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BACKGROUND RESULTS CONCLUSION Oat (Avena sativa L.) is a valuable crop due to its strong adaptability to marginal environments, making it an important component of agricultural systems in regions where other cereals may not thrive. The application of chemical fertilizer can influence oat hay and grain yield significantly. However, large‐scale meta‐analytical studies of the size and variability of oat hay and grain yields in response to fertilizer addition are still lacking. Based on 83 studies worldwide, this meta‐analysis quantifies the impact of the addition of fertilizer on oat hay and grain yields under varying environmental conditions (e.g., soil nutrient levels, texture, and climate).The results confirmed that the fertilizer application increased oat hay yield by 48.9% and grain yield by 36.2%. This study demonstrated that balanced fertilization with nitrogen, phosphorus, and potassium generally enhances oat hay and grain yield despite large temporal and spatial variations. Boosted regression tree (BRT) models suggest that changes in hay and grain yield were primarily dominated by soil pH and nitrogen fertilizer. The response ratio (the natural logarithm of the mean values of hay yield or grain yield with and without fertilization, respectively) of hay yield declined linearly with soil pH. Elevation was the second most important factor affecting the change in response ratio of hay yield and the third most important factor affecting the change in response ratio of grain yield but climatic conditions were not the dominant factors affecting changes in oat hay or grain yield.Overall, these results will benefit producers considering site‐specific fertilization management of oat. They could increase yields and save investment in fertilizer, and help to facilitate the genetic breeding of oat varieties with high nutrient use efficiency. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs. A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers. The stray gas can dissolve in groundwater leading to chemical and biological reactions, which could negatively affect groundwater quality and contribute to atmospheric emissions. The knowledge of light hydrocarbon solubility in the aqueous environment is essential for the numerical modelling of flow and transport in the subsurface. Herein, we compiled a database containing 2129 experimental data of methane, ethane, and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure. Two machine learning algorithms, namely regression tree (RT) and boosted regression tree (BRT) tuned with a Bayesian optimization algorithm (BO) were employed to determine the solubility of gases. The predictions were compared with the experimental data as well as four well-established thermodynamic models. Our analysis shows that the BRT-BO is sufficiently accurate, and the predicted values agree well with those obtained from the thermodynamic models. The coefficient of determination (R2) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 9.97 × 10−8. The leverage statistical approach further confirmed the validity of the model developed. [ABSTRACT FROM AUTHOR]

The aims of this research are (i) to compare random forest (RF), boosted regression tree (BRT), and multinomial logistic regression (MnLR) models to prepare the prediction maps of soil great group and subgroup levels, (ii) determination of the most important environmental covariates influencing the production of digital soil mapping (DSM) in an arid climate, (iii) to evaluate the efficiency of spectra indices extracted from Sentinel-2A digital images and data capability of ALOS-PALSAR radar data, and (iv) investigating the effect of sub-surface genetic horizons in the modeling of different types of soil map classes distribution. The principal component analysis method was employed to select the best set from the pool of environmental covariates (n = 46) such as geomorphometric parameters (GPs), RS indices, and diagnostic soil properties (DSP). The relative importance results indicate that Gypsic (GYP) subsurface horizon, standardized height (StH), slope length (SL), and normalized different vegetation index (NDVI) had an important role in the prediction of soil classes compared to the other selected covariates. DSM methodology was used in this research by incorporating of RF model and representative soil-forming factors that can be used for preparing the maps of soil classes in low-relief areas with a similar soil-landscape relationship. Totally, in this study places a spotlight on the profound impact of sub-surface genetic horizons, shedding light on their pivotal role in accurately modeling soil class distributions. These findings not only advance our comprehension of soil variability in arid regions but also hold immense implications for the burgeoning field of pedometrics. [ABSTRACT FROM AUTHOR]

Mass transfer-based models are among the extensively applied reference evapotranspiration (ETo) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ETo values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ETo). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function. [ABSTRACT FROM AUTHOR]

Native forests host important pools of soil organic carbon (SOC). This is a key element not only for ecosystem functioning but also for the global carbon cycle. Globally, and particularly in Argentina, native forests are being rapidly replaced by other land uses, raising questions about the impact of these transformations on SOC and its environmental controls. Based on the construction of the largest SOC database in Argentina to date, we investigated the patterns and controls of changes in SOC stocks associated with the replacement of native forests by other land uses. We constructed the database with a total of 818 sites with SOC data (0–30 cm depth), covering the main ecoregions, to which we added environmental information (e.g. satellite data, soil database and climate database), to study the environmental controls on SOC change after deforestation and on the original SOC content of native forests. Considering all ecoregions and all land use alternatives together, we found an average decrease in SOC stock of 18.2 Mg C ha−1, which represents a loss of more than a quarter of the original SOC stock of the native forest sites. A boosted regression tree explained 89% of the variation in SOC stock change and indicated that the initial forest SOC stock and the post‐deforestation land use were the most important variables explaining this variation (relative influence of 30.9% and 18.2%, respectively). The replacement of native forests by rainfed annual crops resulted in the largest decrease in SOC (−28 Mg C ha−1), which was twice as large as the decrease observed in rangelands (−14 Mg C ha−1). On the contrary, neither irrigated croplands nor tree plantations of fast‐growing species caused a decrease in SOC stocks (p >.10). Climate and soil texture had an indirect effect on SOC changes through a strong influence on the initial SOC stocks in native forests (p <.01). Our study highlighted the significant impact of land use change on SOC stocks, overshadowing other relevant environmental controls. Understanding how the SOC pool responds to land use change, environmental conditions and management practices is essential to increase the effectiveness of practices implemented to improve soil properties and mitigate climate change. [ABSTRACT FROM AUTHOR]

