Distribution Of Grasslands Research Articles

The Spatio-Temporal Patterns and Driving Forces of Land Use in the Context of Urbanization in China: Evidence from Nanchang City.

Land use change has been one of the common problems in the context of urbanization in China. Social economy and land use interact with each other, and it is especially important for human society to adhere to sustainable development, and to deal with the contradictory relationship between the social-economic needs and land use change. The objectives of this study are: (1) Obtain time-series land-use classification data and its spatial distribution in Nanchang City; (2) Identify the characteristics and driving force of spatial-temporal land use changes in Nanchang City from 2000 to 2020; (3) Discuss the relationship between the urban expansion and social economy in Nanchang City. The results show that the spatial distribution of land use in Nanchang City has changed significantly from 2000 to 2020, and the largest area of land-use type in Nanchang City has been cropland. The cropland has continuously declined, and the urban area has increased significantly. A lot of cropland has been transformed into urban areas, and land use degree in Nanchang City has significantly increased. The spatial pattern of land use has greatly changed, and the city spatial pattern has become more aggregated, while the spatial distribution of cropland, forest and grassland has become more fragmented. Moreover, there has been an obvious correlation between social-economic development and the level of land use, and GDP has been the main driver of land use change. The central urban area of Nanchang city has been the main hotspot of land use change.

Ecological and syntaxonomical characteristics of early spring therophytic ephemeral grasslands (alliance Romuleion) in the northeastern Mediterranean

Our study deals with dry grasslands dominated by Romulea bulbocodium, Romulea linaresii subsp. graeca and Romulea columnae that develop in early spring in the eastern Mediterranean region. Based on numerical classification, we describe two new associations: Romuleo bulbocodii-Poetum bulbosae ass. nova and Ornithogalo exscapii-Poetum bulbosae ass. nova. The studied communities are classified within the Romuleion alliance, order Poetalia bulbosae, and class Poetea bulbosae. We analyzed all available relevés of Romuleion communities from the eastern Mediterranean and ordination analysis (NMDS) revealed that temperature and altitude are the most important ecological factors influencing the vegetation composition and distribution of dry grasslands of the Romuleion alliance. Newly described associations from Montenegro are floristically and ecologically similar to the Alyssum alyssoides-Poa bulbosa community from Epirus (Greece).

Transformer attention network and unmanned aerial vehicle hyperspectral remote sensing for grassland rodent pest monitoring research

The proliferation of grassland rodents has aggravated the degradation process of desert grassland. Additionally, it carries many viruses that threaten human and animal health. Accurate spatial distribution in grassland between rodent populations, vegetation, and bare soil is essential for developing rodent control measures. However, the traditional survey method of grassland rodent pest information is time-consuming and costly, and the period is long. In addition, satellite remote sensing cannot meet the accuracy requirements for the identification of grassland rat holes due to the limitation of spatial resolution. To realize intelligent grassland rodent infestation monitoring, this paper adopts an unmanned aerial vehicle hyperspectral remote sensing platform for data acquisition. Meanwhile, a transformer attention network (TAN) is proposed for grassland rodent infestation information extraction. The network adopts a two-stage feature extraction structure that effectively improves the classification performance of the model. In each stage, first, local features are extracted by a fixed convolution kernel to enhance detailed texture features; second, the extracted local features are refined using the contour convolution module to enrich feature information at the edges of the feature map; finally, the transformer attention module is used to focus on the global pixels, thus suppressing background information and enhancing effective information output. The results show that the overall accuracy (OA), average accuracy, and kappa coefficient of the TAN network can reach 97.71%, 98.44%, and 0.9538, respectively. Compared with several networks, such as two-dimensional CNN, three-dimensional CNN, HybridSN, and CTN, the OA values of the TAN network were improved by 2.59%, 2.45%, 2.94%, and 1.03%, respectively. The results of this study effectively improve the efficiency of the grassland rodent information survey and provide a solid theoretical basis for the investigation and statistics of grassland rodent infestation.

