Morphological modularity in the vertebral column of Felidae (Mammalia, Carnivora)
© The Author(s). 2017
Received: 3 February 2017
Accepted: 22 May 2017
Published: 9 June 2017
Previous studies have demonstrated that the clear morphological differences among vertebrae across the presacral column are accompanied by heterogeneous functional signals in vertebral shape. Further, several lines of evidence suggest that the mammalian axial skeleton is a highly modular structure. These include its composition of serial units, a trade-off between high shape variance and strong conservation of vertebral count, and direct association of regions with anterior expression sites of Hox genes. Here we investigate the modular organisation of the presacral vertebral column of modern cats (Felidae, Carnivora, Mammalia) with pairwise comparisons of vertebral shape covariation (i.e. integration) and evaluate our results against hypotheses of developmental and functional modularity. We used three-dimensional geometric morphometrics to quantify vertebral shape and then assessed integration between pairs of vertebrae with phylogenetic two-block partial least square analysis (PLS).
Six modules were identified in the pairwise analyses (vertebrae included are designated as ‘C’ for cervical, ‘T’ for thoracic, and ‘L’ for lumbar): an anterior module (C1 to T1); a transitional module situated between the last cervicals and first thoracics (C6 to T2); an anterior to middle thoracic set (T4 to T8); an anticlinal module (T10 and T11); a posterior set composed of the last two thoracics and lumbars (T12 to L7); and a module showing covariation between the cervicals and the posterior set (T12 to L7). These modules reflect shared developmental pathways, ossification timing, and observed ecological shape diversification in living species of felids.
We show here that patterns of shape integration reflect modular organisation of the vertebral column of felids. Whereas this pattern corresponds with hypotheses of developmental and functional regionalisation in the axial skeleton, it does not simply reflect major vertebral regions. This modularity may also have permitted vertebral partitions, specifically in the posterior vertebral column, to be more responsive to selection and achieve higher morphological disparity than other vertebral regions.
KeywordsIntegration Two-block PLS Axial skeleton Vertebra Carnivora Modules Geometric morphometrics
Numerous studies have demonstrated that organisms can be partitioned into sets of phenotypic traits or structures that show coordinated patterns of variation or evolution. These sets of traits, termed phenotypic modules, can be defined as units composed of multiple traits that display high levels of covariation with other traits within that unit, but relatively weak covariation with traits outside of the unit. The related concept of integration refers to the overall magnitude of covariation of phenotypic traits, and can refer to a single module, which would be expected to display relatively high within-module integration, or may span multiple modules or structures [1–3]. The integration of traits, and their organisation into discrete phenotypic modules, has been hypothesised to arise and/or evolve as a product of shared developmental origin or pathways, genetic pleiotropy, or common function [1, 2, 4, 5]. Strong integration within modules, and reduced integration between modules, is further hypothesised to promote coordination among functionally-related traits, while allowing independence and differential specialization of distinct modules [2, 6–9]. With its serial organisation and composition of vertebral units, distinguishable morphological differences among regions (cervical, thoracic, and lumbar), and direct association of those regions with expression sites of genes in the Hox family, the presacral axial skeleton would appear to encapsulate the concepts of regionalisation and modularity [4, 10–14].
Although regionalisation of the vertebral column can be observed in amniotes in general , the mammalian axial skeleton shows the greatest differentiation in regional vertebral shape [10, 15–20]. This increased divergence is accompanied by strict constraints in regional vertebral number, particularly in the cervical region with seven vertebrae present in almost all of the ~5000 mammalian species. Total presacral vertebral count is also highly conserved [21–23], although some variation does occur . This invariability with regards to vertebral count has been suggested to signal strong canalisation (i.e. limitation of variation between individuals due to the tendency of organisms to “follow predetermined developmental pathways in spite of environmental and genetic perturbations” , page 44, and also see ) and developmental stability in the axial skeleton, and is thought to have evolved early in mammalian history [22, 23]. Additionally, rather than being the target of selection themselves, highly fixed vertebral numbers in mammals may reflect developmental constraints related to the muscularisation of the diaphragm and the advantages of involving the lumbar region in abdomen expansion during inspiration and in sagittal bending during locomotion [6, 22].
