Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming

The expansion of scientific image data holds great promise to quantify individuals, size distributions and traits. Computer vision tools are especially powerful to automate data mining of images and thus have been applied widely across studies in aquatic and terrestrial ecology. Yet marine benthic communities, especially infauna, remain understudied despite their dominance of marine biomass, biodiversity and playing critical roles in ecosystem functioning. Here, we disaggregated infauna from sediment cores taken throughout the spring transition (April–June) from a near‐natural mesocosm setup under experimental warming (Ambient, +1.5°C, +3.0°C). Numerically abundant mudsnails were imaged in batches under stereomicroscopy, from which we automatically counted and sized individuals using a superpixel‐based segmentation algorithm. Our segmentation approach was based on clustering superpixels, which naturally partition images by low‐level properties (e.g., colour, shape and edges) and allow instance‐based segmentation to extract all individuals from each image. We demonstrate high accuracy and precision for counting and sizing individuals, through a procedure that is robust to the number of individuals per image (5–65) and to size ranges spanning an order of magnitude (40k individuals and revealed overall growth in response to springtime warming. We illustrate that image processing and segmentation workflows can be built upon existing open‐access R packages, underlining the potential for wider adoption of computer vision tools among ecologists. The image‐based approach also generated reproducible data products that, alongside our scripts, we have made freely available. This work reinforces the need for next‐generation monitoring of benthic communities, especially infauna, which can display differential responses to average warming.