Ecological risk assessment and food webs: identifying ecosystem tipping points under multistress

生态风险评估和食物网：确定多重压力下的生态系统临界点

• 批准号: 2596558
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2596558
• 关键词: Ecological; risk; assessment; food; webs

Predicting the response of ecological systems to environmental change is hampered by their inherent complexity and a lack of long-term, high-resolution data. One area where this challenge is particularly acute is ecological risk assessment (ERA), where there is a pressing need to scale from our knowledge of chemical impacts on individual organisms to implications for community and ecosystem processes. Predictive models may serve to bridge this knowledge gap. However, building predictive models for the vast majority of ecological systems is extremely challenging because of system diversity and the array of ecosystem functions at different ecological scales. Even where such modelling is possible, current efforts remain highly system-specific. Thus, the realisable value of models in the context of ERA lies in their potential to identify generalisable phenomena.In the domain of ecological time series analysis, one area which has seen a rapid advancement in the last decade is the field of early warning signals (EWSs). These methods infer the approaching state-change of a system through observed changes in its spatio-temporal dynamics, which can act as symptoms of declining resilience-the capacity to recover quickly from perturbations-before reaching an irreversible transition. Such approaches are phenomenological, aiming to identify general, broadly applicable signals without considering the underlying dynamics of the system or the external drivers of change (e.g. chemical stressors). Indeed, EWSs have been identified in abundance-based data, e.g. increasing autocorrelation in the population abundance; trait-based data, e.g. declining mean body size of individuals; and spatial data, e.g. increased correlation of abundances in connected patches. The generalisability of early warning signals gives hope that they can act as a prioritisation tool to identify the characteristics that make systems particularly vulnerable to chemical stressors (e.g. community structure or trait composition) and identify trigger points that the risk assessment should focus on. Although this approach could be a valuable risk-assessment tool, it is not yet clear how to operationalise the use of such signals. Moreover, we know relatively little about how EWSs manifest in ecological communities and ecosystem processes in the face of multiple stressors. Generating predictions about stressor-driven early warning signals in biodiverse communities requires a modelling framework that is multi-species and allows multiple stressor impacts on physiology, behaviour, survival and reproduction. The bioenergetic food web model (BEFW) is one such model.This PhD will use the food web models (notably BEFW) to develop EWS theory for the community- and ecosystem-level processes where multiple chemical stressors are affecting multiple species. Specifically, the student will:*Evaluate the potential for using such signals for assessment of impending resilience loss.*Determine the robustness of these signals to different assumptions about stressor impacts and community structure.*Identify the set of maximally informative signals of resilience loss among abundance, trait and spatial metrics.*Assess the generality of the optimal set of signals by simulating pollutant impacts using commercially available food chain models (e.g. US EPA AQUATOX)

预测生态系统对环境变化的响应受到其固有复杂性和缺乏长期高分辨率数据的阻碍。这一挑战尤其严峻的一个领域是生态风险评估（ERA），迫切需要从我们对单个生物的化学影响的认识扩展到对社区和生态系统过程的影响。预测模型可能有助于弥合这种知识差距。然而，由于系统多样性和不同生态尺度的生态系统功能阵列，为绝大多数生态系统建立预测模型极具挑战性。即使在可能建立这种模型的地方，目前的努力仍然是高度系统特异性的。因此，在时代背景下，模型的可实现价值在于它们识别可推广现象的潜力。在生态时间序列分析领域，近十年来发展迅速的一个领域是预警信号领域。这些方法通过观察到的系统的时空动态变化来推断系统的接近状态变化，这可以作为在达到不可逆过渡之前恢复能力下降的症状-从扰动中快速恢复的能力。这些方法是现象学的，旨在识别一般的，广泛适用的信号，而不考虑系统的潜在动力学或变化的外部驱动因素（例如化学压力源）。事实上，ews已经在基于丰度的数据中被识别出来，例如，种群丰度的自相关性增加；基于特征的数据，例如个体平均体型的下降；空间数据，例如，连通斑块中丰度的相关性增加。早期预警信号的通用性给人们带来了希望，即它们可以作为一种确定优先次序的工具，以确定使系统特别容易受到化学压力源（例如，群落结构或性状组成）的特征，并确定风险评估应重点关注的触发点。尽管这种方法可能是一种有价值的风险评估工具，但目前尚不清楚如何操作使用这种信号。此外，面对多重压力源，我们对生态环境污染在生态群落和生态系统过程中的表现知之甚少。在生物多样性群落中对压力源驱动的早期预警信号进行预测，需要一个多物种的建模框架，并允许多种压力源对生理、行为、生存和繁殖产生影响。生物能量食物网模型（BEFW）就是这样一个模型。本博士将使用食物网模型（特别是BEFW）为多种化学压力源影响多种物种的群落和生态系统水平过程发展EWS理论。具体来说，学生将：*评估使用这些信号来评估即将发生的弹性损失的潜力。*确定这些信号对压力源影响和社区结构的不同假设的稳健性。*在丰度、特征和空间指标中确定一组最具信息量的恢复力丧失信号。*利用商业上可获得的食物链模型（如美国环保署AQUATOX）模拟污染物影响，评估最优信号集的普遍性