Duets convey information about pair and individual identities in a Neotropical bird

Abstract

Vocal individuality is essential for social discrimination but has been poorly studied in animals that produce communal signals (duets or choruses). Song overlapping and temporal coordination make the assessment of individuality in communal signals more complex. In addition, selection may favor the accurate identification of pairs over individuals by receivers in year-round territorial species with duetting and long-term pair bonding. Here, we studied pair and individual vocal signatures in the polyphonal duets of rufous horneros Furnarius rufus, a Neotropical bird known for its long-term pair bonds. Hornero partners engage in duets to deter territorial intruders and protect their partnership year-round and can discern duets from neighbors versus strangers. Using a dataset of 471 duets from 43 pairs in 2 populations, we measured fine-scale acoustic features across different duet levels (e.g., complete duets to non-overlapping syllable parts) and analysis levels (pair or individual). Permuted linear discriminant function analyses classified pairs and individuals more accurately than expected by chance (means: 45% and 47% vs. 4 and 2%). Pair identity explained more variance in the multivariate acoustic features of duets than individual or population identities. The initial frequency of the duet showed strong potential for encoding pair identity. The acoustic traits contributing most to individual vocal signatures varied between sexes, which might facilitate the simultaneous assessment of duetters' identities by receivers. Our study indicates that vocal individuality may exist even in species with intricate and innate communal signals and elucidates the mechanisms employed by horneros in their social discrimination ability.

Keywords: coordination; duetting; social discrimination; suboscine; vocal individuality; vocal signature.

Figure 1.

Hierarchical structure of rufous hornero’s duets. At the pair level of analysis, 3 levels of duet were considered: complete duets, overlapping duet sections, and overlapping syllable parts. At the individual level of analysis, 2 levels of analysis were considered: duet phrases and non-overlapping syllable parts. The spectrogram was retrieved and modified from Diniz et al. (2021)

Figure 2.

Percentage of populations (A), pairs (B), sexes, and individuals (C) of rufous horneros correctly cross-classified (bars) compared to chance expectation (dashed lines), based on permuted discriminant function analyses (pDFAs) on duet traits. pDFAs using pair-level traits (A and B) were conducted across different hierarchical levels of duet (CoD: complete duet; OvS: overlapping duet section; OvP: overlapping syllable parts) and types of acoustic measurements (Sp: spectro-temporal; Ts: temporal structure). pDFAs using individual-level duet traits (C) were conducted on spectro-temporal measurements of non-overlapping syllable parts (Sp) and/or temporal structure measurements of duet phrases (Ts).

Figure 3.

Variance in individual-level (A) and pair-level (B) duet traits explained by the identities of population, pair, sex, and individual. Variance explained is interpreted as partial R² values obtained by PERMANOVAs. Specifically, for pair-level duet traits, the analysis considers population and pair identities, whereas for individual-level duet traits, the analysis encompasses population, pair, sex, and individual identities. PERMANOVAs using pair-level traits (A and B) were conducted across different hierarchical levels of duet (CoD: complete duet; OvS: overlapping duet section; OvP: overlapping syllable parts) and types of acoustic measurements (Sp: spectro-temporal; Ts: temporal structure). PERMANOVAs using individual-level duet traits (C) were conducted on spectro-temporal measurements of non-overlapping syllable parts (Sp) and/or temporal structure measurements of duet phrases (Ts).

Figure 4.

The potential of several spectro-temporal measurements and temporal structure of syllables of rufous hornero duets for discrimination of populations, pairs, sexes, and individuals of each sex, according with Potential of Identity Coding (PIC) values. In this study, we assume that values higher than 1 (indicated by dashed lines) might facilitate discrimination (Hahn et al. 2013). The acoustics measurements with the 10 highest PIC values for each class are shown as acronyms. For detailed descriptions of acoustic measurements and PIC values, refer to Tables 2–3.