Standardizing food web reconstruction from metabarcoding data using iterative rarefaction and trait-based trophic inference model.

High-throughput metabarcoding combined with trait-based trophic inference models enables the reconstruction of food webs from community eDNA. However, uneven sequencing depth can bias inferred food web structure. Network metrics derived from unstandardized read counts may reflect sampling effort rather than ecological processes. Here, we present an objective and reproducible framework using iterative rarefaction with trait-based trophic inference to standardize food web metrics prior to ecological analysis. Our approach links rarefaction depth to network-level properties by generating rarefaction curves based on food web metrics rather than taxonomic richness. An optimal rarefaction threshold is determined using a first derivative criterion, and rarefied networks are reconstructed repeatedly to obtain robust mean metric estimates. We applied this method to soil protist communities assessed using 18S rRNA metabarcoding. The method reduced sequencing-depth bias and revealed environmental drivers that were masked in non-rarefied analyses. This framework provides a standardized solution for comparing food webs derived from amplicon datasets across environmental gradients. Integrates trait-based trophic inference with iterative rarefaction of metabarcoding communities. Selects rarefaction thresholds using food web network properties rather than species accumulation curve. Provides reliable estimates of food web structure independent of sequencing effort.