In addition to the almost universally fixed count of seven vertebrae in the cervical region in mammals, species of the order Carnivora also show little variation in thoracolumbar count, generally between 19 and 20 vertebrae . Moreover, some families, such as Felidae (i.e. cats), display absolutely no variation in vertebral numbers between taxa: all felid species present 27 presacral vertebrae which are traditionally divided into the three main vertebral column regions (i.e. cervical, thoracic, and lumbar) by clear morphological differences [15, 27–30]. In accordance with the observed trade-off between vertebral count invariability and high morphological disparity, both linear and landmark-based analyses of vertebral shape have shown evident functional regionalisation in the axial skeleton of felids. These analyses revealed regions which differ in magnitude of phylogenetic and ecological signal (e.g. specialisation related to locomotor mode) and both ontogenetic and evolutionary allometric scaling [29, 31, 32]. Specifically, the highest covariation between vertebral shape and prey size choice or locomotory mode (i.e. the two main ecological categories that have been used to describe felid ecology in the literature [33–39]) were found in the posterior region of the vertebral column, composed of the vertebrae caudal to the posterior attachment of the diaphragm, from T10 to L7. Conversely, vertebrae in the cervical region displayed high phylogenetic signal and little significant ecological signal [29, 31].
These examples of conspicuous morphological and functional regionalisation are strong indicators of modularity in the vertebral column, and not surprisingly, modularity has indeed already been described, or at least suggested, at different levels within the mammalian axial skeleton (e.g. [4, 29, 40]). One example of a hypothesised vertebral module is composed of the mid-cervicals C3 to C5. These vertebrae, whose somites have migratory muscle precursor cells which are committed to diaphragm transformation, have been suggested to be involved in the muscularisation of the septum and consequent fixed cervical number across almost all mammals .
A larger hypothesised module stems from the relatively fixed count of total thoracolumbar vertebrae and has been suggested to arise from close association of these two regions, with any changes in regional vertebral number being counteracted by the inverse change in the opposite series, and thus no change to the total count (i.e. homeotic changes) [4, 11, 21, 23, 40, 41].
Our previous studies of vertebral shape evolution in felids have already suggested some hypotheses of modularity specific to this study system. The observation of regionalised patterns of allometric scaling in a linear morphometric study both supported the mid-cervical vertebral module and suggested the presence of three additional modules: an anterior cervicothoracic module, a lumbar module, and a functional ‘anticlinality module’ composed of the T10-T12 vertebrae . Additionally, we have previously demonstrated that presacral vertebral shape in felids is driven by the developmental origins of vertebral components, with two morphological modules found in adult vertebral shape: the ‘centrum’ and the ‘neural spine-related’ modules (referred to as the ‘developmental two-module model’ therein to reflect the different somitic origins of these modules; [27, 40, 42]). Interestingly, this model of modularity, although widespread through most the presacral column, was not supported in vertebrae that are positioned immediately at or adjacent to the borders of morphological vertebral column regions: specifically, C4, T1, T8, L6 and L7. This observation led to the suggestion of a disruption of developmental modularity – or a functional overprint – in order to maintain the larger modular organisation of the vertebral column .
We further conducted separate analyses of intervertebral integration for the two intravertebral developmental modules (centrum and neural spine). Specifically, the same pairwise phylogenetic PLS analyses were conducted across the presacral vertebral column, but traits were limited to those from either the neural spine or the centrum [27, 40, 42]. Following from our previous results showing the widespread developmental two-module model of intravertebral covariation, this latter analysis allows us to assess if the patter of intervertebral covariation across the vertebral column is the same for the whole vertebral morphology and for when only trait units regarding each of these modules are considered (Additional file 1: Tables S1 and S2 for landmarks’ identity, following ).
Vertebral shape covariation
After Benjamini-Hochberg correction (multiple comparisons correction, see Material and Methods; Table 1 and Additional file 1: Table S4), the number of covariation tests that were not significant increased to 113, leaving 58 significant results. Those tests that were rendered not significant after this correction were concentrated between the first cervicals (C1 – C4) and C7 and T1, C1 and the end-thoracics and lumbars, and some of the covariation results between the pre-diaphragmatic thoracics (i.e. thoracic vertebrae between T1 and T8). Thus, the overall pattern of intervertebral modularity was similar after correction for multiple comparisons.
Covariation across centrum versus neural spine modules throughout the vertebral column
Correction of this analysis’ significance level with the Benjamini-Hochberg procedure reduced and rendered non-significant most pairwise comparisons between C1 – C6 and T12 – L4, but had little effect on most other modules (Table 2 and Additional file 1: Table S5).
The results presented here provide new information on the structural organisation of the vertebral column in felids, and potentially mammals in general. In light of the results presented here, the ‘traditional regions’ hypothesis (i.e. ‘the cervical, thoracic and lumbar regions in the felid vertebral column form discrete morphological modules’) and the ‘cervicothoracic and lumbar modules’ hypothesis (i.e. ‘two modules composed of multiple vertebrae that share common allometric patterns: an anterior cervicothoracic and a lumbar module’) could be rejected or considered insufficiently explanatory. Although high covariation was found between vertebrae within each of these regions, those either did not include all or most vertebrae which compose the regions or, more commonly, sets of highly covarying vertebral shapes were inclusive of vertebrae beyond the traditional boundaries. Specifically, in all of the analyses performed, with the exception of the phylogenetic PLS of the neural-spine landmarks, covariation in the anterior portion of the axial skeleton included high pairwise covariation between cervicals and the first thoracics. Additionally, all cervicals analysed here, with the exception of C4 displayed high covariation with the last thoracics and lumbar vertebrae.
