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Discipline
Biological
Keywords
Bioacoustics
Ornithology
Zoology
Observation Type
Standalone
Nature
Standard Data
Submitted
Mar 23rd, 2016
Published
Jun 21st, 2016
  • Abstract

    The vocal variation in birds that produce innate vocalizations reflects the differences that occur within the species' gene pool. These variations can indicate whether the species is undergoing a process of isolation by distance. In this case, the vocalization is an auxiliary tool for understanding the action of evolutionary processes in birds. Thus, we analyzed vocalizations of Ferruginous Antbird (a suboscine bird that has innate vocalizations) and correlated the vocal features to the geographical distances among individuals. We collected vocalizations from animal sound archives and analyzed frequency and time parameters of each sound file. The variables were summarized by a principal component analysis (PCA) and then we compared the distances among these components with the geographical distances among samples using Mantel's test. Thus, we have found evidence that vocal differences are related to geographic separation: the larger the distance between individuals, the greater the difference in vocalizations. These dissimilarities occur both in time and in frequency of vocalizations, showing that processes of geographic isolation are occurring within the species.

  • Figure
  • Introduction

    Song variation has been mainly studied in the Oscine Passerines, a group of birds in which learning and auditory feedback exert significant influence on song development. However, there is evidence that suboscine birds, such as the Ferruginous Antbird (Drymophila ferruginea), develop their vocalizations normally in the absence of vocal tutors. This fact could suggest that genetic components exert more influence on the vocal development than auditory feedback and extensive learning.

    If the vocal differences between distant populations of suboscine species were related to their geographical distances, this could imply that these populations are undergoing isolation by distance. This can be an indication of allopatric divergence within the species, which is an initial step to the process of speciation. Such vocal variation may be used as an auxiliary tool in taxonomic studies between species and even characterize different subspecies.

  • Objective

    We aim to determine if the vocalizations of the Ferruginous Antbirds have variations that are correlated to the geographical distances among individuals.

  • Results & Discussion

    The PCA generated 6 components with eigenvalues greater than|1.0|(Fig. 1D). These components explained 90.19% of the variance among the samples on the 16 acoustic variables. PC1 had high positive loadings for frequency features (F1, F2, F4, F5, F6, F8, and F9). PC2 had positive loadings for three time features (T4, T5 and T6). PC3 had positive loadings for T3 and T5, and negative loadings to F3 and F7. PC4 had negative loadings for time features (T2 and T3). PC5 had a positive loading for F10. PC6 showed no predominance among loading values. The component analysis showed the relevance of each variable. PC1 was strongly related to spectral features while PC2 was related to temporal ones. The two components together summarized almost 50% of the variation, showing that both time and frequency features varied among samples and that they are equally important to define the vocal variations within the species. The Mantel test showed a significant and positive relationship between geographic and vocal distances (N = 10000 permutations; r = 0.4502; p = 0.0003), but it did not find any relationship between the vocal distances and differences among the years of recording (N = 10000 permutations; r = -0.0658; p = 0.6949).

    Several studies demonstrate vocal variation across geographic distances. However, they are focused on variation in the oscine passerines. The papers that have looked for variations in vocalizations on non-oscine birds are mainly restricted to comparing between shorter distances (less than 50 km). Our work is one of the few studies demonstrating geographical variation on suboscine vocalizations over large distances (average distance between pairs of individuals = 483 km) (e.g. on Alder Flycatcher (Empidonax alnorum) and on Common Cuckoo (Cuculus canorus)) and the first one to explore such effect in the Thamnophilidae family.

    These results suggest that vocal differences and geographic distances are closely correlated. This could be explained by the "isolation by distance" model. Differences in vocalizations of Thamnophilidae species are mainly affected by genetic factors, and extensive learning plays no significant role in vocal ontogeny. Therefore, the correlation between distance and vocal differences may be a reflection of variations on the genetic structure originated by isolation of Ferruginous Antbird populations.

  • Conclusions

    The vocal variation on Ferruginous Antbird is related to the geographic distance between individuals. This fact may indicate that there is a certain degree of genetic isolation between populations of this species, which may in turn be a reflection of geographic distance.

  • Limitations

    Our study does not cover molecular analysis, despite making inferences about this genetic aspects based on references. Such studies in this species need to be done to investigate if the vocal variation is indeed a reflection of genetic variation.

