Agricultural grasslands play an important role in conserving the biodiversity of the European cultural landscape. Both, litter cover and soil nutrient availability, change with grassland management, but it is not well-studied how seedling recruitment and growth of multiple grassland species are influenced by their single or combined effects. Therefore, we studied the effects of nitrogen fertilization (100 kg N per year and ha) and litter cover (250 gdw per m2) on seedling recruitment and growth of 75 temperate grassland species (16 graminoid species, 51 forb species, 8 legume species) in a full factorial microcosm experiment. Overall, fertilizer reduced seedling emergence, while litter cover increased it even when combined with fertilization. Fertilization increased seedling height and biomass, and the combination of fertilizer and litter resulted in even stronger responses. Litter cover alone did not influence seedling biomass or seedling height. While the overall direction of treatment effects was similar across functional groups, their strengths were mostly weaker in graminoids than in non-legume forbs and legumes. Positive litter effects on seedling emergence were stronger in large-seeded species. Positive fertilization effects on seedling growth were stronger in small-seeded species, while their seedling biomass was negatively affected by litter cover. In summary, our results show for multiple grassland species that the combination of litter cover and fertilization modulates their single effects. The varying sensitivity of how grassland species representing different functional groups and seed sizes respond with their seedling emergence and growth to litter cover and nitrogen fertilization indicates that the consequences of land-use change on grassland diversity and composition already start to manifest in the earliest stages of the plant life cycle.

Galician forests in northwestern Spain are subject to frequent wildfires with high environmental and economic costs. In addition, due to the consequences of climate change, these fires are becoming more virulent, occurring throughout the year, and taking place in populated areas, in some cases involving the loss of human life. Therefore, forest fire prevention is even more relevant than mitigating its consequences. Given the costs involved in forestry work, alternative measures to reduce fuel load and create vegetation gaps are needed. One involves grazing by an endemic species of feral horses (Equus ferus atlanticus) that feed on thicket-forming gorse (Ulex europaeus). In a 100-ha forest fenced study area stocked with 11 horses, four 50 m2 enclosed plots prevented the access of these wild animals to the vegetation, with the aim of manipulating their impact on the reduction of forest biomass. The measurement of biomass volumes is an important method that can describe the assessment of wildfire risks, unfortunately, high-resolution data collection at the regional scale is very time-consuming. The best result can be using drones (unmanned aerial vehicles - UAVs) as a method of collecting remotely sensed data at low cost. From September 2018 to November 2020, we collected information about aboveground biomass from these four enclosed plots and their surrounding areas available for horses to forage, via UAV. These data, together with environmental variables from the study site, were used as input for a fire model to assess the differences in the surface rate of spread (SROS) among grazed and ungrazed areas. Our results indicated a consistent but small reduction in the SROS between 0.55 and 3.10 m/min in the ungrazed enclosured plots in comparison to their grazed surrounding areas (which have an SROS between 15 and 25 m/min). The research showed that radar remote sensing (UAV) can be used to map forest aboveground biomass, and emphasized the importance and role of feral horses in Galicia as a prevention tool against wildfires in gorse-dominated landscapes.

Wild desert grasslands are characterized by diverse habitats, uneven plant distribution, similarities among plant class, and the presence of plant shadows. However, the existing models for detecting plant species in desert grasslands exhibit low precision, require a large number of parameters, and incur high computational cost, rendering them unsuitable for deployment in plant recognition scenarios within these environments. To address these challenges, this paper proposes a lightweight and fast plant species detection system, termed YOLOv8s-KDT, tailored for complex desert grassland environments. Firstly, the model introduces a dynamic convolutional KernelWarehouse method to reduce the dimensionality of convolutional kernels and increase their number, thus achieving a better balance between parameter efficiency and representation ability. Secondly, the model incorporates triplet attention into its feature extraction network, effectively capturing the relationship between channel and spatial position and enhancing the model\'s feature extraction capabilities. Finally, the introduction of a dynamic detection head tackles the issue related to target detection head and attention non-uniformity, thus improving the representation of the target detection head while reducing computational cost. The experimental results demonstrate that the upgraded YOLOv8s-KDT model can rapidly and effectively identify desert grassland plants. Compared to the original model, FLOPs decreased by 50.8%, accuracy improved by 4.5%, and mAP increased by 5.6%. Currently, the YOLOv8s-KDT model is deployed in the mobile plant identification APP of Ningxia desert grassland and the fixed-point ecological information observation platform. It facilitates the investigation of desert grassland vegetation distribution across the entire Ningxia region as well as long-term observation and tracking of plant ecological information in specific areas, such as Dashuikeng, Huangji Field, and Hongsibu in Ningxia.

