Slope Degree Research Articles

Relationship Between Source-Sink Landscape Pattern and Antibiotics in Surface Water in Peri-urban Watershed

Quantitively identifying the effect of land use patterns on antibiotics in surface water has significance in maintaining water quality and protecting residents' health in urban and rural regions. In this study, a typical peri-urban watershed, located in the Yangtze River Delta, was selected as the study area. Based on surface water sampling, laboratory analysis, and source-sink landscape model (SSLM) analysis, the component and distribution characteristics of antibiotics in surface water in different sub-watersheds were analyzed. The effects of source and sink landscape patterns on antibiotic concentrations in surface water were identified. The results of this study showed substantial differences in types and concentrations of antibiotics in surface water in different sub-watersheds. The total concentrations of antibiotics in surface water ranged from 1.12 ng·L-1 to 53.74 ng·L-1. From upstream to downstream, the area of "source" landscape increased, and the area of "sink" landscape decreased based on landscape pattern analysis. The results of non-metric multidimensional scaling (NMDS) showed that sub-watersheds with similar "source-sink" landscape patterns were detected as having similar antibiotics types and concentrations in surface water. Land use composition, distance, elevation, and slope degree had substantial impacts on antibiotic concentrations in surface water. The results of this study also found that location-weighted landscape index (LWLI) was positive correlated with antibiotics concentrations in surface water based on correlation analysis and redundancy analysis. The sub-watersheds with high LWLI values usually had relatively higher antibiotic concentrations in surface water. This study indicated that optimization of "source" and "sink" landscapes at the watershed scale can decrease antibiotic contamination in surface water. Furthermore, SSLM is an effective tool in landscape optimization at the watershed scale.

Vegetation characteristics in The Soil Geomorphology Perspective

This research lies in the hamlet of the village wells are Klompangan, Ajung, Jember. Research carried out aims to analyze the coordinates of the location, the degree of slope (elevation), soil pH, soil texture, soil moisture, the availability of organic materials, and the color of soil on land relate with tobacco and soil geomorphology aspect. Measurement and research carried out on 3 May 2019 by using descriptive method. Location of site selection has taken 500 meter minimum distance of each point by taking a 5 point location. With research and measurement of physical and chemical aspects of the soil in tobacco fields. From the research and measurement in mind that the average pH in the region ranges from 5.9, with the texture of sandy loam rara average, at an average altitude of 84mdpl, average humidity 9.2. Moreover, this study shows that there is a relationship between tertiary volcanic landforms and tobacco growth as an example of vegetation type.

Towards an Assessment of the Ephemeral Gully Erosion Potential in Greece Using Google Earth

Gully erosion may cause considerable soil losses and produce large volumes of sediment. The aim of this study was to perform a preliminary assessment on the presence of ephemeral gullies in Greece by sampling representative cultivated fields in 100 sites randomly distributed throughout the country. The almost 30-ha sampling surfaces were examined with visual interpretation of multi-temporal imagery from the online Google Earth for the period 2002–2019. In parallel, rill and sheet erosion signs, land uses, and presence of terraces and other anti-erosion features, were recorded within every sample. One hundred fifty-three ephemeral gullies were identified in total, inside 22 examined agricultural surfaces. The mean length of the gullies was 55.6 m, with an average slope degree of 9.7%. Vineyards showed the largest proportion of gullies followed by olive groves and arable land, while pastures exhibited limited presence of gullies. Spatial clusters of high gully severity were observed in the north and east of the country. In 77% of the surfaces with gullies, there were no terraces, although most of these surfaces were situated in slopes higher than 8%. It was the first time to use visual interpretation with Google Earth image time-series on a country scale producing a gully erosion inventory. Soil conservation practices such as contour farming and terraces could mitigate the risk of gully erosion in agricultural areas.

Forest stand susceptibility mapping during harvesting using logistic regression and boosted regression tree machine learning models

