Evidence map›Paper›PMID 41673109›Full record

ArticleCommunications biology2026

Simple mechanistic traits outperform complex syndromes in predicting avian dispersal distances.

Guillermo Fandos, Robert A Robinson, Damaris Zurell

Abstract read
In one paragraph

Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Guillermo FandosInstitute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany. gfandos@ucm.es.ORCID http://orcid.org/0000-0003-1579-9444
Robert A RobinsonBritish Trust for Ornithology, The Nunnery, Thetford, Norfolk, UK.ORCID http://orcid.org/0000-0003-0504-9906
Damaris ZurellInstitute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany.ORCID http://orcid.org/0000-0002-4628-3558

Funding

Comunidad de Madrid PR17/24-31914Deutsche Forschungsgemeinschaft (German Research Foundation) ZU 361/1-1
6 · The paper itself

Abstract

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.

Indexed as

Animal DistributionBirdsAnimalsBiological EvolutionModels, BiologicalPhenotypePhylogeny

Identifiers

PMID41673109
PMCPMC12992614

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.