The evolution of functional disparity in the avian skull

This Project has been filled

Birds represent one of the most spectacular adaptive radiations in earth’s history, with over 10,000 extant species spanning a range of ecologies and environments. Much of birds’ diversity is encapsulated by the beak, a structure that is strongly associated with dietary ecology and feeding behaviour. However, recent large-scale studies of beak and skull shape evolution have shown that this relationship is more complex than commonly perceived: factors like food type and feeding behaviour predict as little as 10% of the variation in beak shape[1, 2], and allometry, phylogeny, and developmental integration play significant roles in shaping the avian skull[3-5] (perhaps providing an explanation for the apparent canalisation of beak shape evolution along shared pathways[6]). This has led to suggestions of the beak acting as a generalised, multi-purpose tool, rather than a specialised feeding structure. But an alternative interpretation of the same result could be one of many-to-one mapping of structure to function, where structures under different developmental or phylogenetic constraints result in different shapes that produce similar performance outputs or dietary niches[7]. This phenomenon is particularly apparent in the Aequorlitornithes, or “core waterbirds”: many waterbirds (e.g. penguins, puffins, pelicans) have a diet primarily of fish, yet all obtain them with dramatically different beak morphologies and behaviours. What is critically lacking from studies so far, therefore, is explicit tests of the functional performance of different beak and skull geometries, and how this relates to the evolution of feeding in birds.

With an initial focus on the Aequolitornithes, this project will investigate several performance metrics present in avian skulls using new and existing three-dimensional models from photogrammetry, structured light, and CT scanning. These scans will be used to conduct four-bar motion and morphological analysis of the highly kinetic palatal architecture and hinges of extant birds[8] to investigate how variation in each component affects the mechanical sensitivity of the system, and influences the overall performance output[9]. Metrics such kinematic transmission and mechanical advantage will then be compared with existing data on beak shape and diet in a phylogenetic framework to test the extent and nature of many-to-one mapping of structure to function, and gain insight into which aspects of morphology are actually connected with mechanics and diet. Specifically, it can be hypothesised that these performance metrics will be a better predictor of diet and beak use than beak shape is, and convergence in performance will show up even when morphological convergence does not.

This data will provide a greater understanding of force transmission in kinetic biological structures more generally, as well as laying the groundwork for a greater understanding of the evolution of cranial kinesis in stem birds. Further analysis will be done on the structural strength of different beak and palatal architectures using second moment of area and finite element analysis[10]. Once again, this data will be tested for the presence of many-to-one mapping, and in addition will provide insight into the engineering principles governing these strong but exceptionally lightweight and thin-walled biological structures.

This project will ultimately assess the extent to which species biodiversity and morphological disparity are indicative of disparity in dietary performance more generally. In so doing, it will offer new insights into the way in which we think of biodiversity measures within ecosystems, and the macroevolutionary context in which this diversity is acquired. It will also produce and make available digital morphological models[11], and fundamental biomechanical data of interest to a range of functional morphologists, macroevolutionary biologists, ecologists, palaeontologists, and biomimetic engineers. The student will receive training in 3D scanning and reconstruction of digital morphologies, working in museum collections, advanced techniques in computational functional morphology, and the use of R for data analysis within a phylogenetic framework.

References
[1] Navalon et al. 2019 Evolution 73:422-435; [2] Felice et al. 2019 Proc R Soc B 286:20182677; [3] Bright et al. 2016 PNAS 113:5352-5357; [4] Bright et al. 2019 BMC Evol Biol 19:104; [5] Felice and Goswami 2017 PNAS 115:555-560; [6] Cooney et al. 2017 Nature 542:344-347; [7] Anderson et al. 2011 Nature 476:206-209; [8] Olsen and Westneat, 2016 J Morph 277:1570-1853; [9] Anderson and Patek, 2015 Proc R Soc B 282:20143088; [10] Bright, 2014 J Paleo 88:760-769; [11] Davies et al. 2017 Proc R Soc B 284:20170794.