  • Methods

    Study Species

    The Ferruginous Antbird (Drymophyla ferruginea Temminck, 1822) is a species distributed in areas of dense Evergreen Forest of the Atlantic domain (Fig. 1A). It is found only in the Southeast of Brazil, mostly near coastal areas, but also extending inland from Minas Gerais and Sao Paulo (Fig. 1B). It inhabits the understory and mid-story of thickets in primary forests and also in second-growth woodland. It is an active forager that feeds on arthropods. This species is not globally threatened, and it is fairly common throughout its range.

    File Survey

    Vocalization recordings were obtained from Laboratory of Ornithology (School of Sciences, São Paulo State University), Xeno-Canto (www.xeno-canto.org), and Macauley Library (Cornell Lab of Ornithology, Cornell University). Each sound file represented a different individual. Files with excessive noise, poor recording quality, overlapping vocalizations, or any other factor that could hinder audio and spectrogram inspection were excluded from the sample. We sought to include recordings that cover as much as possible the species' geographic range that was proposed by. The coordinates and the year of recording were provided by the recordists of each vocalization. A total of 45 sound archives were sampled. 

    Vocalization Analysis

    All the vocalizations were measured blind both to the identity and the location of the vocalization, to prevent the measurements from being biased in any way. The identity of the recording was extracted after the measurements. The digitized sound files were standardized (44.100 Hz, 16 bits of resolution, WAV format) using Sony SoundForge Pro 11 (Sony Creative Software, 2010). We randomly selected 3 vocalizations from each sound file, totaling 135 analyzed vocalizations. The vocalizations were selected according to the morphology of their syllables, which were visible by the spectrogram (Hanning Window, FFT = 512 points, Overlap = 93,75%) generated in the Sonic Visualizer software (Queen Mary, University of London, 2013). We employed the oscillogram to measure the time variables and the amplitude spectra (Hanning Window, resolution = 2048 points) to measure the frequencies (Fig. 1C and D). A limit of -42 db below the peak amplitude was taken on the power spectra to avoid the noise frequency bands. They were used to measure these variables because spectrograms have a trade-off between temporal and frequency resolution, which may lead to major problems on measurement. Both were generated using Sony SoundForge Pro 11 software and the variables were measured by the same person.

    The following variables were obtained: T1- duration of the first syllable(s); T2- duration of the interval between the two syllables(s); T3- duration of the first frequency modulation (FM) of the second syllable(s); T4- duration of the second FM of the second syllable(s); T5- duration of the second syllable(s); T6- duration of the entire vocalization; F1- Maximum frequency of the first syllable (kHz); F2- Minimum frequency of the first syllable (kHz); F3- Frequency bandwidth (MAX-MIN) of the first syllable (kHz); F4- Peak frequency of the first syllable (kHz); F5- Maximum frequency of the second syllable (kHz); F6- Minimum frequency of the second syllable (kHz); F7- Frequency bandwidth (MAX-MIN) of the second syllable (kHz); F8- Peak frequency of the first FM of the second syllable (kHz); F9- Peak frequency of the second FM of the second syllable (kHz); F10- Difference between the peak frequencies of the second and the first FM of the second syllable (F9 minus F8) (kHz).

    Statistical Analysis

    We calculated the mean of the variables using three vocalizations for each sound file. We performed a principal component analysis (PCA) on the mean of variables to reduce its number, to avoid the colinearity of some variables and to decrease the data redundancy. We selected the components that had eigenvalues greater than|1.0|and extracted the scores of the principal component space for each individual. We created a "vocal distance" matrix among the individuals using the Euclidean distance between each pair of individuals in the principal component space. A matrix of "geographical distances" among individuals was created by Geographic Distance Matrix Generator 1.2.3 software (Center for Biodiversity and Conservation, American Museum of Natural History) employing the coordinates of latitude and longitude that were obtained from the sound files. We used the Mantel test to examine if the both matrices are correlated to each other. The factor "year of recording" was analyzed by a second Mantel's test. This test demonstrated that there was no effect of the year of recording on the characteristics of vocalizations. We also tested if the differences among the years of recording affects the vocal distances with another Mantel test. The "temporal distance" matrix were created using  the Euclidean distance between the years of recording of each pair of individuals.

  • Funding statement

    This work was supported by National Council of Technological and Scientific Development (CNPq).

  • Acknowledgements

    We are very thankful to Rafael M. Martins for the photo, and to all the recordists that registered the vocalizations used in this work.

  • Ethics statement

    Not applicable.

