Changes in lifestyles and body weight affected mammal life-history evolution but little is known about how they shaped species’ sensory systems. Since auditory sensitivity impacts communication tasks and environmental acoustic awareness, it may have represented a deciding factor during mammal evolution, including apes. Here, we statistically measure the influence of phylogeny and allometry on the variation of five cochlear morphological features associated with hearing capacities across 22 living and 5 fossil catarrhine species. We find high phylogenetic signals for absolute and relative cochlear length only. Comparisons between fossil cochleae and reconstructed ape ancestral morphotypes show that Australopithecus absolute and relative cochlear lengths are explicable by phylogeny and concordant with the hypothetized ((Pan,Homo),Gorilla) and (Pan,Homo) most recent common ancestors. Conversely, deviations of the Paranthropus oval window area from these most recent common ancestors are not explicable by phylogeny and body weight alone, but suggest instead rapid evolutionary changes (directional selection) of its hearing organ. Premodern (Homo erectus) and modern human cochleae set apart from living non-human catarrhines and australopiths. They show cochlear relative lengths and oval window areas larger than expected for their body mass, two features corresponding to increased low-frequency sensitivity more recent than 2 million years ago. The uniqueness of the “hypertrophied” cochlea in the genus Homo (as opposed to the australopiths) and the significantly high phylogenetic signal of this organ among apes indicate its usefulness to identify homologies and monophyletic groups in the hominid fossil record.

Copyright: © 2015 Braga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

We first used an exploratory factor analysis to uncover the underlying relationships among the five cochlear variables and to identify the features that best explained the observed variability in our sample. We subsequently used these features and phylogenetic generalized linear models [ 10 , 18 , 19 ] to measure the strength of their phylogenetic signal and to determine whether some living species evinced cochlear shifts for their body mass after correcting for gene-based phylogeny. We also used Bayesian Markov chain Monte Carlo to reconstruct ancestral ape auditory conditions at each internal node of the hominoid phylogeny. We finally tested whether ape evolution shown in the fossil Oreopithecus, australopiths (Australopithecus and Paranthropus), Homo erectus and Neanderthals occurred in the direction predicted by our explicit allometric and phylogenetic analyses.

The external cochlear length (ECL, in mm), number of turns (TUR, expressed as the sum of full circle rotations and the angle between lines “AB”—center to apex—and “AC”—center to base), and relative length (RECL = ECL/TUR, in mm), the curvature gradient (CUR, expressed as a dimensionless ratio between the radii of the larger first—noted “R2”—and the smaller last spiral turns—noted “R1”), and the oval window area (OWA in mm 2 ).

Here we used microfocus x-ray computed tomography (micro-ct) to investigate five vestibular/cochlear features (for simplicity, called “cochlear” throughout this paper) associated with hearing capacities [ 8 , 13 , 27 , 29 , 32 ]: the external cochlear length (ECL), number of turns (TUR), and relative length (RECL = ECL/TUR), the curvature gradient (CUR), and the oval window area (OWA) located near the cochlear entrance where the opening of the vestibular system attaches to the stapedial footplate ( Fig 1 ). Our study sample was composed of all the five main apes’ living lineages (Homo, Pan, Gorilla, Pongo, Nomascus/ Hylobates). It also included fossil species (Oreopithecus, Australopithecus, Paranthropus, Homo erectus and Neanderthals) ( Fig 1 ; for detailed information see S1 Table ). We also investigate cochlear features in 13 living cercopithecoid species considered as outgroups. Our measurements were supplemented with literature data for non-catarrhine mammals and for three Middle Pleistocene humans from Spain often considered as early Neanderthals (OWA in fossils from Sima de los Huesos; S1 Table ).

