Estimating the biomass of soil macrofauna communities using composite images

Soil invertebrate biomass is a key driver of ecosystem processes, however, biomass is rarely measured in soil ecological studies, largely because existing methods are labor-intensive or lack individual-level resolution. We developed a simple, low-tech, image-based method to estimate soil macroinvertebrate biomass. This method uses a desktop CCD scanner to digitize composite samples and open-source software (ImageJ) to extract morphometric descriptors from multiple individuals simultaneously. A total of 1153 individuals, representing 96 species and seven taxonomic classes, were scanned and weighed to develop predictive allometric equations for fresh and dry biomass. Multiple linear regression integrating surface area and perimeter yielded the most robust predictors for biomass. For fresh mass, the general model demonstrated high reliability at both the community level (R 2 = 0.98, MAPE = 7.4%) and the individual level (R 2 = 0.98, MAPE = 25.8%). Dry mass showed similarly strong performance, with class-specific equations substantially improving predictive accuracy. We further quantified biases introduced by alcohol preservation with strong retraction observed in soft-bodied organisms such as earthworms (e.g., a 34% reduction in surface area in 90% alcohol). We derived correction coefficients to adjust predicted biomass values, effectively reducing errors caused by preservation-induced shrinkage. This method enables rapid and reproducible estimation of biomass at both community and individual levels, the latter providing valuable individual-level data for functional trait analyses. Its accessibility makes it suitable for laboratories with limited resources and for retrospective use on preserved collections.