Global‐change drivers of ecosystem functioning modulated by natural variability and saturating responses

Humans are altering global environment at an unprecedented rate through changes in biodiversity, climate, nitrogen cycle, and land use. To address their effects on ecosystem functioning, experiments most frequently explore one driver at a time and control as many confounding factors as possible. Yet, which driver exerts the largest influence on ecosystem functioning and whether their relative importance changes among systems remain unclear. We analyzed experiments in the Patagonian steppe that evaluated the aboveground net primary production (ANPP) response to manipulated gradients of species richness, precipitation, temperature, nitrogen fertilization (N), and grazing intensity. We compared the effect on ANPP relative to ambient conditions considering intensity and direction of manipulations for each driver. The ranking of responses to drivers with comparable manipulation intensity was as follows: biodiversity>grazing>precipitation>N. For a similar intensity of manipulation, the effect of biodiversity loss was 4.0, 3.6, and 1.5, times larger than N deposition, decreased precipitation, and increased grazing intensity. We interpreted our results considering two hypotheses. First, the response of ANPP to changes in precipitation and biodiversity is saturating, so we expected larger effects when the driver was reduced, relative to ambient conditions, than when it was increased. Experimental manipulations that reduced ambient levels had larger effects than those that increased them. Second, the sensitivity of ANPP to each driver is inversely related to the natural variability of the driver. In Patagonia, the ranking of natural variability of drivers is as follows: precipitation>grazing>temperature>biodiversity>N. So, in general, the ecosystem was most sensitive to drivers that varied the least. Comparable results from Cedar Creek (MN) support both hypotheses and suggest that sensitivity to drivers varies among ecosystem types. Given the importance of understanding ecosystem sensitivity to predict global‐change impacts, it is necessary to design new experiments located in regions with contrasting natural variability and that include the full range of drivers.     

Codominant water control on global interannual variability and trends in land surface phenology and greenness

Identifying the relative importance of climatic and other environmental controls on the interannual variability and trends in global land surface phenology and greenness is challenging. Firstly, quantifications of land surface phenology and greenness dynamics are impaired by differences between satellite data sets and phenology detection methods. Secondly, dynamic global vegetation models (DGVMs) that can be used to diagnose controls still reveal structural limitations and contrasting sensitivities to environmental drivers. Thus, we assessed the performance of a new developed phenology module within the LPJmL (Lund–Potsdam–Jena managed Lands) DGVM with a comprehensive ensemble of three satellite data sets of vegetation greenness and ten phenology detection methods, thereby thoroughly accounting for observational uncertainties. The improved and tested model allows us quantifying the relative importance of environmental controls on interannual variability and trends of land surface phenology and greenness at regional and global scales. We found that start of growing season interannual variability and trends are in addition to cold temperature mainly controlled by incoming radiation and water availability in temperate and boreal forests. Warming‐induced prolongations of the growing season in high latitudes are dampened by a limited availability of light. For peak greenness, interannual variability and trends are dominantly controlled by water availability and land‐use and land‐cover change (LULCC) in all regions. Stronger greening trends in boreal forests of Siberia than in North America are associated with a stronger increase in water availability from melting permafrost soils. Our findings emphasize that in addition to cold temperatures, water availability is a codominant control for start of growing season and peak greenness trends at the global scale.     

1982-2013年基于GIMMS-NDVI的新疆植被覆盖时空变化

利用美国国家航天航空局（NASA）全球检测与模型组（Global Inventor Modeling and Mapping Studies,GIMMS）的归一化植被指数数据（NDVI）和英国东英格利亚大学气候研究所（Climate Research Unit,CRU）全球气温降水数据（1982至2013年）,研究新疆1982-2013年植被覆盖格局的时空变化。运用一元线性回归法分析近32年来新疆NDVI变化趋势;运用Theil-Sen median与Mann-Kendall检验研究新疆NDVI格局及趋势特征;并将检验的结果和Hurst指数的结果相结合,研究新疆NDVI格局的可持续性特征。研究表明：（1）新疆植被覆盖在空间分布上差异明显,其中北疆优于南疆,西北优于东南;（2）近32年来新疆年NDVI均值在0.10-0.12之间波动,且存阶段变化性;（3）新疆植被改善趋势的区域占总面积的25.89%,轻微退化的区域占总面积的18.00%;（4）从可持续性来看,新疆大部分地区植被变化将保持现在的趋势,但局部地区具有反持续性,持续性改善的面积占全疆总面积的24.39%,持续性轻微退化的区域占15.73%,另外59.88%为严重退化和未来变化趋势无法确定区域。开展NDVI空间格局的变化研究,对于干旱区新疆来说具有重要的理论和实际意义。     

