening trend, followed by nitrogen deposition (9%), climate change (8%) and land cover change (LCC) (4%). CO2 fertilization effects explain most of the greening trends in the tropics, whereas climate change resulted in greening of the high latitudes and the Tibetan Plateau. LCC contributed most to the regional greening observed in southeast China and the eastern United States. The regional effects of unexplained factors suggest that the next generation of ecosystem models will need to explore the impacts of forest demography, differences in regional management intensities for cropland and pastures, and other emerging productivity constraints such as phosphorus availability.

The uncertainties of China's gross primary productivity (GPP) estimates by global data-oriented products and ecosystem models justify a development of high-resolution data-oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982-2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend (p < .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP. The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability (IAV) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP. The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold-wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.© 2017 John Wiley & Sons Ltd.

The Qinling-Daba (Qinba) Mountains, a key ecological zone of terrestrial ecosystem, has experienced a significant change of vegetation coverage in recent years, which is characterized by rapid climate change. Using MODIS-NDVI dataset, the current study investigated the patterns of spatiotemporal variation of vegetation coverage in the Qinba Mountains during the period 2000-2014. In addition, possible environmental factors affecting this variation were identified. Sen+Mann-Kendall model and partial correlation analysis were used to analyze the data, followed by the calculation of Hurst index in order to analyze future trends of vegetation coverage. The results of the study showed that (1) the Normalized Difference Vegetation Index (NDVI) of the study area revealed a significant increase during 2000-2014 (linear tendency 2.8%/10a). During this period, a stable increase was detected before 2010 (linear tendency 4.32%/10a), followed by a sharp decline after 2010 (linear tendency -6.59%/10a). (2) In terms of spatiotemporal variation, vegetation cover showed a "high in the middle and low in surroundings" pattern. High values of vegetation coverage were mainly found in the Qinba Mountains of Shaanxi Province, while low values of vegetation coverage were mainly observed in Longnan, Tianshui, and Gannan prefectures occupied by arable land. (3) The area covered with vegetation was larger than the degraded area, accounting for 81.32% and 18.68% of the total area, respectively. Piecewise analysis revealed that 71.61% of the total study area showed a decreasing trend in vegetation coverage during 2010-2014, of which, the extremely significant and significant decrease accounted for 6.38% and 3.45%, respectively. (4) The reverse characteristics of vegetation coverage change were stronger than the same characteristic in the Qinba Mountains. Some 46.89% of the entire study area is predicted to decrease in future, while 34.44% of the total area will follow a continuous increasing trend. (5) The change of vegetation coverage was mainly attributed to the deficit of precipitation. At the same time, vegetation coverage during La Nina years was larger than that during El Nino years. Statistical analysis showed that positive and negative anomaly pixels accounted for 28.37% and 71.63%, respectively during El Nino years and 80.48% and 19.52%, respectively during La Nina years. (6) Human activities can induce ambiguous effects on vegetation coverage: both positive effect (through the implementation of the ecological restoration project) and negative effect (through urbanization) were observed.

利用MODIS-NDVI数据,辅以趋势分析、Hurst指数及偏相关分析等方法,本文探讨了2000-2014年秦巴山区植被覆盖时空变化特征及未来趋势,并对其驱动因素进行分析。研究发现：① 近15年秦巴山区植被覆盖呈显著增加趋势,增速为2.8%/10a,其中2010年之前植被覆盖呈持续增加趋势,增速为4.32%/10a,而2010年之后呈连续下降态势,降速为-6.59%/10a;② 空间上,植被覆盖格局呈现&#x0201c;中间高、四周低&#x0201d;的分布特征,高值区主要分布在陕西境内的秦岭山地和大巴山山地;③ 秦巴山区植被覆盖呈增加和减少趋势的面积分别占81.32%和18.68%;然而,分段结果表明,2010-2014年有71.61%的区域植被覆盖呈下降趋势;④ 秦巴山区植被覆盖变化的反向特征强于同向特征,其中46.89%的区域将由改善转为退化,而持续改善地区仅占34.44%;⑤ 植被覆盖变化主要归因于降水的减少,同时拉尼娜年的植被覆盖整体好于厄尔尼诺年;⑥ 人类活动对植被覆盖造成双重影响,是植被覆盖变化的另一重要影响因素。