To evaluate the metabarcoding pipeline described in Herrera-Mesías et al. (submitted) a total of 230 wild bee specimens were used. The samples were collected by S.O. and a field assistant using hand nets during 10 sampling events from 27 April to 22 July in 2020 in 7 different sites distributed across the Federal State of Rhineland-Palatinate (Germany). The netted bees were killed with ethyl acetate and immediately stored under cool conditions. From the end of the field day until the pinning of the individuals, all samples were stored frozen to prevent possible degradation of the DNA. Bees were pinned (males with genitals pulled out) and labeled by the end of the field season. For DNA extraction one complete midleg of each individual was removed using fire-sterilized tweezers and transferred to 2 mL Eppendorf tubes. After processing the bees of a sampling event, all surfaces and tools, i.e., tweezers, were sterilized to exclude cross contamination. The legs were pooled per sampling event, the pooled samples labeled with integers 1 through 10 by S.O. and shipped to the Zoology Department of the Musée national d’histoire naturelle Luxembourg, where further molecular analysis (“DNA”) was conducted by F.H-M. and A.W. without specific knowledge of sites or specimens.

The pinned voucher specimens were shipped to two internationally recognized wild bee experts, both with over 15 years of experience in wild bee faunistics and taxonomy, who were asked to identify them to species level (“PIN”). Both experts consented the use of their identifications for the double-blind evaluation of the metabarcoding approach. During the laboratory analysis, the team processing the pooled leg samples had no access to the voucher specimens nor any of their metadata information or the evaluations done by the experts. The wild bee experts never met each other, and their taxon lists were handled by a third party (T.E.) until the DNA pipeline output was completed. The voucher specimens are deposited in the MNHNL invertebrate dry collection for long-term storage and curation (MNHNL127130-127359).

For the metabarcoding pipeline, a two-step PCR protocol using fusion primers based on Elbrecht and Steinke (2019) was used. The tags used for the second PCR are described in Elbrecht and Leese (2017). The laboratory protocols of Weigand and Herrera-Mesías (2020) were used for DNA extraction, as well as for the first PCR. For the second PCR, 1 μl of the amplicon (without cleanup) was used as a template and the reaction volume was modified to a final volume of 50 μl. Both PCRs were run on an Eppendorf Mastercycler nexus eco Thermocycler using programs based on Elbrecht and Steinke (2019) and described in Herrera-Mesías et al. (submitted).

To increase the data robustness and the probability of detecting low biomass specimens, a PCR replicate strategy was followed. Two replicates of each sample plus one positive control (i.e. a mock community of known wild bee community composition) were included in the final setup. The success of both PCR replicates was verified by electrophoresis and their amplicons were purified with a NucleoSpin Gel and PCR Clean-up kit (Macherey-NagelTM). The DNA concentrations of the purified products were measured and equimolarly pooled into the final library (27.42 μl, 48.47 ng/μl). The clean library was sequenced on one lane of an Illumina MiSeq System (2x250 bp) at the Luxembourg Centre for Systems Biomedicine (Belval, Luxembourg).

The resulting DNA metabarcoding sequence data was processed using the JAMP R package (https://github.com/VascoElbrecht/JAMP), with the settings and supplementary tools described in Herrera-Mesías et al. (submitted). Taxonomic sorting was performed by comparing the resulting OTU fasta files against sequences stored in the Barcode of Life Data system (BOLD; Ratnasingham and Hebert 2007) using BOLDigger (Buchner and Leese 2020). As the team performing the bioinformatic analysis was blind to any metadata regarding the potential species composition of the samples, the default thresholds of BOLDigger were considered to find the best fitting hit for OTU taxonomic identification: at least a match of 85% for identification to the level of order, 90% to the level of family, 95% to the level of genus and 98% to the level of species.

The resulting data were pruned using TaxonTableTools (Macher et al. 2021) to remove all non-Hymenoptera OTUs, as well as Hymenoptera OTUs present in only one PCR replicate. Finally, the taxon name assignation of the filtered data was manually reviewed and partly modified from the original BOLD output by A.W. (blind to PIN results) to comply with current taxonomic nomenclature, thus creating a curated taxon list (Suppl. material 1). Only Hymenoptera OTUs present in both replicates with read numbers above 0.01% of abundance for each replicate and identified to species level were included in the final curated table. If a species was represented by multiple OTUs in the dataset, the results were collapsed into a single species entry.

