Review of the subgenus Gerris s.str. in China (Hemiptera: Gerridae), with description of a new species from the Tibetan Plateau

Abstract. The Tibetan Plateau (TP) hosts a variety of vegetation types, ranging from broadleaved and needle-leaved forests at the lower altitudes and in mesic areas to alpine grassland at the higher altitudes and in xeric areas. Accurate and detailed mapping of the vegetation distribution on the TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets for the TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred to as TP_LC10-2022) by leveraging state-of-the-art remote-sensing approaches including Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and four machine learning models using the Google Earth Engine platform. Our TP_LC10-2022 dataset achieved an overall classification accuracy of 86.5 % with a kappa coefficient of 0.854. Upon comparing it with four existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree, which is ignored in existing land cover datasets, occupied 13.99 % of the TP region, and shrublands, which are characterized by distinct forms (deciduous shrublands and evergreen shrublands) that are largely determined by the topography and are missed in existing land cover datasets, occupied 4.63 % of the TP region. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in the TP. TP_LC10-2022 and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8214981 (Huang et al., 2023a) and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023b), respectively. Additionally, the classification map can be viewed at https://cold-classifier.users.earthengine.app/view/tplc10-2022 (last access: 6 June 2024).

The Tibetan Plateau is a vast and elevated plateau in Central Asia and is a source area of the most important rivers of China, India and Indochina, providing water to billions people. In this context, reliable predictions about the evolution of water supply from lacustrine and river systems are valuable to ensure freshwater availability and environmental disaster prevention, especially in time of global warming. For this proposal, organisms that provide a proxy record are particularly valuable. Analysis on ostracods from water bodies of the Tibetan Plateau and palaeoenvironmental records provide the basis to assess the environmental and societal impact of recent global change on the Tibetan Plateau. This study wants to contribute to a better understanding and thus improving the ostracods as indicators for the environmental and social impact of Quaternary and recent global change on the Tibetan Plateau. The analyses were conducted on the Taro Co and Tangra Yumco lake systems, to detect (palaeo)environmental changes and to assess their lake level changes evolution. To compare the obtained results with published records from other lakes, the considered transect was extended with information from articles regarding several water bodies present in different regions of the southern Tibetan Plateau. In the time frame after 18 ka, where more information are available, our results corresponds with only some time-shift, probably due to different dating or different exposure to the Indian Monsoon and the Westerlies although the lacking of information on tectonic and climate models will be needed to assess the different influence of the circulation patterns for the single lakes. In addition of this, the ostracod associations of the Zhada Basin (western Tibetan Plateau) were carried out and, aiming to a future use of ostracods for this less studied sector, a documentation of several unknown species was performed. A new species, Leucocytherella dangeloi was described.

Tibetan Plateau has a crucial impact on the atmospheric circulation changes of Asia and even the northern hemisphere and southern hemisphere, directly affecting the formation and evolution of weather and climate of China, and therefore the studying on weather, climate and their evolving mechanism over Qinghai-Tibet Plateau is of great significance, and this studying is helpful for improving accuracy of forecast disaster weather. Tibetan Plateau is the magnifying glass of global climate change too. The system of ecology and the environment in Tibetan Plateau is very fragile and very sensitive to global climate change, so Tibetan Plateau is a window of studying global climate change. Due to the special geographical conditions of the Tibetan Plateau, the weather stations are scarce over the plateau region, especially in its western region. The introduction and application of satellite remote sensing data on studying on the Tibetan Plateau, in particular, is very important and very necessary. Using satellite remote sensing data, some areas of the Tibetan Plateau is classified into several surface types, regional distributions of the Surface parameters are calculated and discussed according to each type. Further more, each distribution map and straight-bar figure of the Surface parameters is given out. The results indicate: All the regional distributions are characteristic by their terrain nature and the regional distributions are obvious and regular. It is seen that the derived regional distributions of land surface parameters for the whole mesoscale area are in good accordance with the land surface status.