A distinct module composed of vertebrae in the cervicothoracic boundary (i.e. C6 – T2) was found. A developmental covariation had already been suggested for these units based on the migration of cells from somites bound to the forelimbs, which may additionally have been involved in the first evolutionary steps that contributed to the muscularisation of the diaphragm . Due to the lack of vertebrae C3 and C5 in our dataset, it was not possible to test for higher covariation between those and C4, composing the suggested C3 – C5 developmental module in mammals . Nevertheless, C4 presented very high covariation with both C2 and C6, indicating that a C3 – C5 set would likely not be distinguishable as a separate morphological module in the analyses presented here.
High covariation was found between the two last thoracics, T12 and T13, and the lumbars. These two last thoracic vertebrae indeed have morphological characteristics that resemble lumbar shape more than they do the rest of the thoracics, such as a larger centrum, a cranially oriented neural spine and the presence of accessory processes [30, 31, 49]. This result thus supports the ‘thoracolumbar’ modularity hypothesis (i.e. ‘thoracic and lumbar vertebrae show high covariation’), although only with regards to these last thoracics. Additionally, when considering mammals in general, this T12 – L7 modularity could facilitate, or be driven by, the homeotic changes between the thoracic and lumbar regions which can promote vertebral column variation without changes in overall vertebral count [4, 6, 21, 23, 30].
We found strong support for the ‘anticlinality’ hypothesis , although this was only composed of vertebrae T10 and T11, and not T12. This group comprises a biomechanically important region of the axial skeleton for two main reasons. Firstly, T10 is the diaphragmatic vertebra, which marks the dorsocaudal attachment of this septum and is also the first of the thoracic vertebrae to present ribs which are vertebrochondral, commonly named ‘false’ or ‘floating’, instead of vertebrosternal ribs (i.e. vertebrochondral ribs attach to cartilages of another rib instead of directly to the sternum) . This release from the physical constraint of direct attachment allows for greater sagittal bending towards the posterior end of the vertebral column, particularly in the rib-less lumbar region [6, 11, 21, 32]. Secondly, T11 is the anticlinal vertebra, with a much reduced and usually perpendicular neural spine, marking the change in neural spine orientation from a caudally inclined process prior to this vertebra to the cranially orientated process present in vertebrae T12 through L7 [29–31, 49]. This change in neural spine orientation is especially well defined in carnivorans (specifically in Canidae and Felidae) and, along with the observed increase in centrum length, promotes greater motion and sagittal bending of the posterior region of the axial skeleton [29, 31, 49].
In the anterior vertebral column, support for the ‘developmental model disruption’ hypothesis is less clear, as the first two cervicals were not supported as a separate module. However, C4, which did not support the two-module developmental model in our analysis of intravertebral modularity , only displayed significant covariation with two of the other analysed vertebrae. Additionally, two well-supported modules were found either near or involving the suggested boundary between the last cervical and first thoracic: a module composed of C1 – T1, and another of C6 – T2. As discussed above, these vertebrae have been suggested to be highly constrained by development [6, 21, 22], and show significant phylogenetic signal, but no ecological signal, in shape across felids .
The analyses presented here revealed surprisingly strong covariation between the most anterior and most posterior presacral vertebrae (C1 – C7 and T12 – L7 in the phylogenetic analysis, Table 1). This result was unexpected as we had hypothesized higher covariation between more thoracic and lumbar vertebrae instead [4, 51]. However, the origin of this pattern may lie in vertebral ossification timing. A study of ossification sequences in the domestic cat skeleton  reported that thoracic elements developed prior to both the cervical and lumbar regions. In this case, this shared later ossification of cervical and lumbar vertebral elements could relate to the observed covariation of these two regions. Additionally, a more recent study of vertebral ossification in 17 species of mammals  (including one monotreme, six marsupials and ten placentals, but not including any felids) has shown that, although neural arches ossify first and begin ossification in the first cervicals and first thoracics, these are followed by ossification in the other cervicals and lumbar regions. Subsequently, centra ossify first in the thoracic region and ossification spreads both cranially and caudally . This progression of centra ossification in both directions could indeed cause a coincidence in ossification timing in cervicals and posterior T2 – L7. While this potential explanation for the pattern of covariation among these two regions is speculative, it could be tested with more detailed ossification sequence data from felids, vertebral modularity studies across mammals, and biomechanical analyses of the axial skeleton across felids and other mammals.
