Topography Type Research Articles

The Impact of Cultivated Land Fragmentation on Farmers’ Ecological Efficiency of Cultivated Land Use Based on the Moderating and Mediating Effects of the Cultivated Land Management Scale

To date, scholars have increasingly focused on the reduction in crop yields caused by cultivated land fragmentation, yet its effects on the ecological efficiency of cultivated land use are often overlooked. This oversight leads to land resource waste and environmental pollution. It is essential to explore this problem to achieve moderate-scale farming operations and promote the green transformation of agricultural land. This study theoretically analyzed the mechanisms by which cultivated land fragmentation and management scales influence the ecological efficiency of cultivated land use. Based on 2023 household data from Changde and Shaoyang, China, empirical tests were conducted using the stochastic frontier analysis method, Tobit model, and structural equation model. The research results indicate that: (1) The mean ecological efficiency of cultivated land use among the total sample households was 0.822, and the eco-efficiency in the plains was slightly lower than that in the hilly areas. (2) The scale of cultivated land management played a moderating role in the impact of cultivated land fragmentation on ecological efficiency, with differences observed between topographical types. The scale of management can offset part of the negative impact of cultivated land fragmentation on the ecological efficiency of cultivated land use. (3) Regarding the impact of cultivated land fragmentation on the ecological efficiency of cultivated land use, cultivated land management scale changes play a complete mediating role. These findings help provide policy implications to improve the ecological efficiency of cultivated land use. Policy support should be strengthened by promoting moderate-scale cultivated land operations, enhancing the comprehensive remediation of cultivated land fragmentation, and developing skilled farmers for long-term environmental sustainability.

Phylogenetic Effect on Tree Radial Growth Depends on Drought and Tree Sizes

AbstractTree radial growth is one of the most direct measures of tree performance and is also sensitive to climate. Growth performance is the consequence of the interplay between ecological and evolutionary processes. However, the effect of the evolutionary relatedness among species (i.e., phylogeny) on tree radial growth, especially under stressful conditions, remains largely unknown. Furthermore, there is still no ecological evidence for the influence of phylogeny on tree growth across different tree attributes (i.e., tree diameter variation and tree canopy height) and topographic habitat types. We used Blomberg's K to quantify the tree growth phylogenetic signal (TGPS) using two long‐term dendrometer data sets: one a continuous census of 225 tree species at 3‐month intervals in a tropical forest in southwest China from 2009 to 2017; the other, 12 tree species measured at 6‐month intervals in a temperate forest in Washington State, USA from 2013 to 2019. We found that TGPS values were higher in the temperate forest than in the tropical forest. Precipitation, tree diameter, canopy strata, and habitat types all influenced TGPS values. TGPS values were significantly (p < 0.05) and negatively related to precipitation in Xishuangbanna, and the three of four tree diameter classes in the temperate forest, respectively. Stressful growing conditions arose from either based on low precipitation or among large‐diameter trees competing with each other in the upper canopy led to phylogenetic conservatism in trees' radial growth performance. We conclude that phylogeny is pivotal to understanding the growth response differences among species and their responses to climate variability.

Rural land consolidation as an instrument for decreasing farmers' dependence on ecosystem services: Heterogeneity analysis based on consolidation modes and topographic types

Rural land consolidation (RLC) is one of the largest organized activities affecting land use and terrestrial ecosystems, and its effect on ecosystems' spatial patterns and service functions has been closely scrutinized. Previous studies have primarily focused on the response of ecosystem services (ES), but associated changes in farmers' dependence on ES have not yet been adequately explored or completely understood. This study employs a sustainable livelihood framework to identify the mechanism through which RLC affects farmers' dependence on ES, and constructs an index system of dependence on ES (IDES) to quantify farmers' dependence on ES using questionnaire surveys conducted on 949 rural households in Hubei Province, central China. A quasi-experimental difference-in-differences model is subsequently used to investigate the impact of government-dominant mode and new agricultural business entity (NABE)-dominant RLC modes on farmers' dependence on ES across plains and mountainous areas. Results indicated that both RLC modes could decrease farmers' dependence on ES. The reduction effect of the NABE-dominant mode was more significant than that of the government-dominant mode in plains areas. While no significant difference between the two modes is found in mountainous areas. This study broaden research perspectives on RLC effectiveness and aligns with worldwide concerns such as environmental protection and welfare improvement. In addition, the study examine China's experience to offer valuable insight for other developing countries when formulating differentiated RLC policies.

