The Effects of Foraging Ecology and Allometry on Avian Skull Shape Vary across Levels of Phylogeny

Natale, Rossy; Slater, Graham J.

doi:10.1086/720745

Cited by 19 publications

(35 citation statements)

References 98 publications

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“…Further, both ecological and life history traits affect rates of cranial shape evolution across a globally distributed and speciose sample of birds. These results add to the growing body of research suggesting that there is a complex interplay of intrinsic (Bright et al 2016;Navalón et al 2020;Marugán-Lobón et al 2021) and extrinsic factors (Pigot et al 2020;Natale and Slater 2022) contributing to avian skull shape evolution.…”

Section: Discussionsupporting

confidence: 58%

“…In the last five years, there have been significant efforts to robustly quantify this interaction of cranial and beak shape and various ecological and developmental factors, particularly feeding ecology (Bright et al 2016;Cooney et al 2017;Felice and Goswami 2018;Felice et al 2019;Navalón et al 2019;Pigot et al 2020, Natale andSlater 2022) which have demonstrated that this relationship is highly complex and differs across scales and across lineages. Diet has been found to strongly correlate with beak shape in waterfowl (Anseriformes; Olsen 2017), and corvids (Corvidae; Kulemeyer et al 2009), as well as brain shape in kingfishers (Alcedinidae; Eliason et al 2021) and skull shape in shorebirds and relatives (Charadriiformes; Natale and Slater 2022). Conversely, beak and braincase morphology are largely controlled by size in raptors (Bright et al 2016), and diet only predicts 2.4% of skull shape variation in parrots and cockatoos (Psittaciformes; Bright et al2019).…”

Section: Introductionmentioning

confidence: 99%

“…The Galápagos finches are a classic "textbook" example of avian adaptive radiations where beak morphology is considered an adaptation to diet (Grant and Grant 1989). In the last five years, there have been significant efforts to robustly quantify this interaction of cranial and beak shape and various ecological and developmental factors, particularly feeding ecology (Bright et al 2016;Cooney et al 2017;Felice and Goswami 2018;Felice et al 2019;Navalón et al 2019;Pigot et al 2020, Natale andSlater 2022) which have demonstrated that this relationship is highly complex and differs across scales and across lineages. Diet has been found to strongly correlate with beak shape in waterfowl (Anseriformes; Olsen 2017), and corvids (Corvidae; Kulemeyer et al 2009), as well as brain shape in kingfishers (Alcedinidae; Eliason et al 2021) and skull shape in shorebirds and relatives (Charadriiformes; Natale and Slater 2022).…”

Section: Introductionmentioning

confidence: 99%

“…2019; Pigot et al . 2020, Natale and Slater 2022) which have demonstrated that this relationship is highly complex and differs across scales and across lineages. Diet has been found to strongly correlate with beak shape in waterfowl (Anseriformes; Olsen 2017), and corvids (Corvidae; Kulemeyer et al .…”

Section: Introductionmentioning

confidence: 99%

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Ecological and life history drivers of avian skull evolution

Hunt

Felice

Tobias

et al. 2023

Preprint

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One of the most famous examples of adaptive radiation is that of the Galapagos finches, where skull morphology, particularly the beak, varies with feeding ecology. Yet increasingly studies are questioning the strength of this correlation between feeding ecology and morphology in relation to the entire neornithine radiation, suggesting that other factors also significantly affect skull evolution.Here, we broaden this debate to assess the influence of a range of ecological and life history factors, specifically habitat density, migration, and developmental mode, in shaping avian skull evolution. Using 3D geometric morphometric data to robustly quantify skull shape for 354 extant species spanning avian diversity, we fitted flexible phylogenetic regressions and estimated evolutionary rates for each of these factors across the full dataset. The results support a highly significant relationship between skull shape and both habitat density and migration, but not developmental mode. We further found heterogenous rates of evolution between different character states within habitat density, migration, and developmental mode, with rapid skull evolution in species which occupy dense habitats, are migratory, or are precocial. These patterns demonstrate that diverse factors impact the tempo and mode of avian phenotypic evolution, and that skull evolution in birds is not simply a reflection of feeding ecology.

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Section: Discussionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ecological and life history drivers of avian skull evolution

Hunt

Felice

Tobias

et al. 2023

Preprint

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show abstract

“…Tsuboi et al, 2018;Voje et al, 2014): localized scaling shifts could propel developmental modules into new regions of morphospace (Nijhout & McKenna, 2017), but among-clade frequencies have not been evaluated. Some data on sticklebacks suggest that allometric patterns fade as evolutionary constraints over 1-2 Myr (Voje et al, 2022), just as Hunt (2007) found for the influence of (co)variance patterns on speciation directions; Esquerré et al (2017) also found ontogenetic allometries to be "highly evolvable", but the timescale is less clear; see also Natale and Slater (2022) on shorebirds.…”

Section: Ontogenetic Allometry or Multiphase Life Cyclesmentioning

confidence: 95%

Evolvability and Macroevolution: Overview and Synthesis

Jablonski

2022

Evol Biol

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Evolvability is best addressed from a multi-level, macroevolutionary perspective through a comparative approach that tests for among-clade differences in phenotypic diversification in response to an opportunity, such as encountered after a mass extinction, entering a new adaptive zone, or entering a new geographic area. Analyzing the dynamics of clades under similar environmental conditions can (partially) factor out shared external drivers to recognize intrinsic differences in evolvability, aiming for a macroevolutionary analog of a common-garden experiment. Analyses will be most powerful when integrating neontological and paleontological data: determining differences among extant populations that can be hypothesized to generate large-scale, long-term contrasts in evolvability among clades; or observing large-scale differences among clade histories that can by hypothesized to reflect contrasts in genetics and development observed directly in extant populations. However, many comparative analyses can be informative on their own, as explored in this overview. Differences in clade-level evolvability can be visualized in diversity-disparity plots, which can quantify positive and negative departures of phenotypic productivity from stochastic expectations scaled to taxonomic diversification. Factors that evidently can promote evolvability include modularity—when selection aligns with modular structure or with morphological integration patterns; pronounced ontogenetic changes in morphology, as in allometry or multiphase life cycles; genome size; and a variety of evolutionary novelties, which can also be evaluated using macroevolutionary lags between the acquisition of a trait and phenotypic diversification, and dead-clade-walking patterns that may signal a loss of evolvability when extrinsic factors can be excluded. High speciation rates may indirectly foster phenotypic evolvability, and vice versa. Mechanisms are controversial, but clade evolvability may be higher in the Cambrian, and possibly early in the history of clades at other times; in the tropics; and, for marine organisms, in shallow-water disturbed habitats.

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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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The Effects of Foraging Ecology and Allometry on Avian Skull Shape Vary across Levels of Phylogeny

Cited by 19 publications

References 98 publications

Ecological and life history drivers of avian skull evolution

Ecological and life history drivers of avian skull evolution

Evolvability and Macroevolution: Overview and Synthesis

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

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