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Displaying 1–11 of 11 results

Optimal conservation management in uncertain Antarctic environments

Species and ecosystems in Antarctica are threatened. Optimal biodiversity conservation is an interdisciplinary field combining mathematical modelling and optimisation with ecology and conservation. We can use mathematics to understand the system, model how management actions might impact it, and then optimise which actions should be used. For example, we can explore where protected areas should be placed, how species should be managed, or how tourist impacts should be reduced. However, the complexities of conservation in Antarctica necessitate the application of …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Statistical methods for detecting Antarctic ecosystems from space

Satellite images are a frequent and free source of global data which can be used to effectively monitor the environment. We can see how the land is being used, how it’s being changed, what’s there – even where animals are in the landscape. Using these images is essential, particularly for regions where data is expensive to collect or difficult to physically access, like Antarctica. In Antarctica and the sub-Antarctic islands, satellite images can be an easy and quick way to …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Optimal ecosystem management in rapidly changing systems

Delays in acting in collapsing ecosystems can be catastrophic. With every passing year, the chances that the ecosystem has progressed past some point of no return increases. Yet the research and development needed to develop a new technology can take a long time. Balance between these two dynamic processes is needed to determine the optimal length and effort for developing new technologies. This project will develop a method for finding the optimal schedule for developing technological readiness, social acceptability, a …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Predicting alternative states induced by multiple interacting feedbacks: seagrass ecosystems as a case study

This project seeks to explore the complex dynamics that might arise from multiple interacting feedbacks in marine ecosystems, by designing ordinary and/or partial differential equation models of these feedbacks and analysing the steady states and/or temporal dynamics of the proposed model(s).It has been hypothesised that many social and ecological systems exhibit alternative stable states due to feedback processes that keep the ecosystem in one state or the other. The result can be tipping points, which are difficult to predict but …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Ecological interactions in Antarctic ecosystems

Antarctic and sub-Antarctic terrestrial ecosystems are dominated by mosses, lichens, invertebrates and some vascular plants. Marine vertebrates (penguins, seals, seabirds) also play an important role in driving terrestrial processes. All these species are influenced by many environmental and biotic factors, including interactions between species. Determining the impacts of climatic and environmental change on Antarctic and sub-Antarctic biodiversity requires greater understanding of these interactions.Ecological data on species interactions and the drivers of these interactions are an essential part of Antarctic and …

Study level
PhD
Faculty
Faculty of Science
School
School of Biology and Environmental Science
Research centre(s)
Centre for Data Science
Centre for the Environment

Symbiosis in microbial ecosystems

Soil systems are fundamentally important to the health of our planet, but the complexity of soil microbial communities makes them particularly challenging to study. Soil systems are amongst the most diverse microbial ecosystems on Earth in terms of the number of microbial species (and strains) present within individual samples, and in the breadth of functions encoded. Beyond complexity measured by counting distinct community members, interactions between microbial species including symbiosis, parasitism or commensalism are widespread and yet barely studied.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Health
School
School of Biomedical Sciences
Research centre(s)

Centre for Microbiome Research

Identifying emergent ecosystem responses through genes-to-ecosystems integration at Stordalen Mire

Permafrost thaw induced by climate change is predicted to make up to 174 Pg of near-surface carbon (less than 3m below the surface) available for microbial degradation by 2100. Despite having major implications for human health, prediction of the magnitude of carbon loss as carbon dioxide (CO2) or methane (CH4) is hampered by our limited knowledge of microbial metabolism of organic matter in these environments.Genome-centric meta-omic analysis of microbial communities provides the necessary information to examine how specific lineages transform …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Health
School
School of Biomedical Sciences
Research centre(s)

Centre for Microbiome Research

Mathematical modelling of ecosystem tipping points in Antarctica

Slight variations in the seasonal timing of Antarctic ice melt can drastically shift the composition of local shallow-water ecosystems from being dominated by invertebrates to algae instead. Such "tipping point" events may become commonplace in the future due to climate change, not just in Antarctica but in many ecosystems worldwide.This project seeks to develop mathematical models of the interactions between Antarctic environmental conditions and the local shallow-water ecosystem states. These models could then be used to make predictions about the …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Mathematical modelling of ecosystem feedbacks and value-of-information theory

Ecosystems respond to gradual change in unexpected ways. Feedback processes between different parts of an environment can perpetuate ecosystem collapse, leading to potentially irreversible biodiversity loss. However, it is unclear if greater knowledge of feedbacks will ultimately change environmental decisions.The project aims to identify when feedbacks matter for environmental decisions, by generating new methods that predict the economic benefit of knowing more about feedbacks. Combining ecological modelling and value-of-information theory, the outcomes of these novel methods will provide significant and …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Designing efficient sampling algorithms for ensemble ecosystem modelling

This project seeks to use a combination of matrix algebra and advanced Monte Carlo methods for rare-event simulation to identify efficient algorithms for ensemble ecosystem modelling in large ecosystems. This project has the potential to significantly change the methods that researchers useto investigate complex ecosystems for the purposes of environmental management and future predictions.Ensemble ecosystem modelling (EEM) is a novel and increasingly popular method for generating predictions of future species abundance in complex ecosystems represented by generalisations of the Lotka-Volterra …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Quantifying oxygen tolerance in a climate-relevant ecosystem using machine learning

Aims and methodologyPermafrost thaw induced by climate change is predicted to make up to 174 Pg of near-surface carbon (less than 3m below the surface) available for microbial degradation by 2100. Despite having major implications for human health, prediction of the magnitude of carbon loss as carbon dioxide (CO2) or methane (CH4) is hampered by our limited knowledge of microbial metabolism of organic matter in these environments. Genome-centric meta-omic analysis of microbial communities provides the necessary information to examine how …

Study level
Vacation research experience scheme
Faculty
Faculty of Health
School
School of Biomedical Sciences
Research centre(s)

Centre for Microbiome Research

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