To maintain double-blindness between DNA and PIN, the curated table was sent to T.E. who cross-tabulated identification results for each sample for a first comparison. Only then were the results made available to the rest of the team for numerical analysis. To allow comparison among the output of both approaches, the curated taxon list was transformed into a presence/absence table and combined with the results of the morphological approach.

To test the accuracy of in vivo determination of wild bees in the field, one of the authors (C.B.) accompanied bee monitorers during wild bee surveys within the “BienABest” project. Surveys took place from April to September 2020 at nine different sites throughout Germany and were conducted by a total of seven trained bee monitorers, whose experience in bee faunistics and taxonomy varied from some to many years. The monitorers used a reduced-impact monitoring method that includes in vivo identification (IVI) of encountered wild bees along variable transect walks (Neumüller et al. 2020, 2021, VDI-Richtlinie 4340-1 2021). Bees were either identified by the monitorer “on sight” when no closer scrutiny was deemed necessary, or were captured and identified with the help of an observation jar and a magnifier (ID method “capture”). Bees that could still not be identified in vivo were killed for later identification under the microscope. Overall, a total of 552 bee individuals were encountered by the seven monitorers during the surveys, of which 56 individuals (10.14%) were deemed impossible to identify in the field. The remaining 496 individuals were identified alive “on sight” or following “capture” by the monitorer. Of those, 210 individuals (42.34%) were consecutively collected by C.B. and stored in pre-labeled vials for later validations (see below). The remaining 287 individuals either could not be captured or were excluded from the evaluation because they represented species that had already been identified three times by an individual monitorer. This exclusion rule treated sexes separately, i.e., the maximum number of IVI individuals evaluated per species and monitorer was six (three females and three males).

The 210 bees to be included in the laboratory evaluation of IVI were killed with ethyl acetate or by freezing, and pinned by C.B. Furthermore, genitalia of male specimens were extracted and fixed outside the metasoma if required for species identification. The pinned specimens were re-labeled with a unique number code to omit information about date, locality or any other detail that would violate the anonymity of the monitorers.

The pinned bees were first identified by one internationally recognized wild bee expert with many years of experience in bee faunistics, morphotaxonomy and systematics (EXP data set) who worked under the knowledge that the identifications would later be used for IVI evaluation. Consecutively the specimens were sent to four other recognized wild bee experts, with several to many years of experience in bee morphotaxonomy, for independent identification (PIN). These experts were paid at rates typical for freelance work and were also aware that their work was part of a scientific investigation. To reconcile all discrepancies of identifications between the EXP data set and PIN, these were consecutively discussed in detail with the respective PIN-experts. Based on these discussions, and taking into account COI barcodes of two critical bee individuals (see Suppl. material 2 for laboratory protocol), a consensus list (CON data set) was established that represents our most objective assignment of true species affiliation. The voucher specimens are deposited in the collection of the Department for Evolutionary Ecology and Conservation Genomics at the University of Ulm.

For data analysis, the whole data set of wild bee IDs was divided into seven bee sets, each representing the identifications made by one individual monitorer, enabling us to analyze discrepancies between IVI and PIN across monitorers, and to contrast them with the discrepancy among PIN identifications for the same sets of bees. Additionally, a comparison to the consensus list showed the percentage of correctly identified bees per IVI and PIN expert.

To further analyze the congruency and discrepancy of identification within and among DNA and PIN, and within and among IVI and PIN, we calculated Bray-Curtis similarities based on presence/absence taxon tables (DNA evaluation) or quantitative taxon tables (IVI evaluation) using the PRIMER-E software (version 6.1.6; Clarke and Gorley 2001), which was also used to plot dendrograms (hierarchical cluster analysis, complete linkage) based on the calculated similarity matrices.

After trimming and quality filtering, 2,874,629 high quality reads from the original 4,395,456 read pairs were retained (Short Read Archive bioproject: PRJNA876388). About 67.8% of the 1,447,238 original unassigned reads corresponded to PhiX. A total of 17.27% of the original 278 OTUs detected in the dataset were discarded after filtering based on a 0.01% read abundance threshold, remaining 230 OTUs for further analysis. 480 chimeras were discarded as well during clustering. After comparison against the BOLD systems database and replicate consistency analysis with Taxon Table Tools,146 OTU consistently found across replicates were preliminary identified as Hymenoptera taxa to various levels of taxonomic resolution (Suppl. material 3). After filtering, data merging and curation, 91 distinct taxonomic units representing detected wild bee species and species groups were included in the final curated table comparing DNA with PIN (Suppl. material 4).

