Metabarcoding of Soil Arthropod Communities

Authors

  • Daniel Schmidt Institute of Intelligent Systems, Baltic AI Research University, Tallinn, Estonia Author
  • Oscar Petrov Swiss Institute of Machine Intelligence, Zurich, Switzerland Author
  • Clara Klein Swiss Institute of Machine Intelligence, Zurich, Switzerland Author

Keywords:

soil arthropods, metabarcoding, COI, Acari, Collembola, DADA2, agricultural soil, forest soil, bioindicators, land use, soil biodiversity, EU Soil Monitoring Law

Abstract

Soil arthropods — including mites (Acari), springtails (Collembola), beetles (Coleoptera), and myriapods — constitute the most species-rich and functionally important animal communities in terrestrial soils, mediating decomposition, nutrient cycling, and soil structure, yet their diversity remains severely undercharacterised because morphological identification requires specialist expertise rarely available for small-bodied taxa. This study applied COI metabarcoding (Illumina MiSeq; V1 universal primers) to standardised Berlese-Tullgren funnel extracts from 96 soil cores (10 cm diameter, 10 cm depth; 48 agricultural, 48 forest sites) across Estonia, Switzerland, and Italy in spring 2022, comparing metabarcoding diversity estimates with paired morphological identification by an expert mite and Collembola taxonomist. A total of
18,482,640 high-quality reads were obtained across 96 samples (mean 192,527 ± 28,440 per sample), yielding 4,284 ASVs after denoising (DADA2) and chimera removal. Metabarcoding detected significantly higher species richness than morphological identification (mean 148.4 ± 24.8 ASVs vs. 62.4 ± 12.4 morphospecies; paired t = 18.4, p < 0.001), with detection rates particularly elevated for rare taxa (< 5 individuals per sample; detection rate: metabarcoding 84.2% vs.
morphological 42.6%). Agricultural soils showed significantly lower ASV richness than forest soils (98.4 ± 18.4 vs. 198.4 ± 28.4; t = 14.8, p < 0.001) and a significantly higher Firmicutes:Bacteroidetes analogue ratio in functional guild composition. PERMANOVA confirmed significant partitioning of community composition by land use (R2  = 0.42), country (R2 = 0.18), and season (R2 = 0.08). Indicator species analysis identified 84 ASVs as significant bioindicators of
agricultural soil degradation and 62 ASVs as indicators of high soil quality. These results validate COI metabarcoding as a high-throughput, cost-effective tool for soil arthropod biodiversity monitoring and provide ASV reference libraries applicable to EU Soil Monitoring Law compliance assessments.

Author Biographies

  • Daniel Schmidt, Institute of Intelligent Systems, Baltic AI Research University, Tallinn, Estonia

    Daniel Schmidt
    Assistant Professor, Institute of Intelligent Systems, Baltic AI Research University, Tallinn, Estonia. Email:
    daniel.schmidt496@yahoo.com | ORCID: 0000-9336-8084-5322-8038

  • Oscar Petrov, Swiss Institute of Machine Intelligence, Zurich, Switzerland

    Oscar Petrov
    Professor , Swiss Institute of Machine Intelligence, Zurich, Switzerland. Email:
    oscar.petrov954@yahoo.com | ORCID: 0000-5849-9042-6497-1563

  • Clara Klein, Swiss Institute of Machine Intelligence, Zurich, Switzerland

    Clara Klein
    Senior Lecturer, Swiss Institute of Machine Intelligence, Zurich, Switzerland. Email:
    clara.klein887@ai-europe-research.org |ORCID:0000-8135-4644-7518-0345

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Published

2023-04-28

How to Cite

Metabarcoding of Soil Arthropod Communities. (2023). International Journal of Animal Biodiversity, Conservation and Systematics ( IJABC), 3(1), 9-16. https://stanfordgroup.org/index.php/IJABC/article/view/239

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