It is also important to reliably estimate correlated evolution of characters while simultaneously estimating the strength of phylogenetic signal. For instance, the use of phylogenetically informed statistical procedures enhances the ability to detect species that deviate significantly from general allometric equations [ 26 ]. Predictions of ape’s hearing evolution made from cochlear morphological features have not been made to determine whether changes occurred primarily due to body mass increase through genetic/mechanistic interactions or were selected independent of body mass through ecological or functional processes yet to be identified. In the context of the evolution of hearing among apes, changes in body mass may entail changes in cochlear features that facilitated new behaviors. Indeed, the highest audible frequency for a mammal species is negatively correlated with body and ossicle mass, head size [ 27 ] and with the distance between the ears [ 28 ]. In ground-dwelling mammals, the frequency range of hearing is reflected by in the length of the cochear duct [ 5 ] which is in turn scaled with body mass [ 29 ]. However, the tonotopic organization of the cochlea [ 30 ] is complex and represents an intertwining of functional performances, developmental and genetic mechanisms [ 8 , 14 , 29 , 31 ] less prone to environmental pressures than the external/middle ears [ 31 ].

Ape lineages have accumulated changes after their separation [ 15 ]. However, in the absence of proper distinctions of similarities due to shared recent history from homoplasies, it remains challenging to locate ape fossils on their correct monophyletic group [ 20 , 22 ]. Due to the occurrence of homoplasic features in the apes’ fossil record, it remains challenging to define more precisely several genera, including our own genus Homo [ 23 , 24 ]. For instance, comparative studies among several groups of mammals, including apes (hominoids), suggested that the masticatory system might represent a “homoplasy ghetto” [ 23 – 25 ]. Since cladistic analyses often provide the most parcimonious trees, they do not help identifying homoplasic features. Monophyly represents one of the two necessary conditions (in association with adaptative strategies) to identify accurately a genus or any other level of the biological nomenclature [ 17 , 23 , 24 ]. A descriptive statistic that assesses the strength of the phylogenetic signal of sets of traits (already widely used in ecological and evolutionary research) can be complimentary to cladistic investigations of apes’ evolutionary scenarios and to predictions of ancestral states. It can also improve classifications of fossil and living ape species into true monophyletic groups.

In cases where morphology-based or gene-based analyses yield conflicting phylogenetic results due to homoplasy, phylogenetically-based statistical methods (as defined in [ 10 ]) offer possibilities to identify real homologies through measures of the phylogenetic signal contained in morphology. Moreover, since the nomenclature of living and fossil species requires the incorporation of information about both adaptive strategies and monophyly [ 17 ], an approach combining measurements of cochlear gross features with phylogenetically-based statistical methods [ 18 , 19 ] can help understanding key stages in hearing evolution, including in the most recent common ancestors (MRCAs) of apes, great apes (hominids) and hominins (who likely lived respectively in the periods between 23 to 16, 16 to 15 and 8 to 4 millions years ago—Myrs—as indicated through a combination of molecular and paleontological estimates [ 20 , 21 ]), and at the origin of our human genus.

The phylogenetic signal has not been used to investigate evolutionarily the as yet unknown cochlear differences across the five lineages of living apes (Homo, Pan, Gorilla, Pongo, Hylobates) and of their fossil relatives, with their large range of developmental and reproductive strategies. The length of the cochlea has never been measured in early hominin specimens (australopiths and early Homo), and was found to be shorter than in modern humans [ 11 ]. Moreover, it was suggested that cochlear length provided a good estimate of low-frequency hearing in non-human primates [ 12 , 13 ]. If cochlear length is taken as a proxy measure of a shorter basilar membrane length in early hominins (with its sensors tuned to high frequencies at its base and lower frequencies progressively towards the apex), it could be interpreted as consistent with a better low-frequency sensitivity in humans as compared to the australopiths, with similarities between species perhaps associated with responses to similar environmental conditions (homoplasy). Indeed, genes involving hearing show convergent signals due to adaptive selection among some echolocating mammals [ 14 ], parallel accelerated rates of evolution in gorilla and human lineages [ 15 ], and positive selection in humans but not in chimpanzees [ 16 ]. These findings demonstrate the occurrence of homoplasy during mammals’ and apes’ hearing evolution, an obstacle to accurate identifications of monophyletic groups among fossil species.

