Universidade Federal Da Paraíba Centro De Ciências Exatas E Da Natureza Programa De Pós-Graduação Em Ciências Biológicas (Zoologia)

UNIVERSIDADE FEDERAL DA PARAÍBA CENTRO DE CIÊNCIAS EXATAS E DA NATUREZA PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS BIOLÓGICAS (ZOOLOGIA)

INGRID MARIA DENÓBILE TORRES

Evolução acústica em aves: alometria vocal, filtro ambiental e nicho acústico.

JOÃO PESSOA 2017

INGRID MARIA DENÓBILE TORRES

Evolução acústica em aves: alometria vocal, filtro ambiental e nicho acústico.

Dissertação apresentada ao Programa de Pós- Graduação em Ciências Biológicas, Área de Concentração em Zoologia, da Universidade Federal da Paraíba, como requesito parcial para obtenção do título de Mestre em Zoologia.

Orientador: Carlos Barros de Araújo Coorientador: Luiz Carlos Serramo Lopez

JOÃO PESSOA 2017

INGRID MARIA DENÓBILE TORRES

Evolução acústica em aves: alometria vocal, filtro ambiental e nicho acústico.

Data da defesa: 14/02/2017 10:00

JOÃO PESSOA 2017

AGRADECIMENTOS

Aos meus pais, pelo apoio nas minhas decisões e preocupação durante os incontáveis dias de campo. Á minha irmã, pelos ouvidos emprestados para escutar cada história, experiência e presepada de campo. À minha tia, Raquel, e minha voinha, Zélia, que sempre se fizeram presentes na minha vida. Ao meu marido, João, que é a pessoa mais paciente e amorosa do universo, que sem sua motivação, conselhos e companheirismo teria sido impossível terminar com a sanidade intacta.

Aos meus amigos mais próximos, pelo companheirismo, apoio e paciência para escutar minhas neuras, por cada risada, pelos dias que simplesmente não fizemos nada e por me mostrarem que nem tudo deve ser levado tão a sério. Aos meus amigos que “manjam” de estatísticas que antes desconhecia, de scripts, R e afins, por arrumarem um tempinho para me ensinar todo esse paranauê rs.

Ao meu orientador, Carlão, pelo conhecimento compartilhado, pela paciência no decorrer do trabalho, pelos carões quando necessário (rs) e por acreditar em mim. Foi uma ótima parceria que pretendo manter no decorrer da minha vida acadêmica e nos trabalhos pelo mundo afora.

Agradeço também à CAPES e aos professores do PPGCB pelo incentivo, assistência, financiamento e demais fatores que possibilitaram a execução desse projeto.

Aos membros da minha banca, Jeff Podos e Luciano Naka, por terem aceitado participar da minha avaliação e pela excelente revisão da minha dissertação, ótimos conselhos, críticas construtivas e elogios.

Á todos que de alguma forma tornaram este caminho mais fácil de ser percorrido.

RESUMO

A forma com que diversas espécies estão inseridas no espaço acústico é resultado de um longo processo evolutivo. Existe uma relação alométrica negativa entre a massa corporal e as frequências acústicas, podendo esta relação ser otimizada em função da relação sinal-ruído, da intensidade do sinal ou por limites de eficiência de acordo com o tamanho corporal. Adicionalmente, a competição acústica pode gerar divergência no uso da frequência esperada pela alometria fazendo com que ocorra uma estruturação do espaço acústico. Já a eficiência da propagação pode direcionar as vocalizações das espécies menores para a porção grave do espectro. Para analisar a alometria vocal conduzimos regressões lineares do tipo II (Ranged Major Axis) com dados acústicos de psitacídeos neotropicais (Psittacidae - tribo Arini), pombos neotropicais (8 gêneros), dendrocolaptídeos (Dendrocolaptinae), tinamídeos (Tinamidae) e turdídeos (25 espécies). Para verificar a existência de padrões não aleatórios no uso do espaço acústico, utilizamos modelo nulo com o índice de sobreposição de Pianka. Para testar se as espécies menores utilizam frequências mais graves, utilizamos Wilcoxon Sign Rank. Estes foram testados com dados acústicos de uma assembleia de aves da Floresta Nacional de Carajás. Utilizamos a frequência dominante (FDOM), a frequência fundamental mínima (FFMIN) e a frequência fundamental máxima (FFMAX). A relação alométrica foi encontrada em psitacídeos, pombos, dendrocolaptídeos e nas frequências fundamentais de tinamídeos. Não foi encontrada estruturação no uso das frequências acústicas. A FDOM e a FFMIN diferiram significantemente do esperado pela alometria, porém ambas foram mais agudas que o esperado. A comunicação sonora tem sido moldada por um longo processo de seleção natural, através de forças evolutivas distintas, cada uma tendo um papel no sinal acústico.

Palavras-chave: relação sinal-ruído, otimização acústica, alometria vocal, competição acústica.

ABSTRACT

Several species are inserted in the acoustic space due to a long evolutionary process. There is a negative allometric relation between body mass and acoustic frequencies, and this relationship can be optimized through signal-to-noise ratio, signal threshold or efficiency limits according to body size. Additionally, the acoustic competition can cause divergence in the use of the frequency expected by allometry, creating a structured acoustic space. The efficiency of propagation can push the vocalizations of the smaller species to a lower portion of the spectrum. To analyze the vocal allometry, we conducted type II linear regressions (Ranged Major Axis) with acoustic data of New World Parrots (Psittacidae – Arini), New World Doves (8 genera), woodcreepers (Dendrocolaptinae), tinamous (Tinamidae) and thrushes (25 species). To verify the existence of non-random patters in the use of the acoustic space, we used null model with Pianka overlap index. To test whether smaller species use lower frequencies than the expected by allometry, we used Wilcoxon Sign Rank. These were tested with acoustic data from a bird assembly of the Carajás National Forest. We used the dominant frequency (FDOM), the minimum fundamental frequency (FFMIN) and the maximum fundamental frequency (FFMAX). The allometric relation was found in parrots, doves, woodcreepeers and in the fundamental frequencies of tinamous. No structure was found in the use of acoustic frequencies. FDOM and FFMIN differed significantly from those expected by allometry, but both were higher than expected. Sound communication has been shaped by a long process of natural selection, through distinct evolutionary forces, each having a role in the acoustic signal.

Keywords: signal-to-noise ratio, acoustic optimization, vocal allometry, acoustic competition.

SUMÁRIO

INTRODUÇÃO GERAL...... 09 CAPÍTULO 1...... 14 Allometric trends reveal distinct evolution strategies for avian communication. ABSTRACT ...... 16 I. INTRODUCTION ...... 17 II. METHODS ...... 22 A. Data collection ...... 22 1. Psittacidae ...... 22 2. Other taxa from published data ………………………………...... ……. 23 B. Vocal parameters ...... 25 C. Data analysis ...... 25 III. RESULTS ...... 26 IV. DISCUSSION ...... 27 V. CONCLUSION ...... 31 ACKNOWLEDGMENTS ...... 31 REFERENCES ...... 33 TABLES …………………………….………………………………………………….. 41 FIGURES ……………………..………………………………………………………… 42

CAPÍTULO 2 ...... 45 Acoustic ecology of Carajás National Forest bird assemblages, Brazil. ABSTRACT ...... 47 1. INTRODUCTION ...... 48 2. METHODS ...... 50 A. Study Area ...... 50 B. Data Collection ...... 51 C. Vocal Parameters ...... 51 D. Data Analysis ...... 52 - Taxonomic diversity ...... 52 - Acoustic niche partition ...... 53

- Allometric effects on acoustic competition ……………………...... ….. 54 - Environmental filter ...... 56 3. RESULTS ...... 57 - Taxonomic diversity ...... 57 - Acoustic niche partition ...... 57 - Allometric effects on acoustic competition …………………...... …...... 57 - Environmental filter …………………………………………...... …….. 58 4. DISCUSSION ...... 59 - Taxonomic diversity ...... 59 - Acoustic niche partition ...... 59 - Allometric effects on acoustic competition …………………...... ……… 60 - Environmental filter ...... 61 CONCLUSION ...... 62 REFERENCES ...... 63 FIGURES ...... 67

INTRODUÇÃO GERAL

A comunicação animal é o processo pelo qual um emissor envia uma mensagem a um receptor por meio de um sinal, podendo este ser de caráter químico, visual, sonoro, tátil ou elétrico (Snowdon, 2011). Esta mensagem pode envolver diversos aspectos biológicos, desde a atração de parceiros à alertas de predadores (Neiva and Hickson III, 2001), possuindo assim um papel central na perpetuação das espécies, seja em nível de adultos reprodutivos sinalizando para a atração de fêmeas, ou em nível de gametas, por meio da sinalização química (Bradbury and Vehrenkamp, 1998).

Dentre as diversas formas de transmissão de informação, a comunicação acústica se encontra amplamente distribuída no Reino Animal, podendo ser observada em diversos taxa como insetos, anfíbios, peixes, aves e mamíferos (Vielliard and Silva, 2010). A transmissão acústica da informação pode ocorrer tanto em meio gasoso, como também em meio líquido ou sólido, cada um com propriedades físicas próprias, como velocidade, impedância e atenuação (Farina 2014). De fato existe uma infinidade de sons utilizados corriqueiramente para a transmissão de informação, pelos quais os organismos interagem e desempenham funções essenciais à sua sobrevivência (Bradbury and Vehrenkamp, 1998). Por esta razão, ao longo da evolução as espécies desenvolveram paralelamente mecanismos e estruturas específicas para a emissão e recepção do sinal acústico, em função das necessidades impostas pelas pressões ecológicas e ambientais (Vielliard, 2004).

A estrutura morfológica dos organismos é um dos primeiros elementos a limitar o processo de emissão do sinal. A frequência acústica utilizada, por exemplo, parece estar relacionada com a massa corporal, de forma que quanto maior for o animal, menor será a frequência utilizada por ele (Bertelli and Tubaro, 2002; Fletcher, 2004; García et al., 2014; Oblanca and Tubaro, 2012; Ryan and Brenowitz, 1985; Tubaro and Mahler, 1998). Tal processo pode ser explicado, por exemplo, pela limitação acústica de um emissor de emitir frequências com comprimento de onda maior que o comprimento do seu próprio corpo (Bradbury and Vehrenkamp, 1998). Acusticamente um organismo vocaliza de acordo com as características morfológicas do seu aparelho fonador, e diferenças de volume dos pulmões e comprimento do aparelho fonador são determinantes para as características do som emitido (Flether 2004). Em aves, a capacidade de vibração da membrana siringial é limitada pelo tamanho do aparato vocal

(Farina, 2014), o qual tende a se relacionar isometricamente com o tamanho corporal. Ainda assim, por mais que a alometria vocal dite a frequência ótima a ser utilizada por um organismo, existem diversas outras pressões evolutivas que podem modificar o uso das frequências sonoras pelos organismos.

Por exemplo, nem sempre um sinal é transmitido de forma eficaz: a interferência acústica (ou mascaramento) pode reduzir a detecção do sinal, diminuindo sua eficiência na comunicação (Duellman & Pyles, 1983). A ineficiência desta transmissão pode fazer com que os sinais mascarados sejam selecionados negativamente, resultando em um processo muito similar a exclusão competitiva. Dessa forma, espera-se uma segregação das frequências utilizadas pelas espécies de uma comunidade, no sentido de que cada espécie tende a utilizar uma porção singular de recursos com intuito de minimizar os efeitos negativos da competição, gerando uma partição do nicho acústico através da singularidade vocal (de Araújo, 2011; Decker, 2012; Dias, 2013; Silva et al., 2008).

Além de esperar uma estruturação das frequências acústicas devido à competição pelo espaço acústico, o ambiente também pode exercer uma grande influência no canto. A hipótese de adaptação acústica propõe que o ambiente impõe limitações na propagação sonora, influenciando a evolução do canto de forma a maximizar a eficiência dos sons emitidos (Morton, 1975; Pijanowski et al., 2011). Assim, se por um lado a competição acústica promove uma divergência nas frequências utilizadas, por outro, o filtro gerado pelo ambiente promove uma convergência no uso de frequências específicas, cuja transmissão seja privilegiada em função do uso da comunicação. De fato o estudo da evolução vocal é dificultado muitas vezes pela presença de forças evolutivas sobrepostas (Ey and Fischer, 2009).

