New approaches to predictive modelling of high-dimensional count data to study climate impacts on ecological communities. This project will lay methodological foundations for future studies of potential impacts of climate change on ecological communities. A flexible new toolset of predictive modelling approaches will be developed, capable of handling all common data types, which fit easy-to-interpret models, and which are more powerful than currently used methods.
Innovative statistical methods for analysing high-dimensional counts. The aim is to develop fast, modern statistical methods for analysing high dimensional data in ecology at large scales, in particular, for visualising, classifying and predicting ecological communities. The benefit of the project is a set of multivariate tools that can be used to better understand biodiversity and its response to environmental drivers, a challenging statistical problem. The proposed methods for analysing high d ....Innovative statistical methods for analysing high-dimensional counts. The aim is to develop fast, modern statistical methods for analysing high dimensional data in ecology at large scales, in particular, for visualising, classifying and predicting ecological communities. The benefit of the project is a set of multivariate tools that can be used to better understand biodiversity and its response to environmental drivers, a challenging statistical problem. The proposed methods for analysing high dimensional data can provide insight into large scale questions in ecology, such as automated identification of biogeographic boundaries. The expected outcome is a powerful statistical toolset for model-based analysis of high dimensional data, introducing modern multivariate approaches to a high-impact area of ecology.Read moreRead less
Advancing tools for the analysis of high-dimensional data in ecology. This project will accelerate the development of advanced tools for answering fundamental questions concerning the potential impact of climate change on ecological communities. These advanced methodologies, more powerful than currently used methods, will fit easy-to-interpret models which can handle all common data types.
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
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