Automated morphometric analysis and biomass estimation of marine nematodes using a conical frustum segmentation approach

Marine nematodes, the most abundant meiofauna in benthic ecosystems, drive critical nutrient cycling and carbon sequestration, yet accurate biomass estimation remains challenging due to morphological variability and reliance on simplistic geometric models like Andrássy's formula, which introduced systematic biases across taxa. This study developed RAH NemaCalc, an open-source Python-based tool with Tkinter GUI and OpenCV integration, that semi-automates morphometric analysis by segmenting nematodes into 5–20 conical frustums based on length-diameter ratios, calculating lateral surface area with morphology-specific correction factors (ktotal and frustum geometry factor f), and converting to biomass using an empirically determined density of 1.08 g/cm3 derived from sucrose gradient centrifugation of 50 genera. The tool processed high-resolution images of 187 specimens from diverse habitats- 50 from Lakshadweep Islands, 87 from Clarion-Clipperton Zone abyssal sediments, and 50 from the Nemys database yielding processing times of 1.8–2.1 s/image with manual contour verification. Key results showed the frustum method produced significantly different biomass estimates from Andrássy (Wilcoxon p < 0.0001 across datasets), with median volume reductions of 20–28% in coastal/abyssal samples but up to 260% higher biomass in large tapered genera like Linhomoeus and Halichoanolaimus; Bland-Altman analysis confirmed morphology- and size-dependent bias, while 10–20 segments optimized accuracy for tapered forms. RAH NemaCalc thus established a precise, high-throughput standard for nematode biomass quantification, enabling reliable monitoring of anthropogenic impacts like deep-sea mining on ecosystem functions and informing conservation baselines.