Algumas ideias de Platão podem ser interessantes ao abordarmos esse aparente paradoxo. Platão concebia o mundo como uma cópia imperfeita do mundo das ideias. De acordo com ele, o mundo das ideias era perfeito, imutável e eterno, enquanto o mundo real era um desvio do natural. Esse conceito abordado por Platão permite olhar o mundo por meio dos desvios, isto é, quanto um fenômeno natural diverge de um mundo perfeito. Com base no razoado de Platão, podemos considerar a relação alométrica de frequência ideal (ou perfeita), e determinada por uma morfologia intrínseca ao indivíduo. Por outro lado, a presença de pressões evolutivas externas, como aquelas provocadas pela competição com outros indivíduos ou por filtros ambientais, pode

10 provocar desvios da frequência ideal, alterando as frequências com base na eficiência da transmissão de informação.

A forma com que as diversas espécies estão inseridas no espaço acústico e tem sucesso na transmissão do sinal é resultado de um longo processo de seleção natural, e como o resultado de pelo menos três pressões evolutivas distintas: 1) as limitações morfológicas, que pressiona pela utilização da frequência ótima em relação ao tamanho corporal (Fletcher 2004); 2) a competição acústica, que faz com que ocorra uma estruturação do espaço sonoro de forma a evitar o mascaramento acústico (Seddon 2005); 3) os filtros ambientais, que pressiona pelo uso de frequências mais graves que o esperado (Chappuis 1972, Morton 1975). Nesse trabalho buscamos realizar uma análise conjunta das forças que atuam na evolução canto, primeiramente examinando o papel da alometria vocal na evolução do canto de seis grupos de aves, para então relacionar o papel relativo da alometria, competição acústica e filtros ambientais no processo de evolução da comunicação acústica de uma comunidade de aves da Amazônia.

REFERÊNCIAS de Araújo, C. B. (2011). Psitacídeos do Cerrado: sua alimentação, comunicação sonora e aspectos bióticos e abióticos de sua distribuição potencial. (Doutorado) Universidade Estadual de Campinas, 174 pp Bertelli, S., and Tubaro, P. L. (2002). “Body mass and habitat correlates of song structure in a primitive group of birds,” Biol. J. Linn. Soc., 77, 423–430. doi:10.1046/j.1095- 8312.2002.00112.x Bradbury, J. W., and Vehrenkamp, S. (1998). Principles of Animal Communication, Sinauer Sunderland, MA. Chappuis, C. (1971). “Un exemple de l’influence du milieu sur les émissions vocales des oiseaux: l’évolution des chants en forêt équatoriale,” Terre vie, 118, 183–202. Decker, G. (2012). “Macroecologia da diversidade de cantos de thamnofilídeos - aves.,” Dias, A. (2013). Competição por espaço acústico: adaptações de cantos de aves em uma zona de alta biodiversidade do Brasil Central.(Doutorado) Universidade de Brasilia, 87 pp. Ey, E., and Fischer, J. (2009). “the ‘Acoustic Adaptation Hypothesis’—a Review of the Evidence From Birds, Anurans and Mammals,” Bioacoustics, 19, 21–48. doi:10.1080/09524622.2009.9753613 Fletcher, N. H. (2004). “A simple frequency-scaling rule for animal communication,” J. Acoust. Soc. Am., 115, 2334. doi:10.1121/1.1694997 García, N. C., Barreira, A. S., Kopuchian, C., and Tubaro, P. L. (2014). “Intraspecific and interspecific vocal variation in three Neotropical cardinalids ( Passeriformes : Fringillidae ) and its relationship with body mass,” Emu, 114, 129–136. doi:10.1071/MU13010 Morton, E. S. (1975). “Ecological sources of selection on avian sounds,” Am. Nat., 109, 17–34. Neiva, E., and Hickson III, M. (2001). “Os universais, a vida biológica e a comunicação,” Alceu, 1, 17–34. Oblanca, P. D. L., and Tubaro, P. L. (2012). “Song analysis of the South American thrushes (Turdus) in relation to their body mass in a phylogenetic context,” Ornitol. Neotrop., 23, 349–365. Pijanowski, B. C., Villanueva-Rivera, L. J., Dumyahn, S. L., Farina, A., Krause, B. L., Napoletano, B. M., Gage, S. H., et al. (2011). “Soundscape Ecology: The Science of Sound in the Landscape,” Bioscience, 61, 203–216. doi:10.1525/bio.2011.61.3.6 12

Ryan, M. J., and Brenowitz, E. a. (1985). “The Role of Body Size, Phylogeny, and Ambient Noise in the Evolution of Bird Song,” Am. Nat., 126, 87. doi:10.1086/284398 Silva, R. A., Martins, I. A., and Rossa-Feres, D. D. C. (2008). “Bioacústica e sítio de vocalização em taxocenoses de anuros de área aberta no noroeste paulista,” Biota Neotrop., 8, 123–134. doi:10.1590/S1676-06032008000300012 Snowdon, C. T. (2011). “Communicação,” In M. E. Yamamoto and G. L. Volpato (Eds.), Comport. Anim., Editora da Universidade Federal do Rio Grande do Norte, Natal, 2nd ed., pp. 145–152. Tubaro, P. L., and Mahler, B. (1998). “Acoustic frequencies and body mass in New World doves,” Condor, 100, 54–61. doi:10.2307/1369896 Vielliard, J. M. E. (2004). “a Diversidade De Sinais E Sistemas De Comunicação Sonora Na Fauna Brasileira.,” Vielliard, J. M. E., and Silva, M. L. da (2010). “Bioacústica: bases teóricas e regras práticas de uso em ornitologia,” In S. Von Matter, F. C. Straube, V. de Q. Piacentini, I. A. Accordi, and J. F. Cândido Jr (Eds.), Ornitol. e Conserv. Ciência Apl. Técnicas Pesqui. e Levant., Technical Books, Rio de Janeiro, 1st ed., pp. 315–326.

CAPÍTULO 1

Allometric trends reveal distinct evolutionary trajectories for avian communication.

Este capítulo foi formatado de acordo com as normas do periódico Journal of the Acoustical

Society of America ISSN 1520-8524 (eletrônica), 0001-4966 (impressa).

Disponível em http://asa.scitation.org/journal/jas.

Allometric trends reveal distinct evolutionary trajectories for avian communication.

Ingrid M. D. Torresa),

Zoology Postgraduate Program, Federal University of Paraíba, João Pessoa, Paraíba, 58051-900,

Brazil

Luiz C. S. Lopez,

Systematic and Ecology Department, Federal University of Paraíba, João Pessoa, Paraíba, 58051-

900, Brazil

Carlos B. de Araújo

Systematic and Ecology Department, Federal University of Paraíba, João Pessoa, Paraíba, 58051-

900, Brazil

a) Eletronic mail: [email protected] 15

Acoustic signal production is affected by allometric relationships, in which the larger the animal the lower the frequency of its calls. We tested three acoustic evolutionary hypotheses: Signal to

Noise Ratio Hypothesis (SNRH), where evolution maximizes range by increasing the signal-to- noise ratio; Stimulus Threshold Hypothesis (STH), where evolution maximizes range of a specific signal threshold; Body Size Hypothesis (BSH), in which the emission of long wavelengths is enabled by body size. We measured three spectral metrics (Dominant Frequency -

FDOM, Minimum and Maximum Fundamental Frequencies – FFMIN and FFMAX) of

Neotropical Parrots, New World Doves, Woodcreepers, Tinamous, and Thrushes. We used a

Ranged Major Axis regression which shows that body mass is significantly correlated with all spectral parameters in Parrots, Doves, and Woodcreepers, and only with the fundamental frequencies of Tinamous. The SNRH was supported by the FDOM of Parrots. The FFMIN of

Woodcreepers and Tinamous supported SNRH and BSH. The STH was supported by the FFMIN and FFMAX of Parrots with FFMAX also supporting the BSH. The acoustic hypotheses could shed light on the evolutionary processes in avian communication, although our results show that it depends on the taxa and spectral parameters considered.

Keywords: Allometry; signal-to-noise ratio; vocal optimization; Aves.

PACS numbers: 43.80.Ka, 43.64.Tk

I. INTRODUCTION

Allometry is a relative growth in a trait whose increase is considered related to others

(West-Eberhand, 2003). This relationship can be represented by the equation Y= αxβ, where Y is the value of the trait of interest, α is a constant, x is the reference trait, and β is the allometric constant (Gould, 1966). The most commonly used reference trait is body mass, which imposes limits on traits and physiology (Gingerich et al., 1982; Prange et al., 1979; Tomkins et al., 2010).

For example, body size might shape species diversity due to either a faster spatial turnover of small-sized taxa or a higher extinction rates of larger species (Lopez et al. 2016), shape species local abundance as density seems to be related to individual metabolic requirements which should increase with body mass (Damuth 1981), limit home ranges due to size-related spatial requirements of competing individuals derived from resource requirements (Buchmann et al.,

2011), predict fitness through a balance between body-size and the rate of energy acquisition and the efficiency of conversion of energy into offspring (Brown et al., 1993).

Acoustic communication is also influenced by body mass and, as a general rule, the larger the animal the lower the pitch of its calls. Many studies point to negative allometric relationships between body mass and calling frequency (e.g., Bradbury and Vehrenkamp, 1998;

Fletcher, 2004; Ryan and Brenowitz, 1985). In birds, the vibration capacity of the syringeal membrane is limited by the size of the vocal apparatus (Farina, 2014), which tends to correlate with overall body size. While acoustical signals could be used to encode information on the body size of the sender (Macarrão et al., 2012), there is evidence that acoustic signals do not always function as indicators of body size (Gillooly and Ophir, 2010; Rodriguez et al., 2015), but rather reflect the morphological constraints posed to the production of certain frequencies (Fletcher

2004).

However, allometry is not the only process known to be involved in call evolution. In fact, there are many hypotheses concerning the drivers of vocal evolution of spectral parameters, such as: the Acoustical Adaptation Hypothesis (Chappuis, 1971; Morton, 1975), in which the environment shapes calls based on transmission efficiency; the Niche Partition Hypothesis

(Farina, 2014), in which communities segregate available frequencies to reduce overall spectral overlap and interference; or the Species Specificity Hypothesis, commonly known as

Reproductive Character Displacement, which predicts that the calls of closely related species would rapidly differentiate in order to avoid errors of species self-recognition (Seddon, 2005).

Altogether, these evolutionary drivers may explain the residuals found within vocal allometry studies.

There have been studies on the allometric vocal relations of New World Doves (Tubaro and Mahler, 1998), Woodcreepers (Palacios and Tubaro, 2000), Tinamous (Bertelli and Tubaro,

2002), Thrushes (Oblanca and Tubaro, 2012), Cardinals (García et al., 2014) and Songbirds

(Ryan and Brenowitz, 1985), although Fletcher (2004) probably published the most groundbreaking work on vocal allometry in recent years. His mathematical model predicts that evolution would select calls based on propagation efficiency and communication range, and he described several distinct evolutionary mechanisms related to body size that might influence spectral parameters evolution (see below).

Fletcher’s frequency-scaling rule comes from the solution of equations, constructed based on the morphology of the vocal apparatus (see Fletcher 2004). Fletcher’s makes two distinct exercises to examine the evolutionary optimization process within birds. His first approach examines an optimization process that will enhance the range of a signal of a specific stimulus threshold (T). Despite Fletcher describes T as being the minimum stimulus threshold for

18 proper signal detection it may also be thought as the minimum threshold for a specific message to be effective. For instance, while territorial defense signals may encode motivation within signal’s amplitude, the threshold T could be selected on the basis of efficient male-male interactions, in which a certain amplitude threshold could be necessary to convey the intended motivation

(Bradbury and Vehrenkamp 1998).

In his second exercise, he examines the possibility that signal-to-noise ratio is at the center of the optimization process. Under such a scenario, the area of the receiver’s ear is not important, and the spectral parameters would be selected to enhance signal intensity at the receiver’s position. This optimization process could be important in animals that use long range communication such as parrots, that are known to effectively communicate at distances of over a kilometer (de Araújo et al., de Araújo 2011). This makes parrot’s use of communication much different from those of territorial species, and should lead to distinct evolutionary patterns.

Along his work, Fletcher makes different evolutionary assumptions, which mathematically allowed him to establish different values for the allometric constant (k), each supporting a distinct optimization hypothesis: 1) If spectral evolution maximizes the range of a intensity threshold T (Stimulus Threshold Hypothesis – STH), a k value of -0.40 would be expected. Under this model, range efficiency is selected based on a threshold T of the emitted signal, which could be thought as the minimum intensity for stimulus detection as originally discussed by Fletcher, but also as an intensity threshold that encodes the desired emitter’s motivation. 2) If spectral evolution takes background noise into account and maximizes range through increasing the signal-to-noise ratio at the receiver’s position (Signal to Noise Ratio

Hypothesis – SNRH), a k value of -0.27 would be expected. This model is expected for birds that use acoustic signals to communicate over long ranges, such as social birds or territorial birds.