Comparing machine learning algorithms to predict vegetation fire detections in Pakistan

Vegetation fires have major impacts on the ecosystem and present a significant threat to human life. Vegetation fires consists of forest fires, cropland fires, and other vegetation fires in this study. Currently, there is a limited amount of research on the long-term prediction of vegetation fires in Pakistan. The exact effect of every factor on the frequency of vegetation fires remains unclear when using standard analysis. This research utilized the high proficiency of machine learning algorithms to combine data from several sources, including the MODIS Global Fire Atlas dataset, topographic, climatic conditions, and different vegetation types acquired between 2001 and 2022. We tested many algorithms and ultimately chose four models for formal data processing. Their selection was based on their performance metrics, such as accuracy, computational efficiency, and preliminary test results. The model’s logistic regression, a random forest, a support vector machine, and an eXtreme Gradient Boosting were used to identify and select the nine key factors of forest and cropland fires and, in the case of other vegetation, seven key factors that cause a fire in Pakistan. The findings indicated that the vegetation fire prediction models achieved prediction accuracies ranging from 78.7 to 87.5% for forest fires, 70.4 to 84.0% for cropland fires, and 66.6 to 83.1% for other vegetation. Additionally, the area under the curve (AUC) values ranged from 83.6 to 93.4% in forest fires, 72.6 to 90.6% in cropland fires, and 74.2 to 90.7% in other vegetation. The random forest model had the highest accuracy rate of 87.5% in forest fires, 84.0% in cropland fires, and 83.1% in other vegetation and also the highest AUC value of 93.4% in forest fires, 90.6% in cropland fires, and 90.7% in other vegetation, proving to be the most optimal performance model. The models provided predictive insights into specific conditions and regional susceptibilities to fire occurrences, adding significant value beyond the initial MODIS detection data. The maps generated to analyze Pakistan’s vegetation fire risk showed the geographical distribution of areas with high, moderate, and low vegetation fire risks, highlighting predictive risk assessments rather than historical fire detections.

Potential Role and Mechanism of Mulberry Extract in Immune Modulation: Focus on Chemical Compositions, Mechanistic Insights, and Extraction Techniques.

Mulberry is a rapidly growing plant that thrives in diverse climatic, topographical, and soil types, spanning temperature and temperate countries. Mulberry plants are valued as functional foods for their abundant chemical composition, serving as a significant reservoir of bioactive compounds like proteins, polysaccharides, phenolics, and flavonoids. Moreover, these compounds displayed potent antioxidant activity by scavenging free radicals, inhibiting reactive oxygen species generation, and restoring elevated nitric oxide production induced by LPS stimulation through the downregulation of inducible NO synthase expression. Active components like oxyresveratrol found in Morus demonstrated anti-inflammatory effects by inhibiting leukocyte migration through the MEK/ERK signaling pathway. Gallic and chlorogenic acids in mulberry leaves (ML) powder-modulated TNF, IL-6, and IRS1 proteins, improving various inflammatory conditions by immune system modulation. As we delve deeper into understanding its anti-inflammatory potential and how it works therapeutically, it is crucial to refine the extraction process to enhance the effectiveness of its bioactive elements. Recent advancements in extraction techniques, such as solid-liquid extraction, pressurized liquid extraction, superficial fluid extraction, microwave-assisted extraction, and ultrasonic-assisted extraction, are being explored. Among the extraction methods tested, including Soxhlet extraction, maceration, and ultrasound-assisted extraction (UAE), UAE demonstrated superior efficiency in extracting bioactive compounds from mulberry leaves. Overall, this comprehensive review sheds light on the potential of mulberry as a natural immunomodulatory agent and provides insights into its mechanisms of action for future research and therapeutic applications.

