Analysis of desert locust (Schistocerca gregaria) suitability in Yemen: an integrated evaluation based on MaxEnt and space–time cube approaches

ABSTRACT

Desert locust outbreaks pose severe threats to agricultural production and ecological environments. Hence, understanding the distribution of habitat suitability is crucial for establishing effective prevention and control measures. We introduce an innovative method for analyzing the temporal and spatial variations of desert locust habitat suitability in Yemen. This method integrates ecological niche models with space–time cube analysis. First, we gathered key environmental variables that affect desert locust distribution, including vegetation and soil types, precipitation, and temperature. Subsequently, we employed the MaxEnt model to assess habitat suitability for desert locusts in Yemen for 2010 and 2013–2021. Finally, we applied the space–time cube method for spatiotemporal analysis to reveal the spatiotemporal dynamics of habitat suitability distribution. The MaxEnt model results showed that the kappa coefficients exceeded 0.46 and the area under the curve exceeded 0.75. The spatiotemporal analysis results showed that from 2010 to 2021, the Red Sea coastal plains in western Yemen exhibited the highest habitat suitability levels, with significant increases in habitat suitability in the western and central regions. This study offers a new perspective on studying the spatial and temporal characteristics of desert locust habitat suitability distribution and can provides a scientific basis for the management of desert locusts.

KEYWORDS:

• Desert locust
• habitat suitability
• Yemen
• MaxEnt
• space–time cube

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in LSIB 2017 at https://geonode.state.gov/layers/catalog:geonode:LSIB, ERA5-Land Monthly Aggregated at https://doi.org/10.24381/cds.68d2bb30

 Google Scholar

, MOD09GA at https://doi.org/10.5067/MODIS/MOD09GA.061

 Google Scholar

, MCD12Q1 at https://doi.org/10.5067/MODIS/MCD12Q1.061

 Google Scholar

, CHIRPS at https://doi.org/10.1038/sdata.2015.66

 Google Scholar

, NASADEM at https://doi.org/10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001

 Google Scholar

.

Additional information

Funding

This work is supported by the National Key R&D Program of China (2021YFE0194800), Alliance of International Science Organizations (Grant No. ANSO-CR-KP-2021-06), the Project of Northern Agriculture and Livestock Husbandry Technical Innovation Center, Chinese Academy of Agricultural Sciences (BFGJ2022007), Global Vegetation Pest and Disease Dynamic Remote Sensing Monitoring and Forecasting, 2023-2025.