The results from the phylogenetic PLS on centrum or neural-spine-related coordinates also offer some support to this new hypothesis of integration between cervicals and T12-L7, tentatively due to ossification timing (Tables 2 and 3). There was a clear and strong association between the neural-spine landmarks of cervical vertebrae (with the exception of C4) and vertebrae in the T12 – L7 region. This covariation was slightly less consistent but still present in the analysis of the centrum-related landmarks, although in this case the atlas (C1) and C6 also displayed fewer covariations with posterior vertebrae in addition to C4. Additionally, those posterior vertebrae with significant covariation were generally the more anterior ones, from T8 – L4, with the exception of T11, reflecting the direction of centrum ossification. However, we would expect a stronger signal of this covariation in the centrum landmarks, rather than the neural spine landmarks, contrary to our results.
The separate centrum and neural-spine analyses also supported the other modules found in the PLS analyses of whole vertebrae. Results from centrum-only landmarks showed modularity of the vertebral column into an anterior cervicothoracic module from C1 – T2, with five pairwise exceptions between axis (C1) and T2, C4 and T1, C6 and C7, C6 and T1, and C6 and T2. This analysis also showed stronger interaction between the thoracics and lumbars, with a strong T6 – L6 module, and among vertebrae in the T12 – L7 module. Neural-spine traits further supported this T12 – L7 module, as well as the C1 – C7 module (with C1 and C4, C2 and C4, and C4 and C7 as exceptions), and the anticlinality T10 – T11 module.
Here we have performed an empirical analysis of intervertebral integration and compared our results to previously suggested hypotheses of developmental and functional modularity across the presacral vertebral column. Our results demonstrate that modularity is prevalent in the axial skeleton of felids, but that modules do not necessarily agree with the traditional regions of cervical, thoracic and lumbar vertebrae. Instead, vertebral morphological modules reflect five main groupings which organise the vertebral column according to either developmental constraints or function. Those regions have also been shown to differ considerably in their morphological disparity, phylogenetic signal, and ecological specialisation, and have been suggested to present opposing levels of evolvability. Additionally, the observed interaction between the cervicals and lumbars may reflect their shared ossification timing. Finally, the recovered modules supported the hypothesis that overall modularity of the vertebral column reflects the positions of the few vertebrae which show disruption of the intravertebral developmental two-module model. Specifically, the few vertebrae in which the developmental two-module model was not supported form the boundaries of the intervertebral modules found here.
Although this study is limited to a subset of representatives from a single family, the similarities in the modular organisation found here to developmental patterns shared across mammals suggest that these results may reflect a common mammalian condition. Importantly, the modular organisation of the vertebral column demonstrated here highlights that both development and function are important factors shaping vertebral shape diversification. Therefore, it may be the trade-off between these influences that control the disparity observed in the axial skeleton across mammalian families.
An Immersion Microscribe G2X (Solution Technologies, Inc., Oella, Maryland) was used to collect three-dimensional (3D) landmarks on 19 out of the 27 felid presacral vertebrae. These 19 vertebrae comprised the atlas (C1), axis (C2), C4, C6, C7, T1, T2, T4, T6, T8, T10, T11, T12, T13, L1, L2, L4, L6, and L7 (where C stands for cervical, T for thoracic, and L for lumbar). Reasons for vertebrae selection have been detailed extensively in previous studies [29, 31, 40]; in short, the chosen vertebrae cover the observed range in presacral vertebral morphology and include vertebrae which compose the boundaries between traditional vertebral morphological regions (e.g. C7 and T1 forming the boundary between the cervical and thoracic regions).
Analyses were carried out in R version 3.2.3 , using the ‘geomorph’ [54, 55] package. Prior to subsequent analyses, the landmark data for each vertebral type (i.e. vertebrae grouped by vertebral position, e.g. C1, C2, C4, T1, etc.) was separately aligned with a Generalised Procrustes Superimposition (GPA) in order to remove effects of scale, translation and rotation. The stability of the covariance matrices for each vertebrae was assessed by bootstrapping each dataset 10,000 times and comparing the covariance matrices of the original and resampled dataset with random skewers analysis [1, 56]. This analysis demonstrated that covariance matrix repeatability was high, ranging from 0.91 to 0.96 with a median of 0.94 and thus our sampling was sufficient for accurately estimating vertebral covariance matrices.
Vertebral shape covariation
The degree of morphological integration (i.e. shape covariation) per each possible pairwise combination between the vertebrae included here (e.g. C1 and C2, C1 and C4, C2 and C4 etc.) was measured using a two-block partial least square (PLS) analysis [57, 58]. This analysis was performed while accounting for phylogenetic relatedness, and was quantified by following these steps: first, landmark data for each vertebral type (e.g. T10) was separated into single species sets (e.g. Panthera leo only), which were individually aligned with a GPA. Species means per each vertebral type were then calculated from these Procrustes coordinates. Finally, pairwise mean vertebral shape covariation was estimated with a phylogenetic PLS, under a Brownian motion model of evolution . Phylogenetic relationships between the species studied here were calculated using a pruned version of the composite tree by Piras et al. . Statistical significance of each pairwise integration test was evaluated against a null distribution generated by repeating the phylogenetic PLS analysis after randomly permuting specimen rows for one vertebral dataset. Repeating this procedure with 5000 iterations generated the distribution against which the significance of the original results were compared.