Revealing forest structural "fingerprints": An integration of LiDAR and deep learning uncovers topographical influences on Central Amazon forests

Amazon forests are characterized by rich structural diversity. However, the influence of factors such as topography, soil attributes, and external disturbances on structural variability is not always well characterized, and traditional structural metrics may be inadequate to capture this type of complexity. While LiDAR offers expanded structural insights, traditional parameters used in LiDAR analysis, such as mean or maximum canopy height, are not always well directly linked to environmental variables like topography. Emerging approaches merge LiDAR with machine learning to uncover deeper structural complexities. However, work to date may fail to fully utilize the potential of fine-scale LiDAR information. Here we introduce a novel approach, leveraging 2D point cloud images derived from a profiling canopy LiDAR (PCL). The technique targets intricate details within LiDAR point clouds by using deep learning algorithms. With a dataset from the Central Amazon comprising 18 multitemporal transects of 450 m in length, our objective was to detect structural "fingerprints" of varied topographical types along a hillslope, comprising: Riparian, White-sand, and Plateau, and to detect any gradient of structural shifts based on terrain variations here represented by the height above the nearest drainage (HAND). The dataset was trained and tested using a leave-one-group-out approach (LOGO) in which, for each iteration, a complete 450 m multitemporal transect was excluded from training and tested after each iteration. The fast.ai platform and a ResNet-34 architecture, coupled with transfer learning, were used to perform a classification to distinguish between three topographical types. Furthermore, a hybrid model combining a Convolutional Autoencoder, and Partial Least Square (PLS) regression was designed to detect forest structural gradient correlations with HAND variation. Cross-validation achieved a promising high weighted F1 score of 0.83 to classify forests based on the topographical types. Additionally, a combined Convolutional Autoencoder and PLS regression revealed a strong correlation (R2 = 0.76) between actual and predicted HAND. Innovatively combining deep learning with ground-based PCL LiDAR, our study revealed unique Amazon Forest structures connected to topographic variation. Our findings underscore the transformative potential of such integrative approaches for investigating forest dynamics and promise a powerful new tool for understanding climate-related forest structure change.

WATER EROSION RISK AND FOOD SECURITY: A CASE STUDY

Water erosion reduces soil fertility and productivity, negatively impacting crop yields. The transported material not only affects water quality but also significantly shortens the lifespan of water infrastructure. The Catamayo-Chira transboundary basin, spanning an area of 17,199 km2, serves as the primary water source for agriculture in the Ecuador-Peru border. Ongoing sediment production within the basin has drastically decreased the volume of the Poechos reservoir, northern Peru's largest irrigation system, raising concerns about its potential complete filling in the near future. To identify vulnerable areas to water erosion, the Revised Universal Soil Loss Equation (RUSLE) was utilized. By integrating meteorological, topographic, land use, and soil type data, a GIS-based model was developed to map the spatial variation of soil loss. This approach facilitated the identification of critical areas necessitating soil conservation actions. Within the basin, approximately 65% of the land exhibits high to critical erosion due to a combination of intense rainfall, steep slopes, limited vegetation cover, and erodible soils. Soil losses of around 200 tons per year have been observed. In the absence of effective soil loss management practices, sediment production, and transportation continue to pose significant challenges for the expansive agricultural lands in northern Peru, thereby jeopardizing regional food security.

Airborne microbes in five important regions of Chinese traditional distilled liquor (Baijiu) brewing: regional and seasonal variations.