Rock glaciers are geomorphologically valuable indicators of permafrost distribution and form potentially important hydrological resources in the context of future climate change. Despite the widespread distribution of permafrost on the Tibetan Plateau and its reputation as the "water tower of Asia", this region lacks a complete inventory and systematic investigation of rock glaciers. In this study, we develop a deep-learning-based approach for mapping rock glaciers on the Tibetan Plateau. A powerful deep learning network, DeepLabv3+, is trained using Planet Basemaps as training imagery and multi-source rock glacier inventories as training labels. The well-trained model is then used to map new rock glaciers. The visually consistent and cloud-free properties of Planet Basemaps are crucial for developing comprehensive maps of rock glacier distribution; and the rock glacier inventories from multiple regions can improve the volume and diversity of the training dataset. The deep learning mapped results present strong identification and acceptable boundary delineation of rock glaciers, indicating that the deep learning model could serve as a useful tool for facilitating the inventory of rock glaciers over vast regions. Based on the deep learning outputs, we compile 4233 rock glaciers on eight subregions of the Tibetan Plateau, which are widespread in the surrounding regions while being scarcely distributed in inner areas. Talus- and glacier-connected rock glaciers are two major classes, which are dominant on the southeastern and densely distributed on the northwestern Tibetan Plateau, respectively. The regions with steep slopes are favored by rock glacier clusters with high density, and glacier-abundant regions tend to breed large rock glaciers. The proposed rock glacier mapping method effectively speeds up inventorying efforts, which will be used to map and inventory rock glaciers on the entire Tibetan Plateau. The complete inventory will offer a significant contribution to the global catalog and serves as a benchmark dataset for modeling and monitoring the state of permafrost in a changing climate. 

Distribution mapping and risk assessment of lead in topsoil across the Tibetan Plateau.

The region surrounding the Tibetan Plateau (TP) is widely considered a primary global dust source, with mineral dust comprising a significant proportion of aerosols over the TP. Current research on TP dust has mainly focused on transport from the surrounding deserts, with little focus on dust emissions from the TP's interior. The erodibility factor used by the WRF‐Chem (ERODDEF) is 0 for the TP, so the model cannot simulate the dust emissions inside the plateau. Thus, we constructed a high‐resolution erodibility data set (ERODSDS) based on a reliable dust source distribution and intensity map. Based on the modified EROD map, the WRF‐Chem model was used to simulate dust emissions and direct radiative forcing on the TP in 2018. With the modified EROD map, WRF‐Chem can well simulate the temporal variation and spatial pattern of mineral dust on the plateau, which greatly improves the model's dust emissions simulation accuracy on the TP.

Data-driven mapping of the spatial distribution and potential changes of frozen ground over the Tibetan Plateau

The Qinghai-Tibet Plateau (QTP) harbors significant amounts of soil organic carbon (SOC) in the permafrost regions, which are at risk of release as carbon dioxide or methane under global warming, amplifying the greenhouse effect. Despite this, long-term investigations into the spatiotemporal dynamics of SOC in the QTP's permafrost regions remain scarce. Furthermore, spatial scale mismatches between SOC maps and thermokarst landscape maps hinder a comprehensive understanding of carbon cycling mechanisms in these landscapes. Hyperspectral data, with its superior spectral richness, offers the potential to more precisely capture soil spectral characteristics, enhancing the accuracy of SOC estimations. However, the limited availability of long-term hyperspectral datasets for the QTP presents a major challenge to leveraging this technology for SOC estimation.In this study, we developed a physically constrained hyperspectral generative model that integrated spectral response functions and diffusion models, utilizing satellite data from Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and EO-1 Hyperion imagery. This method generated high-accuracy hyperspectral data (MSSIM = 0.96, PSNR = 38.65) for the permafrost regions of the QTP from 2000 to 2020, with a spatial resolution of 30 m and a spectral resolution of 10 nm. Leveraging these generated hyperspectral data, we constructed spectral indices and incorporated climate, topography, and soil characteristics into a dual-input convolutional neural network model. This model enabled the mapping of the spatiotemporal distribution of SOC in the 0-3 m layer across the QTP’s permafrost regions from 2000 to 2020 with resolution of 30 m. Compared to existing approaches, our model achieved a 22.9% improvement in the accuracy of SOC estimation in permafrost regions, highlighting its potential for advancing carbon estimation.

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost.

Abstract. The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs) and validated this map using various ground-based data sets. The soil thermal properties of five soil types across the TP were estimated according to an empirical equation and soil properties (moisture content and bulk density). The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution. Permafrost, seasonally frozen ground, and unfrozen ground covered areas of 1.06 × 106 km2 (0.97–1.15 × 106 km2, 90 % confidence interval) (40 %), 1.46 × 106 (56 %), and 0.03 × 106 km2 (1 %), respectively, excluding glaciers and lakes. Ground-based observations of the permafrost distribution across the five investigated regions (IRs, located in the transition zones of the permafrost and seasonally frozen ground) and three highway transects (across the entire permafrost regions from north to south) were used to validate the model. Validation results showed that the kappa coefficient varied from 0.38 to 0.78 with a mean of 0.57 for the five IRs and 0.62 to 0.74 with a mean of 0.68 within the three transects. Compared with earlier studies, the TTOP modelling results show greater accuracy. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.

