The Morning Dove Still Sings

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However, it is not as simple, as some non-passerines e. Therefore, many more factors other than the syrinx structure e.


Nonetheless, most non-passerines do produce relatively simple songs that are developed during ontogenesis without any social learning Catchpole and Slater Our survey of research on acoustic communication in this group reveals that there is a bias towards certain topics and species. Several papers addressed the topic of the mechanism of sound production ten Cate and Ballintijn ; Ballintijn and ten Cate a , ; Beckers et al. In addition, the relationship between song structure and its functions in territorial defence or mate attraction was studied in detail for certain model species e.

Slabbekoorn and ten Cate , These studies give an excellent background for further research on dove and pigeon vocalisations as they have revealed some fundamental information about the trade-offs between signal characteristics, anatomical limitations and signal functions Tubaro and Mahler ; Slabbekoorn et al. A limitation of previous research is that it is strongly biased toward doves from the Streptopelia genus, and the majority of published work—particularly experimental research—concerns only a few species.

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Morris and Erickson , and Mairy ; after Hutchison et al. Hutchison et al. Hitchcock et al. Thus, our knowledge of acoustic recognition processes in doves seems to be very limited in comparison to dozens of papers quantitatively describing the individually specific song structures and experimentally testing individual recognition or neighbour-stranger discrimination in songbirds e. In this study we would like to fill the gap in knowledge and test if the simple, stereotypical song of a dove species may convey information about identity. Our chosen model species is the Tambourine Dove Turtur Tympanistra , a relatively small wood dove species with a large distribution across sub-Saharan Africa.

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Individuals can be found in numerous forest types, woodlands and plantations spanning various elevations, from lowlands to montane areas Baptista et al. To the best of our knowledge, there are no published papers addressing any aspect of vocalization of this species. The aim of this study was to describe for the first time the basic acoustic parameters of the Tambourine Dove long-distance vocalisation further called song , on the background of their close relatives, and to test whether these songs are specific to each individual, allowing for individual recognition.

The Bamenda Highlands are one of the most important hotspots of bird diversity and endemism in Africa Orme et al. However, intensive logging has reduced formerly continuous forests to isolated patches in recent decades Reif et al.

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The study species is common in this area and is found both in forests and smaller remnants along streams. It avoids open areas but occurs in ecotone areas of habitat transition. Spontaneously singing birds were recorded opportunistically in the morning — and evening — hours. For the analysis we only selected good quality recordings of males that were well separated spatially to ensure that subsequent analyses were done on different males.

When we had recordings from the same place and different years, we analysed only one recording from such location. Sound analyses were done in two steps. Firstly, Raven Pro 1. The aim was to look precisely at the variation in notes that make up the song whilst investigating the parameters which are likely to be individually specific. The only problem with selecting notes based on visual selection was that some notes were very short and very close to subsequent notes, forming something like double-notes.

More complicated notes were found in songs of most individuals but were much rarer usually 1 or 2 per song than single-notes Fig. We realised that classification of notes as single, double or triple may be subjective and depends both on the recording quality and on the experience of the persons conducting signal analysis. Therefore, in the second step we applied automatic measurements allowing for better time—frequency resolution and avoidance of human error that is produced when making on screen selections.

FIR values were chosen based on initial inspection measurements.

We measured simple variables for the whole songs as duration s , number of notes, and minimum, maximum and peak frequency Hz. Then we measured peak frequency later PF; Hz of each note, and the time s between the following notes note-to-note duration, later NTN. Later we used the following notation: PF1 refers to the peak frequency of the first note in a song, PF2 to the peak frequency of the second note, and so on; NTN1 refers to time between the beginning of the first note and the beginning of the second note, NTN2 the time between the second and third note, and so on.

Such manipulations were necessary because of the variation in recording quality and did not influence the measured parameter of song. The main reason for this was usually due to a change of position of the bird or microphone in such a way that from a certain moment the notes building songs, which are quite long, abruptly changed in maximal amplitude. This resulted in—for example—one part of the song being louder, however this did not affect the peak frequency detection and timing between peaks.

We describe songs, notes and note-to-note durations with descriptive statistics to present the general variation of songs in the studied population. In the next step we analysed within and between individual variations of each parameter in order to find song characteristics potentially important for individual recognition. To determine which song characteristics could be potentially useful for individual identification, we first calculated within-individual CV i and between-individual CV b coefficients of variation for each parameter of the whole song and separately for each note and note-to-note duration shared by all individuals.

We used the formula:. PIC values greater than 1 indicated that the within-individual variation was lower than between-individual variation for a particular song characteristic, and therefore it could potentially be used for individual identification. For measured songs we found that the lowest number of notes in a song was twelve. The parameters included in our final analyses were therefore those which could be calculated for the whole song and for the first twelve notes or first eleven note-to-note durations.

