Collaborative Proposal: MSB-FRA: A macrosystems ecology framework for continental-scale prediction and understanding of lakes

合作提案：MSB-FRA：用于大陆尺度预测和湖泊理解的宏观系统生态学框架

• 批准号: 2306364
• 负责人: Erin Schliep
• 金额: $ 25.66万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306364&HistoricalAwards=false
• 关键词: Collaborative; Proposal; MSB; FRA; macrosystems

Lakes are recognized as hotspots for processing carbon, nitrogen, and phosphorus and thus are critical for understanding how human activities affect global cycles of these essential nutrients. However, to estimate the total contribution of lakes in the United States to these global cycles, they have to rely on measurements from a small number of well-studied lakes because scientists do not have the resources to study every lake all the time. The resulting extrapolations to estimate global cycles and predict future change have many uncertainties. Consequently, it is important to understand where and when information from small subsets of lakes can be accurately applied to the wide variety of lake types and landscape settings across the continental United States. To improve future extrapolation efforts and to understand the role of lakes in global nutrient cycles, this award will build an unprecedented database that combines nutrient measurements from existing government and university monitoring programs (for about 15,000 lakes) with lake and landscape characteristics from national publicly-available digital maps for all lakes in the continental United States (about 130,000 lakes). Using this novel and unprecedented database, three components will be studied that are needed to determine the contribution of lakes to continental nutrient cycles. First, lake nutrients will be studied jointly rather than individually to provide insights into the conditions in which cycles are linked or not, which will help to reduce uncertainty in continental estimates of lake nutrients. Second, as scientists expand their studies from a few lakes to the entire continent, the relationships between lake nutrients and their landscape controls can differ in strength and even direction among different regions, further contributing to uncertainties in continental understanding of lake nutrient cycles. Finally, compiling data on every lake increases the chance of discovering novel environmental conditions that have not previously been studied, yet may play important roles in continental-scale nutrient cycles. Through these important research activities, scientists will increase their confidence in estimating the effects of lakes on global cycles. This award contributes to the broader scientific community because the database will be made publicly-available in a timely manner to complement the National Ecological Observatory program and to developing open-source advanced computer tools for analyzing large datasets for this and other big-data studies. In addition, the diverse team (by gender, career-level, and discipline) will train and mentor early-career scientists in interdisciplinary, team-based, and data-intensive science to be leaders in addressing challenging questions such as how future land use intensification and changes in global climate will affect lakes and the services they provide. Ecosystems, such as lakes, are complex, heterogeneous, and strongly influenced by their ecological context?environmental or anthropogenic factors that operate at multiple scales. This complexity makes extrapolating site-level estimates of ecological services, state, and function challenging. The overarching goal of this research is to understand and predict patterns in the three major nutrients for all continental US lakes to inform estimates of lake contributions to continental and global cycles of nitrogen, phosphorus, and carbon. The proposed work will address three important phenomena that limit scientists? ability to extrapolate freshwater nutrients at continental scales. (1) Because cycles of nitrogen, phosphorus, and carbon in inland water interact with each other and are often affected by similar controls, they should be considered as linked, not isolated. (2) As studies expand to view the whole continent, interactions between driver variables at different scales (cross-scale interactions) also increase. (3) A hallmark of the Anthropocene is the rise of novelty in ecosystems--new environmental conditions or new combinations of conditions. Such novelty may confound extrapolation in unknown ways. The proposed research is an unprecedented effort that will: address these important phenomena, develop new continental-scale data products for aquatic macrosystems ecology, and contribute novel, data-intensive analytical methods from computer science and statistics. This award will answer five research questions related to the above phenomena using two approaches. First, funds will be used to build a large, integrated database of all lakes in the continental United States (called LAGOS-US) that includes measures of in situ nutrients collected from tens of thousands of lakes, and ecological-context metrics calculated for all 130,000 continental lakes using geographic information systems and remote sensing datasets. Second, analyses of the database will be conducted for each research question using existing and novel statistical and computer science analytical tools to improve macrosystems ecology knowledge of freshwater nutrients. This award will complement the National Ecological Observatory strengths by providing data for a broader range of aquatic ecosystems and by providing the ecological context for the six continental Observatory lake sites. This award will result in four major intellectual contributions to macrosystems ecology. (1) The identification of regions where coupling and decoupling of nutrients occur, leading to a more comprehensive understanding of relationships between ecological context drivers and linked nutrient cycles. (2) Increased understanding of the types and spatial structure of ecological contexts that are more likely to lead to cross-scale interactions. (3) The identification of the role that novelty in ecological context plays in continental-scale predictions. (4) The transformation of understanding of the ecological contexts that influence biogeochemical cycles at macroscales and lake contributions to these cycles. Given the likely prevalence of such phenomena in other macrosystems, the results will be transferable to other ecosystem types, and more broadly to macrosystems ecology.

