Background Birds have extremely well-developed acoustic communication and have become popular in bioacoustics research. The majority of studies on bird song have been conducted in the temperate zones where usually males of birds sing to attract females and defend territories. In over 360 bird species mostly inhabiting the tropics both males and females sing together in duets. Avian duets are usually formed when a male and female coordinate their songs. We focused on a species with relatively weakly coordinated duets, with male solo as the prevailing vocalisation type. Methods Instead of analysing a set of recordings spread over a long time, we analysed whole day microphone-array recordings of the Yellow-breasted Boubou (Laniarius atroflavus), a species endemic to West African montane rainforests. We described the structure of the solo and duet vocalisations and temporal characteristics of daily activity based on 5,934 vocal bouts of 18 focal pairs and their neighbours. Results Birds had small, sex specific repertoires. All males shared three types of loud whistles functioning as song type repertoires in both solos and duets. Females vocalised with five types of harsh, atonal notes with a more variable and usually lower amplitude. Three of them were produced both as solos and in duets, while two seem to function as alarm and excitement calls given almost exclusively as a solo. Solos were the most common vocalisation mode (75.4%), with males being more vocally active than females. Duets accounted for 24.6% of all vocalisations and in most cases were initiated by males (81%). The majority of duets were simple (85.1%) consisting of a single male and female song type, but altogether 38 unique duet combinations were described. Males usually initiated singing at dawn and for this used one particular song type more often than expected by chance. Male solo and duet activities peaked around dawn, while female solos were produced evenly throughout the day. Discussion Yellow-breasted Boubou is a duetting species in which males are much more vocal than females and duetting is not a dominating type of vocal activity. Duet structure, context and timing of daily production support the joint resource defence hypothesis and mate guarding/prevention hypotheses, however maintaining pair contact also seems to be important. This study provides for the first time the basic quantitative data describing calls, solos and duet songs in the Yellow-breasted Boubou.

The acoustic signals of birds are commonly used for individual recognition. Calls or songs allow discrimination between parent and offspring, between mates and between territorial neighbours and strangers. In this study, we investigated vocal neighbour-stranger discrimination in a nocturnally calling rail species, the Corncrake, Crex crex. We conducted interactive playback experiments with 43 males. All males were tested twice on the same night, and their responses to the calls of a familiar neighbour and the calls of an unfamiliar stranger were measured. The Corncrake males responded more aggressively to the playback of a stranger's calls. They approached the speaker more rapidly, spent more time close to the speaker and physically attacked the speaker more frequently. We found no significant differences between the vocal responses to the playback of neighbours' and strangers' calls. Thus, although calling plays an important role in Corncrake territorial interactions, it is only a first line of defence in which males signal aggression towards intruders by calling, similar to singing in passerines. The lack of differences in vocal responses and the presence of clear differences in other behavioural responses demonstrate that the absence of a differential vocal response does not imply the absence of discrimination. Although the individual nature of the Corncrake call has been mentioned in a few previous studies, this study provides the first experimental evidence that Corncrake males indeed use calls for neighbour-stranger discrimination.Notably, because of the Corncrake's dense wet meadow habitat and its tendency to signal at night, it is probable that acoustic individual discrimination in the Corncrake is crucial for making correct decisions during aggressive encounters with rivals.

Source–filter theory assumes that calls are generated by a vocal source and are subsequently filtered by the vocal tract. The air in the vocal tract vibrates preferentially at certain resonant frequencies, called formants. Formant frequencies can be a good indicator of the caller's characteristics, such as sex, age, body size or individual identity. Although source–filter theory was originally proposed for mammals, formants are also observed in birds, and some bird species have been shown to perceive formants. In this study, we evaluated the hypotheses that formant frequencies (1) are an indicator of body size and (2) can be used for individual discrimination by a nocturnal bird species, the corncrake (Crex crex). We analysed calls of 104 males from Poland and the Czech Republic. Linear regression models showed that the males with a longer head (including the bill length) had a significantly lower formant dispersion and lower fourth and fifth formant frequencies. However, we found no significant relationships between body weight and any filter‐related acoustic measurement. The formant frequencies had smaller within‐ than between‐individual coefficients of variation. This characteristic of the formant frequencies implies a high potential for individual coding. A discriminant function analysis correctly assigned 94.8% of the calls to the caller based on formants from second to fifth. Our results indicated that the formant frequencies are a weak indicator of the body size of the sender in the corncrake. However, even weak dependence between body size and acoustic properties of signal may be important in natural selection process. Alternatively, such a weak dependence may be observed, because receivers ignore the acoustical, formant‐based cues of body size. Simultaneously, the formants might potentially provide acoustic cues to individual discrimination and could be used to census and monitoring tasks.

