DOI: 10.5061/dryad.pvmcvdp0r
title: Integrating remote sensing and meteorological data to identify key drivers of the potential distribution of a grassland grasshopper

description: 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.
description: # Integrating remote sensing and meteorological data to identify key
 drivers of the potential distribution of a grassland grasshopper We have
 submitted our raw data (**Grasshopper_and_factors_data.csv**),
 environmental factors in 2022 (**2022_bio1.asc; 2022_bio13.asc;
 2022_bio14.asc; 2022_bio15.asc; 2022_bio16.asc; 2022_bio17.asc;
 2022_bio18.asc 2022_bio19.asc; 2022_bio20.asc; 2022_bio21.asc;
 2022_bio22.asc; 2022_bio24.asc; 2022_bio25.asc; 2022_bio26.asc;
 2022_bio27.asc; 2022_bio28.asc; 2022_bio30.asc**), and environmental
 factors in 2023 (**2023_bio1.asc; 2023_bio13.asc; 2023_bio14.asc;
 2023_bio15.asc; 2023_bio16.asc; 2023_bio17.asc; 2023_bio18.asc;
 2023_bio19.asc; 2023_bio20.asc; 2023_bio21.asc; 2023_bio22.asc;
 2023_bio24.asc; 2023_bio25.asc; 2023_bio26.asc; 2023_bio27.asc;
 2023_bio28.asc; 2023_bio30.asc**) ## Descriptions ###
 grasshopper_and_factors_data.csv The values of all environmental factors
 are normalized to 0–1 to eliminate the impact of the unit on each
 environmental factor. * *Name*: Currently valid scientific name of
 grasshopper species * *alt*: altitude (m) * *Lat*: latitude (°) * *Lon*:
 longitude  (°) * *time*: Survey year * *Occurrence*: A value of
 1represents grasshopper occurrence (presence). ### environmental factors
 in 2022 and 2023 data. The values of all environmental factors are
 normalized to 0–1 to eliminate the impact of the unit on each
 environmental factor. * *Name*: Currently valid scientific name *
 *2022_bio1.asc*: Elevation * *2022_bio13.asc*: NDVI in August of the
 previous year (NDVI–8) * *2022_bio14.asc*: NDVI in May (NDVI–5) *
 *2022_bio15.asc*：NDVI in June (NDVI–6) * *2022_bio16.asc*：Average
 temperature in winter (Mean–T–W) * *2022_bio17.asc*：Min temperature in
 winter of the previous year (Min–T–W) * *2022_bio18.asc*：Sum temperature
 in spring (Sum–T–S) * *2022_bio19.asc*：Average temperature from July of
 the previous year to June of the experimental year (Mean–T–Y) *
 *2022_bio20.asc*：Average temperature in July of the previous year (T–7) *
 *2022_bio21.asc*：Average temperature in August of the previous year (T–8)
 * *2022_bio22.asc*：Average temperature in September of the previous year
 (T–9) * *2022_bio24.asc*：Sum precipitation in winter of the previous year
 (Sum–P–W) * *2022_bio25.asc*：Sum precipitation from July of the previous
 year to experimental (Sum–P–Y) * *2022_bio26.asc*：Sum precipitation in
 July of the previous year (P–7) * *2022_bio27.asc*：Sum precipitation in
 August of the previous year (P–8) * *2022_bio28.asc*：Sum precipitation in
 September of the previous year (P–9) * *2022_bio30.asc*：Vegetation type
 (VT) * *2023_bio1.asc*: Elevation * *2023_bio13.asc*: NDVI in August of
 the previous year (NDVI–8) * *2023_bio14.asc*: NDVI in May (NDVI–5) *
 *2023_bio15.asc*：NDVI in June (NDVI–6) * *2023_bio16.asc*：Average
 temperature in winter (Mean–T–W) * *2023_bio17.asc*：Min temperature in
 winter of the previous year (Min–T–W) * *2023_bio18.asc*：Sum temperature
 in spring (Sum–T–S) * *2023_bio19.asc*：Average temperature from July of
 the previous year to June of the experimental year (Mean–T–Y) *
 *2023_bio20.asc*：Average temperature in July of the previous year (T–7) *
 *2023_bio21.asc*：Average temperature in August of the previous year (T–8)
 * *2023_bio22.asc*：Average temperature in September of the previous year
 (T–9) * *2023_bio24.asc*：Sum precipitation in winter of the previous year
 (Sum–P–W) * *2023_bio25.asc*：Sum precipitation from July of the previous
 year to experimental (Sum–P–Y) * *2023_bio26.asc*：Sum precipitation in
 July of the previous year (P–7) * *2023_bio27.asc*：Sum precipitation in
 August of the previous year (P–8) * *2023_bio28.asc*：Sum precipitation in
 September of the previous year (P–9) * *2023_bio30.asc*：Vegetation type
 (VT)