Seminar: Lara Köster

DNA analyses have revolutionized species identification and taxonomic work. Yet, persistent challenges arise from little differentiation among and considerable variation within species, particularly among closely related groups. While images are commonly used as an alternative modality for automated identification tasks, their usability is limited by the same concerns. An integrative strategy, fusing molecular and image data through machine learning, holds significant promise for fine-grained species identification. However, a systematic overview and rigorous statistical testing concerning molecular and image preprocessing and fusion techniques, including practical advice for biologists, are missing so far. We introduce a machine learning scheme that integrates both molecular and image data for species identification. Our results can inform integration efforts for various organism groups, improving automated identification across a wide range of eukaryotic species.