Bedair Heba, Hazzazi Yehia, Abo Hatab Asmaa, Halmy Marwa Waseem A, Dakhil Mohammed A, Alghariani Mubaraka S, Sumayli Mari, El-Shabasy A, El-Khalafy Mohamed M
Botany Department, Faculty of Science, Tanta University, Tanta, Egypt.
United States Department of Agriculture (USDA) Forest Service, International Institute of Tropical Forestry, San Juan, Puerto Rico.
Front Plant Sci. 2025 Feb 20;16:1461639. doi: 10.3389/fpls.2025.1461639. eCollection 2025.
Climate change poses significant challenges to the distribution of endemics in the Mediterranean region. Assessing the impact of climate change on the distribution patterns of Mediterranean endemics is of critical importance for understanding the dynamics of these terrestrial ecosystems under the uncertainty of future changes. The population size of the has declined significantly over the previous century across its geographical region. This decline is linked to how ongoing climate change is affecting natural resources like water and the capacity of foraging sites. In fact, it is distributed in 3 fragmented locations in Egypt (Wadi Hashem (5 individuals), Wadi Um Rakham (20 individuals), Burg El-Arab (4 individuals)).
In this study, we examined 's response to predicted climate change over the next few decades (2020-2040 and 2061-2080) using species distribution models (SDMs). Our analysis involved inclusion of bioclimatic variables, in the SDM modeling process that incorporated five algorithms: generalized linear model (GLM), Random Forest (RF), Boosted Regression Trees (BRT), Support Vector Machines (SVM), and Generalized Additive Model (GAM).
The ensemble model obtained high accuracy and performance model outcomes with a mean AUC of 0.95 and TSS of 0.85 for the overall model. Notably, RF and GLM algorithms outperformed the other algorithms, underscoring their efficacy in predicting the distribution of endemics in the Mediterranean region. Analysis of the relative importance of bioclimatic variables revealed Precipitation of wettest month (Bio13) (88.3%), Precipitation of warmest quarter (Bio18) (30%), and Precipitation of driest month (Bio14) (22%) as the primary drivers shaping the potential distribution of . The findings revealed spatial variations in habitat suitability, with the highest potential distribution observed in Egypt, (especially the Arishian sub sector), Palestine, Morocco, Northern Cyprus, and different islands in the Sea of Crete. Furthermore, our models predicted that the distribution range of would drop by more than 25% during the next few decades. Surprisingly, the future potential distribution area of (SSP 126 scenario) for 2061 and 2080 showed that there is increase in the suitable habitats area. It showed high habitat suitability along the Mediterranean coastal strip of Spain, Sardinia, Morocco, Algeria, Tunisia, Libya, Egypt, (especially the Arishian sub sector), Palestine, Lebanon, Northern Cyprus, and different Aegean islands.
气候变化对地中海地区特有物种的分布构成了重大挑战。评估气候变化对地中海特有物种分布模式的影响,对于在未来变化的不确定性下理解这些陆地生态系统的动态至关重要。在过去的一个世纪里,其地理区域内的种群数量显著下降。这种下降与持续的气候变化如何影响水资源等自然资源以及觅食地的承载能力有关。实际上,它分布在埃及的3个分散地点(哈谢姆谷(5只个体)、乌姆拉克姆谷(20只个体)、布尔格阿拉伯(4只个体))。
在本研究中,我们使用物种分布模型(SDMs)来研究其对未来几十年(2020 - 2040年和2061 - 2080年)预测气候变化的响应。我们的分析在SDM建模过程中纳入了生物气候变量,该过程采用了五种算法:广义线性模型(GLM)、随机森林(RF)、增强回归树(BRT)、支持向量机(SVM)和广义相加模型(GAM)。
总体模型的集成模型获得了高精度和良好的模型结果,平均AUC为0.95,TSS为0.85。值得注意的是,RF和GLM算法的表现优于其他算法,突出了它们在预测地中海地区特有物种分布方面的有效性。对生物气候变量相对重要性的分析表明,最湿润月降水量(Bio13)(88.3%)、最暖季降水量(Bio18)(30%)和最干月降水量(Bio14)(22%)是塑造其潜在分布的主要驱动因素。研究结果揭示了栖息地适宜性的空间差异,在埃及(特别是阿里什地区)、巴勒斯坦、摩洛哥、北塞浦路斯和克里特海的不同岛屿观察到最高的潜在分布。此外,我们的模型预测,在未来几十年里,其分布范围将下降超过25%。令人惊讶的是,表示2061年和2080年的未来潜在分布区域(SSP 126情景)显示适宜栖息地面积有所增加。它在地中海沿岸的西班牙、撒丁岛、摩洛哥、阿尔及利亚、突尼斯、利比亚、埃及(特别是阿里什地区)以及巴勒斯坦、黎巴嫩、北塞浦路斯和不同的爱琴海岛屿沿线显示出较高的栖息地适宜性。