In times of global insect decline (Maes and van Dyck 2001, Brooks et al. 2012, Hallmann et al. 2017, Lister and Garcia 2018), insect diversity assessments and monitoring schemes become more important than ever. In the past decades, many insect monitoring projects were launched (e.g. Seibold et al. (2019), Karlsson et al. (2020), Lehmann et al. (2021), Staab et al. (2023)). Malaise traps are widely used to assess population trends of flying insects, as they are standardised traps that are very efficient in collecting flying insects (Matthews and Matthews 1983, Ssymank et al. 2018, Karlsson et al. 2020). Malaise trap samples comprise large numbers of insects from different orders and with high specimen size heterogeneity (Geiger et al. 2016). The largest amount is made up of just two orders, Hymenoptera and Diptera (Srivathsan et al. 2023), which are the most species-rich insect orders (Zhang 2011, Forbes et al. 2018). These groups contain a significant number of so-called ‘dark taxa’, small-sized specimens that lack experts to identify them (Hausmann et al. 2020, Chimeno et al. 2022), which contributes to the taxonomic impediment (Engel et al. 2021). These samples are highly valuable as they are likely to contain undescribed species. Sorting and identifying specimens from large bulk samples is time-consuming and constitutes the main bottleneck of a morphological approach (Piper et al. 2019). Due to the lack of sorting, most of the collected material is stored indefinitely in freezers, awaiting proper identification. Implementation of a strategy for downstream sample handling after biomass assessment heavily depends on the study goal. However, with regards to millions of unidentified specimens, the high potential and value of these samples provide sufficient reason to develop a comprehensive concept for each monitoring approach, as an accurate identification of a species forms the base of every effort made in the fields of ecology, biogeography and conservation.

Metabarcoding is a highly promising method for molecular identification of species from mass samples (Taberlet et al. 2012). In this approach, DNA is extracted from the whole bulk sample (Hajibabaei et al. 2011). A standardised marker region of the mitochondrial cytochrome c oxidase I gene (COI) is amplified and after high-throughput sequencing (Shendure and Ji 2008), the sequences are compared against a reference database for species identification (Ratnasingham and Hebert 2007, Hajibabaei et al. 2011). For metabarcoding, no taxonomic knowledge of experts is needed and it is, therefore, a cost-efficient approach for biodiversity assessments (Hajibabaei et al. 2011, Elbrecht et al. 2017b). However, the molecular identification of a species is limited by the availability of an accurately identified reference barcode (Ekrem et al. 2007, Virgilio et al. 2010). Concerning dark taxa, the lack of reference data results in large numbers of OTUs (operational taxonomic units), whose exact number depends largely on ambiguous species cut-off levels and which cannot be linked to a species name. Without this linkage, we are unable to grasp the biology of a species, its ecological requirements and, therefore, miss the initial goal to learn about species in order to protect them. In addition, small-sized taxa remain undetected, as they contribute less DNA compared to larger specimens (Elbrecht et al. 2017a). Additionally, homogenisation of bulk samples to extract DNA from the entire “biodiversity soup” precludes the possibility to study morphological characteristics of individual specimens and would ultimately not allow for voucher specimens to be made accessible to taxonomic research through their preservation in collections.

The primary impediment of a conventional monitoring approach is the vast number of insect specimens that need to be identified and preserved for extended periods. In this context, Buffington and Gates (2008) presented a cost- and time-efficient method for size-sorting of specimens from large bulk samples: the fractionator. In this approach, the major aim was not genetic identification using size fractions, but rather recovering intact specimens from mass samples for subsequent morphological analysis. The fractionator is a system based on a sieve in a plastic tub that is placed on an orbital shaker. Full fluid-conserved insect samples are immersed in soapy water to reduce surface tension. Stacking the tubs enables the fractionation of multiple samples simultaneously. As a result, specimens from the bulk samples are sorted into two size classes: a large (macro) and a small (micro) fraction. In a recent paper, Elbrecht et al. (2021) showed that size fractioning of specimens from insect bulk samples increases taxon recovery in a metabarcoding concept. However, to our knowledge, the fractionator method is still not widely utilised. In this paper, we evaluate the efficiency of the fractionator to separate Hymenoptera families from a two week Malaise trap sample of a meadow ecosystem to make them available for morphological identification by testing which Hymenoptera families are found to what extent in the different size fractions.

