MorphQ: label-free quantification and visualisation of complex morphology from standardised specimen images

O_LIQuantifying complex morphology from images remains difficult because predefined descriptors capture only selected traits. Yet, supervised machine learning models for images require labels and often produce task-specific features that are hard to interpret as biological traits. C_LIO_LIWe present MorphQ, a label-free, self-supervised method that learns a quantitative morphospace from standardised specimen images. Its encoder produces feature vectors for statistical analysis, and its decoder converts analysed positions in morphospace into human-interpretable images, including hypothetical forms not represented by sampled specimens or sampled taxa. C_LIO_LIUsing 1,868 Lepidoptera species, we tested whether MorphQs label-free features were more useful for downstream analysis than features from principal component analysis (PCA) or a supervised species-classification machine learning model. As a diagnostic probe of downstream biological utility, MorphQ features supported higher low-label family-classification accuracy than comparator features, and retained stronger family-level similarity for species absent from model training, indicating better generalisation to species not seen during model training. C_LIO_LITwo case studies link MorphQ morphospaces to species-level elevation and assemblage-level functional diversity while keeping statistical patterns visually inspectable. MorphQ provides a reproducible framework for constructing interpretable morphological trait spaces when predefined descriptors are incomplete and labelled data are limited. C_LI Data/code for peer review: An anonymised repository containing the source code, trained model weights, example data, configuration files and scripts required to reproduce the analyses is available at https://anonymous.4open.science/r/MorphQ-ECD4/.