The presence of a phylogenetic signal implies that the topology and branch lengths of a given phylogenetic gene-based tree are proportional to the observed variance of evolution for a trait measured among a set of terminal species. Importantly, if the phylogenetic signal of a given trait is low or absent (i.e., phylogenetically-related species are not more similar than expected by chance), our ability to infer ancestral states of such an evolutionary malleable feature will be more limited. Therefore, measures of the phylogenetic signal represent a prerequisite for the study of evolutionary processes. This statistic allows comparisons of features in order to assess potential differences in patterns of evolutionary processes between them.

Body size constraints, lifestyles, and other environment-related parameters moduling survival and reproduction, are key players in the evolution of mammalian features [ 1 – 4 ]. Because sensory organs influence optimal decisions when interacting with environmental signals, it is important to reliably assess their dependence on selection, scaling rules and phylogeny during evolution [ 5 – 7 ]. Since predictions of hearing capabilities in mammal species have been proposed from observations on gross features of the cochlea [ 8 ]—the auditory organ which plays the most important role in determining the bandwidth of hearing [ 9 ]—attention needs to be given on the relative roles of selection, interspecies allometry and phylogenetic relationships in shaping its evolution. In particular, it is important to use descriptive statistics to measure the tendency for evolutionarily related species to resemble each other in cochlear morphology due to their recent shared ancestry (i.e., the phylogenetic signal, as defined in [ 10 ]).

Material and Methods

Ethics statement Our human and non-human samples were composed exclusively of dry skulls donated and curated in Museums from which we obtained permissions to access the specimens that have already been used in several published studies [11,33]. Therefore, no data reported here involved experimentation on subjects but only processing of micro-ct scans. The human skeletal sample is curated in the Institut d’Anatomie Normale et Pathologique of the University of Strasbourg. It was constituted mainly by Professors HWG Waldeyer (1836–1921) and G Schwalbe (1844–1916) before 1918 [34]. The non-human skeletal collections were elaborated in the early twentieth century from mostly wild-shot animals donated to the following museums: Muséum d’Histoire Naturelle de Toulouse (France), Musée Royal de l’Afrique Centrale (Tervuren, Belgium), Musée Zoologique de Strasbourg (France), Senckenberg Forschungsinstitut und Naturmuseum (Frankfurt, Germany). In all cases, the parties involved in the dissection of human cadavers or in the hunting of the animals held the proper permits.

