Skip to main content
SLU publication database (SLUpub)

Research article2016Peer reviewedOpen access

Diet Assessment Based on Rumen Contents: A Comparison between DNA Metabarcoding and Macroscopy

Nichols, Ruth; Åkesson, Mikael; Kjellander, Petter


Dietary choices are central to our understanding of ecology and evolution. Still, many aspects of food choice have been hampered by time consuming procedures and methodological problems. Faster and cheaper methods, such as DNA metabarcoding, have therefore been widely adopted. However, there is still very little empirical support that this new method is better and more accurate compared to the classic methods. Here, we compare DNA metabarcoding to macroscopic identifications of rumen contents in two species of wild free-ranging ungulates: roe deer and fallow deer. We found that the methods were comparable, but they did not completely overlap. Sometimes the DNA method failed to identify food items that were found macroscopically, and the opposite was also true. However, the total number of taxa identified increased using DNA compared to the macroscopic analysis. Moreover, the taxonomic precision of metabarcoding was substantially higher, with on average 90% of DNA-sequences being identified to genus or species level compared to 75% of plant fragments using macroscopy. In niche overlap analyses, presence/absence data showed that both methods came to very similar conclusions. When using the sequence count data and macroscopic weight, niche overlap was lower than when using presence-absence data yet tended to increase when using DNA compared to macroscopy. Nevertheless, the significant positive correlation between macroscopic quantity and number of DNA sequences counted from the same plant group give support for the use of metabarcoding to quantify plants in the rumen. This study thus shows that there is much to be gained by using metabarcoding to quantitatively assess diet composition compared to macroscopic analysis, including higher taxonomic precision, sensitivity and cost efficiency.

Published in

2016, Volume: 11, number: 6, article number: e0157977