Vegetation's responses to climate variability

Interannual precipitation variability dominates the growth of alpine grassland above-ground biomass at high elevations on the Tibetan Plateau

The impact of global climate change on mountainous regions with significant elevational gaps is complex and often unpredictable. In particular, alpine grassland ecosystems, are experiencing changes in their spatial patterns along elevational gradients, which increases their vulnerability to degradation. Therefore, a more detailed understanding of spatiotemporal changes in alpine grassland productivity along elevational gradients and an elevation-dependent characterization of the effects of climatic variables on grassland productivity dynamics are essential. Thus, we conducted a study in the Tibetan Plateau, where we collected 2251 above-ground biomass (AGB) observations collected from 1986 to 2020. Mean annual temperature (TMP), annual precipitation (PRE), interannual precipitation variability (CVP), and snowmelt (SNMM) were chosen as influential variables. Using the Random Forest algorithm, we generated an AGB raster dataset covering the period 1989-2020 based on earth observation data at 30 m resolution to examine the dynamics of alpine grasslands and their response to climate change with respect to elevation. The results showed that the AGB of alpine grassland on the Tibetan Plateau was 49.17 g/m2. We observed an increasing trend in grassland AGB at high elevations, with a growth rate of about 0.28 g/m2 per year within the interval of 3100-4800 m. However, above the elevation of approximately 4400-4600 m, we observed a decoupling trend between grassland AGB and TMP. Moreover, at most elevations, the proportion of maximum partial correlation coefficients for CVP, PRE, and SNMM surpassed that of TMP. We found the dominant role of precipitation variability on grassland AGB dynamics, with 22.80 % and 18.86 % for CVP+ and CVP-, respectively. The proportion of CVP+ did not vary much at different elevations, whereas the proportion of CVP- increased with elevation, varying between 12.85 and 30.25 %. In the future, precipitation on the Tibetan plateau is expected to increase, potentially reversing its original positive impact.

Long-term evidence of differential resistance and resilience of grassland ecosystems to extreme climate events

Grassland ecosystems are affected by the increasing frequency and intensity of extreme climate events (e.g., droughts). Understanding how grassland ecosystems maintain their functioning, resistance, and resilience under climatic perturbations is a topic of current concern. Resistance is the capacity of an ecosystem to withstand change against extreme climate, while resilience is the ability of an ecosystem to return to its original state after a perturbation. Using the growing season Normalized Difference Vegetation Index (NDVI_gs, an index of vegetation growth) and the Standardized Precipitation Evapotranspiration Index (a drought index), we evaluated the response, resistance, and resilience of vegetation to climatic conditions for alpine grassland, grass-dominated steppe, hay meadow, arid steppe, and semi-arid steppe in northern China for the period 1982-2012. The results show that NDVI_gs varied significantly across these grasslands, with the highest (lowest) NDVI_gs values in alpine grassland (semi-arid steppe). We found increasing trends of greenness in alpine grassland, grass-dominated steppe, and hay meadow, while there were no detectable changes of NDVI_gs in arid and semi-arid steppes. NDVI_gs decreased with increasing dryness from extreme wet to extreme dry. Alpine and steppe grasslands exhibited higher resistance to and lower resilience after extreme wet, while lower resistance to and higher resilience after extreme dry conditions. No significant differences in resistance and resilience of hay meadow under climatic conditions suggest the stability of this grassland under climatic perturbations. This study concludes that highly resistant grasslands under conditions of water surplus are low resilient, but low resistant ecosystems under conditions of water shortage are highly resilient.

