Individual identification of birds with complex songs: The case of green-backed flycatchers Ficedula elisae

Abstract

Vocal individual identification has been demonstrated in many animals, with discriminant function analysis (DFA) and spectrographic cross-correlation (SPCC) being the two most frequent methods. Successful vocal individual identification requires high among-individual differences and within-individual stability over time for vocal features. Lack of vocal individual identification is common in songbirds with complex songs, and most vocal individual identification studies are made in bird species with simple vocalizations. Here, we applied vocal individual identification with the two methods on a songbird, green-backed flycatcher Ficedula elisae. We based its complex songs by division into first, second, and third phrases. DFA resulted in a correct distinction rate of 94.5 % between one first-phrase type and another. SPCC similarity was significantly higher within than among types for first and second phrases, respectively. For first-phrase types with recordings from different days during a breeding season, the correct DFA rate was 87.1 %. SPCC similarity within type changed significantly among days, but was still significantly higher than that among types. In conclusion, first phrases of the complex songs met the two requirements and could be effectively used for vocal individual identification in this species. This study filled a gap in vocal individual identification in birds with complex songs.