Evaluating Geographic Patterns of Morphological Diversity in Ferns and Lycophytes Using Deep Neural Networks

Biodiversity Information Science and Standards

article

Alexander E White¹  Michael G Trizna¹  Paul B Frandsen²  Laurence J Dorr³  Rebecca B Dikow¹  Eric Schuettpelz³ 

[1] Data Science Lab, Smithsonian Institution;Brigham Young University;National Museum of Natural History, Smithsonian Institution

关键词: biogeography;    deep learning;    convolutional neural networks;    morphospace;    latitudinal diversity gradient;   

DOI  :  10.3897/biss.3.37559

来源: Pensoft

【 摘 要 】

With digitized herbarium specimens and associated metadata accumulating rapidly in open access repositories, we are now able to exploit data-hungry computer vision techniques in order to evaluate fundamental questions in plant evolution. High among the list of unknowns is the role that ecological factors, such as morphological similarity, play in mediating biogeographic patterns of taxonomic and phylogenetic diversity. Here, we integrate deep convolutional neural networks (CNNs) into a biogeographic study of morphological, taxonomic, and phylogenetic diversity in ferns and lycophytes. We show how CNNs and digitized specimens can be used to extract quantitative estimates of morphospace occupation, and we use these techniques to evaluate diversity-disparity relationships within ferns across latitudes. We also discuss how CNNs can be used to overcome logistical obstacles arising from modern workflows involving millions of images.

【 授权许可】

Unknown