Estimation of fuel load using remote sensing data in Hulunbuir Grassland

Grassland fires are one of the main disturbances in Hulunbuir Grassland and have a great effect on the regional ecological environment and the production and life of herdsmen. The characteristics of fuel load and spatial distribution of grassland in the fire season are the main indices in simulating grassland fire behavior. Based on a field survey of fuel load in Hulunbuir Grassland in different seasons, vegetation indices of optical images (Landsat-8 OLI) and the texture characteristic parameters of radar data (Sentinel-1) were selected as input parameters, and three models, multiple linear regression (MLR), stepwise regression (SR) and random forest (RF), were used to estimate fuel load in the study region. Considering the limitations of optical images in estimating vegetation changes in fire seasons, this study explored a method of estimating vegetation biomass in the growing season and then transforming it to fuel load in fire seasons based on the relationships between biomass and fuel load. The results show that a combination of optical and SAR images is advantageous for estimating grassland fuel load. Of the three estimation models, the accuracy of RF is better than that of SR and better than that of MLR. This study provides a new method for large-scale grassland fuel inversion, and the continuous data of large-scale grassland fuel can improve the simulation accuracy of grassland fire behavior and provide strong support for grassland fire prediction. Furthermore, the policy of national grassland contracts to herdsmen in China has made enormous contributions to decreasing grassland fires.

Phosphorus Fractions in Temperate Grassland Soils and Their Interactions with Agronomic P Tests

Phosphorus (P) is a major nutrient for plant growth and animal health, and its availability in soil is influenced by agronomic management and soil properties. This study examined the distribution of labile and non-labile soil P fractions in grassland and the properties that influence their distribution in temperate grasslands. Eleven fractions were identified as, labile (H2O.P and NaHCO3.P), moderately labile (NaOH.P) and plant non-available (NaOH.P, HCl.P and residual P). The residual P, moderately labile organic (NaOH.Po) and inorganic (NaOH.Pi) fractions comprised 38%, 24% and 15% of the total P (TP), respectively. The residual and organic (Po) fractions were associated with extractable Fe, and the NaOH.Pi fraction was associated with extractable Al. Agronomic reagents (Morgan’s and Mehlich3) targeted specific fractions based on their affinity for different soil elements. Morgan’s P was associated with extractable and residual Ca and in high Ca soils, and overestimated plant-available P by extracting non-labile Ca.P forms (HCl.P). The Mehlich3 P test was associated with NaOH.Pi fraction and showed a preference for Al.P oxides. This study indicates that Mehlich3 reagent, which provides a wider suite of elements such as P, Ca, Al and Fe, will suit as a better indicator for soil P dynamics and improved P advice to farmers. The inclusion of additional soil chemico-physical characteristics would improve agronomic soil testing, benefitting farmers’ advice.

Downscaling of planetary boundaries and sustainability management: A nexus analysis of water, land and major functions at the national-provincial level

This study introduces planetary boundaries as a global sustainability benchmark to expand the evaluation of the major function zoning. We established the relationship between the resource boundaries and China's sustainability management system while calculating the national and provincial indicators of land and water boundaries and major functions. The results showed that the efficiency of land resources was related to the distribution of forest, grassland, and wetland ecosystems; the efficiency of water resources was related to the basin and economic distribution. Linear downscaling methods are less applicable and effective in studies of planetary boundaries. The provincial water and land boundary are not consistent with the top-down decomposition results of planetary boundaries. The spatial mismatch between regional development and population will lead to the overexploitation of national resources and the environment. For basin regions rich in water resources and biome regions rich in ecological resources, the boundary threshold should be increased for major function clustering based on efficiency advantages. For main agricultural production zones and key ecological function zones beyond the water boundary, water allocation should reduce industrial and residential water use, and improve agricultural and ecological water efficiency to ensure food security and ecological safety. For optimization development zones, industrial transfer to the surrounding areas would optimize the industrial water structure and reduce virtual water consumption.

