WISER: Which Ecosystem Service Models Best Capture the Needs of the Rural Poor?

人们普遍承认，贫穷的农村社区往往高度依赖生态系统服务来维持生计，特别是在困难或危机时期，将其作为安全网。然而，理解和管理这些惠益流向穷人的一个主要挑战是缺乏关于穷人对生态系统服务的供应、需求和使用的数据，特别是在对生态系统依赖程度往往最高的发展中国家。最近的工作表明，与常用的全局代理相关的错误(例如，政策制定或管理干预(如不正当的补贴)很可能是巨大的，因此会造成混乱或更糟糕的、误导。鉴于这些问题，旨在提供当前和未来生态系统分布的改进和动态地图的综合建模平台--在某些情况下是建立在桌面流程模型基础上的--的最新改进，在很大程度上可以提供基于生态系统的扶贫干预和政策。尽管这些基于流程的下一代模型似乎在基于生态系统的扶贫工作中发挥了作用，但人们对提供与政策相关的信息所需的复杂程度和数据需求知之甚少。例如，目前还不清楚，即使是最复杂的基于过程的生物物理模型，在基于全球可用的数据集的情况下，是否能够为区域或地方规模的政策决策提供足够准确的信息。同样，也没有人试图量化在综合建模平台内将受益人分类的必要性程度，以提供与最贫穷人口有关的自然资产管理方面的信息。这些分析对于确保下一代模型尽可能有效地产生有用和可信的结果至关重要--也就是说，在数据收集和定制模型开发方面投入最少的资金。我们将评估当前一系列复杂程度不同的建模方法的有效性，以绘制撒哈拉以南非洲多个空间尺度的至少六种生态系统服务--作物生产、储存碳、水可获得性、非木材森林产品(NTFP)、放牧资源和授粉。我们将基于两个广泛的度量来评估模型的性能：模型数据需求和对决策的有用性。首先，我们将评估每个建模层的数据需求，使用数据可用性、空间分辨率和不确定性来对所需投入的强度进行评分。那些对数据要求很高的模型得分会很低。其次，我们将使用统计二元鉴别器检验来评估该模型在决策过程中的有用性。我们将使用同样的方法来评估考虑受益者对决策的影响，方法是将生物物理模型的产出与社会经济措施和模型进行比较，并使用二元判别检验。我们在这个项目中的目标是确定需要应用于绘制对减贫有用的分辨率的ES图的建模的复杂程度。该项目的结果将使决策者能够：1)最好地利用现有的土地利用/覆盖模式，为国家和区域土地利用/覆盖变化政策提供信息，支持土地利用/覆盖变化政策，支持土地利用管理，并促进这些服务受益者之间的平等和正义；以及2)设定优先事项，确定应在哪里投入稀缺资源，以改进对土地利用的有效管理。因此，WISER可能通过评估该区域决策者可用的工具，帮助改善撒哈拉以南非洲约4亿贫困人口的生活。

It is widely acknowledged that poor rural communities are frequently highly dependent on ecosystem services (ES) for their livelihoods, especially as a safety net in times of hardship or crisis. However, a major challenge to the understanding and management of these benefit flows to the poor is a lack of data on the supply, demand and use of ecosystem services by the poor, particularly in the developing world where dependence on ES is often highest. Recent work suggests that errors associated with the commonly used global proxies (eg. benefits transfer) are likely to be substantial and therefore confuse or worse, misdirect, policy formulation or management interventions (e.g. perverse subsidies). Given these issues, recent improvements in integrated modelling platforms - in some cases founded on desktop process-based models - which aim to provide improved and dynamic maps of current and future distributions of ES have much to offer ES-based poverty alleviation interventions and policy.While these next generation process-based models appear to have a role to play in ES-based poverty alleviation efforts, the level of sophistication and data needs that is required to deliver policy relevant information is poorly understood. It is, for example, unclear whether even the most sophisticated process-based biophysical model is able to provide sufficiently accurate information for regional- or local-scale policy decision making when based on globally available datasets. Similarly, there has been no attempt to quantify the degree to which disaggregation of beneficiaries is necessary within integrated modelling platforms to provide information on managing natural assets that is relevant to the poorest people. Such analyses are vital to ensure that next generation models produce useful and credible results as efficiently as possible - that is, with a minimum investment in data collection and bespoke model development.We will evaluate the effectiveness of a range of current modelling approaches of varying degrees of complexity for mapping at least six ecosystem services - crop production, stored carbon, water availability, non-timber forest products (NTFPs), grazing resources, and pollination - at multiple spatial scales across sub-Saharan Africa. We will assess model performance based on two broad metrics: model data requirements and the usefulness to decision-making. Firstly, we will evaluate the data requirements of each modelling tier, using data availability, spatial resolution and uncertainty to score in the intensity of the required inputs. Those models with intensive data requirements will be scored poorly. Secondly, we will evaluate the usefulness of the model in a decision-making process using statistical binary discriminator tests. We will use the same approach to evaluate the impact of consideration of beneficiaries on decision making by comparing the biophysical model outputs with both socioeconomic measures and models also using binary discriminator tests. Our goal in this project is to ascertain the degree of complexity of modelling that needs to be applied to map ES at resolutions that are useful for poverty alleviation. The findings of this project will enable decision makers to: 1) best use existing ES models to inform national and regional land use/cover change policies supporting ES management and promoting equality and justice amongst the beneficiaries of these services; and 2) set priorities determining where scarce resources should be invested to improve effective management of ES. Thus, WISER may help improve the lives of the approximately 400 million people living in poverty in sub-Saharan Africa by evaluating the tools available to policy makers in this region.