Modeling ecological patterns and processes often involve large‐scale and complex high‐dimensional spatial data. Due to the nonlinearity and multicollinearity of ecological data, traditional geostatistical methods have faced great challenges in model accuracy. As machine learning has increased our ability to construct models on big data, the main focus of the study is to propose the use of statistical models that hybridize machine learning and spatial interpolation methods to cope with increasingly large‐scale and complex ecological data. Here, two machine learning algorithms, boosted regression tree (BRT) and least absolute shrinkage and selection operator (LASSO), were combined with ordinary kriging (OK) to model plant invasions across the eastern United States. The accuracies of the hybrid models and conventional models were evaluated by 10‐fold cross‐validation. Based on an invasive plants dataset of 15 ecoregions across the eastern United States, the results showed that the hybrid algorithms were significantly better at predicting plant invasion when compared to commonly used algorithms in terms of RMSE and paired‐samples t‐test (with the p‐value <.0001). Besides, the additional aspect of the combined algorithms is to have the ability to select influential variables associated with the establishment of invasive cover, which cannot be achieved by conventional geostatistics. Higher accuracy in the prediction of large‐scale biological invasions improves our understanding of the ecological conditions that lead to the establishment and spread of plants into novel habitats across spatial scales. The results demonstrate the effectiveness and robustness of the hybrid BRTOK and LASOK that can be used to analyze large‐scale and high‐dimensional spatial datasets, and it has offered an optional source of models for spatial interpolation of ecology properties. It will also provide a better basis for management decisions in early‐detection modeling of invasive species. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Eucalyptus plantations are widespread in the highlands of northern Ethiopia. The species has been used for centuries for various purposes. However, there are controversies surrounding the species with excessive soil nutrient and water consumption. Modelling the spatial distribution of the species is fundamental to understand its ecological and hydrological effects in the region for policy inputs. Therefore, the purpose of this study is to develop a model for mapping the spatial distribution of Eucalyptus globulus. We used the spectral bands of Sentinel-2 data, vegetation indices, and environmental data as predictor variables and three machine learning algorithms (Random Forest, Support Vector Machine, and Boosted Regression Trees) to model the current distribution of Eucalyptus globulus. Eleven of the twenty-five predictor variables were filtered using a variance inflation factor (VIF). 419 in situ georeferenced data points were used for training, and validating the models. The area under the curve (AUC), kappa statistic (K), true skill statistic (TSS), Root Mean Squared Error and coefficient of determination (R2) were used to validate the models’ performance. The model validation metrics confirmed the highest performance of Random Forest. The prediction map of Random Forest revealed that Eucalyptus globulus was fairly detected in non-Eucalyptus globulus woody vegetation (R2 = 0.86, P < 0.001; RMSE = 0.31). We found that the Green Normalized Difference Vegetation Index and environmental variables, such as elevation and distance from the road, were the most important predictor variables in explaining the distribution of Eucalyptus globulus. Our findings demonstrate that machine learning algorithms with Sentinel-2 spectral bands and vegetation indices compounded with environmental data can effectively model the spatial distribution of Eucalyptus globulus.

The key to managing urban heat island (UHI) is identifying and cooling the core areas of UHI. Studies have shown that spatial differences in urban green space (UGS) indicators have a significant impact on spatial differences in land surface temperature (LST). However, previous studies have mainly explored UGS and LST in the entire region, with few focusing on the urban core area. In this study, we extracted the urban core area based on the Point of Interest (POI) and kernel density methods. Correlation and regression analyses were then used to investigate the correlation between UGS indicators and seasonal LST in the urban core area. Our results confirm the significant cooling effect of UGS at the urban core scale. The effects of UGS landscape composition and configuration on LST varied across seasons and across urban green space ratio (UGSR) gradients. In the warm season, increasing the UGSR proved to be the most effective way to mitigate the urban thermal environment. In urban core areas where expanding UGS is not possible or in cores with high UGSR, designing UGS with complex shapes and clusters can better achieve the cooling effect. These results suggest that the influence of UGS landscape features on the urban surface thermal environment at the urban core area scale is very complex. Therefore, implementing urban greening to mitigate the UHI problem requires considering the premise of multifactorial analysis.

Gradients of latitudinal diversity are one of the biogeographic features calling the most attention in ecology and macroecology. However, in pelagic communities of the marine environment, geographic trends and patterns are poorly known. We evaluated the latitudinal variation in species richness of marine planktonic copepods in the Eastern Pacific using spatial statistical models and approaches that mitigate and account for biases in occurrence data. A Boosted Regression Tree (BRT) and regression-Kriging based models allowed us to estimate and predict alpha diversity in poorly sampled regions, whereas beta diversity patterns were assessed using generalized dissimilarity analysis (GDM). Species richness showed a bimodal pattern, with a maximum of 291 species in the Northern Hemisphere and Tropical Eastern Pacific Ocean. Particulate Organic Carbon, salinity (max), spatial autocovariate, range of salinity and temperature, and Mixed Layer Depth, explained 85.2% of the latitudinal variability of copepods. Beta diversity was structured into four macrozones associated with the main water masses of the North and South Pacific.Our analytical approaches can overcome the limitations of data gaps, predicting greater diversity in subtropical and coastal areas, while providing insights into key drivers modulating spatial diversity patterns. [ABSTRACT FROM AUTHOR]

Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation. [ABSTRACT FROM AUTHOR]

Soil, as a valuable natural resource, provides a large number of services and plays an important role in the environment and world economy. Soil degradation and erosion reduce the quality and quantity of the soil and are important natural and anthropogenic processes that affect many countries. Water erosion is the most common type of soil degradation in the world, and Asia has about 50% of the total water erosion area of the world. Gullies are a typical erosion type, and gully formation is an important process of soil erosion and degradation in semi-arid and arid areas, especially areas impacted by human activities and land uses. Because of arid and semi-arid climate, piping and gully erosion is an active phenomenon in the agricultural lands, bare land, and rangeland areas of the Shazand watershed, Markazi Province, central Iran. The goal of this research was to identify the priority conditioning factors of gully erosion, map the susceptibility of the Shazand watershed to gully erosion, and compare some of the applied machine learning techniques based on their accuracy. Prioritization of conditioning factors using a random forest (RF) algorithm demonstrated that distance from the roads, altitude, and rainfall has the greatest impact on gully occurrence in the Shazand watershed. The RF, boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), and mixture discriminant analysis (MDA) algorithms were applied to create gully erosion susceptibility maps in the study area. The receiver operating characteristic curve (ROC) and area under the curve (AUC) performance metrics were used to validate susceptibility maps. The AUC values of 0.850, 0.831, 0.760, 0.751, and 0.758 were achieved for the RF, BRT, FDA, GLM, and MDA algorithms, respectively. Due to the negative and destructive effects of gully erosion, its management and control is a critical component in the management of natural resources and land uses. Determining the importance of factors affecting erosion is very important to manage and reduce the erosion in the study area. The susceptibility maps of gully erosion prepared in this study are a substantial information resource for decision makers, planners, and engineers concerned with human impacts on natural resources and land uses. About 40% of the study area has high to very high susceptibility to the gully erosion, so control and management of this phenomenon is very important in Shazand watershed. The areas identified with high and very high erosion susceptibility in the Shazand watershed need more care to mitigate the consequences of gully erosion and soil degradation. Also, prioritizing factors will increase the focus on more important factors, and management activities will be more successful. [ABSTRACT FROM AUTHOR]