基于卫星遥感数据的黄淮海地区植被覆盖时空变化特征

利用1982—2003年GIMMS NDVI遥感数据,在进行合成、重采样和时间序列滤波处理的基础上,采用线性趋势分析、经验正交函数分解等方法,对中国黄淮海地区植被覆盖的时空特征进行了研究。结果表明:22年来黄淮海地区植被覆盖总体上呈略微增加的趋势,且该区域生长季有提前和延长的趋势;黄淮海大部分地区植被活动在增强的同时,局部地区出现了植被退化现象;从季节变化上看,春季上升和夏季下降趋势明显;林地为主的自然植被、草甸类自然植被和所有农业植被未变化类别占主导地位,而草原植被则以增加趋势为主。主要生长季的NDVI距平EOF分析表明,第1模态的主要特征是区域中间、北部和南端为正,四周为负变化;第2模态的主要特征是从东南向西北由正到负变化且正值区明显偏多;第3模态从东南向西北呈现"正-负-正"的空间分布,其中负值区大部分为以林地为主的自然植被区和一年一熟农业植被,正值区大部分为农耕区和草原牧区,该模态大致反映了农牧区和林区的NDVI分布型。     

Continental‐scale determinants of population trends in European amphibians and reptiles

The continuous decline of biodiversity is determined by the complex and joint effects of multiple environmental drivers. Still, a large part of past global change studies reporting and explaining biodiversity trends have focused on a single driver. Therefore, we are often unable to attribute biodiversity changes to different drivers, since a multivariable design is required to disentangle joint effects and interactions. In this work, we used a meta‐regression within a Bayesian framework to analyze 843 time series of population abundance from 17 European amphibian and reptile species over the last 45 years. We investigated the relative effects of climate change, alien species, habitat availability, and habitat change in driving trends of population abundance over time, and evaluated how the importance of these factors differs across species. A large number of populations (54%) declined, but differences between species were strong, with some species showing positive trends. Populations declined more often in areas with a high number of alien species, and in areas where climate change has caused loss of suitability. Habitat features showed small variation over the last 25 years, with an average loss of suitable habitat of 0.1%/year per population. Still, a strong interaction between habitat availability and the richness of alien species indicated that the negative impact of alien species was particularly strong for populations living in landscapes with less suitable habitat. Furthermore, when excluding the two commonest species, habitat loss was the main correlate of negative population trends for the remaining species. By analyzing trends for multiple species across a broad spatial scale, we identify alien species, climate change, and habitat changes as the major drivers of European amphibian and reptile decline.     

1982-2018年中国植被覆盖变化非线性趋势及其格局分析

探究植被覆盖变化是评估陆地生态系统环境变化的重要手段,但现有研究多采用线性趋势来表达植被覆盖的变化情况而忽略了趋势的非线性。本文使用GLASS FVC数据,利用BFAST方法和格局分析,探讨了1982-2018年我国植被覆盖变化的非线性趋势及其分布格局。结果表明：（1）与线性趋势方法的对比发现,BFAST的检测结果揭示了四川盆地、黄土高原等地的植被覆盖显著增加趋势其实存在中断,青海和东北等地植被覆盖经历了由退化到改善的过程而并非简单的线性增加,而青藏高原中东部等地则由原先的改善趋势变为了退化趋势。（2）将非线性趋势结果进行分类,其中单调型增加类型占比最多,达到33.58%,主要分布在内蒙古、陕西及河南等地;单调型减少占比1.82%,主要分布在东南沿海地区;中断型增加占比22.91%,主要分布在四川盆地东部和华北地区;中断型减少占比2.68%,主要分布在青藏高原东南部;由增到减占比4.20%,主要分布在青海等地;由减到增占比14.62%,主要分布在吉林等地。大范围的植被覆盖增加趋势充分反映了我国过去几十年植被的改善,但同时存在的减少趋势表明潜在的植被退化风险仍不可忽视。（3）不同趋势类型发生改变的时间有所差异,总体上1988-1999年间发生的改变较少,而2000-2011年间发生的改变较多,我国21世纪以来实施的大规模生态保护和恢复工程对植被的改善过程有重要影响。（4）分布格局上,植被覆盖改善趋势类型（单调型增加,中断型增加,由减到增）呈现大聚集,小分散的特点,具有复杂的形状;退化趋势类型（单调型减少,中断型减少,由增到减）的面积均较小,分布也相对离散。全国尺度上趋势空间格局呈现一定规律但分布的异质性较大,区域尺度上植被覆盖经受的干扰显著,变化过程实际也是较为复杂的。本研究表明,使用非线性趋势方法和格局分析,可以更准确地评估植被覆盖的时空变化,从而为生态环境相关工作的开展提供科学的参考。     

