The evolutionary transition from solitary to group living has fundamentally changed how organisms interact with their environment. However, to reap the rewards of group living, from collective intelligence to predator avoidance, g...
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30/06/2025
UZH
1M€
Presupuesto del proyecto: 1M€
Líder del proyecto
UNIVERSITAT ZURICH
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Fecha límite participación
Sin fecha límite de participación.
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Información proyecto ECOLBEH
Duración del proyecto: 67 meses
Fecha Inicio: 2019-11-13
Fecha Fin: 2025-06-30
Líder del proyecto
UNIVERSITAT ZURICH
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
1M€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
The evolutionary transition from solitary to group living has fundamentally changed how organisms interact with their environment. However, to reap the rewards of group living, from collective intelligence to predator avoidance, group members must maintain cohesion by making collective decisions. Theory suggests that individuals’ ability to influence collective decisions could be determined by a range of factors, including their state, social role, relationships with other group members, the composition of their social group, and the physical environment. Although these factors could all interact, most empirical studies have investigated them in isolation. The aim of this project is to take a ‘whole-system’ approach by quantifying (1) how individual-level factors determine leadership, (2) how movement decisions are modulated by long-term social and genetic relationships among group members, and (3) whether the mechanisms by which groups reach consensus are resilient to rapid environmental change. This research program will add unprecedented ecological validity and replication to the field of collective decision making. I will leverage state-of-the-art technology and develop novel methods to simultaneously collect and analyse multi-scale data in a wild population of highly social birds. These data include the movement of individuals within and between groups (high-resolution GPS), social interactions (direct observations), kinship (whole-genome sequencing), physiology (heart rate), and environmental conditions (drone-based mapping). The analytical methods I will develop (e.g. multi-layered dynamic social network analysis) will provide new tools for the research community, while integrating these unique data across replicated groups and over multiple generations, the project will bring new depth to the field. This depth is essential to address the questions that are central to our understanding of social behaviour in mobile animal groups, including humans.