Effects of Soil Properties on the Distribution of Woody Plants in Communally Managed Rangelands in Ngaka Modiri Molema District, North-West Province, South Africa

Soil properties are important drivers of species distribution and community structure in grassland. This study was undertaken to assess the influence of soil properties on woody plant distribution around six selected communally managed rangelands in the District. At each communal rangeland, a total of 25 plots of 20 × 20 m were surveyed to record the density, frequency, and composition of woody species. Soil samples were collected for thirteen soil variables. A Tukey HSD (Tukey’s honestly significant difference) post hoc test was used to compare soil properties and canonical correspondence analysis (CCA) to relate the soil properties to the woody species distribution. The study recorded a total of 17 woody species in 9 families. Fabaceae was the most dominant family, and Senegalia mellifera was the most abundant and frequent encroaching species. Most of the species were native, whereas Prosopis velutina was the only invasive alien species recorded. Senegalia mellifera, P. velutina, and Terminalia sericea were considered the most encroaching in the study sites, with densities exceeding 2000 TE ha-1 (i.e., tree equivalent). CCA results exhibited the strong effect of soil variables on the distribution of woody plant species. CCA ordination analyses showed that K was the most influential soil variable on woody species distributions, followed by Mg, CEC, Na, pH, sand, clay and silt. In terms of woody distribution, the CCA diagram showed similarities between Disaneng, Logageng and Tshidilamolomo. This study provides baseline information on woody species diversity for future management of this ecosystem.

The urgent need to develop a new grassland map in China: based on the consistency and accuracy of ten land cover products.

Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years. Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets (GLC2000, GlobCover, CCI-LC, MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×104 to 332.46×104 km2, with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of east-central Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products, with an overall accuracy (OA) reaching 77.51% and a kappa coefficient of 0.51; CLCD is slightly less accurate than MCD12Q1 (OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its 30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China's grasslands and call for researchers and the government to actively produce a new generation of grassland maps.

Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization

The timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in the normalized vegetation index in the time-series data of different ground objects were compared. February to March, during grassland senescence, was the optimum phenological stage for grassland extraction. The spectral, textural of Sentinel-2, and topographic features of the Shuttle Radar Topography Mission (SRTM) were used for the classification. The features were optimized using the recursive feature elimination (RFE) feature importance selection algorithm. The overall accuracy of the random forest (RF) classification algorithm was 91.2%, the producer’s accuracy of grassland was 96.7%, and the user’s accuracy of grassland was 89.4%, exceeding that of the cart classification (Cart), support vector machine (SVM), and minimum distance classification (MDC). The SWIR1 and elevation were the most important features. The results show that Yunnan has abundant grassland resources, accounting for 18.99% of the land area; most grasslands are located in the northwest at altitudes above 3200 m and in the Yuanjiang River regions. This study provides a new approach for feature optimization and grassland extraction in mountainous areas, as well as essential data for the further investigation, evaluation, protection, and utilization of grassland resources.

How climate, topography, soils, herbivores, and fire control forest-grassland coexistence in the Eurasian forest-steppe.

