Theories about critical transitions and threshold-dependent changes (TDC) in natural systems have been largely developed in fluid systems using bifurcation theory. Application of these theories to terrestrial systems has not been as convincing, possibly because they differ from fluid systems in terms of their levels of spatial interconnectivity. Here, we use new theory to understand the mechanisms of decline in already 'degraded' terrestrial systems. Terrestrial systems consist of varying numbers of discrete spatial units (modules) with low interconnectivity between them, whereas fluid systems approach a single spatial unit with near ubiquitous interconnectivity. Furthermore, bifurcation theory applies optimally to self-organised and complex system states rather than simplified, homogenised or 'degraded' states. We hypothesise that, due to lower spatial interconnectivity (i.e., more discrete spatial units), terrestrial systems are more likely to undergo Turing bifurcations (localised state changes which arise due to differential diffusion, giving rise to spatial patterns) rather than the conventional 'saddle-node'/fold bifurcations (a local bifurcation in which two equilibria of a dynamical system collide). Degraded terrestrial systems, such as agroecosystems, may therefore be quasi-stable states where further degradation might be characterised by spatial reorganisation predicted by Turing bifurcations rather than the abrupt decline expected following 'saddle-node'/fold bifurcations. This PhD focusses on agroecosystems to provide new evidence to understand the mechanisms that maintain system stability, lead to further decline or recovery, and control the rates of change in overall system state. We will capture near real-time imaging over on-going resilience plot experiments. We will combine these primary data with existing spatiotemporal datasets from the North Wyke Farm Platform (NWFP) already captured at the sub-field, field, farm and landscape scale, as national/continental satellite data. Combined, these datasets allow for the detection of the localised abrupt changes within discrete spatial units (evidence of spatial reorganisation) that are anticipated under Turing bifurcations.

Anthropogenic disturbance, coupled with climate change, is a leading cause of biodiversity loss. In Ecuador, approximately 97% of the Chocó biodiversity hotspot is now deforested, underscoring the need to understand and predict species' responses to Anthropogenic change. This project will interrogate behavioural and microbial responses in the diablito poison frog, Oophaga sylvatica as a test case to assess how species respond to disturbance and climate change. The composition of microbiomes can change rapidly across environments, and microbiomes can play an important role in disease resistance in their host species. Understanding links between environmental change and the microbiome is therefore an important step in understanding disease susceptibility under Anthropogenic change. Oophaga sylvatica is an ideal model to test links between Anthropogenic change, behaviour, and the microbiome on susceptibility to the fungal skin pathogen Batrachochytrium dendrobatidis (Bd): O. sylvatica sequester toxic alkaloids from arthropod prey and their skin alkaloids can display microbial inhibitory activity in vitro. Oophaga sylvatica's diet and skin alkaloid profile have also been shown to be impacted by anthropogenic change. We hypothesise that variation in diet and skin alkaloids is driven by microhabitat choice, and that microbiome composition will have knock-on effects on susceptibility to Bd.

This PhD will deliver fundamental research in support of managing saltmarshes, a coastal ecosystem with a purported, yet unqualified, role as a nursery habitat for UK fisheries species. Saltmarshes provide multiple ecosystem services, including flood protection, blue carbon sequestration and water quality remediation (Hudson et al. 2021). They are central to Nature Based Solutions (NBS) of Shoreline Management Planning (SMP2), biodiversity conservation and emerging blue-carbon offsetting. Marshes are a priority habitat for restoration and conservation (25 Year Environment Plan, Defra) and integral to national biodiversity management (e.g., England Biodiversity 2020 Strategy). Historically, British saltmarshes were depleted to < 1/4 of their cover, many through land-conversion for agriculture (Hudson et al. 2021). Restoring marshes is costly, involving significant engineering through 'managed realignment', where defences are breached to convert terrestrial land into saltmarsh. Currently, the annual rate of restoration lags strongly behind the rate of losses (45 vs105 hectares/year) (Miles & Richardson 2018). The biggest barrier to implementing restoration is insufficient data to enable otherwise powerful instruments such as NBS, Payment for Ecosystem services (PES) and National Habitat Compensation Programmes (Hudson et al. 2021). This PhD will incentivise these instruments through quantitative substantiation of the ecological benefits of marsh restoration and conservation to fisheries species. Several species that use marshes have high fisheries value (e.g., seabass, mullet, brown shrimp), but are threatened (eel) or their management is limited by data scarcity on nursery habitats (e.g., seabass, Defra 2021). Working with and at the Wildfowl & Wetland Trust (WWT), the student will couple environmental research to stakeholder and policy analyses, to identify barriers and solutions to boosting marsh management for fisheries species. The project includes contrasts between natural and restored sites to inform on restoration benefits to fisheries.

Climate-change-related droughts in the Amazon rainforest can cause widespread tree mortality, resulting in large carbon emissions, with potential feedbacks to regional and global climate (Phillips et al. 2009; Brienen et al. 2015). However, much of our knowledge of tropical forest drought responses comes from deep water table depth (WT) forests. Shallow WT forests are severely under-researched, despite occupying ~50% of the Amazon basin, and being expected to respond differently to drought (Costa, Schietti, Stark, Smith 2022). Recent results indicate that shallow WT areas benefit from moderate drying, which reduces anoxia and extends the growing season (Esteban et al. 2021, Sousa et al. 2022). As such, shallow WT forests may be more resilient to drought, potentially capable of offsetting large carbon losses anticipated from deep WT forests. To accurately project the future of this globally important ecosystem and set conservation priorities, we need to understand the contribution of shallow WT forests to Amazon forest carbon balance and how soil water availability influences tropical forest vulnerability or resilience to drought. We will investigate how soil water availability (using WT as a proxy) influences Amazon forest responses to seasonal dry periods (and if observed, droughts). Specifically, we will quantify seasonal patterns of key components of forest carbon-vertical leaf area distributions (from ground-based lidar) and tree woody growth (from dendrometers) -and test potential drivers, including soil moisture, microenvironmental conditions, and tree hydraulic strategy.

The presence of a strong magnetic field in tokamaks leads to Magneto Hydro Dynamic (MHD) Lorentz force effects, which fundamentally impact the heat transfer characteristics of a liquid metal flow. The effect of MHD on the flow characteristics is of utmost importance for the design and performance of liquid breeder blankets, but the field is relatively poorly understood. Studies of these MHD effects started already in the early '80s. However, in most cases, the experimental setups were of relatively simple geometry (typical single rectangular or circular test section). Thus, there is very limited experimental data for multiple parallel or crossflow channels, which are more relevant to the actual breeder blanket design. The project will provide feedback to existing blanket designs as well as experimental data for mathematical model validation. The proposed study is expected to provide a new and deeper understanding of the prediction of MHD effects on fusion-relevant flows and heat-exchangers and guidance for increasing thermal performance of blankets, which can improve the design and viability of fusion reactors.