Economic and environmental consequences of harvesting operations are important in forestry because of the damages caused by tree felling and timber transportation. In this research, we analyzed forest stand damage susceptibility using GIS-based logistic regression (LR) and boosted regression tree (BRT) machine learning models for the Shourab Forests. These forests contain valuable stands of Hyrcanian forests in northern Iran that represent many of the plant covers, geographical, and forest management features of this ancient ecosystem. To this end, we applied traversed transect to cover the forest unit. This allowed us to sample any damaged residual tree intersected by the transect resulting in a dataset of 152 damaged trees in the study area. Of this dataset, we utilized 106 damaged trees for training purposes, while the remaining instances (46 damage locations) served as the verification dataset. We utilized eight input factors as stand damage conditioning variables to model damage susceptibility, including distance from the skid trails, distance from the road, forest density, type of forest, topographic position index (TPI), length of slope (LS), altitude, slope aspect, and slope degree. Using these predictive variables, we calculated damage susceptibility using LR and BRT machine learning models, and, subsequently, devised the outputs in ArcGIS to produce the final stand damage susceptibility maps. As the statistical evaluative criteria, we calculated the area under the curve (AUC) and Akaike Information Criterion (AIC) for the validation and training data sets. According to the validation results, the BRT model (AIC = −109.04; AUC = 0.91) strongly outperformed the LR model (AIC = 96.14; AUC = 0.77). According to the results, among the explanatory variables, the slope degree and slope length had more significant impact on the stand damage. Moreover, the susceptibility maps depicted that more than one-third of the forest area were surveyed in high and very high damage susceptibility classes. According to the results mentioned above, using machine learning technique is an inevitable approach in controlling and mitigating the damages caused by human interferences in natural ecosystems especially in mountain forests. Our findings revealed that the susceptibility maps produced by BRT are highly relevant and beneficial for planning forest harvest operations.

Geostructural and Geomechanical Study of the Piastrone Quarry (Seravezza, Italy) Supported by Photogrammetry to Assess Failure Mode

The use of non-contact-techniques for rock mass characterization has been growing significantly over the last decade. However, their application to stability assessment of ornamental stone has not yet received much attention from researchers. This study utilizes rock mass data both in terms of slope orientations and degree of fracturing obtained from a point cloud, a set of three-dimensional (3D) points representing a rock mass surface, to (1) investigate the influence of geostructures at different scales and (2) assess quarry stability by determining areas susceptible to different failure types. Multi-resolution point clouds are obtained through several photogrammetric survey techniques to identify important structural elements of the site. By integrating orientation data of discontinuity planes, obtained with a traditional survey, and of traces, outlined on point clouds, several joint sets were identified. Kinematic tests revealed various potential failure modes of the rock slope. Moreover, an analysis of the influence of the discontinuity strength determined by the presence of rock bridges was carried out. The study revealed that the strength of the quarry face is governed by the presence of rock bridges that act to improve the stability condition of the rock fronts.

Modification of the Fetal Profile Line to Measure Reversal of Forehead Slope after Early Repair of Frontoethmoidal Encephalocele.

Objective Congenital frontoethmoidal encephaloceles are associated with a shallow sloping forehead. We (1) sought to determine if early repair reverses abnormal forehead slope, and (2) assessed a modification of the fetal profile (FP) line to assess results. Design Study of two cases. Participants Newborns with frontoethmoidal encephaloceles repaired prior to the age of 4 months with cranial base bone grafting. Main Outcome Measures Forehead slope was assessed using a modification of the FP line, defined as the line that passes through the anterior border of the mandible and nasion, on pre and postoperative magnetic resonance imaging (MRI) in the midsagittal plane. A modified FP (mFP) line anterior to the forehead was " - ", while a posterior (normal) mFP line was " + ." The largest distance from the mFP line to the forehead was measured. Results Both infants underwent bifrontal craniotomy, excision of encephalocele, and repair of cribriform plate defect using full-thickness autologous parietal bone before the age of 4 months. Preoperatively, the mFP line was -20.6 mm in case 1, and -9.8 mm in case 2. In both cases, follow-up MRI showed excellent reversal of forehead slope and normal calvarium development. The mFP line improved to +7.4 (age = 16 months) in case 1, and +7.6 (age = 11 months) in case 2. The parietal bone donor site ossified completely within 3 months in both cases. Conclusion Early repair with bone grafting can promote normal frontal bone development and improve forehead slope. The mFP line is a useful method to measure degree of forehead slope.