Recent advances in ecology and biogeography demonstrate the importance of fire and large herbivores - and challenge the primacy of climate - to our understanding of the distribution, stability, and antiquity of forests and grasslands. Among grassland ecologists, particularly those working in savannas of the seasonally dry tropics, an emerging fire-herbivore paradigm is generally accepted to explain grass dominance in climates and on soils that would otherwise permit development of closed-canopy forests. By contrast, adherents of the climate-soil paradigm, particularly foresters working in the humid tropics or temperate latitudes, tend to view fire and herbivores as disturbances, often human-caused, which damage forests and reset succession. Towards integration of these two paradigms, we developed a series of conceptual models to explain the existence of an extensive temperate forest-grassland mosaic that occurs within a 4.7 million km2 belt spanning from central Europe through eastern Asia. The Eurasian forest-steppe is reminiscent of many regions globally where forests and grasslands occur side-by-side with stark boundaries. Our conceptual models illustrate that if mean climate was the only factor, forests should dominate in humid continental regions and grasslands should prevail in semi-arid regions, but that extensive mosaics would not occur. By contrast, conceptual models that also integrate climate variability, soils, topography, herbivores, and fire depict how these factors collectively expand suitable conditions for forests and grasslands, such that grasslands may occur in more humid regions and forests in more arid regions than predicted by mean climate alone. Furthermore, boundaries between forests and grasslands are reinforced by vegetation-fire, vegetation-herbivore, and vegetation-microclimate feedbacks, which limit tree establishment in grasslands and promote tree survival in forests. Such feedbacks suggest that forests and grasslands of the Eurasian forest-steppe are governed by ecological dynamics that are similar to those hypothesised to maintain boundaries between tropical forests and savannas. Unfortunately, the grasslands of the Eurasian forest-steppe are sometimes misinterpreted as deforested or otherwise degraded vegetation. In fact, the grasslands of this region provide valuable ecosystem services, support a high diversity of plants and animals, and offer critical habitat for endangered large herbivores. We suggest that a better understanding of the fundamental ecological controls that permit forest-grassland coexistence could help us prioritise conservation and restoration of the Eurasian forest-steppe for biodiversity, climate adaptation, and pastoral livelihoods. Currently, these goals are being undermined by tree-planting campaigns that view the open grasslands as opportunities for afforestation. Improved understanding of the interactive roles of climate variability, soils, topography, fire, and herbivores will help scientists and policymakers recognise the antiquity of the grasslands of the Eurasian forest-steppe.

DISTRIBUTION OF GRASSLANDS IN A TERRITORIAL ASPECT UNDER THE IMPACT OF NATURAL AND ANTHROPIC FACTORS

Grasslands are spread all over the globe, their distribution being possible by various modern methods. The spatial representation of the grasslands, in correlation with other categories of land use, provides us with an overview of the activities of the geographical area and also represents a first stage in the field research. In this context, the purpose of the research is to analyze the territorial distribution of grasslands through geographic information systems (GIS), based on land use maps and to identify natural and anthropic factors with direct and/or indirect impact in their spatial modeling. The study area is characterized by variable conditions of landform, being partially located in the following physical-geographical units, Vinga plain, Gătaiei plain, Timiș plain, Lipova Hills, The Sacoș-Zăgujeni Hills and Anina Mountains. As representative of the analyzed area, we chose five ATUs, framed in a working transect. In the sub-areas of the analyzed work transect there are variations in the way of organizing the land, respectively in the territorial distribution of the grasslands. Thus, the largest areas of grassland are present in ATU Recaş (6253,83 ha) which has a hilly appearance (with altitudes between 90 – 287 m). Large areas of grasslands are also found in ATU Fârliug (3.163,23 ha) and ATU Pișchia (2255,25 ha) located in different landform conditions. The smallest areas of grasslands are found in ATU Vermeș, respectively 861,63 ha. From the point of view of the territorial distribution, the areas of grasslands shall be grafted on all forms of landform and under all environmental conditions in the territory under consideration. The share of grassland depends on natural factors (soil, climate, landform) but also on the other production activities, specific to each area, which is also found in the distribution of the other land use categories, for each ATU.

Ecological Impact Prediction of Groundwater Change in Phreatic Aquifer under Multi-Mining Conditions

