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

人们普遍认为，贫困农村社区的生计往往高度依赖生态系统服务，特别是在困难或危机时期作为安全网。然而，了解和管理这些流向穷人的惠益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世界。最近的研究表明，与常用的全局代理(例如。利益转移)可能数额巨大，因此混淆或更糟地误导政策制定或管理干预（例如不正当补贴）。鉴于这些问题，最近综合建模平台的改进- -在某些情况下是建立在基于桌面进程的模型上的- -旨在提供改进的、动态的当前和未来生态系统分布地图，这对基于生态系统的扶贫干预措施和政策大有帮助。虽然这些新一代基于过程的模式似乎可以在基于系统的减贫努力中发挥作用，但人们对提供政策相关信息所需的复杂程度和数据需求了解甚少。例如，目前尚不清楚的是，即使是最复杂的基于过程的生物物理模型，在基于全球可用数据集的情况下，是否能够为区域或地方尺度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模型平台内对受益人进行分类的必要程度，以便提供与最贫穷的人有关的管理自然资产的信息。这样的分析对于确保下一代模型尽可能高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的投资最小。我们将评估当前一系列不同复杂程度的建模方法在撒哈拉以南非洲多个空间尺度上绘制至少六种生态系统服务的有效性，这些生态系统服务包括作物生产、碳储量、水分供应、非木材林产品（NTFPs）、放牧资源和授粉。我们将基于两个广泛的度量来评估模型性能：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对所需输入的强度进行评分。那些需要大量数据的模型得分很低。其次，我们将使用统计二元判别器检验来评估模型在决策过程中的有用性。我们将使用相同的方法来评估考虑受益人对决策的影响，将生物物理模型的输出与社会经济措施和模型进行比较，也使用二元判别检验。我们在这个项目中的目标是确定需要应用于以有助于减轻贫困的分辨率绘制环境观测图的模型的复杂程度。该项目的研究结果将使决策者能够：1)最好地利用现有的生态系统模型，为国家和区域土地利用/覆盖变化政策提供信息，支持生态系统管理，促进这些服务受益者之间的平等和正义；2)确定优先事项，确定稀缺资源应投资于何处，以提高ES的有效管理。因此，WISER可以通过评估该地区决策者可用的工具，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.