It is of great significance to identify the spatiotemporal stress distribution characteristics to ensure the safety of a super-high arch dam during the initial operation stage. Taking the 285.5 m-high Xiluodu Dam as an example, the spatiotemporal distribution characteristics were analyzed based on the five-year observation data after impoundment. Statistical and boosted-regression-tree-based prediction models for the dam stress were established. The boosted-regression-tree-based prediction model is more accurate than the statistical model. The monitoring indicators for the measuring points of focused locations were determined using the confidence interval estimation method. The results show that the dam was in a compression state, and the arching effect was obvious. The arch direction compressive stress gradually increased and stabilized, and the maximum appeared in the middle of the upstream face of the crown cantilever monolith. For the crown cantilever, the cantilever direction stress at the dam heel was significantly affected by the interior temperature recovery, and the arch direction stress in the middle of the upstream face was significantly affected by the reservoir water level. The measuring points of the focused locations with reliable observation data can be selected as a monitoring index to guide the initial operation. [ABSTRACT FROM AUTHOR]

Abstract Modeling ecological patterns and processes often involve large‐scale and complex high‐dimensional spatial data. Due to the nonlinearity and multicollinearity of ecological data, traditional geostatistical methods have faced great challenges in model accuracy. As machine learning has increased our ability to construct models on big data, the main focus of the study is to propose the use of statistical models that hybridize machine learning and spatial interpolation methods to cope with increasingly large‐scale and complex ecological data. Here, two machine learning algorithms, boosted regression tree (BRT) and least absolute shrinkage and selection operator (LASSO), were combined with ordinary kriging (OK) to model plant invasions across the eastern United States. The accuracies of the hybrid models and conventional models were evaluated by 10‐fold cross‐validation. Based on an invasive plants dataset of 15 ecoregions across the eastern United States, the results showed that the hybrid algorithms were significantly better at predicting plant invasion when compared to commonly used algorithms in terms of RMSE and paired‐samples t‐test (with the p‐value

Optimizing the spatial distribution of urban functional zones (UFZs) effectively improves the thermal environment. This study utilized an enhanced regression tree model and relied on Ecosystem Spaceborne Thermal Radiometer Experiment data to analyze the relative contributions and marginal effects of 2-D/3-D urban morphological factors on the diurnal land surface temperature (LST) in Shenyang, China. The results showed that public and residential areas dominated Shenyang's UFZs. The temperature in industrial areas was the highest during the day, and residential and commercial functional areas are high-temperature concentration areas. Furthermore, the effects of the urban spatial morphology on the LST differed between diverse time points and UFZs. The digital elevation model and the normalized difference vegetation index contributed significantly to daytime and nighttime LSTs. Construction indicators, such as the normalized difference built-up index and the proportion of construction land, significantly impacted commercial services. Residential daytime LST had a large contribution value, and the sum of its contribution rates reached approximately 30%. Population greatly contributed to the nighttime LST of the industrial and residential zones, accounting for 16.77% and 22.06%, respectively. Vegetation contributed to the cooling effect on daytime LST in summer, especially in industrial areas, contributing 29.79%. In addition, 3-D indicators, such as building height and building density, contributed to diurnal LST. Finally, when the proportion of construction land reached approximately 45%, it negatively affected LST. In this study, the main factors affecting day and night LSTs were identified, and this work acts as a relevant strategic reference for alleviating the urban heat island effect.

Sustainable intensification of cultivated land use (SICLU) and large-scale operations (LSO) are widely acknowledged strategies for enhancing agricultural performance. However, the existing literature has faced challenges in precisely defining SICLU and constructing comprehensive indicators, which has hindered the exploration of factors influencing LSO within the SICLU framework. To address this gap, we integrated self-efficacy theory into the design of an index framework for evaluating SICLU. We subsequently employed econometric models to analyze the significant factors that impact LSO. Our findings reveal that SICLU can be divided into four key dimensions: intensive management, efficient output, resource conservation, and ecological environment optimization. Furthermore, it is crucial to incorporate belief-based cognitive factors into the index system, as farmers' understanding of fertilizer and pesticide application significantly influences their willingness to engage in LSO. Moreover, we identify grain market turnover as the most influential factor in promoting LSO, with single-factor contribution rates reaching 70.9% for cultivated land transfer willingness and 62.5% for the total planting areas. Interestingly, unlike irrigation and agricultural machinery inputs, increased labor inputs correspond to larger planting areas for farmers. This trend may be attributed to reduced labor availability because of rural labor migration, whereas the reduction in irrigation and agricultural input is contingent on innovations in production practices and the transfer of cultivated land management rights. Importantly, SICLU dynamically influences LSO, with each index related to SICLU having an optimal range that fosters LSO. These insights offer valuable guidance for policymakers, emphasizing farmers as their central focus, with the adjustment of input and output factors as a means to achieve LSO as the ultimate goal. In conclusion, we propose research avenues for further enriching the SICLU framework to ensure that it aligns with the specific characteristics of regional agricultural development. [ABSTRACT FROM AUTHOR]