  • References
  • 1
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    Matters11/20

    Vocal variation of Ferruginous Antbird (Drymophila ferruginea) is related to geographic distances

    Affiliation listing not available.
    Abstractlink

    The vocal variation in birds that produce innate vocalizations reflects the differences that occur within the species' gene pool. These variations can indicate whether the species is undergoing a process of isolation by distance. In this case, the vocalization is an auxiliary tool for understanding the action of evolutionary processes in birds. Thus, we analyzed vocalizations of Ferruginous Antbird (a suboscine bird that has innate vocalizations) and correlated the vocal features to the geographical distances among individuals. We collected vocalizations from animal sound archives and analyzed frequency and time parameters of each sound file. The variables were summarized by a principal component analysis (PCA) and then we compared the distances among these components with the geographical distances among samples using Mantel's test. Thus, we have found evidence that vocal differences are related to geographic separation: the larger the distance between individuals, the greater the difference in vocalizations. These dissimilarities occur both in time and in frequency of vocalizations, showing that processes of geographic isolation are occurring within the species.

    Figurelink

    Figure 1.

    (A) Male individuals of Ferruginous Antbird (photo taken by Rafael M. Martins, 2015).

    (B) Distribuition of Ferruginous Antbird. The red circles represent individuals whose vocalizations were collected. The numbers above and below them represent the number of individuals whose vocalizations were recorded in the same place.

    (C) Visual representations of the vocalizations. (1) The spectrogram displays the two syllables of the vocalization. (2) The oscillogram shows measurement of the temporal variables. (3) The power spectra of each section (i-iv, as shown in the oscillogram) shows the measurement of spectral variables.

    (D) Principal components (PC) scores for each variable. The eigenvalue and the percentage of variance summarized by the PCs are assigned below each component. Scores values greater than|0.5|were highlighted in gray.

    Introductionlink

    Song variation has been mainly studied in the Oscine Passerines[1], a group of birds in which learning and auditory feedback exert significant influence on song development[2]. However, there is evidence that suboscine birds, such as the Ferruginous Antbird (Drymophila ferruginea), develop their vocalizations normally in the absence of vocal tutors[3][4]. This fact could suggest that genetic components exert more influence on the vocal development than auditory feedback and extensive learning[5][6].

    If the vocal differences between distant populations of suboscine species were related to their geographical distances, this could imply that these populations are undergoing isolation by distance. This can be an indication of allopatric divergence within the species, which is an initial step to the process of speciation[7]. Such vocal variation may be used as an auxiliary tool in taxonomic studies between species and even characterize different subspecies[8].

    Objectivelink

    We aim to determine if the vocalizations of the Ferruginous Antbirds have variations that are correlated to the geographical distances among individuals.

    Results & Discussionlink

    The PCA generated 6 components with eigenvalues greater than|1.0|(Fig. 1D). These components explained 90.19% of the variance among the samples on the 16 acoustic variables. PC1 had high positive loadings for frequency features (F1, F2, F4, F5, F6, F8, and F9). PC2 had positive loadings for three time features (T4, T5 and T6). PC3 had positive loadings for T3 and T5, and negative loadings to F3 and F7. PC4 had negative loadings for time features (T2 and T3). PC5 had a positive loading for F10. PC6 showed no predominance among loading values. The component analysis showed the relevance of each variable. PC1 was strongly related to spectral features while PC2 was related to temporal ones. The two components together summarized almost 50% of the variation, showing that both time and frequency features varied among samples and that they are equally important to define the vocal variations within the species. The Mantel test showed a significant and positive relationship between geographic and vocal distances (N = 10000 permutations; r = 0.4502; p = 0.0003), but it did not find any relationship between the vocal distances and differences among the years of recording (N = 10000 permutations; r = -0.0658; p = 0.6949).

    Several studies demonstrate vocal variation across geographic distances. However, they are focused on variation in the oscine passerines[9][10][11][12][13]. The papers that have looked for variations in vocalizations on non-oscine birds are mainly restricted to comparing between shorter distances (less than 50 km)[14][15][16]. Our work is one of the few studies demonstrating geographical variation on suboscine vocalizations over large distances (average distance between pairs of individuals = 483 km) (e.g.[6] on Alder Flycatcher (Empidonax alnorum) and[17] on Common Cuckoo (Cuculus canorus)) and the first one to explore such effect in the Thamnophilidae family.

    These results suggest that vocal differences and geographic distances are closely correlated. This could be explained by the "isolation by distance" model. Differences in vocalizations of Thamnophilidae species are mainly affected by genetic factors, and extensive learning plays no significant role in vocal ontogeny[4]. Therefore, the correlation between distance and vocal differences may be a reflection of variations on the genetic structure originated by isolation of Ferruginous Antbird populations.