Baijiu is one of the six primary distilled spirits in the world. It is produced through the solid-state fermentation of grains in the open environment, so high-quality Baijiu brewing largely depends on terrior. Environmental microbes are one of the most important factors affecting the quality, quantity, and flavors of Baijiu. As atmosphere is a pool and transport pathway for microbes from the ambient environment to Baijiu brewing ecosystems, we explored the functional microbes of Baijiu brewing in five important regions. The regions fell into two topographical types, namely, plain and river-valley. In total, 41 functional microbes were identified rich (relative abundance >0.1%) in at least one of the regions, such as the fungi of Aspergillus, Candida, Cladosporium, Debaryomyces, Penicillium, Pichia, Rhizopus, Saccharomyces, and Wickerhamomyces and the bacteria of Acetobacter, Bacillus, Clostridium, Enterobacter, Lactobacillus, Methanosarcina, Methanobacterium, Methanobrevibacter, and Pseudomonas. However, some functional bacteria (e.g., Clostridia, Gluconacetobacter, and Weissella) and fungi (e.g., Dekkera, Eurotium, Issatchenkia, Mucor, and Phoma) were not rich or were not detected in the atmosphere. Airborne microbiomes and the Phylogenetic Diversity (PD) index were significantly different between the main brewing season (winter) and the summer break in each region, except for the fungi in one region. In winter, airborne microbiomes were significantly different among almost all the regions. The relative abundance of bacterial fermentation function in each region increased from summer to winter. The relative abundances of fungal yeast function were higher in winter for the plain regions but were higher in summer for the river-valley regions. In sum, our results suggested that: (1) atmosphere was one but not the sole important source of functional microbes for Baijiu brewing and (2) microbiomes in different regions might be quite different but they could share some major functions related to Baijiu brewing.

Analysis of Clothes of Persons With Disability with Palsy Cerebral for Adolescents Based on Topography

This study aims to determine needs about form of clothing for disabled people with cerebral palsy for young women. The research was conducted at Development Foundation for Children with Disabilities, Jakarta. 5 respondents with disabilities were grouped into 2 group based on topography or the number of disabilities in their bodies. The research method used is qualitative with a case study research approach. The result of this study indicates the need for dress form according to the conditions of type of disability based on the topography, namely diplegia and triplegia. Respondents still need help from others in dressing; experiencing difficulties in dressing; comfort and like the position of the front opening; can use types of openings such as zippers, buttons, and velcro; having difficulty with eye and hand coordination so it is more comfortable to use buttons that are not too small such as medium size buttons with less buttons. Respondents use pants for their daily clothes; baggy pants size maxi, midi and angkel size. The type of materials used are materials that fall, not too stiff and cool, colors suitable for adolescents can be dark but not black so they are not easily dirty, comfortable and prefer looser clothes; comfortable wearing more closed clothes; comfortable using thin cotton. On average, respondents having no problem in choosing colors on clothes, but rather on models and openings that need special creation based on topographic types.

High-Accuracy Mapping of Soil Parent Material Types in Hilly Areas at the County Scale Using Machine Learning Algorithms

Conventional maps of soil parent material (SPM) types obtained by field survey and manual mapping or predictions from other map data have limited accuracy. Digital soil mapping of SPM types necessitates accurate acquisition of SPM distribution information, which is still a challenge in hilly areas. This study developed a high-accuracy method for SPM identification in hilly areas at the county scale. Based on geographic information system technology, seven feature variables were extracted from the geological map, geomorphic map, digital elevation model, and remote sensing image data of Shanggao County, Jiangxi Province, China. Different feature combination schemes were designed to develop SPM identification models based on random forest (RF), support vector machine (SVM), and maximum likelihood classification (MLC) algorithms. The best SPM identification results were obtained from the RF algorithm using the combination of geological type, geomorphic type, elevation, and slope. Confusion matrices were constructed based on a field survey of 586 validation samples, and the results were evaluated in terms of overall accuracy, precision, recall, F1 score, and Kappa coefficient. The overall accuracy and Kappa coefficient of the results from the optimal RF model were 83.11% and 0.79, respectively, which were 26.11% and 0.31 higher than those of the conventional map, respectively. Its precision and recall for various SPM types were greater than 75%. A comprehensive comparison of the accuracy, uncertainty, and plotting performance of the SPM recognition results reveals that the RF algorithm outperforms the SVM algorithm and the MLC algorithm. Geological type was the largest contributor to SPM identification, followed by geomorphic type, elevation, and slope. The importance of different feature variables varied for distinct SPM types. The accuracy of SPM identification was not improved by selecting more feature variables, such as land use type, normalised difference vegetation index, and topographic wetness index. This study demonstrates the feasibility of high-accuracy county-level SPM mapping in hilly areas based on the RF algorithm using geological type, geomorphic type, elevation, and slope as feature variables. As hilly areas have typical topographic features and SPM types, the proposed method of SPM mapping can be useful for application in other similar areas. There are a few limitations in this study with regard to data quality and resolution, feature variable selection, classification algorithm generalisation, and study area representativeness, which may affect the outcomes and need to be solved.