Evaluasi Inverse Kinematics untuk Robot Quadruped Menggunakan Sensor Accelerometer

Quadruped robot is one of the types of robots that move using legs 4 compiled by some of the servo motor as a driving force on each foot ft the DOF is used. However, problems arise when this robot is confronted on the inclined plane, because the burden is borne out every servo motor on the feet will be different, so can make a fast servo motor damaged. This research was conducted on the design of the quadruped robot system for stability on the inclined plane using the accelerometer sensor and the application of the inverse kinematics method with PID control of Ziegler-Nichols. The results of tests obtained response robots in stabilizing the body when faced with the inclined plane with some degree of slope of the pitch and roll. In this research was conducted some test for quadruped robot: static Testing robot against the angel of the pitch in the standby retrieved response average robot in stabilizing the body is 245 ms, static Testing robot against the angle of roll in standby retrieved response average robot in stabilizing the body is 280 ms, dynamic Testing robot against the roll and pitch in standby retrieved response average robot in stabilizing the body is 8 seconds, Static Testing robot to stabilizing the body against the angel of roll in running the largest robot oscillations obtained 10 degrees, dynamic Testing robot to stabilizing the body against the angle of roll in run retrieved response average robot in stabilizing the body is 490 ms.

An assessment of metaheuristic approaches for flood assessment

This study is performed to determine the importance of metaheuristic approaches and their accuracy with respect to the spatial modeling process emphasising on flood phenomena. In this study, four ensemble metaheuristic approaches, including the combinations of ANFIS (Adaptive Neuro-Fuzzy Inference System) with the genetic algorithm (GA), simulated annealing (SA), imperialist competitive algorithm (ICA), and differential evolution (DE), are used for flood zoning in Dingnan County, China. Based on historical records, aerial photo interpretation, field surveys, and Google Earth, the location of 115 floods are recorded in the study area. Different categorical and continuous factors affecting floods, including the plan curvature, altitude, distance to rivers, slope degree, rainfall, land use, lithology, stream power index, topographic wetness index, soil type, aspect, normalized difference vegetation index, and profile curvature, are identified in the study area and entered in GIS software. Flood susceptibility maps (FSMs) are validated using the ROC curve. The results confirm that the AUCs of the four combined metaheuristic models are larger than 79%. The highest AUC value is obtained for the ANFIS–GA ensemble (0.903), followed by the ANFIS–SA (0.843), ANFIS–DE (0.812), and ANFIS–ICA (0.798). The RMSEs obtained for the training data of the different models are 0.2562 (ANFIS-DE), 0.3121 (ANFIS-GA), 0.3345 (ANFIS-ICA), and 0.3398 (ANFIS-SA). The results of this study show that the proposed ensemble approaches are useful for flood hazard management and land use planning in Dingnan County, China, and other places.

Une approche des dynamiques d’implantation des habitats à la fin du Néolithique provençal

L’espace provençal du 3e millénaire av. n. è. laisse entrevoir un certain nombre d’éléments qui mettent en scène la complexité archéologique de la fin du Néolithique. En Provence, le travail de J. Cauliez a ouvert la voie à un cadre chronoculturel plus robuste et tissé une trame plus diversifiée des composantes céramiques de la fin du Néolithique. Cependant, les dernières analyses des vestiges archéologiques liés à ces contextes requièrent à notre sens une étude complémentaire fondée sur les logiques d’implantation des sites d’habitat afin de mieux saisir l’ensemble des mécanismes socio-culturels émergeant à l’aube de l’âge du Bronze. Grâce à la mise en place d’une base de données relationnelle couplée à un système d’information géographique, il a été possible de générer de nouvelles informations spatiales sur un corpus de sites du Néolithique final provençal (260 gisements appartenant strictement au Néolithique final, et respectivement, 8, 19, 29 et 7 gisements attribués aux 4 horizons définis par J. Cauliez). Ainsi, après des analyses statistiques multivariées exploratoires sur les données spatiales, il a été possible de caractériser finement les implantations de chacun des gisements étudiés à l’échelle de la Provence et de dégager des tendances principales dans les choix d’installation de ces populations à partir du milieu du 3e millénaire av. n. è.

Spatial assessment of landslide risk using two novel integrations of neuro-fuzzy system and metaheuristic approaches; Ardabil Province, Iran

This article addresses the spatial analysis of landslide susceptibility in the Ardabil province of Iran. To this end, two well-known optimization algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO) are synthesized with an adaptive neuro-fuzzy inference system (ANFIS) to create the ensembles of GA-ANFIS and PSO-ANFIS. Besides, the statistical index (SI) model is also performed to be compared with the mentioned intelligent techniques. Fourteen landslide conditioning factors including elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI), and lithology were considered within the geographic information system (GIS). Out of 253 identified landslides, 177 points (70% of them) were randomly selected and used for the training phase, and the remaining 76 points (30% of them) were used to evaluate the accuracy of the SI, GA-ANFIS, and PSO-ANFIS models. Referring to the calculated area under the receiver operating characteristic curve (AUROC) index, the GA-ANFIS (AUROC = 0.914) and SI (AUROC = 0.821) showed the best performance, respectively in the training and testing phases. Notably, ANFIS-PSO emerged as the faster prediction method compared to the GA-ANFIS. Also, from spatial analysis, it was revealed that around 95%, 87%, and 97% of the training landslides, and 96%, 84%, and 76% of the testing landslides are located in hazardous areas.