    Conclusionslink

    The vocal variation on Ferruginous Antbird is related to the geographic distance between individuals. This fact may indicate that there is a certain degree of genetic isolation between populations of this species, which may in turn be a reflection of geographic distance.

    Limitationslink

    Our study does not cover molecular analysis, despite making inferences about this genetic aspects based on references. Such studies in this species need to be done to investigate if the vocal variation is indeed a reflection of genetic variation.

    Methodslink

    Study Species

    The Ferruginous Antbird (Drymophyla ferruginea Temminck, 1822) is a species distributed in areas of dense Evergreen Forest of the Atlantic domain (Fig. 1A). It is found only in the Southeast of Brazil, mostly near coastal areas, but also extending inland from Minas Gerais and Sao Paulo (Fig. 1B). It inhabits the understory and mid-story of thickets in primary forests and also in second-growth woodland. It is an active forager that feeds on arthropods. This species is not globally threatened, and it is fairly common throughout its range[18].

    File Survey

    Vocalization recordings were obtained from Laboratory of Ornithology (School of Sciences, São Paulo State University), Xeno-Canto (www.xeno-canto.org), and Macauley Library (Cornell Lab of Ornithology, Cornell University). Each sound file represented a different individual. Files with excessive noise, poor recording quality, overlapping vocalizations, or any other factor that could hinder audio and spectrogram inspection were excluded from the sample. We sought to include recordings that cover as much as possible the species' geographic range that was proposed by[18]. The coordinates and the year of recording were provided by the recordists of each vocalization. A total of 45 sound archives were sampled. 

    Vocalization Analysis

    All the vocalizations were measured blind both to the identity and the location of the vocalization, to prevent the measurements from being biased in any way. The identity of the recording was extracted after the measurements. The digitized sound files were standardized (44.100 Hz, 16 bits of resolution, WAV format) using Sony SoundForge Pro 11 (Sony Creative Software, 2010). We randomly selected 3 vocalizations from each sound file, totaling 135 analyzed vocalizations. The vocalizations were selected according to the morphology of their syllables, which were visible by the spectrogram (Hanning Window, FFT = 512 points, Overlap = 93,75%) generated in the Sonic Visualizer software (Queen Mary, University of London, 2013). We employed the oscillogram to measure the time variables and the amplitude spectra (Hanning Window, resolution = 2048 points) to measure the frequencies (Fig. 1C and D). A limit of -42 db below the peak amplitude was taken on the power spectra to avoid the noise frequency bands. They were used to measure these variables because spectrograms have a trade-off between temporal and frequency resolution, which may lead to major problems on measurement[19]. Both were generated using Sony SoundForge Pro 11 software and the variables were measured by the same person.

    The following variables were obtained: T1- duration of the first syllable(s); T2- duration of the interval between the two syllables(s); T3- duration of the first frequency modulation (FM) of the second syllable(s); T4- duration of the second FM of the second syllable(s); T5- duration of the second syllable(s); T6- duration of the entire vocalization; F1- Maximum frequency of the first syllable (kHz); F2- Minimum frequency of the first syllable (kHz); F3- Frequency bandwidth (MAX-MIN) of the first syllable (kHz); F4- Peak frequency of the first syllable (kHz); F5- Maximum frequency of the second syllable (kHz); F6- Minimum frequency of the second syllable (kHz); F7- Frequency bandwidth (MAX-MIN) of the second syllable (kHz); F8- Peak frequency of the first FM of the second syllable (kHz); F9- Peak frequency of the second FM of the second syllable (kHz); F10- Difference between the peak frequencies of the second and the first FM of the second syllable (F9 minus F8) (kHz).

    Statistical Analysis

    We calculated the mean of the variables using three vocalizations for each sound file. We performed a principal component analysis (PCA) on the mean of variables to reduce its number, to avoid the colinearity of some variables and to decrease the data redundancy. We selected the components that had eigenvalues greater than|1.0|and extracted the scores of the principal component space for each individual. We created a "vocal distance" matrix among the individuals using the Euclidean distance between each pair of individuals in the principal component space. A matrix of "geographical distances" among individuals was created by Geographic Distance Matrix Generator 1.2.3 software (Center for Biodiversity and Conservation, American Museum of Natural History) employing the coordinates of latitude and longitude that were obtained from the sound files. We used the Mantel test to examine if the both matrices are correlated to each other. The factor "year of recording" was analyzed by a second Mantel's test. This test demonstrated that there was no effect of the year of recording on the characteristics of vocalizations. We also tested if the differences among the years of recording affects the vocal distances with another Mantel test. The "temporal distance" matrix were created using  the Euclidean distance between the years of recording of each pair of individuals.

