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: 1753854
• 负责人: Emily Stanley
• 金额: $ 49.36万
• 依托单位: University of Wisconsin-Madison
• 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=1753854&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，这些变化会对生态系统产生巨大影响。例如，湖泊中的有害藻华可能会因环境条件的微妙变化而突然出现。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，通过经常测量水质，可能会发现藻类大量繁殖的早期预警。技术的最新进展现在使得使用传感器真实的实时测量水质成为可能。 这些高频数据提供了一种方法来开发有害藻类即将大量繁殖的早期预警。一个有效的预警系统将有助于科学家了解有害藻华的原因，并制定预防战略。这项研究将研究如何利用空间模式，除了随时间的变化，以更有效地发现在湖泊中的有害藻华预计会发生的早期预警。其目标是创建一种新的方法来预测生态系统何时何地将发生重大变化。有害藻华是一个严重的水质和公共卫生问题，研究结果将有助于管理。在水华开始之前发出早期预警可以帮助管理人员保护海滩、水质和其他重要的公共资源。这一项目还将涉及与记者的外联活动，以帮助教育公众如何努力防止有害藻华，对生态系统空间格局的经验性评价需要一种反复生成空间丰富数据的手段。快速湖沼学自动测量（FLAMe）平台快速确定一系列变量的空间分布，包括诊断藻类水华的浮游植物色素。该项目将使用FLAMe平台生成颜料图和各种空间统计数据。空间格局将研究湖泊在自然营养负荷梯度和一组富营养化湖泊。将建立湖泊浮游植物动态空间模型，以评价现有指标，并可能制定新的指标。湖泊附近的阈值藻华假设有更高的空间方差，自相关性，偏度比更贫营养（低营养输入）或富营养化湖泊（高营养输入）。空间预警将在一个完整的湖泊操作进行测试，比较一个营养丰富的湖泊，一个不丰富的参考湖。丰富的湖泊被假设为增加空间方差，自相关，偏度之前的发展的水华。这项研究将提供湖泊营养状况变化的空间格局的新知识，以及关于空间变异性和变化指标的新见解。该研究将系统地探索水华的空间指标，并提出可在其他类型生态系统中检验的假设。该奖项反映了NSF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。

项目成果

A Comparison of Ecological Memory of Lake Ice‐Off in Eight North‐Temperate Lakes

北温带八个湖泊冰湖生态记忆比较

• DOI: 10.1029/2020jg006232
• 发表时间: 2021
• 期刊: Journal of Geophysical Research: Biogeosciences
• 作者: Dugan, Hilary A.

Evaluating the performance of temporal and spatial early warning statistics of algal blooms

• DOI: 10.1002/eap.2616
• 发表时间: 2022-05-19
• 期刊: ECOLOGICAL APPLICATIONS
• 影响因子: 5
• 作者: Buelo, C. D.;Pace, M. L.;Ha, D. T.
• 通讯作者: Ha, D. T.

Earlier winter/spring runoff and snowmelt during warmer winters lead to lower summer chlorophyll‐ a in north temperate lakes

冬季/春季径流提前和暖冬期间的融雪导致北温带湖泊夏季叶绿素含量降低

• DOI: 10.1111/gcb.15797
• 发表时间: 2021
• 期刊: Global Change Biology
• 影响因子: 11.6
• 作者: Hrycik, Allison R.;Isles, Peter D.;Adrian, Rita;Albright, Matthew;Bacon, Linda C.;Berger, Stella A.;Bhattacharya, Ruchi;Grossart, Hans‐Peter;Hejzlar, Josef;Hetherington, Amy Lee
• 通讯作者: Hetherington, Amy Lee

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.

Lake Mendota long term water quality model

门多塔湖长期水质模型

• DOI: 10.6073/pasta/f21259ba038cb517b876131b2fda41a1
• 发表时间: 2023
• 期刊: Environmental Data Initiative
• 作者: Hanson, Paul C