In aeolian sandy grass shoal catchment areas that rely heavily on groundwater, mining-induced geological deformation and aquifer drainage are likely to cause irreversible damage to natural groundwater systems and affect the original circulation of groundwater, thus threatening the ecological environment. This study aimed to predict the impact of groundwater level decline on vegetation growth in the Hailiutu River Basin (HRB), which is a coal-field area. Based on remote-sensing data, the land use/cover change was interpreted and analyzed, and the central areas of greensward land in the basin were determined. Subsequently, the correlation between groundwater depth and grassland distribution was analyzed. Then, the groundwater system under natural conditions was modeled using MODFLOW, and the groundwater flow field in 2029 was predicted by loading the generalized treatment of coal mine drainage water to the model. The change in groundwater depth caused by coal mining and its influence on the grassland were obtained. The results show that coal mining will decrease the groundwater depth, which would induce degradation risks in 4 of the original 34 aggregation centers of greensward land that originally depended on groundwater for growth in HRB because they exceeded the groundwater threshold. The prediction results show that the maximum settlement of groundwater level can reach 5 m in the northern (Yinpanhao), 6 m in the eastern (Dahaize), and 10 m in the southern (Balasu) region of HRB. Attention should be paid to vegetation degradation in areas where groundwater depth exceeds the minimum threshold for plant growth.

Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images

Southwest China has abundant grassland resources, but they are mainly scattered across fragmented mountainous terrain with frequently cloudy and rainy weather, making their accurate identification by remote sensing challenging. Therefore, the goal of this study was to generate prefecture-level city-scale mountainous grassland distribution data to support the development of sustainable grassland husbandry. Here, we proposed a sample selection method and comprehensively utilized multi-source data to obtain the quasi-10 m southwest grassland distribution data. The sample selection method was to first determine the sample selection range based on multi-source land use/cover database, and then to randomly select the samples under the constraint of secondary land use types, multiple factors of terrain and pure pixels. This method can deal with the difficulty in identifying the fragmented grassland distribution caused by steep mountains and hills. In addition, a multispectral time series dataset was constructed based on the fusion of Landsat 8 OLI and Sentinel-2A/B data due to cloudy and rainy weather and was used as one of the input features along with synthetic aperture radar Sentinel-1 time series data and the terrain multi-factor data. Finally, a remote sensing method to accurately identify grassland distribution in southwest China was constructed based on the Google Earth Engine (GEE) platform. Taking Zhaotong City, a prefecture-level city in Yunnan Province, as an example, a thematic map of grassland distribution with an overall accuracy of 88.21% was obtained using the above method. This map has been used by the local government of Zhaotong City in their planning of the development of sustainable grassland husbandry.

On the distribution and productivity of mountain grasslands in the Gran Paradiso National Park, NW Italy: A remote sensing approach

Mountain grazing lands are key constituents of the natural, economical and cultural heritage, but at the same time sensitive to climate and land use change, hence requiring urgent adaptation and management strategies. These must be based on a better understanding of the distribution of mountain pastoral resources across space and time.In this study we model the distribution and the productivity of grassland surfaces in a topographically complex protected area (Gran Paradiso National Park, 710 km2) in north-western Italian Alps. The objective of our work was threefold: a) modelling the distribution of mountain grasslands across the entire park at a 20-meters spatial resolution, b) classify pastoral surfaces according to productivity classes, and c) according to thirteen pastoral categories. We used a random forest approach to combine a massive terrain vegetation survey as ground truth, with remote-sensing-derived, climatic and topographic layers as predictors.Grassland presence/absence was classified with high accuracy (up to 88%) and, compared to the standard Copernicus European Grassland Product, revealed the presence of extensive high altitude grassland areas potentially available for wild herbivores. Grassland productivity was modelled with remarkably high accuracy both according to three broad productivity classes (90% accuracy) and to a more detailed classification into thirteen pastoral categories (83% accuracy). Productivity estimates agree well with satellite-derived leaf area index maps and with area-averaged NDVI seasonal patterns.We conclude that combining tailored field campaigns and high-resolution remote sensing allows for robust prediction of grassland distribution and productivity even in complex terrains. This information can contribute to improve the management of pastoral resources and promote effective adaptation strategies.