Variation of Physical and Chemical Properties of Soils under Different Cropping Systems in the Watershed of Kpocomey, Southern Benin

Soils degradation is one of the constraints in food security achievement in Benin. This study aimed at assessing the effect of cropping systems and slope on soil physical and chemical properties in the watershed of Kpacomey located in the Aplahoue district. Soil samples were collected from three parallel transects along the slope. Sampling was carried out under different treatments combining cropping systems (Maize-Cassava, pure Palm grove, Palm grove-Maize-Cassava and Teak Plantation) along with slope levels (low slope, medium slope and high slope degree). The impact of cropping systems and slope on soil properties was assessed by determining the physical and chemical parameters. The cropping systems significantly (p 3) was recorded under the Palm grove-Maize-Cassava cropping system while the highest (1.47 g/cm3) was obtained with pure Palm grove cropping system. Root biomass was more abundant (0.28%) with the pure Palm grove cropping system. However, root biomass was significantly (p 0.05) on the effect of cropping systems and slope. Moreover, cropping systems resulted in significant effects (p < 0.05). Soil organic matter and soil-assimilated phosphorus content were significantly influenced by the effect of the slope. These findings indicated that cropping systems and slope are significant drivers in soil degradation in the Kpacomey watershed and bringing out cropping systems that best aim at soil conservation.

Multiple environmental factors analysis of flash flood risk in Upper Hanjiang River, southern China.

Identifying the environmental factors and analyzing the causal mechanism of flash floods help to manage the risk. Maximum 24-h precipitation (MP), digital elevation (DE), slope degree (SD), soil type (ST), drainage density (DD), and vegetation cover (VC) are selected as the risk factors of flash floods in this study. Precipitation is the important meteorological components in flash floods; thus spatial characteristics of precipitation trend have been analyzed by using Mann-Kendall tests, and a positive trend of precipitation in Upper Hanjiang River is detected. Then, association rule mining approach is proposed to investigate the multiple environmental factors of flash floods, in which both single and multiple dimension data mining have been conducted by Apriori algorithm. Considering the high rate of 5-year return period floods in the flash flood inventory, further association rule mining after sampling has been conducted in order to deeply mine the causal patterns of flash floods in different risk magnitudes. Results show that soil type, slope degree, and digital elevation are the dominant environmental factors of flash floods in the study area, and precipitation is one of the important causal factors in severe flash flood hazards. It is also highlighted that flash floods might easily occur even with a slight rainfall present due to the instability of sand clay and saturated soil moisture. The proposed novel use of field data and data mining has the potential for providing procedures and solutions for an effective interpretation of flash flood mechanism. The results are expected to be applicable for decision-making and sustainable management in flooding risk.

RMR and RHRS of Ngaizel Road cutting section, Aizawl, Mizoram

Due to unfavourable topographical features roads in hilly areas are usually small as compared to plain areas, therefore several problems may arise whenever rockfall occurs. One of the busiest domestic road in Aizawl i.e. Ngaizel road often encountered rockfall along its cutting slope. This road act as an important gateway which connects the southern parts of Mizoram. This road is packed with human activities which adds on the hazard level to rise high. The main factors that contribute to rockfalls include slope degree, geomechanical properties of rock mass and anthropogenic activities. Taking into consideration of all the adverse effect of rockfalls, RMR (Rock Mass Rating) become indispensable to identify the actual condition of rock mass on the slope of the study area. RMR classification is carried out to determine the geomechanical condition of slope rock mass and Rockfall Hazard Rating System (RHRS) is applied for identifying the hazard-prone area of rockfall along the cut slope of Ngaizel road. RHRS depends on various parameter which include slope height, annual precipitation, block size, block volume, decision sight distance, average vehicle risk, road width and rockfall history which mainly concern about the safety of pedestrians along the road. The RMR and RHRS study along Ngaizel cut slope provides a detailed and useful information about the geological condition and delineating the hazard level. The study also gave awareness and alertness about the actual condition of the area.

