Study on the prediction method of grasshopper occurrence risk in Inner Mongolia based on the maximum entropy model during the growing period.

Grasshoppers represent a significant biological challenge in Inner Mongolia's grasslands, severely affecting the region's animal husbandry. Thus, dynamic monitoring of grasshopper infestation risk is crucial for sustainable livestock farming. This study employed the Maxent model, along with remote sensing data, to forecast Oedaleus decorus asiaticus occurrence during the growing season, using grasshopper suitability habitats as a base. The Maxent model's predictive accuracy was high, with an AUC of 0.966. The most influential environmental variables for grasshopper distribution were suitable habitat data (34.27%), the temperature-vegetation dryness index during the spawning period (18.81%), and various other meteorological and vegetation factors. The risk index model was applied to calculate the grasshopper distribution across different risk levels for the years 2019-2022. The data indicated that the level 1 risk area primarily spans central, eastern, and southwestern Inner Mongolia. By examining the variable weights, the primary drivers of risk level fluctuation from 2019 to 2022 were identified as accumulated precipitation and land surface temperature anomalies during the overwintering period. This study offers valuable insights for future O. decorus asiaticus monitoring in Inner Mongolia.

Analysis of Spatiotemporal Variation in Habitat Suitability for Oedaleus decorus asiaticus Bei-Bienko on the Mongolian Plateau Using Maxent and Multi-Source Remote Sensing Data.

O. decorus asiaticus is a major grasshopper species that harms the development of agriculture on the Mongolian Plateau. Therefore, it is important to enhance the monitoring of O. decorus asiaticus. In this study, the spatiotemporal variation in the habitat suitability for O. decorus asiaticus on the Mongolian Plateau was assessed using maximum entropy (Maxent) modeling along with multi-source remote sensing data (meteorology, vegetation, soil, and topography). The predictions of the Maxent model were accurate (AUC = 0.910). The key environmental variables affecting the distribution of grasshoppers and their contribution were grass type (51.3%), accumulated precipitation (24.9%), altitude (13.0%), vegetation coverage (6.6%), and land surface temperature (4.2%). Based on the assessment results of suitability by Maxent model, the model threshold settings, and the formula for calculating the inhabitability index, the 2000s, 2010s, and 2020s inhabitable areas were calculated. The results show that the distribution of suitable habitat for O. decorus asiaticus in 2000 was similar to that in 2010. From 2010 to 2020, the suitability of the habitat for O. decorus asiaticus in the central region of the Mongolian Plateau changed from moderate to high. The main factor contributing to this change was accumulated precipitation. Few changes in the areas of the habitat with low suitability were observed across the study period. The results of this study enhance our understanding of the vulnerability of different regions on the Mongolian Plateau to plagues of O. decorus asiaticus and will aid the monitoring of grasshopper plagues in this region.

Assessment on Potential Suitable Habitats of the Grasshopper Oedaleus decorus asiaticus in North China based on MaxEnt Modeling and Remote Sensing Data.

Grasshopper populations can quickly grow to catastrophic levels, causing a huge amount of damage in a short time. Oedaleus decorus asiaticus (Bey-Bienko) (O. d. asiaticus) is the most serious species in Xilingol League of the Inner Mongolia Autonomous Region. The region is not only an important grassland but also a site of agricultural heritage systems in China. Therefore, projecting the potential geographic distribution of O. d. asiaticus to provide an early warning is vital. Here, we combined temperature, precipitation, soil, vegetation, and topography with remote sensing data to screen the predictors that best characterize the current geographical distribution of O. d. asiaticus. A MaxEnt model approach was applied to project the potential suitable distribution of O. d. asiaticus in Xilingol League (the Inner Mongolia Autonomous Region of China) combined with a set of optimized parameters. The modeling results indicated that there were six main habitat factors that determined the suitable distribution of O. d. asiaticus such as the soil type (ST), grassland type (GT), elevation, precipitation during the growing period (GP), precipitation during the spawning period (SP), and normalized difference vegetation index during the overwintering period (ONDVI). The simulated result was good, with average AUC and TSS values of 0.875 and 0.812, respectively. The potential inhabitable areas of grasshoppers were 198,527 km2, distributed mainly in West Urumqi, Xilinhot City, East Urumqi, Abaga Banner, and Xianghuang Banner of Xilingol League. This study is valuable to guide managers and decision-makers to prevent and control the occurrence of O. d. asiaticus early on and this study may facilitate meaningful reductions in pesticide application.