The number of taxonomic units detected by DNA in individual samples varied between 11 and 22. All the species intentionally pooled in the mock community sample (positive control) were detected. From the ten samples considered in the analysis, only one (S2) presented a perfect congruence between the metabarcoding results (“DNA”) and the evaluations of both taxonomic wild bee experts (“PIN1” and “PIN6”), based on the values of the Bray-Curtis index and the visual analysis of the dendrogram (Fig. 1). Two more DNA-based species lists were identical to the PIN1 expert results (S7, S9), none to PIN6. In six of the remaining samples, the DNA pipeline outcome was grouped closer to PIN1 on a terminal branch, with higher similarity than the resulting one from the comparison of the results of both experts. In three samples (S1, S3 and S8), the results of both PIN experts were more similar with each other than with the results of the DNA pipeline. When results are considered within the same sample, the Bray-Curtis similarity was 80% or higher among all three methods, with the lowest similarity observed between the pipeline and both experts in S3.

Figure 1. 

Dendrogram based on Bray-Curtis index to illustrate congruency of wild bee identification among the metabarcoding pipeline (DNA) and two PIN experts. X-axis shows index values expressed as percentage Bray-Curtis similarity.

Across samples, the average congruency between the DNA and PIN (“PINav”) was 88.98% (Table 1). The mean congruency within PIN was slightly higher (93.65%). When PIN identifications were considered separately, the results of DNA were in better agreement with the evaluation of the first expert (“PIN1”) than with the second one (“PIN6”) or with their average outcome. The highest disagreement between DNA and PIN was observed in S3, where DNA detected five additional species and missed one identified by both PIN experts, reaching a congruency of only 66.67%.

The total sample size of evaluated bees was reduced from originally 210 bees to 208 bees due to critical damage in two specimens caused by repeated shipping. The number of identified bees per monitorer/bee set varied from 19 to 46 bee individuals. Fig. 2 shows the similarity of species identifications by IVI, PIN and the CON data set based on the Bray-Curtis similarity index. The greatest congruency was found in bee set 4, in which the monitorer (IVI4) and three out of four PIN experts as well as the CON data set produced a perfectly identical taxon list. In bee set S2, S3 and S6 identification results differed at least slightly among the consulted IVI and PIN experts. The largest discrepancies were found in bee sets S1 and S3 (86.90% and 80.77%, respectively).

Figure 2. 

Dendrogram based on Bray-Curtis similarity index to illustrate congruency of wild bee identification among IVI and PIN experts. X-axis shows index values expressed as percentage Bray-Curtis similarity. PIN experts 1 to 4 are the same among the seven bee sets, whereas the IVI expert is different for each bee set.

Averaged across bee sets, there was a taxon list congruency between IVI and PIN of 91.81%. PIN results among themselves showed an average taxon list congruency of 92.96% (Table 2). Overall, and in comparison with the CON dataset, the average percentage of correctly identified bee individuals was 95.13% for IVI and 95.19% for PIN (see Table 2). Apparent misidentifications of IVI and PIN experts appeared mainly within bee genera Andrena, Bombus, Halictus and Megachile. In addition, some bee individuals of Lasioglossum spp. were misidentified by PIN experts (Suppl. material 5).

The overall congruency found between the metabarcoding pipeline (DNA) and morphological identification results (PIN) on a per-sample basis analysis (88.98% mean congruency) agrees well with previous findings reported by Gueuning et al. (2019). In their study, based on a multi locality setting in Switzerland, over 90% of the traditionally identified morphospecies were also detected by DNA metabarcoding.

Despite the high overall similarity of the results obtained by DNA and PIN in our study, 26 cases of disagreement were present (Suppl. material 4), which are worth further discussion: In 12 cases, the molecular results support the assessment of one morphotaxonomic expert against the other, resolving conflicting morphological evaluations. Incongruence between DNA and both PIN experts can partially be explained by unclear species delimitation. There is a historical controversy regarding whether Andrena ovatula and Andrena albofasciata should be consider as one or two species (Westrich et al. 2011; Schmidt et al. 2015; Praz et al. 2022). In our study, the metabarcoding pipeline supported the presence of A. albofasciata against A. ovatula in S4 and S10, in opposition to the morphological analysis, but was in agreement with both PIN experts regarding detecting only A. ovatula in S5. As DNA recognized these taxa as two separate OTUs in our dataset based on a 97% genetic similarity threshold, this suggests the presence of a second species, potentially overlooked by PIN, within what has been traditionally considered Andrena ovatula sensu lato. These results are in agreement with recent analyses that have resolved the controversy by consistently demonstrating the existence of two distinct species within the complex, A. ovatula and A. afzeliella (Kirby, 1802) (=A. albofasciata), based on molecular, morphological and ecological evidence (Praz et al. 2022). Therefore, the nomenclature of DNA barcodes currently available in BOLD should be updated accordingly to match this new taxonomic consensus, further improving the detection capacity of molecular approaches.

