Predicting the Potential Distribution of Three Allergenic Invasive Ambrosia (ragweed) Species in Asia

IF 6 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Journal of Environmental Informatics Pub Date : 2021-05-18 DOI:10.3808/JEI.202000444
Z. Qin, J. E. Zhang, A. DiTommaso, J. Díez, Y. Zhao, F. Wang
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引用次数: 3

Abstract

Three ragweed species native to North America (Ambrosia artemisiifolia L., A. psilostachya DC, and A. trifida L.) that have been introduced into Asia are now spreading quickly in many regions. Predicting which specific areas may be vulnerable to the invasion of these allergenic Ambrosia species can provide valuable insights for early detection and for prioritizing preventive actions. Species distribution models, based on native and non-Asian occurrence records for these three Ambrosia species, were generated with the maximum entropy (Maxent) approach respectively. Spatial filtering and target-group background methods were used to address sampling bias. Models fitted with different levels of complexity under present conditions were compared and evaluated with independent Asian records. Models showing lower over-fitting and higher performance were then selected to assess their future distribution under two types of Representative Concentration Pathways (RCP4.5 and RCP8.5), using four General Circulation Models (GCMs). Predicted habitats for A. artemisiifolia in 2050 would contract in regions having been colonized, despite a limited increase in parts of China. This species may experience a southward range shift in China. Under all future climate scenarios, A. trifida was predicted to decrease its potential establishment while A. psilostachya would expand its range, especially in habitats being colonized currently. Special attention should be given to Hunan, Jiangxi Provinces and scattered along southeastern coastal regions of China as well as parts of Turkey and northwest Iran, Azerbaijan, considering that future potential distribution of A. artemisiifolia and A. psilostachya might increase in these areas respectively. The findings provide valuable information for assessing the risk that these three Ambrosia species pose to many Asian countries and for prioritizing early detection and prevention strategies.
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三种致敏入侵豚草种在亚洲的潜在分布预测
原产于北美的三种豚草(Ambrosia artemisiifolia L., A. psilostachya DC .和A. trifida L.)已被引入亚洲,目前正在许多地区迅速蔓延。预测哪些特定区域可能容易受到这些过敏性安氏菌物种的入侵,可以为早期发现和优先采取预防措施提供有价值的见解。利用最大熵(Maxent)法分别建立了三种Ambrosia的本地和非亚洲发生记录的物种分布模型。采用空间滤波和目标群体背景方法来解决抽样偏差。在当前条件下拟合不同复杂程度的模型与独立的亚洲记录进行了比较和评估。然后选择具有较低过拟合和较高性能的模型,使用四种一般循环模型(GCMs)评估它们在两种典型浓度路径(RCP4.5和RCP8.5)下的未来分布。预计到2050年,在已被殖民的地区,蒿属植物的栖息地将会收缩,尽管中国部分地区的蒿属植物数量会有有限的增长。本种在中国可能经历向南的范围转移。在未来的气候情景下,三叶草的潜在种群数量将会减少,而拟犀草的分布范围将会扩大,尤其是在目前已被殖民的生境中。应特别注意湖南、江西和分散在中国东南沿海地区以及土耳其部分地区和伊朗西北部、阿塞拜疆等地的蒿属植物,因为这些地区未来的潜在分布可能会增加。这些发现为评估这三种Ambrosia物种对许多亚洲国家构成的风险以及优先考虑早期发现和预防策略提供了有价值的信息。
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来源期刊
Journal of Environmental Informatics
Journal of Environmental Informatics ENVIRONMENTAL SCIENCES-
CiteScore
12.40
自引率
2.90%
发文量
7
审稿时长
24 months
期刊介绍: Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include: - Planning of energy, environmental and ecological management systems - Simulation, optimization and Environmental decision support - Environmental geomatics - GIS, RS and other spatial information technologies - Informatics for environmental chemistry and biochemistry - Environmental applications of functional materials - Environmental phenomena at atomic, molecular and macromolecular scales - Modeling of chemical, biological and environmental processes - Modeling of biotechnological systems for enhanced pollution mitigation - Computer graphics and visualization for environmental decision support - Artificial intelligence and expert systems for environmental applications - Environmental statistics and risk analysis - Climate modeling, downscaling, impact assessment, and adaptation planning - Other areas of environmental systems science and information technology.
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