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

合作研究：空间动力学、早期预警和有害藻华

• 批准号: 1754712
• 负责人: Michael Pace
• 金额: $ 39.37万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1754712&HistoricalAwards=false
• 关键词: Collaborative; Research; Spatial; Dynamics; Early

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.

当环境的微小变化接近临界点时，这些变化可能会对生态系统产生重大影响。例如，湖泊中有害的藻类水华可能会因环境条件的微妙变化而突然出现。要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。一些科学家提出，可能存在通过频繁测量水质来检测到藻类水华的早期预警。最近的技术进步使使用传感器实时测量水质成为可能。这些高频数据为开发有害藻类即将爆发的早期预警提供了一种方法。一个有效的早期预警系统将帮助科学家了解有害的藻类大量繁殖的原因，并制定预防策略。这项研究将研究如何利用空间模式，除了随着时间的变化，更有效地检测预计将发生有害藻华的湖泊的早期预警。其目标是创造一种新的方法来预测生态系统将在何时何地发生重大变化。这些结果将有助于管理，因为有害的藻华是一个严重的水质和公共卫生问题。水华开始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。该项目还将与记者进行接触，帮助教育公众有关预防有害藻华的努力。生态系统空间格局的经验评估需要一种重复生成空间丰富数据的手段。快速湖沼学自动化测量(FLAMP)平台快速确定一系列变量的空间分布，包括浮游植物色素，这些色素是藻类水华的诊断指标。该项目将使用火焰平台生成颜料地图和各种空间统计数据。将研究跨越自然营养物质负荷梯度的湖泊和一组富营养化湖泊的空间格局。将开发湖泊浮游植物的动态空间模型，以评估现有指标，并可能开发新的指标。与营养较少(低营养投入)或富营养化(高营养投入)的湖泊相比，藻华爆发阈值附近的湖泊具有更高的空间变异性、自相关性和偏斜度。空间早期预警将在整个湖泊操作中进行测试，该操作将营养丰富的湖泊与未丰富的参考湖进行比较。富营养化的湖泊在水华发展之前，被假设增加了空间变化、自相关和偏斜度。这项研究将提供随着营养状况变化而变化的湖泊空间格局的新知识，以及关于空间变异性和变化指标的新见解。这项研究将系统地探索水华的空间指标，并产生可在其他类型的生态系统中验证的假设。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。

项目成果

Phytoplankton biomass, dissolved organic matter, and temperature drive respiration in whole lake nutrient additions

• DOI: 10.1002/lno.11738
• 发表时间: 2021-04
• 期刊: Limnology and Oceanography
• 影响因子: 4.5
• 作者: M. Pace;C. Buelo;S. Carpenter

DOC , grazers, and resilience of phytoplankton to enrichment

DOC、食草动物和浮游植物对富集的恢复力

• DOI: 10.1002/lol2.10280
• 发表时间: 2022
• 期刊: Limnology and Oceanography Letters
• 影响因子: 7.8
• 作者: Carpenter, Stephen R.;Pace, Michael L.;Wilkinson, Grace M.
• 通讯作者: Wilkinson, Grace M.

Resilience of phytoplankton dynamics to trophic cascades and nutrient enrichment

浮游植物动态对营养级联和营养富集的恢复力

• DOI: 10.1002/lno.11913
• 发表时间: 2021
• 期刊: Limnology and Oceanography
• 影响因子: 4.5
• 作者: Carpenter, Stephen R.;Arani, Babak M. S.;Van Nes, Egbert H.;Scheffer, Marten;Pace, Michael L.
• 通讯作者: Pace, Michael L.

Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records?

我们能否从古生态记录中检测到生态系统的关键转变和恢复能力变化的信号？

• DOI: 10.1002/ecs2.2438
• 发表时间: 2018
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Taranu, Zofia E.;Carpenter, Stephen R.;Frossard, Victor;Jenny, Jean-Philippe;Thomas, Zoë;Vermaire, Jesse C.;Perga, Marie-Elodie
• 通讯作者: Perga, Marie-Elodie

A modeling analysis of spatial statistical indicators of thresholds for algal blooms

• DOI: 10.1002/lol2.10091
• 发表时间: 2018-10-01
• 期刊: LIMNOLOGY AND OCEANOGRAPHY LETTERS
• 影响因子: 7.8
• 作者: Buelo, C. D.;Carpenter, S. R.;Pace, M. L.
• 通讯作者: Pace, M. L.