Migration of vegetation boundary between alpine steppe and meadow on a century-scale across the Tibetan Plateau

The distribution of alpine vegetation is highly sensitive to climate change, which attracted the attention of climate scientists as well as ecologists. However, the dominant factors of vegetation distribution showed greatly spatiotemporal variation, especially the alpine grasslands on the Tibetan Plateau, which is known as an amplifier of climate warming. In this study, to identify the dominant factors of vegetation distribution, we verified the reliability and accuracy of the classification of alpine steppe and alpine meadow by Random Forest and tried to provide a new reference for the classification schemes. Dataset collected from the field investigations (200 sites) and previous publications(200 sites) were used in this study for model calibration and validation. Then climate models of CMIP6 were used to forecast the underlying transfers and changes of vegetation boundary in the future. Our results revealed that precipitation may be the dominant driver in the alpine grassland distribution, with the highest relative importance of 41.26%. Through different climate scenarios from 2000 to 2100, the vegetation boundary showed a shift from northeast to southwest. Within the SSP5-8.5 scenario it moves from 94.50° to 93.49° and shifted from north to south by 0.34°. Our results demonstrated the significant role of precipitation in the alpine grassland distribution and revealed the migration of the alpine grassland ecosystem under different climate change scenarios. Our work may be useful to deal with global climate change, early warning, and grassland protection, as well as adaptive management of grassland.

Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau.

Grassland is the vegetation type with the widest coverage on the Qinghai-Tibet Plateau. Under the influence of multiple factors, such as global climate change and human activities, grassland is undergoing temporal and spatially different disturbances and changes, and they have a significant impact on the grassland ecosystem of the Qinghai-Tibet Plateau. Therefore, timely and dynamic monitoring of grassland disturbances and distinguishing the reasons for the changes are essential for ecological understanding and management. The purpose of this research is to propose a knowledge-based strategy to realize grassland dynamic distribution mapping and analysis of grassland disturbance changes in the region that are suitable for the Qinghai-Tibet Plateau. The purpose of this study is to propose an analysis algorithm that uses first annual mapping and then establishes temporal disturbance rules, which is applicable to the integrated exploration of disturbance changes in highland-type grasslands. The characteristic indexes of greenness and disturbance indices in the growing period were constructed and integrated with deep neural network learning to dynamically map the grassland for many years. The overall accuracy of grassland mapping was 94.11% and that of Kappa was 0.845. The results show that the area of grassland increased by 11.18% from 2001 to 2017. Then, the grassland disturbance change analysis method is proposed in monitoring the grassland distribution range, and it is found that the area of grassland with significant disturbance change accounts for 10.86% of the total area of the Qinghai-Tibet Plateau, and the disturbance changes are specifically divided into seven types. Among them, the type of degradation after disturbance mainly occurs in Tibet, whereas the main types of vegetation greenness increase in Qinghai and Gansu. At the same time, the study finds that climate change, altitude, and human grazing activities are the main factors affecting grassland disturbance changes in the Qinghai-Tibet Plateau, and there are spatial differences.

Zoniranje travne i šumske vegetacije u Republici Srpskoj prema Copernicus bazi podataka

The paper presents the distribution of grassland and forest vegetation in the Republika Srpska, zoning by natural regions and altitude zones and the changes that occurred in the period 2000-2018. The territory of Republika Srpska belongs to three large natural regions of different physical and geographical characteristics, with specific environmental factors that affect the distribution of vegetation. Altitude significantly affects the occurrence and types of vegetation, so a digital elevation model with a 25 m resolution was used for the analysis of vertical zonality, in order to process data on the distribution of grasslands and forests by altitude zones. Copernicus Grassland and Forest databases were used, for the period 2012-2018. Given climate change and increasing droughts, which indirectly affect the quantity and quality of vegetation, monitoring and managing grasslands and forests will be an important challenge for Republika Srpska. The results of the research show that the grassland according to the Copernicus Grassland database occupies 19.7%, and the forest vegetation according to the Copernicus Forest covers 60.1%, which is almost 80% of the territory of Republika Srpska. Grasslands are most widespread in the area of medium-high and high karst fields of eastern Herzegovina. There are more pastures in the lower altitude zones, while natural grasslands predominate in the higher zones. Forests predominate in the central mountain-valley region. Broad-leaved forests dominate at lower altitudes and coniferous forests at higher altitudes. The European program for monitoring the Earth's surface via satellite remote sensing is a new technology, so the results obtained for the territory of Republika Srpska can be considered a novelty, which is the most significant contribution of this paper.

