Using Quantitative Shape Analysis to Investigate the Coevolution of Brain Shape and Flight Loss in Birds
Poster Number
9
Faculty Mentor Name
Chris Torres
Research or Creativity Area
Natural Sciences
Abstract
Birds exhibit widely diverse brain morphology, which can provide insights into their ecological adaptations and evolutionary history. They are the most diverse clade of terrestrial vertebrates with over 11,000 extant species recognized. Additionally, they occupy a wide variety of ecological roles and complex cognitive behaviors such as tool use, problem solving, vocal learning, and sociality. Investigations of avian brain shape change hold potential to elucidate the relationships of brain structure to ecology, and cognitive ability. Fossils provide physical records of organisms but usually only preserve hard anatomical structures like bones and teeth. Birds’ brain cavities closely match brain shape, a feature unique among reptiles that makes birds an ideal model system for investigating the coevolution of brain morphology and ecology. Thus, the internal anatomy of fossil bird skulls can be used to create endocasts that approximate brain size and external shape, providing insights into cognitive and sensory capabilities for long-extinct organisms. Rails are a particularly useful group of birds to study the evolution of brain morphology because they occupy diverse habitats and exhibit a wide range of behaviors, body sizes, and flight capabilities. There is also evidence that various rail species have independently lost flight multiple times relatively recently, providing a well-defined phylogenetic framework, with well-established species relationships and divergence timelines.
This research utilizes high-resolution medical imaging and qualitative and quantitative shape comparisons to assess coevolution of brain morphology and ecology in rails. First, we collect microCT scan data for bird skulls from museums or other databases. We then use this data to create 3D digital models of brain cavities as proxies for external brain morphology. We then reconstructed 3D digital models of the birds’ brain cavities (i.e., “endocasts”) using the segmentation program Dragonfly (Comet). The Principle of Proper Mass suggests that the relative size of brain regions are correlated with functional complexity and the external brain surface correlates with neuronal cell density. Essentially this tells us that external brain morphology is a reliable indicator of internal structure. We then use qualitative comparisons of those endocasts across our sample to preliminarily identify brain structures/regions exhibiting marked morphological variation.
Future work will involve quantitative comparisons of our models within a phylogenetic context. Within R, we will use geometric morphometrics to quantify the statistically significant variation in brain shapes across our samples by placing landmarks on reconstrued endocasts. Additionally we will use Principal component analysis to reduce the dimensionality of our data. Then we will use phylogenetically informed statistical methods to investigate coevolution between brain morphology and ecology, which will allow us to determine whether similarities in brain shape are due to shared ancestry or convergent evolution. Elucidating the drivers of evolutionary shifts in brain morphology holds potential to expand our understanding of broader patterns in avian brain evolution. Overall, this research lays the groundwork for inferring ecological shifts in extinct birds for which direct data on brain morphology but not ecology are available.
Location
University of the Pacific, DeRosa University Center
Start Date
26-4-2025 10:00 AM
End Date
26-4-2025 1:00 PM
Using Quantitative Shape Analysis to Investigate the Coevolution of Brain Shape and Flight Loss in Birds
University of the Pacific, DeRosa University Center
Birds exhibit widely diverse brain morphology, which can provide insights into their ecological adaptations and evolutionary history. They are the most diverse clade of terrestrial vertebrates with over 11,000 extant species recognized. Additionally, they occupy a wide variety of ecological roles and complex cognitive behaviors such as tool use, problem solving, vocal learning, and sociality. Investigations of avian brain shape change hold potential to elucidate the relationships of brain structure to ecology, and cognitive ability. Fossils provide physical records of organisms but usually only preserve hard anatomical structures like bones and teeth. Birds’ brain cavities closely match brain shape, a feature unique among reptiles that makes birds an ideal model system for investigating the coevolution of brain morphology and ecology. Thus, the internal anatomy of fossil bird skulls can be used to create endocasts that approximate brain size and external shape, providing insights into cognitive and sensory capabilities for long-extinct organisms. Rails are a particularly useful group of birds to study the evolution of brain morphology because they occupy diverse habitats and exhibit a wide range of behaviors, body sizes, and flight capabilities. There is also evidence that various rail species have independently lost flight multiple times relatively recently, providing a well-defined phylogenetic framework, with well-established species relationships and divergence timelines.
This research utilizes high-resolution medical imaging and qualitative and quantitative shape comparisons to assess coevolution of brain morphology and ecology in rails. First, we collect microCT scan data for bird skulls from museums or other databases. We then use this data to create 3D digital models of brain cavities as proxies for external brain morphology. We then reconstructed 3D digital models of the birds’ brain cavities (i.e., “endocasts”) using the segmentation program Dragonfly (Comet). The Principle of Proper Mass suggests that the relative size of brain regions are correlated with functional complexity and the external brain surface correlates with neuronal cell density. Essentially this tells us that external brain morphology is a reliable indicator of internal structure. We then use qualitative comparisons of those endocasts across our sample to preliminarily identify brain structures/regions exhibiting marked morphological variation.
Future work will involve quantitative comparisons of our models within a phylogenetic context. Within R, we will use geometric morphometrics to quantify the statistically significant variation in brain shapes across our samples by placing landmarks on reconstrued endocasts. Additionally we will use Principal component analysis to reduce the dimensionality of our data. Then we will use phylogenetically informed statistical methods to investigate coevolution between brain morphology and ecology, which will allow us to determine whether similarities in brain shape are due to shared ancestry or convergent evolution. Elucidating the drivers of evolutionary shifts in brain morphology holds potential to expand our understanding of broader patterns in avian brain evolution. Overall, this research lays the groundwork for inferring ecological shifts in extinct birds for which direct data on brain morphology but not ecology are available.
