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SOUNDCHANGE

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Listening to change: acoustic monitoring of multi-trophic community dynamics in...

Listening to change: acoustic monitoring of multi-trophic community dynamics in the face of increasingly variable and extreme environmental conditions 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... ver más

31/08/2025

CNRS

153K€

Presupuesto del proyecto: 153K€

Líder del proyecto

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-04-14

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

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Cerrada hace 2 años

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2 Participantes

CNRS

152.62K€ | Lider

UGA

N.D. | Participante

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Duración del proyecto: 28 meses Fecha Inicio: 2023-04-14
Fecha Fin: 2025-08-31

Líder del proyecto

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 153K€

Descripción del proyecto 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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