Spatial and Temporal Variability of Grassland Grasshopper Habitat Suitability and Its Main Influencing Factors

Abstract

Grasshoppers are highly destructive pests, and their outbreak can directly damage livestock development. Grasshopper outbreaks can be monitored and forecasted through dynamic analysis of their potential geographic distribution and main influencing factors. By integrating vegetation, edaphic, meteorological, topography, and other geospatial data, this study simulated the grasshopper suitability index in Hulunbuir grassland using maximum entropy species distribution modeling (Maxent). The Maxent model showed high accuracy, with the training area under the curve (AUC) value ranging from 0.897 to 0.973 and the testing AUC ranging from 0.853 to 0.971 for the past 13 years. The results showed that suitable areas, including the most suitable area and moderately suitable area, accounted for a small proportion and were mainly located in the eastern and southern parts of the study area. According to model analysis based on 51 environmental factors, not all factors played a significant role in the grasshopper cycle. Moreover, differences in environmental factors drive the spatial variability of suitable areas for grasshoppers. The monitoring and prediction of potential outbreak areas can be improved by identifying major environmental factors having large variability between suitable and unsuitable areas. Future trends in grasshopper suitability indices are likely to contradict past trends in most of the study area, with only approximately 33% of the study area continuing the past trend. The results are expected to guide future monitoring and prediction of grasshoppers in Hulunbuir grassland.

Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data

“…Compared with other studies [ 54 ], this study not only constructed an index system and models for grasshopper occurrence suitability, but also analyzed and compared the similarities and differences in the environmental conditions. We comprehensively considered 16 environmental variables corresponding to four categories: topography, meteorology, vegetation and soil, in order to determine the key factors that impact grasshopper occurrence in typical steppe and meadow steppe.…”

“…Grasshoppers have always been the most significant grassland pests in China [ 2 , 54 ]. Grasshoppers have caused widespread agricultural and social economic damage [ 5 , 6 , 54 ].…”

“…Grasshoppers have always been the most significant grassland pests in China [ 2 , 54 ]. Grasshoppers have caused widespread agricultural and social economic damage [ 5 , 6 , 54 ]. In this paper, the machine learning model (Maxent) and remote sensing data were used to develop a species–environmental matching model, study the spatial distribution of grasshopper occurrence in the major grasshopper occurrence area of Inner Mongolia, identify the related environmental variables, and summarize the suitable environmental conditions for grasshopper occurrence.…”

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

“…In this study, the suitable threshold of the Maxent model was set to 0.5. Based on the literature and expert experience [ 8 , 14 , 60 , 61 ], the suitability of each habitat factor was divided into low suitability (<0.5), medium suitability (0.5–0.6), and high suitability (>0.6). In calculations, areas with low suitability were assigned values of 1, areas with medium suitability were assigned values of 2, and areas with high suitability were assigned values of 3.…”

Impact of Land Use Change on the Habitat Quality Evolution in Three Gorges Reservoir Area, China

“…Currently, most studies on habitat quality use the indicator system evaluation method [ 12 ] and the model method [ 13 ], but the indicator system method has not yet formed a unified evaluation index and standard, whereas the model method is relatively more accurate and data are easier to obtain, as well as less time consuming, less costly and more visible [ 14 ]. The habitat quality models mainly used at this stage are the Habitat Suitability Model (HSI) [ 15 ], the Maximum Entropy Model (Mx Ent) [ 16 ], and the InVEST model [ 17 , 18 ]. Among them, the InVEST model is mainly used for the assessment, management, and decision-making of ecosystem services, and the HQ module of this model is mostly used for habitat quality measurement in research [ 19 , 20 ].…”