High-density three-dimensional morphometric analyses support conserved static (intraspecific) modularity in caecilian (Amphibia: Gymnophiona) crania

Marshall, Ashleigh F; Bardua, Carla; Gower, David J.; Wilkinson, Mark; Sherratt, Emma; Goswami, Anjali

doi:10.1093/biolinnean/blz001

Cited by 37 publications

(72 citation statements)

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“…; Marshall et al. ). Second, if several alternative modular hypotheses have been proposed for the same structure (based on developmental, genetic, or functional grounds), it may be of interest to characterize the degree of modular signal under each of these hypotheses and determine which provides the best description of the observed patterns of morphological covariation (see Márquez ; Goswami and Finarelli ; Felice and Goswami ).…”

Section: Methodsmentioning

confidence: 98%

“…First, it may be of interest to determine whether the "strength" of modular signal is greater in one dataset as compared to another. To date, no explicit analytical approach has been proposed to statistically compare the strength of modular signal across datasets, though some studies have evaluated whether general patterns of modularity are conserved across taxa (e.g., Goswami 2006;Sanger et al 2012;Marshall et al 2019). Second, if several alternative modular hypotheses have been proposed for the same structure (based on developmental, genetic, or functional grounds), it may be of interest to characterize the degree of modular signal under each of these hypotheses and determine which provides the best description of the observed patterns of morphological covariation (see Márquez 2008;Goswami and Finarelli 2016;Felice and Goswami 2018).…”

Section: Covariance Ratio Effect Sizes (Z Cr )mentioning

confidence: 99%

“…Concomitant with these advances is an increasing number of empirical studies that characterize patterns of modularity in distinct phenotypic traits, and across a wide variety of taxa (for recent examples, see Parsons et al 2012;Parr et al 2016;Felice and Goswami 2018;Larouche et al 2018; Bardua et al 2019). Likewise, evolutionary biologists have striven to decipher whether patterns of modularity are similar among taxa and traits, and across levels of biological organization (Drake and Klingenberg 2010;Renaud et al 2012;Sanger et al 2012;Felice and Goswami 2018;Bardua et al 2019;Marshall et al 2019). Some studies have investigated whether patterns of modularity are conserved across taxa (e.g., Goswami 2006;Marshall et al 2019), however, direct quantitative or statistical comparisons of modularity patterns are generally lacking.…”

mentioning

confidence: 99%

“…Likewise, evolutionary biologists have striven to decipher whether patterns of modularity are similar among taxa and traits, and across levels of biological organization (Drake and Klingenberg 2010;Renaud et al 2012;Sanger et al 2012;Felice and Goswami 2018;Bardua et al 2019;Marshall et al 2019). Some studies have investigated whether patterns of modularity are conserved across taxa (e.g., Goswami 2006;Marshall et al 2019), however, direct quantitative or statistical comparisons of modularity patterns are generally lacking. We assert that a critical aspect of this endeavor should be to determine whether the "strength" of modularity is similar across datasets.…”

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confidence: 99%

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Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data

2019

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The study of modularity is paramount for understanding trends of phenotypic evolution, and for determining the extent to which covariation patterns are conserved across taxa and levels of biological organization. However, biologists currently lack quantitative methods for statistically comparing the strength of modular signal across datasets, and a robust approach for evaluating alternative modular hypotheses for the same dataset. As a solution to these challenges, we propose an effect size measure (ZCR) derived from the covariance ratio, and develop hypothesis‐testing procedures for their comparison. Computer simulations demonstrate that ZCR displays appropriate statistical properties and low levels of mis‐specification, implying that it correctly identifies modular signal, when present. By contrast, alternative methods based on likelihood (EMMLi) and goodness of fit (MINT) suffer from high false positive rates and high model mis‐specification rates. An empirical example in sigmodontine rodent mandibles is provided to illustrate the utility of ZCR for comparing modular hypotheses. Overall, we find that covariance ratio effect sizes are useful for comparing patterns of modular signal across datasets or for evaluating alternative modular hypotheses for the same dataset. Finally, the statistical philosophy for pairwise model comparisons using effect sizes should accommodate any future analytical developments for characterizing modular signal.

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Section: Methodsmentioning

confidence: 98%

Section: Covariance Ratio Effect Sizes (Z Cr )mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data

2019

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“…A semi‐automated procedure in the R package Morpho version 2.5.1 (Schlager 2017) was used to project surface semilandmarks from a template onto each present cranial region, as described (Schlager 2017) and previously implemented (Felice and Goswami 2018; Marshall et al. 2019; Bardua et al. 2019b).…”

Section: Methodsmentioning

confidence: 99%

Evolutionary integration of the frog cranium

et al. 2020

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Evolutionary integration (covariation) of traits has long fascinated biologists because of its potential to elucidate factors that have shaped morphological evolution. Studies of tetrapod crania have identified patterns of evolutionary integration that reflect functional or developmental interactions among traits, but no studies to date have sampled widely across the species‐rich lissamphibian order Anura (frogs). Frogs exhibit a vast range of cranial morphologies, life history strategies, and ecologies. Here, using high‐density morphometrics we capture cranial morphology for 172 anuran species, sampling every extant family. We quantify the pattern of evolutionary modularity in the frog skull and compare patterns in taxa with different life history modes. Evolutionary changes across the anuran cranium are highly modular, with a well‐integrated “suspensorium” involved in feeding. This pattern is strikingly similar to that identified for caecilian and salamander crania, suggesting replication of patterns of evolutionary integration across Lissamphibia. Surprisingly, possession of a feeding larval stage has no notable influence on cranial integration across frogs. However, late‐ossifying bones exhibit higher integration than early‐ossifying bones. Finally, anuran cranial modules show diverse morphological disparities, supporting the hypothesis that modular variation allows mosaic evolution of the cranium, but we find no consistent relationship between degree of within‐module integration and disparity.

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Correlated evolution of beak and braincase morphology is present only in select bird clades

Xu,

Natale

2024

Journal of Morphology

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Complex morphological structures, such as skulls or limbs, are often composed of multiple morphological components (e.g., bones, sets of bones) that may evolve in a covaried manner with one another. Previous research has reached differing conclusions on the number of semi‐independent units, or modules, that exist in the evolution of structures and on the strength of the covariation, or integration, between these hypothesized modules. We focus on the avian skull as an example of a complex morphological structure for which highly variable conclusions have been reached in the numerous studies analyzing support for a range of simple to complex modularity hypotheses. We hypothesized that past discrepancies may stem from both the differing densities of data used to analyze support for modularity hypotheses and the differing taxonomic levels of study. To test these hypotheses, we applied a comparative method to 3D geometric morphometric data collected from the skulls of a diverse order of birds (the Charadriiformes) to test support for 11 distinct hypotheses of modular skull evolution. Across all Charadriiformes, our analyses suggested that charadriiform skull evolution has been characterized by the semi‐independent, but still correlated, evolution of the beak from the rest of the skull. When we adjusted the density of our morphometric data, this result held, but the strength of the signal varied substantially. Additionally, when we analyzed subgroups within the order in isolation, we found support for distinct hypotheses between subgroups. Taken together, these results suggest that differences in the methodology of past work (i.e., statistical method and data density) as well as clade‐specific dynamics may be the reasons past studies have reached varying conclusions.

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High-density three-dimensional morphometric analyses support conserved static (intraspecific) modularity in caecilian (Amphibia: Gymnophiona) crania

Cited by 37 publications

References 80 publications

Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data

Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data

Evolutionary integration of the frog cranium

Correlated evolution of beak and braincase morphology is present only in select bird clades

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