Further research on cryptic diversity following a similar approach would contribute to reach final conclusions regarding the status of similarly challenging species complexes, such as the Halictus simplex-complex. Although our dataset pooled species within this complex into one entity for the overall comparisons, DNA was able to precisely identify H. langobardicus regardless of the sex of the individual, whereas PIN was only able to assign a species-level annotation to males (Suppl. material 4).

Given that the genetic results of controversial species complexes involve an additional level of analysis (Schmidt et al. 2015), a sufficient number of validated DNA reference barcodes should be a pre-requirement to perform metabarcoding on taxonomically problematic sibling species. Whenever possible, barcodes from local specimens reliably identified by known taxonomic experts should be preferred as reference material, thus to reach accurate interpretations.

Another factor potentially affecting the congruency of metabarcoding results with morphological analysis is environmental contamination. For example, in seven cases the pipeline detected additional wild bee species to the ones reported by the taxonomic experts. Five false positive detections were found in S3 (Bombus lapidarius, Bombus pascuorum, Andrena cineraria, Chelostoma florisomne and Dasypoda hirtipes), one in S5 (Halictus confusus) and one in S8 (Melecta luctuosa). The additional species in S3 correspond to easily identifiable wild bees and three of them were completely absent in the whole wild bee set, which means that they cannot have been overlooked by PIN. Most likely, DNA traces from an outside source are likely responsible for these additional findings. Carry-over DNA from other specimens in the field, the sampling containers, or from specimen handling before DNA extraction represents a more likely explanation than cross-contamination in the laboratory as no other bees were being processed within the laboratory premises at the time of the double-blind experiment. The same situation may explain the presence of H. confusus in S5 and of M. luctuosa in S8. Tag-switching as an alternative explanation for the false positive results of species generally present in the overall data set seems unlikely, as tag combinations with high Levenshtein distances (=>3) were chosen to avoid the artificial generation of existing tag combinations given the sequencing platform used (Salipante et al. 2014; Elbrecht and Steinke 2019).

False positives and false negatives are known drawbacks affecting taxonomic assessment results originating from PCR-based high throughput sequencing techniques, potentially leading to taxonomic biases such as “biodiversity inflation” (Zhou et al. 2013; Tang et al. 2015; Gueuning et al. 2019). Identifying contaminants in wild bee metabarcoding datasets can be hard, because amplification bias may result in false positives, with read numbers equal or higher than the read numbers of true positives (Tang et al. 2015). Even if the false positive found in S8 had fewer reads than any true positive within the sample, their numbers were still over the defined threshold and similar to the read numbers of true positives found in other samples (see Suppl. material 3). Strategies that boost data robustness, such as increasing the number of PCR replicates of the same biological sample (Alberdi et al. 2018; Weigand and Macher 2018) or adjusting the value of filtering thresholds during bioinformatic pruning may be helpful to separate out potential false positives.

Finally, three false negatives were also found in the metabarcoding dataset (Sphecodes gibbus in S1, Lasioglossum pauxillum in S3 and Melecta albifrons in S6). In this case, insufficient sequencing depth seems a more likely explanation than obscurity due to primer bias, as all missing species show low primer-template mismatch with the selected primer pair (Herrera-Mesías et al. submitted). In the experiment, the sequencing run produced fewer overall read numbers than the ones reported by similar works (13.8 million reads in Gueuning, et al. (2019); 11.7 million in Herrera-Mesías et al. (submitted)). Compared to the 47,471 average reads per community of Gueuning et al. 2019, the average number of reads per sample replicate obtained in the double-blind experiment was almost three fold higher (134,340 reads after trimming and quality filtering). However, it was less than a third of the 460,074 average reads per replicate included in the final dataset of Herrera-Mesías et al. (submitted). Therefore, insufficient sequencing depth may have negatively affected specimens of low biomass represented by single individuals in certain sample mixtures. This seems to be the case for L. pauxillum in S3. The species presented 12 reads in the first replicate (threshold of 14 reads) of the sample and 20 reads in the second (threshold of 14 reads), just barely below the 0.01% inclusion threshold (Suppl. material 3). Adjusting the pooling scheme of the library considering different criteria and additional metadata regarding the sample in question (i.e., final DNA concentration in relation to the number of specimens for each bulk sample, size sorting, etc.) may help to reduce the likelihood of false negatives.