Three-dimensional linkage between meteorological drought and vegetation drought across China

Advance knowledge of the linkage between meteorological drought and vegetation drought is relevant for the risk of droughts and the impacts on vegetation health. This study employs a 3-dimensional clustering identification method to capture drought events and their characteristics (i.e., drought severity, intensity, area, center, and trajectory) in vegetated regions of China during 1982-2018. The probability of vegetation droughts, triggered by different types of drought events, is investigated by using a K-means trajectory clustering method and copula with the vegetation health index (VHI). Moreover, the impacts of moisture deficit and high temperature caused by drought on vegetation are examined with the vegetation condition index (VCI) and temperature condition index (TCI). The analysis has identified a total of 93 drought events in 1982-2018. The drought occurrences have become more concentrated in space along the time and droughts frequently occur in spring and summer. Drought events are categorized into three types, and droughts in type 1 lead to vegetation droughts with larger area, droughts in type 2 lead to vegetation droughts with stronger intensity, droughts in type 3 pose the least threat to vegetation. Additionally, the impacts of moisture deficit and high temperature have significant seasonal difference and contradictory trends over time. For example, grassland is most sensitive to moisture deficit in summer, while forest is the most sensitive to moisture deficit in spring and winter. The complex response of vegetation to drought is resulted from the combined effects of moisture and heat stress and different regional climate and vegetation types.

Responses of vegetation to changes in terrestrial water storage and temperature in global mountainous regions

As an important component of terrestrial ecosystem, vegetation acts as a sensitive recorder of changes in hydroclimatic conditions. Long-term time series of remote sensing-based vegetation indices and their influencing environmental driving factors, such as human activities and climate change, have been widely discussed in the literature. Globally, however, little is known about the hydroclimatic processes controlling vegetation changes in mountainous regions, which are conceived as more sensitive to climate change than other landscapes. The present study aims to quantify the respective roles of two dominant hydroclimatic factors, namely, TWS (i.e., terrestrial water storage) and T_air (i.e., temperature), in the spatio-temporal changes of mountainous vegetation over global six contrasting climate zones (i.e., tropical, arid, subtropical, temperate, sub-frigid, and frigid zones) during the period 2003-2016 based on EVI (i.e., enhanced vegetation index), TWS, T_air, and elevation data. Results indicate that the mean EVI shows a larger increasing trend (+0.85 %/decade, p-value < 0.01) and a larger decreasing trend in TWS (-85 mm/decade, p-value < 0.01) across the global mountainous regions than other global regions combined together (+0.61 %/decade, p-value < 0.01), particularly over high latitudes. With the increasing latitudes, the positive effect of temperature more dominates mountainous vegetation growth than moisture, as evidenced by the increasing trends of EVI with warming. However, in certain low-latitude mountainous regions (e.g., East Africa, South Asia, the western Tibetan Plateau, Brazil Plateau, and the southern Rocky Mountains), mountainous vegetation may face degradation due to water deficit induced by increased snowmelt, especially among the high-elevation ecosystems. The water availability controls vegetation activities more than T_air in the mid- and low-latitude regions, including the tropical, arid, and subtropical climate zones. These findings indicate that the potential shifts in mountainous vegetation may occur under the notable interactions with hydroclimatic factors, as the high-latitudes are experiencing ongoing warming and the mid- and low-latitudes are getting dryer.

Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan

Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers - spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth - for Kazakhstan during 2000-2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.

Vegetation Changing Patterns and Its Sensitivity to Climate Variability across Seven Major Watersheds in China

Climate changes have profound impacts on vegetation and further alter hydrological processes through transpiration, interception, and evaporation. This study investigated vegetation's changing patterns and its sensitivity to climate variability across seven major watersheds in China based on a hybrid regionalization approach and a novel, empirical index-Vegetation Sensitivity Index (VSI). Vegetation showed linearly increasing trends in most of the seven watersheds, while decreases in vegetation were mostly found in the source regions of the Yangtze River Basin (YZRB) and Yellow River Basin (YRB), the forest and grassland areas of the Songhua River Basin (SHRB) and Liao River Basin (LRB), the Yangtze River Delta, and the Pearl River Delta during the growing season. The selected watersheds can be categorized into 11 sub-regions, and the regionalization result was consistent with the topography and vegetation types; the characteristics of vegetation dynamics were more homogeneous among sub-regions. Vegetation types such as forests and shrubland in the central parts of the YZRB were relatively more vulnerable to climate variations than the grasslands and alpine meadows and tundra (AMT) in the source regions of the YZRB and YRB and the Loess Plateau of the YRB. In arid and semi-arid regions, precipitation had a profound impact on vegetation, while, at low latitudes, solar radiation was the main controlling factor. Such comprehensive investigations of the vegetation-climate relationship patterns across various watersheds are expected to provide a foundation for the exploration of future climate change impacts on ecosystems at the watershed scale.

