Format: ORAL

Katherine A. Wolcott 1, Edward L. Stanley 2 , Osman A. Gutierrez 3, Stefan Wuchty 1,4, Barbara A. Whitlock1

1 Department of Biology, University of Miami, Coral Gables, FL 33124, USA 2 Department of Natural History, Florida Museum of Natural History, Gainesville, FL, USA 3 Subtropical Horticultural Research Station, USDA-ARS, Miami, FL 33158, USA 4 Department of Computer Science, University of Miami, Miami, FL, 33146, USA

We are beginning to realize the potential for discovery using big data and biodiversity archives. As 3-dimensional (3D) imaging with micro-computed tomography (micro-CT) and photogrammetry becomes more common, workflows to streamline processes and develop novel studies using large-scale digital imaging are needed, especially for botanical specimens in a vertebrate-focused field. We present a case study on the 3D pollination biology of Theobroma cacao and its relatives (Malvaceae: Byttnerioideae) that highlights discoveries possible through combining cutting-edge imaging modalities and big data, which we call Morphology 2.0. Despite the importance of chocolate, little is known about its specialized pollination (likely by micro-dipterans and/or -hymenopterans) and even less about other Byttnerioideae. First, we used micro-CT, 3D geometric morphometrics (GMM), and scanning electron microscopy to precisely quantify plant-pollinator geometry, functional size limits for pollinators, and floral reward structures in cacao.Then, we extended our methods to Ayenia euphrasiifolia, an endemic of endangered Florida pine rocklands. To gain baseline data on putative pollinators, we used a GoPro camera trap (modified for subjects 1cm), 3D imaging with micro-CT and DSLR camera photogrammetry, then 3D GMM analysis. After refining methods for cacao and Ayenia, we applied them to other Byttnerioideae (Guazuma, Herrania, Byttneria, Commersonia). Processing and analysis pipelines are open source and available on GitHub, CT datasets on MorphoSource, and 3D models for outreach on Sketchfab. By combining lab and field-based imaging technologies, we developed a novel tool for studying pollination biology of an important crop. Our 3D imaging also enabled high resolution comparative study of one of the nine major Malvaceae subclades, characterized by unusual floral morphology not captured in 2D.Moreover, we produced a large annotated imaging dataset (N200) that will be repurposed to train computer vision models that automate 3D shape prediction and digitization of botanical specimens.