However, even though range might be important to territorial defense, it might be not the case if the call has a range larger than the bird’s territory, especially if an intensity threshold of a signal along territorial border is of greater importance for territorial defense. 3) Fletcher also discusses the hypothesis put forward by Bradbury and Vehrenkamp (1998), in which the performance of an animal would be restricted if the wavelengths used in its vocalizations were larger than its body size (Body Size Hypothesis – BSH) and, in this case, a k value of -0.33 would be expected. Birds should face constraints to sing at wavelengths larger than their body, and according to this model this would be the major issue behind allometric relations. A deeper discussion on the mathematical approach may be found both in Fletcher (2004) and in the Supplementary material.

Fletcher used the data of Ryan and Brenowitz (1985) to test his evolutionary hypothesis, but the data didn’t seem to fit well the model. In fact, in spite of the importance of Fletcher’s work, there are some major concerns. First, a mixture of fundamental and dominant frequencies, which could alter the resulting analyses, represented the raw dataset of spectral parameters used by him. Additionally, sound measurements are very subjective and depend upon the observer, the sound recording and editing procedures, as well on the FFT size used (Vielliard and Silva, 2004;

Zollinger et al., 2012).

Second, as Darwin (1859) first pointed out, closely related species should demonstrate greater similarities as compared to species from different families, which may have extreme adaptations (such as the elongation of the vocal tract) that could lead to a significant vocal deviations if these species are considered altogether. Morton’s raw data (1970, 1975; also used by

Ryan and Brenowitz 1985 and Fletcher 2004), for example, includes examples of species that have been separated for long periods of time, which are expected to show significant morphological divergence in their vocal abilities (Derryberry et al., 2012; Gaban-Lima and

Höfling, 2006; Huber and Podos, 2006; Podos et al., 2004). Studies of long-separated groups could therefore include considerable evolutionary history and lead to data scattering and a low-fit models.

Thirdly, Fletcher assumes that bird species will maximize their communication distance based on the threshold of the emitted signal, which may not always be true. Depending on the use of vocal communication, it is possible that, at some point, a species would have no need for additional range, and no additional fitness would derive from enhancing it. For example, during sexual selection, females may select signals based on call characteristics other than active communication space. Also, Fletcher argues that the SNRH does not seem to explain frequency variations, due to the animal’s neural system that can presumably filter out noise – a situation known as selective masking.

Here we aim to uncover the vocal evolutionary strategies adopted by closely related species, such as Neotropical Parrots, New World Doves (Tubaro and Mahler, 1998),

Woodcreepers (Palacios and Tubaro, 2000), Tinamous (Bertelli and Tubaro, 2002) and Thrushes

(Lavinia Oblanca and Tubaro, 2012), using three distinct spectral metrics (dominant frequency, minimum and maximum fundamental frequencies). First we focused on examining whether there is a relationship between body size and spectral parameters of the taxa studied. We expect to find a strong negative relationship between body mass and calling frequency, as the vibration capacity of the syringeal membrane is limited by the size of the vocal apparatus (Farina, 2014). Then, we examine whether these relations occur due to a basic process of geometric sizing, which the scaling coefficient corresponds to an isometric relation (i.e. Body Size Hypothesis). We expect to find deviations from isometric scaling, as distinct evolutionary factors may be shaping distinct trajectories for avian communication (e.g. Stimulus Threshold Hypothesis and Signal to Noise

Ratio Hypothesis) as shown by Fletcher (2004). By using the slopes and its confidence intervals we examined the existence of support to the Body Size Hypothesis (-0.33), the Stimulus

Threshold Hypothesis (-0.44) or/and he Signal to Noise Ratio Hypothesis (-0.27) by comparing the results of the allometric slopes calculated to the theoretical values presented by Fletcher

(2004).

II. METHODS

A. Data collection

Data from two different sources were used: flight calls of New World Parrots, which were manually analyzed by us, and published studies containing call measurements from other taxa. Data acquisition is explained in detail below.

1. Psittacidae

New World Parrots (Tribe Arini) are currently represented by 164 species of neotropical

Parrots (Forshaw, 2010). These taxa have vast vocal repertoires composed of discrete calls with specific messages such as: contact, hostility, congregation, alarm, guttural calls, and flock calls

(de Araújo, 2011; Moura, 2007). The most important biological information coded within a call is species identification (Vielliard, 1987), and parrot species' identities seem to be coded within their contact calls, ubiquitous calls uttered while individuals are in flight (Medina‐García et al.,

2015; de Moura et al., 2011).

Parrot contact calls were searched within the sound archives of the Fonoteca Neotropical

Jacques Vielliard (FNJV; Campinas, SP, Brazil), Macaulay Library (Ithaca, NY, USA), in online repositories such as Xeno-canto and WikiAves, and in published audio guides such as Voices of

New World Parrots (Whitney et al., 2002). A total of 967 recordings were reviewed and, based

22 on their signal-noise ratios, 418 were selected. In order to avoid pseudoreplication, only a single call per recording was used and our analyses were limited to five calls for each species (to reduce sampling effort variation). A single average value was used for each species. The species used were selected both for their high fidelity calls and the availability of body mass data. This procedure allowed us to examine a total of 118 Neotropical parrot species. The average body masses of the species were obtained either in the literature (Dunning, 2008) or were kindly provided by zoological museums (Museu de Zoologia da Universidade de São Paulo – MZUSP,

Museu Nacional do Rio de Janeiro – MNRJ, Universitets København, Zoologisk Museum -

ZMUC). Neotropical parrots demonstrate great variations in their body masses, making them a perfect group for studying vocal allometrics. Our data contain body masses as low as 23 g

(Forpus passerinus) and as high as 1331 g (Anodorhynchus hyacinthinus).

To standardize the recordings and allow comparisons (Zoolinger et al. 2012), the recordings were edited by applying a high pass filter (125Hz) with a graphic equalizer and normalization to 0 dB, which amplifies the signal until its peak reaches the 0 dB threshold. Both sound editing and analysis were made using Cool Edit Pro software (Syntrillium Software

Corporation, 2002) through direct measurements of spectrograms and power spectra (256 FFT, window width 75%, logarithmic energy plot with range of 120dB, triangular windowing function).

2. Other taxa from published data

Acoustical data of New World Doves from Tubaro and Mahler (1998) were used, which included 44 new world dove species with body masses ranging from 30 to 320 g. The call used was the advertising call, which, according to those authors, is basically innate and thus highly

23 stereotyped and considered homologous to the songs of Passerines in terms of its biological/sexual function. Although this group is not monophyletic, their radiation to the

Neotropics occurred between 39 and 55 million years ago, when their lineage became independent from old world doves (Pereira et al., 2007).

The Woodcreepers analyzed by Palacios and Tubaro (2000) comprised a total of 39 species having body masses ranging from 14 to 155 g. Those authors analyzed the song used to defend territories and attract females. The study group, Dendrocolaptinae, is a neotropical monophyletic group that split from the other Furnariidae groups approximately 20 to 25 million years ago

(Derryberry et al., 2011; Raikow, 1994).

Data of the Tinamous genera published by Bertelli and Tubaro (2002) were used, comprising a group of 36 species having body masses ranging from 125 to 2000 g. The authors examined the songs commonly used for territorial defense and mate attraction. Tinamous are neotropical volant terrestrial birds whose monophyly is well established; they occupy habitats varying from open areas to dense forests (which is consistent with a separation time of approximately 17 million years) (Bertelli, 2016; Bertelli et al., 2014).

Lastly, data of thrushes from Lavinia Oblanca and Tubaro (2012) were used, represented by a set of 25 species belonging to the South American clade of the genus Turdus, with body masses ranging from 52 to 143 g. Those authors used male songs in their analyses, which are known to have species-specific recognition functions as acoustic communication signals (Da

Silva et al., 2000). The Turdus genera is paraphyletic, with four known clades: African clade,

Central American-Caribbean clade, South American clade, and the Eurasian clade. The South

American clade is well-supported (Klicka et al., 2005; Voelker et al., 2007), with a divergence time of between 1.2 and 3.4 million years (Pan and Lei, 2007).

B. Vocal parameters

We chose three spectral parameters widely used within bioacoustics studies, which could be measured analogously among the various taxa: the dominant frequency (FDOM), defined as the frequency with the highest energy. We also used fundamental frequencies that unlike dominant frequency have no single representation value, but rather depends upon the selection of landmarks along frequency-modulated calls. Here we use minimum fundamental frequency (FFMIN), and maximum fundamental frequency (FFMAX), which are the minimum and maximum value found within the fundamental harmonic, respectively. If there are morphological limits to frequency emission by a bird, maximum and/or minimum frequencies are probably the best values to find it, while representing the limit of observed emitted frequencies.

The measurements used are graphically indicated in figure 1.

C. Data analysis

Statistical analyses were made using the lmodel2 package (Legendre, 2014), implemented in R software version 3.3.1 (R Core Team, 1997). Considering that allometric trends can be described by logarithmic transformation of the power function, the allometric equation F = BMk can be expressed as Log (F) = LogB + k.Log(M), where F is frequency, B a constant, M is body mass and k is the allometric constant (For more details on the logarithm form of the equation, refer to Christiansen, 2002; Gould, 1966), allowing us to use linear regression between log-transformed values of mass and frequency to estimate the allometric constants. As both traits, frequency and body mass, are not controlled by the researcher, a type I regression would underestimate the true slope, making it preferable to use a type II regression (details are discussed in Legendre and Legendre, 2000). We used a Ranged Major Axis (RMA) regression

25 between body mass and spectral parameters (FDOM, FFMIN, FFMAX) to estimate the allometric constants of each bird group with 95% confidence intervals. The Ranged Major Axis analysis is the most suitable allometric technique for inferring functional relationships as its slope is independent of the correlation coefficient, and X and Y may use different units (Aiello, 1992;

Green, 2001; Legendre and Legendre, 2000). To determine which vocal optimization hypothesis is supported for each avian taxa, we compared the attained constants to the values attained by

Fletcher (2004).

Slope estimates and strength of log-log correlations to body mass using traditional regression are very similar to those obtained taking phylogeny into account (Gillooly and Ophir,

2010; McKechnie and Wolf, 2004; Ricklefs and Starck, 1996), and while it might be useful to consider phylogeny to assess evolutionary mechanisms, phylogenetically ‘corrected’ results do not seem to invalidate results obtained without phylogeny (Bello et al., 2015). As we here test a theoretical model deduced without consideration of phylogeny, we opted not to consider phylogenetic relationships into the regression analysis made.

III. RESULTS

Body mass was significantly correlated with all spectral parameters in Neotropical

Parrots, New World Doves and Woodcreepers, and with the fundamental frequencies (FFMIN,

FFMAX) of Tinamous (Table 2, Figure 2). No allometric correlations were found among

Thrushes (Table I). Additionally, the percentages of variation of the spectral parameters explained by body mass varied between groups. While New World Doves, Woodcreepers and

Tinamous had presented low coefficients of determination (r2 varying from 4% to 26%), New

World Parrots showed a much better fit, with body mass explaining 68% of FDOM and FFMIN variation within the group.

The reduced explanatory power found within some groups prevented accurate estimations of their allometric constants (Figure 3), making it difficult to discriminate the factors that may be restraining allometric relations, as the confidence intervals supported more than one hypotheses. In Woodcreepers and Tinamous the FFMIN was supported by both SNRH and BSH.

In New World Parrots the FDOM was supported only by SNRH and FFMAX shaped by STH and

BSH, while FFMIN came close to being shaped by STH, but neither hypothesis was supported by it.

IV. DISCUSSION

We examined here the applicability of Fletcher’s (2004) vocal allometric model in several avian groups, and used the allometric constants obtained to examine the support for each of the evolutionary mechanisms proposed by that author. Our study provided mixed support for

Fletcher’s allometric model, as groups such as Neotropical Parrots, Woodcreepers, New World

Doves and Tinamous demonstrated considerable fit to the allometric model. However Thrushes did not demonstrate any apparent relationship between their body masses and spectral parameters. The mechanisms behind spectral evolution seem to be variable among species, and our data show that avian groups have distinct allometric value constants depending upon the spectral metric used in the analysis. Theory predicts that efficient vocalizations should be selected by vocal evolution (e.g., Chappuis 1971), but the allometric constraints of such evolutionary optimization have not been widely explored, even though physical analyses could be used to predict vocal parameters found in nature (Fletcher 2004).