Quantifying Vegetation Vulnerability to Climate Variability in China

Climate variability has profound effects on vegetation. Spatial distributions of vegetation vulnerability that comprehensively consider vegetation sensitivity and resilience are not well understood in China. Furthermore, the combination of cumulative climate effects and a one-month-lagged autoregressive model represents an advance in the technical approach for calculating vegetation sensitivity. In this study, the spatiotemporal characteristics of vegetation sensitivity to climate variability and vegetation resilience were investigated at seasonal scales. Further analysis explored the spatial distributions of vegetation vulnerability for different regions. The results showed that the spatial distribution pattern of vegetation vulnerability exhibited spatial heterogeneity in China. In spring, vegetation vulnerability values of approximately 0.9 were mainly distributed in northern Xinjiang and northern Inner Mongolia, while low values were scattered in Yunnan Province and the central region of East China. The highest proportion of severe vegetation vulnerability to climate variability was observed in the subhumid zone (28.94%), followed by the arid zone (26.27%). In summer and autumn, the proportions of severe vegetation vulnerability in the arid and humid zones were higher than those in the other climate zones. Regarding different vegetation types, the highest proportions of severe vegetation vulnerability were found in sparse vegetation in different seasons, while the highest proportions of slight vegetation vulnerability were found in croplands in different seasons. In addition, vegetation with high vulnerability is prone to change in Northeast and Southwest China. Although ecological restoration projects have been implemented to increase vegetation cover in northern China, low vegetation resilience and high vulnerability were observed in this region. Most grasslands, which were mainly concentrated on the Qinghai–Tibet Plateau, had high vulnerability. Vegetation areas with low resilience were likely to be degraded in this region. The areas with highly vulnerable vegetation on the Qinghai–Tibet Plateau could function as warning signals of vegetation degradation. Knowledge of spatial patterns of vegetation resilience and vegetation vulnerability will help provide scientific guidance for regional environmental protection.

Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin

Background

Climate change is an important factor driving vegetation changes in arid areas. Identifying the sensitivity of vegetation to climate variability is crucial for developing sustainable ecosystem management strategies. The Irtysh River is located in the westerly partition of China, and its vegetation cover is more sensitive to climate change. However, previous studies rarely studied the changes in the vegetation coverage of the Irtysh River and its sensitivity to climate factors from a spatiotemporal perspective.

Methods

We adopted a vegetation sensitivity index based on remote sensing datasets of high temporal resolution to study the sensitivity of vegetation to climatic factors in the Irtysh River basin, then reveal the driving mechanism of vegetation cover change.

Results

The results show that 88.09% of vegetated pixels show an increasing trend in vegetation coverage, and the sensitivity of vegetation to climate change presents spatial heterogeneity. Sensitivity of vegetation increases with the increase of coverage. Temperate steppe in the northern mountain and herbaceous swamp and broadleaf forest in the river valley, where the normalized difference vegetation index is the highest, show the strongest sensitivity, while the desert steppe in the northern plain, where the NDVI is the lowest, shows the strongest memory effect (or the strongest resilience). Relatively, the northern part of this area is more affected by a combination of precipitation and temperature, while the southern plains dominated by desert steppe are more sensitive to precipitation. The central river valley dominated by herbaceous swamp is more sensitive to temperature-vegetation dryness index. This study underscores that the sensitivity of vegetation cover to climate change is spatially differentiated at the regional scale.