Remote sensing data provide a high ability to represent habitat characteristics and use in species distribution models. The purpose of this study is to determine the most important of remote sensing predictors, including climatic indices (precipitation and temperature), primary and secondary topographic indices (elevation, slope, direction, Topographic Position Index (TPI) and Terrain Roughness Index (TRI) and remote sensing indices (Global Environmental Monitoring Index (GEMI), Leaf Area Index (LAI), Modified Normalized Difference Water Index (MNDWI), Modified Simple Ratio Index (MSR), Normalized Burn Ratio Index (NBR) and Visible Atmospherically Resistant Index (VARI)) using two species distribution models (Boosted Regression Tree and Random Forest) to predict the presence of J. excelsa in Khalkhal County of Ardabil province and northern part of Zanjan province using SAHM software. The evaluation of prediction models using AUC chart (Area under curve) showed that it is at an excellent level for both the BRT model (0.991) and the RF model (0.974). The most important affecting habitat desirability based on the BRT method include annual precipitation, slope, digital elevation model, temperature, GEMI index and TRI index variables respectively. The most important variables affecting habitat desirability based on the RF method, respectively, include annual precipitation, digital elevation model, GEMI index, slope, VARI index, temperature, MSR index, TRI index, TPI index, NBR index, MNDWI index, LAI index and aspect. The region mapped in the study as suitable habitats' for the species could be used in the planning strategies with the aim of evaluating the susceptible habitats, the possibility of conservation, reproduction and breeding. Considering that the modeling method choice is the main source of variability in predictions and choosing the best prediction model is not simple, therefore, it is suggested to use a combination of these models instead of relying on the outputs of a single model. [ABSTRACT FROM AUTHOR]

Financial fraud detection is a challenging problem due to four primary reasons: the constantly changing fraudulent behavior, the lack of a mechanism to track fraud data, the specific limitations of available detection techniques (such as machine learning algorithms), and the highly dispersed financial fraud dataset. Thus, it can be declared that teaching algorithms are complex. The current study used machine learning techniques, including support vector machine regression and boosted regression tree, to detect financial fraud in the Iranian stock market. The findings indicated that the boosted regression tree machine model has the lowest RMSE. Furthermore, concerned with the sensitivity value of the models, the boosted regression tree model has the highest sensitivity in the sense that they had correctly detected the absence of financial fraud Tehran Stock Exchange market the Tehran Stock Exchange market. The boosted regression tree has the highest kappa coefficient indicating the appropriate performance of this model compared to other models used in the research. [ABSTRACT FROM AUTHOR]

Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world’s largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. “logistic regression (LR)”, “random forest (RF)”, and “boosted regression tree (BRT)”, were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems. [ABSTRACT FROM AUTHOR]

Marine fish experience shifts in their distribution due to changes in the physical and biological environments. These shifts pose challenges for fishery businesses and management in international fisheries organizations. In the western and central Pacific Ocean, changes in the spatial distribution of skipjack tuna (Katsuwonus pelamis) due to climate variability often influence fishing activities and economic benefits. This study provides an operational forecast to enhance the decision-making process for fishery managers and fishermen by informing them about fish distributions in the coming months. Monthly forecasts of skipjack tuna habitat are generated utilizing a species distribution model (boosted regression trees) in combination with real-time environmental forecasts. An alternative method (dynamic time warping) is proposed to improve monthly chlorophyll forecasts, which are crucial for enhancing skipjack tuna habitat forecasting. To assess the prediction skills of the model, a retrospective analysis was conducted, forecasting skipjack tuna distribution over 9-month periods and comparing the forecasts with observed occurrences. The results demonstrate that 67.9% of the observed skipjack tuna catches occurred within the forecasted habitats, indicating that the forecast model skillfully predicted skipjack tuna movements 9 months in advance. These monthly updated forecasts can serve as a potential tool for resource managers in developing effective management strategies and fishers in planning fishing operations sustainably and responsibly. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop sensitive gully erosion models by implementing a machine learning algorithm (Support Vector Machine and Boosted Regression Tree) in the Moher basin. First, gully areas are identified, and then 13 variables predisposing to gully erosion (Slope, Slope Direction, Topographic Wetness Index, Streem Power Index, Terrain Ruggedness Index, Distance from Waterway, Drainage Density, Distance from Road, Land use, NDVI, Avera annual Rainfall, Geology, and Soil Texture) were selected. The variance inflation coefficient was used to evaluate multicollinearity between variables. Finally, a gully erosion sensitivity map was prepared in the environment (R). Also, the effect of physical and chemical characteristics of soil on gully erosion was investigated using Multivariate Regression. Regarding the importance of variables, Geology has the most significant effect on gully erosion in the SVM model, Land use, and the BRT model. The predicted sensitivity map was validated with the help of the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) in the Support Vector Machine and Boosted Regression Tree models were calculated as 0.92 and 0.94, respectively, which led to accurate prediction. Also, the results showed that the sand variable (9.299), sodium absorption ratio (7.967), and TNV (6.185) have the most significant effect on gully erosion [ABSTRACT FROM AUTHOR]

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation.

Gradients of latitudinal diversity are one of the biogeographic features calling the most attention in ecology and macroecology. However, in pelagic communities of the marine environment, geographic trends and patterns are poorly known. We evaluated the latitudinal variation in species richness of marine planktonic copepods in the Eastern Pacific using spatial statistical models and approaches that mitigate and account for biases in occurrence data. A Boosted Regression Tree (BRT) and regression-Kriging based models allowed us to estimate and predict alpha diversity in poorly sampled regions, whereas beta diversity patterns were assessed using generalized dissimilarity analysis (GDM). Species richness showed a bimodal pattern, with a maximum of 291 species in the Northern Hemisphere and Tropical Eastern Pacific Ocean. Particulate Organic Carbon, salinity (max), spatial autocovariate, range of salinity and temperature, and Mixed Layer Depth, explained 85.2% of the latitudinal variability of copepods. Beta diversity was structured into four macrozones associated with the main water masses of the North and South Pacific.Our analytical approaches can overcome the limitations of data gaps, predicting greater diversity in subtropical and coastal areas, while providing insights into key drivers modulating spatial diversity patterns.

Efficient energy conversion and utilization remain paramount in addressing the growing energy demand and environmental concerns. Concentrating photovoltaic thermal (CPVT) systems have emerged as promising solutions by integrating photovoltaic (PV) cells with thermal components for simultaneous electricity and heat generation. In this paper, we propose the application of the Boosted Regression Tree (BRT) algorithm to predict the entropy generation rate in a CPVT system equipped with a perforated twisted tube turbulator. Brief introduction of numerical analysis of local and global rates of frictional (S˙fr) and thermal (S˙th) irreversibilities in a CPVT system equipped with a perforated twisted tube turbulator. The results approve the efficacy of the BRT algorithm in predicting the entropy generation rate. Through comprehensive simulations and data analysis, we establish a predictive model that considers factors such as solar irradiance, fluid flow rate, tube geometry, and turbulator characteristics. The BRT model exhibits remarkable accuracy in capturing the nuanced interplay of these factors, enabling reliable estimations of entropy generation rate.

