Scaling from eDNA to biomass: controlling allometric relationships improves precision in bycatch estimation

Autor: P Urban, D Bekkevold, H Degel, B K Hansen, M W Jacobsen, A Nielsen, E E Nielsen
Rok vydání: 2023
Předmět:
Zdroj: Urban, P, Bekkevold, D, Degel, H, Hansen, B K, Jacobsen, M W, Nielsen, A & Nielsen, E E 2023, ' Scaling from eDNA to biomass: controlling allometric relationships improves precision in bycatch estimation ', ICES Journal of Marine Science, vol. 80, no. 4, fsad027, pp. 1066-1078 . https://doi.org/10.1093/icesjms/fsad027
ISSN: 1095-9289
1054-3139
DOI: 10.1093/icesjms/fsad027
Popis: Environmental DNA (eDNA) has attracted interest in relation to fisheries, with its possibilities for species identification and promises for species quantification. In the context of fisheries catches, eDNA can be most useful for the estimation of bycatch proportions. The assessment of species mixtures in large catches (>1000 t) is challenging, especially when morphologically similar species are to be differentiated. We used an experimental set-up to simulate industrial sprat fishery catches, and tested two types of water (blood water and discharge water) derived from this simulated fishery for their suitability in reliable species quantification. We analysed nine mixtures of sprat and herring—the main bycatch species. Species-specific quantitative PCR was used for species identification and quantification. Species-to-species weight fractions and eDNA fractions in mixtures showed a strong correlation. Accounting for size-based differences in DNA abundance (allometrically scaled weight) reduced the estimated standard error on weight fraction prediction from 0.064 to 0.054 in blood water, and from 0.080 to 0.075 in discharge water when comparing the weight-based model with the allometrically scaled weight model, respectively. Accounting for allometric scalling in genetic analyses of fisheries process water can serve as a more precise method for the assessment of bycatch, thus in a wider sense improve the quality of fisheries-dependent data.
Databáze: OpenAIRE
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