湖泊被认为是处理碳，氮和磷的热点，因此对于了解人类活动如何影响这些基本营养素的全球周期至关重要。 但是，为了估计美国湖泊对这些全球周期的全部贡献，他们必须依靠少数研究良好的湖泊的测量值，因为科学家没有任何时间来研究每个湖泊。 估计全球周期和预测未来变化的外推出了许多不确定性。因此，重要的是要了解来自小湖泊子集的信息以及何时可以准确地应用于整个美国大陆的各种湖泊类型和景观环境。为了改善未来的推断努力并了解湖泊在全球营养周期中的作用，该奖项将建立一个前所未有的数据库，该数据库结合了现有政府和大学监测计划（约15,000个湖泊）的养分测量，以及湖泊和景观特征，来自全国持续美国的所有湖泊（约130,000，000，000，000，000，000，000，000，000，000，000，000，000，000，000，000，000，000，000年）。 使用这个新颖和前所未有的数据库，将研究三个组件，以确定湖泊对大陆营养周期的贡献。 首先，将共同研究湖泊营养素，而不是单独研究，以提供有关循环是否相连的条件的见解，这将有助于减少湖泊养分大陆估计值的不确定性。其次，随着科学家将研究从几个湖泊扩展到整个大陆，湖泊营养物质与其景观控制之间的关系在不同地区之间的强度甚至方向上都会有所不同，这进一步导致了大陆对湖泊营养周期的不确定性。最后，编译每个湖泊的数据增加了发现以前未曾研究过的新型环境条件的机会，但可能在大陆规模的营养周期中起重要作用。通过这些重要的研究活动，科学家将增加对估计湖泊对全球周期影响的信心。该奖项为更广泛的科学界做出了贡献，因为该数据库将及时公开获得，以补充国家生态天文台计划，并开发开源高级计算机工具，以分析该大型数据集的大型数据集，以进行此和其他大数据研究。此外，多样化的团队（通过性别，职业水平和纪律）将在跨学科，基于团队和数据密集型科学领域培训和指导早期职业生涯的科学家，以解决诸如未来土地使用强化以及全球气候变化如何影响他们提供的湖泊及其提供的服务，以解决诸如未来的土地使用强化以及如何影响他们提供的挑战性问题。生态系统（例如湖泊）是复杂的，异质的，并且受其生态环境的影响强烈？环境或人为因素在多个尺度上运行。这种复杂性使得推断生态服务，状态和功能具有挑战性的现场级别估计值。这项研究的总体目的是了解和预测美国所有大陆湖泊三种主要营养素中的模式，以告知对氮，磷和碳的大陆和全球循环的湖泊贡献。拟议的工作将解决限制科学家的三个重要现象？能够在大陆尺度推断淡水营养素的能力。 （1）由于内陆水中的氮，磷和碳的循环相互作用，并且经常受到相似对照的影响，因此应将它们视为链接，而不是分离。 （2）随着研究的扩展以查看整个大陆，驱动器变量在不同尺度（跨尺度相互作用）之间的相互作用也会增加。 （3）人类世的标志是生态系统中新颖性的兴起 - 新的环境条件或条件的新组合。这种新颖性可能会以未知的方式混淆推断。拟议的研究是一项前所未有的努力：解决这些重要现象，开发用于水生宏观系统生态学的新大陆尺度数据产品，并从计算机科学和统计学中贡献了新颖的，数据密集型的分析方法。该奖项将使用两种方法回答与上述现象有关的五个研究问题。首先，资金将用于在美国大陆（称为Lagos-US）中建立一个大型的集成数据库，其中包括从数以万计的湖泊中收集的原位营养物质的度量，以及使用地理信息系统和遥感数据集计算的所有130,000大陆湖泊计算出的生态膜型指标。其次，将使用现有和新颖的统计和计算机科学分析工具对数据库进行分析，以改善淡水营养素的生态知识。该奖项将通过为更广泛的水生生态系统提供数据，并通过为六个大陆天文台湖面的生态环境提供数据来补充国家生态观测的优势。该奖项将为宏观系统生态学做出四个主要的智力贡献。 （1）鉴定出养分耦合和脱钩的区域，从而更全面地了解生态环境驱动因素与连接的养分周期之间的关系。 （2）对生态环境的类型和空间结构的了解更可能导致跨尺度相互作用。 （3）鉴定新颖性在生态环境中的作用在大陆规模的预测中发挥了作用。 （4）对影响宏观和湖泊对这些周期的生物地球化学周期的生态环境的理解的转变。鉴于这种现象在其他宏观系统中可能普遍存在，结果将可以转移到其他生态系统类型，并更广泛地转移到宏观系统生态学。

