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Related Experiment Video

Updated: Jun 16, 2026

Fatty Acid 13C Isotopologue Profiling Provides Insight into Trophic Carbon Transfer and Lipid Metabolism of Invertebrate Consumers
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Fatty Acid 13C Isotopologue Profiling Provides Insight into Trophic Carbon Transfer and Lipid Metabolism of Invertebrate Consumers

Published on: April 17, 2018

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

Víctor Lecegui1,2, Vincent E J Jassey3, Aaron Pérez-Haase1,2

  • 1Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Barcelona, Spain.

Methodsx
|June 15, 2026
PubMed
Summary

This study introduces a novel framework for analyzing ecological food webs using DNA metabarcoding. The method standardizes data by linking rarefaction depth to network properties, reducing bias from sequencing effort and revealing environmental drivers.

Keywords:
18S rRNAEcological networksEnvironmental gradientsMachine learningProtistsSequencing-depth biasWetlands

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Area of Science:

  • Ecology
  • Molecular Ecology
  • Bioinformatics

Background:

  • High-throughput metabarcoding and trait-based trophic inference can reconstruct food webs from environmental DNA (eDNA).
  • Uneven sequencing depth in metabarcoding data can introduce significant bias into inferred food web structures and network metrics.
  • Current methods often rely on unstandardized read counts, potentially conflating sampling effort with ecological processes.

Purpose of the Study:

  • To develop an objective and reproducible framework for standardizing food web metrics derived from metabarcoding data.
  • To mitigate biases caused by uneven sequencing depth in amplicon-based food web reconstructions.
  • To enable more reliable comparisons of food web structures across different environmental conditions.

Main Methods:

  • Integration of trait-based trophic inference with iterative rarefaction of metabarcoding community data.
  • Development of rarefaction curves based on food web network properties, rather than traditional taxonomic richness.
  • Determination of optimal rarefaction thresholds using a first derivative criterion for robust metric estimation.

Main Results:

  • The proposed framework successfully reduced sequencing-depth bias in soil protist food web analysis.
  • Environmental drivers influencing food web structure became apparent after standardization, which were masked in non-rarefied analyses.
  • The method provides reliable estimates of food web structure that are independent of sequencing effort.

Conclusions:

  • This iterative rarefaction framework offers a standardized solution for analyzing amplicon-derived food webs.
  • The approach facilitates robust comparisons of food web structures across environmental gradients.
  • The method enhances the ecological interpretability of metabarcoding data by minimizing technical artifacts.