Elucidating the spatial and temporal patterns of grassland ecosystem service value (ESV) changes under different karst geomorphic types (KGTs) is crucial for promoting regional sustainable development and enhancing human well-being. Karst ecosystems are characterized by high spatial heterogeneity. However, analyses of the drivers of spatial and temporal changes in ESV in karst grasslands at multiple scales are lacking. In this study, the South China Karst (SCK) region was selected as the focus area, the gross ecosystem product (GEP) accounting method was used to quantify the grassland ESV from 2000 to 2020, and the GeoDetector model was used to elucidate the spatial and temporal evolution of the GEP, the drivers, and their interactions in different KGTs. The results indicate the following: (1) Over the past 20 years, the grassland GEP of SCK has increased from ¥ 14,844.24 × 108 in 2000 to ¥ 17,174.90 × 108 in 2020. Among the various KGTs, the karst gorge exhibited the fastest GEP increase (24.93 %) and karst hilly depressions the slowest (6.22 %). (2) The karst grassland GEP showed a strong positive spatial correlation with significant clustering characteristics (p < 0.05). (3) There are significant differences in the factors influencing the GEP of grasslands with different KGT values, and although they are generally influenced by factors such as NPP, precipitation, and population density, anthropogenic factors are becoming increasingly important. In addition, the multifactor interaction explained GEP better than the single factor. Based on our findings, we propose targeted grassland ESV restoration approaches and management recommendations for various KGTs dominated by distinct factors. Our results provide a scientific basis for decision-making regarding karst ecosystem service enhancement and value realization.

UNASSIGNED: Grassland-livestock balance is an important principle of sustainable development of grassland livestock production and grassland ecosystem health. Grassland degradation becomes more serious at global scales and especially at the area that is sensitive to climate change and human activities. Decreases in pasture biomass and shifts in plant community composition in degraded grasslands can largely affect grazing behaviors of livestock. Up to date, however, it is unclear that whether livestock behaviors change across spatial and temporal scales and what key factors are to shape observed behavioral patterns of livestock.
UNASSIGNED: Here, yak behaviors including grazing, rumination and walking on the eastern Qinghai-Tibetan Plateau (QTP) were monitored by a continuous visual observation, to investigate temporal and spatial variations of grazing behavior of yaks (Bos grunniens); based on the data from public database in the past 18 years, a meta-analysis was conducted to examine the main factors that affect grazing behaviors and intake of yaks.
UNASSIGNED: We showed that grazing behaviors of yaks differed significantly within hours, among hours of each day and among days as well as across different observation sites. Intake rate of yaks was higher in the morning than in the afternoon, but walking speed showed an inverse trend compared with intake rate. Resting, altitude, the mean annual precipitation (MAP), the mean annual temperature (MAT), forage ash, yak age and season were the main predictors for yak intake, and forage and yak individual characteristics had direct effects on grazing behaviors and intake of yaks.
UNASSIGNED: The findings confirm that grazing behaviors of yaks can vary even at small temporal scales and regional scales, which is closely related to the shift in forage quality and biomass caused by environmental changes. The study suggests that multiple factors can be responsible for the variation in livestock behaviors and shifts in behavioral patterns may consequently lead to positive or negative feedback to grassland ecosystems through plant-animal interactions.

Elevational gradients constitute excellent systems for understanding the mechanisms that generate and maintain global biodiversity patterns. Climatic gradients associated with elevation show strong influence on species distribution in mountains. The study of mountains covered by the same habitat type is an ideal scenario to compare alternatives to the energy hypotheses. Our aim was to investigate how changes in climatic conditions along the elevational gradient drive α- and β-diversity of four taxa in a mountain system located within a grassland biome. We sampled ants, spiders, birds and plants, and measured climatic variables at six elevational bands (with 10 sampling sites each) established between 470 and 1,000 masl on a mountain from the Ventania Mountain System, Argentina. Species richness per site and β-diversity (turnover and nestedness) between the lowest band and upper sites were estimated. For most taxa, species richness declined at high elevations and energy, through temperature, was the major driver of species richness for ants, plants and birds, prevailing over productivity and water availability. The major β-diversity component was turnover for plants, spiders and birds, and nestedness for ants. The unique environmental conditions of the upper bands could favour the occurrence of specialist and endemic species.

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain\'s socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km2) and grassland (531.3 km2); and forests were converted to other LULC classes, namely woodland (766.5 km2), agriculture (706.1 km2), grassland (34.6 km2), bushland (31.9 km2), settlement (20.5 km2), and bare land (14.3 km2). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources\' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource-based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources.

