Digitization PEN: Functional Quantitative Characters for Ecology and Evolution (FuncQEE)

An award is made to Chicago State University to join the oVert Thematic Collection Network (TCN) as a Partner to an Existing Network (PEN). Computed tomography (CT) scanning will be used to provide 3D data relating to structural diversity for ca. 1,700 specimens of rodents. Rodents are among the most common but also among the most diverse mammals on the planet. They are found in virtually every terrestrial ecosystem, with species ranging from guinea pigs, to prairie dogs, kangaroo rats, flying squirrels, capybaras, lemmings, house mice, and sewer rats. Across this diversity, their bodies vary considerably in size and shape as they adapted to distinct habitats (e.g., Arctic tundra, grasslands, temperate and tropical forests), locomotion (e.g. terrestrial, arboreal, swimming, burrowing habits), feeding preferences (e.g., insectivores, granivores, herbivores, or generalists), and more. Given these vast differences, this project will provide 3D digitized data making it possible to examine and quantify the characters found in this radiation of diversity throughout the rodent tree of life. Undergraduate students at an MSI (Chicago State University) will be trained in CT-scan image processing The project will generate computed tomography (CT) scans as a basis for 3D modeling of structural diversity for 1,700 specimens of rodents. Sampling will focus on less common species. The 3D data will be available to researchers and educators via MorphoSource. These data will enable studies on functional quantitative characters for ecology and evolution (FuncQEE) as well as studies and lessons ranging from physiology to biomedical applications. The project will bring together scientists and educators from Chicago State University, University of Michigan, University of New Mexico, University of Florida, and Sandia National Laboratory. Master's degree candidates and undergraduate students will receive training, including an effort to provide opportunities for underrepresented, first generation, and/or low-income students in science careers in cutting edge visualization and analytical approaches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.