MorphOTU: image-derived morphological operational units for open-set biodiversity assessment

Abstract The absence of a scalable system for organizing the vast majority of unidentified species is a central obstacle in biodiversity science. Molecular methods can generate OTUs without species names but require sequencing infrastructure and often remain difficult to link to observable morphology, whereas most computer-vision methods still rely on closed-set species labels. These limitations hamper biodiversity quantification under the open, incomplete conditions that characterize real ecosystems. Here, we introduce morphOTUs, a general image-based framework that constructs operational units of biodiversity directly from phenotypes. Using morphOTU, we derive image-based OTUs across five standardized benchmark datasets spanning flowers, wood anatomy, and beetle dorsal habitus. These units closely approximate reference species-level groupings, including closely related species, retain coherent structure when most species are “unseen’’ during training, and accurately approximate α-diversity metrics under sparse labeling or limited sampling. Furthermore, morphOTUs remain effective on a heterogeneous, long-tailed real-world insect survey dataset, demonstrating robustness beyond standardized imaging conditions. Visual explanations reveal that morphOTU consistently focuses on biologically meaningful traits and captures continuous phenotypic variation. By providing a scalable and open-set framework for quantifying phenotypic diversity, morphOTUs enable biodiversity assessment that includes unnamed species and unlock the ecological value of rapidly expanding digital image repositories.