Vegetation Productivity Dynamics in Response to Climate Change and Human Activities under Different Topography and Land Cover in Northeast China

Net primary productivity (NPP) is the total amount of organic matter fixed by plants from the atmosphere through photosynthesis and is susceptible to the influences of climate change and human activities. In this study, we employed actual NPP (ANPP), potential NPP (PNPP), and human activity-induced NPP (HNPP) based on the Hurst exponent and statistical analysis to analyze the characteristics of vegetation productivity dynamics and to evaluate the effects of climate and human factors on vegetation productivity in Northeast China (NEC). The increasing trends in ANPP, PNPP, and HNPP accounted for 81.62%, 94.90%, and 89.63% of the total area, respectively, and ANPP in 68.64% of the total area will continue to increase in the future. Climate change played a leading role in vegetation productivity dynamics, which promoted an increase in ANPP in 71.55% of the area, and precipitation was the key climate factor affecting ANPP. The aggravation of human activities, such as increased livestock numbers and intensified agricultural activities, resulted in a decrease in ANPP in the western grasslands, northern Greater Khingan Mountains, and eastern Songnen Plain. In particular, human activities led to a decrease in ANPP in 53.84% of deciduous needleleaf forests. The impact of climate change and human activities varied significantly under different topography, and the percentage of the ANPP increase due to climate change decreased from 71.13% to 53.9% from plains to urgent slopes; however, the percentage of ANPP increase due to human activities increased from 3.44% to 21.74%, and the effect of human activities on the increase of ANPP was more obvious with increasing slope. At different altitudes, the difference in the effect of these two factors was not significant. The results are significant for understanding the factors influencing the vegetation productivity dynamics in NEC and can provide a reference for governments to implement projects to improve the ecosystem.

Climate Dynamics of the Spatiotemporal Changes of Vegetation NDVI in Northern China from 1982 to 2015

As an important part of a terrestrial ecosystem, vegetation plays an important role in the global carbon-water cycle and energy flow. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological station data, climate reanalysis data, and land cover data, this study analyzed the climate dynamics of the spatiotemporal variations of vegetation NDVI in northern China from 1982 to 2015. The results showed that growth season NDVI (NDVIgs) increased significantly at 0.006/10a (p < 0.01) in 1982–2015 on the regional scale. The period from 1982 to 2015 was divided into three periods: the NDVIgs increased by 0.026/10a (p < 0.01) in 1982–1990, decreased by −0.002/10a (p > 0.1) in 1990–2006, and then increased by 0.021/10a (p < 0.01) during 2006–2015. On the pixel scale, the increases in NDVIgs during 1982–2015, 1982–1990, 1990–2006, and 2006–2015 accounted for 74.64%, 85.34%, 48.14%, and 68.78% of the total area, respectively. In general, the dominant climate drivers of vegetation growth had gradually switched from solar radiation, temperature, and precipitation (1982–1990) to precipitation and temperature (1990–2015). For woodland, high coverage grassland, medium coverage grassland, low coverage grassland, the dominant climate drivers had changed from temperature and solar radiation, solar radiation and precipitation, precipitation and solar radiation, solar radiation to precipitation and solar radiation, precipitation, precipitation and temperature, temperature and precipitation. The areas controlled by precipitation increased significantly, mainly distributed in arid, sub-arid, and sub-humid areas. The dominant climate drivers for vegetation growth in the plateau climate zone or high-altitude area changed from solar radiation to temperature and precipitation, and then to temperature, while in cold temperate zone, changed from temperature to solar radiation. These results are helpful to understand the climate dynamics of vegetation growth, and have important guiding significance for vegetation protection and restoration in the context of global climate change.

Effective ecosystem monitoring requires a multi-scaled approach

Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence-based policy and management. However, the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.

Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index

Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.

