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Status : Active
Research Topic : image quality
Field of Research : Ecology
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Ecology (3)
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  • Researchers (23)
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT190100244

    Funder
    Australian Research Council
    Funding Amount
    $738,020.00
    Summary
    Establishing a global framework to trace the provenance of seafood. The global importance and demand for seafood is higher than ever; yet, sustainable seafood production is threatened by seafood fraud. This research will develop a new technology that will trace the geographic origins of seafood from catch to table and empower authorities to combat fraud. In doing so, this research will use natural chemical variation in biominerals to build maps of ocean chemistry and create universal markers of .... Establishing a global framework to trace the provenance of seafood. The global importance and demand for seafood is higher than ever; yet, sustainable seafood production is threatened by seafood fraud. This research will develop a new technology that will trace the geographic origins of seafood from catch to table and empower authorities to combat fraud. In doing so, this research will use natural chemical variation in biominerals to build maps of ocean chemistry and create universal markers of seafood provenance. These markers will be intrinsically tamper-proof: enabling the chemical geolocation of seafood across international trade routes. The outcome of this research will address a global environmental challenge and, in doing so, deliver benefits to the Australian economy, consumer and environment.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190101848

    Funder
    Australian Research Council
    Funding Amount
    $475,000.00
    Summary
    Next-generation models to predict cyanobacteria harmful algal blooms. This project aims to address the need for improved predictions of cyanobacteria (blue-green algae) harmful algal blooms. Accurate predictions of blooms with computer models are important to support management strategies to prevent their occurrence. This project is expected to generate new knowledge of strain-level variation in cyanobacteria that leads to toxic blooms. This project will lead to new knowledge of the significance .... Next-generation models to predict cyanobacteria harmful algal blooms. This project aims to address the need for improved predictions of cyanobacteria (blue-green algae) harmful algal blooms. Accurate predictions of blooms with computer models are important to support management strategies to prevent their occurrence. This project is expected to generate new knowledge of strain-level variation in cyanobacteria that leads to toxic blooms. This project will lead to new knowledge of the significance of strain-level variation in cyanobacteria harmful algal blooms, how strains influence toxin production and models for prediction of bloom and toxins. The project will generate significant benefits for water security for the purposes human consumption and recreation, and ecosystem health.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240100269

    Funder
    Australian Research Council
    Funding Amount
    $676,935.00
    Summary
    A paradigm shift for predictions of freshwater harmful cyanobacteria blooms. This project aims to advance model predictions to generate novel insights into the triggers of freshwater harmful cyanobacteria blooms. Current models are poorly adapted for this purpose because they fail to account for antecedent environmental forcing. The project is expected to create new knowledge of cyanobacteria dynamics from simulating the adaptive responses of individual cyanobacteria cells, colonies or filaments .... A paradigm shift for predictions of freshwater harmful cyanobacteria blooms. This project aims to advance model predictions to generate novel insights into the triggers of freshwater harmful cyanobacteria blooms. Current models are poorly adapted for this purpose because they fail to account for antecedent environmental forcing. The project is expected to create new knowledge of cyanobacteria dynamics from simulating the adaptive responses of individual cyanobacteria cells, colonies or filaments to temperature, light and nutrient history. Three field studies will be used to validate a new individual based model. The outcomes of this project will be valuable for managing freshwater ecosystems that are increasingly subject to blooms in a warming climate, and for testing suitable mitigation and control strategies.
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