The negative relationships found here between acoustic frequencies and body masses have been reported previously for groups such as songbirds (Ryan and Brenowitz, 1985; Wiley,

1991), Warblers (Badyaev and Leaf, 1997), and Cardinalids (García et al., 2014), and is considered to reflect basic anatomical and physiological trait constraints involved in song production (Lambrechts, 1996). As lower frequency sounds travel further, while high frequency sounds suffer more attenuation (Morton, 1998; Wiley, 1991), body mass will constrain low frequency production and mold the spectral parameters used as well communication efficiency

(Badyaev and Leaf, 1997; Morton, 1975; Ryan and Brenowitz, 1985) by imposing performance limits to the use of call frequency (Bradbury and Vehrenkamp 1998).

The avian groups studied here demonstrated different degrees of fit to the allometric model. While the spectral parameters of the Thrushes showed no relation to body size,

Woodcreepers, Tinamous, and New World Doves showed low fits with low coefficients of determination (r2), and Neotropical Parrots showed extremely high fits, with coefficients of determination near 0.7 for some spectral parameters. It is possible that while the groups analyzed have great differences in terms of the age of their clade, some of them may carry considerable evolutionary histories and morphological adaptations that add residuals to the analysis. It is likely that the influence of other factors in some of the avian groups studied, such as predicted by the

Acoustical Adaptation hypothesis and Acoustical Niche Hypothesis, made it impossible to discriminate between Fletcher’s hypotheses due to high dispersion values found for allometric slopes. This is especially evident in groups with the narrowest range of body mass, which presented the weakest relationships between body mass and spectral parameters. Limited variation in body mass might bias the result and those groups seem to fail to present any correlation between mass and spectral parameters.

Additionally, allometric relationships seem to depend on the spectral parameters examined, as our results showed mixed support for Fletcher’s hypothesis. The FDOM of the

Neotropical Parrots supported the SNRH, while the FFMIN of the Woodcreepers and Tinamous supported both the SNRH and the BSH. Thus, some of spectral metrics used here seemed to be selected on the basis of the maximization of the signal-to-noise ratio, rather than the stimulus threshold. This goes against Fletcher’s conclusion, as his data pointed to STH and body sizes as the main drivers of spectral evolution (Fletcher 2004). Background noise has been found to provoke numerous adverse effects in birds, such as increases in missed nestling detections

(Leonard and Horn, 2012), frequency changes due to noisy environments (Slabbekoorn et al.,

2012), reduced reproductive success (Blickley and Patricelli, 2010), avoidance of areas (Forman,

2000), changes in foraging behavior (Quinn et al., 2006), the inability to attract mates and defend territories (Slabbekoorn and Ripmeester, 2008), and a reduced ability to avoid predators (Quinn et al., 2006). The masking of acoustic signals can severely limit communication ranges and detection, thereby impairing essential ingredients of avian sexual and social life – which could then reflect in reduced fitness. Those results support the hypothesis that background noise is an important driver of vocal evolution of spectral parameters in Parrots, and information transmission efficiency should be improved by enhancing the signal to noise ratio within the group. Other social birds that makes use of long-range social signals such as New World Jays might also present the same pattern (Rosa et al., 2016).

On the other hand STH was supported by Neotropical Parrots FFMAX. Despite it also supported to BSH, slope values are not accordingly to the by SNRH. These parameters do support Fletcher’s results in which acoustical optimization would come from maximizing the signal to a specific intensity threshold level. Sound attenuation can be influenced by the

29 landscape (ground effects, topography, vegetation cover) and by climatic variables (i.e., air absorption, wind gusts, humidity, fog, rain, temperature) with resulting reductions of acoustic communication efficiency (Albert, 2004; Farina, 2014). However, it is possible that the fundamental frequencies are involved in other physical phenomena, such as resonance or sound radiation, which demonstrate tight relationships with the morphology of the vocal tract (Fletcher and Tarnopolsky, 1999; Reed et al., 2012). Thus, the fine-tuning between the fundamental frequencies and the morphology of the vocal tract could still lead to improved vocal ranges, if the specific intensity threshold level is the one to best enhance sound radiation or resonance related phenomena.

Even though data scattering prevented deeper discussions, our results indicate that the physical characteristics of an acoustical signal are related to the context. For example, while birds and mammals are known to use harsh low-frequency signals when in hostile interactions, they use higher frequency pure tones during friendly interactions (Morton, 1977). Unfortunately, call intensity is frequently ignored during repertoire descriptions, but a broad discussion can still be made. We must consider that some bird species can shift frequencies in response to biological or environment factors, a condition known as vocal plasticity. Thus, a species could shift its frequency calls resulting in a shift of the allometric constant (Bee et al., 2016). In other words, vocal plasticity could cause a species change frequency in order to prioritize a specific intensity threshold level (STH) or signal-to-noise ratios (SNRH). However, despite such bioacoustics phenomenon may lead to data scattering it is unlikely that the frequency of an entire taxon, which has been related to body mass through one of Fletcher’s hypotheses, to be a result of vocal plasticity. Such patterns might be better explained through the results of distinct evolutionary drivers. For instance, while territorial defense signals are used to secure resources from

30 conspecifics over a large active space, the intensity of the signal itself may also encode motivation, so that close range high-amplitude signals, such as those predicted by STH, could be more important in close range territorial interactions (Bradbury and Vehrenkamp 1998).

Contrariwise, groups such as parrots are known to use high amplitude calls that assure long-range communication and flock fusion/fission during feeding (de Araújo et al., 2011). In such groups, range efficiency would be the main driver for vocal evolution of spectral parameters, and could explain the support for SNRH found in Neotropical Parrots dominant frequencies.

V. CONCLUSION

We must agree with Fletcher’s conclusion that it is “interesting that the sort of optimization that would be predicted, based upon simple physical arguments, is so close to that found in nature”. Avian communication has been shaped by distinct evolutionary forces, each force having a role in the detectability of the calls. The signals must be separated from background noise, and the optimization proposed by Fletcher could shed light on the evolutionary processes involved. We found evidence for all of Fletcher’s hypotheses, even though it depends on the spectral metric and also on the focus taxa.

ACKNOWLEDGMENTS

We thank CAPES for financial support; the Macaulay Library (Cornell) and Fonoteca

Neotropical Jacques Vielliard (Unicamp) for providing access to their audio collections; Fabio

Nunes (Aquasis), Luiz Fábio Sileira (MZUSP), Marcos Raposo (National Museum UFRJ), and

Jon Fjeldså (Zoological Museum of the University of Copenhagen) for providing us additional

31 body mass data. Jeff Podos and Luciano Naka, for the amazing feedback on early versions of the manuscript.

REFERENCES

Aiello, L. C. (1992). “Allometry and the analysis of size and shape in human evolution,” J. Hum.

Evol., 22, 127–147. doi:10.1016/0047-2484(92)90034-7

Albert, D. G. (2004). Past Research on Sound Propagation through forests. Technical report, US

Army Engineer Research and Development Center, New Hampshire, 23 pages. de Araújo, C. B. (2011). Psitacídeos do Cerrado: sua alimentação, comunicação sonora e

aspectos bióticos e abióticos de sua distribuição potencial (Psittacids of the Cerrado:

feeding, sound communication and biotic and abiotic aspects of its potential distribution).

(PhD Dissertation) Universidade Estadual de Campinas, 174 pages. de Araújo, C. B., Marcondes-Machado, L. O., and Vielliard, J. (2011). “Vocal Repertoire of the

Yellow-Faced Parrot (Alipiopsitta xanthops),” Wilson J. Ornithol., 123, 603–608.

doi:10.1676/10-102.1

Badyaev, A., and Leaf, E. (1997). “Habitat Associations of Song Characteristics in Phylloscopus

and Hippolais Warblers,” Auk, 114, 40–46. doi:10.2307/4089063

Bello, F., Berg, M. P., Dias, A. T. C., Diniz-Filho, J. A. F., Götzenberger, L., Hortal, J., Ladle, R.

J., and Leps, J. (2015). “On the need for phylogenetic ‘corrections’ in functional trait-based

approaches,” Folia Geobot., 50, 349–357. doi:10.1007/s12224-015-9228-6

Bertelli, S. (2016). “Advances on tinamou phylogeny: an assembled cladistic study of the volant

palaeognathous birds,” Cladistics, 1, 1–24. doi:10.1111/cla.12172

Bertelli, S., Chiappe, L. M., and Mayr, G. (2014). “Phylogenetic interrelationships of living and

extinct Tinamidae, volant palaeognathous birds from the New World,” Zool. J. Linn. Soc.,

172, 145-184. doi:10.1111/zoj.12156

Bertelli, S., and Tubaro, P. L. (2002). “Body mass and habitat correlates of song structure in a

primitive group of birds,” Biol. J. Linn. Soc., 77, 423–430. doi:10.1046/j.1095-

8312.2002.00112.x

Blickley, J. L., and Patricelli, G. L. (2010). “Impacts of Anthropogenic Noise on Wildlife:

Research Priorities for the Development of Standards and Mitigation,” Int. Wildl. Law

Policy, 13, 274–292. doi:10.1080/13880292.2010.524564

Bradbury, J. W., and Vehrenkamp, S. (1998). Principles of Animal Communication, Sinauer

Sunderland, MA, pp.75-99.

Brown, T. J., Marquet, P. A., and Taper, M. L. (1993). “Evaluation of body size: consequences of

an energetic definition of fitness,” Am. Nat., 142, 573–584.

Buchmann, C. M., Schurr, F. M., Nathan, R., and Jeltsch, F. (2011). “An allometric model of

home range formation explains the structuring of animal communities exploiting

heterogeneous resources,” Oikos, 120, 106–118. doi:10.1111/j.1600-0706.2010.18556.x

Chappuis, C. (1971). “Un exemple de l’influence du milieu sur les émissions vocales des oiseaux:

l’évolution des chants en forêt équatoriale”(“An example of the influence of the environment

on the vocal emissions of birds: the evolution of songs in the equatorial forest”), Terre vie,

118, 183–202.

Christiansen, P. (2002). “Mass allometry of the appendicular skeleton in terrestrial mammals,” J.

Morphol., 251, 195–209. doi:10.1002/jmor.1083

Corporation, S. S. (2002). “Cool Edit Pro.,” Retrieved from

https://www.adobe.com/special/products/audition/syntrillium.html

Darwin, C. (1859). On the origin of the species by natural selection, J. Murray, Lodon, pp.54-82.

Derryberry, E. P., Claramunt, S., Derryberry, G., Chesser, R. T., Cracraft, J., Aleixo, A., Pérez-

Emán, J., Remsen Jr, J. V. and Brumfield, R. T. (2011). “Lineage diversification and

morphological evolution in a large-scale continental radiation: The neotropical ovenbirds and

woodcreepers (aves: furnariidae),” Evolution (N. Y)., 65, 2973–2986. doi:10.1111/j.1558-

5646.2011.01374.x

Derryberry, E. P., Seddon, N., Claramunt, S., Tobias, J. A., Baker, A., Aleixo, A., and Brumfield,

R. T. (2012). “Correlated Evolution of Beak Morphology and Song in the Neotropical

Woodcreeper Radiation,” Evolution (N. Y)., 66, 2784–2797. doi:10.1111/j.1558-

5646.2012.01642.x

Dunning, J. B. (2008). CRC Handbook of avian body masses., CRC Press, Florida, 2nd ed,

pp.148-155.

Farina, A. (2014). Soundscape Ecology: Principles, patterns, methods ans applications. Springer,

New York, pp.44-58. doi:10.1007/978-94-007-7374-5

Fletcher, N. H. (2004). “A simple frequency-scaling rule for animal communication,” J. Acoust.

Soc. Am., 115, 2334. doi:10.1121/1.1694997

Fletcher, N. H., and Tarnopolsky, A. (1999). “Acoustics of the avian vocal tract,” J. Acoust. Soc.

Am., 105, 35. doi:10.1121/1.424620

Forman, R. T. T. (2000). “Estimate of the area affected ecologically by the road system in the

United States,” Conserv. Biol., 14, 31–35. doi:10.1046/j.1523-1739.2000.99299.x

Forshaw, J. M. (2010). Parrots of the world, Princeton University Press, New Jersey, pp.77-144.

Gaban-Lima, R., and Höfling, E. (2006). “Comparative anatomy of the syrinx in the tribe Arini

(Aves: Psittacidae),” Brazilian J. Morphol. Sci., 23, 501–512.

García, N. C., Barreira, A. S., Kopuchian, C., and Tubaro, P. L. (2014). “Intraspecific and

interspecific vocal variation in three Neotropical cardinalids ( Passeriformes : Fringillidae )

and its relationship with body mass,” Emu, 114, 129–136. doi:10.1071/MU13010

Gillooly, J. F., and Ophir, A. G. (2010). “The energetic basis of acoustic communication,” Proc.

