Predicting Current and Future Habitat Suitability of an Endemic Species Using Data-Fusion Approach: Responses to Climate Change

IF 2.4 3区 环境科学与生态学 Q2 ECOLOGY Rangeland Ecology & Management Pub Date : 2024-04-05 DOI:10.1016/j.rama.2024.03.002
Atiyeh Amindin , Hamid Reza Pourghasemi , Roja Safaeian , Soroor Rahmanian , John P. Tiefenbacher , Babak Naimi
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Abstract

Fritillaria imperialis L., an indicator plant species in Iran, is facing threats and its population has declined in recent years. To provide insights into the drivers affecting its loss, this research aims to identify the effects of three groups of factors, including climate, soil, and physiographic variables, on the current and future spatial distributions of F. imperialis. For this purpose, we used five machine learning algorithms as well as an ensemble forecasting of species distribution approach to explain the geographical distributions of the species as a function of these factors. In addition, we used two shared socio-economic pathways scenarios – SSP 1-2.6 and SSP 5-8.5 – to project the future distributions of F. imperialis in 2030, 2050, 2070, and 2090. Based on evaluation indices, area under the ROC curve (AUC) and true skill statistic (TSS), the Random Forest (RF) model generated the strongest prediction of the distribution of F. imperialis (TSS>0.9 and AUC>0.9). No significant difference observed among the three datasets (climate-only variables, climate + physiography variables, and climate + physiography + soil variables) in terms of AUC values. In models using climate + physiography + soil datasets, soil electrical conductivity, clay, and pH emerged as the most important variables affecting the growth and development of F. imperialis while climate factors played a lesser role in its distribution. Future projections revealed different patterns when using the optimistic (SSP 1-2.6) and pessimistic (SSP 5-8.5) socio-economic pathway scenarios and either the climate only or climate + physiography models. The climate + physiography + soil model produced similar prediction patterns for the scenarios. The climate-only models predicted larger areas suitable for crown imperial in the future than did the climate + physiography + soil model. These results emphasize the consideration of factors beyond climate scenarios when modeling biological responses to global warming and regional climate change.

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利用数据融合方法预测特有物种当前和未来的栖息地适宜性:应对气候变化
Fritillaria imperialis L.是伊朗的一种指示植物物种,近年来正面临威胁,其数量也在减少。为了深入了解影响其消失的驱动因素,本研究旨在确定三组因素(包括气候、土壤和地貌变量)对帝国蓟草当前和未来空间分布的影响。为此,我们使用了五种机器学习算法以及物种分布的集合预测方法来解释这些因素对该物种地理分布的影响。此外,我们还使用了两种共同的社会经济路径情景--SSP 1-2.6 和 SSP 5-8.5--来预测帝王鱼在 2030 年、2050 年、2070 年和 2090 年的未来分布。根据评价指标、ROC 曲线下面积(AUC)和真实技能统计量(TSS),随机森林(RF)模型对帝王鱼分布的预测最强(TSS>0.9 和 AUC>0.9)。三个数据集(仅气候变量、气候 + 地形变量和气候 + 地形 + 土壤变量)的 AUC 值无明显差异。在使用气候+地貌+土壤数据集的模型中,土壤导电率、粘土和 pH 值成为影响帝王镰刀菌生长发育的最重要变量,而气候因素对其分布的影响较小。在使用乐观的(SSP 1-2.6)和悲观的(SSP 5-8.5)社会经济路径方案以及仅气候模型或气候 + 地貌模型进行未来预测时,发现了不同的模式。气候 + 地貌 + 土壤模型对各种情景的预测模式相似。与气候 + 地貌 + 土壤模型相比,纯气候模型预测的未来适合冠状病毒的区域更大。这些结果表明,在模拟生物对全球变暖和区域气候变化的反应时,应考虑气候情景以外的因素。
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来源期刊
Rangeland Ecology & Management
Rangeland Ecology & Management 农林科学-环境科学
CiteScore
4.60
自引率
13.00%
发文量
87
审稿时长
12-24 weeks
期刊介绍: Rangeland Ecology & Management publishes all topics-including ecology, management, socioeconomic and policy-pertaining to global rangelands. The journal''s mission is to inform academics, ecosystem managers and policy makers of science-based information to promote sound rangeland stewardship. Author submissions are published in five manuscript categories: original research papers, high-profile forum topics, concept syntheses, as well as research and technical notes. Rangelands represent approximately 50% of the Earth''s land area and provision multiple ecosystem services for large human populations. This expansive and diverse land area functions as coupled human-ecological systems. Knowledge of both social and biophysical system components and their interactions represent the foundation for informed rangeland stewardship. Rangeland Ecology & Management uniquely integrates information from multiple system components to address current and pending challenges confronting global rangelands.
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