基于植被信息季节变换的植被覆盖度变化——以福建省连江县为例

基于植被覆盖度的植被信息遥感变化检测已成为研究植被及其相关生态系统变化的主要途径,但由于云覆盖等天气条件的影响,很难获得不同年份同一季节覆盖整个研究区的光学遥感影像来进行植被变化检测,而采用季节差异的影像必然会影响植被变化检测的结果.为此,本研究利用中高分辨率遥感数据的空间分辨率优势和MODIS遥感数据的时间分辨率优势,基于二者关系的拟合,提出一种植被信息季节变换的方法,将不同季节影像的植被覆盖度变换到研究所需的季节上.结果表明: 将该方法应用到福建敖江流域连江片区发现,植被信息变换的效果较好,经过将覆盖研究区的2007年冬季和2013年春季的中高分辨率影像的植被信息统一变换到夏季后,2007年的植被覆盖度由66.5%上升到79.7%,2013年由58.6%上升到77.9%,有效消除了因季节差异而对植被覆盖度估算产生的误差,提高了结果的准确性.     

黄土高原泾河流域长时间序列的归一化植被指数动态变化及其驱动因素分析

泾河流域土地开发历史悠久, 是黄土高原水土流失的典型区域。研究气候变化和人类活动影响下泾河流域的植被覆盖变化及其原因, 对黄土高原的植被恢复、水土保持和景观管理等都具有重要意义。该研究应用GIMMS归一化植被指数NDVI、土地覆盖分类数据和气候数据, 采用趋势分析和相关分析方法, 研究了泾河流域1982-2005年植被覆盖变化趋势及其驱动因素。研究表明: 泾河流域24年间79.64%的区域NDVI无显著变化趋势, NDVI趋势显著增加的区域占16.33%, 主要集中在流域中部和南部, NDVI趋势显著减小的区域占4.03%, 主要集中在流域北部。流域所有气象站点的降水量均无显著变化趋势, 气温均呈显著升高趋势。分析发现气候变化不能很好地解释NDVI趋势的空间分异, 人为因素更为重要。从土地利用分析结果来看, NDVI不同趋势下各土地利用类型比例无明显变化, 但NDVI显著增加区以耕地为主, 显著减小区以草地为主, 由此推断NDVI的显著增加趋势主要由耕地NDVI增加引起, 显著减小趋势可能与林地减少和草地退化有关。通过分析不同分区的土地利用数据和社会经济资料, 着重探讨了造成植被覆盖显著变化趋势的人为因素。     

Comparison of forest above‐ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation‐based estimates

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above‐ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model‐data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB₉₅) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB₉₅, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.     

2000—2015年西南地区土地利用与植被覆盖的时空变化

西南地区是我国重要的生态资源区和生态脆弱区,在国家“绿水青山”战略发展中具有重要地位。本研究基于1 km空间分辨率的土地利用数据集,结合土地利用转移矩阵,定量分析2000—2015年间西南地区土地利用变化特征及其驱动力。并基于MODIS遥感植被指数,利用像元二分模型计算西南地区植被覆盖度,分析归一化植被指数(NDVI)和植被覆盖度的变化规律。结果表明: 研究期间,西南地区的主要地类是林地、农田和草地。建设用地面积增加5874 km²,增长率为55.8%;农田面积减少最多,下降6211 km²,其次是草地,减少2099 km²。2000—2015年间,西南地区建设用地的转入面积最多,主要由农田(贡献率68.2%)、林地(贡献率19.2%)和草地(贡献率13.1%)转化而来,转化的区域多靠近城区。农田的转出面积和转出率分别为7079 km²和2.2%,占所有转出类型面积的46.0%。林地多由草地(贡献率61.8%)转化而来,转化区域多分布在贵州中南部和云南西部等地。全区NDVI和植被覆盖度均呈显著增加趋势,说明研究区整体呈变绿趋势。其中,自然植被和农田的NDVI均显著增长,建设用地扩张地区的NDVI下降,说明自然植被和农田主导了该地区植被变化。通过残差分析发现,气候变化和人类活动对研究区变绿趋势的贡献显著。     

Changes in satellite‐derived spring vegetation green‐up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis

The change in spring phenology is recognized to exert a major influence on carbon balance dynamics in temperate ecosystems. Over the past several decades, several studies focused on shifts in spring phenology; however, large uncertainties still exist, and one understudied source could be the method implemented in retrieving satellite‐derived spring phenology. To account for this potential uncertainty, we conducted a multimethod investigation to quantify changes in vegetation green‐up date from 1982 to 2010 over temperate China, and to characterize climatic controls on spring phenology. Over temperate China, the five methods estimated that the vegetation green‐up onset date advanced, on average, at a rate of 1.3 ± 0.6 days per decade (ranging from 0.4 to 1.9 days per decade) over the last 29 years. Moreover, the sign of the trends in vegetation green‐up date derived from the five methods were broadly consistent spatially and for different vegetation types, but with large differences in the magnitude of the trend. The large intermethod variance was notably observed in arid and semiarid vegetation types. Our results also showed that change in vegetation green‐up date is more closely correlated with temperature than with precipitation. However, the temperature sensitivity of spring vegetation green‐up date became higher as precipitation increased, implying that precipitation is an important regulator of the response of vegetation spring phenology to change in temperature. This intricate linkage between spring phenology and precipitation must be taken into account in current phenological models which are mostly driven by temperature.     

Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.     

Satellite‐based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air‐photo studies have documented recent changes in high‐latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24‐year (1986–2010) Landsat time series in a latitudinal transect across the boreal forest‐tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last 40 years. Using a per‐pixel (30 m) trend analysis, 30% of the observable (cloud‐free) land area experienced a significant (P < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak‐summer conditions. The average NDVI trend (0.007 yr^?1) corresponds to a leaf‐area index (LAI) increase of ~0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer. Across the entire transect, the area‐averaged LAI increase was ~0.2 during 1986–2010. A higher area‐averaged LAI change (~0.3) within the shrub‐tundra portion of the transect represents a 20–60% relative increase in LAI during the last two decades. Our Landsat‐based analysis subdivides the overall high‐latitude greening trend into changes in peak‐summer greenness by cover type. Different responses within and among shrub, graminoid, and tree‐dominated cover types in this study indicate important fine‐scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low‐biomass vegetation types over multi‐decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.     

Defining the importance of including transient ecosystem responses to simulate C‐cycle dynamics in a global change model

The ability of plant species to migrate is one of the critical issues in assessing accurately the future response of the terrestrial biosphere to climate change. This ability is confined by both natural and human‐induced changes in land cover. In this paper we present land‐cover and Carbon (C) cycle models designed to simulate the biospheric consequences of different types of land‐cover changes. These models, imbedded in the larger integrated assessment model IMAGE 2, were used to demonstrate the importance of considering spatial aspects for global C‐cycle modelling. A gradual‐migration, an unlimited‐migration and a no‐migration case were compared to show the range of possible consequences. Major differences between these cases were simulated for land‐cover patterns and the carbon budget. A large geographical variation in the biospheric response was also simulated. The strongest response was simulated in high‐latitude regions, especially for the migration cases in which land‐cover changes were permitted. In low‐latitudes regions the differences between the migration cases were smaller, mainly due to the effects of land‐use changes. The geographical variation among, and the different responses, the migration cases clearly demonstrate how essential it is to assess biospheric responses to climate change and land use simultaneously. Moreover, it also shows the urgent need for enhanced understanding of spatial and temporal dynamics of the biospheric responses.     

基于遥感与GIS的渭库绿洲生态系统服务价值时空变化研究

生态系统服务价值(ESV)研究是改善土地利用方式,对促进地区生态系统保护和修复具有重要指导意义。选择生态环境较为脆弱的渭库绿洲为研究区域,以1994和2016年遥感影像为基础数据进行生态服务价值评估,应用格网、敏感性分析、空间自相关等空间统计学方法来进一步揭示研究区域生态系统服务价值(ESV)动态变化特征。结果表明:(1)1994—2016年间渭库绿洲土地利用变化/土地覆被变化较为显著,耕地,建设用地面积呈增长趋势,水域、草地和未利用地面积呈递减趋势,耕地和草地面积变化最为剧烈。(2)1994—2016年,渭库绿洲的生态系统服务总价值呈减少趋势,由4895.67×10~6元减少到3864.78×10~6元,减少了1030.89×10~6元,年均减少0.96%;空间上表现为生态服务价值增值区及减值区呈包围态势,生态系统服务高值区域在不断缩小;同时生态系统单项服务价值呈现有增有减的趋势,废物处理、水源涵养、生物多样性保护、娱乐文化等四项单项服务价值呈现下降趋势,而食物生产、气候调节、气体调节和原材料、土壤形成与保护等其他五项单项服务价值出现上升趋势。(3)敏感性分析表明,所有土地利用类型生态系统服务价值系数的敏感性指数(CS)均小于1,说明生态价值系数(VC)对生态系统服务价值的变化影响较小,模型能够合理评估该区域生态服务价值波动。(4)研究区生态系统服务价值表现出明显的空间自相关与空间聚集现象,生态服务价值高值聚集区主要分布在渭干河上游"克孜尔水库"周围和塔里木河北岸,并呈减少趋势,注意保护高值区的空间稳定性;低值聚集区主要集中在绿洲南部靠近塔克拉玛干沙漠边缘和北部靠近天山中部的山区以及库车河下游荒漠区域,这类区域在空间范围上呈连片连绵趋势,空间分布格局上基本保持稳定。(5)受气候变化和人类活动的双重影响下,草地和水域面积的大幅度下降,耕地和建设用地面积的增加,导致生态服务价值系数较高的土地越来越少,使得研究区的生态服务价值趋于减弱。