权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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：
University of Bristol
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

来源：
https://gtr.ukri.org/projects?ref=studentship-2439776
关键词：
Indicators regime shifts freshwater ecosystems

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.

以生物群落组成突然的、往往是不可逆转的变化为特征的制度转变，可能对社会所依赖的生态系统服务产生灾难性影响。这一现象的典型例子来自淡水生态系统，其中群落结构的变化可能导致蓝藻主导的生态系统，对人类和牲畜健康产生潜在的负面影响。因此，能够及时预测即将发生的制度变化以避免这些变化是一个关键目标，对管理稀少的淡水资源具有重大影响。该项目将使用长期监测数据和先进的建模技术来描述群落组成在制度转变之前和期间如何变化，并测试和开发可推广的方法来预测和预防未来的这种转变。预测生态学的一个关键目标是预测生态系统快速变化的可能性，从而形成“早期预警信号”。这些是可推广的方法，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，这些信号是即将到来的政权转移的症状。这些信号的潜在功效已经在模拟研究中得到了广泛的证明，但在很大程度上还没有在现实世界的数据中得到检验，部分原因是缺乏对观察到的状态变化前后的长期监测。这就提出了它们是否适合为自然资本的管理战略提供信息的问题。该项目将采用双管齐下的方法来解决这一知识差距：分析一个经过充分研究的湖泊生态系统的长期监测数据，并对湖泊群落进行补充理论建模。加利利海是以色列唯一的天然淡水湖，因此是一个重要的资源。它的重要性意味着近50年来进行了广泛的监测，提供了从1969年到2018年鱼类、浮游动物和浮游植物物种的丰度、生物量和密度的特殊数据，以及湖泊化学成分的变化。1994-1995年，该湖的群落发生了重大变化，导致水质严重恶化，有害藻华增加。因此，在已知的制度转变之前、期间和之后，可以获得多个物种和营养水平的数据，使其成为测试和开发预警信号方法的理想选择。这些异常数据将用于参数化湖泊群落的大小光谱模型，从而允许对系统扰动的多个模拟结果进行评估。该项目将：(1)评估加利利海的政权转移是否可以在其发生之前被预测，(2)确定这些警告信号提前可检测到的程度，(3)在试图预测系统未来动态时，检查是否更好地关注特定物种，营养水平，或将群落动态视为一个整体，以及(4)确定未来应该收集哪些数据来预测政权转移。除导师提供的专业训练外，学生亦须修习一系列的通识课程，包括统计、计算机、研究伦理、知识产权与企业、生物资讯学、抽样方法及研究技巧等，以提升他们的就业能力和个人发展。