Search

This paper established a mathematical method for the spectral feature extraction of ratholes, based on UAV hyperspectral imaging technology. The degradation of grasslands is a major challenge to terrestrial ecosystems. Rodents not only promote soil erosion and accelerate the process of grassland degradation, but also carry diseases that can easily cause epidemics. The calculation of the number of rodent holes and grassland vegetation cover is an important indicator for monitoring and evaluating grassland degradation. Manual surveys have drawbacks in efficiently monitoring large areas and are human- and material-costly, hardly meeting the current needs of grassland degradation monitoring. Therefore, there is an urgent need to conduct real-time dynamic monitoring of grassland rathole distributions and grassland degradation processes. In this study, a low-altitude remote sensing platform was constructed by integrating a hyperspectral imager with a UAV to collect spectral data of the desert steppes in central Inner Mongolia Autonomous Region, China. Then, the spectral features of ratholes were extracted via radiation correction, noise reduction, and principal component analysis (PCA). Meanwhile, the spectral features of vegetation and bare soil were extracted based on the normalized difference vegetation index (NDVI), which was inputted to calculate the vegetation cover. The results showed that the single-band map extracted based on PCA could effectively determine the location of ratholes, where the overall accuracy and kappa coefficient were 97% and 0.896, respectively. Therefore, the method proposed in this study can accurately identify the location of desert steppe rodent holes. It provides a high-precision technical means for scientific and effective control of grassland rodent infestation and also provides a higher technical means for grassland degradation. [ABSTRACT FROM AUTHOR]

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry. [ABSTRACT FROM AUTHOR]

Simple Summary: Grasshoppers are among the most dangerous agricultural pests of China. However, the monitoring, prediction and control of grasshoppers are complex and difficult. Therefore, it is crucial to detect the key factors affecting the spatial distribution of grasshopper occurrence, understand the role of the environmental factors in grasshopper occurrence, and study whether different laws exist between different grass types. Here we conduct a species–environmental matching model integrated by Maxent model and remote sensing data to identify the spatial distribution of grasshopper occurrence in Inner Mongolia of China, analyze the related environmental variables and detect the most relevant environmental factors for grasshopper occurrence both in typical steppe and meadow steppe. Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland. [ABSTRACT FROM AUTHOR]

Uncovering the complex periodic variations in soil moisture can provide a significant reference for climate prediction and hydrological process simulation. We used wavelet analysis to quantify and identify the multi-scale periodic variations of soil moisture in the desert steppe of Inner Mongolia from 2009 to 2019 and analyzed the differences between the multi-scale periodic changes in soil moisture at the bottom (BS) and upper slope (US). The results show that the interannual variability in soil moisture at the BS and US has a significant upward trend. Moreover, the amount and volatility decrease with the increase in soil depth in the range of 0–30 cm, and the soil moisture at the BS is 26.4% higher than the US. The soil moisture has periodic changes of 0.5a, 1a, 2a, 3a and 3.5a in the desert steppe environment of Inner Mongolia. The periodic structure and intensity of different slope positions are greatly different. Soil moisture at the US has more complex multi-scale periodic changes, and the periodic oscillations of 3.5a, 3a, and 1a are dominant. The periodic oscillations of 0.5a and 1a are dominant at the BS. At the BS, the intensity of periodic oscillation of 1a after January 2015 has weakened. The weakening of soil moisture by temperature, rainfall and soil temperature caused the change in the multiple time-scale periodic oscillation of soil moisture. [ABSTRACT FROM AUTHOR]

