Collaborative Research: Spatial Dynamics, Early Warnings and Harmful Algal Blooms

当环境的微小变化接近临界临界点时，就会对生态系统产生巨大影响。例如，由于环境条件的细微变化，湖泊中的有害藻华可能突然出现。及时了解它们可能发生的时间和地点，以防止它们或减少它们的影响可能是相当具有挑战性的。一些科学家认为，通过频繁测量水质，可能会发现藻华的早期预警。最近的技术进步使得利用传感器实时测量水质成为可能。这些高频率的数据提供了一种方法来开发有害藻类即将爆发的早期预警。一个有效的早期预警系统将帮助科学家了解有害藻华的成因，并制定预防策略。这项研究将研究如何利用空间模式，以及随时间的变化，更有效地发现有害藻华可能发生的湖泊的早期预警。目标是创造一种新的方法来预测何时何地会发生生态系统的大变化。由于有害藻华是一个严重的水质和公共卫生问题，研究结果将对管理有用。在水华开始之前的早期预警可以帮助管理者保护海滩、水质和其他重要的公共资源。该项目还将包括与记者接触，帮助公众了解如何防止有害的藻华。生态系统空间格局的实证评价需要一种反复生成空间丰富数据的手段。快速湖沼自动测量（FLAMe）平台可快速确定一系列变量的空间分布，包括诊断藻华的浮游植物色素。该项目将使用FLAMe平台生成颜料地图和各种空间统计数据。将研究湖泊在自然营养负荷梯度和一组富营养化湖泊的空间格局。将开发湖泊浮游植物动态空间模型，以评价现有指标，并可能开发新的指标。据推测，藻华阈值附近的湖泊比贫营养湖泊（低营养投入）或富营养湖泊（高营养投入）具有更高的空间方差、自相关性和偏度。空间预警将在整个湖泊操作中进行测试，该操作将一个营养丰富的湖泊与一个营养不丰富的参考湖泊进行比较。富营养化湖泊的空间变异、自相关和偏度在水华形成前增加。该研究将提供湖泊营养状况变化的空间格局的新知识，以及空间变异性和变化指标的新见解。该研究将系统地探索水华的空间指标，并产生在其他类型生态系统中可检验的假设。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。

Small changes in the environment can have large effects on ecosystems when these changes approach a critical tipping point. For example, harmful algal blooms in lakes can appear suddenly in response to subtle changes in environmental conditions. Knowing when and where they are likely to happen in time to prevent them or to reduce their impact can be quite challenging. Some scientists have suggested that there may be early warnings of algal blooms detectable by frequent measurements of water quality. Recent advances in technology now make it possible to measure water quality in real time using sensors. These high frequency data provide a way to develop early warnings of imminent blooms of harmful algae. An effective early warning system will help scientists understand what causes harmful algal blooms and develop strategies to prevent them. This research will study how to use spatial patterns, in addition to changes over time, to more effectively detect early warnings in lakes where harmful algal blooms are expected to occur. The goal is to create a new approach to predicting when and where large changes to ecosystems will occur. The results will be useful for management as harmful algal blooms are a serious water quality and public health problem. Early warnings before blooms begin could help managers protect beaches, water quality, and other important public resources. This project will also involve outreach to journalists to help educate the public about efforts to prevent harmful algal blooms.Empirical evaluation of spatial pattern in ecosystems requires a means of repeatedly generating spatially rich data. The Fast Limnology Automated Measurement (FLAMe) platform rapidly determines the spatial distribution of a suite of variables including phytoplankton pigments that are diagnostic of algal blooms. The project will generate pigment maps and a variety of spatial statistics using the FLAMe platform. Spatial patterns will be studied for lakes across a natural nutrient loading gradient and for a set of eutrophic lakes. Dynamic spatial models of lake phytoplankton will be developed to evaluate existing indicators and possibly develop new ones. Lakes near thresholds for algal blooms are hypothesized to have higher spatial variance, autocorrelation, and skewness than either more oligotrophic (low nutrient inputs) or more eutrophic lakes (high nutrient inputs). Spatial early warnings will be tested in a whole lake manipulation that compares a nutrient enriched lake to an unenriched reference lake. The enriched lake is hypothesized to increase in spatial variance, autocorrelation, and skewness prior to the development of a bloom. The research will provide new knowledge of spatial patterns in lakes as nutrient status changes, as well as new insights about spatial variability and change indicators. The study will systematically explore spatial indicators for blooms and generate hypotheses testable in other types of ecosystems.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.