HiReS: A Method for Automated Morphometric Trait Extraction from High-Resolution Plankton Images

Abstract

Trait-based analyses in plankton ecology require measurements from large numbers of individuals, yet morphometric data are typically collected manually from small subsets. Although deep learning methods enable automated detection and segmentation, extracting quantitative trait data from full-resolution images remains challenging due to memory limitations. We present HiReS (High-Resolution Segmentation), an open-source workflow for automated morphometric trait extraction from large plankton images. HiReS partitions images into overlapping chunks, performs YOLO-based instance segmentation, reconstructs polygon annotations in full-image space, removes truncated and duplicate detections, and computes geometric descriptors. We evaluated the workflow using manually annotated and automated segmentations of Daphnia pulex, Daphnia galeata, and Simocephalus vetulus. Automated measurements reproduced the structure of manual trait distributions and showed strong agreement at both sample and individual levels. A consistent positive bias was observed, reflecting a multiplicative scaling offset rather than distortion of relative trait structure. Subsampling analyses further showed that model-derived medians can outperform manual estimates at low sampling depths. HiReS provides a reproducible and computationally efficient framework for extracting morphometric traits from full-resolution plankton images.