Despite the lack of a perfect match with the expert evaluations, the results of the DNA metabarcoding pipeline are similar enough to be advised as a viable alternative to microscopy-based assessment, especially when considering its high congruency to the PIN1 results. Moreover, this approach offers several advantages for broad-scale assessments in the context of conservation biology projects, when large quantities of wild bees may be challenging and costly to identify (Lebuhn et al. 2013; Creedy et al. 2020). The number of specimens here analyzed could be increased 10-fold without substantially rising laboratory expenses, work effort, or compromising the quality of results. However, increasing the number of samples can also reduce the number of sequences per replicate, potentially increasing the risk of false negatives. Therefore, each analysis must consider the desired sequencing depth per sample as well as the performance of the platform selected to determine the maximum number of samples that can be pooled on the same run (Elbrecht and Steinke 2019).

Finally, DNA metabarcoding presents a crucial limitation for wild bee monitoring purposes, as it should only be used for qualitative assessment. An alternative molecular, cost-effective but specimen-based solution allowing qualitative results can be offered by high-throughput or next-generation sequencing DNA barcoding (Creedy et al. 2019; Gueuning et al 2019).

We found that IVI of bee individuals considered feasible for alive determination in the field by the monitorer led to similar rates of correct identification as PIN, i.e., 95% as judged post-hoc based on the curated consensus list (CON). This may seem surprising, because IVI took place in the field without a dissecting microscope. For a better understanding of the results, it is necessary to look more closely at the different error sources that led to incongruencies between the expert identifications.

First, biased expectations appeared to have caused misidentifications especially in IVI, where monitorers had knowledge of local bee communities from previous visits. This kind of mistake seems to have generated several cases of incorrect bumblebee identification. For example, in case of BBV86 and BBV98 (see Suppl. material 5) a similar but more noteworthy species was chosen instead of the abundant Bombus lapidarius. In another case a female Megachile leachella (BBV188) was confused with Megachile pilidens. Whereas M. leachella was not previously known to occur in the locality, the similar M. pilidens had been expected from previous encounters (pers. comm. of monitorer with C.B.).

In contrast, PIN appears to have been more susceptible to mistakes like misplaced entries in excel sheets or mix-up of specimens. Such errors were suggested by unlikely misidentifications as in BBV42, a worker bumblebee Bombus lapidarius that had been identified as Halictus subauratus, a bee that could not be more different. In addition, some errors arose from biases in the used identification keys or reference collections. For example, the popular (and generally very good) identification key for bumblebees by Mauss (1987) does not cover the full range of (corbicula hair) color variability of Bombus humilis. Its use by PIN experts was associated with repeated misidentification of Bombus humilis workers as Bombus ruderarius, for which reddish corbicula hair is a well known trait (BBV13, BBV14, BBV16, pers. comm. with C.B.). The alternative distinctive trait (shape of labrum bottom edge) given by the key was not considered by the experts and another evident characteristic of the specimens (bright facial hair; untypical for B. ruderarius) was neither explicitly treated by the key nor noticed by the experts. Biased reference collections appeared to have caused other errors in PIN. For example, the expert who incorrectly identified a female of Megachile maritima (BBV169) as Megachile willughbiella did so based on divergent reference material collected from populations outside of Germany (pers. comm. with C.B.). In discussions with C.B., some PIN experts stated their insufficient experience with species outside of their region of expertise as a possible source of error.

Due to the design of the study there might be a number of intrinsic biases that could have increased the accuracy of IVI relative to PIN. First, IVI experts had a free choice regarding which of the encountered bees they considered feasible for IVI (during evaluated monitorings approximately 90% of individuals were considered feasible for IVI, a rate that corresponds well with IVI rates during regular BienABest monitorings; BienABest project, unpublished results). Thus, they could directly influence the sample of bees/identifications that was being evaluated. In addition, IVI experts were very aware of being evaluated, and were constantly reminded of the fact by the presence of C.B. who collected their IVI bees. PIN experts, while also having been informed that their results will be used in a double-blind evaluation, did not work under close observation. This discrepancy in experienced scrutiny could have led to different likelihoods of careless mistakes.