R. Soc. B-Biological Sci., 277, 1325–1331. doi:10.1098/rspb.2009.2134

Gingerich, P. D., Smith, B. H., and Rosenberg, K. (1982). “Allometric scaling in the dentition of

primates and prediction of body weight from tooth size in fossils,” Am. J. Phys. Anthropol.,

58, 81–100. doi:10.1002/ajpa.1330580110

Gould, S. J. (1966). “Allometry and size in ontogeny and phylogeny,” Biol. Rev., 41, 587–640.

doi:10.1111/j.1469-185X.1966.tb01624.x

Green, A. J. (2001). “Mass/length residuals: Measures of body condition or generators of

spurious results?,” Ecology, 82, 1473–1483. doi:10.1890/0012-

9658(2001)082[1473:mlrmob]2.0.co;2

Huber, S. K., and Podos, J. (2006). “Beak morphology and song features covary in a population

of Darwin’s finches (Geospiza fortis),” Biol. J. Linn. Soc., 88, 489–498. doi:10.1111/j.1095-

8312.2006.00638.x

Klicka, J., Voelker, G., and Spellman, G. M. (2005). “A molecular phylogenetic analysis of the

‘true thrushes’ (Aves: Turdinae),” Mol. Phylogenet. Evol., 34, 486–500.

doi:10.1016/j.ympev.2004.10.001

Lambrechts, M. M. (1996). “Organization of birdsong and constraints on performance,” In D. E.

Kroodsma and E. H. Miller (Eds.), Ecol. Evol. Acoust. Commun. birds, Cornell University

Press, New York, pp. 305–320.

Lavinia Oblanca, P. D., and Tubaro, P. L. (2012). “Song analysis of the South American thrushes

(Turdus) in relation to their body mass in a phylogenetic context,” Ornitol. Neotrop., 23,

349–365.

Legendre, A. P. (2014). “lmodel2: Model II Regression.,” Retrieved from https://cran.r-

project.org/package=lmodel2

Legendre, P., and Legendre, L. (2000). Numerical Ecology, Elsevier, Amsterdam, 2nd ed,

pp.504-516.

Leonard, M. L., and Horn, a. G. (2012). “Ambient noise increases missed detections in nestling

birds,” Biol. Lett., 8, 530–532. doi:10.1098/rsbl.2012.0032

Medina-García, A., Araya-Salas, M., and Wright, T. F. (2015). “Does vocal learning accelerate

acoustic diversification? Evolution of contact calls in Neotropical parrots,” J. Evol. Biol., 28,

1782–1792.

Morton, E. S. (1975). “Ecological sources of selection on avian sounds,” Am. Nat., 109, 17–34.

Morton, E. S. (1977). “On the Occurrence and Significance of Motivation-Structural Rules in

Some Bird and Mammal Sounds,” Am. Nat., 111, 855. doi:10.1086/283219

Morton, E. S. (1998). “Degradation and signal ranging in birds: memory matters,” Behav. Ecol.

Sociobiol., 42, 135–137. doi:10.1007/s002650050421

Moura, L. N. De (2007). Comportamento do Papagaio-do-mangue Amazona amazônica:

gregarismo , ciclos nictemerais e comunicação sonora. Universidade Federal do Pará, 133

pages. de Moura, L. N., Silva, M. L., and Vielliard, J. (2011). “Vocal repertoire of wild breeding

Orange-winged Parrots Amazona amazonica in Amazonia,” Bioacoustics, 20, 331–339.

Palacios, M. G., and Tubaro, P. L. (2000). “Does beak size affect acoustic frequencies in

woodcreepers ?,” Condor, 102, 553–560.

Pan, Q. W., and Lei, F. M. (2007). “Phylogenetic relationships between Turdus species:

Mitochondrial cytochrome b,” Ornis Fenn., 84, 1–11.

Pereira, S. L., Johnson, K. P., Clayton, D. H., and Baker, A. J. (2007). “Mitochondrial and

nuclear DNA sequences support a Cretaceous origin of Columbiformes and a dispersal-

driven radiation in the Paleocene ,” Syst. Biol., 56, 656–672.

doi:10.1080/10635150701549672

Podos, J., Southall, J. A., and Rossi-Santos, M. R. (2004). “Vocal mechanics in Darwin’s finches:

correlation of beak gape and song frequency,” J. Exp. Biol., 207, 607–619.

doi:10.1242/jeb.00770

Prange, H. D., Anderson, J. F., and Rahn, H. (1979). “Scaling of Skeletal Mass to Body Mass in

Birds and Mammals,” Am. Nat., 113, 103. doi:10.1086/283367

Quinn, J. L., Whittingham, M. J., Butler, S. J., and Cresswell, W. (2006). “Noise, predation risk

compensation and vigilance in the chaffinch Fringilla coelebs,” J. Avian Biol., 37, 601–608.

doi:10.1111/j.2006.0908-8857.03781.x

Raikow, R. J. (1994). “A phylogeny of the woodcreepers (Dendrocolaptinae),” Auk, 111, 104–

114. doi:10.2307/4088509

Reed, S. E., Boggs, J. L., and Mann, J. P. (2012). “A GIS tool for modeling anthropogenic noise

propagation in natural ecosystems,” Environ. Model. Softw., 37, 1–5.

doi:10.1016/j.envsoft.2012.04.012

Ricklefs, R. E., and Starck, J. M. (1996). “Applications of phylogenetically independent

contrasts: a mixed progress report,” Oikos, 77, 167–172. doi:10.2307/3545598

Rodriguez, R. L., Araya-Salas, M., Gray, D. a., Reichert, M. S., Symes, L. B., Wilkins, M. R.,

Safran, R. J., and Hobel, G (2015). “How acoustic signals scale with individual body size:

common trends across diverse taxa,” Behav. Ecol., 26, 168–177. doi:10.1093/beheco/aru174

Ryan, M. J., and Brenowitz, E. (1985). “The role of body size, phylogeny, and ambient noise in

the evolution of bird song,” Am. Nat., 126, 87. doi:10.1086/284398

Seddon, N. (2005). “Ecological adaptation and species recognition drives vocal evolution in

neotropical suboscine birds,” Evolution (N. Y)., 59, 200–215.

Da Silva, M. L., Piqueira, J. R., and Vielliard, J. M. (2000). “Using Shannon entropy on

measuring the individual variability in the Rufous-bellied thrush Turdus rufiventris vocal

communication,” J. Theor. Biol., 207, 57–64. doi:10.1006/jtbi.2000.2155

Slabbekoorn, H., and Ripmeester, E. A. P. (2008). “Birdsong and anthropogenic noise:

implications and applications for conservation,” Mol. Ecol., 17, 72–83. doi:10.1111/j.1365-

294X.2007.03487.x

Slabbekoorn, H., Yang, X.-J., and Halfwerk, W. (2012). “Birds and anthropogenic noise: singing

higher may matter,” Am. Nat., 180, 142-145. doi:10.1086/665991

Team, R. C. (1997). R: A language and environment for statistical computing, R Found. Stat.

Comput., Available: http://www.r-project.org, (date last viewed: 10-Oct-05). Retrieved

October 10, 2005, from http://www.r-project.org

Tomkins, J. L., LeBas, N. R., Witton, M. P., Martill, D. M., and Humphries, S. (2010). “Positive

Allometry and the Prehistory of Sexual Selection,” Am. Nat., 176, 141–148.

doi:10.1086/653001

Tubaro, P. L., and Mahler, B. (1998). “Acoustic frequencies and body mass in New World

doves,” Condor, 100, 54–61. doi:10.2307/1369896

Vielliard, J. M. E. (1987). “O uso da bioacústica na observação de aves” (“The use of

bioacoustics in birdwatching”) II Encontro Nac. Anilhadores Aves, Rio de Janeiro, pp. 98–

121.

Vielliard, J., and Silva, M. L. Da (2004). “A Bioacústica como ferramenta de pesquisa em

Comportamento animal” (“Bioacustics as a research tool in Animal Behavior”), In G. Assis,

R. Brito, and W. L. Martin, Estudos do Comportamento II, Universidade Federal do Pará,

Belém, pp. 1–15.

Voelker, G., Rohwer, S., Bowie, R. C. K., and Outlaw, D. C. (2007). “Molecular systematics of a

speciose, cosmopolitan songbird genus: Defining the limits of, and relationships among, the

Turdus thrushes,” Mol. Phylogenet. Evol., 42, 422–434. doi:10.1016/j.ympev.2006.07.016

West-Eberhand, M. J. (2003). Developmental plasticity and evolution., Oxford University Press,

Oxford, pp. 308-310.

Whitney, B. M., Parker III, T. A., Budney, G. F., Munn, C. A., and Bradbury, J. W. (2002).

Voices of New World Parrots, Macauley Libr. Nat. Sounds, Ithaca, New York.

Wiley, R. H. (1991). “Associations of Song Properties with Habitats for Territorial Oscine Birds

of Eastern North America,” Am. Nat., 138, 973-993. doi:10.1086/285263

Zollinger, S. A., Podos, J., Nemeth, E., Goller, F., and Brumm, H. (2012). “On the relationship

between, and measurement of, amplitude and frequency in birdsong,” Anim. Behav., 84, e1–

e9. doi:10.1016/j.anbehav.2012.04.026

Table I: Linear regressions (p-values) between Log transformed acoustic parameters (FDOM,

FFMIN and FFMAX) and the body masses of Neotropical Parrots, New World Doves,

Woodcreepers, Tinamous, and Thrushes. Values in bold type represent significant slopes

(p<0.05).

Neotropical New World Woodcreepers Tinamous Thrushes Parrots Doves

FDOM (Hz) >0.001 >0.001 >0.001 0.13 0.25

FFMIN (Hz) >0.001 0.003 0.006 0.03 0.48

FFMAX (Hz) >0.001 0.002 0.001 0.04 0.36

Figure 1: Spectrogram, oscilogram and power spectrum of a Diopsittaca nobilis flight call, illustrating the vocal parameters measured.

Figure 2: Allometric models of the acoustic parameters (FDOM, FFMIN and FFMAX) of

Neotropical Parrots, New World Doves, Woodcreepers, Tinamous, and Thrushes. 43

Figure 3: Allometric constants and their confidence intervals for each spectral parameter of

Neotropical Parrots, New World Doves, Woodcreepers and Tinamous. The dashed lines mark the expected allometric slopes for each of Fletcher’s hypotheses.

CAPÍTULO 2

Acoustic ecology of Carajás National Forest bird assemblages, Brazil.

Este capítulo foi formatado de acordo com as normas do periódico Journal of Ornithology;

ISSN: 2193-7192 (impresso) ISSN: 2193-7206 (eletrônico)

Disponível em: http://link.springer.com/journal/10336

Acoustic ecology of Carajás National Forest bird assemblages, Brazil.

Ingrid M. D. Torresa) ORCID 0000-0003-4336-0682

Zoology Postgraduate Program, Federal University of Paraíba, João Pessoa, Paraíba, 58051-900,

Brazil.

Luiz C. S. Lopez, Carlos B. de Araújo

Systematic and Ecology Department, Federal University of Paraíba, João Pessoa, Paraíba, 58051-

900, Brazil.

a) Eletronic mail: [email protected] 46

Abstract

Communication among birds constitutes the foundation of all of their social interactions. We examined the structure of acoustic signals of an avian assemblage in the Carajás National Forest,

PA, Brazil, addressing the following questions: 1) Does that avian assemblage show non-random patterns of acoustic niche partitioning? 2) Is competition higher among small (thirty-gram) species? 3) Do smaller species use lower frequencies than expected by their allometry? We recorded songs during 86 days between 2013 and 2016 using three spectral parameters: dominant frequency (FDOM), minimum fundamental frequency (FFMIN), and maximum fundamental frequency (FFMAX). We conducted null model analyses using Pianka’s index of Niche Overlap to determine whether there was evidence of nonrandom structuring in the acoustic niche. We conducted a Wilcoxon Sign Rank between what was expected by allometry and what was actually found in the acoustic spectra of the avian assemblage to test whether smaller species use lower frequencies. We recorded and analyzed 347 audio samples of 138 species. The null model analysis showed a lack of niche structuring within the avian assemblage calls, with all spectral parameters differing significantly from the frequencies expected by allometry; the frequencies used, however, were higher than those expected. Communication is a dynamic process in which a signal emitted by one individual can influence the behavior of another, and the successful insertion of many species into the same acoustic space in terms of their signal transmission is the result of a long process of natural selection.

