ArticleEnvironmental monitoring and assessment2024
Global spatial distribution of Prosopis juliflora - one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models.
Article in Environmental monitoring and assessment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Optimising the elastic constants of Vachellia nilotica, Prosopis juliflora and Vachellia leucophloea/ceramic fillers epoxy composites using GRA.Scientific reports · 2026Article
- Predicting Ecological Risks ofBiology · 2025Article
- Study on sweet potatoes in relation to nutritional profile against worst invasive plant (Prosopis juliflora) under arid climate of Fujairah.Scientific reports · 2025Article
- Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.PloS one · 2025Article
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Authors and funding
2 authors.
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Abstract
Climate change is one of the factors contributing to the spread of invasive alien species. As a result, it is critical to investigate potential invasion dynamics on a global scale in the face of climate change. We used updated occurrence data, bioclimatic variables, and Köppen-Geiger climatic zones to better understand the climatic niche dynamics of Prosopis juliflora L. (Fabaceae). In this study, we first compared several algorithms-MaxEnt, generalized linear model (GLM), artificial neural network (ANN), generalized boosted model (GBM), generalized additive model (GAM), and random forest (RF)-to investigate the relationships between species-environment and climate for mesquite. We identified the global climate niche similarity sites (NSSs) using the coalesce approach. This study focused on the current and future climatic suitability of P. juliflora under two global circulation models (GCMs) and two climatic scenarios, i.e., Representative Concentration Pathways (RCPs), 4.5 and 8.5, for 2050 and 2070, respectively. Sensitivity, specificity, true skill statistic (TSS), kappa coefficient, and correlation were used to evaluate model performance. Among the tested models, the machine learning algorithm random forest (RF) demonstrated the highest accuracy. The vast swaths of currently uninvaded land on multiple continents are ideal habitats for invasion. Approximately 9.65% of the area is highly suitable for the establishment of P. juliflora. Consequently, certain regions in the Americas, Africa, Asia, Europe, and Oceania have become particularly vulnerable to invasion. In relation to RCPs, we identified suitable area changes (expansion, loss, and stability). The findings of this study show that NSSs and RCPs increase the risk of invasion in specific parts of the world. Our findings contribute to a cross-border continental conservation effort to combat P. juliflora expansion into new potential invasion areas.
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