It is of great significance to identify the spatiotemporal stress distribution characteristics to ensure the safety of a super-high arch dam during the initial operation stage. Taking the 285.5 m-high Xiluodu Dam as an example, the spatiotemporal distribution characteristics were analyzed based on the five-year observation data after impoundment. Statistical and boosted-regression-tree-based prediction models for the dam stress were established. The boosted-regression-tree-based prediction model is more accurate than the statistical model. The monitoring indicators for the measuring points of focused locations were determined using the confidence interval estimation method. The results show that the dam was in a compression state, and the arching effect was obvious. The arch direction compressive stress gradually increased and stabilized, and the maximum appeared in the middle of the upstream face of the crown cantilever monolith. For the crown cantilever, the cantilever direction stress at the dam heel was significantly affected by the interior temperature recovery, and the arch direction stress in the middle of the upstream face was significantly affected by the reservoir water level. The measuring points of the focused locations with reliable observation data can be selected as a monitoring index to guide the initial operation.

Reference evapotranspiration (ETo) is a vital climate parameter affecting plants' water use. ETo can generate large deficits in soil moisture and runoff in different regions and seasons, leading to uncertainties in drought warning systems. A novel multivariate variational mode decomposition integrated with a boosted regression tree (i.e., MVMD-BRT) is constructed to forecast daily ETo. Firstly, the correlation matrix based on cross-correlation was computed to investigate the significant input predictor lags of daily ETo. Secondly, the MVMD technique decomposes the significant input lags into signals called intrinsic mode functions (IMFs). Thirdly, the IMFs were then employed in the BRT to build the MVMD-BRT model for daily ETo forecasting. A comparative assessment of MVMD against multivariate empirical mode decomposition (MEMD) was also performed on the same lines to develop the MEMD-BRT model. The MVMD-BRT model is compared against the random forest (RF) and hybrid MVMD-RF, MEMD-RF, extreme learning machine (ELM), and hybrid MVMD-ELM, MEMD-ELM, and cascaded feedforward neural network (CFNN) along with its hybrid MVMD-CFNN models for two stations in Queensland, Australia using a set of goodness-of-fit metrics. The results prove that the MVMD-BRT provide accurate daily ETo forecasting against the benchmark models. The MVMD-BRT model yielded the highest accuracy in terms of (WIE = 0.9070, NSE = 0.8421, LME = 0.6529, KGE = 0.8792) and (WIE = 0.8966, NSE = 0.8396, LME = 0.6521, KGE = 0.8803) for Brisbane and Gympie stations against the comparing models.

Soil erosion reduces soil fertility and land productivity while enhancing desertification, which seriously threatens ecological security, carbon sequestration, and sustainable economic and social development. Therefore, it is fundamental to precisely assess the long-term dynamics of soil erosion and explore its drivers to control the risk of soil erosion. Currently, machine learning-based analyses of the factors driving soil erosion are lacking in the Beiluo River Basin (BRB), China. This paper aimed to investigate the spatiotemporal dynamics of soil erosion by the Chinese soil loss equation coupled with the gully erosion factor, and then reveal its driving factors using the Boosting Regression Tree method (BRT) and estimate the influence of drivers' interactions on soil erosion in the BRB between 1990 and 2017. The findings demonstrated that the expanded Chinese soil loss equation accurately explained the variations in soil erosion in the BRB. The range of average soil erosion rates was 2,852.83–800.35 t km−2 a−1. Steep gully farmland, particularly areas with relatively rough terrain, was more vulnerable to intense erosion. Soil erosion rates for different geomorphologies decreased as follows: hilly–gully areas > gully–plateau areas > alluvial river plains > Rocky Mountains. The major drivers affecting the soil erosion rate were the biomass control factor (B) and slope, followed by slope length (L) and slope steepness (S). B–soil erodibility (1990), B–LS (2000), LS–rainfall erosivity (2010), and slope–rainfall erosivity (2017) interactions had the greatest influence on soil erosion, with interaction intensities of 32.3%, 17.75%, 46.05%, and 35.69%, respectively. The average soil erosion rates according to land classification types decreased as follows: farmland > grassland > forest. The results indicate that water and soil conservation in the study area benefited greatly from implementation of the Grain for Green Program, with forests reducing erosion more effectively than grassland.

The impact of landscape patterns on urban plant diversity has received significant attention; however, previous studies have primarily focused on two-dimensional (2D) patterns. Limited investigations have been conducted to explore the effects of three-dimensional (3D) landscape patterns on urban plant diversity. Spontaneous plants are ideal objects for studying the response of plant diversity to urban environments due to their independence from intentional human inputs. Buildings, as a crucial symbol of urbanization, exhibit strong 3D characteristics in urban landscapes. Green spaces, the most important source of plant propagules in cities, are closely related to spontaneous plant diversity in urban areas. Therefore, we examined how 2D/3D building and green space patterns influence urban spontaneous plant species richness and compared the responses between different categories (native and non-native). We surveyed spontaneous plants in the built-up areas of Shenzhen, a highly urbanized region situated along China’s southern coast. Our survey recorded 278 species belonging to 208 genera and 77 families. Results from boosted regression tree models (BRT) indicated significant correlations between spontaneous plant species richness and various metrics, including building coverage ratio (BCR), mean building height (MAH), green space coverage ratio (GCR), edge density of green spaces (ED_G), spatial congestion degree (SCD), and floor area ratio (FAR). These metrics exhibited complex nonlinear correlations with the species richness of spontaneous plants. Furthermore, native and non-native species richness responded differently to building and green space patterns; while native species richness was equally affected by green space and building patterns, non-native species richness was more strongly impacted by building patterns than green space ones. Our study quantified the nonlinear relationships between urban spontaneous plant species richness and 2D/3D green space and building patterns, demonstrating that optimizing green space and building patterns can be a practical approach for managing and promoting spontaneous plant diversity in highly urbanized areas.