This study aimed to investigate the impact of different nitrogen forms on soil physicochemical properties and microbial community structure in perennial alpine cultivated grasslands, in order to provide scientific basis for developing nitrogen addition strategies for perennial alpine cultivated grasslands. In June 2022, a 4-year-old Qinghai grassland mixed with Poa pratensis Qinghai and Festuca sinensis Qinghai was established at the Bakatai Farm in Gonghe County, Hainan Tibetan Autonomous Prefecture, Qinghai Province. The study was conducted without fertilization as a control (CK), and three different forms of nitrogen treatments were set up, namely, U:urea (amide nitrogen), A:ammonium sulfate (ammonium nitrogen), and N:calcium nitrate (nitrate nitrogen); the nitrogen application rate for each treatment was 67.5 kg·(hm2·a)-1, and the composition and diversity of soil nutrients and microbial communities under different treatments were analyzed. The results showed that the input of exogenous ammonium nitrogen significantly increased NH4+-N content, AP content, and EC; amide nitrogen input significantly increased SOC content and TN content; and nitrate nitrogen input significantly increased NO3--N content, AN content, and TC content. Exogenous nitrogen input changed the structure of soil bacterial and fungal communities, as well as the relative abundance of dominant phyla and genera, but it did not significantly affect the alpha diversity of bacterial and fungal communities. Principal coordinate analysis (PCoA) showed that different forms of nitrogen addition had a significant impact on the Beta diversity of bacterial communities, whereas the impact on fungal communities was not significant. Redundancy analysis (RDA) indicated that nitrogen addition mainly changed the composition and structure of microbial communities through soil ammonium nitrogen. Overall, ammonium nitrogen fertilizer should be given priority in the soil remediation process of perennial cultivated grasslands in the Qinghai Tibet Plateau.

The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors on the phenology of the Qinghai-Xizang Plateau, resulting in a lack of understanding of the mechanisms underlying phenological changes on the Qinghai-Xizang Plateau. In this study, we used remote sensing data interpretation to analyze the spatial and temporal variability of grassland phenology on the Qinghai-Xizang Plateau from 2002 to 2021, focusing on precipitation, temperature, altitude, soil, and other aspects to reveal the dominant factors of phenological variability using an interpretable machine learning method (SHAP) and to quantify the interactive effects of multiple factors on phenology. The results showed that:① The growing season start (SOS) of grasslands on the Qinghai-Xizang Plateau mostly ranged from 110 to 150 d, with 56.32 % of grasslands showing an early SOS trend; the growing season end (EOS) mostly ranged from 290-320 d, with 67.65 % of grasslands showing a delayed EOS trend; and the growing season length (LOS) mostly ranged from 120 to 210 d, with 65.50 % of the grasslands showing a trend towards longer growing season lengths. ② SOS in grasslands on the Qinghai-Xizang Plateau was mainly influenced by moisture conditions, in which soil moisture between 10 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the advancement of SOS and peaked at approximately 20 kg·m-2. EOS was mainly influenced by temperature, with higher temperatures in September and October having a stronger effect on EOS latency promotion and peaking at over 8 ℃ and -0.5 ℃, respectively. The main influencing factors of LOS were more consistent with SOS, in which soil moisture between 15 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the prolongation of LOS and peaked at approximately 18 kg·m-2. ③ There was an obvious interactive effect of water and heat and other factors on phenology; after soil moisture reached 20 kg·m-2 in the 0-10 cm soil layer in March, SOS was more advanced in low-precipitation and low-altitude areas. Better moisture conditions were more conducive to EOS delay at temperatures above 0 ℃ in October, and soil moisture in high precipitation areas promoted LOS prolongation more when soil moisture was between 12 and 22 kg·m-2 in 0-10 cm in March. The results also demonstrated that interpretable machine learning methods could provide a new approach to the analysis of the multifactorial effects of phenological change.

The biodiversity of grasslands is important for ecosystem function and health. The protection and mana-gement of grassland biodiversity requires the collection of the information on plant diversity. Hyperspectral remote sensing, with its unique advantages of extensive coverage and high spectral resolution, offers a new solution for long-term monitoring of plant diversity. We first reviewed the development history of hyperspectral remote sensing technology, emphasized its advantages in monitoring grassland plant diversity, and further analyzed its specific applications in this field. Finally, we discussed the challenges faced by hyperspectral remote sensing technology in its applications, such as the complexity of data processing, accuracy of algorithms, and integration with ground-based remote sensing data, and proposes prospects for future research directions. With the advancement of remote sensing technology and the integrated application of multi-source data, hyperspectral remote sensing would play an increasingly important role in grassland ecological monitoring and biodiversity conservation, which could provide scientific basis and technical support for global ecological protection and sustainable development.
草原的生物多样性具有多种生态功能,而草原生物多样性的保护与管理工作需要收集草原的植物多样性信息。高光谱遥感以其独特的大范围覆盖和高光谱分辨率优势,为草原植物多样性的长期监测提供了新的解决方案。本文首先回顾了高光谱遥感技术的发展历程,强调了高光谱数据在草原植物多样性监测中的独特优势,并进一步分析了其在草原植物多样性监测中的具体应用。最后,讨论了高光谱遥感技术在当前应用中面临的挑战,如数据处理复杂性、算法精度,以及与地面遥感数据的整合问题,并对未来研究方向提出展望。随着遥感技术的不断进步和多源数据的综合应用,高光谱遥感将在草原生态监测与生物多样性保护方面发挥更加重要的作用,为全球生态保护和可持续发展提供科学依据和技术支持。.