Prevalence and related factors of epilepsy in children and adolescents with cerebral palsy: a systematic review and meta-analysis.

To study the worldwide prevalence and associated factors of epilepsy in children and adolescents with Cerebral Palsy (CP) and to analyze the differences between various subgroups. We identified all potential studies on the prevalence of epilepsy in children and adolescents with CP from PubMed, Web of Science, and Embase. The search time was from the establishment of the database to November 2022. Randomized effects meta-analysis models were used to calculate the prevalence of epilepsy in CP. Subgroup analysis and meta-regression were utilized to further explore heterogeneity between articles and prevalence disparities between subgroups. The funnel plot and Egger's test were used to investigate potential publication bias. Seventy-two articles, comprising 53,969 children and adolescents with CP, were included in this study. The results indicated a total epilepsy prevalence of 38.0% (95% CI: 34.8%-41.2%) in CP. The prevalence of epilepsy was 46.4% (95% CI: 41.4%-51.5%) in clinical sample-based studies and 31.6% (95% CI: 28.7%-34.5%) in population-based studies. Meta-regression demonstrated that the sample source, neonatal seizure, family history of epilepsy, EEG or cranial imaging abnormalities, intellectual/cognitive impairment, and topographical types of CP were heterogeneous contributors to the epilepsy prevalence in CP. Approximately one-third of children and adolescents with CP have epilepsy, and the sample source can significantly impact the total prevalence of epilepsy. Neonatal seizures, family history of epilepsy, EEG abnormalities, cranial imaging abnormalities, severe intellectual disability, and quadriplegia may be contributing factors to epilepsy comorbid in CP. Further study is required to verify the strength of these associations with epilepsy. This study aids in identifying the clinical characteristics of young people with CP at risk of developing epilepsy, which may assist clinicians in the early prevention and diagnosis of epilepsy within this population.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=367766, identifier CRD42022367766.

Factors affecting the transferability of bioindicators based on stream fish assemblages

The development of multimetric indices (MMIs) to measure the biotic condition of aquatic habitats is based on metrics derived from biological assemblages. Considering fish assemblages, the inconsistencies in metrics responses outside of the places where they were developed limit MMI transferability and applicability to other locations, requiring local calibration. The factors behind the low transferability of these MMIs are still poorly understood. We investigated how environmental dissimilarity and spatial distance influence the transferability of metrics generated from local stream fish assemblages to other regions. We also tested whether functional and taxonomic metrics respond differently to the spatial distance. We used data from 239 fish assemblages from streams distributed across a Brazilian, the upper Parana basin and characterized each site according to the level of anthropogenic disturbance at the landscape scale using an Anthropogenic Pressure Index (API). We divided the upper Parana basin into sub-basins and used two of them to create template response models of the metrics in relation to the API. We used these response models to predict the responses outside the template sub-basins. Our response variable representing a metric of transferability was the absolute difference between metric's predicted and observed value for each site (prediction error). We thus modeled the prediction error in relation to the predictor variables that were i) the environmental dissimilarity between each site with the average of the sites from template sub-basins (climatic, topographic and soil type variables) and ii) the spatial distance (overland and watercourse distance) between each site and the center of the template sub-basin. We found that errors in metric predictions were associated with both environmental dissimilarity and spatial distance. Furthermore, functional and taxonomic metrics responded equally to spatial distance. These results indicate the need for local calibration of metrics when developing MMIs, especially if the protocols already available come from distant and environmentally dissimilar places.

