Project description
Integrating spatial patterns in ecological machine learning
In ecological research, producing accurate spatial maps of critical variables such as biodiversity and climate is essential. Current machine learning methods, like random forest, are effective but often overlook intricate spatial patterns inherent in ecological processes. This limitation hinders our understanding of complex ecosystems and compromises the accuracy of predictions. With the support of the Marie Skłodowska-Curie Actions programme, in the PRISM project tackles this by integrating and validating spatial patterns within machine learning models. Drawing from geography, ecology, and computer science, PRISM adopts an open science approach to disseminate findings widely. It fosters collaboration between researchers and institutions, enriching skills and methodologies. Ultimately, PRISM promises to enhance ecological research, offering more precise predictions and deeper insights into spatial patterns.
Objective
Ecological research necessitates the production of spatial maps representing an array of critical variables such as biodiversity, climate, land cover, and soil carbon storage. While current machine learning methods, such as the well-known random forest (RF), have been effective in generating maps for these variables, they often overlook the intricate spatial patterns inherent in ecological processes. The PRISM project seeks to introduce a novel approach that addresses this limitation by integrating and validating spatial patterns within machine learning models. The project will draw upon insights from various fields, including geography, landscape ecology, statistics, and computer science. To ensure the widespread dissemination and accessibility of its findings, PRISM will adopt a comprehensive open science approach, including manuscript publications, the development of open-source software, and the sharing of repositories containing data and code, enabling others to reproduce and build upon the project's results. Through this project, an exchange of knowledge is anticipated between the researcher and the host institution, fostering a collaborative partnership. Under the supervisor's mentorship, the researcher will acquire essential skills in group organization, grant preparation, and research leadership. The researcher will enrich the host institution by creating innovative methods for spatial data analysis, implementing impactful teaching methodologies, and sharing the principles of open science. Ultimately, the PRISM project is poised to fuel the researcher's interdisciplinary growth, positioning him as a valuable asset in both academia and industry. The project's outcomes have the potential to improve how ecological research is conducted, leading to more accurate predictions and a deeper understanding of complex spatial patterns in ecological systems.
Fields of science
- natural sciencescomputer and information sciencesdata science
- natural sciencesbiological sciencesecologylandscape ecology
- natural sciencesearth and related environmental sciencessoil sciencesland-based treatment
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
48149 MUENSTER
Germany