Mechanisms of strain localisation in the lithosphere

This thesis examines the development of shear-zone localisation in the continental lithosphere. I use non-Newtonian, viscous models to examine the controls on strain localisation with depth and on the development of horizontal shear-zones in regions away from strength contrasts. I show how the vertical extent of strain localisation is principally controlled by lithology and geothermal gradient, and how the horizontal extent of localisation is a consequence of strain-weakening and the geometry of strength contrasts. I explore how strain localisation develops from an initial isolated weak inclusion. The progress of strain localisation follows a power-law growth that is strongly non-linear. When applied to the rheological laws for common lithospheric minerals, the temperature and stress-dependence provide a direct means of predicting the depth below which localisation does not occur. I apply the calculations to four major continental strike-slip zones and find observations from seismic data agree with the calculations. Localisation to the base of the lithosphere is not supported by the calculations or the geophysical data. I use a model configured to resemble the India-Asia convergence that includes an isolated weak region within the Tibetan Plateau area and, in selected experiments,strong regions representing the Tarim and Sichuan Basins. I rotate a strong India region into a weaker Asia and observe the evolving strain. Shear zones develop adjacent and propagate outwards from the weak region. Where the Basins are present then high strain- rate zones develop adjacent to them and the overall distribution of strain within the model is altered. A high strain-weakening component enables shear-zones to localise. Micro-plate models assume the pre-existence of such high strain regions but I show how a continuum model can initiate and grow localised deformation within a region of generally diffuse deformation

What is natural? The importance of a long-term perspective in biodiversity conservation and management

Ecosystems change in response to factors such as climate variability, invasions, and wildfires. Most records used to assess such change are based on short-term ecological data or satellite imagery spanning only a few decades. In many instances it is impossible to disentangle natural variability from other, potentially significant trends in these records, partly because of their short time scale. We summarize recent studies that show how paleoecological records can be used to provide a longer temporal perspective to address specific conservation issues relating to biological invasions, wildfires, climate change, and determination of natural variability. The use of such records can reduce much of the uncertainty surrounding the question of what is ‘natural’ and thereby start to provide important guidance for long-term management and conservation

Fire in the Swamp Forest: Palaeoecological Insights Into Natural and Human-Induced Burning in Intact Tropical Peatlands

Tropical peat swamp forests are invaluable for their role in storing atmospheric carbon, notably in their unique below-ground reservoirs. Differing from terra firme forests, the peat-forming function of tropical swamps relies on the integrity of discrete hydrological units, in turn intricately linked to the above-ground woody, and herbaceous vegetation. Contemporary changes at a local, e.g., fire, to global level, e.g., climatic change, are impacting the integrity, and functioning of these ecosystems. In order to determine the level of impact and predict their likely future response, it is essential to understand past ecosystem disturbance, and resilience. Here, we explore the impact of burning on tropical peat swamp forests. Fires within degraded tropical peatlands are now commonplace; whilst fires within intact peat swamp forests are thought to be rare events. Yet little is known about their long-term natural fire regime. Using fossil pollen and charcoal data from three peat cores collected from Sarawak, Malaysian Borneo, we looked at the incidence and impact of local and regional fire on coastal peat swamp forests over the last 7,000 years. Palaeoecological results demonstrate that burning has occurred in these wetland ecosystems throughout their history, with peaks corresponding to periods of strengthened ENSO. However, prior to the Colonial era c. 1839 when human presence in the coastal swamp forests was relatively minimal, neither local nor regional burning significantly impacted the forest vegetation. After the mid-nineteenth century, at the onset of intensified land-use change, fire incidence elevated significantly within the peatlands. Although fire does not correlate with past vegetation changes, the long-term data reveal that it likely does correlate with the clearance of forest by humans. Our results suggest that human activity may be strongly influencing and acting synergistically with fire in the recent past, leading to the enhanced degradation of these peatland ecosystems. However, intact tropical peat swamp forests can, and did recover from local fire events. These findings support present-day concerns about the increase in fire incidence and combined impacts of fire, human disturbance and El Niño on peat swamp forests, with serious implications for biodiversity, human health and global climate change

BCCNet: Bayesian classifier combination neural network

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications.Comment: Presented at NeurIPS 2018 Workshop on Machine Learning for the Developing Worl

Sensitivity of global terrestrial ecosystems to climate variability

The identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global relevance1. Here we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognizing that systems closer to critical thresholds respond more sensitively to external perturbations2. We develop a new metric, the vegetation sensitivity index, that identifies areas sensitive to climate variability over the past 14 years. The metric uses time series data derived from the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index3, and three climatic variables that drive vegetation productivity4 (air temperature, water availability and cloud cover). Underlying the analysis is an autoregressive modelling approach used to identify climate drivers of vegetation productivity on monthly timescales, in addition to regions with memory effects and reduced response rates to external forcing5. We find ecologically sensitive regions with amplified responses to climate variability in the Arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems—be they natural or with a strong anthropogenic signature—to environmental variability, which is the first step towards addressing why some regions appear to be more sensitive than others, and what impact this has on the resilience of ecosystem service provision and human well-being.acceptedVersio

BCCNet: Bayesian classifier combination neural network

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications

Globally important plant functional traits for coping with climate change

The last decade has seen a proliferation of studies that use plant functional traits to assess how plants respond to climate change. However, it remains unclear whether there is a global set of traits that can predict plants’ ability to cope or even thrive when exposed to varying manifestations of climate change. We conducted a systematic global review which identified 148 studies to assess whether there is a set of common traits across biomes that best predict positive plant responses to multiple climate changes and associated environmental changes. Eight key traits appear to best predict positive plant responses to multiple climate/environmental changes across biomes: lower or higher specific leaf area (SLA), lower or higher plant height, greater water-use efficiency (WUE), greater resprouting ability, lower relative growth rate, greater clonality/bud banks/below-ground storage, higher wood density, and greater rooting depth. Trait attributes associated with positive responses appear relatively consistent within biomes and climate/environmental changes, except for SLA and plant height, where both lower and higher trait attributes are associated with a positive response depending on the biome and climate/environmental change considered. Overall, our findings illustrate important and general trait-climate responses within and between biomes that help us understand which plant phenotypes may cope with or thrive under current and future climate change.publishedVersio