Comparing approaches to spatially explicit ecosystem service modeling: A case study from the San Pedro River, Arizona
Comparing approaches to spatially explicit ecosystem service modeling: A case study from the San Pedro River, Arizona
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DOI：
10.1016/j.ecoser.2013.07.007
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发表时间：
2013-09-01
影响因子：
7.6
通讯作者：
Winthrop, Robert
中科院分区：
文献类型：
--
作者：
Bagstad, Kenneth J.;Semmens, Darius J.;Winthrop, Robert
虽然近年来生态系统服务建模工具的数量不断增加，但这些工具的定量比较研究一直缺乏。在本研究中，我们将两种领先的开源、空间明确的生态系统服务建模工具--生态系统服务人工智能（ARIES）和生态系统服务和权衡综合评估（InVEST）--应用于美国亚利桑那州东南部的圣佩德罗河流域和墨西哥北方索诺拉。我们模拟了两个建模系统都可以解决的本地重要服务-碳，水和风景区。然后，我们应用管理相关的方案，城市增长和牧豆树管理，以量化生态系统服务的变化。InVEST和ARIES使用不同的建模方法和生态系统服务指标;对于碳，指标更相似，结果比视域或水更容易比较。然而，研究结果表明，在比较我们的情景的影响时，生态系统服务和结论的收益和损失相似。结果更紧密地对齐的规模城市增长的情景和更不同的网站规模的mesquite管理的情景。后续研究，包括在不同地理环境下的测试，可以提高我们对这些和其他生态系统服务建模工具的优势和劣势的理解，因为它们更接近于支持日常资源管理的准备。由爱思唯尔公司出版
Although the number of ecosystem service modeling tools has grown in recent years, quantitative comparative studies of these tools have been lacking. In this study, we applied two leading open-source, spatially explicit ecosystem services modeling tools - Artificial Intelligence for Ecosystem Services (ARIES) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) - to the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. We modeled locally important services that both modeling systems could address - carbon, water, and scenic viewsheds. We then applied managerially relevant scenarios for urban growth and mesquite management to quantify ecosystem service changes. InVEST and ARIES use different modeling approaches and ecosystem services metrics; for carbon, metrics were more similar and results were more easily comparable than for viewsheds or water. However, findings demonstrate similar gains and losses of ecosystem services and conclusions when comparing effects across our scenarios. Results were more closely aligned for landscape-scale urban-growth scenarios and more divergent for a site-scale mesquite-management scenario. Follow-up studies, including testing in different geographic contexts, can improve our understanding of the strengths and weaknesses of these and other ecosystem services modeling tools as they move closer to readiness for supporting day-to-day resource management. Published by Elsevier B.V.