Accurate, large-scale, and temporally explicit lake mapping is critical for water resource management, hazard risk assessment, and understanding lake responses to climate change. The Tibetan Plateau (TP) hosts a high density of lakes, many of which are small and difficult to detect due to highly heterogeneous environments, including mountain shadows, glacier and snow cover, clouds, and turbid waters. Existing studies often produce multi-year composite datasets, which enhance the detectability of lakes by emphasizing their long-term occurrence. However, these datasets ultimately remain static, reflecting the long-term aggregated distribution of lakes without capturing their spatial extent in any specific year. In this study, we processed 17,942 Sentinel-2 images acquired from July to October 2020 using an automated, deep learning–based water extraction framework to extract lakes across the TP. The method demonstrated high accuracy under challenging conditions, with Intersection over Union (IoU) values exceeding 92% in cloudy, glacial, and mountainous test areas. Applying this framework, we generated a comprehensive 2020 lake inventory, identifying 57,841 lakes larger than 0.01 km2. Approximately 65% of the total lake coverage was concentrated in the Inner Plateau Basin, with the 4,500–5,000 m elevation exhibiting the highest density (29,200 lakes covering 32,892.51 km2, representing 50.5% of all lakes and 59.3% of total lake area). Compared with previous studies, this dataset improves the detection of small lakes and provides a temporally explicit, year-specific map of lake distributions, distinguishing multiple lake types (large natural lakes, glacial lakes, thermokarst lakes, and reservoirs). This high-resolution, comprehensive dataset constitutes a valuable resource for hydrological, climatic, and ecohydrological research across TP. The lake dataset generated in this study has been archived and made publicly available through Zenodo (https://doi.org/10.5281/zenodo.15639602).

Since the first 2 cases observed in southern Germany and the correct identification of a parasite at the origin of the disease by the famous scientist Rudolf Virchow in 1855, the borders of the endemic area of alveolar echinococcosis (AE) have never stopped to expand. The parasite was successively recognized in Switzerland, then in Russia, Austria and France which were long considered as the only endemic areas for the disease. Cases were disclosed in Turkey in 1939; then much attention was paid to Alaska and to Hokkaido, in Japan. The situation totally changed in 1991 after the recognition of the Chinese endemic areas by the international community of scientists. The world map was completed in the beginning of the 21st century by the identification of AE in most of the countries of central/eastern Europe and Baltic States, and by the recognition of cases in central Asia. Up to now, the disease has however never been reported in the South hemisphere and in the United Kingdom. In the mid-1950s, demonstration by Rausch and Schiller in Alaska, and by Vogel in Germany, of the distinction between 2 parasite species responsible respectively for cystic echinococcosis (“hydatid disease”) and AE put an end to the long-lasting debate between the "dualists", who believed in that theory which eventually proved to be true, and the "unicists", who believed in a single species responsible for both diseases. At the end of the 20th century, molecular biology fully confirmed the "dualist" theory while adding several new species to the initially described E. granulosus; within the past decade, it also confirmed that little variation existed within Echinococcus (E.) multilocularis species, and that AE-looking infection in some intermediate animal hosts on the Tibetan plateau was indeed due to a new species, distinct from E. multilocularis, named E. shiquicus. Since the 1970s, the unique ecological interactions between the landscape, the hosts, and E. multilocularis have progressively been delineated. The important role of the rodent/lagomorph reservoir size for the maintenance of the parasite cycle has been recognized within the last 2 decades of the 20th century. And the discovery of a close relationship between high densities of small mammals and particularities in land use by agriculture/forestry has stressed the responsibility of political/economic decisions on the contamination pressure. Urbanization of foxes in Europe and Japan and the major role of dogs in China represent the new deals at the beginning of the 21st century regarding definitive hosts and prevention measures.

