Restoring amphibian populations in chytrid-impacted landscapes. This project aims to address an outstanding problem in wildlife disease ecology: how can we enable susceptible amphibians to persist in the face of the chytrid pathogen, which has devastated amphibian biodiversity? This project expects to generate new knowledge by experimentally trialling two highly promising interventions: immunising animals and creating disease refugia through simple habitat manipulations. Outcomes of this project ....Restoring amphibian populations in chytrid-impacted landscapes. This project aims to address an outstanding problem in wildlife disease ecology: how can we enable susceptible amphibians to persist in the face of the chytrid pathogen, which has devastated amphibian biodiversity? This project expects to generate new knowledge by experimentally trialling two highly promising interventions: immunising animals and creating disease refugia through simple habitat manipulations. Outcomes of this project include a framework for predicting how interventions might enable host-pathogen coexistence. This project should provide significant benefits including enhanced understanding of wildlife disease dynamics that will pave the way for interventions to restore amphibian biodiversity in chytrid-impacted landscapes.Read moreRead less
Enabling wider use of mechanistic models for biodiversity forecasts . Forecasting species distributions is challenging yet necessary. The pattern-based models commonly used are error-prone. Mechanistic models, best equipped for the task, are limited by lack of data. This project aims to enable wider use of mechanistic models by developing new methods for dealing with incomplete trait data and uncertainty. It expects to generate new knowledge about how species’ traits define the environments in w ....Enabling wider use of mechanistic models for biodiversity forecasts . Forecasting species distributions is challenging yet necessary. The pattern-based models commonly used are error-prone. Mechanistic models, best equipped for the task, are limited by lack of data. This project aims to enable wider use of mechanistic models by developing new methods for dealing with incomplete trait data and uncertainty. It expects to generate new knowledge about how species’ traits define the environments in which they persist. Anticipated outcomes include enhanced capacity to apply mechanistic models to conservation problems, methods for communicating uncertainties and models for tens of species of immediate conservation interest. This will enable more reliable biodiversity forecasts, supporting better decision-making.
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Tapping into non-English-language science in tackling global challenges. This project aims to transform the conventional practice of English-biased evidence use to multilingual evidence synthesis to enable us to better tackle global challenges. The project expects to lay the foundations and provide platforms for multilingual, unbiased evidence-based solutions to global issues including biodiversity loss, climate adaptation and animal-origin diseases. Expected outcomes include a database of non-E ....Tapping into non-English-language science in tackling global challenges. This project aims to transform the conventional practice of English-biased evidence use to multilingual evidence synthesis to enable us to better tackle global challenges. The project expects to lay the foundations and provide platforms for multilingual, unbiased evidence-based solutions to global issues including biodiversity loss, climate adaptation and animal-origin diseases. Expected outcomes include a database of non-English-language evidence on the three global issues of focus, machine learning tools, and machine translation platforms that make non-English-language evidence accessible. This should benefit national/international policies and practices by making a neglected source of evidence available for science-led decision-making.Read moreRead less
Improved management of marine habitats by learning from historical change. This project aims to greatly improve the cost-effectiveness of actions to protect and restore shallow subtidal marine habitats by quantifying the severity and distribution of recent human impacts. Environmental change will be quantified as the difference between contemporary and historical assemblages encompassing thousands of invertebrate species, and by reading historical chronicles coded by mollusc shells layered in se ....Improved management of marine habitats by learning from historical change. This project aims to greatly improve the cost-effectiveness of actions to protect and restore shallow subtidal marine habitats by quantifying the severity and distribution of recent human impacts. Environmental change will be quantified as the difference between contemporary and historical assemblages encompassing thousands of invertebrate species, and by reading historical chronicles coded by mollusc shells layered in sediments. The roles of different stressors (warming, dredging, eutrophication, introduced species, sediment runoff) will be distinguished. Expected outcomes include continental-scale understanding of factors that facilitate ecosystem decline and recovery, and of sites and species traits most affected by ongoing threats.Read moreRead less
Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in up to 500 million people. With over 10,000 species, the influence of grass type, location and climate on pollen in the air is not yet known. This is a key issue since subtropical and temperate grasses differ in response to environmental factors. The project aims to use artificial intelligence on digital camera images to learn to ....Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in up to 500 million people. With over 10,000 species, the influence of grass type, location and climate on pollen in the air is not yet known. This is a key issue since subtropical and temperate grasses differ in response to environmental factors. The project aims to use artificial intelligence on digital camera images to learn to see local grass flowers and integrate this with air sensors trained to detect grass pollen types. The expected outcomes are new capacities to track airborne grass pollen types. These outcomes can transform how pollen can be monitored to reduce the burden of allergies, and provide evidence of changing airborne pollen loads.
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A systemic environmental impact metric for companies and investors. Environmental-Social-Governance (ESG) metrics are marketed as measures of environmental performance, but they often track exposure to environmental risk rather than generation of environmental impacts. This project aims to develop and test a science-based, systemic environmental impact score for corporate activities. Expected outcomes include new knowledge of cross-scale interactions in the Earth system and tools to assess a bus ....A systemic environmental impact metric for companies and investors. Environmental-Social-Governance (ESG) metrics are marketed as measures of environmental performance, but they often track exposure to environmental risk rather than generation of environmental impacts. This project aims to develop and test a science-based, systemic environmental impact score for corporate activities. Expected outcomes include new knowledge of cross-scale interactions in the Earth system and tools to assess a business or investment’s systemic environmental impacts from activities including water extraction, deforestation and carbon emissions. These outcomes should provide benefits including improved business decision-making on impact mitigation, environmental quality, productivity and corporate environmental reputation.Read moreRead less