气候变化情景下亚马逊地区养蜂的适宜性及其未来分布

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Darwin Gómez-Fernández , Ligia García , Jhonsy O. Silva-López , Jaris Veneros Guevara , Erick Arellanos Carrión , Rolando Salas-Lopez , Malluri Goñas , Nilton Atalaya-Marin , Manuel Oliva-Cruz , Nilton B. Rojas-Briceño
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引用次数: 0

摘要

养蜂在全球粮食生产和野生物种保护中发挥着重要作用。然而,在秘鲁,确定气候变化情景下的领土适宜性和未来分布是一个研究相对不足的领域。根据共享社会经济路径(SSP),本研究评估了亚马逊地区在两个预测时期(2041-2060年和2081-2100年)的养蜂适宜性及其在气候变化情景下的变化。方法框架整合了层次分析法(AHP)和地理信息系统(GIS),并使用Hadley Centre全球地球模式-全球耦合配置3.1 (HadGEM3-GC31-LL)进行未来气候分析。该地区的养蜂适宜性是根据11个标准确定的:4个气候标准,3个地形标准和4个环境标准。结果表明:该地区养蜂适宜性分布为:3.4% (1417.90 km2)为“高”适宜性,79.2% (33318.61 km2)为“中等”适宜性,17.2% (7230.26 km2)为“边际”适宜性,0.2% (83.64 km2)为“不适宜”。此外,在预估期间,在SSP2-4.5情景下,整个区域的平均温度预计将增加约3°C,在SSP5-8.5情景下,预计将增加6 - 8°C。该地区北部降水将减少,而西南部降水将增加。在高度适宜养蜂的地区,预计气温将上升至10.8°C,在3°C至8°C之间频繁变化,影响500多平方公里。此外,预估降水减少高达311毫米/年,主要变化范围为- 49.5至- 32.8毫米/年,覆盖约600平方公里。因此,建议实施策略来缓解即将到来的挑战,特别是如果ssp下的模型经济发展继续下去。这项研究模拟和绘制了适合养蜂和未来气候行为的地区。该模型旨在指导养蜂人和地方当局制定可持续做法和实施预防措施,以应对未来的气候挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Suitability of the Amazonas region for beekeeping and its future distribution under climate change scenarios
Beekeeping plays an important role in global food production and the conservation of wild species. However, determining territorial suitability and future distribution under climate change scenarios is a relatively understudied area in Peru. This study assessed the beekeeping suitability of the Amazonas region and its variation under climate change scenarios in two projected periods (2041–2060 and 2081–2100), according to Shared Socioeconomic Pathways (SSP). The methodological framework integrated the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), and the Hadley Centre Global Earth Model - Global Coupled configuration 3.1 (HadGEM3-GC31-LL) was used for future climate analysis. The beekeeping suitability of the region was determined based on eleven criteria: four climatic, three topographic, and four environmental. The results indicate that beekeeping suitability is distributed as follows: 3.4 % (1417.90 km2) with ‘High’ suitability, 79.2 % (33,318.61 km2) with ‘Moderate’ suitability, 17.2 % (7230.26 km2) with ‘Marginal’ suitability, and 0.2 % (83.64 km2) as ‘Not suitable’. Moreover, the average temperature across the region is projected to increase by approximately 3 °C under the SSP2–4.5 scenario and between 6 °C and 8 °C under the SSP5–8.5 scenario during the projected periods. Precipitation will decrease in the northern part of the region, while the southwestern part will experience an increase. In the highly suitable beekeeping area, a temperature increases up to 10.8 °C is expected, with frequent variations around 3 °C to 8 °C, affecting more than 500 km2. Additionally, a reduction in precipitation up to 311 mm/year is projected, with predominant variations ranging from −49.5 to −32.8 mm/year over approximately 600 km2.Therefore, it is suggested to implement strategies to mitigate these upcoming challenges, particularly if the modeled economic development under the SSPs continues. This study modeled and mapped areas with present conditions suitable for beekeeping and future climate behavior. The modeling aims to guide beekeepers and local authorities in developing sustainable practices and implementing preventive measures to address future climatic challenges.
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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