Locusta migratoria (L.) (Orthoptera) in a warming world: unravelling the ecological consequences of climate change using GIS

Abstract

The migratory locust, Locusta migratoria (L.), a significant grasshopper species known for its ability to form large swarms and cause extensive damage to crops and vegetation, is subject to the influence of climate change. This research paper employs geographic information system (GIS) and MaxEnt ecological modelling techniques to assess the impact of climate change on the distribution patterns of L. migratoria. Occurrence data and environmental variables are collected and analysed to create predictive models for the current and future distribution of the species. The study highlights the crucial role of climate factors, particularly temperature and precipitation, in determining the locust's distribution. The MaxEnt models exhibit high-performance indicators, accurately predicting the potential habitat suitability of L. migratoria. Additionally, specific bioclimatic variables, such as mean temperature and annual precipitation, are identified as significant factors influencing the species' presence. The generated future maps indicate how this species will invade new regions especially in Europe. Such results predict the risk of this destructive species for many agriculture communities as a direct result of a warming world. The research provides valuable insights into the complex relationship between locust distribution and environmental factors, enabling the development of effective strategies for locust management and early warning systems to mitigate the impact on agriculture and ecosystems.

Prediction of the potential distribution of Chimonobambusa utilis (Poaceae, Bambusoideae) in China, based on the MaxEnt model

“…One of the most widely used SDMs is the maximum entropy model (MaxEnt) ( Liu et al 2021 ), which evaluates habitat suitability, based on species distribution coordinates and environmental data ( Dai et al 2022 ). Being a machine-learning algorithm, the model provides accurate predictions and ease of use ( Pan et al 2020 , Jian et al 2022 ), frequently used for predicting species distributions, protecting rare plants and animals and managing invasive species spread ( Wang et al 2021 , Ma et al 2022 , Zhang et al 2022 , Hosni et al 2024 ). By combining the MaxEnt model with ArcGIS, researchers can analyse potential species distribution changes due to climate change, offering valuable insights for developing effective strategies to mitigate its impact on species by scientists and policy-makers.…”