Belmont Forum Collaborative Research: Open Library of Pollinator Biodiversity and Ecosystem Services Scenarios

需要对社会经济和全球环境趋势对生物多样性和生态系统服务的复杂相互作用进行创新研究，以帮助开发更多信息丰富的情景，以应对环境和人类发展挑战。为了克服这些挑战，需要结合自然-人类系统的方法和分析。这些方案提供了生物多样性和生态系统服务的改进情景，将土地利用、入侵物种、过度开发、生物多样性、环境变化和污染等直接和间接驱动因素的产出结合起来。由此产生的模型提供了一种最先进的方法，可以实现更准确的定量评估、更好的土地利用和更有效的生态系统服务。采用这一方法，本研究的目标是为传粉媒介开发一个用户友好的生物多样性和生态系统服务模型开放库，利益相关者可以使用该模型根据不同的环境情景，特别是与气候变化相关的环境情景，提供地方和全球预测图。开展该项目的动机来自这样一个事实，即生物多样性的丧失，特别是传粉媒介的丧失，对粮食安全、农业和经济上重要的植物生产力以及人类福祉产生负面影响。工作将包括与主要利益相关者社区共同开发建模软件，并比较目前用于生成情景和不断变化的条件对传粉媒介丰度和多样性的影响的不同建模方法的结果。这项工作的更广泛影响包括美国科学家与阿根廷、西班牙和荷兰科学家之间的国际合作。通过更好地了解生态系统变化对传粉媒介的影响以及气候变化对传粉媒介的影响，它们还具有改善全球粮食安全和作物生产力的巨大潜力。其他影响包括提供基于科学的研究结果，供决策者以及面临传粉媒介丰度和生物多样性下降的非政府组织和公共行政人员使用。这项工作还通过与关键利益相关者团体共同开发新的预测模型来建立科学基础设施，以提供更多的效用，并通过社交媒体和制作展示该项目及其工作的短视频提供公众宣传。该项目包括对一名研究生进行劳动力培训，并通过支持一名性别在科学领域代表性不足的研究者，扩大代表性不足群体在科学领域的参与。该奖项支持参与由来自23个国家的26个资助机构组成的联盟通过贝尔蒙特论坛征集“生物多样性和生态系统服务情景”提案竞争性选择的项目的美国研究人员。该呼吁是一项多边倡议，旨在支持有助于开发情景、模型和决策支持工具的研究项目，以了解和解决我们星球面临的关键问题。竞赛的目标是改进和应用参与式情景方法，以提高研究的相关性和接受度，并解决影响驱动因素和政策干预建模方法方面的差距。它还开发和交流与模型相关的不确定性水平，以改善数据可访问性并填补知识空白。利用这种方法，该资助项目解决了传粉媒介的生物多样性和丰度问题及其与作物授粉相关的社会环境经济影响。本研究通过建模和数据收集/汇总，探讨了传粉媒介种群响应和对生态系统服务影响的各种情景，开发了与土地利用变化、人类干预引起的变化以及气候和环境变化相关的驱动因素相关的情景。预测生物多样性及其相关服务的未来情景是为保护和农业资源规划提供信息的有力工具。该研究比较了三种模型族。这些都是基于(1)数据挖掘，(2)统计关系和(3)机制模型。所有这些都将使用从位于三大洲的四个不同国家新收集和以前收集的数据进行参数化。独立开发模型的输出将用于识别知识缺口，并提供有关各种模型预测能力中剩余不确定性的见解。该研究采用开源环境k.LAB，这是一个平台，旨在提供访问网络可访问模型的集成网络，与关键利益相关者合作，共同开发一个用户友好的开放传粉媒介建模场景库。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Innovative research on the complex interaction of socio-economic and global environmental trends on biodiversity and ecosystem services is needed to help develop more informative scenarios for addressing environmental and human development challenges. To overcome these challenges coupled natural-human systems approaches and analyses are needed. These provide improved scenarios of biodiversity and ecosystem services that couple the outputs of direct and indirect drivers such as land use, invasive species, overexploitation, biodiversity, environmental change, and pollution. The resulting models provide a methodological state-of-the art that results in more accurate quantitative assessments, better land use, and more effective ecosystem services. Employing this methodology, this research has a goal to develop a user-friendly open library of biodiversity and ecosystem service models for pollinators that stakeholders can use to deliver local and global predictive maps, based on different environmental scenarios, in particular, those associated with climate change. Motivation for the project comes from the fact that the loss of biodiversity, in particular with regard to pollinators, negatively impacts food security, agricultural and economically important plant productivity, and human well-being. Work will include the co-development of modelling software with key stakeholder communities and the comparison of results from different modelling approaches presently used to generate scenarios and impacts of evolving conditions on pollinator abundance and diversity. Broader impacts of the work include international collaboration between US scientists and those from Argentina, Spain, and The Netherlands. They also involve a strong potential for improving food security and crop productivity, world-wide, through a better understanding of the impacts of ecosystem changes on pollinators and how these might be impacted by climate change. Other impacts include the provision of science-based findings that can be used by decision and policy makers as well as non-governmental organizations and public administrators faced with declining pollinator abundance and biodiversity. The work also builds infrastructure for science by generating new co-developed predictive models with key stakeholder groups to provide increased utility and provides public outreach via social media and generation of short videos showcasing the project and its work. This project includes workforce training of a graduate student and broadens participation of underrepresented groups in sciences by supporting an investigator whose gender is underrepresented in the sciences. This award supports US researchers participating in a project competitively selected by a coalition of 26 funding agencies from 23 countries through the Belmont Forum call for proposals on "Scenarios of Biodiversity and Ecosystem Services". The call was a multilateral initiative designed to support research projects that contribute to the development of scenarios, models, and decision-support tools for understanding and solving critical issues facing our planet. The goal of the competition was to improve and apply participatory scenario methods to enhance research relevance and its acceptance and to address gaps in methods for modelling impact drivers and policy interventions. It was also to develop and communicate levels of uncertainty associated with the models, to improve data accessibility and fill gaps in knowledge. Using this methodology, the funded project addresses the issue of pollinator biodiversity and abundance and its socio-enviro-economic impacts related to crop pollination. This research examines various scenarios of pollinator population response and impact on ecosystems services using modelling and data collection/aggregation, the development of scenarios tied to drivers related to land-use changes, changes caused by human intervention, and changes in climate and the environment. Scenarios predicting the future of biodiversity and its associated services are a powerful tool to inform conservation and agricultural resource planning. The research compares three families of models. These are based on (1) data mining, (2) statistical relationships and (3) mechanistic models. All will be parameterized using newly collected and formerly collected data available from four different countries located on three continents. The output of the independently developed models will be used to identify gaps in knowledge and provide insights about the residual uncertainties in the predictive capacity of the various models. The research employs the open source environment, k.LAB, which is a platform designed to give access to an integrated network of web-accessible models, to co-develop a user-friendly open library of modelled pollinator scenarios in collaboration with key stakeholders.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.