Deep Learning on field photography reveals the morphometric diversity of Colombian Freshwater Fish

Neotropical freshwater fish are among the most morphologically diverse vertebrates, but their study has long depended on preserved specimens, limiting our understanding of their natural body shapes due to preservation-induced distortions. Field photography provides a powerful, noninvasive alternative to capture fish morphology as it occurs in nature. However, automatically extracting accurate shape information from these images remains a major challenge, especially for highly diverse taxa. Here, we present an AI-based workflow that integrates Segment Anything, to automate fish segmentation and shape extraction from field photographs. We applied this workflow to CavFish-Colombia, a curated dataset of 1,749 images representing 393 Colombian freshwater fish species, obtained using the PhotaFish standardized imaging system. Achieving more than 97% segmentation accuracy, our workflow enables precise and consistent extraction of natural fish body shapes. We provide the first structured morphospace of Colombian freshwater fish based on natural body shapes, quantified through descriptors such as area, perimeter, and invariant moments. This morphospace reveals distinct gradients in body size and contour complexity, spanning from large, robust species with rounded forms to small, elongate species related to locomotion and habitat use. Our results demonstrate that AI-driven field photograph analysis can transform large-scale morphological studies, delivering accurate, rapid, and scalable data for biodiversity evaluations, functional trait analyses, and ecological research. This noninvasive morphological monitoring, directly from field images, opens new opportunities to assess fish morphology and analyze shape variation as it naturally occurs, capturing more accurate representations of living specimens.