Ocean and climate drivers affect the distribution and abundance of marine life on a global scale. Marine ecological forecasting seeks to predict how living marine resources respond to physical variability and change, enabling proactive decision‐making to support climate adaptation. However, the skill of ecological forecasts is constrained by the skill of underlying models of both ocean state and species‐environment relationships. As a test of the skill of data‐driven forecasts for fisheries, we developed predictive models of catch‐per‐unit‐effort (CPUE) of tuna and billfish across the south‐west Pacific Ocean, using a 12‐year time series of catch data and a large ensemble climate reanalysis. Descriptors of water column structure, particularly temperature at depth and upper ocean heat content, emerged as useful predictors of CPUE across species. Enhancing forecast skill over sub‐seasonal to multi‐year timescales in any system is likely to require the inclusion of sub‐surface ocean data and explicit consideration of regional physical dynamics. [ABSTRACT FROM AUTHOR]

We developed machine learning models to retrieve surface soil moisture (0-4 cm) from high resolution multispectral imagery, terrain attributes, and local climate covariates. Using a small unmanned aircraft system (UAS) equipped with a multispectral sensor we captured high resolution imagery in part to create a high-resolution digital elevation model (DEM) as well as quantify relative vegetation photosynthetic status. We tested four different machine learning algorithms. The boosted regression tree algorithm provided the best accuracy model with mean absolute error of 3.8 % volumetric water content. The most important variables for the prediction of soil moisture were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing data and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991-2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.

Landscape and quality attributes are major ecosystem characteristics closely associated with soil conservation service (SCS). However, the intrinsic mechanisms by which these two attributes influence SCS are still unclear. Therefore, this study quantitatively analyzed the landscape pattern, ecological quality, and SCS in the Lianshui River watershed (a typical soil and water loss area of red soil in southern China) and its sub-watersheds in 2019. The boosted regression tree model was used to explore the influence of 15 factors (i.e., landscape and quality attributes) on SCS at the sub-watershed scale. According to the results, compared with the landscape attribute, the quality attribute of the watershed ecosystem could better explain the spatial heterogeneity of SCS across 66 sub-watersheds. The overall degree of influence of five quality factors on SCS reached 57.81%, with the highest being the normalized differential build-up and bare soil index (NDBSI), at 25.11%. Among 10 landscape factors, aggregation had the greatest influence on SCS, at 28.64%. The relationships between key influencing factors and SCS were non-monotonic and non-linear, with threshold effects. For example, NDBSI values of 0.18–0.41 had a positive influence on SCS, while NDBSI values of 0.41–0.65 had a negative influence on SCS. The findings broaden our understanding of the response of SCS to changes in landscape and quality attributes at the sub-watershed scale, and could offer comprehensive support for soil erosion management in the watershed ecosystem.

Assessing changes in the extent and intensity of cropland use is essential to understanding the processes underlying agricultural development. However, our knowledge of the spatiotemporal dynamics of cropland intensification, and how they connect to the livelihoods of rural households, is currently limited. This paper aims to quantify key components of cropland intensification in China through trend analysis of cropping intensity and land productivity over time. This information is then used to model the effects of cropland intensification on farmers' livelihoods. We found that most croplands were under intensive use characterized by steady cropping frequency or multi‐cropping from 2001 to 2018, while the variation in cropping frequency exhibited a significant north–south spatial disparity. High cropping intensity increased land productivity. However, over 25% of the total cropland area experienced productivity improvements that were characterized as inconsistent. Our work suggests that the economic output of farming is greatly driven by land management intensity and that fertilizer use is the predominant driver of this. We also found that cropping intensity at the landscape scale showed no correlation with agricultural income, but land productivity correlated significantly with both land management intensity and rural livelihood metrics. The findings presented here highlight the importance of integrating the long‐term consistency of land productivity and rural livelihoods into the research framework of land use intensification. Doing so advances the current understanding of diverse cropland use change in China. [ABSTRACT FROM AUTHOR]

Dew is one of the most important supplementary water sources in arid and semi-arid regions. The influence of dew amount on the water balance can be evaluated for water management, ecosystem protection, and the optimization of agricultural practices. Moreover, the influencing factors of dew formation can provide some insights into how climate change may impact water availability and ecosystems. However, it is still unclear in the dew formation, due to the difficulty of observation without considering the dew in the water balance. This study aims to quantify the dew amount for a better understanding of dew formation. A series of lysimeter experiments were carried out to continuously observe the dew amount in the Mu Us desert from October 2020 to September 2021. Among them, the dew amount included the fog, dew, and hoar frost formation. In addition, the meteorological factors were monitored at the experimental site, including air temperature, precipitation, wind speed, relative humidity, net radiation, and surface temperature. All data were recorded every ten minutes. The dew amount was evaluated for the water balance during formation. Boosted regression tree (BRT) model was used to investigate the influence of meteorological factors on dew formation. The results were as follows. 1) The dew amount was 47.1 mm in the experimental period, corresponding to 19.6% of the annual rainfall. The dew started to form at night when the air temperature was significantly higher than the soil surface temperature. The dew was mainly formed from October 2020 to January 2021 (cold months), with a maximum amount of 9.00 mm in December 2020, and a minimum of 0.55 mm in June 2021. The dew amount was larger in the colder months than that in the warmer ones. 2) The BRT model showed that the largest contribution (43.4%) was found in the relative humidity to the dew formation. It infers that relative humidity was a key factor in the formation of dew. The contribution rates of the net radiation at night and the wind speed to the dew formation were 21.7% and 15.2%, respectively. The much fewer contributions to the dew formation were the air temperature and vapor pressure deficit. The optimal combination of parameters was achieved in the dew formation, such as the relative humidity greater than 53%, net radiation at night less than -37 W/m², and wind speeds ranging from 0.5 to 1.8 m/s. 3) The surrounding air temperature and relative humidity of Artemisia ordosica posed the more significant influence on the dew formation, compared with bare ground. The errors of the water balance were reduced from 25.3% to 5.7%, considering the amount of dew in the semi-arid regions. The dew amount was quantified to improve the accuracy of the water balance. The finding can provide a strong reference to accurately evaluate the water resources and then better understand the formation process of dew amounts under the coverage of Artemisia ordosica. In addition, the Artemisia ordosica in the water cycle can be expected to protect the vegetation ecosystems in the arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