Keywords: Acoustic competition, niche overlap, environmental filter, Aves.

1. INTRODUCTION

Communication among birds constitutes the foundation of all of their social interactions, from mate choice to parent-offspring interactions (Bradbury and Vehrenkamp 1998). Acoustical communication is the main process involved in information transmission (Vielliard and Silva

1990) in forest habitats, where visual communication is more restricted. Acoustic signals will not be transmitted efficiently at all times, however, as acoustic interference (or masking), for example, can reduce signal detection and diminish the ability of the receiver to detect a signal and impose difficulties on discriminating one signal from another (Brumm and Slabbekoorn 2005;

Duellman and Pyres 2013). Signal transmission inefficiency may provoke negative selection for masked signals, resulting in a competitive process in which signals will tend to segregate from one another (Chek et al. 2003). The concept of ecological competition should therefore be extended to acoustic competition, and divergence in the use of frequencies should be expected in which each species uses a unique portion of the available acoustical resources to reduce the negative effects of competition (de Araújo 2011). Empirical evidence does, in fact, show (for example) that populations of sympatric species show higher degrees of call differentiation as compared to allopatric populations, indicating the existence of call segregation due to competition processes (Nelson and Marler, 1990; Seddon 2005).

Body mass is also known to influence acoustic communication and, as a general rule, the larger the animal the lower is the pitch of its calls (Ryan and Brenowitz 1985; Tubaro and Mahler

1998; Bertelli and Tubaro 2002; Fletcher 2004; Lavinia Oblanca and Tubaro 2012; García et al.

2014). Vocal allometry suggests that each species will have an ideal dominant frequency in relation to its body mass, leading to better communication efficiency (Fletcher 2004). As body masses are not evenly distributed among species, and species richness is greater (for example)

48 near 30 grams (Olson et al. 2009), acoustic competition would be expected to occur more intensely among thirty-gram birds. The effects of acoustic competition are therefore expected to lead to the partitioning of the acoustic niche (Silva et al. 2008; de Araújo 2011; Decker 2012;

Dias 2013), although it might not have the same effects on species with different body masses.

The environment, likewise, is known to be a major driver of call evolution, as acoustical signals decay and degrade after leaving their source. In a perfect environment, with no sound reflection or absorption, sounds will suffer a simple attenuation of 6dB upon doubling the distance from its source (Mockford et al. 2011) due to spherical decay. As natural environments are not homogenous, however, sound signals will suffer additional attenuations and reflection due to environment structure and/or meteorological characteristics (such as wind speed, temperature, humidity, topography, and vegetation). During sound propagation, signal structuring becomes progressively degraded (Mathevon 1998; Naguib and Haven-Wiley 2001) and, as predicted by the

Acoustical Adaptation Hypothesis, this degradation can be diminished by using lower frequencies

(with their higher transmission efficiencies) (Chappuis 1971; Morton 1975) over longer distances

(McComb et al. 2003), or by using signals with little frequency modulation (Lohr et al. 2003).

Thus, while acoustic competition could promote divergences in frequency use, environmental filters might likewise promote convergence of the use of lower frequencies (which transmit information more efficiently than higher frequencies). Under that scenario, we would expect small species using higher frequencies (which suffer higher degradation) to be the most influenced by environmental filters.

Beyond the effects of sexual selection and phylogenetic effects, which should both affect call evolution, we can view the evolution of bird song as being the result of at least three distinct evolutive pressures: 1) allometric (or morphological) pressure, which drives the use of an ideal

49 frequency related to a given species’ body size; 2) acoustic competition, which leads to the structuring of the acoustic space to reduce acoustic masking; and, 3) environmental filtering, which pressures the use of lower frequencies due to their transmission efficiencies. The study of bird song evolution is therefore difficulty by the overlapping of these pressures, which will act simultaneously on signal acoustical parameters (Ey and Fischer 2009). In the present study, we examined the acoustic relationships of the avian assemblage in the Carajás National Forest, PA,

Brazil, and addressed the following questions: 1) Does the avian assemblage show non-random patterns of acoustic niche use? We expect to find a structured acoustic niche, as competition can be molding the use of the acoustic space to avoid overlap. 2) Is competition higher among smaller species? We expect to find a fierce competition in smaller bodied birds, as they have a greater diversity of species and, due to vocal allometry, they might use a similar range of frequencies. 3)

Do smaller species use lower frequencies than expected by allometric relationships? We expect to find that the smaller species (which use the higher frequencies range due their size) will tend to use lower frequencies than expected as a way to reduce sound degradation during propagation

(which is more severe in higher frequencies).

2. METHODS

A. Study Area

Our study was conducted within the Carajás National Forest (FLONA Carajás) in Pará

State, Brazil, which is managed by the Chico Mendes Institute for Biodiversity Conservation

(ICMBio). The FLONA Carajás was created in 1998 (Brasil 1998) with an area of 392,725 hectares, occupying most of the municipalities of Canaã dos Carajás and Parauapebas and a small portion of Água Azul do Norte. It is situated along one of the eastern limits of a continuous

50 forested area in the state (Martins et al. 2012). This area has been part of a sustainable-use area used for iron ore extraction by Vale S/A for more than three decades. We surveyed six major sites administered by the Bioindicators Monitoring Project – Vale S/A where two-kilometer trails were crossed by five perpendicular 250 meter transects (totaling six trails and 30 transects) (Fig. 1).

The sampling areas included two vegetation types: Dense Ombrophilous Forest on plateaus, and

Open Ombrophilous Forest on the hillsides of shallow fluvial valleys (Martins et al. 2012).

B. Data Collection

Audio recordings were made in March/2013 (3 days), February, August and

September/2014 (15 days), January, February, March, April and May/2015 (37 days), and in

March, April and May/2016 (31 days), totaling 86 days. Avian calls were recorded with a Sony

PCM-M10 recorder coupled to a Rodes NTG2 unidirectional microphone, with a Sound Device

702 coupled to a Sennheiser ME67 unidirectional microphone, and with a Sony PCM-D50 recorder with a Shure Beta 58 microphone coupled to a parabola dish (19 m focus, 60 cm diameter), all with a 48 kHz sampling rate and 24-bit resolution. To standardize the recordings and allow comparisons (Zoolinger et al. 2012), we edited the recordings by applying a high pass filter (at 125Hz) with a graphic equalizer and normalized the recordings to 0 dB. Both sound editing and analysis were made using Cool Edit Pro software (Syntrillum Software Corporation

2002) through direct measurements of spectrograms and power spectra.

C. Vocal Parameters

We chose three spectral parameters widely used within bioacoustics studies, which could be measured analogously among the various taxa: the dominant frequency (FDOM), defined as the

51 frequency with the highest energy. We also used fundamental frequencies that unlike dominant frequency have no single representation value, but rather depends upon the selection of landmarks along frequency-modulated calls. Here we use minimum fundamental frequency (FFMIN), and maximum fundamental frequency (FFMAX), which are the minimum and maximum value found within the fundamental harmonic, respectively. If there are morphological limits to frequency emission by a bird, maximum and/or minimum frequencies are probably the best values to find it, while representing the limit of observed emitted frequencies. The measurements used are graphically indicated in Fig 2.

D. Data Analysis

- Taxonomic diversity

We used 10 MacKinnon species lists to compare sampling efforts made during field trips in

2015 and 2016 (morning hours 6:00 to 9:30) in each perpendicular transect, totaling 198 hours of sampling (MacKinnon and Phillips 1993; Herzog et al. 2002; Ribon 2010). This survey data was used to build a rarefaction curve and to estimate species richness using nonparametric estimators

(Chao2 and ICE), which were then compared to the number of species recorded in the field. This allowed us to estimate how well the regional avian community was represented in our acoustical sampling. Each of these estimators has its advantages and disadvantages. ICE tends to overestimate species richness in small samples, while Chao2 often underestimates species richness estimates (Magurran 2013). The results of these methods were expected to complement each other and closely represent the actual local avian assemblage. The analyses were performed using EstimateS software (Colwell 2013).

-Acoustic niche partition

As the FDOM is the most common variable used in acoustic analyses, we classified the

FDOM of the sampled species into one-third octave bands to determine which bandwidths were most used by the avian assemblage (Zollinger et al. 2012). To determine whether there was evidence of nonrandom structuring in the acoustic niche, null model analyses were performed using Pianka’s index of Niche Overlap (Pianka 1973). The following matrix settings have been used in many studies of niche overlap using acoustic variables: species corresponding to rows, acoustic variables corresponding to columns, and the values of each cell corresponding to the arithmetic mean of the variables (e.g., Bourne and York 2001, Oliveira 2014, Protázio et al. 2014,

Lopez et al. 2016).

There may be large variations in the frequencies used by some species that are driven by factors promoting vocal plasticity (e.g., Hu and Cardoso, 2010; Nelson and Marler, 1994;

Schuster et al., 2002; Tubaro and Lijtmaer, 2006). Therefore, the arithmetic means of the acoustic spectra may not faithfully represent the use of the acoustic space, making it difficult to make inferences about competition. One way to include this variability is to categorize the acoustic data in one-third octave bands. This categorization of spectral frequencies into one-third octave bands is very common in acoustical studies (Ma et al. 2013; Chen et al. 2014; Kastelein et al. 2014;

Scobie et al. 2014; Sills et al. 2014; Erbe et al. 2015; Holt and Johnston 2015; Papale et al. 2015;

Tougaard 2015), and has been used to make inferences concerning spectral overlap, traffic noise, and frequencies used by avian communities (Halfwerk et al. 2011). Additionally, niche overlap analysis explores resource utilization using only one resource dimension per model (Gotelli and

Graves 1996).

We used only the FDOM as a niche dimension, with species corresponding to rows, one- third octave bands as variables (columns), and the value of each cell corresponding to the number of recordings in which the FDOM fitted the bandwidth. We used all measured FDOM to categorize the one-third octave bands, not the average values for the species as shown in the example in Table 1. Taking into account possible variations in FDOM, we included in the analysis only those species for which we managed to obtain at least three recordings (approximately 33 species). The matrix was reshuffled (1000 randomizations) to simulate random patterns that would be expected in the absence of assemblage structure, using randomization algorithm 2, which maintains the integrity of the zero structure of the matrix. The null model analyses were performed using the EcoSimR package (Gotelli et al 2015), implemented in R software version

3.3.1 (R Core Team, 1997). We compared the random values to those observed at Carajás to determine whether or not acoustic niche structuring could be found in our dataset.

Table 1 Examples of acoustic data categorized in 1/3 octave bands, corresponding to the matrix settings used in this study. File Species FDOM File Species FDOM 160318_06 Aratinga jandaya 4078 130310_00 Anodorhynchus hyacinthinus 1874.6 T1942 Aratinga jandaya 3456 T1262 Anodorhynchus hyacinthinus 1687 130312_04 Aratinga jandaya 3846 T1264 Anodorhynchus hyacinthinus 1867 T1193 Aratinga jandaya 4406 T880 Anodorhynchus hyacinthinus 1687

1/3 octave 1600 2000 2500 3150 4000 bands Species intervals (1414.2 - 1781.8) (1781.8 - 2244.9) (2244.9 - 2828.4) (2828.4 - 3563.6) (3563.6 - 4489.8) Anodorhynchus hyacinthinus 2 2 0 0 0 Aratinga_ andaya 0 0 0 1 3

- Allometric effects on acoustic competition

To examine the hypothesis that competition will be greater among small species, we

54 examined the residuals of the allometric regression, which should be greater due to the divergent effects of competition. As actual change in a physical stimuli is not expected to be linearly related to the perceived change (termed by psychophysics as Weber’s law), 20Hz differences should not be perceived equally when the base frequency changes from 100Hz to 120Hz or from 4000Hz to

4020Hz. Music, for example, is build upon sensory intervals that take into account frequency sensations, not frequency itself. While an A1 has a frequency of 110Hz, and A2 of 220Hz, A3 has a frequency of 440Hz. In other words, an octave is achieved in music by doubling the frequency; western music will further divide an octave into 12 additional tonal intervals. A fifth, for example, may be calculated by multiplying the base frequency by 3/2, so that the fifth of A1 is 165Hz, while the fifth of A2 is 330Hz. These tonal differences are the basis of musical sensations, and how music is actually made.

