SG: Evaluating synchrony among ecosystem productivity, benthic cyanobacterial growth, and toxin production dynamics in rivers

SG：评估河流中生态系统生产力、底栖蓝藻生长和毒素产生动态之间的同步性

• 批准号: 2042915
• 负责人: Joanna Blaszczak
• 金额: $ 19.99万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042915&HistoricalAwards=false
• 关键词: SG; Evaluating; synchrony; among; ecosystem

Cyanobacteria growing in freshwater ecosystems, such as lakes and rivers, can produce toxins that threaten aquatic life and human health. This project advances our understanding of controls on the timing of peak cyanobacterial growth and cyanotoxin production in rivers where blooms are becoming more common. This project monitors the Eel, Russian, and Klamath rivers in northern California and uses the data to develop predictive models of cyanobacterial growth and toxin production. This research advances the ability of managers to predict where and when toxins produced by benthic cyanobacteria reach concentrations that exceed human and animal health risk thresholds. The researchers are also broadening participation in aquatic ecosystem science by supporting a summer intern in partnership with the Klamath Basin Monitoring Program and Karuk Tribe Department of Natural Resources and by supporting a graduate student. The investigators are also organizing two experiential field trips for K-12 students in partnership with the Mid-Klamath Watershed Council. The results of this work are critical to river managers in identifying and managing risks associated with exposure to algal toxins and transmitting that information to the public. All data and modeling approaches developed in this project are being shared publicly on web-accessible sites.This project advances understanding of the timing and occurrence of cyanobacterial growth and cyanotoxin production in rivers by using high-frequency sensor data and new approaches in ecological modeling. To improve predictions of cyanobacterial growth and toxin production through time, the investigators are focusing on two questions: 1) To what degree do processes that shape overall productivity dynamics in rivers predict the growth of toxin-producing cyanobacteria? and 2) How do environmental conditions modify the relationship between cyanobacterial growth and cyanotoxin production over the growing season? These questions are being addressed through the use of aquatic environmental sensors and coupled field sampling for two years at locations with previously documented occurrences of cyanobacteria that produce anatoxins, a neurotoxin produced by many species of freshwater cyanobacteria. Data on whole-ecosystem productivity rates are being used in combination with survey data to inform a series of models testing the relative importance of drivers of cyanobacterial growth and cyanotoxin production in rivers through time. These analyses are laying the foundation for future forecasts of harmful cyanobacterial blooms and toxin production in United States rivers.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.

在湖泊和河流等淡水生态系统中生长的蓝细菌会产生威胁水生生物和人类健康的毒素。该项目推进了我们对控制蓝藻生长高峰和蓝藻毒素产生的时间的理解，在河流中水华越来越普遍。该项目监测了加州北方的鳗鱼河、俄罗斯河和克拉马斯河，并利用这些数据开发了蓝藻生长和毒素产生的预测模型。这项研究提高了管理人员预测底栖蓝藻产生的毒素何时何地达到超过人类和动物健康风险阈值的浓度的能力。研究人员还通过与克拉马斯流域监测项目和卡鲁克部落自然资源部合作支持一名暑期实习生，并支持一名研究生，扩大了对水生生态系统科学的参与。调查人员还与中克拉马斯流域理事会合作，为K-12学生组织了两次实地考察。这项工作的结果对于河流管理者确定和管理与接触藻毒素有关的风险并向公众传播这一信息至关重要。该项目开发的所有数据和建模方法都在可访问的网站上公开共享。该项目通过使用高频传感器数据和生态建模的新方法，促进了对河流中蓝藻生长和蓝藻毒素产生的时间和发生的理解。为了改善对蓝藻生长和毒素产生的预测，研究人员集中在两个问题上：1）在多大程度上塑造河流中整体生产力动态的过程预测产毒蓝藻的生长？以及2）在生长季节，环境条件如何改变蓝藻生长和蓝藻毒素产生之间的关系？这些问题正在解决，通过使用水生环境传感器和耦合现场采样两年的位置与以前记录的蓝藻产生类毒素，许多物种的淡水蓝藻产生的神经毒素。关于整个生态系统生产率的数据正在与调查数据相结合，为一系列模型提供信息，这些模型测试了随着时间的推移河流中蓝藻生长和蓝藻毒素生产驱动因素的相对重要性。这些分析为今后预测美国河流中有害的蓝藻水华和毒素产生奠定了基础。该奖项反映了国家科学基金会的法定使命，并被认为值得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来支持。