Samples and data collection The inner ear is not notably influenced by postnatal growth and development [35], so that both juveniles and adults could be sampled and compared directly. We used micro-ct data obtained from dry skulls, or isolated petrous parts (pars petrosa) of the temporal bone, representing 86 juvenile and adult specimens of unknown sex and age. Sex diagnosis can be relatively straightforward in museum specimens representing adult dominant males of the two most dimorphic ape genera (Gorilla and Pongo). However, when museum specimens of unknown sex represent juveniles or non-dominant males, they cannot be sexed reliably. The specimens were distributed among the following 9 hominoid and 13 cercopithecoid contemporaneous species: Homo sapiens (n = 22), Pan paniscus (n = 7), Pan troglodytes (n = 9), Gorilla gorilla (n = 7), Pongo pygmaeus (n = 8), Nomascus concolor (n = 1), Hylobates moloch (n = 1), Hylobates lar (n = 1), Hylobates agilis (n = 2), Papio hamadryas (n = 2), Papio cynocephalus (n = 5), Papio ursinus (n = 1), Papio anubis (n = 2), Mandrillus sphinx (n = 1), Macaca radiata (n = 1), Macaca sylvanus (n = 2), Cercopithecus mona (n = 1), Cercopithecus hamlyni (n = 1), Cercocebus torquatus (n = 1), Colobus angolensis (n = 3), Colobus guereza (n = 4), Piliocolobus badius (n = 4) (for detailed information see S1 Table). New and published [11] morphometric data were also obtained from adult hominoid specimens sampling five fossil taxa: Oreopithecus bambolii (BAC 208), a Mediterranean species which survived in isolation until 7.0–6.5 Myrs; Australopithecus africanus (STS 5) / Australopithecus sp. (StW 329, StW 98 and StW 255) from the late Pliocene deposits of the Sterkfontein site (South Africa), Paranthropus robustus (TM 1517, SK 879, SKW 18) and Homo erectus (SK 847) from the early Pleistocene sites of Kromdraai B (TM 1517) and Swartkrans (SK 879, SKW 18 and SK 847) (South Africa), and Neanderthals (Kr 38.20 and Kr 39.23) from the late Pleistocene site of Krapina (Croatia). All but three specimens in our sample (Oreopithecus numbered BAC 208; and two Neanderthal specimens numbered Kr 38.20 and Kr 39.23) were obtained using five micro-ct systems (see details in S1 Table): the XtremeCT (Scanco Medical; http://www.scanco.ch) at the Institut de Médecine et de Physiologie Spatiales, Toulouse, France (http://www.medes.fr/); the Optiv CT160 (http://www.hexagonmetrology.fr) at Sematec Metrology, Oyonnax, France; the BIR ACTIS 225/300 from the Max Planck Institute for Evolutionary Anthropology in Leipzig (Germany); the X-Tek (Metris) XT H225L industrial CT system at the South African Nuclear Energy Corporation, Pelindaba (NECSA, www.necsa.co.za); and the Nikon Metrology XTH 225/320 LC dual source industrial CT system at the Palaeosciences Centre in the University of the Witwatersrand, Johannesburg (www.wits.ac.za/microct). The micro-ct data set for the Oreopithecus specimen was made available at: http://www.geo.unifi.it/ricerca/bambolii.htm) [36]. The micro-ct data set for the two Neanderthal specimens was made available at www.nespos.org. To the exception of the STS 5 skull (scanned at an isometric voxel size of 76.15 microns, μm), all the micro-ct data had isometric voxel dimensions ranging from 7.0 to 41.0 μm (S1 Table), hence allowing a good visualization of the cochlear structures. These relatively small voxel dimensions could be obtained due to the use of small skulls (mainly juvenile specimens) and isolated petrosals.

Measurement methodology All measurements were calculated using Matlab R 2012a (7.14, Mathworks). We first imported the μCT into the Avizo software package (www.vsg3d.com/avizo) for the 3D reconstruction of the air filled cochlea and oval window fossa. The ECL was measured by placing landmarks at small intervals along the outer circumference of the cochlea, between the cupula (apex) and the point marking the origin of the basal turn, where there is a saddle between the cochlear part and the vestibule, very close to the inferior margin of the round window (Fig 1). This method has already been used [13] making our measurements directly comparable. The ECL was used as a proxy for the length of the basilar membrane (as suggested in [13]) even if this measurement method likely overestimated the true length of the basilar membrane. The number of turns (TUR) and the curvature (CUR) were also expressed as continuous variables obtained (using Matlab) from the coordinates of the landmarks placed on the outer circumference of the cochlea (Fig 1). We first computed the center of the cochlear spiral (noted “A”, Fig 1) from the local chords defined by the landmarks placed at its two extremities. This method has already been used [37]. We then calculated the equations of the two circles best fitted to the landmarks placed respectively on the first and last spiral turns and centered on “A” (shown in red and green, Fig 1). The CUR values corresponded to the ratio between the radii of the larger and the smaller circle (noted respectively “R2” and “R1”, Fig 1). We finally defined two distinct lines joining the center “A” with the two extremities of the spiral. The TUR values corresponded to the sum of full circle rotations and the angle between the two lines (Fig 1). As for ECL values, the TUR and CUR parameters were rounded to the nearest tenth. The OWA was visualized in 3D after extracting an isosurface of its fossa (Fig 1). An oblique slice visually considered to best-fit the complete outline of the oval window was reconstructed. The OWA was then measured from its segmentation on this oblique slice. No data on ECL, RECL, TUR and CUR in non-human living hominoids have been published so far.

