Timeline to collapse

崩溃的时间表

• 批准号: NE/T006579/1
• 负责人: Christopher Clements
• 金额: $ 69.18万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT006579%2F1
• 关键词: Timeline; collapse

Biological systems on which humanity relies for food, fresh water, and clean air are becoming increasingly stressed. One way in which such stress manifests is through loss of resilience: ecosystems are increasingly at risk of rapid change, characterised by sudden collapses in the abundance of populations. Increasing anthropogenic stressors thus leave us at a critical point for ecosystem management, where to preserve biodiversity and the services on which society relies, we need to reliably detect what systems are at most risk of collapse, and thus where conservation effort should be targeted.Such predictions are hampered not only by the nature of ecological systems, which are inherently high dimensional and non-linear, but also by a lack of long-term high-resolution data for many populations and communities. Such limitations mean that process-to-pattern methods such as mechanistic modelling approaches - whereby detailed models on a specific system are parameterised and perturbed to assess whether that system is at risk - are unsuitable for predicting the fates of most systems.An alternative school of thought has suggested a pattern-to-process approach, whereby signals observed in data are used to infer changes in the structure of a system which may lead to its collapse. These pattern-to-process phenomenological approaches required fewer data and make fewer assumptions about the structure of the system, providing generalisable rule-of-thumb methods to warn of approaching disaster. Were they to be reliable, such methods would be an invaluable tool to help manage and conserve biodiversity in a rapidly changing world.However, the promise of such phenomenological methods is negated by the lack of basic testing. For example, whilst many warning methods have been developed (based fluctuations in population abundances, declines in body size, and changes in the spatial distribution of populations) thus far no work has compared the relative predictive efficacy of these methods. Moreover, we know nothing about how key drivers of stability such as community and landscape complexity affect our ability to make reliable predictions, catastrophic failings if such methods are to be used to inform the management of heterogeneous populations and communities in the real world.In this project, we will take a bold new approach to the advancement and testing of phenomenological warning signals by developing a spatially-explicitly multi-species experimental system which will allow high-resolution, high-dimensional data to be generated on multiple species at the landscape scale. We will use this system to (i) provide the first simultaneous test of currently proposed phenomenological warning methods, (ii) develop and test novel warning signals derived from ecological theory based on changes in the behaviour of individuals and assess how these perform in relation to previously proposed methods, and (iii) assess the reliability of warning signal methods across various spatial and community complexities, and thus their suitability for informing conservation decision making.The work we are proposing is fundamental not only to or understanding of resilience loss in biological systems, but also to practical on-the-ground management of key resources on which human society relies. Our focus during this work will be not only to understand how integral drivers of population dynamics such as spatial and community complexity affect our ability to make reliable predictions, but how reliable the developed methods will be when subjected to the vagaries and inconsistencies seen in real-world conservation data. Thus, this project spans a range of disciplines, generating important insights in fields including demography and community ecology, whilst targeting significant downstream socio-economic impact by developing robust predictive frameworks to help minimise biodiversity loss in the face of anthropogenic forcing.

人类赖以获得食物、淡水和清洁空气的生物系统正变得越来越紧张。这种压力的一种表现方式是丧失韧性：生态系统越来越面临快速变化的风险，其特征是种群数量突然崩溃。因此，越来越多的人为压力使我们处于生态系统管理的临界点，在那里我们需要保护生物多样性和社会所依赖的服务，我们需要可靠地检测哪些系统最有可能崩溃，从而确定保护工作的目标。这种预测不仅受到生态系统的性质的阻碍，生态系统本身就是高维和非线性的，而且缺乏许多人口和社区的长期高分辨率数据。这些局限性意味着，过程到模式的方法，如机械建模方法--对特定系统的详细模型进行参数化和扰动，以评估该系统是否处于危险之中--不适合预测大多数系统的命运。另一种思想流派提出了模式到过程的方法，即使用在数据中观察到的信号来推断可能导致系统崩溃的结构变化。这些从模式到过程的现象学方法需要的数据更少，对系统结构的假设也更少，从而提供了可推广的经验方法来警告即将到来的灾难。如果它们是可靠的，这些方法将是在快速变化的世界中帮助管理和保护生物多样性的宝贵工具。然而，由于缺乏基本的测试，这些现象学方法的前景被否定了。例如，虽然已经开发了许多预警方法(基于种群丰度的波动、身体大小的下降和种群空间分布的变化)，但到目前为止还没有工作比较这些方法的相对预测效果。此外，我们不知道群落和景观复杂性等稳定的关键驱动因素如何影响我们做出可靠预测的能力，如果要使用这些方法来向现实世界中异质种群和社区的管理提供信息，则会出现灾难性的失败。在这个项目中，我们将采取一种大胆的新方法来推进和测试现象学警告信号，开发一个空间上明确的多物种实验系统，该系统将允许在景观尺度上生成关于多个物种的高分辨率、高维数据。我们将使用这个系统来(I)提供对目前提出的现象学警告方法的第一次同时测试，(Ii)根据个体行为的变化开发和测试来自生态学理论的新警告信号，并评估这些警告信号方法相对于先前建议的方法的表现，以及(Iii)评估警告信号方法在各种空间和群落复杂性中的可靠性，从而评估它们是否适合为保护决策提供信息。我们提议的工作不仅是对生物系统中弹性丧失的基础或理解，也是对人类社会所依赖的关键资源的实际实地管理。在这项工作中，我们的重点不仅是了解人口动态的整体驱动因素(如空间和群落复杂性)如何影响我们做出可靠预测的能力，而且还将了解所开发的方法在受到现实世界保护数据中出现的变幻莫测和不一致的影响时的可靠性。因此，该项目跨越了一系列学科，在人口学和社区生态学等领域产生了重要的见解，同时通过开发强大的预测框架来瞄准重大的下游社会经济影响，以帮助在面临人为强迫时将生物多样性的损失降至最低。

项目成果

Landscape configuration affects probability of apex predator presence and community structure in experimental metacommunities.

• DOI: 10.1007/s00442-022-05178-9
• 发表时间: 2022-05
• 期刊: Oecologia
• 影响因子: 2.7

Landscape configuration affects probability of apex predator presence and community structure in experimental metacommunities

景观配置影响实验元群落中顶级捕食者存在的概率和群落结构

• DOI: 10.21203/rs.3.rs-768807/v1
• 发表时间: 2021
• 作者: Wolfe E

Phenotypic response to different predator strategies can be mediated by temperature.

• DOI: 10.1002/ece3.10474
• 发表时间: 2023-09
• 期刊: Ecology and evolution
• 影响因子: 2.6

Spatiotemporal thermal variation drives diversity trends in experimental landscapes.

• DOI: 10.1111/1365-2656.13867
• 发表时间: 2023-02
• 期刊: JOURNAL OF ANIMAL ECOLOGY
• 影响因子: 4.8
• 作者: Wolfe, Ellie;Cerini, Francesco;Besson, Marc;O'Brien, Duncan;Clements, Christopher F.
• 通讯作者: Clements, Christopher F.