Calculating slope gradient variations in the submarine landforms by R and Python statistical libraries

Multiple factors affect submarine geomorphology causing variations in the gradient slope: geological settings (rock composition, structure, permeability, erodibility of the materials), submarine erosion, gravity flows of water streams, tectonics, sediments from the volcanic arcs, transported by transverse submarine canyons. Understanding the slope geomorphology is important for the precise bathymetric mapping. However, analysis of such a complex geomorphic structure as ocean trench requires numerical computation and advanced statistical analysis of the data set. Such methods are proposed by R and Python programming languages that include libraries of machine learning algorithms for the data processing used in this research: {tidyverse}, {ggsignif}, {ggplot} and {magrittr} by R, StatsModels, Matplotlib, NumPy, Pandas and Seaborn by Python. The research workflow can be summarized in five steps: 1) Partial least squares regression analysis; 2) Violin plots, modified box plot approach; 3) Modelling variations of depth and slope gradient, facetted in multi-panel plots by 4 tectonic plates; 4) Calculating normalized steepness angle; 5) Sorting, ranking and grouping of the cross-sectioning profiles by gradient slope degree, to estimate differences in the geomorphic shapes. As a result of the ranking performed in step 5, slopes were classified into five classes based on the calculated tangent angles: strong, very strong, extreme, steep, very steep. The results show differences in the gradient slope between various segments of the Mariana Trench located in four tectonic plates: Mariana, Caroline, Pacific and Philippine Sea, performed by statistical data modelling. Programming codes and snippets are presented for repeatability of the methods in similar research tasks.

Acromion morphology and its relationship with sub acromial Impingement

Background And Objectives: Shoulder pain due to Impingement syndrome is a common clinical entity. The cause of which are generally supported by typical changes in Acromion morphology on standard radiographs. We Evaluated 5 commonly used radiographic parameters of acromial morphology and assessed the association between different radiographic characteristics on the one hand and sub-acromial impingement on the other. Methods: Measurement of acromial type (Bigliani), acromial Slope (AS), acromial tilt(AT), Lateral acromial angle (LAA), and acromion index (AI) were done on standard radiographs of 100 patients with sub acromial impingement and 100 controls without sub-acromial pathology. Results: The acromial type III according to Bigliani was associated with Impingement, A statistically significant difference between controls and impingement patients was found for AS. AT of controls was significantly smaller than that of impingement patients. LAA of controls was not significantly different from that of impingement patients. AI of controls was significantly lower than of impingement patients. A good correlation was found between acromial type and AS. Interpretation And Conclusions: A low lateral acromial angle and a large lateral extension of the acromion are associated with a higher prevalence of Impingement. Type II Acromion is most common type whereas Type III is a risk factor for Impingement. Higher degrees of Acromion slope and lower degrees Tilt are found in Impingement syndrome. Keywords: Acromion, Impingement, Bigliani

Lattice Boltzmann simulation of wind-driven rainfall on a slope surface

In overland flow modeling, the influences of the slope degree, rain intensity and wind condition can be significant. In this study, the effects of the slope gradient and wind-driven rain were integrated in a nonlinear shallow water model based on the lattice Boltzmann method (LBM). Since slope gradient and wind condition can not only affect the flow rate, but also its momentum, we enhanced the mathematical model and derived two LBM schemes that include their hydrodynamic influence on the surface flow. Then, this model was verified by several typical cases. The results indicated that the momentum component of rainfall should be considered during the overland flow simulation influenced by wind, otherwise it would result in an intrinsic error of approximately [Formula: see text] in simulated results.

Development and Construction of Electric Propulsion System for Go-Kart

Go-kart ride is accessible to everybody, regardless the old age and driving expertness. Economic and environmental influences have raised the demand to change from conventional combustion engine to electric propulsion systems for go-kart. This project is focusing on design and development of propulsion and energy storage system. Electric propulsion system will improve energy efficiency and significantly reduce greenhouse gas emissions. This project is lead to practical experiment to analyze and explore the action of modifying combustion engine powered go-kart to an electric powered go-kart. Several aspects need to be considered to design a good performance of an electric go kart that involves hardware and electrical energy propulsion system. In this project the calculation of force and power was done base on both the weight of go-kart and driver. The power calculation with certain degree of slope was also done to determine the maximum power demand for motor selection. The project is expected to drive a total weight of 120kg including the weight of the go-kart and the driver. The design is also included the installation of the gear, propulsion, battery and controller in electric go kart. The electrical component that consist in go-kart are battery, brushless DC motor and controller, wiring harness with on, enable and forward/reverse switches and motor cable. These selections of components are based on calculation analysis for each selected device. Lastly, the performance of electric go kart will be analyzed in term of power, speed and energy work. As the results, the propulsion system could drive the go kart and speeds can be regulated, also the maximum speed of the go-kart reaches its desired speed.