Study of snow cover/depth evolution characteristics in Tianshan region of China based on geographical partition

Based on the digital elevation data, snow depth and snow cover remote sensing data, this paper divides six snow evolution areas and geographical partitions, extracts the geographical partitions of each evolution area and obtains the geographical characteristics of the evolution area for analysis. The results show that: (1) From 2003 to 2017, the average snow area decreased at a rate of − 0.004, and the average snow depth increased at a rate of 0.03. (2) The snow in the middle altitude hill with shady gentle slope area is the most obvious in the seasonal evolution, and the percentage of this region in the seasonal snow evolution area is 5.46%, the snow depth in the middle altitude hill with sunny and gentle slopes area increased and decreased significantly in the past 15 years, and the percentage of this region in the SD significant changes evolution area was 6.32%. The snow in the low relief middle altitude mountain with shady and moderate slope area not only shows obvious seasonal evolution, but also increases and decreases significantly in snow depth. And the percentage of this region in the seasonal snow significant evolution area is 5.82%. (3) The geographical partitions with the largest area in all evolution areas is the middle altitude hill with sunny and gentle slopes area (4.75%). (4) The geographical partition with the largest variation of snow depth in Tianshan region is the low relief middle altitude mountain with shady and moderate slope area (12.02 cm). (5) The snow accumulation and melting are obvious in the range of 1000–3500 m above altitude, different geomorphology types lead to obvious differences in snow characteristics. The snow melting is most obvious in the gentle slope area of the low topographic relief geomorphology types, and the snow accumulation is most obvious in the steep slope area of the middle relief geomorphology types.

КОНЦЕНТРАЦИЯ ЦИНКА, МАРГАНЦА, МЕДИ В ПОКРОВНОМ ВОЛОСЕ КРУПНОГО РОГАТОГО СКОТА В ЗАВИСИМОСТИ ОТ УСЛОВИЙ СОДЕРЖАНИЯ, СЕЗОННОСТИ И ТОПОГРАФИЧЕСКОГО УЧАСТКА ТЕЛА

The article presents studies on the concentration of trace elements in the coat hair of cattle. They reflect the amount of copper, manganese and zinc in the hair coat of cattle under the influence of seasonality, type of maintenance, topographical area of the animal's body. Based on the results, it can be concluded that, in contrast to the factor of the topographic site, the seasonality and type of maintenance possess a pronounced impact on the concentrations of the studied trace elements in the hair coat. And the variability within the studied groups emphasizes the individuality of the accumulation of trace elements by every individual animal. This reflects the multifunctionality of the hair coat, which consists not only in performing the thermoregulatory function, but also in reflecting the mineral status of the animal's body.

Stereotactic radiosurgery as the initial management option for small-volume hypothalamic hamartomas with intractable epilepsy: a 35-year institutional experience and systematic review.