Body mass In order to investigate how interspecific differences in auditory structures may be caused by allometry, we compiled body mass data in living taxa [38–40] by averaging adult male and adult female values. Thus, we did not take into account interspecific differences in sexual dimorphism. Average weights may be overestimated in the particular case of genera in which sexual dimorphism is marked, like for instance Gorilla, Pongo and Papio. However, a twofold error in estimating body mass will cause a shift in log values of less that 10% of the entire range of data. Moreover, for most taxa, sufficient data on sexual dimorphism of the auditory structures are not yet available to permit a closer investigation of the interspecific allometric relationships. For fossil species we used estimates of body mass: 32 kg for Oreopithecus [41], 35.5 and 36 kg for respectively Australopithecus and Paranthropus [42], and 42 kg for early Homo erectus [42].

Inter-species differences We evaluated interspecies cochlear differences using a Monte Carlo permutation test (with the RStudio free software, Version 0.96.331) and the p-values for two samples t-tests applied to each pair of species (Table 1). For the randomization tests, we assumed that the cumulative distribution functions for the two samples were identical under the null hypothesis H0. The significance level was set at 5%. PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. Interspecies RECL and OWA differences using a Monte Carlo permutation test with p-values for two samples t-tests applied to each pair of hominoid species. https://doi.org/10.1371/journal.pone.0127780.t001

Catarrhine consensus phylogenetic tree We used 10kTrees for Primates, V2 (http://10ktrees.fas.harvard.edu/) [43] to download the 50% majority rule catarrhine consensus tree (i.e., with nodes present on 50% or more of all trees) with ‘molecular-calibrated’ branch lengths (phylogram) rather than a time-calibrated ultrametric tree (chronogram, i.e. with path lengths identical for all species). We made this choice because of evidence for changes in mutation rate in primate evolution on large timescales, including an approximately 30% branch length decrease in humans compared to baboons since their common ancestor [44].

Phylogenetic signal: Brownian model of evolution and Pagels’ λ The phylogenetic signal measures the statistical dependence among observations for species related by a phylogenetic tree. The basic principle is to test whether a given tree better fits a set of species data observed at its tips as compared with the fit obtained when the same data have been randomly permuted across the tips (i.e., when the topology of the tree is destroyed). Our test was implemented via a phylogenetic generalised least squares (PGLS) approach. In PGLS mode, the phylogenetic tree is converted into a variance-covariance matrix, with the diagonal elements reporting the path length for each species (the root-to-tips distances; the variance) and the off-diagonal elements reporting the time of shared evolution for each pair of species (the distances from the root to the most recent common ancestor of each pair of species; the covariance). The covariance between the values in two tips of the tree is defined as the product of the trait values for the two tips, each measured as deviations from the ancestral state at the root node of the phylogeny. When two tips share a greater proportion of common history, their expected phylogenetic covariance is relatively high. In order to compute the phylogenetic covariances, we used the most common model for the evolution of continuously valued traits: the Brownian model [10]. Under this model, the expected variance for the trait value at a given tip of the tree is directly proportional to the summed branch length from the root to that tip. Therefore, the expected covariance between two values at the tips of the tree is directly proportional to the shared history of the taxa represented by the two tips. We used the likelihood ratio test to determine whether a Brownian model fitted our data and to compare two models of evolution: (i) a model that correspond to a standard Brownian constant-variance random-walk model with one parameter (variance of evolution) and (ii) a directional random-walk model with two parameters (variance of evolution and a parameter that reflects the degree of directional change). The likelihood ratio test compares the log-likelihood of the null hypothesis model (no directional trend exists) to that of the alternative hypothesis model (a directional trend exists) (S2 Table). To measure the phylogenetic signal, we used the parameter lambda (λ) (Pagels’ λ) (S3 Table) which is multiplied to each off-diagonal elements in the variance-covariance matrix of shared evolutionary time between any pair of species in the gene-based phylogeny. The λ parameter reveals whether the phylogeny correctly predicts the patterns of covariance among species on a given trait, and its value can differ for different traits on the same phylogeny. The Pagels’ λ statistics varies between λ = 0 (the tree becomes more "star" like with all the branches emanating from a common node) and λ = 1 (the original tree is recovered). The λ parameter is typically estimated to obtain a value that maximizes the likelihood of the data. The statistical tests for a phylogenetic signal are successively performed under the null hypotheses that λ = 0, and that λ = 1. We reported tests for significant departure of λ from 0 and 1 (S3 Table). The procedure is as follows: (i) we estimate the maximum likelihood (ML) value of λ in our data, and get the log- likelihood of this model; (ii) we run a model with λ fixed at its maximum value of 1; (iii) we use a likelihood ratio test to decide whether a model with ML λ fits the data better than a model with λ = 1. This tells us whether the phylogenetic signal in the data is equal or less than expected under the Brownian model given the phylogeny; (iv) we repeat the procedure and compare the model with ML λ with one in which λ = 0. A likelihood ratio test of a model with ML λ versus a model with λ = 0 will tell us if the phylogenetic signal in the data is greater than 0. The likelihood ratio test is calculated as: 2 * (log-likelihood of best fitting model—log-likelihood of worst fitting model). The best fitting model has the highest likelihood. The likelihood ratio is the absolute (i.e. positive) value of the difference between Log-likelihoods of the two competing nested models. We assess the significance of this value against a χ2 distribution with degrees of freedom (df) equal the difference in the number of estimated parameters between competing models (the directional random-walk model has two parameters and the Brownian motion model has one parameter). If the result is significant, then the directional random-walk model describes the data significantly better than the Brownian model, and should therefore be preferred, for instance when estimating ancestral states. More details can be found in Blomberg et al. [10], Revell et al. [45] and http://www.anthrotree.info/wiki/projects/pica/The_AnthroTree_Website.html [46]. All the tests were made for each trait (cochlear parameters and body mass) separately on: (i) the entire sample of 22 catarrhines available in this study, (ii) the hominoid clade only (9 species), (iii) the cercopithecoid clade only (13 species). Because these calculations can be difficult when made using small numbers of species (i.e., less than 20), we consider our results based on all catarrhine species as more robust. We used the Unix executable program BayesTraits, Version 1 [47] (www.evolution.rdg.ac.uk/BayesTraits.html).

