Almaazmi Alya, Al-Ruzouq Rami, Shanableh Abdallah, El-Keblawy Ali, Jena Ratiranjan, Gibril Mohamed Barakat A, Hammouri Nezar Atalla, Talib Manar Abu
GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, 27272, UAE.
Civil and Environmental Engineering Department, University of Sharjah, Sharjah, 27272, UAE.
Environ Monit Assess. 2025 Mar 20;197(4):440. doi: 10.1007/s10661-025-13876-z.
Prosopis juliflora, one of the most invasive trees, adversely affects the ecosystem and native plant communities in arid lands. This disrupts biodiversity and depletes water resources, posing significant ecological and economic challenges. Several attempts have been made to control this species in the United Arab Emirates (UAE) deserts but with little success. This study identifies and maps environmental variables influencing P. juliflora habitats using machine learning (ML); employs maximum entropy (MaxEnt) and statistical techniques to estimate its presence in Sharjah, UAE, home to one of its most intense populations; and conducts validation and sensitivity analysis. Eleven environmental variables representing geological, geomorphological, hydrological, eco-indicators, and climatological factors were selected to map the spread of the associated P. juliflora hazard. Variables were selected using collinearity and variance inflation factor (VIF) to eliminate bias, and ML techniques assigned weights based on overall accuracy (OA) and the Kappa coefficient before model implementation. Finally, a statistical comparison with MaxEnt was conducted to map P. juliflora habitats, classifying suitability as very high, high, low, and very low while estimating model accuracy. The results indicated that MaxEnt achieved a higher area under the curve (AUC 0.98) and more logical outcomes than statistical models (AUC 0.85) due to its superior handling of collinearity, complex environmental interactions, and capability of minimizing overfitting. The main findings show that the variable weights for MaxEnt and statistical models are primarily influenced by precipitation (27.0% and 18.18%), groundwater depth (14.9% and 26.8%), and total dissolved solids (TDS) (20.9% and 26.22%), respectively, indicating a shift in habitat distribution towards the eastern regions of the study area. Habitat mapping of P. juliflora is essential for local stakeholders and policymakers in decision-making regarding species conservation, sustainable land use, and climate adaptation. The findings conclude that ML offers a viable approach for habitat modeling of invasive species in similar arid regions worldwide.
牧豆树是最具入侵性的树木之一，对干旱地区的生态系统和本地植物群落产生不利影响。这破坏了生物多样性，耗尽了水资源，带来了重大的生态和经济挑战。在阿拉伯联合酋长国（阿联酋）的沙漠中，人们多次尝试控制这种物种，但收效甚微。本研究利用机器学习（ML）识别并绘制影响牧豆树栖息地的环境变量；采用最大熵（MaxEnt）和统计技术估计其在阿联酋沙迦（牧豆树密集种群之一的所在地）的分布情况，并进行验证和敏感性分析。选择了代表地质、地貌、水文、生态指标和气候因素的11个环境变量，以绘制相关牧豆树危害的扩散情况。使用共线性和方差膨胀因子（VIF）选择变量以消除偏差，在模型实施前，ML技术根据总体准确率（OA）和卡帕系数分配权重。最后，与MaxEnt进行统计比较以绘制牧豆树栖息地，将适宜性分为非常高、高、低和非常低，并估计模型准确率。结果表明，由于MaxEnt在处理共线性、复杂环境相互作用以及最小化过拟合方面具有优势，其曲线下面积（AUC为0.98）比统计模型（AUC为0.85）更高，结果也更合理。主要研究结果表明，MaxEnt和统计模型的变量权重分别主要受降水量（27.0%和18.18%）、地下水位（14.9%和26.8%）和总溶解固体（TDS）（20.9%和26.22%）的影响，这表明栖息地分布向研究区域的东部地区转移。牧豆树的栖息地绘图对于当地利益相关者和政策制定者在物种保护、可持续土地利用和气候适应方面的决策至关重要。研究结果得出结论，ML为全球类似干旱地区入侵物种的栖息地建模提供了一种可行的方法。