    Funding Statementlink

    This work was supported by National Council of Technological and Scientific Development (CNPq).

    Acknowledgementslink

    We are very thankful to Rafael M. Martins for the photo, and to all the recordists that registered the vocalizations used in this work.

    Conflict of interestlink

    The authors declare no conflicts of interest.

    Ethics Statementlink

    Not applicable.

    No fraudulence is committed in performing these experiments or during processing of the data. We understand that in the case of fraudulence, the study can be retracted by ScienceMatters.

    Referenceslink
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      The Evolution of Geographic Variation in Birdsong
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    2. Kroodsma, D. E.
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      Acoustic Communication in Birds, Academic Press, New York, 2/1982, pages 1-23 chrome_reader_mode
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    4. Janeene M. Touchton, Nathalie Seddon, Joseph A. Tobias
      Captive Rearing Experiments Confirm Song Development without Learning in a Tracheophone Suboscine Bird
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      Song divergence at the edge of Amazonia: an empirical test of the peripatric speciation model
      Biological Journal of the Linnean Society, 90/2007, pages 173-188 DOI: 10.1111/j.1095-8312.2007.00753.xchrome_reader_mode
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      GEOGRAPHIC VARIATION IN THE SONG OF WILLOW FLYCATCHERS: DIFFERENTIATION BETWEEN EMPIDONAX TRAILLII ADASTUS AND E. T. EXTIMUS
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      Microgeographic Song Variation in the Nuttall's White-Crowned Sparrow
      The Condor, 89/1987, pages 261-275 DOI: 10.2307/1368479chrome_reader_mode
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      Geographic Variation in Syllables of House Finch Songs
      The Auk, 116/1999, pages 666-676 DOI: 10.2307/4089328chrome_reader_mode
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      Geographic variation in songs of the Common Yellowthroat
      The Auk, 131/2014, pages 175-185 DOI: 10.1642/auk-12-187.1chrome_reader_mode
    12. Kim G Mortega, Heiner Flinks, Barbara Helm
      Behavioural response of a migratory songbird to geographic variation in song and morphology
      Frontiers in Zoology, 11/2014, page 85 DOI: 10.1186/s12983-014-0085-6chrome_reader_mode
    13. Scott M. Ramsay, Ken A. Otter
      Geographic variation in White-throated Sparrow song may arise through cultural drift
      Journal of Ornithology, 156/2015, pages 763-773 DOI: 10.1007/s10336-015-1183-8chrome_reader_mode
    14. Robert B. Payne, Paul Budde
      Song Differences and Map Distances in a Population of Acadian Flycatchers
      The Wilson Bulletin, 91/1979, pages 29-41 chrome_reader_mode
    15. Scott F. Lovell, M. Ross Lein
      Song variation in a population of Alder Flycatchers
      Journal of Field Ornithology, 75/2004, pages 146-151 DOI: 10.1648/0273-8570-75.2.146chrome_reader_mode
    16. M. Ross Lein
      Song Variation In Buff-Breasted Flycatchers (Empidonax fulvifrons)
      The Wilson Journal of Ornithology, 120/2008, pages 256-267 DOI: 10.1676/07-067.1chrome_reader_mode
    17. Chentao Wei, Chenxi Jia, Lu Dong,more_horiz, Wei Liang
      Geographic variation in the calls of the Common Cuckoo (Cuculus canorus): isolation by distance and divergence among subspecies
      Journal of Ornithology, 156/2015, pages 533-542 DOI: 10.1007/s10336-014-1153-6chrome_reader_mode
    18. K. J. Zimmer, M. L. Isler
      Family Thamnophilidae (Typical Antbirds)
      Handbook of the Birds of the World: Broadbills to Tapaculos, Lynx Editions, 8/2003, pages 448-681 chrome_reader_mode
    19. Sue Anne Zollinger, Jeffrey Podos, Erwin Nemeth, Franz Goller, Henrik Brumm,
      On the relationship between, and measurement of, amplitude and frequency in birdsong
      Animal Behaviour, 84/2012, pages e1-e9 DOI: 10.1016/j.anbehav.2012.04.026chrome_reader_mode
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