Non-phylogenetic and phylogenetic interspecific linear regressions We used both traditional (non-phylogenetic) and phylogenetic bivariate and multivariate linear regressions to investigate the relationship between log-transformed mean species values for cochlear parameters and body mass considered as the independent variable (for detailed information see S1 Text). We determined whether the variation in each cochlear parameter was conceived as being tied to, or best expressed as the variation in body mass. The non-phylogenetic regressions (S4 Table) and the Akaike information criterion (AIC) [48] were computed using the RStudio free software (Version 0.96.331R) while the phylogenetic regressions (S5 Table) were computed using Bayes Traits (Version 1). In the non-phylogenetic approach, we used two distinct methods to get meaningful inter-species allometric information out of our data: the p-values of linear regressions and the Akaike information criterion (AIC) (S1 Text). The AIC method is a measure of the relative quality of a statistical model, for a given set of data, and provides a mean to select the best model. We also applied least-squares (LS) and reduced major axis (RMA) line-fitting techniques to our species mean data. The advantage of RMA regression is that unlike the LS one, it does not assume that the independent variable (x-variable) is measured without error [49]. Then, as body mass is taken as the independent variable, the RMA residuals for cochlear parameters will be biased in the same direction. Both LS and RMA regression equations have been calculated for the entire sample of catarrhines. Moreover, to test for potential grade shifts within catarrhines, we examined the scaling of cochlear parameters within cercopithecoids and hominoids separately. The significance level was set at 5%. Residual analyses were used to discriminate between species that had cochlear values deviating from the catarrhine inter-species allometric plan. We evaluated graphically how well the non-phylogenetic linear bivariate models fitted the data and how the data met the assumptions of the linear model. To evaluate deviations from the linear model assumptions we examined various diagnostic plots (S1 Text, S6 Table). In the phylogenetic approach, we investigated whether accounting for phylogeny impacted on the estimates of the slope of cochlear traits on body weight, by using phylogenetic generalized least-squares (PGLS) bivariate and multiple regressions. The λ parameter was estimated while simultaneously calculating the correlation. We explored the effects of specific variables on the explanatory power of the models by statistically comparing models with versus without the variables in question using the log-likelihood ratio (LR) test (see above).