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Data from: Unravelling the causes and consequences of dispersal syndromes in a wild passerine

Cite this dataset

Nicolaus, Marion et al. (2022). Data from: Unravelling the causes and consequences of dispersal syndromes in a wild passerine [Dataset]. Dryad.


Evidence accumulates that dispersal is correlated with individual behavioural phenotype (‘dispersal syndrome’). The evolutionary causes and consequences of such covariation depend on the degree of plasticity vs inheritance of the traits, which requires challenging experiments to implement in mobile organisms. Here, we combine a forced dispersal experiment, natural colonisation and longitudinal data to establish if dispersal and aggression levels are integrated and to test their adaptive nature in pied flycatchers (Ficedula hypoleuca). We found that (forced) dispersers behaved more aggressively in their first breeding year after dispersal and decreased their aggression in following years. Strength of dispersal syndrome and direction of fecundity selection on aggression in newly colonised areas varied between years. We propose that the net benefits of aggression for dispersers increase under harsh conditions (e.g. low food abundance). This hypothesis now warrants further testing. Overall, this study provides unprecedented experimental evidence that dispersal syndromes can be remodelled via adaptive plasticity depending on the individuals’ local breeding experience and/or year-specific ecological conditions. It highlights the importance of individual behavioural variation in population dynamics.


These data were collected in the field through 'classical' monitoring of ringed bird breeding in nestboxes (laying date, clutch size, number and mass of fledglings etc). Aggression behavioural data were collected in the field via simulated territorial intrusion (see manuscript for details).

Collected data were immediately entered into an Access database. Relevant data were extracted trhough queries and then filtered and analysed in R (see provided scripts)

Usage notes

Missing values are indicated by NA

check R scripts to see how data were selected and to see which dataset was used in each analysis


Dutch Research Council, Award: ALWOP.2014.109