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Published February 26, 2024 | Version submitting
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Dispersal syndromes on European Birds

  • 1. Potsdam University
  • 2. ROR icon Universidad Complutense de Madrid
  • 3. ROR icon British Trust for Ornithology
  • 4. ROR icon University of Potsdam

Description

Dispersal syndromes in European bird dispersal

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Repository folder structure
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1. data

All dispersal estimates come from Fandos et al. 2023.

2. scripts

Where all of the executed code lives. This includes pipelines, scripts, and figure files. 


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The pipeline and workflow for analysing dispersal syndromes
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1. Join dispersal estimates with biological and ecological traits
2. Correlation between variables
3. Bayesian phylogenetic mixed models
4. Results and plots from dispersal syndromes
5. Evaluating and cross-predicting dispersal estimates

Abstract

Understanding the determinants and predictability of dispersal remains a crucial challenge in ecology and evolution. Dispersal syndromes, which describe patterns in the covariation of traits associated with dispersal, can help to gain a deeper understanding of the evolutionary dynamics of dispersal and its implications for range dynamics and ecosystem functioning in the face of global changes. However, the extent to which such dispersal syndromes are generalizable across a large taxonomic scale has been hampered by low availability of standardised dispersal data across species. In this study, we used the most comprehensive and up-to-date empirical dispersal dataset for European birds to investigate the formation of dispersal syndromes and their effectiveness in predicting dispersal across species. We found that body mass, feeding guild and life-history traits shape dispersal syndromes in birds. Yet, only body mass and life history accurately predicted dispersal for unassessed species, while multi-trait dispersal syndromes had some limitations for predicting dispersal for phylogenetically distant species. These results shed light on the complex nature of bird dispersal and emphasize the need for refined approaches in elucidating the mechanisms and constraints underlying dispersal evolution. Our study highlights the importance of considering multiple factors and expanding empirical datasets to enhance our understanding of dispersal in avian populations.

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