Liu Xuan, Xu Li, Zheng Jianghua, Lin Jun, Li Xuan, Liu Liang, Tian Ruikang, Mu Chen
College of Geography and Remote Sensing Sciences Xinjiang University Urumqi China.
Xinjiang Key Laboratory of Oasis Ecology Xinjiang University Urumqi China.
Ecol Evol. 2024 Nov 11;14(11):e70517. doi: 10.1002/ece3.70517. eCollection 2024 Nov.
The great gerbil () is a gregarious rodent in Central Asia and is one of the major pests found in desert forest and grassland areas. The distribution changes and migration routes of in Central Asia under climate change remain unexplored. This study employed multi-model ensemble, correlation analysis, jackknife method, and minimum cumulative resistance (MCR) model to simulate the potential habitat of under current and future (2030 and 2050) climate scenarios and estimate its possible migration routes. The results indicate that the ensemble model integrating Random Forest (RF), Gradient Boosting Machine (GBM), and Maximum Entropy Model (MaxEnt) performed best within the present climate context. The model predicted the potential distribution of in Central Asia with an area under the curve (AUC) of 0.986 and a True Skill Statistic (TSS) of 0.899, demonstrating excellent statistical accuracy and spatial performance. Under future climate scenarios, northern Xinjiang and southeastern Kazakhstan will remain the core areas of distribution. However, the optimal habitat region will expand relative to the current one. This expansion will increase with the rising CO emission levels and over time, potentially enlarging the suitable area by up to 39.49 × 10 km. In terms of spatial distribution, the suitable habitat for is shifting toward higher latitudes and elevations. For specific migration routes, tends to favor paths through farmland and grassland. This study can provide guidance for managing and controlling under future climate change scenarios.
大沙鼠是中亚地区一种群居性啮齿动物,是沙漠森林和草原地区的主要害虫之一。气候变化下中亚地区大沙鼠的分布变化和迁移路线仍未得到探索。本研究采用多模型集成、相关分析、刀切法和最小累积阻力(MCR)模型,模拟当前和未来(2030年和2050年)气候情景下大沙鼠的潜在栖息地,并估计其可能的迁移路线。结果表明,在当前气候背景下,集成随机森林(RF)、梯度提升机(GBM)和最大熵模型(MaxEnt)的集成模型表现最佳。该模型预测中亚地区大沙鼠的潜在分布,曲线下面积(AUC)为0.986,真技能统计量(TSS)为0.899,显示出优异的统计准确性和空间性能。在未来气候情景下,新疆北部和哈萨克斯坦东南部仍将是大沙鼠分布的核心区域。然而,最佳栖息地范围相对于当前将有所扩大。随着二氧化碳排放水平的上升,这种扩大将随时间增加,潜在适宜面积可能扩大至39.49×10平方千米。在空间分布方面,大沙鼠适宜栖息地正朝着更高纬度和海拔转移。对于具体迁移路线,大沙鼠倾向于选择穿过农田和草原的路径。本研究可为未来气候变化情景下大沙鼠的管理和控制提供指导。