SpaceUseDrivers | Unravellling the biological determinants of space use patterns in animals

Summary
Patterns of space use by animals appear to be immensely diverse. However, recent work has suggested that they can all emerge from the interplay between only four drivers: cognitive abilities, movement constraints, social behaviour, and environmental conditions. Despite the considerable implications of space use patterns for ecological processes (e.g. nutrient fluxes, disease dynamics, invasions, extinctions), we still lack a unified theory of the causal links between the observed breadth of space use patterns and their drivers. This is mainly because there is little integration between field studies of the different types of movement (e.g. territoriality, nomadism, migration), and an uncomfortable divide between mechanistic movement models and data.
In this project, I will combine for the first time individual-based modelling and recent advances in statistical analysis applied on a unique portfolio of tracking data comprising three ungulate, two seabird, and one fish species. This approach will enable me to 1) develop a universal individual-based model of animal movement that can generate all existing types of space use patterns, 2) infer what biological mechanisms and parameters are implied by the patterns observed in nature, and 3) introduce a new methodology to the movement ecology research field. I will use this new integrative framework to specifically investigate four hypotheses on the drivers of site fidelity (residency), spatial segregation, migration and aggregation.
This project will not only greatly advance our understanding of the determinants of space use patterns, but will also be a crucial precursor to predicting how complex natural and anthropogenic environmental changes may impact animal populations.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/794760
Start date: 01-02-2019
End date: 20-06-2021
Total budget - Public funding: 183 454,80 Euro - 183 454,00 Euro
Cordis data

Original description

Patterns of space use by animals appear to be immensely diverse. However, recent work has suggested that they can all emerge from the interplay between only four drivers: cognitive abilities, movement constraints, social behaviour, and environmental conditions. Despite the considerable implications of space use patterns for ecological processes (e.g. nutrient fluxes, disease dynamics, invasions, extinctions), we still lack a unified theory of the causal links between the observed breadth of space use patterns and their drivers. This is mainly because there is little integration between field studies of the different types of movement (e.g. territoriality, nomadism, migration), and an uncomfortable divide between mechanistic movement models and data.
In this project, I will combine for the first time individual-based modelling and recent advances in statistical analysis applied on a unique portfolio of tracking data comprising three ungulate, two seabird, and one fish species. This approach will enable me to 1) develop a universal individual-based model of animal movement that can generate all existing types of space use patterns, 2) infer what biological mechanisms and parameters are implied by the patterns observed in nature, and 3) introduce a new methodology to the movement ecology research field. I will use this new integrative framework to specifically investigate four hypotheses on the drivers of site fidelity (residency), spatial segregation, migration and aggregation.
This project will not only greatly advance our understanding of the determinants of space use patterns, but will also be a crucial precursor to predicting how complex natural and anthropogenic environmental changes may impact animal populations.

Status

CLOSED

Call topic

MSCA-IF-2017

Update Date

28-04-2024
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Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.3. EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions (MSCA)
H2020-EU.1.3.2. Nurturing excellence by means of cross-border and cross-sector mobility
H2020-MSCA-IF-2017
MSCA-IF-2017