There is some discussion of whether phylogeny should be corrected for when analysing patterns of integration, as removing this signal might conceal real genetic or developmental integration . However, the phylogenetic PLS methodology used  has been widely accepted (e.g. [62–64]), and the application of this correction here reveals the major patterns of vertebral column organisation even in a highly conservative scenario.
The estimated degree of integration (i.e. covariation between pairs of vertebrae) and the statistical significance of each test (p-value; significance cut-off used a p-value <0.05 threshold, but see below) were then compiled in matrices where sets of vertebrae showing significant shape covariation (i.e. modules) could be visualised.
Covariation across centrum versus neural spine modules throughout the vertebral column
A second phylogenetic PLS analysis was carried out using the Procrustes-aligned mean species coordinates for landmarks present in the centrum or neural spine modules only (see Additional file 1: Table S2 for landmarks identity). Landmark assignment to these modules was based on developmental origins of vertebral components in amniotes  and ossification centres in felids . Additionally, analysis of intravertebral morphological modularity across felids has shown that this model is supported in most presacral vertebrae .
Multiple comparisons and statistical significance
Because each individual vertebrae was involved in multiple comparisons, the significance test results (i.e. p-values) of each of the PLS analyses were corrected using a Benjamini-Hochberg procedure with a false discovery rate at 0.05, a relatively strict value . The Benjamini-Hochberg correction is a method for taking into account false positives (i.e. cases in which the raw p-value is below the chosen threshold, e.g. 0.05, purely due to chance) in multiple comparisons analyses. We chose to use this procedure instead of the more common Bonferroni correction due to the latter method’s tendency to find a sizeable number of false negatives in analyses that include a large number of comparisons (e.g. a Bonferroni-corrected significance test for an analysis containing 171 comparisons, such as the one presented here, at an initial significance threshold of p-value <0.05, would entail that only p-values <0.0003 were to be considered significant) . The way in which the Benjamini-Hochberg method classifies p-values according to their significance is by using a ranking system. First, all raw p-values are ordered from smallest to largest and ranked from i = 1 (lowest) to m = the total number of tests. These ranked raw p-values are then compared to their ‘Benjamini-Hochberg critical values, calculated as (i/m)Q, where Q is the chosen false discovery rate (0.05 here). The largest p-value which is still lower than their critical value plus all other lower raw p-values are classified as significant [65, 66]. This method also calculates Benjamini-Hochberg’-corrected p-values for easier visualisation, which are displayed here along with the raw p-values.
Allometry and vertebral integration
Allometry in vertebral shape was not corrected for prior to the analyses of intervertebral integration. Allometric shape changes (i.e. those directly driven by changes in body size) have been suggested to be a strong driver contributing towards morphological integration, particularly when analyses are performed between partitions within a single structure, because allometric effects may integrate a single structure uniformly [1, 9, 67]. However, our previous work on vertebral shape in the species studied here  has demonstrated that allometry varies across the presacral vertebral column, but only explain around 11% of vertebral shape differences across felids (mean 11.1%, median 9.9%). Further, body mass evolution in felids has been shown to be highly dependent on phylogenetic relationships [34, 36, 39], therefore correcting for size after having applied the phylogenetic correction performed here could potentially overcorrect and introduce error into our analyses. Finally, keeping in mind that the aim of this study was to investigate patterns of integration across the vertebral column, correcting for a factor that may be one of the constituents of such integration would potentially obscure real biological patterns of covariation between the vertebrae studied here.
We thank two anonymous reviewers for their helpful comments in improving this manuscript. For access to specimens at their respective museum collections, we thank R. Portela Miguez and R. Sabin at the Natural History Museum, London; M. Lowe and R. Asher at the University Museum of Zoology, Cambridge; C. Lefèvre at the Muséum National d’Histoire Naturelle, Paris; J. Chupasko at the Harvard Museum of Natural History, Cambridge; E. Westwig at the American Museum of Natural History, New York; W. Stanley at the Field Museum of Natural History, Chicago; and D. Lunde at the Smithsonian National Museum of Natural History, Washington D.C. We thank Dr. John R. Hutchinson, Dr. Andrew R. Cuff and Dr. Stephanie E. Pierce, also members of the ‘Team Cat’, and Dr. Ryan N. Felice and Dr. Thomas J.D. Halliday for their constant support and relevant discussions regarding earlier versions of this manuscript. We also thank the members of the ADaPTIVE and UCL-Imperial-NHM Journal Club for their feedback and comments.