Young patients with hypothalamic hamartomas (HHs) often present with intractable epilepsy. Currently there are no established management guidelines for HH. The authors retrospectively reviewed their single-institution experience to delineate the role of stereotactic radiosurgery (SRS). Seven patients with HHs (4 females; median age 13.7 years, range 2.5-25 years) with no prior resection underwent SRS between 1987 and 2022. The clinical history, epilepsy profile, radiographic findings, and neurological outcomes were characterized. HH topographical types were classified according to the Régis classification. Outcome measures included Engel seizure classification, HH response, and the need for additional surgical interventions. All patients had Engel class IV epilepsy. A Leksell Gamma Knife was used to deliver a median margin dose of 18 Gy (range 16-20 Gy) to a median hamartoma volume of 0.37 cm3 (range 0.20-0.89 cm3). Seizure reduction was confirmed in 6 patients, and 2 patients had regression of their hamartoma. Two patients underwent resection and/or laser interstitial thermal therapy after SRS. At follow-up, 1 patient was seizure free, 4 patients achieved Engel class II, 1 patient had Engel class III, and 1 patient had Engel class IV seizure outcomes. SRS as the initial management option for HH was associated with a low risk of adverse effects. In this institutional series reviewing small-volume HHs treated with SRS, no adverse radiation effect was detected, and the majority of patients experienced seizure reduction. SRS should be considered as the first-line treatment for seizure control in patients with small-volume HHs.

Studying of settler network of the Ternopil area, by realization of typification of settlements

A brief general analysis of research and publications of scientists who worked in the field of typification of settlements in various directions was carried out. Approaches to the classification of settlements by population, their functional and genetic features and status in different historical periods are considered. Most researchers, in particular, attach great importance to the analysis of the historical and geographical aspects of the emergence of settlements, and as a result, the formation of settlement networks. Emphasis is placed on the importance of natural conditions, in particular the relief and density of the river network, which directly had a significant impact on the choice of places for the formation of the settlement network. It is observed that there is a direct connection between the topographic type and the planning structure of settlements. An analysis of processed and analyzed data was carried out, in particular on the population of the Ternopil region for the period from 2003 to 2022. The settlements of the Ternopil region by population were analyzed and systematized, and the typification of villages, cities and towns by population was carried out, for the region as a whole and for each of the districts separately. A clearly expressed tendency to reduce the number of the region's population during the period under consideration was noted, and the predominance of the share of rural residents in the overall structure of the region's population was also revealed. It was found that more than 42% of the settlements (the largest group) are settlements with a population of 101 to 500 inhabitants. Typification of settlements by topographical types is carried out, in the section of the Ternopil Oblast and each of its districts separately. Taking into account the peculiarities of the relief and the presence of a dense network of small rivers, on the banks of which the vast majority of settlements in the Ternopil region are usually located, the main topographic types of settlements were distinguished: watershed, stream-river, and valley-river, which is the most numerous, is divided into subtypes. Settlements of the Ternopil region are grouped and separated according to the main planning forms, namely: simple linear, complex linear, circular, random-group, quarter and combined. The impact of topographic types on the formation of the planning structure of settlements is analyzed. Settlements were typified according to their planning structure, and the main features and regularities characteristic of different regions of the region were identified. The most common planning forms of settlements in the region were branched, lying to complex linear forms and linear-multi-row, most of such settlements are located on the banks of rivers with branching streets from lower to upper terraces. For a more complete understanding of the peculiarities of the formation of the settlement network of the Ternopil region, it is considered appropriate to trace the historical aspects and periods of their occurrence, and the functions assigned to a specific individual settlement from the moment of its foundation. Considering the natural features of the region, the largest share among the settlements of the region is naturally dominated by rural settlements specializing in agriculture, it is also worth highlighting settlements that performed defense, trade and administrative functions, most of which have retained their status as cities to this day. As a result of the last classification, clear patterns of combination and interweaving of the historical and geographical aspects of the placement of settlements with the natural features of the area and their genetic and functional type were revealed. Keywords: placing of population, settlement, typification, topographical types of settlements, plan forms of settlements, genetically-functional types of settlements.

Agents Affecting the Plant Functional Traits in National Soil and Water Conservation Demonstration Park (China).

