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.

制度转变的特点是生物群落组成发生突然的、往往是不可逆转的变化，可能对社会所依赖的生态系统服务产生灾难性影响。这种现象的典型例子源于淡水生态系统，其中群落结构的变化可能导致蓝藻占主导地位的生态系统，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政权转变以避免它们是一个关键目标，对稀缺淡水资源的管理具有重大影响。该项目将使用长期监测数据与先进的建模技术相结合，来描述社区组成在政权更迭之前和期间如何变化，并测试和开发通用方法来预测和防止未来的此类转变。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。这些是通用方法，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，这些信号表明即将发生政权转变。这些信号的潜在功效已在模拟研究中得到广泛证明，但在很大程度上尚未在现实世界的数据上进行测试，部分原因是在观察到的政权转变之前和之后缺乏长期监测。这引发了人们对它们是否适合为自然资本管理策略提供信息的质疑。该项目将采用双管齐下的方法来解决这一知识差距：分析来自经过充分研究的经历了政权转变的湖泊生态系统的长期监测数据，以及湖泊群落的补充理论模型。加利利海是以色列唯一的天然淡水湖，因此也是重要的资源。它的重要性意味着近 50 年来一直进行了广泛的监测，提供了 1969 年至 2018 年鱼类、浮游动物和浮游植物物种的丰度、生物量和密度以及湖泊化学成分变化的特殊数据。 1994年至1995年间，该湖的群落经历了一次重大转变，导致水质严重恶化，有害藻类大量繁殖。因此，在已知的政权转变之前、期间和之后可以获得多个物种和营养级的数据，这使其成为测试和开发预警信号方法的理想选择。这些特殊数据将用于参数化湖泊群落的尺寸光谱模型，从而可以评估系统扰动的多个模拟结果。该项目将：(1) 评估加利利海的政权转变是否可以在其发生之前预测到，(2) 确定提前多长时间可以检测到此类警告信号，(3) 检查是否最好集中精力特定物种、营养级，或在尝试预测系统未来动态时将群落动态视为一个整体，以及 (4) 确定未来应收集哪些数据来预测政权转变。除了导师提供的专业培训外，学生还将参加全方位的一般课程，以提高他们的就业能力和个人发展，包括统计、计算、研究伦理、知识产权和企业、生物信息学、抽样方法和研究技能。