This work was supported by Leverhulme Trust grant RPG 2013-124 to AG. This research received support from the SYNTHESYS project http://www.synthesys.info/ which is financed by European Community Research Infrastructure Action under the FP7 “Capacities” Program. The SYNTHESYS grant was awarded to MR.
Availability of data and materials
The datasets generated during and/or analysed during the current study are not publicly available due to still being used for further analyses by the authors but are available from the corresponding author on reasonable request.
MR conceived of the study, designed the analyses, conducted data collection and analyses, and drafted the manuscript; AG conceived of the study, designed the analyses, and drafted the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Goswami A, Polly PD. Methods of studying morphological integration and modularity. In: Alroy J, Hunt G, editors. Quantitative methods in paleobiology, vol. 16. Cambridge: Paleontological Society Special Publications; 2010. p. 213–43.Google Scholar
- Olson EC, Miller RL. Morphological integration. Chicago: University of Chicago Press; 1958.Google Scholar
- Terentjev PV. Biometrische Untersuchungen über die Morpho-Logischen Merkmale von Rana ridibunda Pall: (Amphibia, Salientia). Biometrika. 1931;23(1/2):23–51.View ArticleGoogle Scholar
- Buchholtz EA. Modular evolution of the cetacean vertebral column. Evol Dev. 2007;9(3):278–89.View ArticlePubMedGoogle Scholar
- Bolker JA. Modularity in development and why it matters to evo-devo. Am Zool. 2000;40(5):770–6.Google Scholar
- Buchholtz EA. Crossing the frontier: a hypothesis for the origins of meristic constraint in mammalian axial patterning. Zoology. 2014;117(1):64–9.View ArticlePubMedGoogle Scholar
- Clune J, Mouret JB, Lipson H. The evolutionary origins of modularity. Proc Biol Sci. 2013;280(1755):20122863.View ArticlePubMedPubMed CentralGoogle Scholar
- Cheverud JM. Developmental integration and the evolution of pleiotropy. Am Zool. 1996;36:44–50.View ArticleGoogle Scholar
- Klingenberg CP. Morphological integration and developmental modularity. Annu Rev Ecol Evol Syst. 2008;39(1):115–32.View ArticleGoogle Scholar
- Head JJ, Polly PD. Evolution of the snake body form reveals homoplasy in amniote Hox gene function. Nature. 2015;520(7545):86–9.View ArticlePubMedGoogle Scholar
- Polly PD, Head JJ, Cohn MJ. Testing modularity and dissociation: the evolution of regional proportions in snakes. In: Zelditch ML, editor. Beyond heterochrony: the evolution of development. 1st ed. United States of America: Wiley-Blackwell; 2001. p. 392.Google Scholar
- Guinard G, Marchand D. Modularity and complete natural homeoses in cervical vertebrae of extant and extinct penguins (Aves: Sphenisciformes). Evol Biol. 2010;37(4):210–26.View ArticleGoogle Scholar
- Burke AC, Nelson CE, Morgan BA, Tabin C. Hox genes and the evolution of vertebrate axial morphology. Development. 1995;121:4333–46.Google Scholar
- Wellik DM. Hox patterning of the vertebrate axial skeleton. Dev Dyn. 2007;236(9):2454–63.View ArticlePubMedGoogle Scholar
- Boszczyk BM, Boszczyk AA, Putz R. Comparative and functional anatomy of the mammalian lumbar spine. Anat Rec. 2001;264:157–68.View ArticlePubMedGoogle Scholar
- Buchholtz EA. Vertebral osteology and swimming style in living and fossil whales (Order: Cetacea). J Zool. 2001;253:175–90.View ArticleGoogle Scholar
- Buchholtz EA, Stepien CC. Anatomical transformation in mammals: developmental origin of aberrant cervical anatomy in tree sloths. Evol Dev. 2009;11(1):69–79.View ArticlePubMedGoogle Scholar
- Jones KE, German RZ. Ontogenetic allometry in the thoracolumbar spine of mammal species with differing gait use. Evol Dev. 2014;16(2):110–20.View ArticlePubMedGoogle Scholar
- Pierce SE, Clack JA, Hutchinson JR. Comparative axial morphology in pinnipeds and its correlation with aquatic locomotory behaviour. J Anat. 2011;219(4):502–14.View ArticlePubMedPubMed CentralGoogle Scholar
- Shapiro LJ. Morphological and functional differentiation in the lumbar spine of lorisids and galagids. Am J Primatol. 2007;69(1):86–102.View ArticlePubMedGoogle Scholar