项目成果

On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics

关于年度和半年谐波驱动的典型季节性温度循环的空间和时间变化

• DOI: 10.1002/env.2665
• 发表时间: 2020
• 期刊: Environmetrics
• 影响因子: 1.7
• 作者: North, Joshua S.;Schliep, Erin M.;Wikle, Christopher K.
• 通讯作者: Wikle, Christopher K.

Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

• DOI: 10.1002/lol2.10134
• 发表时间: 2019-12-27
• 期刊: LIMNOLOGY AND OCEANOGRAPHY LETTERS
• 影响因子: 7.8
• 作者: Wagner, Tyler;Lottig, Noah R.;Zhou, Jiayu
• 通讯作者: Zhou, Jiayu

Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets

综合数据集中直接方法和基于凯氏定氮方法的总氮数据比较

• DOI: 10.1002/lom3.10338
• 发表时间: 2019
• 期刊: Limnology and Oceanography: Methods
• 作者: Stanley, Emily H.;Rojas‐Salazar, Shirley;Lottig, Noah R.;Schliep, Erin M.;Filstrup, Christopher T.;Collins, Sarah M.
• 通讯作者: Collins, Sarah M.

Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes

形态氮数据融合模型，用于识别环境驱动因素并改进湖泊中氮的估算

• DOI: 10.1214/20-aoas1371
• 发表时间: 2020
• 期刊: The Annals of Applied Statistics
• 作者: Schliep, Erin M.;Collins, Sarah M.;Rojas-Salazar, Shirley;Lottig, Noah R.;Stanley, Emily H.
• 通讯作者: Stanley, Emily H.

Ecological prediction at macroscales using big data: Does sampling design matter?

使用大数据进行宏观生态预测：抽样设计重要吗？

• DOI: 10.1002/eap.2123
• 发表时间: 2020
• 期刊: Ecological Applications
• 影响因子: 5
• 作者: Soranno, Patricia A.;Cheruvelil, Kendra Spence;Liu, Boyang;Wang, Qi;Tan, Pang‐Ning;Zhou, Jiayu;King, Katelyn B. S.;McCullough, Ian M.;Stachelek, Jemma;Bartley, Meridith
• 通讯作者: Bartley, Meridith