RT Journal Article
T1 Simple mechanistic traits outperform complex syndromes in predicting avian dispersal distances
A1 Fandos Guzmán, Guillermo
A1 Robinson, Robert A.
A1 Zurell, Damaris
AB Dispersal is a fundamental ecological and evolutionary process, but identifying its determinants and predicting it across species remains a major challenge. Dispersal syndromes, which describe patterns of covariation among traits related to dispersal, are thought to capture general rules of dispersal evolution and its ecological consequences. Based on the most comprehensive empirical dispersal dataset available for European birds, we test how dispersal syndromes form and how well they predict dispersal across species. We found that distinct dispersal processes were governed by different trait combinations, with body mass consistently predicting overall dispersal, whereas flight efficiency was key for long-distance dispersal events. However, multi-trait dispersal syndromes performed poorly for phylogenetically distant species and were outperformed by models based on single mechanistic traits, especially body mass, life history, and, to a lesser extent, flight efficiency. Thus, single traits with clear mechanistic meaning predict avian dispersal ability better than complex syndromes. These findings highlight the complexity of avian dispersal and emphasize the need for refined mechanistic approaches to understand the constraints shaping dispersal evolution. Together, our study calls for broader empirical efforts and more mechanistic frameworks to uncover the evolutionary and ecological drivers of dispersal.
PB Nature Research
YR 2026
FD 2026-02
LK https://hdl.handle.net/20.500.14352/135112
UL https://hdl.handle.net/20.500.14352/135112
LA eng
NO Fandos, G., Robinson, R.A. & Zurell, D. Simple mechanistic traits outperform complex syndromes in predicting avian dispersal distances. Commun Biol 9, 376 (2026). https://doi.org/10.1038/s42003-026-09676-x
NO Acknowledgements:D.Z. and G.F. received funding from the German Science Foundation DFG (grant no. ZU 361/1-1), G.F. also received funding from the Community of Madrid (Spain) and the Universidad Complutense de Madrid (Grant No. PR17/24-31914).
NO Deutsche Forschungsgemeinschaft (Deutschland)
NO Comunidad de Madrid
NO Universidad Complutense de Madrid
DS Docta Complutense
RD 30 abr 2026