- Narita Y, Kuratani S. Evolution of the vertebral formulae in mammals: a perspective on developmental constraints. J Exp Zool B Mol Dev Evol. 2005;304(2):91–106.View ArticlePubMedGoogle Scholar
- Buchholtz EA, Bailin HG, Laves SA, Yang JT, Chan MY, Drozd LE. Fixed cervical count and the origin of the mammalian diaphragm. Evol Dev. 2012;14(5):399–411.View ArticlePubMedGoogle Scholar
- Müller J, Scheyer TM, Head JJ, Barrett PM, Werneburg I, Ericson PG, et al. Homeotic effects, somitogenesis and the evolution of vertebral numbers in recent and fossil amniotes. Proc Natl Acad Sci USA. 2010;107(5):2118–23.Google Scholar
- Asher RJ, Lin KH, Kardjilov N, Hautier L. Variability and constraint in the mammalian vertebral column. J Evol Biol. 2011;24(5):1080–90.View ArticlePubMedGoogle Scholar
- Lazić MM, Carretero MA, Crnobrnja-Isailović J, Kaliontzopoulou A. Effects of environmental disturbance on phenotypic variation: an integrated assessment of canalization, developmental stability, modularity, and allometry in lizard head shape. Am Nat. 2015;185(1):44–58.View ArticlePubMedGoogle Scholar
- Waddington CH. Canalization of development and the inheritance of acquired characters. Nature. 1942;150:563–5.View ArticleGoogle Scholar
- Boyd JS. Studies on the appearance of the centres of ossification of the axial skeleton in the feline fœtus. Anat Histol Embryol. 1976;5(3):193–292.View ArticleGoogle Scholar
- Galis F, Carrier DR, van Alphen J, van der Mije SD, Van Dooren TJ, Metz JA, et al. Fast running restricts evolutionary change of the vertebral column in mammals. Proc Natl Acad Sci USA. 2014;111(31):11401–6.Google Scholar
- Randau M, Goswami A, Hutchinson JR, Cuff AR, Pierce SE. Cryptic complexity in felid vertebral evolution: shape differentiation and allometry of the axial skeleton. Zool J Linnean Soc. 2016;178(1):183–202.View ArticleGoogle Scholar
- De Iuliis G, Pulerà D. The cat. In: The dissection of vertebrates: a laboratory manual. 1st ed. Burlington: Academic Press; 2006. p. 131–226.Google Scholar
- Randau M, Cuff AR, Hutchinson JR, Pierce SE, Goswami A. Regional differentiation of felid vertebral column evolution: a study of 3D shape trajectories. Organ Divers Evol. 2016;17(1):305–319.View ArticleGoogle Scholar
- Jones KE. Evolutionary allometry of the thoracolumbar centra in felids and bovids. J Morphol. 2015;276(7):818–31.View ArticlePubMedGoogle Scholar
- Ewer RF: The carnivores. Chicago: Cornell University Press; 1973.Google Scholar
- Cuff AR, Randau M, Head JJ, Hutchinson JR, Pierce SE, Goswami A. Big cat, small cat: reconstructing body size evolution in living and extinct Felidae. J Evol Biol. 2015;28(8):1516–25.View ArticlePubMedGoogle Scholar
- Leyhausen P. Cat behavior: the predatory and social behavior of domestic and wild cats. Garland: STMP Press; 1979.Google Scholar
- MacDonald D, Macdonald DW, Loveridge AJ: The biology and conservation of wild felids. Chicago: Oxford University Press; 2010.Google Scholar
- Meachen-Samuels JA, Van Valkenburgh B. Craniodental indicators of prey size preference in the Felidae. Biol J Linn Soc. 2009;96(4):784–99.View ArticleGoogle Scholar
- Meachen-Samuels JA, Van Valkenburgh B. Forelimb indicators of prey-size preference in the Felidae. J Morphol. 2009;270(6):729–44.View ArticlePubMedGoogle Scholar
- Sunquist M, Sunquist F. Wild cats of the world. Chicago: University of Chicago Press; 2002.Google Scholar
- Randau M, Goswami A. Unravelling intravertebral integration, modularity and disparity in Felidae (Mammalia). Evol Dev. 2017;19:85–95.View ArticlePubMedGoogle Scholar
- Raff RA. The shape of life. Chicago: The University of Chicago Press; 1996.Google Scholar
- Christ B, Huang R, Scaal M. Amniote somite derivatives. Dev Dyn. 2007;236(9):2382–96.View ArticlePubMedGoogle Scholar
- Hautier L, Weisbecker V, Sanchez-Villagra MR, Goswami A, Asher RJ. Skeletal development in sloths and the evolution of mammalian vertebral patterning. Proc Natl Acad Sci U S A. 2010;107(44):18903–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Pierce SE, Ahlberg PE, Hutchinson JR, Molnar JL, Sanchez S, Tafforeau P, et al. Vertebral architecture in the earliest stem tetrapods. Nature. 2013;494(7436):226–9.Google Scholar
