Discovery Early Career Researcher Award - Grant ID: DE240100477
Funder
Australian Research Council
Funding Amount
$449,592.00
Summary
Quantifying climate change impacts for wetlands in agricultural landscapes. This project aims to quantify the impacts of changed water availability on wetland biodiversity. Research will focus on high conservation value wetlands in agricultural regions, which face significant climatic risk. Novel integration of biodiversity theory with hydroecological and spatial modelling is expected to generate new understanding of how water availability drives wetland diversity. Intended outcomes include new ....Quantifying climate change impacts for wetlands in agricultural landscapes. This project aims to quantify the impacts of changed water availability on wetland biodiversity. Research will focus on high conservation value wetlands in agricultural regions, which face significant climatic risk. Novel integration of biodiversity theory with hydroecological and spatial modelling is expected to generate new understanding of how water availability drives wetland diversity. Intended outcomes include new techniques to model wetland biodiversity, building of international collaborations and enhanced ability to support policy development to ameliorate climate-related wetland impacts. This should promote sustainable management of water and biodiversity in farmlands, benefitting productive capacity and environmental amenity.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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