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埃塞俄比亚气候变化情景下文化基石(阿迪·阿贝巴)时空栖息地适宜性分布建模

Modeling the Spatiotemporal Habitat Suitability Distributions of Cultural Keystone (Adey Abeba) Under Climate Change Scenarios in Ethiopia.

作者信息

Hailegiorgis Tsige, Lemessa Debissa, Melese Daniel, Abebe Mikiyas, Nemomissa Sileshi

机构信息

Department of Plant Biology and Biodiversity Management Addis Ababa University Addis Ababa Ethiopia.

Department of Biology Kotebe University of Education Addis Ababa Ethiopia.

出版信息

Ecol Evol. 2025 Sep 6;15(9):e72115. doi: 10.1002/ece3.72115. eCollection 2025 Sep.

Abstract

Bidens macroptera symbolizes the change of a season, marking the transition from the rainy season to autumn, heralding the new year for Ethiopians. Despite a general understanding of its geographic regions, significant gaps remain in identifying the habitat distribution and key predictor variables of Bidens macroptera through species distribution modeling (SDM) in the context of climate change. We developed an ensemble species distribution model using 2 statistical and 3 machine learning algorithms. We collected 119 presence and pseudoabsence points to train and validate bioclimatic variables through 5-fold cross-validation with the "SDM" package in R software. We calculated the Variance Inflation Factor (VIF) to assess multicollinearity among environmental variables. Projections were made for medium (SSP 2-4.5) and extreme (SSP 5-8.5) greenhouse gas emissions for the periods of the 2050s and 2070s. The performance of the models was evaluated by Area Under the Curve (AUC) and True Skill Statistic (TSS). A weighted average threshold value at the sum of sensitivity and specificity of the TSS was used to classify habitat suitability using ArcGIS 10.4. The ensemble model showed strong performance, with an AUC ranging from 0.96 to 0.98 and a TSS of 0.84-0.92. Individually, MaxEnt outperformed with an AUC of 98% and a TSS of 92%. The mean temperature of the driest quarter (Bio 9) emerged as the most influential, followed by soil pH and slope. With the current climate conditions, 89.81% of habitats are classified as unsuitable, while 5.64% are least suitable, 2.46% moderately suitable, and 2.09% highly suitable. However, all future projections revealed a decline in suitable habitats, increasing the risk of local extinction. Therefore, it is essential to develop a conservation plan and strengthen climate change adaptation strategies to mitigate habitat loss for this iconic highland species.

摘要

大翅鬼针草象征着季节的变化,标志着从雨季到秋季的过渡,为埃塞俄比亚人迎来新的一年。尽管人们对其地理区域有大致了解,但在气候变化背景下,通过物种分布模型(SDM)来确定大翅鬼针草的栖息地分布和关键预测变量方面仍存在重大差距。我们使用2种统计算法和3种机器学习算法开发了一个集成物种分布模型。我们收集了119个存在点和伪缺失点,通过R软件中的“SDM”包进行5折交叉验证,以训练和验证生物气候变量。我们计算了方差膨胀因子(VIF)来评估环境变量之间的多重共线性。针对2050年代和2070年代的中等(SSP 2-4.5)和极端(SSP 5-8.5)温室气体排放情景进行了预测。通过曲线下面积(AUC)和真技能统计量(TSS)评估模型性能。使用TSS的敏感性和特异性之和的加权平均阈值,通过ArcGIS 10.4对栖息地适宜性进行分类。集成模型表现出强大的性能,AUC范围为0.96至0.98,TSS为0.84-0.92。单独来看,MaxEnt表现最佳,AUC为98%,TSS为92%。最干燥季度平均温度(Bio 9)是最具影响力的因素,其次是土壤pH值和坡度。在当前气候条件下,89.81%的栖息地被归类为不适宜,5.64%为最不适宜,2.46%为中度适宜,2.09%为高度适宜。然而,所有未来预测都显示适宜栖息地减少,增加了局部灭绝的风险。因此,制定保护计划并加强气候变化适应策略以减轻这种标志性高地物种的栖息地丧失至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69dd/12413564/7cf102358390/ECE3-15-e72115-g004.jpg

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