Increasing sensitivity of alpine grasslands to climate variability along an elevational gradient on the Qinghai-Tibet Plateau

Monitoring and mapping the sensitivity of grassland ecosystems to climate change is crucial for developing sustainable local grassland management strategies. The sensitivity of alpine grasslands to climate change is considered to be high on the Qinghai-Tibet Plateau (QTP), yet little is known about its spatial pattern, and particularly the variations between different elevations. Here, based on the Normalized Difference Vegetation Index (NDVI) and three climate variables (air temperature, precipitation, and solar radiation), we modified a vegetation sensitivity index-approach to capture the relative sensitivity of alpine grassland productivity to climate variability on the QTP during 2000-2016. The results show that alpine grasslands on the southern QTP are more sensitive to climate variability overall, and that the climate factors driving alpine grassland dynamics are spatially heterogeneous. Alpine grasslands on the southern QTP are more sensitive to temperature variability, those on the northeastern QTP display strong responses to precipitation variability, and those on the central QTP are primarily influenced by a combination of radiation and temperature variability. The sensitivity of alpine grasslands to climate variability increases significantly along an elevational gradient, especially to temperature variability. This study underscores that alpine grasslands at higher elevations on the QTP are more sensitive to climate variability than those at lower elevations at the regional scale.

Climatic determinants impacting the distribution of greenness in China: regional differentiation and spatial variability

This study investigated climatic determinants for regional greenness in China and spatially variable correlations between climatic determinants and vegetation in specific regions using the geographical detector and geographically weighted regression (GWR) methodologies. The analyses were based on normalized difference vegetation index (NDVI) and interpolations of climatic determinants from 652 Chinese meteorological stations. The study period (1982-2013) was divided into two stages (T1-T2) before and after the inflection year identified by the accumulative anomaly of NDVI. Three typical regions (R1-R3) were then selected according to the same NDVI variation trend as China in the two periods. Precipitation was the dominant climatic factor of NDVI in China, and the effect of temperature on greenness increased with warming from T1 to T2. In a relatively arid region (R1), the effect of precipitation in T2 was further strengthened compared to T1. Meanwhile, the effect of minimum temperature in T2 also increased compared with T1 in a relatively humid region (R2), becoming the major climatic determinant. In addition to the regional differentiation, spatial variability was investigated by comparing normalized coefficients of GWR for climatic determinants; this showed significant spatial heterogeneity within each region. Temperature impact areas also existed within precipitation-dominated regions (R1 and R3), where areas of precipitation impact expanded from T1 to T2. Furthermore, regression coefficients between NDVI dynamics and climate variability revealed relationships between regional differentiation and spatial variability. For example, the increasing precipitation rate could mediate the adverse impacts on greenness caused by the higher warming rate in relatively arid regions (R1).

NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015

Vegetation in Northern Hemisphere, being sensitive to climate change, plays an important role in the carbon cycles between land and the atmosphere. The response of vegetation to climate change was analyzed at pixel, biome and regional scale in Amur-Heilongjiang River Basin (AHRB) for growing season, spring, summer and autumn using Normalized Difference Vegetation Index and gridded climate data for the period 1982-2015. NDVI and climate variables trend detection methods and correlation analysis were applied. The potential impacts of human activities on growing season NDVI dynamics were investigated further using residual trend analysis. Results showed that at river basin scale, growing season vegetation experienced a discontinuous greening trend with two reversals, demonstrating that NDVI initially increased to mid-1990s, then declined to mid-2000s, and finally rebounded to 2015. This may be attributed to the shifting between drought and wet trends, indicating growing season NDVI was mainly regulated by precipitation. Temperature was the dominant factor on affecting spring vegetation growth while autumn NDVI showed negative correlation with precipitation due to the relation of precipitation with sunshine hours available for photosynthesis. The response of vegetation growth to climatic variations varied among vegetation types. Grassland NDVI exhibited positive correlation with precipitation in all time ranges. NDVI of needleleaved forest, broadleaved forest, mixed forest and woodland were positively correlated with temperature in all seasons, while showing significant negative correlation with autumn precipitation. Residual trend analysis revealed that human activities might lead to the vegetation degradation in China farming zone of AHRB. Fires also play an important role in regulating vegetation dynamics in the region. Results of our analysis can be used by national governments from three countries of AHRB in managing and negotiating vegetation resources of the region.

