Indicators of regime shifts in freshwater ecosystems

淡水生态系统政权转变的指标

基本信息

  • 批准号:
    2439776
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Studentship
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    已结题

项目摘要

Regime shifts, characterised by sudden, often irreversible, changes in the composition of biological communities, can have catastrophic impacts on the ecosystem services which society relies on. Classic examples of this phenomenon originate from freshwater ecosystems, where shifts in the structure of a community can lead to cyanobacteria-dominated ecosystems, with potential negative impacts on human and livestock health. Being able to predict impending regime shifts in time to avert them is consequently a critical goal with significant implications for the management of scarce freshwater resources. This project will use long-term monitoring data in combination with advanced modelling techniques to characterise how community composition can change prior to and during a regime shift, and test and develop generalisable methods to predict and prevent such shifts in the future. A key goal in predictive ecology is forecasting the potential for rapid changes in ecosystems, leading to the development of "early warning signals". These are generalisable methods which aim to predict changes in the composition of a community by detecting signals in time series data which are symptomatic of an approaching regime shift. The potential efficacy of such signals has been widely shown in simulation studies, but remains largely untested on real-world data, in part due to the lack of long-term monitoring before and after observed regime shifts. This has raised questions about their suitability to inform management strategies for natural capital. This project will tackle this knowledge gap using a two-pronged approach: analysis of long-term monitoring data from a well-studied lake ecosystem which has undergone a regime shift, and complementary theoretical modelling of the lake community. The Sea of Galilee is the only natural freshwater lake in Israel, and consequently a key resource. Its importance has meant extensive monitoring has been carried out for nearly 50 years, providing exceptional data on the abundances, biomasses, and densities of fish, zooplankton, and phytoplankton species from 1969 until 2018, as well as changes in the lake's chemical composition. The lake's community underwent a major shift in 1994-1995, resulting in a severe deterioration in water quality and an increase in harmful algal blooms. Consequently, data on multiple species and trophic levels are available prior to, during, and after a known regime shift, making it ideal for testing and developing warning signal methods. These exceptional data will be used to parameterise a size-spectra model of the lake community, allowing multiple simulated outcomes of perturbations on the system to be assessed. This project will: (1) assess whether the regime shift in the Sea of Galilee could have been predicted prior to its occurrence, (2) determine how far in advance such warning signals are detectable, (3) examine whether it is better to focus on specific species, trophic levels, or look at the community dynamics as a whole when trying to predict a system's future dynamics, and (4) identify what data should be collected in the future to predict regime shifts. In addition to the specialist training provided by the supervisors, the student will undertake a full range of general courses to enhance their employability and personal development, including training on Statistics, Computing, Research Ethics, Intellectual Property and Enterprise, Bioinformatics, Sampling Methodology, and Research Skills.
政权转变的特征是突然的,通常是不可逆转的生物群落组成的变化,可能会对社会依赖的生态系统服务产生灾难性的影响。这种现象的经典例子起源于淡水生态系统,在淡水生态系统中,社区结构的转变可能会导致蓝细菌为主的生态系统,并对人类和牲畜健康产生潜在的负面影响。因此,能够预测即将到来的政权随时间变化以避免它们是一个关键目标,对稀缺淡水资源的管理产生了重大影响。该项目将使用长期监控数据与先进的建模技术结合使用,以表征社区组成在政权转移之前和期间如何变化,并测试和开发可通用的方法,以预测和防止将来的这种转变。预测生态学的一个关键目标是预测生态系统快速变化的潜力,从而导致“预警信号”的发展。这些是可普遍的方法,旨在通过检测时间序列数据中的信号来预测社区组成的变化,这是接近政权转移的症状。这种信号的潜在疗效已在模拟研究中广泛显示,但在很大程度上仍未在现实世界数据上测试,部分原因是在观察到的状态转移之前和之后缺乏长期监测。这就提出了有关其适用于为自然资本管理策略提供信息的疑问。该项目将使用两管齐下的方法来解决这一知识差距:对经过良好研究的湖泊生态系统的长期监测数据的分析,该数据经历了政权的转变,以及对湖社区的互补理论建模。加利利海是以色列唯一的天然淡水湖,因此是关键资源。它的重要性意味着已经进行了近50年的广泛监测,从1969年至2018年提供了有关鱼类,浮游生物和浮游植物物种的丰度,生物量和密度的非凡数据,以及湖泊化学成分的变化。该湖的社区在1994年至1995年发生了重大转变,导致水质严重恶化,有害藻类繁殖的增加。因此,有关多种物种和营养水平的数据,可以在已知的状态转移之前,之中和之后获得,这是测试和开发警告信号方法的理想选择。这些特殊数据将用于参数湖社区的尺寸 - 光谱模型,从而允许评估系统上的多次扰动结果。该项目将:(1)评估加利利海的政权在发生之前是否可以预测,(2)确定可检测到的警告信号是可以检测到的,(3)检查是否更好地专注于特定物种,营养级别,或者在试图预测系统的未来动态时,应确定数据的整体动态,并确定数据的不断变化。除了主管提供的专业培训外,学生还将举办一系列一般课程,以增强其就业能力和个人发展,包括有关统计,计算,研究伦理,研究伦理,知识产权和企业,生物信息学,抽样方法论和研究技能的培训。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Planktonic functional diversity changes in synchrony with lake ecosystem state.
  • DOI:
    10.1111/gcb.16485
  • 发表时间:
    2023-02
  • 期刊:
  • 影响因子:
    11.6
  • 作者:
  • 通讯作者:
EWSmethods: an R package to forecast tipping points at the community level using early warning signals and machine learning models
EWSmethods:一个 R 包,使用早期预警信号和机器学习模型来预测社区层面的临界点
  • DOI:
    10.22541/au.166801190.00303336/v3
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    O'Brien D
  • 通讯作者:
    O'Brien D
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其他文献

Metal nanoparticles entrapped in metal matrices.
  • DOI:
    10.1039/d1na00315a
  • 发表时间:
    2021-07-27
  • 期刊:
  • 影响因子:
    4.7
  • 作者:
  • 通讯作者:
Ged?chtnis und Wissenserwerb [Memory and knowledge acquisition]
  • DOI:
    10.1007/978-3-662-55754-9_2
  • 发表时间:
    2019-01-01
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:
A Holistic Evaluation of CO2 Equivalent Greenhouse Gas Emissions from Compost Reactors with Aeration and Calcium Superphosphate Addition
曝气和添加过磷酸钙的堆肥反应器二氧化碳当量温室气体排放的整体评估
  • DOI:
    10.3969/j.issn.1674-764x.2010.02.010
  • 发表时间:
    2010-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:

的其他文献

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核燃料模拟物的现场辅助烧结
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  • 批准号:
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