Search Results (5)

The Tibetan Plateau (TP) is experiencing higher warming rates than elsewhere, which may affect regional vegetation growth. Particularly on the Eastern Tibetan Plateau (ETP), where the topography is diverse and rich in biodiversity, it is necessary to clarify the drivers of climate and topography on vegetation cover. In this research, we selected the Shaluli Mountains (SLLM) in the ETP as the study area, monitored the spatial and temporal dynamics of the regional vegetation cover using remote sensing methods, and quantified the drivers of vegetation change using Geodetector (GD). The results showed a decreasing trend in annual precipitation (PRE) (−2.4054 mm/year) and the Palmer Drought Severity Index (PDSI) (−0.1813/year) in the SLLM. Annual maximum temperature (TMX) on the spatial and temporal scales showed an overall increasing trend, and the regional climate tended to become warmer and drier. Since 2000, fractional vegetation cover (FVC) has shown a fluctuating upward trend, with an average value of 0.6710, and FVC has spatially shown a pattern of “low in the middle and high in the surroundings”. The areas with non-significant increases (p > 0.05) and significant increases (p < 0.05) in FVC accounted for 46.03% and 5.76% of the SLLM. Altitude (q = 0.3517) and TMX (q = 0.3158) were the main drivers of FVC changes. As altitude and TMX increased, FVC showed a trend of increasing and then decreasing. The results of this study help us to clarify the influence of climate and topography on the vegetation ecosystem of the ETP and provide a scientific basis for regional biodiversity conservation and sustainable development. Full article

Forests are widely distributed in terrestrial ecosystems, covering about one-third of the global land area. They play a key role in sequestering carbon, releasing oxygen, mitigating climate change, and maintaining ecosystem balance. The ecology of the Tibetan Plateau is very fragile, but the impact of environmental change on regional forest ecosystems is not yet clear. Located in the Eastern Tibetan Plateau, the Shaluli Mountain has the richest biodiversity and the widest distribution of forests on the Tibetan Plateau. Assessing the dynamics of forest change is the basis for correctly formulating forest management measures, and is important for regional biodiversity conservation. However, traditional field surveys have the shortcomings of high cost, being time-consuming, and having poor regional coverage in forest dynamics monitoring. Remote sensing methods can make up for these shortcomings. Therefore, in this study, satellite remote sensing images were used to extract forest information from 2000 to 2020 in Shaluli Mountain, and the main drivers of forest change were analyzed with full consideration of the Spatially Stratified Heterogeneity (SSH) of environmental factors. The results found that the forest area increased from 23,144.20 km² in 2000 to 28,429.53 km² in 2020, and the average Percentage of Forest Cover (PFC) increased from 19.76% to 21.67%, with significant improvement in forest growth. The annual minimum temperature (TMN), altitude, annual maximum temperature (TMX), and annual precipitation (PRE) were the main driving factors of forest change, with an average driving power (q-value) of 0.4877, 0.2706, 0.2342, and 0.2244, and TMN was the primary limiting factor for forest growth. In addition, the driving power of all environmental factors on forest change increased from 2000 to 2020. The results of this study can provide a basis for the development of forest management strategies, and provide reference materials for regional biodiversity conservation. Full article

The Shaluli Mountains are located in the southeastern part of the Tibetan Plateau at an elevation of 2500–5000 m. They are characterized by a typical vertical distribution of climate and vegetation and are considered a global biodiversity hotspot. We selected ten vegetation types at different elevation gradients representing distinct forests in the Shaluli Mountains to assess the macrofungal diversity, including subalpine shrub, Pinus spp., Populus spp., Pinus spp. and Quercus spp., Quercus spp., Abies spp., Picea spp. and Abies spp., Picea spp., Juniperus spp., and alpine meadow. In total, 1654 macrofungal specimens were collected. All specimens were distinguished by morphology and DNA barcoding, resulting in the identification of 766 species belonging to 177 genera in two phyla, eight classes, 22 orders, and 72 families. Macrofungal species composition varied widely among vegetation types, but ectomycorrhizal fungi were predominant. In this study, the analysis of observed species richness, the Chao1 diversity index, the invsimpson diversity index, and the Shannon diversity index revealed that the vegetation types with higher macrofungal alpha diversity in the Shaluli Mountains were composed of Abies, Picea, and Quercus. The vegetation types with lower macrofungal alpha diversity were subalpine shrub, Pinus spp., Juniperus spp., and alpine meadow. The results of curve-fitting regression analysis showed that macrofungal diversity in the Shaluli Mountains was closely related to elevation, with a trend of increasing and then decreasing with rising elevation. This distribution of diversity is consistent with the hump-shaped pattern. Constrained principal coordinate analysis based on Bray–Curtis distances indicated that macrofungal community composition was similar among vegetation types at similar elevations, while vegetation types with large differences in elevation differed significantly in macrofungal community composition. This suggests that large changes in elevation increase macrofungal community turnover. This study is the first investigation of the distribution pattern of macrofungal diversity under different vegetation types in high-altitude areas, providing a scientific basis for the conservation of macrofungal resources. Full article

