Towards globally customizable ecosystem service models
Towards globally customizable ecosystem service models
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DOI：
10.1016/j.scitotenv.2018.09.371
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发表时间：
2019-02-10
影响因子：
9.8
通讯作者：
Villa, Ferdinand
中科院分区：
文献类型：
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作者：
Martinez-Lopez, Javier;Bagstad, Kenneth J.;Villa, Ferdinand
科学家、利益相关者和决策者在对生态系统服务进行建模时面临着采用简单方法还是复杂方法的权衡。复杂的方法可能是时间和数据密集型的，使其实施更具挑战性，难以推广，但可以产生更准确和更适合当地的结果。相比之下，简单的方法可以更快地进行评估，但可能会牺牲准确性和可信度。人工智能生态系统服务（ARIES）建模平台已经能够提供一系列简单到复杂的ES模型，这些模型可以被广泛的用户访问。在本文中，我们描述了一系列的五个“第1层”ES模型，用户可以在世界上任何地方运行，没有用户输入，同时提供了选项，可以轻松地定制模型与上下文特定的数据和参数。这种方法能够快速ES量化，因为模型会自动适应应用环境。我们提供了定制的ES评估在不同大陆的三个地点的例子，并演示了ARIES的空间多标准分析模块，使空间优先级ES为不同的受益群体的使用。这里描述的模型使用公开的全球和大陆规模的数据作为默认值。高级用户可以修改数据输入要求、模型参数或整个模型结构，以利用高分辨率数据和特定于上下文的模型公式。研究界提供的数据和方法成为不断增长的知识库的一部分，使世界各地的用户能够更快，更好地进行环境服务评估。通过与ES建模社区合作，根据用户需求，时空背景和分析规模进一步开发和定制这些模型，我们的目标是覆盖从简单到复杂的评估的整个弧，当需要增加复杂性和准确性时，最大限度地减少用户的额外成本。(C)2018作者由爱思唯尔公司出版
Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five "Tier 1" ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. (C) 2018 The Authors. Published by Elsevier B.V.