Implementing cost-effective monitoring programs for wild bees remains challenging due to the high
costs of sampling and specimen identification. To reduce costs, next generation sequencing (NGS)-
based methods have lately been suggested as alternatives to morphology-based identifications. To
provide a comprehensive presentation of the advantages and weaknesses of different NGS-based
identification methods, we assessed three of the most promising ones, namely metabarcoding,
mitogenomics and NGS barcoding. Using a regular monitoring dataset (723 specimens identified
using morphology), we found that NGS barcoding performed best for both species presence/absence
and abundance data, producing only few false positives (3.4%) and no false negatives. In contrast,
the proportion of false positives and false negatives was higher using metabarcoding and
mitogenomics. Although strong correlations were found between biomass and read numbers,
abundance estimates significantly skewed the communities’ composition in these two techniques.
NGS barcoding recovered the same ecological patterns as morphology. Ecological conclusions based
on metabarcoding and mitogenomics were similar to those based on morphology when using
presence/absence data, but different when using abundance data. In terms of workload and cost,
we show that metabarcoding and NGS barcoding can compete with morphology, but not
mitogenomics which was consistently more expensive. Based on these results, we advocate that
NGS barcoding is currently the seemliest NGS method for monitoring of wild bees. Furthermore, this
method has the advantage of potentially linking DNA sequences with preserved voucher specimens,
which enable morphological re-examination and will thus produce verifiable records which can be
fed into faunistic databases.