Many vocalizing animals produce the discrete elements of their acoustic signals in a specific sequential order, but we know little about the biological relevance of this ordering. For that, we must characterize the degree by which individuals differ in how they organize their signals sequentially and relate these differences to variation in quality and fitness. In this study, we fulfilled these tasks in male collared flycatchers (Ficedula albicollis). We characterized the sequential order of syllables with a network analysis approach and studied the consistency of network variables on distinct time-scales (within-day, between-day, and between-year), and assessed their relationship with such quality indicators as age, body condition, arrival date, and fitness-related proxies like survival to the next year and pairing success. We found that the syllables were associated non-randomly with one another and both the frequency differences of consecutive syllables and the number of motif types were higher in the original than in randomized syllable sequences. Average degree and small-worldness showed considerable among-individual differences and decreasing repeatability with increasing time-scale. Furthermore, we found relationships between male age and average degree among and within individuals. Accordingly, older males produce syllable sequences by using common syllables less often than younger individuals. However, the network variables showed no relationship with fitness-related variables. In conclusion, the sequential organization of birdsong has the potential to encode individual-specific characteristics, which thus could be used as signals in social interactions and thus potentially could be subject to sexual selection.

Data based on field recordings, segmented and manually clustered syllables from the song of collared flycatcher. We also included repeated recordings from the same individuals from the same day, different days, or from different years. Based on the syllables sequence, we generated directed unweighted networks and calculated 4 network measure variables for all recordings: average degree, average minimum path, clustering coefficient, and small-worldness. We also searched for the motifs with different lengths. Furthermore, we calculated the frequency differences between the consecutive syllables. The details of the processing are provided in the manuscript.

4 datasets are included in 1 Excel file:

• data_176ind - the network measures and male traits for 176 individuals
• data_repeatability - the network measures for repeated recordings
• data_acoustic - the mean frequency differences of consecutive syllables for original and randomized sequences
• data_motifs - the number of motif types 

In dataset, each row represents data for one recording.