Finally, we conducted several separate stepwise discriminant function analyses DFAs. Prior probabilities were computed from group sizes individuals in this case.

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The initial DFA was built with simple variables which could be measured for the whole songs duration, number of syllables, frequency of song etc. However, after initial screening of songs it was clear that they differ in number of notes and in order to compare characteristics based on note measurements we had to limit analyses to notes common for all cases similarly as did Budka et al. In the studied population we found songs containing between 12 and 59 notes.

Thus, to analyse the whole dataset according to the note variation we had to limit analyses to the first 12 PF and first 11 NTN. Songs of the Tambourine Dove in the studied population consisted of a series of very short notes, lasting on average 0. Notes were simple, unmodulated whistles except for the initial syllable which was quieter and sometimes sounded more like a gurgle. It was not found in all recorded songs as it was not always sung and it was difficult to detect in the lower amplitude recordings Fig.

Songs consisted of 12—59 notes, Consequently, songs were quite long, lasting from 7. All notes had a very narrow and almost identical bandwidth; thus, their peak frequency seems to be the most important parameter in the frequency domain. The temporal pattern of notes within a song was similar in all songs.

Alternatively, but more rarely, durations were different due to the presence or absence of the initial note on the recording, as this was often much softer than the rest of the song Fig. The prior probability of assigning a song to the correct individual by chance was 2. In the first DFA we used parameters which could be measured for the whole song, i. We found that DFA correctly classified individuals in In the third DFA we used note-to-note duration between the first 12 notes, and we found that DFA correctly classified individuals in Finally, we used whole-songs measurements, peak frequency and note-to-note duration for the first 12 notes, and we found that DFA correctly classified individuals in These analyses reveal that songs of the Tambourine Dove convey information with a very high potential for identity coding.

It is also clear that identity information is coded with a time pattern of note production rather than their peak frequencies which have more of an overlap between individuals. In the next step we conducted a series of DFAs using peak frequency only, note-to-note duration only, or both.

Climate threats facing the Mourning Dove

The aim of these analyses was to find out how information might be added using the following notes in order to improve the correct discrimination of individuals. For note-to-note durations, correct classification varied from When both, peak frequency and note-to-note duration were included in DFAs, the percentage of correct classification was between However, in a few initial syllables the peak frequency may also be important, as the joining of time and frequency information improved correct classification Fig.

In the final step, we used DFA to find out how identity information changes when we restrict data included in the models to five neighbouring PFs or NTNs only. Five notes were used as the initial analysis revealed that the efficiency of discrimination did not have a considerable improvement when more notes were included Fig. These analyses may be used to test the potential for identity coding if the receiver of the signal did not pay attention to the song from its beginning. Peak frequencies alone carried less identity information than note-to-note durations.

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When both variables were included into DFAs it improved the model. As illustrated in Fig. The Fig. Again, it seems that information about identity in the Tambourine Dove is encoded in the beginning of a song.

get link One on the X -axis indicates that classification rate was calculated based on peak frequency of notes from one to five and so on. Finally, we used recordings from automatic recorders to check if songs recorded at the same points but separated by over a month are still classified as belonging to the same individuals. We used five songs per point and per time, i. DFA achieved in leave-one-out classification The song of the Tambourine Dove we studied is equivalent to the perch coos described for other pigeons and doves in that it is a long-range signal aimed to attract potential mates and deter rivals Baptista et al.

To accomplish these functions, a song has to convey information about the signalling species, as well as the quality, motivation and identity of the signaller Bradbury and Vehrencamp As the song of the Tambourine Dove is relatively simple, one may ask how this information transfer can be achieved? When comparing the Tambourine Dove song with those of the well-studied Streptopelia doves, they have a considerably longer song duration with more notes within a song, and more variable number of notes within songs.

Beside song duration, which was quite variable, songs of the studied species were highly stereotyped and characterized by a low overall frequency and narrow frequency band. Songs mostly consisted of unchanging, short and tonal-whistle notes see Fig. Such results may suggest that frequency and note characteristics could be cues for species recognition. However, real recognition is likely more intricate. The Tambourine Dove is one of the five species of the genus Turtur. All of them occur in Africa, inhabit habitats from forest to more or less wooded savanna where their ranges may overlap Baptista et al.

In addition, there is no obvious difference in the pattern of note organisation within a song for all species, with the notes of a song accelerating when closer to the end. Currently, there is no study focused on the between-species differences in Turtur species songs, and so it is hard to quantitatively represent how similar or dissimilar the songs are. With some confidence, one can state that the songs of these species are very similar, and if they are different, this difference is sophisticated, using multidimensional characters of the song.

The unmodulated-whistle of the notes and the low frequency of the song for Turtur doves, seem to be an adaptation for signalling in forested habitats.