Plant functional traits (PFTs) can reflect the response of plants to environment, objectively expressing the adaptability of plants to the external environment. In previous studies, various relationships between various abiotic factors and PFTs have been reported. However, how these factors work together to influence PFTs is not clear. This study attempted to quantify the effects of topographic conditions, soil factors and vegetation structure on PFTs. Four categories of variables were represented using 29 variables collected from 171 herb plots of 57 sites (from different topographic and various herb types) in Xindian SWDP. The partial least squares structural equation modeling showed that the topographic conditions and soil properties also have a direct effect on plant functional traits. Among the topographic conditions, slope (SLO) has the biggest weight of 0.629, indicating that SLO contributed the most to plant functional traits and vegetation structure. Among soil properties, maximum water capacity (MWC) contributes the most and is followed by soil water content (SWC), weighted at 0.588 and 0.416, respectively. In a word, the research provides new points into the quantification of the correlation between different drivers that may be important for understanding the mechanisms of resource utilization, competition and adaptation to the environment during plant recovery.

Abiotic environmental factors drive the diversity, compositional dynamics and habitat preference of ectomycorrhizal fungi in Pannonian forest types.

Ectomycorrhizal (ECM) fungi are among the most diverse and dominant fungal groups in temperate forests and are crucial for ecosystem functioning of forests and their resilience toward disturbance. We carried out DNA metabarcoding of ECM fungi from soil samples taken at 62 sites in the Bükk Mountains in northern Hungary. The selected sampling sites represent the characteristic Pannonian forest types distributed along elevation (i.e., temperature), pH and slope aspect gradients. We compared richness and community composition of ECM fungi among forest types and explored relationships among environmental variables and ECM fungal alpha and beta diversity. The DNA sequence data generated in this study indicated strong correlations between fungal community composition and environmental variables, particularly with pH and soil moisture, with many ECM fungi showing preference for specific zonal, topographic or edaphic forest types. Several ECM fungal genera showed significant differences in richness among forest types and exhibited strong compositional differences mostly driven by differences in environmental factors. Despite the relatively high proportions of compositional variance explained by the tested environmental variables, a large proportion of the compositional variance remained unexplained, indicating that both niche (environmental filtering) and neutral (stochastic) processes shape ECM fungal community composition at landscape level. Our work provides unprecedented insights into the diversity, landscape-level distribution, and habitat preferences of ECM fungi in the Pannonian forests of Northern Hungary.

Experimental study and a physical model on the geomorphic response mechanisms of meandering rivers under progressive sediment reduction

As influenced by human activities, including high dam construction, soil and water conservation, and sand mining in the upper reaches of rivers, the amount of sediment entering the lower reaches of the river decreases significantly. Human activities disturb the natural balance between river evolution and water as well as sediment movement and induce new characteristics of river evolution. After the construction and operation of the Danjiangkou Reservoir and the Three Gorges Reservoir of the Yangtze River (China), different degrees of bend bypassing and shoal cutting in the bends downstream of the dams are observed. The reason for this interrelation has not been explained sufficiently. Based on the analysis of prototype observation data and physical model experiments, we have investigated the evolution mechanism of different topographic types of meandering rivers under the condition of decreasing sediment inflow. The data generated by our model experiment document that the sediment concentration of the convex bank flow with a large degree of curvature of the bend is lower than that of the concave bank flow for a reduction of the sediment concentration from 100% to 0%. Moreover, the convex bank of the bend gradually collapses and retreats, whereas the main channel of the river gradually swings, with a swing range of 0–800 m, toward the convex bank. This phenomenon becomes more significant with progressive reduction of the sediment inflow. Combined with theoretical analysis, we interpret that the drastic reduction in the amount of sand that has destroyed the relative natural balance of sediment transport in the riverbanks and river channels in the meandering river section is the main reason for the occurrence of bypassing and shoal cutting in the meandering river section of plain alluvial rivers. Especially during the mid-to high-water period, the capacity of sediment transport in the mainstream belt exceeds the sediment concentration in the water body significantly and is coupled with the change of the runoff process caused by the regulation of the reservoir operation. As a consequence, the river beach is constantly scouring and developing. The evolution is generally characterized by bend bypassing and shoal cutting. The results of our study can be transposed to other alluvial plain rivers and may constitute valuable reference data.

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold.

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.