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to: build predictive spatial models for VME distribution and resilience under current and future climate scenarios; define and quantify functional traits of VME taxa; identify key functional groups, map
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position in a ‘landscape of fear’. Objective 3: Develop spatially explicit models to predict how nest footprints combine to generate landscape-level variation in microclimate, carbon flux, and plant
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different VME types. Build predictive spatial models for VME distribution and resilience under future climate scenarios. Training The candidate will gain skills in: Deep-sea survey techniques using remotely
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and the potential effects of this method of fishing on scallop stocks, other species and the wider ecosystem. Through analysis of existing data, aquarium and field-based experiments, and modelling
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in combination with the Porexpert Research Suite to construct numerical models that simulate fluid flow, allowing for the calculation of permeability. These results will be integrated with the field
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-ML to identify potential for upscaling a GIS model versus field collected geochemistry data to inform areas that would benefit from soil erosion mitigation and protection from land clearance. Training
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biodiversity. However, current plankton monitoring is insufficient, limiting our ability to detect biodiversity shifts, model ocean responses to climate stressors, and inform effective conservation policies