The increasing frequency of global drought events poses a significant threat to the stability of grassland ecosystems' functionality. The Inner Mongolian grasslands stand out as one of the world's most drought-prone regions, facing elevated drought risks compared to other biomes. An in-depth comprehension of the impact of drought on grassland ecosystems is paramount for their long-term sustainability. Using the Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2018, this study identified various drought events within the Inner Mongolian grasslands, encompassing moderate drought, severe drought, and extreme drought. The resistance of the vegetation to the different drought conditions, assessed through net primary productivity (NPP) as a metric (reflecting its capacity to maintain its original level during drought periods), was examined. The research findings indicated that the period from 2001 to 2018 witnessed a substantial increase in both the frequency and the extent of drought events compared to the period from 1982 to 2000, particularly concerning severe and extreme droughts. The areas most severely impacted by extreme drought were the Xilingol League and the Alxa League. From 1982–2000 to 2001–2018, under moderate drought conditions, vegetation resistance exhibited a minor decrease in the central and eastern regions but experienced a slight increase in the western region. In contrast, under severe drought conditions, the western region saw a significant decrease in vegetation resistance. Remarkably, under extreme drought conditions, the western region showed a substantial increase in vegetation resistance, while the central and eastern regions experienced a slight decrease. Across all three drought conditions, as precipitation levels declined, the resistance of the meadow–steppe–desert ecosystems demonstrated a high–low–high distribution pattern. The temperate desert steppe exhibited a minimal vulnerability to drought, boasting resistance levels exceeding 0.9. Notably, extreme drought had the most pronounced impact on the temperate meadow steppe, temperate steppe, and temperate desert steppe, particularly within the temperate meadow steppe category. Given these findings, the authorities responsible for grassland management should prioritize regions characterized by frequent drought occurrences and low drought resistance, such as Ulanqab City, the Xilingol League, and the western part of Hulun Buir City. Safeguarding steppe ecosystems is of paramount importance for stabilizing vegetation productivity and land carbon sinks, especially under the anticipated exacerbation of climate conditions in the future. [ABSTRACT FROM AUTHOR]

Dominant species play a principal role in controlling and maintaining ecosystem stability. Stipa breviflora is the dominant species in desert steppe. Changes in the stability of a plant population will further affect the stability of the broader habitat, such as the desert steppe. In the desert steppe ecosystem, it is not clear what level of grazing intensity is best for improving the grazing tolerance and stability of the vegetation. And, the study of this question should involve a multi-dimensional, comprehensive analysis. This study will utilize variance analysis, plant population stability, and trade-off index to study S. breviflora, the dominant species in the desert steppe in Inner Mongolia, and its performance under four grazing intensities (control, CK, 0 sheep·ha−1·half year−1; light grazing, LG, 0.93 sheep·ha−1·half year−1; moderate grazing, MG, 1.82 sheep·ha−1·half year−1; and heavy grazing, HG, 2.71 sheep·ha−1·half year−1) over six scales (5 cm × 5 cm; 10 cm × 10 cm; 20 cm × 20 cm; 25 cm × 25 cm; 50 cm × 50 cm; and 100 cm × 100 cm). The characteristics of the population stability of S. breviflora were explored. The results showed that the response of S. breviflora's stability to heavy grazing was multidimensional. Heavy grazing reduced the population stability of S. breviflora. Across different dimensions, base coverage was the first of the population stability metrics of S. breviflora to destabilize, followed by projection coverage, density, and height. Heavy grazing also affected the trade-offs of S. breviflora's population stability across different dimensions. In general, the trade-off degree decreased as the grazing intensity increased, and it increased as the scale increased. [ABSTRACT FROM AUTHOR]