Horizontal and vertical spatial distribution and loss of grassland in Bosnia and Herzegovina

Two databases related to grassland in Bosnia and Herzegovina (B&H) have been used in this research-the Copernicus Grassland (GRA) and CORINE Land Cover (CLC). The data have been processed by using GIS tools, and the spatial distribution of grass vegetation in relation to three biogeographical and four elevation zones has been determined. By using the Copernicus Water and Wetness (WaW) database, an insight into the coverage of grass vegetation in wetlands has been obtained. In addition, the analysis of changes in databases determined the total amount and location of the largest grassland losses. Based on GRA database, B&H is one of the richest countries, with 22.4% of its territory covered by grassland. According to the CLC database, pastures occupy 6.3% and natural grasslands 5.6% of the territory of B&H. However, grassland is exposed to various anthropogenic and natural processes that affect its distribution, quality, functions, etc. The CLC database has been processed by extracting the grasslands (pastures and natural grasslands) and calculating their spatial coverage and changes which happened during the three six-year periods from 2000 to 2018. The trend of decreasing pasture areas and increasing natural grasslands has been noticed. The pastures are most endangered by the processes of conversion to other types of agricultural land, while natural grassland increased spatial coverage during the period 2012-2018 mostly due to the revitalization and grazing of burned areas in the region of Herzegovina. Grasslands have been particularly targeted for afforestation and cropland conversion at present.

Discrete Population Competition Model of Poisonous Weed Invasion and Spatial Distribution

Poisonous weeds invade ecosystems and compete with edible forage grassland, changing the population spatial distribution. In this paper, a discrete population competition model for the interaction between poisonous weeds and edible forage grass is established, and a diffusion coefficient to describe the invasion and diffusion effect of poisonous weeds is introduced. By using cellular automata methods, the influence of invasive diffusion of poisonous weeds on the spatial distribution of poisonous weed and edible forage populations is simulated. These findings indicated that the invasion of poisonous weeds affected the ecological distribution of grassland, and that the spatial distribution of poisonous weeds also changed from initial uniform distribution to aggregated distribution.

Population genomics indicates micro‐refuges and riverine barriers for a southern South American grassland nightshade

AbstractAimPleistocene climate oscillations influenced the species distribution and genetic diversity in grasslands, and such climatic changes promoted expansion and contraction cycles, leading to fragmentation and isolation of evolutionary lineages in refuges. This study aimed to infer the evolutionary processes that most influenced the genetic diversity of a South American nightshade Petunia inhabiting subtropical grasslands under the Pleistocene influence.LocationPampa and Chaco.TaxonThe widely distributed herb Petunia axillaris subsp. parodii (Solanaceae).MethodsWe obtained high‐density genome coverage throughout the geographical distribution of Petunia axillaris subsp. parodii. We estimated the genetic diversity and structure to evaluate population differentiation and applied a model‐based demographic analysis to investigate the scenarios that could have influenced the species' evolutionary history. We evaluated the association between neutral and outlier polymorphisms with environmental variables to distinguish the influence of the geographical distance and environmental differences between populations.ResultsThere are three evolutionary lineages in P. axillaris subsp. parodii. The origin and differentiation of these lineages were related to the Pleistocene refuges and rivers acting as barriers to gene flow. We also identified 496 outlier loci related to adaptation to environmental conditions.Main conclusionThe Pleistocene climate changes drove lineage diversification isolated in micro‐refugia. Rivers and changes in their courses may have also acted on the population divergence, serving as barriers to gene flow or even as corridors for species range expansion. These findings contribute to the knowledge of the evolutionary processes that influenced the lineage diversification of South American subtropical grassland species.