Climate change is affecting biodiversity by altering the geographical distribution range of species, and this effect is amplified in climate-sensitive areas. Studying the geographic distribution of flagship species in response to climate change is important for the long-term conservation of species and the maintenance of regional biodiversity. Therefore, we collected field survey records from 2016 to 2020 and conducted field surveys of black-necked cranes in the Shaluli Mountains (SLLMs) in May–June and August–October 2021; 103 breeding records were acquired totally, and the geographical distribution range under the current and four future climate scenarios was modeled with the MaxEnt model to predict the impact of climate change on its distribution and habitat quality. The results showed that 152 black-necked cranes were surveyed in seven counties of SLLMs in total; the estimated number of black-necked cranes in the entire SLLMs was about 200. The currently suitable habitat area is 27,122 km², mainly distributed in gentle meadows and wetland habitats along the lake where the Annual Mean Temperature is −1 °C and the Mean Diurnal Range (16 °C) and Precipitation Seasonality (105) are comparatively large. Furthermore, the breeding range would expand to varying degrees under future climate scenarios and showed a migration trend toward the northwest and higher elevation. Besides, as time goes by, the habitat for black-necked cranes in SLLMs would become more homogeneous and more suitable. The conservation effectiveness of the existing reserve network would keep stable with climate change, although there are large conservation gaps between protected areas, and these gaps will gradually expand over time. Overall, this study provides a preliminary understanding of the population and distribution and predicts the future distribution of black-necked cranes in the SLLMs. It also demonstrates the importance of SLLMs for protecting the central population of black-necked cranes and maintaining regional biodiversity. Therefore, we recommend long-term monitoring and conservation of the black-necked crane population and wetland resources in the region. Full article

The quantification of snow cover changes and of the related water resources in mountain areas has a key role for understanding the impact on several sectors such as ecosystem services, tourism and energy production. By using NASA-Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2018, this study analyzes changes in snow cover in the High Mountain Asia region and compares them with global mountain areas. Globally, snow cover extent and duration are declining with significant trends in around 78% of mountain areas, and the High Mountain Asia region follows similar trends in around 86% of the areas. As an example, Shaluli Shan area in China shows significant negative trends for both snow cover extent and duration, with −11.4% (confidence interval: −17.7%, −5.5%) and −47.3 days (confidence interval: −70.4 days, −24.4 days) at elevations >5500 m a.s.l. respectively. In spring, an earlier snowmelt of −13.5 days (confidence interval: −24.3 days, −2.0 days) in 4000–5500 m a.s.l. is detected. On the other side, Tien Shan area shows an earlier snow onset of −28.8 days (confidence interval: −44.3 days, −8.2 days) between 2500 and 4000 m a.s.l., governed by decreasing temperature and increasing snowfall. In the current analysis, the Tibetan Plateau shows no significant changes. Regarding water resources, by using Gravity Recovery and Climate Experiment (GRACE) data it was found that around 50% of areas in the High Mountain Asia region and 30% at global level are suffering from significant negative temporal trends of total water storage (including groundwater, soil moisture, surface water, snow, and ice) in the period 2002–2015. In the High Mountain Asia region, this negative trend involves around 54% of the areas during spring period, while at a global level this percentage lies between 25% and 30% for all seasons. Positive trends for water storage are detected in a maximum 10% of the areas in High Mountain Asia region and in around 20% of the areas at global level. Overall snow mass changes determine a significant contribution to the total water storage changes up to 30% of the areas in winter and spring time over 2002–2015. Full article