No upward shift of alpine grassland distribution on the Qinghai-Tibetan Plateau despite rapid climate warming from 2000 to 2014

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.

A remote sensing method for mapping alpine grasslines based on graph-cut

Climate change has induced substantial shifts in vegetation boundaries such as alpine treelines and shrublines, with widespread ecological and climatic influences. However, spatial and temporal changes in the upper elevational limit of alpine grasslands ("alpine grasslines") are still poorly understood due to lack of field observations and remote sensing estimates. In this study, taking the Tibetan Plateau as an example, we propose a novel method for automatically identifying alpine grasslines from multi-source remote sensing data and determining their positions at 30-m spatial resolution. We first identified 2895 mountains potentially having alpine grasslines. On each mountain, we identified a narrow area around the upper elevational limit of alpine grasslands where the alpine grassline was potentially located. Then, we used linear discriminant analysis to adaptively generate from Landsat reflectance features a synthetic feature that maximized the difference between vegetated and unvegetated pixels in each of these areas. After that, we designed a graph-cut algorithm to integrate the advantages of the Otsu and Canny approaches, which was used to determine the precise position of the alpine grassline from the synthetic feature image. Validation against alpine grasslines visually interpreted from a large number of high-spatial-resolution images showed a high level of accuracy (R2 , .99 and .98; mean absolute error, 22.6 and 36.2 m, vs. drone and PlanetScope images, respectively). Across the Tibetan Plateau, the alpine grassline elevation ranged from 4038 to 5380 m (5th-95th percentile), lower in the northeast and southeast and higher in the southwest. This study provides a method for remotely sensing alpine grasslines for the first-time at large scale and lays a foundation for investigating their responses to climate change.

Dynamic characteristics and synergistic effects of ecosystem services under climate change scenarios on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) supplies many ecosystem services (ESs) that maintain local and global pan-Asian populations and ecosystems. The effects of climate change on ES provision in the QTP will have far-reaching impacts on the region and the many downstream ecosystems and countries that depend on ESs from the "Third Pole". This study undertook a systematic assessment of ES provision, trade-offs and synergies between four ESs (raw material provision, water yield, soil retention, and carbon storage) under future climate scenarios (representative concentration pathway). The results show that: (1) the total amount of the four ESs on the QTP is predicted to increase from 1980 to 2100 for three climate change scenarios. (2) The spatial pattern of ESs on the QTP will not change significantly in the future, and the grassland and forest ESs in the central and southern regions are predicted to increase significantly. (3) The synergistic interactions among ESs were generally consistent at three spatial scales (10 km (pixel), county and watershed scales), but with more significant synergistic effects at the watershed scale. This demonstrates the necessity for the examination of scale-dependent ES dynamics and interactions. This study will supply a reference for further research on long-term ES assessments, especially the dynamic ES changes and the spatial scale dependency of the ES interactions, and provide evidence-based strategies for formulating ecosystem management on the QTP under climate change.

Scenarios of potential vegetation distribution in the different gradient zones of Qinghai-Tibet Plateau under future climate change

The spatial distribution of potential vegetation types in Qinghai-Tibet Plateau presents a significant vertical zonation. Explicating the vertical differences of potential vegetation distribution under future climate change in Qinghai-Tibet Plateau is an important issue for understanding the response of terrestrial ecosystem to climate change. Based on the observed climate data in 1981-2010 (T0), the scenario data of RCP 2.6, RCP 4.5 and RCP 8.5 released by CMIP5 in 2011-2040 (T1), 2041-2070 (T2) and 2071-2100 (T3), and the digital elevation model (DEM) data, the Holdridge life zone (HLZ) model has been improved to simulate the scenarios of potential vegetation distribution in the different gradient zones of Qinghai-Tibet plateau. The shift model of mean center has been improved to calculate the shift direction and distance of mean center in the potential vegetation types. The ecological diversity index was introduced to compute the ecological diversity change of potential vegetation. The simulated results show that there are 17 potential vegetation types in Qinghai-Tibet Plateau. Wet tundra, high-cold moist forest and nival are the major potential vegetation types and cover 56.26% of the total area of Qinghai-Tibet Plateau. Under the three scenarios, the nival would have the largest decreased area that would be decreased by 3.340 × 10⁴ km² per decade, and the high-cold wet forest would have the greatest increased area that would be increased by 3.340 × 10⁴ km² on average per decade from T0 to T3. The potential vegetation types distributed in the alpine zone would show the fastest change ratio (11.32% per decade) and that in low mountain and other zone would show the slowest change ratio (7.54% per decade) on average. The ecological diversity and patch connectivity of potential vegetation would be decreased by 0.108% and 0.290% per decade on average from T0 to T3. In general, the potential vegetation types distributed in the high elevation area generally have a higher sensitivity to climate change in Qinghai-Tibet plateau in the future.

