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，以帮助制定更具信息量的情景，以应对环境和人类发展挑战。为了克服这些挑战，需要采用自然-人类系统耦合的方法和分析。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在一起。由此产生的模型提供了一种最先进的方法，导致更准确的定量评估、更好的土地利用和更有效的生态系统服务。采用这种方法，这项研究的目标是为传粉者开发一个用户友好的生物多样性和生态系统服务模型的开放库，利益攸关方可以使用该库根据不同的环境情景，特别是与气候变化有关的情景，提供当地和全球预测地图。该项目的动机来自这样一个事实，即生物多样性的丧失，特别是传粉者的丧失，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了负面影响。工作将包括与主要利益攸关方社区共同开发建模软件，以及比较目前用于产生不断变化的条件对传粉者丰富度和多样性的情景和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。它们还涉及通过更好地了解生态系统变化对传粉者的影响以及气候变化可能如何影响这些影响，在世界范围内改善粮食安全和作物生产力的巨大潜力。其他影响包括提供以科学为基础的调查结果，供决策者和政策制定者以及面临授粉者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攸关方团体生成新的共同开发的预测模型来为科学建设基础设施，以提供更高的实用性，并通过社交媒体和生成展示项目及其工作的短片来提供公共宣传。该项目包括对一名研究生进行劳动力培训，并通过支持一名在科学界性别代表性不足的研究人员，扩大未被充分代表的群体在科学领域的参与。该奖项支持美国研究人员参与一个由来自23个国家的26个资助机构组成的联盟通过贝尔蒙论坛征集关于“生物多样性和生态系统服务的情景”的建议的项目。这一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，以了解和解决地球面临的关键问题。竞赛的目标是改进和应用参与性情景方法，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的建模方法方面的差距。它还将开发和传播与模型相关的不确定程度，以改善数据可获取性并填补知识空白。利用这一方法，资助的项目解决了传粉者的生物多样性和丰富性及其与作物授粉有关的社会-环境-经济影响问题。这项研究通过建模和数据收集/汇总、与土地利用变化、人为干预引起的变化以及气候和环境变化有关的驱动因素的发展，审查了传粉者种群反应和对生态系统服务的影响的各种情景。预测生物多样性及其相关服务的未来的情景是为保护和农业资源规划提供信息的强大工具。这项研究比较了三个模型家族。它们基于(1)数据挖掘、(2)统计关系和(3)机械模型。所有这些都将使用新收集的和以前收集的来自三大洲四个不同国家的数据进行参数化。独立开发的模型的产出将用于查明知识差距，并就各种模型的预测能力中剩余的不确定性提供见解。这项研究采用了开源环境k.LAB，这是一个旨在提供网络可访问模型的集成网络的平台，与主要利益相关者合作开发了一个用户友好的模型传粉者场景开放库。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.