Convergence ROL: RCN: Cross-Scale Processes Impacting Biodiversity

地球上的生物多样性--估计由1000万或更多不同的物种组成--为地球提供关键的生态系统服务，包括营养、气体和水的循环，以及提供食物、药品、能源和住所。由于生物多样性对地球的健康至关重要，因此了解生物多样性是如何产生、维持和丧失的非常重要。然而，这个话题非常复杂。生物多样性分布模式和生态系统服务由跨越组织、时间维度和空间尺度的多个层级的过程来调节。这个研究协调网络汇集了一组不同的研究人员，以整合数据并探索将迅速推动该领域发展的新概念。研究人员将明确研究跨越层级的过程，以确定仅通过调查单个部件无法预测的新特性。在不同组织规模工作的生物学家将领导这项工作，与拥有机器学习、建模和数学专业知识的研究人员协调，以确保所需的网络基础设施将与新的生物多样性和生态预测理论同步发展。一年一度的会议和讲习班将为学生和教职员工提供生物多样性信息学和科学交流方面的各种培训机会。该网络将通过合作机构的生物多样性、环境生物学和计算机科学实验室之间的交流，建立学生沉浸研究的机会和广泛的跨学科培训。根据参与者的反馈，研究协调网络将采用以学生为中心的最佳合作研究实践，并将其报告给科学界。设想的产品包括观点、合成、概念验证和数据驱动的出版物，以及在科学会议、网络研讨会和学习模块上的演示。该项目还将为不同职业水平、机构和文化背景的科学家提供出色的教育和网络机会，有助于在美国建立一支多样化和训练有素的劳动力队伍。调控生物多样性和生态过程的复杂机制的非线性使预测变得困难，需要多样化的数据和新颖的分析方法来使预测更加准确。这个研究协调网络通过将不同的生物学家、环境生物学家、计算机科学家和数学家聚集在一起，探索调节生命规则的跨尺度过程，以及分析它们所需的理论、模型和网络基础设施，来促进融合。这个研究协调网络将集中于四个主要主题：1)如何将跨尺度过程纳入生物多样性格局模型和对其提供的生态系统功能的预测；2)探索和扩展新的生物多样性监测方法，以更好地了解跨尺度的模式和过程(特别是通过使用近距离和遥感方法)；3)生物信息学和网络基础设施方面的挑战和可能的解决方案，以促进处理复杂生物和环境数据的存储、整合和可视化的新方法；以及4)提高公众对生物多样性的复杂性、价值和作用的认识的新方法。这种融合的方法有望为生物多样性和生态预测的基本问题提供新的见解，这将对生物科学和更广泛的社会产生广泛影响。

Biodiversity on Earth -- comprising an estimated 10 million or more different species -- provides crucial ecosystem services to the planet, including the cycling of nutrients, gases, and water, provision of food, medicine, energy, and shelter. Because biodiversity is essential to the health of the planet, it is important to understand how it is generated, maintained and lost. This topic, however, is extraordinarily complex. Biodiversity distribution patterns and ecosystem services are regulated by processes that operate across multiple hierarchical levels of organization, temporal dimensions, and spatial scales. This Research Coordination Network brings together a diverse set of researchers to integrate data and explore novel concepts that will rapidly advance the field. Researchers will explicitly investigate processes that span hierarchical levels to identify novel properties that could not have been predicted by investigating the individual parts alone. Biologists working at different scales of organization will lead the effort, in coordination with researchers with expertise in machine learning, modeling, and mathematics to ensure the required cyberinfrastructure will advance in sync with new biodiversity and ecological forecasting theory. Annual meetings and workshops will offer diverse training opportunities in biodiversity informatics and scientific communication to students and faculty. The network will establish student research immersion opportunities and extensive cross-disciplinary training through exchanges among biodiversity, environmental biology, and computer sciences laboratories at the collaborating institutions. Based on participant feedback, the Research Coordination Network will adapt best student-centric practices of collaborative research, and report them to the scientific community. Envisioned products include perspective, synthetic, proof-of-concept, and data-driven publications, and presentations at scientific meetings, webinars, and learning modules. The project also will provide outstanding education and networking opportunities to scientists at different career levels, institutions, and cultural backgrounds, contributing to the establishment of a diverse and well-trained workforce in the U.S.The non-linearity of the complex mechanisms regulating biodiversity and ecological processes makes predictions difficult, and requires diverse data and novel analytical methods to make forecasting more accurate. This Research Coordination Network promotes convergence by bringing together a diverse set of biologists, environmental biologists, computer scientists, and mathematicians to explore the cross-scale processes regulating the Rules of Life, and the theory, models, and cyberinfrastructure needed to analyze them. This Research Coordination Network will focus on four major topics: 1) how to incorporate cross-scale processes into models of biodiversity patterns and predictions about the ecosystem functions they provide; 2) exploration and expansion of novel biodiversity monitoring approaches to better understand patterns and processes acting across scales (particularly through the use of proximal and remote sensing methods); 3) challenges and possible solutions in bioinformatics and cyberinfrastructure to foster new ways to handle storage, integration, and visualization of complex biological and environmental data; and 4) novel approaches to enhance public awareness about the complexity, value, and role of biodiversity. This convergence approach promises to provide novel insights into fundamental questions about biodiversity and ecological forecasting that will have a broad impact on the biological sciences, and society more generally.