Advances in biodiversity modelling - analysis of high-dimensional counts. The aim is to develop flexible models for the analysis of high-dimensional count data, in particular, for studying species interactions and the response of communities to environmental factors. This project is significant because increasingly, important research questions are answered using data with many response variables, with a particular need when studying ecological communities and their response to environmental imp ....Advances in biodiversity modelling - analysis of high-dimensional counts. The aim is to develop flexible models for the analysis of high-dimensional count data, in particular, for studying species interactions and the response of communities to environmental factors. This project is significant because increasingly, important research questions are answered using data with many response variables, with a particular need when studying ecological communities and their response to environmental impacts. This project aims to develop the first models that can be used directly to draw valid community-level conclusions for common ecological data types. The expected outcome is a powerful toolset for fully model-based inference from high-dimensional counts, introducing modern approaches to a high-impact area of ecology.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100904
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Connections between imperfect detection and ecological inference. This project is designed to resolve whether or when it is important to account for imperfect detection when modelling communities of species. Robust conservation and environmental decisions require reliable estimates of biodiversity, yet current modelling methods may be biased because they fail to account for the imperfect detection of species. Improving the models requires good understanding about levels and patterns of species d ....Connections between imperfect detection and ecological inference. This project is designed to resolve whether or when it is important to account for imperfect detection when modelling communities of species. Robust conservation and environmental decisions require reliable estimates of biodiversity, yet current modelling methods may be biased because they fail to account for the imperfect detection of species. Improving the models requires good understanding about levels and patterns of species detectability, which is currently lacking. The project intends to bridge this gap by producing a global synthesis of species detectability across taxa, geographical regions and survey methods. The project then aims to evaluate the performance and limitations of existing and emerging community modelling methods in ecology to enable better biodiversity conservation decisions.Read moreRead less
New insights from point event data in ecology. This project aims to develop new tools for analysing point event data from multiple species and sources, to better predict species distribution and potential response to climate change. The project proposes joint statistical models for such multivariate data, for greater accuracy and for insights about which species are related in distribution and in environmental response. The new toolset expects to provide significant benefits including improved u ....New insights from point event data in ecology. This project aims to develop new tools for analysing point event data from multiple species and sources, to better predict species distribution and potential response to climate change. The project proposes joint statistical models for such multivariate data, for greater accuracy and for insights about which species are related in distribution and in environmental response. The new toolset expects to provide significant benefits including improved understanding of the drivers of species distribution and interaction, and potential response to a changing climate.Read moreRead less