How do data-mining models consider arsenic contamination in sediments and variables importance?

Arsenic (As) is one of the most important dangerous elements as more than 100 million of people are exposed to risk, globally. The permissible threshold of As for drinking water is 10μg/L according to both the WHO's drinking water guidelines and the Iranian national standard. However, several studies have indicated that As concentrations exceed this threshold value in several regions of Iran. This research evaluates an As-susceptible region, the Tajan River watershed, using the following data-mining models: multivariate adaptive regression splines (MARS), functional data analysis (FDA), support vector machine (SVM), generalized linear model (GLM), multivariate discriminant analysis (MDA), and gradient boosting machine (GBM). This study considers 12 factors for elevated As concentrations: land use, drainage density, profile curvature, plan curvature, slope length, slope degree, topographic wetness index, erosion, village density, distance from villages, precipitation, and lithology. The susceptibility mapping was conducted using training (70%) and validation (30%). The results of As contamination in sediment showed that classifications into 4 levels of concentration are very similar for two models of GLM and FDA. The GBM calculated the areas of highest arsenic contamination risk by MARS and SVM with percentages of 30.0% and 28.7%, respectively. FDA, GLM, MARS, and MDA models calculated the areas of lowest risk to be 3.3%, 23.0%, 72.0%, 25.2%, and 26.1%, respectively. The results of ROC curve reveal that the MARS, SVM, and MDA had the highest accuracies with area under the curve ROC values of 84.6%, 78.9%, and 79.5%, respectively. Land use, lithology, erosion, and elevation were the most important predictors of contamination potential with a value of 0.6, 0.59, 0.57, and 0.56, respectively. These are the most important factors. Finally, these data-mining methods can be used as appropriate, inexpensive, and feasible options to identify As-susceptible areas and can guide managers to reduce contamination in sediment of the environment and the food chain.

Greater effect of warming on community composition with increased precipitation and in moister landscape location

AbstractQuestionsWe asked how plant community composition responded to experimentally produced warmer and drier climate conditions at the landscape scale with existing variation in local species composition and environmental conditions. We aimed to identify changes in community composition overall and the species with greatest response in abundance, and hypothesized that locally restricted species may be more sensitive to warming than more widespread species within the landscape based on the assumption that they have a narrower niche breadth with respect to environmental conditions.LocationSemiarid, northern Mongolian steppe.MethodsOpen‐top passive warming chambers (OTCs) elevated temperatures at two slope locations that differed in elevation, degree of slope, environmental conditions, and species composition. The OTC treatment was crossed with watering on the drier upper slope. Community composition differences among treatments were examined using canonical analysis of principal coordinates (CAP), which identified species contributing the most to differences. In response to warming, we also compared species locally restricted to one slope location with locally widespread species.ResultsOpen‐top passive warming chambers affected community composition more where soil moisture was greater, at the lower slope location and where warming was combined with supplemental watering on the drier upper slope. Locally restricted species responded negatively to the OTC while locally widespread species showed no overall response.ConclusionsCommunity composition responses to warming differ even within the landscape over and above the initial differences that exist in community structure and abiotic factors. Our results suggest that a warmer and drier climate will impact community composition sooner under more mesic conditions, affect locally restricted species more strongly, and reduce variation in species composition across the landscape. To better predict community responses to future warming, we must consider combined and interactive effects with changes in precipitation and extant water availability.

Detection of spatial-temporal variations in forest canopy surface temperature in response to urbanization: a case study from Longyan, China

Urban forests are fundamental components of localized surface energy budgets. Understanding the factors controlling urban forest surface temperatures (UFSTs) should be helpful in mitigating the negative effects of urbanization on urban energy budgets. This study aimed to identify the factors controlling the spatial-temporal pattern of UFSTs by utilizing a variety of data layers and spatial statistical analysis methods. Our results showed that UFST values become more spatially heterogeneous as urbanization progresses. Elevation and degree of slope were the main factors explaining the increase in spatial heterogeneity. Human activities were also significantly related to variations in UFST. Interactions between human activities and almost all environmental factors were related to higher UFST values. Therefore, human activity directly impacts on the spatial heterogeneity of UFST and indirectly affects variations in landscape patterns. Human activities compatible with ecologically sustainable development should be considered for mitigating the deterioration of urban thermal environments.