Our knowledge of species diversity in biodiversity hotspots remains incomplete. The Qinghai–Tibet Plateau (QTP) and the mountainous region of southwestern China have long been regarded as biodiversity hotspots. However, despite considerable efforts, numerous plant species may still elude formal description. Rhodiola L. (Crassulaceae) encompasses ca. 58 perennial herb species, which have been used as an important traditional medicinal plant for centuries. Rampant exploitation has put some species at risk of extinction. Rhodiola has also been recognized as a promising model for investigating radiation speciation in the QTP. However, the phylogenetic relationships among major clades in the genus are still not well resolved, and the underlying causes of cytonuclear discordance briefly mentioned in previous studies remain unexplored. Through phylogenomic analyses utilizing data from both the nuclear genome and plastome of 42 species, we identified six major clades in Rhodiola and found extensive cytonuclear discordance, which was primarily attributed to hybridization and introgression occurring among clades or closely related species. In addition, the integration of morphological, phylogenomic, population genomic, and ecological evidence resulted in the identification and description of a new species of Rhodiola: R. renii sp. nov., and the reclassification of a previously Pseudosedum species merged into Rhodiola. Our results highlight the significant role of hybridization and introgression in the evolution of Rhodiola and probably other rapid‐radiated groups in the QTP, and emphasize the need for increased species discovery efforts in biodiversity hotspots such as the QTP and its adjacent mountainous areas.

A sensitivity study was performed to investigate the responses of potential natural vegetation distribution in China to the separate and combined effects of temperature, precipitation and [CO2], using the process-based equilibrium terrestrial biosphere model BIOME4. The model shows a generally good agreement with a map of the potential natural vegetation distribution based on a numerical comparison using the ΔV statistic (ΔV = 0.25). Mean temperature of each month was increased uniformly by 0–5 K, in 0.5- or 1-K intervals. Mean precipitation of each month was increased and decreased uniformly by 0–30%, in 10% intervals. The analyses were run at fixed CO2 concentrations of 360 and 720 ppm. Temperature increases shifted most forest boundaries northward and westward, expanded the distribution of xeric biomes, and confined the tundra to progressively higher elevations. Precipitation increases led to a greater area occupied by mesic biomes at the expense of xeric biomes. Most vegetation types in the temperate regions, and on the Tibetan Plateau, expanded westward into the dry continental interior with increasing precipitation. Precipitation decreases had opposite effects. The modelled effect of CO2 doubling was to partially compensate for the negative effect of drought on the mesic biomes and to increase potential ecosystem carbon storage by about 40%. Warming tended to counteract this effect, by reducing soil carbon storage. Forest biomes showed substantial resilience to climate change, especially when the effects of increasing [CO2] were taken into account. Savannas, dry woodland and tundra biomes proved sensitive to temperature increases. The transition region of grassland and forest, and the Tibetan plateau, was the most vulnerable region.

Forage grass is very important for food security. The development of artificial grassland is the key to solving the shortage of forage grass. Understanding the spatial distribution of forage grass in alpine regions is of great importance for guiding animal husbandry and the rational selection of forage grass management measures. With its powerful computing power and complete image data storage, Google Earth Engine (GEE) has become a new method to address remote sensing data collection difficulties and low processing efficiency. High-resolution mapping of pasture distributions on the Tibetan Plateau (China) is still a difficult problem due to cloud disturbance and mixed planting of forage grass. Based on the GEE platform, Sentinel-2 data and three classifiers, this study successfully mapped the oat pasture area of the Shandan Racecourse (China) on the eastern Tibetan Plateau over 3 years from 2019 to 2021 at a resolution of 10 m based on cultivated land identification. In this study, the key phenology windows were determined by analysing the time series differences in vegetation indices between oat pasture and other forage grasses in the Shandan Racecourse, and monthly scale features were selected as features for oat pasture identification. The results show that the mean Overall Accuracy (OA) of Random Forest (RF) classifier, Support Vector Machine (SVM) classifier, and Classification and Regression Trees (CART) classifier are 0.80, 0.69, and 0.72 in cultivated land identification, respectively, with corresponding the Kappa coefficients of 0.74, 0.58, and 0.62. The RF classifier far outperforms the other two classifiers. In oat pasture identification, the RF, SVM and CART classifiers have high OAs of 0.98, 0.97, and 0.97 and high Kappa values of 0.95, 0.94, and 0.95, respectively. Overall, the RF classifier is more suitable for our research. The oat pasture areas in 2019, 2020 and 2021 were 347.77 km2 (15.87%), 306.19 km2 (13.97%) and 318.94 km2 (14.55%), respectively, with little change (1.9%) from year to year. The purpose of this study was to explore the identification model of forage grass area in alpine regions with a high spatial resolution, and to provide technical and methodological support for information extraction of the forage grass distribution status on the Tibetan Plateau.