Summary
Species are often predicted to lag behind the unprecedented pace of ecological changes imposed by current human activities. Such changes result not only in modification of the mean environmental values in an ecosystem but also in increasingly extreme and variable environmental conditions. These extreme and variable conditions operate at fine spatio-temporal scales. A mechanistic understanding of these dynamics in response to new environmental conditions thus requires monitoring, analysing and modelling at such fine spatio-temporal resolution. Passive acoustic monitoring (PAM) is a rapidly growing field of research that allows for an unparalleled temporal resolution and a wide taxonomic diversity of sound producing species in terrestrial and aquatic environments. In this context, I will use PAM to understand fine scale spatio-temporal community dynamics in response to increasingly extreme and variable environmental conditions. For that I plan to develop a methodological and conceptual framework to understand community dynamics at unprecedented spatio-temporal resolution. First, I will use unsupervised learning to characterise acoustic community composition, creating acoustic groups that I will ecologically identify. I will then use these groups to assess spatio-temporal variation of community composition in these environments and their associations using network models. Finally, I will use different change scenarios to investigate the potential impact of these changes on community dynamics. This research will increase our understanding of the importance of extreme and variable environmental conditions in driving community dynamics.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101106862 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | - 211 754,00 Euro |
Cordis data
Original description
Species are often predicted to lag behind the unprecedented pace of ecological changes imposed by current human activities. Such changes result not only in modification of the mean environmental values in an ecosystem but also in increasingly extreme and variable environmental conditions. These extreme and variable conditions operate at fine spatio-temporal scales. A mechanistic understanding of these dynamics in response to new environmental conditions thus requires monitoring, analysing and modelling at such fine spatio-temporal resolution. Passive acoustic monitoring (PAM) is a rapidly growing field of research that allows for an unparalleled temporal resolution and a wide taxonomic diversity of sound producing species in terrestrial and aquatic environments. In this context, I will use PAM to understand fine scale spatio-temporal community dynamics in response to increasingly extreme and variable environmental conditions. For that I plan to develop a methodological and conceptual framework to understand community dynamics at unprecedented spatio-temporal resolution. First, I will use unsupervised learning to characterise acoustic community composition, creating acoustic groups that I will ecologically identify. I will then use these groups to assess spatio-temporal variation of community composition in these environments and their associations using network models. Finally, I will use different change scenarios to investigate the potential impact of these changes on community dynamics. This research will increase our understanding of the importance of extreme and variable environmental conditions in driving community dynamics.Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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