The effects of the Weber’s law have been widely studied in terms of intensity, where a small deviation was found while the discrimination of intensity is independent of frequency but better at higher amplitudes (Jesteadt et al. 1977). Considering Weber’s law together with spectral competition allows us to make two distinct predictions. First we would expect the allometric residuals to be higher at higher frequencies, as the spectral differences selected by evolution to reduce competition should be sufficient to insure sensory differences (this being achieved with smaller spectral differences at lower frequencies). We would also expect, however, that tonal differences, measured by the ratios between two frequencies (Backus 2977), would show a negative linear slope in relation to mass, as the selected tonal differences should be greater in small species (showing greater species richness – and thus higher competition pressure). In order to test these two predictions, we first prepared a bar plot of the masses of the detected birds to examine if there was, in fact, a higher diversity among small birds in Carajás. We then prepared a

55 scatterplot of the residuals, expecting to observe a reduction in their values, as sensory differences can be attained with smaller shifts to lower frequencies (larger birds). Lastly, we constructed a linear regression model using the tonal differences (calculated by dividing the residuals by the expected frequency) and mass, in which we suspected a negative slope if competition (and tonal dispersion) was indeed greater between small birds. In order to improve visualization, we show mass classes until 300g in the graph, since the classes that follow have only between 1 and 2 species.

- Environmental filter

To test whether birds with smaller body masses use lower frequencies than expected by allometry, we divided the data into quartiles – a common measure of relative standing

(Mendenhal et al. 2009). To test if the first quartile (smallest species) uses lower frequencies, we first calculated the allometric relationships for the other three quartiles (larger species) in terms of of all three spectral parameters (FFMIN, FFMAX, FDOM); based on the results, we estimated the expected values for the first quartile using the allometric equation f=B*Mk, where f is the spectral

(dominant frequency) variable, B is a constant, M is the body mass (kg), and k is the allometric constant. We then tested if the first species quartile had calls with lower frequencies than expected by their the allometric parameters, using a Wilcoxon Sign Rank test. To calculate the allometric relationships between body mass and dominant frequencies, we used OLS regression, as this method produces fitted values with the smallest error (Legendre and Legendre 2000). Statistical analyses were implemented in R software (R CORE TEAM 1997).

RESULTS

- Taxonomic diversity

We accumulated 390 Mackinnon lists, from which we identified a total of 258 bird species belonging to 48 families in the six areas. The most representative families were Thamnophilidae

(31 species), Thraupidae (20 species), Psittacidae (15 species), Tyrannidae (15 species) and

Dendrocolaptidae (15 species), representing together 37% of all species. Richness estimates varied from 294 species (Chao2) to 300 species (ICE) within the six study areas (Fig. 2).

-Acoustic niche partition

We were able to record 384 audio files belonging to 138 different species, of which 347 were selected for analysis based on their signal-to-noise ratios. The FDOM varied from 328.1 Hz to 9107.4 Hz, while FFMIN varied from 204.0 Hz to 6742.6 Hz, and the FFMAX from 472.0 Hz to 10357.0 Hz. Dominant frequencies (FDOM) in the 3150Hz band (in 1/3 octaves) were the bandwidths most used by the avian assemblage of FLONA Carajás, with 37 species, followed by the 2500 and 4000 1/3 octave bands (with 31 and 29 species respectively) (Fig. 3).

We found a mean niche overlap of 0.2323 as compared to a random expected overlap value of 0.21991. The probability that the observed overlap mean was lower than that randomly expected was 0.872 (Fig. 4), so that the comparison of our data to the null model indicated a lack of acoustic competition in the Carajás avian assemblage according to frequency parameters.

-Allometric effects on acoustic competition

The Carajás avian community showed a higher richness of small birds (Fig 6). The scatterplot comparing the module of the residuals and body mass was triangular in shape, converging to zero

57 residuals towards larger masses – indicating that higher frequencies have larger dispersion. We found tonal differences larger than three octaves in the values expected by the allometric regression, but most tonal dispersions were not higher than 1 octave (Fig. 6). The linear regression of tonal differences and mass were not statistically significant – indicating that there is no over dispersion due to higher competition under conditions of high species richness (Fig. 6).

Table 2: OLS results between tonal differences and body mass.

FDOM FFMIN FFMAX r² 0.74 0.38 0.63 p 0.0008 0.006 0.002

F 1,129 0.1086 0.7721 0.2299

- Environmental filters

All spectral parameters significantly adjusted to the allometric model constructed with 3/4 of the largest species (Table 2), allowing us to correctly estimate the expected frequencies

(FDOM, FFMIN and FFMAX) of the first quartile (smallest birds); our data significantly differed from the frequencies expected by allometry, with p= 0.0075, 0.0037, and 0.031 respectively.

However, in contrast to our initial predictions, frequencies were higher than expected (Fig. 7, Fig.

8 and Fig. 9).

Table 3: Result of the OLS regressions between body masses and frequencies.

FDOM FFMIN FFMAX p 2.23E-07 4.40E-06 2.76E-10 r² 0.2426 0.1962 0.3382

F 1,97 31.07 23.68 49.56 k -0.224 -0.23 -0.312 B 1239.852 624.885 1032.238

DISCUSSION

- Taxonomic diversity

Based on the estimated richness obtained by Chao2 and ICE, we can affirm that 86% of the estimated species were registered in the FLONA Carajás, indicating that the sampling method employed could be used with that bird assemblage. Despite our efforts, however, we successfully recorded only 53% of the registered species and 46% of the estimated species. There are 594 known species in the FLONA (Aleixo et al. 2012), but as we only sampled the Ombrophilous

Forest (excluding the Deciduous Forest, the savannah-like areas (canga), and the area surrounding the FLONA), lower species richness was encountered.

-Acoustic niche and competition

The most-used dominant frequency 1/3 octave bands (3150, 2500, 4000) were all close together, and were observed in 70% of the recorded species. Acoustic competition may be more severe in those bandwidths, as the call of one species may provoke higher acoustical interferences in the calls of other species. Nevertheless, in addition to spectral partitioning, other mechanisms of niche partition made be acting to reduce call overlapping. Species may call in different microhabitats (e.g., high in the forest canopy or on the forest floor) or adjust their calling periods to reduce overlap and competition (Chek et al. 2003).

The lack of overlap reduction in the acoustic niche suggests an absence of competition call structuring in the dominant frequency used at Carajás. Overlapping is especially common in songbirds, as they may simultaneously produce signals while displaying to attract mates or defending their territories during dawn choruses, or during aggressive interactions between rivals

(Todt and Naguib 2000; Naguib and Mennill 2010; Masco et al. 2016). Each bird species may

59 respond differently to this overlapping, with some males shifting their calling frequencies due to low spectrum noise (Brumm 2004); they may also alter their song lengths to shorter calls, avoid areas with overlapping calling (Hall et al. 2006), sing at higher rates (Poesel and Dabelsteen

2005), or extend their song lengths during overlapping (Dabelsteen et al. 1996).

Evidence of acoustic partitioning has been relatively more intensively studied in frogs

(Bourne and York 2001; Chek et al. 2003; Silva et al. 2008; Sinsch et al. 2012; Protázio et al.

2014), but competition for any kind of resource may have as a final result ecological character displacement, in which there are evolutionary processes of differentiation between species in relation to resource use. Our data, however, indicates that competition does not have a role in the evolution of calling in the species studied, as the overlap in the use of that resource (the spectral dimension of the acoustical space) in not different from that expected by chance.

-Allometric effects on acoustic competition

As was expected, smaller species showed higher species richness, which might be expected to lead to larger frequency dispersions. However, the higher dispersions (larger absolute residuals) found within higher frequencies seem to be result of sensory characteristics of the birds, in which the stimulus differences necessary to produce sensory perception are higher at higher frequencies.

Despite our finding that the residuals of the allometric regression do, in fact, converge to 0 in larger birds, we did not find any relation to body mass in the analysis made through the use of tonal differences (used as proxies for perceived spectral differences). Thus, our results go against our initial predictions that competition is higher among smaller species. This result supports the previous one, in which no structuring was observed in the acoustic niche – suggesting an absence of any competitive role in call evolution within the Carajás bird assembly.

- Environmental filters

Despite our initial prediction that small birds should use lower call frequencies than expected by allometry to enhance transmission efficiency, our results clearly showed that the smaller species in the FLONA avian assemblage actually sing at higher frequencies than expected. Although counterintuitive, these results may be related to the role of the call itself, in which that signal is primarily used by males to advertise and defend their territories or to attract mates, actions that do not necessarily require long-distance projections (Naguib and Haven-Wiley

2001). It is possible that, at some point, a species would have no need to attain additional range as, for example, when its members defend small territories or defend their territories from multiple points – so that no additional fitness would result from improving their call range. It is also important to consider the motivations for the calls: relatively low frequency calls may be used in hostile situations, and higher frequency calls in “friendly” situations, meaning that communication may vary according to the underlying context (Morton 1977).

Additionally, as the structures of acoustical signals become progressively degraded during the sound propagation (Naguib and Haven-Wiley 2001), especially at higher frequencies; this could result in some positive effects for the receiver – since the birds could also obtain information from sound degradation – as, for example, the capacity to discriminate signaler distance. It would obviously be advantageous to a territorial male to be able to locate an opponent bird and adjust its territorial response (Naguib 1996; Mathevon 1998; Mouterde et al. 2014).

Additionally, it is possible that during the miniaturization of birds, their flight capacities could show dependence on mass reductions, so that morphological structures not involved in flight itself might well suffer stronger size-reduction selection. Despite the fact that there should be advantages in singing lower pitched sounds in closed habitats (Chappuis 1972, Morton 1975),

61 pressure on flight efficiency could act against it, and average it out. In this case, non-flight structures, such as the syrinx, could suffer allometric reductions that would result in the generation of higher than expected frequencies, such as seen in our results.

CONCLUSION

Communication is a dynamic process in which a signal emitted by one individual can influence the behavior of another. If such behavioral changes bring gains in terms of fitness, it is expected that those signals will be selected for based on higher information transmission efficiency. Our results, however, indicate that competition does not have a strong role in the call evolution of birds in the Carajás region, either in the structuring of their acoustical niches, or in posing more severe restrictions on species that sing at similar frequencies. We cannot ignore, however, there can be factors, other than the spectral features, that could collaborate to minimizing the competition. The acoustic space has multiple dimensions, which create many combinations of the parameters, such as spectral features, temporal and spatial parameters. For example, some species may use similar dominant frequency, but diverge in timing of signaling.

The addition of temporal parameters could result in a structured acoustic niche.

REFERENCES

Aleixo A, Carneiro LS, Dantas S de M (2012) Aves. In: Martins FD, Castilho AF, Campos J, et al. (eds) Fauna da Floresta Nacional de Carajás: estudos sobre vertebrados terrestres. Nitro imagens, São Paulo, Backus J (2977) The Acoustical Foundations of Music., 2nd edn. W. W. Norton & Company, United States Bertelli S, Tubaro PL (2002) Body mass and habitat correlates of song structure in a primitive group of birds. Biol J Linn Soc 77:423–430. doi: 10.1046/j.1095-8312.2002.00112.x Bourne GH, York H (2001) Vocal behaviors are related to nonrandom structure of anuran breeding assemblages in Guyana. Ethol Ecol Evol 13:313–329. Bradbury JW, Vehrenkamp S (1998) Principles of Animal Communication. Sinauer Sunderland, MA Brasil (1998) No Title. Brasil Brumm H (2004) noise on song amplitude The impact of environmental in a territorial. J Anim Ecol 73:434–440. doi: 10.1111/j.0021-8790.2004.00814.x Brumm H, Slabbekoorn H (2005) Acoustic Communication in Noise. Adv Study Behav 35:151– 209. doi: 10.1016/S0065-3454(05)35004-2 Chappuis C (1971) Un exemple de l’influence du milieu sur les émissions vocales des oiseaux: l’évolution des chants en forêt équatoriale. Terre vie 118:183–202. Chek AA, Bogart JP, Lougheed SC (2003) Mating signal partitioning in multi-species assemblages: A null model test using frogs. Ecol Lett 6:235–247. doi: 10.1046/j.1461- 0248.2003.00420.x Chen G, Decker B, Prakash V, et al (2014) Prolonged noise exposure-induced auditory threshold shifts in rats. Hear Res 317:1–8. doi: 10.1016/j.heares.2014.08.004 Colwell RK (2013) EstimateS: Statistical estimation of species richness and shared species from samples. Corporation SS (2002) Cool Edit Pro. Dabelsteen T, McGregor PK, Shepherd M, et al (1996) Is the signal value of overlapping different from that of alternating during matched singing in great tits? Anim Behav 27:189–194. de Araújo CB (2011) Psitacídeos do Cerrado: sua alimentação, comunicação sonora e aspectos bióticos e abióticos de sua distribuição potencial. Universidade Estadual de Campinas Decker G (2012) Macroecologia da diversidade de cantos de thamnofilídeos - aves. Dias A (2013) Competição por espaço acústico: adaptações de cantos de aves em uma zona de alta biodiversidade do Brasil Central. (PhD Dissertation) Universidade de Brasília, 1–87. Duellman WE, Pyres RA (2013) Acoustic Resource Partitioning in Anuran Communities. Copeia 3:639–645. Erbe C, Verma A, Mccauley R, et al (2015) Progress in Oceanography The marine soundscape of the Perth Canyon. Prog Oceanogr 137:38–51. doi: 10.1016/j.pocean.2015.05.015 Ey E, Fischer J (2009) the “Acoustic Adaptation Hypothesis”—a Review of the Evidence From Birds, Anurans and Mammals. Bioacoustics 19:21–48. doi: 10.1080/09524622.2009.9753613 Fletcher NH (2004) A simple frequency-scaling rule for animal communication. J Acoust Soc Am 115:2334. doi: 10.1121/1.1694997 García NC, Barreira AS, Kopuchian C, Tubaro PL (2014) Intraspecific and interspecific vocal variation in three Neotropical cardinalids ( Passeriformes : Fringillidae ) and its relationship