Simple Summary: The Chinese Red Steppe Cattle (CRS) is a well-known dual-purpose (meat and milk) cattle breed. Here, the genetic variation and population structure of CRS were studied using 100 K SNP genotyping data. The results show that the genetic structure of CRS is different from other populations, the level of genetic diversity is high, and the level of inbreeding is low. In conclusion, our research provides the genetic basis for the prominent characteristics of CRS, which can be used to improve the breeding program of CRS in the future. Chinese Red Steppe Cattle (CRS), a composite cattle breed, is well known for its milk production, high slaughter rate, carcass traits, and meat quality. Nowadays, it is widely bred in Jilin and Hebei Province and the Inner Mongolia Autonomous region. However, the population structure and the genetic basis of prominent characteristics of CRS are still unknown. In this study, we systematically describe their population structure, genetic diversity, and selection signature based on genotyping data from 61 CRS individuals with GGP Bovine 100 K chip. The results showed that CRS cattle had low inbreeding levels and had formed a unique genetic structure feature. Using two complementary methods (including comprehensive haplotype score and complex likelihood ratio), we identified 1291 and 1285 potentially selected genes, respectively. There were 141 genes annotated in common 106 overlapping genomic regions covered 5.62 Mb, including PLAG1, PRKG2, DGAT1, PARP10, TONSL, ADCK5, and BMP3, most of which were enriched in pathways related to muscle growth and differentiation, milk production, and lipid metabolism. This study will contribute to understanding the genetic mechanism behind artificial selection and give an extensive reference for subsequent breeding. [ABSTRACT FROM AUTHOR]

In recent decades, frequent compound dry and hot events have posed a great threat to humans and the ecological environment, especially in Inner Mongolia, which has arid and semi-arid characteristics. In this study, monthly temperature and precipitation data from 115 meteorological stations in Inner Mongolia from 1982 to 2020 were used to establish a standardized dry and hot index (SDHI). Theil–Sen median trend analysis, Mann–Kendall test, partial correlation analysis, and stepwise multiple regression models were used to characterize the changes in compound dry and hot events and the normalized difference vegetation index (NDVI) from 1982 to 2020, and the relationship between the SDHI and NDVI was quantitatively evaluated. The results showed that the overall SDHI values in Inner Mongolia showed a significant decrease at a rate of 0.03/year from 1982 to 2020, indicating an increase in the severity of compound dry and hot events. NDVI values showed a significant increasing trend and NDVI showed mutated 2001. Among the grassland vegetation types, SDHI and NDVI trends were more significant in forests, and meadow steppe, desert steppe, and desert were more susceptible to compound dry and hot events, and forests had the greatest severity of compound dry and hot events. The results of the partial correlation analysis showed that the average value of the partial correlation coefficient between the SDHI and NDVI was 0.68, and the area of positive correlation was 84.13%. Spatially, it showed strong response characteristics in the middle and gradual weakening towards the east and west sides. The correlation between NDVI and climatic conditions varied greatly in different vegetation areas. The forest area is most sensitive to the influence of temperature, and the desert steppe area is most affected by compound dry and hot events. The overall vegetation growth in Inner Mongolia was most affected by temperature conditions, followed by compound dry and hot conditions, and the influence of drought conditions was the least significant. The results of the relative importance analysis confirmed this. The research results provide a more detailed understanding of compound dry and hot events in arid and semi-arid regions and useful insights and support for ecological protection. [ABSTRACT FROM AUTHOR]

Global climate change is altering the amounts of ice and snow in winter, and this could be a major driver of soil microbial processes. However, it is not known how bacterial and fungal communities will respond to changes in the snow cover. We conducted a snow manipulation experiment to study the effects of snow removal on the diversity and composition of soil bacterial and fungal communities. A snow manipulation experiment was carried out on the meadow steppe in Hulunbuir, Inner Mongolia, China, during the winter period October 2019–March 2020. Soil samples were collected from the topsoil (0–10 cm) in mid-March 2020 (spring snowmelt period). Snow removal significantly reduced soil moisture and soil ammonium concentration. Lower snow cover also significantly changed the fungal community structure and beta diversity. Snow removal did not affect the bacterial community, indicating that fungal communities are more sensitive to snow exclusion than bacterial communities. The relative importance analysis (using the Lindeman–Merenda–Gold method) showed that available nitrogen (AN), soil water content (SWC), total organic carbon (TOC), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) together explained 94.59% of the variation in soil fungal beta diversity, where AN was identified as the most important predictor. These finding provide insights into potential impacts of climate warming and associated reduced snow cover on soil microbial communities and processes. [ABSTRACT FROM AUTHOR]