Combining the Optimized Maximum Entropy Model to Detect Key Factors in the Occurrence of Oedaleus decorus asiaticus in the Typical Grasslands of Central and Eastern Inner Mongolia

Abstract

Grasshoppers pose a significant threat to both natural grassland vegetation and crops. Therefore, comprehending the relationship between environmental factors and grasshopper occurrence is of paramount importance. This study integrated machine learning models (Maxent) using the kuenm package to screen MaxEnt models for grasshopper species selection, while simultaneously fitting remote sensing data of major grasshopper breeding areas in Inner Mongolia, China. It investigated the spatial distribution and key factors influencing the occurrence of typical grasshopper species in grassland ecosystems. The modelling results indicate that a typical steppe has a larger suitable area. The soil type, above biomass, altitude, and temperature, predominantly determine the grasshopper occurrence in typical steppes. This study explicitly delineates the disparate impacts of key environmental factors (meteorology, vegetation, soil, and topography) on grasshopper occurrence in typical steppes. Furthermore, it provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be instrumental in formulating future management measures to guarantee grass ecological environment security and the sustainable development of grassland.

Keywords: MaxEnt; grasshopper; remote sensing; typical steppe.

Figure 1

Location of the study area.

Figure 2

Distribution of correlation coefficients between habitat factors. The darker the blue, the higher the two environmental factors; the opposite is true for red.

Figure 3

All candidate model data and the best model selected based on statistical significance, omission rate, and AICc criteria.

Figure 4

MaxEnt models for the typical central and eastern Inner Mongolian steppe regions represented by ROC curves and omission curves. (a) The ROC curve comprises a red line, a blue line, and an area under the curve (AUC) value. (b) The crimson line represents the model’s fit to the training data, while the blue line represents the model’s fit to the test data. The omission curve shows the differences between testing and training omission and the predicted area in relation to the cumulative threshold choice. Suitable conditions were predicted above the threshold value while unsuitable conditions were predicted below it. The omission rate should ideally be close to the predicted omission, according to the cumulative threshold definition.

Figure 5

Modeling suitability of grasshopper occurrence in the typical central and eastern Inner Mongolian steppes determined by MaxEnt models. Four levels of suitability are shown, namely, high suitability (>0.75, red color), moderate suitability (0.5–0.75, orange color), low suitability (0.25–0.5, yellow color), and unsuitability (<0.25, gray color).

Figure 6

Jackknife of regularized training for the MaxEnt model of Oedaleus asiaticus occurrence in the typical central and eastern Inner Mongolian steppes.

Figure 7

Percentage Contribution and Permutation Importance of environment variables for O. asiaticus in the MaxEnt.

Figure 8

Response curves for the major habitat factors in the model predictions. The response curves show the relationship between the probability of grasshopper occurrence and habitat factors. The values shown are the mean of 100 replicate runs; blue edges show the ± SD of 100 replicates. For each panel, the x-axis represents the variable, and the y-axis represents the probability of grasshopper occurrence, Blue represents the standard deviation, while red represents the mean value.