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

WISER：哪种生态系统服务模式最能满足农村贫困人口的需求？

• 批准号: NE/L001322/1
• 负责人: James Bullock
• 金额: $ 16.17万
• 依托单位: NERC CEH (Up to 30.11.2019)
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FL001322%2F1
• 关键词: WISER; Ecosystem; Service; Models; Best

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.

人们普遍承认，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，在理解和管理流向穷人的这些惠益方面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，特别是在发展中世界，那里对环境服务的依赖程度往往最高。最近的工作表明，与常用的全球代理（如。利益转移）可能是巨大的，因此会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流程的模型上-旨在提供关于环境服务当前和未来分布的改进和动态地图，这在很大程度上可以提供基于环境服务的扶贫干预措施和政策。人们对提供政策相关信息所需的复杂程度和数据需求知之甚少。例如，即使是最复杂的基于过程的生物物理模型，如果以全球现有数据集为基础，是否能够为区域或地方规模的决策提供足够准确的信息，这一点也不清楚。同样，也没有试图量化在综合建模平台内对受益人进行分类的必要程度，以提供与最贫穷人口相关的自然资产管理信息。这些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少。我们将评估一系列复杂程度不同的当前建模方法的有效性，这些方法用于绘制至少六种生态系统服务--作物生产、碳储存、水可用性、非木材森林产品（NTFPs），放牧资源和授粉-在撒哈拉以南非洲的多个空间尺度。我们将基于两个广泛的指标来评估模型性能：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来评估所需输入的强度。那些具有密集数据要求的模型将得分很低。其次，我们将使用统计二进制检验来评估模型在决策过程中的有用性。我们将使用同样的方法，通过比较生物物理模型输出与社会经济措施和模型，也使用二进制可接受性测试，以评估考虑受益人对决策的影响。我们在这个项目中的目标是要确定的复杂程度的建模，需要应用到地图ES的决议，是有益的扶贫。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，促进这些服务的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，以改善生态系统的有效管理。因此，WISER可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。

项目成果

Machine learning for ecosystem services

• DOI: 10.1016/j.ecoser.2018.04.004
• 发表时间: 2018-10
• 期刊: Ecosystem Services
• 影响因子: 7.6
• 作者: S. Willcock;Javier Martínez-López;D. Hooftman;K. Bagstad;S. Balbi;A. Marzo;Carlo G. Prato;S. Sciandrello;G. Signorello;B. Voigt;F. Villa;J. Bullock;I. Athanasiadis

Land cover change and carbon emissions over 100 years in an African biodiversity hotspot.

非洲生物多样性热点地区 100 年来的土地覆盖变化和碳排放。

• DOI: 10.1111/gcb.13218
• 发表时间: 2016
• 期刊: Global change biology
• 影响因子: 11.6
• 作者: Willcock S

Mapping social-ecological systems: Identifying 'green-loop' and 'red-loop' dynamics based on characteristic bundles of ecosystem service use

• DOI: 10.1016/j.gloenvcha.2015.07.008
• 发表时间: 2015-09-01
• 期刊: GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS
• 影响因子: 8.9
• 作者: Hamann, Maike;Biggs, Reinette;Reyers, Belinda
• 通讯作者: Reyers, Belinda

A Continental-Scale Validation of Ecosystem Service Models

• DOI: 10.1007/s10021-019-00380-y
• 发表时间: 2019-04
• 期刊: Ecosystems
• 影响因子: 3.7
• 作者: S. Willcock;D. Hooftman;S. Balbi;R. Blanchard;T. Dawson;P. O’Farrell;T. Hickler;M. Hudson;M. Lindeskog;Javier Martínez-López;M. Mulligan;B. Reyers;C. Shackleton;N. Sitas;F. Villa;Sophie M. Watts;F. Eigenbrod;J. Bullock

Do ecosystem service maps and models meet stakeholders' needs? A preliminary survey across sub-Saharan Africa

生态系统服务地图和模型是否满足利益相关者的需求？

• DOI: 10.1016/j.ecoser.2016.02.038
• 发表时间: 2016
• 期刊: Ecosystem Services
• 影响因子: 7.6
• 作者: Willcock S