Acoustically identifying individuals may be a helpful technique when it is necessary to monitor animal populations over space and time. Previous studies have largely focused on the theoretical exploitation of vocal individuality or have looked at a small number of individuals. Here, we examined whether vocal individuality can be used to track the movement of individuals within a population (in this case when the number of individuals is greater than 100) and unknown beforehand. As a model species, we used the Corncrake (Crex crex)-a highly secretive bird whose calls are characterized by an individual-specific feature: pulse-to-pulse duration (PPD). When we performed classical discriminant function analyses on PPD, we correctly identified a high percentage of individuals ([98 %), even when sample size was larger than 100. However, a comparison of PPD similarity within and between individuals showed that, while birds can be correctly discriminated, unambiguous identification is impossible when the number of individuals is unknown beforehand. Therefore, we were only able to assess the probability that two calls belonged to the same individual. The results of this study show that acoustic identification in the Corncrake, and probably in other animal species, is mainly useful in detecting general behavioral patterns within populations. For instance, we discovered that more than 50 % of males change territories during the breeding season, probably to find females. Physical marking methods seem to be more reliable to tracking specific individuals. However, those methods usually consider limited numbers of individuals. Therefore, generalizing results to the population scale can also be misleading.

Birds are commonly used as bio-indicators of the quality of environments and the changes to them. Therefore, ecologists put a lot of effort into the monitoring of their population trends. One of the methods used for bird population monitoring is autonomous sound recording. Current studies provide inconsistent results when the number of detected species by autonomous sound recorders was compared with that delivered by an observer. In our study, observers counted birds using a point-count method at 64 random points in forest and farmland. At the same points, autonomous sound recorders recorded the soundscape four separate times (including counting by observer period) and the species present in the recordings were later identified by observers in the lab. We compared the number of species detected by simultaneous observations and recordings, as well as the number of species detected by recorders during four different surveys. Additionally, we calculated the Sorensen index to compare the species composition during different surveys at the same point. We found that observers detected more species than autonomous sound recorders. However, differences in the number of detected species were habitat dependent–observers detected more species than recorders in farmland, but not in the forest. When the time for recording was doubled, recorders were more effective than observers during a single survey. The average Sorensen index between the four repeated surveys performed by autonomous sound recorders ranged from 0.58 to 0.67, however we did not find significant differences in the number of species detected during different surveys conducted at the same point. Our study showed that 10-minutes sampling from the same point gives various species composition estimates but not species richness estimates between different surveys. Therefore, even when recorders detect less species than observers during the simultaneous surveys, increasing the survey duration of recorders may alter this difference. The use of autonomous sound recording for monitoring bird populations should be promoted, especially in forest habitats, as this technique is easier to standardise, eliminates many errors observed in the traditional point-count approach, enables conducting survey during adverse field conditions and delivers more reliable results for the majority of the species.

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Identity signals have been studied for over 50 years but, and somewhat remarkably, there is no consensus as to how to quantify individuality in animal signals. While there is a variety of different metrics to quantify individuality, these methods remain un‐validated and the relationships between them unclear. We contrasted three univariate and four multivariate identity metrics (and their different computational variants) and evaluated their performance on simulated and empirical datasets. Of the metrics examined, Beecher's information statistic (HS) performed closest to theoretical expectations and requirements for an ideal identity metric. It could be also easily and reliably converted into the commonly used discrimination score (and vice versa). Although Beecher's information statistic is not entirely independent of study sampling, this problem can be considerably lessened by reducing the number of parameters or by increasing the number of individuals in the analysis. Because it is easily calculated, has superior performance, can be used to quantify identity information in single variable or in a complete signal and because it indicates the number of individuals who can be discriminated given a set of measurements, we recommend that individuality should be quantified using Beecher's information statistic in future studies. Consistent use of Beecher's information statistic could enable meaningful comparisons and integration of results across different studies of individual identity signals.

Identity signals have been studied for over 50 years but there is no consensus as to how to quantify individuality. While there are a variety of different metrics to quantify individual identity, or individuality, these methods remain un-validated and the relationships between them unclear. We contrasted three univariate and four multivariate metrics (and their different computational variants) and evaluated their performance on simulated and empirical datasets. Of the metrics examined, Beecher's information statistic (HS) was the best one and could easily and reliably be converted into the commonly used discrimination score (and vice versa) after accounting for the number of individuals and calls per individual in a given dataset. Although Beecher's information statistic is not entirely independent of sampling parameters, this problem can be removed by reducing the number of parameters or by increasing the number of individuals. Because it is easily calculated, has superior performance, can be used to describe single variables or signal as a whole, and because it tells us the maximum number of individuals that can be discriminated given a set of measurements, we recommend that individuality should be quantified using Beecher's information statistic.