When AI encounters natural history: Morphological OTUs reshape our understanding of Earth's life

Biodiversity can be quantified only after organisms are assigned to reproducible units, yet most individuals encountered in nature lack reliable species-level identifications. Molecular operational taxonomic units can organize unnamed diversity, whereas image-based approaches generally depend on predefined species levels. Here, we show that operational biodiversity units can be derived directly from phenotypes. We developed morphOTU, a framework combining self-supervised representation learning, operational metric supervision, and adaptive hierarchical clustering to organize specimen images in continuous phenotypic space. Across five benchmark datasets comprising flowers, wood anatomy, and beetle habitus, morphOTUs recovered coherent species-level structure and produced -diversity estimates close to those obtained from expert identifications. This structure remained informative when species were excluded from representation learning, when labeled data were sparse, and per-species sampling was limited. In a heterogeneous field-survey dataset of 4,717 insects representing 269 species across 12 orders, fine-tuning on only 28 common species produced diversity estimates close to expert labels (Shannon index, 3.75 versus 3.53). Visual explanations localized variation to biologically meaningful structures, including body outliers, surface sculpture, floral symmetry, and wood vessels. Morphological units therefore provide an operational layer for organizing and quantifying biodiversity before, alongside, or in the absence of formal species names.