Charles C. Davis, Lucas C. Marinho, and André M. Amorim. 11/2020. “
Andersoniodoxa, a replacement name for Andersoniella (Malpighiaceae).” Phytotaxa, 470, 1, Pp. 121-122.
PDF Charles C. Davis, Julien Champ, Daniel S. Park, Ian Breckheimer, Goia M. Lyra, Junxi Xie, Alexis Joly, Dharmesh Tarapore, Aaron M. Ellison, and Pierre Bonnet. 7/31/2020. “
A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.” Frontiers in Plant Science, 11, 1129.
PDF Haiwen Li, Bing Liu, Charles C. Davis, and Yong Yang. 6/30/2020. “
Plastome phylogenomics, systematics, and divergence time estimation of the Beilschmiedia group (Lauraceae).” Molecular Phylogenetics and Evolution, 151, 106901.
PDF Camille M. M. DeSisto, Daniel S. Park, Charles C. Davis, Veronarindra Ramananjato, Jadelys L. Tonos, and Onja H. Razafindratsima. 6/18/2020. “
An invasive species spread by threatened diurnal lemurs impacts rainforest structure in Madagascar.” Biological Invasions, Pp. 1-14.
PDF Charles C. Davis, Lucas C. Marinho, and André M. Amorim. 6/2020. “
Andersoniella: A new genus of neotropical malpighiaceae.” Harvard Papers in Botany, 25, 1, Pp. 51-56.
PDF Katelin D Pearson, Gil Nelson, Myla FJ Aronson, Pierre Bonnet, Laura Brenskelle, Charles C Davis, Ellen G Denny, Elizabeth R Ellwood, Hervé Goëau, Mason J Herberling, Alexis Joly, Titouan Lorieul, Susan J Mazer, Emily K Meineke, Brian J Stucky, Patrick Sweeney, Alexander E White, and Pamela S Soltis. 5/13/2020. “
Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.” BioScience.
AbstractMachine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.
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