Collaborative Research: The Effects of Cross-Scale Interactions on Freshwater Ecosystem State Across Space and Time

由于气候和土地利用强烈影响生态系统及其为社会提供的服务，因此了解单个因素及其相互作用对于制定有效的环境管理和政策不可或缺。跨尺度的相互作用，其中一个尺度的因素与另一个尺度的因素相互作用，特别令人感兴趣，因为它们的复杂性和缺乏研究。这项研究的主要目标是开发工具来测量和了解气候和土地利用本身以及相互作用的因素如何在时间和空间尺度上影响湖泊生态系统，即使这些因素本身也在变化。湖泊是解决跨尺度相互作用问题的独特研究系统;例如，周围湖泊流域的农业土地利用可以与湖泊所在地区的气候相互作用，导致不同气候带的湖泊可能对类似的周围环境投入（如流域农业土地利用的营养投入）作出不同反应。该项目将确定和测量控制湖泊养分和水质的最重要的跨尺度相互作用，并将以景观湖沼学概念框架为指导。来自三所大学的合作团队将收集有关湖泊，营养物和流域的大型数据集，包括美国11个州的5，000多个湖泊生态系统，时间跨度长达30年。几种新的和创新的统计建模方法将用于检测和建模跨尺度的相互作用，包括贝叶斯分层建模（一种用于学习和建模数据中复杂关系的统计方法）。识别容易发生跨尺度相互作用的条件或环境，是预测、管理和恢复生态系统（如湖泊）所必需的，以应对地方到区域尺度的变化。这项工作的研究框架，设计和分析提供了一种创新的方法，有可能改变对大规模生命系统的研究，超越所研究的湖泊。此外，由于水资源政策中使用的常用测量湖泊水质变量将用于本分析，因此本项目的结果将直接通知负责湖泊和水管理的州和联邦机构。最后，一些本科生，研究生和博士后研究人员将因此受到培训，帮助培养新一代的生物学家，他们具有解决大规模研究和政策问题的技能。

Because climate and land use strongly affect ecosystems and the services that they provide to society, understanding of both individual factors and their interactions is integral for developing effective environmental management and policy. Cross-scale interactions, wherein a factor at one scale interacts with a factor at another scale, are of particular interest, given their complexity and lack of study. The main goal of this research is to develop tools to measure and understand how climate and land use, by themselves and as interacting factors, affect lake ecosystems across scales of time and space, even as these factors are themselves, changing. Lakes are unique study systems to address questions of cross-scale interactions; for example, agricultural land use in a surrounding lake watershed can interact with the climate of the region in which the lake is located, leading to situations where lakes in different climatic zones may respond differently to similar surrounding environmental inputs, such as nutrient inputs from agricultural land use in their watersheds. This project will identify and measure the most important cross-scale interactions that control lake nutrients and water quality and will be guided by a landscape limnology conceptual framework. A collaborative team from three universities will collect a large dataset on lakes, nutrients, and watersheds, including over 5,000 lake ecosystems in 11 U.S. states spanning up to 30 years. Several new and innovative statistical modeling approaches will be used to detect and model cross-scale interactions, including Bayesian hierarchical modeling (a statistical method for learning and modeling complex relationships in data). Identifying the conditions or the environments prone to cross-scale interactions is needed to forecast, manage, and restore ecosystems, such as lakes, responding to change operating at local to regional scales. The research framework, design, and analysis of this work provide an innovative approach that has the potential change the conduct of research on large-scale, living systems, beyond the lakes under study. Additionally, because commonly-measured lake water quality variables used in water resource policy will be used in this analysis, results from this project will directly inform state and federal agencies responsible for lake and water management. Finally, several undergraduate, graduate, and post-doctoral researchers will be trained as a result, helping to foster a new generation of biologists with skills in tackling broad-scaled research and policy problems.