The location, effort, number of captures, and time of fishing were all used in this study to assess the geographic distribution of Parastromateus niger in the Taiwan Strait. Other species distribution models performed worse than generalized linear models (GLMs) based on six oceanographic parameters. The sea surface temperature (SST) was between 26.5 °C and 29.5 °C, the sea surface chlorophyll (SSC) level was between 0.3–0.44 mg/m3, the sea surface salinity (SSS) was between 33.4 °C and 34.4 °C, the mixed layer depth was between 10 °C and 14 °C, the sea surface height was between 0.57 °C and 0.77 °C, and the eddy kinetic energy (EKE) was between 0.603 °C. According to the statistical findings, SST is merely a small effect compared to SSS, SSC level, and EKE in terms of impacting species distribution. By combining four effective single-algorithm models with no obvious bias, an ensemble habitat model was created. The ranges of 117°E–119°E and 22°N–24°N have the highest annual distributions of S.CPUE and nominal CPUE. [ABSTRACT FROM AUTHOR]

Study region: Northwest of Guangxi Province, southwest China. Study focus: Knowing the role of isotopes in precipitation can improve understanding of climate change and eco-hydrological processes in complex critical-zone systems. However, knowledge of the influence of local meteorological factors on variations of precipitation isotopes remains limited as high-resolution rainwater sampling is lacking. New hydrological insights for the region: The temporal characteristics (intra-annual, seasonal, and monthly scales) of precipitation were analyzed using daily precipitation isotopes data from 2011 to 2020 and event-resolving data from 2019 to 2020 at Huanjiang in southwest China. The boosted regression tree model was used to explore the relationship between local meteorological factors and δ18O values. On an intra-annual scale, δ18O, δD, and d-excess values exhibited significant sinusoidal fluctuation, being lower and higher during the wet and dry seasons respectively. Temperature is the most sensitive meteorological factor to monsoon climate response. On the seasonal scale, ET0 explains 37.5% of isotopic variations in the dry season, thus indicating that the influence of evaporation on the rainwater isotopes variation should be considered. The “amount effect” of rainwater isotopes was clearly shown on the monthly scale. However, precipitation isotopes did not follow the “amount effect” on an inter-event scale, which can be explained by large-scale convection activity. These findings can improve interpretations of isotopic variations in precipitation in humid subtropical climate zones.

The location, effort, number of captures, and time of fishing were all used in this study to assess the geographic distribution of Parastromateus niger in the Taiwan Strait. Other species distribution models performed worse than generalized linear models (GLMs) based on six oceanographic parameters. The sea surface temperature (SST) was between 26.5 °C and 29.5 °C, the sea surface chlorophyll (SSC) level was between 0.3–0.44 mg/m3, the sea surface salinity (SSS) was between 33.4 °C and 34.4 °C, the mixed layer depth was between 10 °C and 14 °C, the sea surface height was between 0.57 °C and 0.77 °C, and the eddy kinetic energy (EKE) was between 0.603 °C. According to the statistical findings, SST is merely a small effect compared to SSS, SSC level, and EKE in terms of impacting species distribution. By combining four effective single-algorithm models with no obvious bias, an ensemble habitat model was created. The ranges of 117°E–119°E and 22°N–24°N have the highest annual distributions of S.CPUE and nominal CPUE.

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Ranges of species around the world are expected to contract in response to climate change. Species distribution models (SDMs) are a powerful tool for predicting changes in habitat availability, but the variables selected to create SDMs influence their performance. In addition to climate, habitat characteristics and species traits can play a role in predicting species distribution. In this paper, we consider how variable selection influences the accuracy of SDMs when applied to isolated subpopulations of two widely distributed bird species: great gray owl (Strix nebulosa) and willow flycatcher (Empidonax traillii). In the Sierra Nevada of California, these species are restricted largely to discrete patches of meadow habitat within a forest matrix, providing the potential to identify specific locations to target conservation efforts. We contrast predictions made by SDMs that consider climatic variables alone with those that incorporate both climate and geophysical variables. Adding geophysical variables resulted in differing model predictions. For willow flycatchers, adding geophysical variables improved predictive performance. In the case of great gray owls, models with and without geophysical variables had nearly identical performance under historical conditions but differed starkly in their predictions. The full model (climatic and geophysical variables) predicted habitat availability to decrease moderately, whereas the climate‐only model predicted nearly complete loss of favorable habitat by 2099. The climate‐only model is consistent with expectations based on previous SDMs of birds across North America, but previous studies also assumed homogeneity in species traits and range‐wide habitat requirements. The full model appears more consistent with recent trends in great gray owl numbers in the Sierra Nevada specifically, where the population has remained relatively stable over recent decades. Given contradictions in our model predictions, care should be taken when trying to apply similar SDMs to other systems. [ABSTRACT FROM AUTHOR]