Continuous warming shift greening towards browning in the Southeast and Northwest High Mountain Asia

Remote sensing and ground vegetation observation data show that climate warming promotes global vegetation greening, and the increase in air temperature in High Mountain Asia (HMA) is more than twice the global average. Under such a drastic warming in climate, how have the vegetation dynamics in HMA changed? In this study, we use the Normalized Difference Vegetation Index (NDVI) from 1982 to 2015 to evaluate the latest changes in vegetation dynamics in HMA and their climate-driving mechanisms. The results show that over the past 30 years, HMA has generally followed a "warm-wet" trend, with temperatures charting a continuous rise. During 1982-1998 precipitation increased (1.16 mm yr-1), but depicted to reverse since 1998 (- 2.73 mm yr-1). Meanwhile, the NDVI in HMA increased (0.012 per decade) prior to 1998, after which the trend reversed and declined (- 0.005 per decade). The main reason for the browning of HMA vegetation is the dual effects of warming and precipitation changes. As mentioned, the increase in air temperature in HMA exceeds the global average. The increase of water vapor pressure deficit caused by global warming accelerates the loss and consumption of surface water, and also aggravates the soil water deficit. That is to say, the abnormal increase of land evapotranspiration far exceeds the precipitation, and the regional water shortage increases. Climate change is the primary factor driving these vegetation and water dynamics, with the largest proportion reaching 41.9%.

Precipitation Drives the NDVI Distribution on the Tibetan Plateau While High Warming Rates May Intensify Its Ecological Droughts

Climate change has significantly affected the ecosystem of the Tibetan Plateau. There, temperature rises and altered precipitation patterns have led to notable changes in its vegetation growth processes and vegetation cover features. Yet current research still pays relatively little attention to the regional climatic determinants and response patterns of such vegetation dynamics. In this study, spatial patterns in the response of the normalized difference vegetation index (NDVI) to climate change and its dynamic characteristics during the growing season were examined for the Tibetan Plateau, by using a pixel-scale-based geographically weighted regression (GWR) based on the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data, as well as data for temperature and moisture indices collected at meteorological stations, for the period 1982–2015. The results show the following. Spatial nonstationary relationships, primarily positive, were found between the NDVI and climatic factors in the Tibetan Plateau. However, warming adversely affected vegetation growth and cover in some arid and semiarid regions of the northeast and west Tibetan Plateau. Additionally, precipitation played a dominant role in the NDVI of the Tibetan Plateau in the largest area (accounting for 39.7% of total area). This suggests that increased moisture conditions considerably facilitated vegetation growth and cover in these regions during the study period. Temperature mainly played a dominant role in the NDVI in some parts of the plateau sub-cold zone and some southeastern regions of the Tibetan Plateau. In particular, the minimum temperature was the dominant driver of NDVI over a larger area than any of the other temperature indices. Furthermore, spatial regressions between NDVI dynamics and climatic variability revealed that a faster warming rate in the arid and semiarid regions impeded vegetation growth through mechanisms such as drought intensification. Moisture variability was found to act as a key factor regulating the extent of vegetation cover on the south Tibetan Plateau.

Dynamic Changes of NDVI in the Growing Season of the Tibetan Plateau During the Past 17 Years and Its Response to Climate Change

The fragile alpine vegetation in the Tibetan Plateau (TP) is very sensitive to environmental changes, making TP one of the hotspots for studying the response of vegetation to climate change. Existing studies lack detailed description of the response of vegetation to different climatic factors using the method of multiple nested time series analysis and the method of grey correlation analysis. In this paper, based on the Normalized Difference Vegetation Index (NDVI) of TP in the growing season calculated from the MOD09A1 data product of Moderate-resolution Imaging Spectroradiometer (MODIS), the method of multiple nested time series analysis is adopted to study the variation trends of NDVI in recent 17 years, and the lag time of NDVI to climate change is analyzed using the method of Grey Relational Analysis (GRA). Finally, the characteristics of temporal and spatial differences of NDVI to different climate factors are summarized. The results indicate that: (1) the spatial distribution of NDVI values in the growing season shows a trend of decreasing from east to west, and from north to south, with a change rate of −0.13/10° E and −0.30/10° N, respectively. (2) From 2001 to 2017, the NDVI in the TP shows a slight trend of increase, with a growth rate of 0.01/10a. (3) The lag time of NDVI to air temperature is not obvious, while the NDVI response lags behind cumulative precipitation by zero to one month, relative humidity by two months, and sunshine duration by three months. (4) The effects of different climatic factors on NDVI are significantly different with the increase of the study period.

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.

The demise of caterpillar fungus in the Himalayan region due to climate change and overharvesting

Demand for traditional medicine ingredients is causing species declines globally. Due to this trade, Himalayan caterpillar fungus (Ophiocordyceps sinensis) has become one of the world's most valuable biological commodities, providing a crucial source of income for hundreds of thousands of collectors. However, the resulting harvesting boom has generated widespread concern over the sustainability of its collection. We investigate whether caterpillar fungus production is decreasing-and if so, why-across its entire range. To overcome the limitations of sparse quantitative data, we use a multiple evidence base approach that makes use of complementarities between local knowledge and ecological modeling. We find that, according to collectors across four countries, caterpillar fungus production has decreased due to habitat degradation, climate change, and especially overexploitation. Our statistical models corroborate that climate change is contributing to this decline. They indicate that caterpillar fungus is more productive under colder conditions, growing in close proximity to areas likely to have permafrost. With significant warming already underway throughout much of its range, we conclude that caterpillar fungus populations have been negatively affected by a combination of overexploitation and climate change. Our results underscore that harvesting is not the sole threat to economically valuable species, and that a collapse of the caterpillar fungus system under ongoing warming and high collection pressure would have serious implications throughout the Himalayan region.