[Composition, Characteristics, and Accumulation of Antibiotics in the Soil in Agricultural Land].

Exploring the composition and accumulation of antibiotics in agricultural land soil for soil for quality management of agricultural land and control of antibiotic pollution is of great significance. A total of 95 soil samples were collected from farmland soil in a typical agricultural and sideline production base of northern China. In this study, the concentrations of 10 antibiotics, including tetracycline antibiotics (TCs), macrolide antibiotics (MLs), and sulfonamide antibiotics (SAs), were determined in soil samples from different land use types using HPLC-MS/MS. In addition, the composition and spatial distribution of the antibiotics were compared. The relationships between the concentration distributions and the distance from livestock farms, highways, and rivers were analyzed. Moreover, the composition and accumulation of antibiotics in the soil with different planting patterns and soil properties were preliminarily discussed. The results showed that the concentrations of antibiotics in the soil were low-level, while the detection rate was high; in particular, the detection rate of the total amount of antibiotics was as high as 100%. In the study area, TCs were the dominant antibiotic types, accounting for 94% of the total. The coefficient variation (CV) was high, which reflected a significant difference in the spatial variation of these antibiotics. The spatial distribution and accumulation of antibiotics in the soil in this area were affected by the intensity of human activity. The detection rate and concentrations of the various antibiotics decreased with increasing distance between the soil sample and livestock farms, highways, and rivers. Among these, there was a significant negative correlation between the concentration of antibiotics and the distance between livestock farms and the soil samples (P<0.05). The detection rate of three types of antibiotics in soil samples from within 50 meters of a river reached 100%. The total concentration of the 10 antibiotics was the highest in orchards, followed by vegetable plots, and mixed fruit and vegetable areas. Furthermore, the sources and concentrations of antibiotics in a peach orchard and open-air vegetable field were significantly different from those in a walnut orchard, greenhouse vegetable field, and mixed field. Moreover, the soil pH, soil organic matter (SOM), cation exchange capacity (CEC), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) were selected to characterize soil properties. Redundancy analysis showed that soil properties such as pH, SOM, CEC, AP, AK have a greater impact on the distribution of antibiotics. The distribution of antibiotics was most closely related to AK, and the effect of TN was relatively weak. The results of this study suggested that the composition and accumulation of soil antibiotics in the area were affected by human activities and soil properties.

Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods.

Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. We explored reflectance spectroscopy as an alternative method for assessing heavy metals. Four spectral transformation methods, first-order differential (FDR), second-order differential (SDR), continuum removal (CR) and continuous wavelet transform (CWT), are used for the original spectral data. Spectral preprocessing effectively eliminated the noise and baseline drifting and also highlighted the locations of the spectral feature bands. Partial least squares regression (PLSR) and radial basis function neural network (RBF) were used to study the hyperspectral inversion of four heavy metals (Cr, As, Ni, Cd). The inversion models of four heavy metals were established in the bands with the highest correlation coefficient. The inversion effects were evaluated by the coefficient of determination (R2), root mean square error (RMSE) and residual predictive deviation (RPD) indexes. The R values of the correlation coefficient were significantly improved after smoothing and spectral transformation compared to the original waveband. The method combining continuous wavelet transform (CWT) with radial basis function neural network (RBF) had the best inversion effect on the four heavy metals. When compared to partial least squares regression (PLSR), the RMSE values were reduced by approximately 2. The CWT-RBF method can be used as a means of inversion of heavy metals in mining wasteland reclaimed land.