with body mass. Emu 114:129–136. doi: 10.1071/MU13010 Gotelli NJ, Graves GR (1996) Null models in ecology. Smithsonian Institution Press, Washington, USA Halfwerk W, Holleman LJM, Lessells CKM, Slabbekoorn H (2011) Negative impact of traffic noise on avian reproductive success. 210–219. doi: 10.1111/j.1365-2664.2010.01914.x Hall ML, Illes A, Vehrencamp SL (2006) Overlapping signals in banded wrens: long-term effects of prior experience on males and females. Behav Ecol 17:260–269. Herzog SK, Kessler M, Cahill TM (2002) Estimating species richness of tropical bird communities from rapid assessment data. Auk 119:749–769. Holt DE, Johnston CE (2015) Traffic noise masks acoustic signals of freshwater stream fish. Biol Conserv 187:27–33. doi: 10.1016/j.biocon.2015.04.004 Hu Y, Cardoso GC (2010) Which birds adjust the frequency of vocalizations in urban noise? Anim Behav 79:863–867. doi: 10.1016/j.anbehav.2009.12.036 Jesteadt W, Wier CC, Green DM (1977) Intensity discrimination as a function of frequency and sensation level. J Acoust Soc Am 61:169–177. doi: 10.1121/1.381278 Kastelein RA, Hoek L, Terhune JM, Jennings N (2014) Hearing thresholds of a harbor porpoise ( Phocoena phocoena ) for playbacks of seal scarer signals , and effects of the signals on behavior. doi: 10.1007/s10750-014-2035-x Lavinia Oblanca PD, Tubaro PL (2012) Song analysis of the South American thrushes (Turdus) in relation to their body mass in a phylogenetic context. Ornitol Neotrop 349–365. Legendre P, Legendre L (2000) Numerical Ecology, 2nd edn. Elsevier, Amsterdam Lohr B, Wright TF, Dooling RJ (2003) Detection and discrimination of natural calls in masking noise by birds: estimating the active space of a signal. Anim Behav 65:763–777. doi: 10.1006/anbe.2003.2093 Lopez LCS, Figueiredo MSL, Fracasso MP de A, et al (2016) The role of local versus biogeographical processes in influencing diversity and body-size variation in mammal assemblages. Ecol Evol 6:1447–1456. doi: 10.1002/ece3.1978 Ma C, Qiao C, Wang Y, Zhao S (2013) A Real-time Processing Method of Underwater Ambient Noise Spectrum Based on One-third Octave. 522–525. doi: 10.4028/www.scientific.net/AMM.333-335.522 MacKinnon S, Phillips K (1993) A field guide to the birds of Borneo, Sumatra, Java and Bali. Oxford University Press, Oxford Magurran AE (2013) Measuring biological diversity. Blackwell Publishing, Oxford Martins FD, Castilho AF, Campos J, et al (2012) Fauna da Floresta Nacional de Carajás: estudos sobre vertebrados terrestres. Nitro imagens, São Paulo Masco C, Allesina S, Mennill DJ, Pruett-Jones S (2016) The Song Overlap Null model Generator (SONG): a new tool for distinguishing between random and non-random song overlap. Bioacoustics 25:29–40. doi: 10.1080/09524622.2015.1079734 Mathevon N (1998) Degraded Temporal Sound Features As a Function of Distance and Potential As Cues for Ranging in Birds. Bioacoustics 9:17–33. doi: 10.1080/09524622.1998.9753377 McComb K, Reby D, Baker L, et al (2003) Long-distance communication of acoustic cues to social identity in African elephants. Anim Behav 65:317–329. doi: 10.1006/anbe.2003.2047 Mendenhal W, Beaver RJ, Beaver BM (2009) Introduction to probability and statistics, 13th edn. Brooks/Cole Cengage Learning, Canada Mockford EJ, Marshall RC, Dabelsteen T (2011) Degradation of rural and urban great tit song: Testing transmission efficiency. PLoS One. doi: 10.1371/journal.pone.0028242

Morton ES (1975) Ecological sources of selection on avian sounds. Am Nat 109:17–34. Morton ES (1977) On the Occurrence and Significance of Motivation-Structural Rules in Some Bird and Mammal Sounds. Am Nat 111:855. doi: 10.1086/283219 Naguib M, Haven-Wiley R (2001) Estimating the distance to a source of sound: mechanisms and adaptations for long-range communication. Anim Behav 62:825–837. doi: 10.1006/anbe.2001.1860 Naguib M, Mennill DJ (2010) The signal value of birdsong: Empirical evidence suggests song overlapping is a signal. Anim Behav. doi: 10.1016/j.anbehav.2010.06.001 Nelson DA, Marler P (1990) The perception of bird song and an ecological concept of signal space. In Comparative perception: complex signals, vol. 2 (eds W. C. Stebbins & M. A. Berkley), pp. 443–478. New York, NY: Wiley. Nelson DA, Marler P (1994) Selection-based learning in bird song development. Proc Natl Acad Sci United States Am 91:10498–10501. Oliveira G. (2014) Processos ecológicos e evolutivos na comunicação acústica entre grilos : partição de nicho acústico e seleção de sítios de chamado. 55. doi: 10.1017/CBO9781107415324.004 Olson VA, Davies RG, Orme CDL, et al (2009) Global biogeography and ecology of body size in birds. Ecol Lett 12:249–259. doi: 10.1111/j.1461-0248.2009.01281.x Papale E, Gamba M, Perez-gil M, Martin VM (2015) Dolphins Adjust Species-Specific Frequency Parameters to Compensate for Increasing Background Noise. 1–15. doi: 10.1371/journal.pone.0121711 Pianka ER (1973) The structure of lizard communities. Annu Rev Ecol Syst 4:53–74. Poesel A, Dabelsteen T (2005) Territorial responses of male blue tits to simulated dynamic intrusions: effects of song overlap and intruder location. Protázio A dos S, Albuquerque RL, Falkenberg LM, Mesquita DO (2014) Acoustic ecology of an anuran assemblage in the arid Caatinga of northeastern Brazil. Ribon R (2010) Amostragem de aves pelo método de listas de Mackinnon. In: Von Matter S, Straube FC, Accordi I, et al. (eds) Ornitologia e conservação: ciência aplicada, técnicas de pesquisa e levantamento. Technical Books, Rio de Janeiro, pp 31–44 Ryan MJ, Brenowitz E a. (1985) The Role of Body Size, Phylogeny, and Ambient Noise in the Evolution of Bird Song. Am Nat 126:87. doi: 10.1086/284398 Schuster S, Zoolinger SA, Lesku JA, Brumm H (2002) On the evolution of noise-dependent vocal plasticity in birds. Biol Lett 8:913–916. doi: 10.1098/rsbl.2012.0676 Scobie CA, Sciences B, Tg AB (2014) Wildlife Monitoring With Unmanned Aerial Vehicles : Quantifying Distance to Auditory Detection. 1–5. doi: 10.1002/wsb.700 Seddon N (2005) Ecological adaptation and species recognition drives vocal evolution in neotropical suboscine birds. Evolution (N Y) 59:200–215. Sills JM, Southall BL, Reichmuth C (2014) Amphibious hearing in spotted seals ( Phoca largha ): underwater audiograms , aerial audiograms and critical ratio measurements. 726–734. doi: 10.1242/jeb.097469 Silva RA, Martins IA, Rossa-Feres DDC (2008) Bioacústica e sítio de vocalização em taxocenoses de anuros de área aberta no noroeste paulista. Biota Neotrop 8:123–134. doi: 10.1590/S1676-06032008000300012 Sinsch U, Lumkemann K, Rossar K, et al (2012) Acoustic Niche Partitioning in an Anuran Community Inhabiting an Afromontane Wetland (Butare, Rwanda). African Zool 47:60–73. doi: 10.3377/004.047.0122

TEAM RC (1997) R: A language and environment for statistical computing. In: R Found. Stat. Comput. http://www.r-project.org. Accessed 10 Oct 2005 Todt D, Naguib M (2000) Vocal interactions in birds The use of song as a model in communication. Adv Study Behav 29:247–296. Tougaard J (2015) Underwater Noise from a Wave Energy Converter Is Unlikely to Affect Marine Mammals. 1–7. doi: 10.1371/journal.pone.0132391 Tubaro PL, Lijtmaer D a (2006) Environmental Correlates of Song Structure in Forest Grosbeaks and Saltators. Condor 108:120–129. doi: 10.1650/0010- 5422(2006)108[0120:ECOSSI]2.0.CO;2 Tubaro PL, Mahler B (1998) Acoustic frequencies and body mass in New World doves. Condor 100:54–61. doi: 10.2307/1369896 Vielliard JME, Silva WR (1990) Nova metodologia de levantamento quantitativo da avifauna e primeiros resultados no interior do Estado de São Paulo. An IV ENAV 1:117–151. Zoolinger SA, Podos J, Nemeth E, et al (2012) On the relationship between, and measurement of, amplitude and frequency in birdsong. Anim Behav 84:e1–e9. doi: 10.1016/j.anbehav.2012.04.026

Fig. 1 A) Brazil. B) Pará State. C) the FLONA Carajás area. Each group of points in the FLONA area represents a survey site with 5 perpendicular 250 meter transects, totaling 6 sites and 30 transects.

Figure 1: Spectrogram, oscilogram and power spectrum of a Diopsittaca nobilis flight call, illustrating the vocal parameters measured.

350

300

250

200 ICE 150 Chao 2

100 S(est) Specie Richness Specie 50

0 0 100 200 300 400 -50 Mackinnon Lists

Fig. 2 Species accumulation curve, using MacKinnon lists and richness estimators.

40 35 30

25 20

Species 15 10 5 0

Third octave Bands (Hz)

Fig. 3 Number of species in each FDOM third octave band.

Fig. 4 Niche overlap analysis, indicating a lack of structuring in the avian assemblage.

Body mass classes 80

30 Speccies Richness Speccies 20

30 45 60 75 90

120 135 150 165 180 195 210 225 240 255 270 285 300

105

- - - - -

------

0 0

15 15 30 45 60 75

90 90

120 120 135 150 165 180 195 210 225 240 255 270 285 105 105 Bird masses, in grams

Fig. 5 Bar plot of species richness per body mass class.

FFMIN FFMAX FDOM

) ) )

z z z

H H H

( ( (

s s s

l l l

a a a

u u u

d d d

i i i

s s s

e e e

R R R

- - 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Weight (kg) Weight (kg) Weight (kg)

FFMIN FFMAX FDOM

) ) )

s s s

e e e

v v v

a a a

t t t

c c c

o o o

( ( (

e e e

c c c

n n n

e e e

r r r

e e e

f f f

i i i

d d d

l l l .

a a a

n n n

o o o

T T T

. .

0 0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Weight (kg) Weight (kg) Weight (kg) Fig. 6 Scatterplots of the allometric residuals (upper row) and residual tonal differences (lower row).

9000

8000

7000

6000

5000

4000

Frequency (Hz) Frequency 3000

2000

1000

0 EXP-FDOM FDOM

Fig. 7 The expected FDOM by allometry and the found FDOM.

7000

6000

5000

4000

3000 Frequency (Hz) Frequency 2000

1000

0 EXP-FFMIN FFMIN

Fig. 8 The expected FFMIN by allometry and the found FFMIN.

12000

10000

8000

6000

Frequency (Hz) Frequency 4000

2000

0 EXP-FFMAX FFMAX

Fig. 9 The expected FFMAX by allometry and the found FFMAX.