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

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)