Integrating remote sensing and meteorological data to identify key drivers of the potential distribution of a grassland grasshopper

A detailed understanding of the mechanisms linking environmental factors
to insect pest outbreaks is crucial. Combining these key factors to
establish reliable models for the early identification of potential pest
distribution is essential for protecting ecosystems and reducing economic
losses. We identified and analyzed the study object Oedaleus decorus
asiaticus (O. decorus) for key environmental factors and their thresholds
influencing the distribution. An ensemble model (EM) incorporating these
key environmental factors was then constructed to delineate the potential
suitable areas for this species. The results indicated that: (1) EM offers
significant advantages for monitoring suitable areas for O. decorus. The
accuracy of the model was assessed using area under the curve (AUC) and
true skill statistics (TSS), which yielded values of 0.973 and 0.833,
respectively. (2) Climate is the determining factor directly influencing
the distribution of O. decorus, particularly because temperature often
affects the entire life cycle of this taxon. The total precipitation in
August is crucial in their distribution. Other topographical features,
apart from elevation, exert minor influence in their distribution.
Vegetation biomass during the oviposition and incubation periods
influenced the distribution of this species. The distribution of their
suitable areas is strongly influenced by vegetation type but shows little
correlation with soil type. (3) The suitable areas of this species in 2022
and 2023 were identified. The most suitable areas of O. decorus are
distributed in the central, southeastern, and northeastern regions of
Inner Mongolia, which overlapped with the zones of agro–pastoralism. Our
findings provide positive implications for grasshopper monitoring and
control in Inner Mongolia. The theoretical and methodological approaches
developed here can be extended to guide the monitoring and prediction of
other pests.