The relationship between the amount of experience of the expert and the accuracy of identification results is less than clear. All experts included in this study (IVI and PIN, also for the DNA comparison) were recognized experts of bee morphotaxonomy with at least some years, but mostly many years, of experience. If there was a difference at all, the amount of experience was slightly higher and less variable among PIN than among IVI experts. The IVI monitorer considered least experienced did indeed deliver the least accurate identification result of only 84.6% in comparison to the consensus list. However, the respective bee set (S3) was also the one that had the lowest congruency among PIN experts (90.6%), suggesting that the set was difficult.

In general, IVI as conducted within the BienABest project yielded accurate identifications in nineteen out of twenty bees (95%). It needs to be emphasized that such accuracy can only be achieved by highly trained experts, a resource that is in short supply (Drew 2011) and needs to be replenished by concerted efforts of universities, NGOs, national authorities and funding agencies. Probably, IVI will remain limited to a certain part of bee diversity that is feasible for IVI. Exactly how large this part is, is a matter of debate. According to a list (“Ampelliste”) prepared by experts during their work on the VDI-Richtlinie 4340-1 (2021) just about 50% of females of German bee species can currently be identified alive. In the male sex the percentage is considered to be even lower (30%). It remains to be seen if this percentage can be increased in the future with the help of digital tools that allow scrutiny of additional taxonomical characters. Currently, such a tool is being developed within the BienABest project for identification of 300 bee species of Central Europe via smartphone app, which includes high quality pictures to guide reliable identification under field conditions.

There is a controversial debate on whether the use of IVI is in fact necessary and desirable for wild bee monitoring (Gezon et al. 2015). Generally, the effect of invasive sampling on insect populations, and bees in particular, has not been well studied (Packer and Darla-West 2021). We are aware of only one study that was dedicated to assess the effect on wild bee communities: Gezon et al. (2015) found no negative effects of several years of bi-weekly pan trapping and netting on bee communities in the Rocky Mountains (Colorado, USA). However, the study was conducted in large tracts of near natural habitat, and it is questionable whether the results can be transferred to the degraded and fragmented bee habitats in Central Europe (e.g., Steffan-Dewenter et al. 2006). It seems plausible to assume that repetitive removal of reproductive individuals can affect local populations of already endangered species, especially in solitary bees which are characterized by low reproductive rates and which often demonstrate a highly localized distribution (Westrich 2019). This is supported by at least one study that used colored vane traps and found conspicuous declines of attracted species in one locality (Gibbs et al. 2017). Depending on locality and monitoring design, IVI may be the way of erring on the safe side.

IVI and most DNA metabarcoding approaches relying on bulk samples might have another disadvantage, as both strategies usually do not deposit extensive reference material. A reference collection for future comparison is often a legal requirement or at least important to judge about spatio-temporal patterns of individual species in times of changing taxonomies, e.g. within species complexes. Voucher specimens are also relevant in case upcoming taxonomic methods require biomaterial or morphometric data to address open taxonomic questions, or for educational purposes (Lister et al. 2011; Monfils et al. 2017; Kharouba et al. 2019), or to validate particularly noteworthy findings. Moreover, voucher specimens stored in local natural history collections represent an important resource for the construction of future taxonomic lists, including potentially overlooked findings relevant to the development of national conservation strategies (Herrera-Mesías and Weigand 2021). The most common referencing strategy of DNA metabarcoding approaches – if any – is the deposition of DNA vouchers. However, in cases of surprising results, DNA vouchers will make it difficult to further judge about the unexpected results.

In the metabarcoding setup here applied, DNA was extracted from individual legs while the rest of the voucher specimens were archived in the invertebrate collection of the MNHNL. Although this led to an increase in the hands-on-times and costs per sample, it preserves specimens for future conservation studies (Herrera-Mesías and Weigand 2021). Single specimen barcoding or HTS barcoding might also be helpful in the context of wild bee monitoring (Schmidt et al. 2015; Gueuning et al. 2019), especially when abundance data are desirable and total specimen numbers feasible to handle.

Regarding IVI, additional documentation could be provided by depositing high-quality images taken from live bees confined in observation jars, as is currently done by some experts. However, this requires appropriate equipment and imposes substantial additional effort during field work. Also, there currently exists no general depository for digital specimens of wild bees.