Vulnerability of the global terrestrial ecosystems to climate change

Climate change has far-reaching impacts on ecosystems. Recent attempts to quantify such impacts focus on measuring exposure to climate change but largely ignore ecosystem resistance and resilience, which may also affect the vulnerability outcomes. In this study, the relative vulnerability of global terrestrial ecosystems to short-term climate variability was assessed by simultaneously integrating exposure, sensitivity, and resilience at a high spatial resolution (0.05°). The results show that vulnerable areas are currently distributed primarily in plains. Responses to climate change vary among ecosystems and deserts and xeric shrublands are the most vulnerable biomes. Global vulnerability patterns are determined largely by exposure, while ecosystem sensitivity and resilience may exacerbate or alleviate external climate pressures at local scales; there is a highly significant negative correlation between exposure and sensitivity. Globally, 61.31% of the terrestrial vegetated area is capable of mitigating climate change impacts and those areas are concentrated in polar regions, boreal forests, tropical rainforests, and intact forests. Under current sensitivity and resilience conditions, vulnerable areas are projected to develop in high Northern Hemisphere latitudes in the future. The results suggest that integrating all three aspects of vulnerability (exposure, sensitivity, and resilience) may offer more comprehensive and spatially explicit adaptation strategies to reduce the impacts of climate change on terrestrial ecosystems.

Water memory effects and their impacts on global vegetation productivity and resilience

Memory effects refer to the impacts of antecedent climate conditions on current vegetation productivity. This temporal linkage has been found to be strong in arid and semi-arid regions. However, the dominant climatic factors that determine such patterns are still unclear. Here, we defined'water-memory effects' as the persistent effects of antecedent precipitation on the vegetation productivity for a given memory length (from 1 to up to 12 months). Based on satellite observations and climate data, we quantified the length of water-memory effects and evaluated the contributions of antecedent precipitation on current vegetation. Our results showed that vegetation productivity was highly dependent on antecedent precipitation in arid and semi-arid regions. The average length of water memory was approximately 5.6 months. Globally, water-memory effects could explain the geographical pattern and strength of memory effects, indicating that precipitation might be the dominant climatic factor determining memory effects because of its impact on water availability. Moreover, our results showed vegetation in regions with low mean annual precipitation or a longer water memory has lower engineering resilience (i.e. slower recovery rate) to disturbances. These findings will enable better assessment of memory effects and improve our understanding of the vulnerability of vegetation to climate change.

Correlation between vegetation and environment at different levels in an arid, mountainous region of China

Vegetation patterns and spatial organization are influenced by the changing environmental conditions and human activities. However, the effect of environment on vegetation at different vegetation classification levels has been unclear. We conducted an analysis to explore the relationship between environment and vegetation in the land use/land cover (LULC), vegetation group, vegetation type, and formation and subformation levels using redundancy analysis with seven landscape metrics and 33 environmental factors in the upper reaches of the Heihe River basin in an arid area of China to clarify this uncertainty. Atmospheric counter radiation was the most important factor at the four levels. The effect of soil was the second determinant factor at three levels (except in vegetation formation and subformation level). The number of variables whose relationship to vegetation reached significant levels varied from 26 to 28, and 20 variables were the same at all four levels. The factors affecting vegetation were basically the same at vegetation group level and vegetation‐type level. It was sufficient to analyze the relationship between environmental and vegetation patterns only in LULC, vegetation group and vegetation formation and subformation level in mountainous regions; different factors should be considered at different vegetation levels.