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Application of eDNA metabarcoding for high-resolution reconstruction of the trophic web of an Arctic fjord

Piroli, C.; Marinchel, N.; Galli, S.; Russo, T.; Azzaro, M.; Filiciotto, F.; Di Marco, G.; D'Agostino, A.; Gismondi, A.; Profeta, A.

2026-01-09 ecology
10.64898/2026.01.09.698612 bioRxiv
Show abstract

In the face of a rapidly changing Arctic, the ecosystem of Kongsfjorden was put under the spotlight to explore its community composition and structural dynamics. An eDNA metabarcoding approach was implemented to carry out a Food Web Analysis. eDNA samples were collected using metaprobes, innovative passive samplers, deployed under two different sampling configurations: in association with set fish traps in the coastal area and a towed sampling along a central transect, an offshore domain of the fjord. Amplification of the mithocondrial COI and ribosomal 18S genes was conducted in order to obtain a comprehensive view of metazoans and protists communities, respectively. The output taxa from the metabarcoding process constituted trophic webs nodes while producers-consumers and prey-predators relationships were identified through a literature review. Qualitative food networks were successfully obtained for each sampled site and for the two domains identified in the ecosystem, the coastal and offshore areas. Moreover, these networks were characterized by using four food web indicators: Species Richness (N), Number of links (L), Direct Connectance (C) and Generality (G). Differences in the apical part of the webs instantly emerged, as well as a clear separation between the coastal and offshore domain. Analyzing the values of the trophic indicators allowed for a deeper consideration regarding the nets structure and relative stability. Overall, eDNA proved sensitive and precise in capturing differences between the two domains and in providing insights into ecosystem structure. Moreover, eDNA-based Food Web Analysis could set the basis for long term monitoring studies in the same area, being cost-effective, rapid and easy to implement when compared to traditional methods.

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