BACKGROUND: Oat (Avena sativa L.) is recognized for its impressive productivity in marginal environments, and the sowing rate is an important crop management practice that potentially enhances oat productivity. Previous studies have reported the effect of sowing rate on oat yield; however, the results from such studies are inconsistent. Thus, based on 43 studies across eight countries, this study aimed to assess changes in hay and grain yields in response to sowing rate and, in combination with a boosted regression tree, to evaluate and rank the dominant factors (e.g. climate conditions, soil conditions, and sowing rate) affecting changes in hay and grain yields of oat. RESULTS: The results revealed that increasing the sowing rate significantly increased the response ratio of grain yields and hay yields by averages of 7.3% and 7.9% respectively. However, the response ratios of grain yields and hay yields in response to changes in sowing rate were affected by different factors. Climate condition and mean annual precipitation primarily affected the response ratios of hay yields, whereas the sowing rate dominated changes in the response ratios of grain yields, with the response ratios of grain yields peaking at a sowing rate of 85 kg ha−1. CONCLUSION: Optimizing the sowing rate with site‐specific environmental conditions could be a potential strategy for profitable oat production, given that oat can be produced under marginal environments (e.g. cool–wet climates and soil with low fertility). © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Abstract Little is currently known about preservation of plant DNA in lake sediments. Most prior information originates from laboratory experiments while systematic field‐based studies are still lacking. Here, we used the “g” and “h” universal primers for the P6 loop region of the chloroplast trnL (UAA) intron to amplify plant DNA from 219 lake surface sediments from China and Siberia. We introduce (i) the percentage of sequence counts with the best identity ≥95%, (ii) weighted average identity, (iii) weighted average DNA fragment length, and iv) rarefied richness of terrestrial seed plants of plant DNA metabarcoding as proxies for sedimentary DNA preservation and relate them to five environmental variables (lake water conductivity, lake water pH, mean July air temperature, and sampling depth, lake size) using boosted regression tree (BRT) analyses. Our results suggest that lake water chemical characteristics, that is, electrical conductivity and pH, are the most important variables for the preservation of plant DNA in lake sediments. Intermediate water conductivities (100–500 μS cm−1) and neutral to slightly alkaline water pH (7–9) may facilitate plant DNA preservation. Furthermore, deep lakes seem to support plant DNA preservation as indicated by relatively high rarefied richness. We also find high rarefied richness in small lakes compared with large lakes, but this result needs to be assessed by more studies in the future. None of our BRT models shows that mean July air temperature is a key variable to limit plant DNA preservation. To conclude, our results suggest that sedimentary DNA studies can preferentially select deep lakes characterized by intermediate water conductivities and neutral to slightly alkaline pH conditions.

• There are considerable differences of LST across UFBs in day and night. • Influence of driving factors on LST is strongly dependent on times and UFBs. • There are small differences of warming and cooling thresholds across UFBs. • Mitigation measures should take into account differences of times and UFBs. A comprehensive and in-depth understanding of the formation mechanisms of the urban thermal environment is the basis for thermal environment regulation, however, there is insufficient knowledge regarding how driving factors influence daytime and nighttime land surface temperature (LST) within urban functional blocks (UFBs). We selected Xi'an, China as a case study, integrating remote sensing data including ECOSTRESS, Landsat-8, and Gaofen-1, along with geographic data including road network, areas of interest, points of interest, building footprint, and mobile phone signaling. It divided 10 types of UFBs, inverted daytime and nighttime LST, calculated 5 types of driving factors, and finally analyzed the contribution and marginal effects of driving factors on day-night LST using boosted regression tree. The results showed that LST and its driving factors differed significantly in different times and UFBs. Industrial blocks and urban villages had higher LST in the daytime, while residential blocks, commercial blocks, and public service blocks had higher LST in nighttime. Industrial blocks were the dominant blocks that drove the overall LST up during the day, while residential blocks were the dominant blocks at night. Location distance (-) and population density (+) affected LST in all UFBs during day and night, NDVI (-), building density (+), and floor area ratio (-) were key factors for most UFBs during daytime, and NDVI (+), surface albedo (-), and point density of interest (+) were key factors during nighttime. Most of the driving factors had significant influence thresholds, but there were small differences across UFBs. This paper aims to elucidate the mechanisms by which driving factors influence urban LST during day and night across different UFBs, thereby providing new support for more targeted thermal environment regulation and diurnal trade-offs at the block scale. [ABSTRACT FROM AUTHOR]

• Diurnal LST variations across different landscape types were compared. • Trees exhibit lower LST than low vegetation during the daytime and transition time. • 3D landscapes exhibited a greater impact on diurnal LST than 2D landscapes. • Nonlinear relationships between key factors and diurnal LST were revealed. The diverse urban features considerably influence land surface temperature (LST). Numerous studies have extensively investigated the impacts of 2D/3D urban features on LST. However, the diurnal impacts have not been comprehensively understood. Therefore, taking 1, 510 blocks within the 4th ring road of Beijing as samples, this study examined the influence of urban features (2D: low vegetation, water, and impervious surface; 3D: buildings and trees) on diurnal LST at the block scale based on ECOSTRESS LST using boosted regression tree (BRT) algorithm. The results showed that trees exhibit better cooling effects than low vegetation except at 3:09. The 3D features exhibited a greater impact on diurnal LST compared with 2D features, and it contributed strongly to daytime LST, which was 27.8 % on average higher than the 2D features. Building and tree densities had the greatest influence on daytime LST, while building height influenced nighttime LST the most. The impacts of key metrics on diurnal LST were nonlinear, and the relationship had diurnal variations. Taller buildings and trees were negatively correlated with LST during the daytime and transition time, but positively correlated with nighttime LST. The findings provide insights into landscape optimization strategies to improve block thermal comfort. [ABSTRACT FROM AUTHOR]

• In this study, we extracted the urban core area based on the Point of Interest (POI) and kernel density methods. • The effects of UGS landscape composition and configuration on LST varied across seasons and across urban green space ratio (UGSR) gradients. • In the warm season, increasing the UGSR proved to be the most effective way to mitigate the urban thermal environment. • In urban core areas where expanding UGS is not possible or in cores with high UGSR, designing UGS with complex shapes and clusters can better achieve the cooling effect. The key to managing urban heat island (UHI) is identifying and cooling the core areas of UHI. Studies have shown that spatial differences in urban green space (UGS) indicators have a significant impact on spatial differences in land surface temperature (LST). However, previous studies have mainly explored UGS and LST in the entire region, with few focusing on the urban core area. In this study, we extracted the urban core area based on the Point of Interest (POI) and kernel density methods. Correlation and regression analyses were then used to investigate the correlation between UGS indicators and seasonal LST in the urban core area. Our results confirm the significant cooling effect of UGS at the urban core scale. The effects of UGS landscape composition and configuration on LST varied across seasons and across urban green space ratio (UGSR) gradients. In the warm season, increasing the UGSR proved to be the most effective way to mitigate the urban thermal environment. In urban core areas where expanding UGS is not possible or in cores with high UGSR, designing UGS with complex shapes and clusters can better achieve the cooling effect. These results suggest that the influence of UGS landscape features on the urban surface thermal environment at the urban core area scale is very complex. Therefore, implementing urban greening to mitigate the UHI problem requires considering the premise of multifactorial analysis. [ABSTRACT FROM AUTHOR]