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Nasal turbinals, scrolled thin bones of the nasal cavity, increase surface area for conditioning inspired air or for olfaction in mammals. To assess function in Eptesicus fuscus (Big Brown Bat), we quantify surface area of respiratory and olfactory turbinals from birth to adult size, using data from microCT scans before and after iodine staining. Surface area of each turbinal is significantly correlated with postnatal age and cranial length. The surface area of the maxilloturbinal and first ethmoturbinal (ET I) grows faster, relative to skull size, than surface area of caudal ethmoturbinals or the frontoturbinal. Histological examination of selected specimens reveals ET I grows disproportionately more presumptive respiratory mucosa than olfactory mucosa, supporting the hypothesis that ET I has a dual function. Lastly, we find that distribution of olfactory mucosa in the caudal nasal cavity diminishes with age. Our findings suggest a reduction in olfactory function in E. fuscus, perhaps due to a diminished role in food acquisition by this aerial insectivore. 

Abstract Modern computational and imaging methods are revolutionizing the fields of comparative morphology, biomechanics, and ecomorphology. In particular, imaging tools such as X-ray micro computed tomography (µCT) and diffusible iodine-based contrast enhanced CT allow observing and measuring small and/or otherwise inaccessible anatomical structures, and creating highly accurate three-dimensional (3D) renditions that can be used in biomechanical modeling and tests of functional or evolutionary hypotheses. But, do the larger datasets generated through 3D digitization always confer greater power to uncover functional or evolutionary patterns, when compared with more traditional methodologies? And, if so, why? Here, we contrast the advantages and challenges of using data generated via (3D) CT methods versus more traditional (2D) approaches in the study of skull macroevolution and feeding functional morphology in bats. First, we test for the effect of dimensionality and landmark number on inferences of adaptive shifts during cranial evolution by contrasting results from 3D versus 2D geometric morphometric datasets of bat crania. We find sharp differences between results generated from the 3D versus some of the 2D datasets (xy, yz, ventral, and frontal), which appear to be primarily driven by the loss of critical dimensions of morphological variation rather than number of landmarks. Second, we examine differences in accuracy and precision among 2D and 3D predictive models of bite force by comparing three skull lever models that differ in the sources of skull and muscle anatomical data. We find that a 3D model that relies on skull µCT scans and muscle data partly derived from diceCT is slightly more accurate than models based on skull photographs or skull µCT and muscle data fully derived from dissections. However, the benefit of using the diceCT-informed model is modest given the effort it currently takes to virtually dissect muscles from CT scans. By contrasting traditional and modern tools, we illustrate when and why 3D datasets may be preferable over 2D data, and vice versa, and how different methodologies can complement each other in comparative analyses of morphological function and evolution.