- Böhmer C, Rauhut OW, Wörheide G. Correlation between Hox code and vertebral morphology in archosaurs. Proc R Soc B. 2015;282:1810.View ArticleGoogle Scholar
- Molnar JL, Pierce SE, Bhullar B-AS, Turner AH, Hutchinson JR. Morphological and functional changes in the vertebal column with increasing aquactic adaptation in crocodylomorphs. R Soc Open Sci. 2015;2:1–22.View ArticleGoogle Scholar
- Werneburg I, Wilson LA, Parr WC, Joyce WG. Evolution of neck vertebral shape and neck retraction at the transition to modern turtles: an integrated geometric morphometric approach. Syst Biol. 2015;64(2):187–204.View ArticlePubMedGoogle Scholar
- Goswami A, Smaers JB, Soligo C, Polly PD. The macroevolutionary consequences of phenotypic integration: from development to deep time. Philos Trans R Soc B. 2014;369(1649):1–15.View ArticleGoogle Scholar
- Slijper EJ. Comparative biologic-anatomical investigations on the vertebral column and spinal musculature of mammals. Amsterdam: North-Holland Pub; 1946.Google Scholar
- Schluter D. Adaptive radiation along genetic lines of least resistance. Evolution. 1996;50(5):1766–74.View ArticlePubMedGoogle Scholar
- Jenkins FA Jr. The postcranial skeleton of African cynodonts: problems in the early evolution of the mammalian postcranial skeleton, vol. 36. New Haven: Yale University; 1971.Google Scholar
- Carbone C, Mace GM, Roberts SC, Macdonald DW. Energetic constaints on the diet of terrestrial carnivores. Nature. 1999;402:286–8.View ArticlePubMedGoogle Scholar
- R Core Team. R: A language and environment for statistical computing. 3.2.3 ed. Vienna: R Foundation for Statistical Computing; 2015.Google Scholar
- Adams DC, Collyer ML, Sherrat E. geomorph: software for geometric morphometric analyses. R package version 2.1.x. 2.1 ed; 2015.Google Scholar
- Adams DC, Otarola-Castillo E. geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods Ecol Evol. 2013;4:393–9.View ArticleGoogle Scholar
- Melo D, Garcia G, Hubbe A, Assis AP, Marroig G. EvolQG - An R package for evolutionary quantitative genetics. F1000Res. 2016;4(925):1–25.Google Scholar
- Bookstein FL, Gunz P, Mitterœcker P, Prossinger H, Schæfer K, Seidler H. Cranial integration in Homo: singular warps analysis of the midsagittal plane in ontogeny and evolution. J Hum Evol. 2003;44(2):167–87.View ArticlePubMedGoogle Scholar
- Rohlf FJ, Corti M. Use of two-block partial least-squares to study covariation in shape. Syst Biol. 2000;49(4):740–53.View ArticlePubMedGoogle Scholar
- Adams DC, Felice RN. Assessing trait covariation and morphological integration on phylogenies using evolutionary covariance matrices. PLoS One. 2014;9(4):e94335.View ArticlePubMedPubMed CentralGoogle Scholar
- Piras P, Maiorino L, Teresi L, Meloro C, Lucci F, Kotsakis T, et al. Bite of the cats: relationships between functional integration and mechanical performance as revealed by mandible geometry. Syst Biol. 2013;62(6):878–900.Google Scholar
- Polly PD, Lawing AM, Fabre AC, Goswami A. Phylogenetic principal components analysis and geometric morphometrics. Hystrix. 2013;24(1):33–41.Google Scholar
- Kane EA, Higham TE. Complex systems are more than the sum of their parts: using integration to understand performance, biomechanics, and diversity. Integr Comp Biol. 2015;55(1):146–65.View ArticlePubMedGoogle Scholar
- Hu Y, Ghigliotti L, Vacchi M, Pisano E, Detrich HW 3rd, Albertson RC. Evolution in an extreme environment: developmental biases and phenotypic integration in the adaptive radiation of antarctic notothenioids. BMC Evol Biol. 2016;16(1):142.View ArticlePubMedPubMed CentralGoogle Scholar
- Klaczko J, Sherrat E, Setz EZF. Are diet preferences associated to skulls shape diversification in xenodontine snakes? PLoS One. 2016;11(2):e0148375.View ArticlePubMedPubMed CentralGoogle Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.Google Scholar
- McDonald JH. Handbook of biological statistics. 3rd ed. Baltimore: Sparky House Publishing; 2014.Google Scholar
- Klingenberg CP, Marugán-Lobón J. Evolutionary covariation in geometric morphometric data: analyzing integration, modularity, and allometry in a phylogenetic context. Syst Biol. 2013;62(4):591–610.View ArticlePubMedGoogle Scholar