Sample collection

Fractionation

Identification and data analysis

Overall, 2,449 Hymenoptera specimens were included in the macro fraction and 3,016 in the micro fraction (5,465 specimens in total, Table 1). Specimens were intact, clean and free of Lepidoptera scales after fractionation. All Apocrita were identified to family level, which resulted in a total of 33 families. For 24 out of 34 families (including ‘Symphyta’), separation was efficient, corresponding to 71% of all sorted groups. The families Crabronidae, Sphecidae, Torymidae, Chrysididae, Cynipidae, Gasteruptiidae, Ichneumonidae, Mymarommatidae, Proctotrupidae, Formicidae, Pompilidae, Sapygidae, Vespidae and ‘Symphyta’ were efficiently separated into the macro fraction (Fig. 1, Table 1). For the micro fraction, the families Ceraphronidae, Megaspilidae, Aphelinidae, Encyrtidae, Eulophidae, Mymaridae, Tetracampidae, Trichogrammatidae, Platygastridae and Scelionidae were separated efficiently. Separation was not efficient for the families Eupelmidae, Eurytomidae, Ormyridae, Pteromalidae, Signiphoridae, Bethylidae, Dryinidae, Figitidae, Diapriidae and Braconidae.

Figure 1.  

Efficiency of size fractionation for Hymenoptera families (including ‘Symphyta’). For specimen numbers, see Table 1.

This study illustrates the efficiency of the fractionator by Buffington and Gates (2008) for Hymenoptera specimens in a meadow ecosystem over a two-week period and its potential to improve workflows dealing with specimen-rich Malaise trap samples. The results show that 71% of Hymenoptera families including the suborder ‘Symphyta’ were separated efficiently for this sample. For the families Eupelmidae, Eurytomidae, Ormyridae, Pteromalidae, Signiphoridae, Bethylidae, Dryinidae, Figitidae, Diapriidae and Braconidae, separation was not clear. This is due to the small number of total specimens for some of these families (e.g. Signiphoridae) and the size range of specimens that varies within different taxa of these groups (e.g. Braconidae). The best way to work with such families is to know the approximate size range of a specific subfamily or species group and choose the fraction accordingly. In some cases, it would be necessary to have a look at both size fractions. The single Mymarommatidae specimen in this sample ended up in the macro fraction. Species of the family Mymarommatidae have a maximum size of 0.75 mm (Noyes 2019). The single specimen found in this sample, therefore, most likely ended up in the macro fraction by chance. One limitation of our study is that it is based on a single Malaise trap sample from a grassland site in a two week interval. Therefore, the size distribution of taxa of the same family might differ considering their phenology, which means that our results may look different at another point in time of the year. Given the enormous number of specimens and the large diversity of hymenopteran families that we found, we are, nevertheless, confident that our dataset represents a good proxy for the efficiency of the fractionator.

Buffington and Gates (2008) chose a fractioning duration of 2 hours. Our results show that most families of Hymenoptera can be separated effectively with a fractioning duration of 30 minutes. This is a quarter of the duration used by Buffington and Gates (2008) and, therefore, saves a considerable amount of time. Further, specimens spend less time in the soapy water with low concentrations of ethanol and can be transferred faster to high percentage ethanol for better conservation. To have the option for subsequent analyses, it is advisable to preserve 50 ml of the initial ethanol in a vial before the sample is poured into the sieve for fractionation. In a recent paper, Brühl et al. (2021) used ethanol from Malaise trap samples to detect pesticide residues in nature conservation areas. This shows that not only the insect specimens, but also the ethanol in which the insects were conserved, have high potential to answer questions in the field of nature conservation.

The benefits of using the fractionator for sorting out microhymenoptera were already highlighted in Buffington and Gates (2008) (e.g. clean specimens) and we can add that it facilitates the sorting process as microhymenoptera tend to get stuck in hairs and bristles of larger Hymenoptera and Diptera specimens. Here, we want to highlight the advantages of including the fractionator in a standard Malaise trap monitoring workflow. The proposed method makes large bulk samples more easily accessible and attractive to work with, as experts do not need to spend time manually sorting through massive amounts of material. It can easily be implemented in the monitoring workflow, as it is time- and cost-efficient. To minimise cross-contamination, particularly during subsequent molecular analysis, it is recommended to sterilise the fractionation equipment between samples. Further, Elbrecht et al. (2017a) showed that sample presorting reduces sequencing depth five-fold in a metabarcoding approach resulting in reduced sequencing costs. They also showed that 30% more taxa can be recovered in sorted compared to unsorted samples. Size fractionation is also used in the workflow of the “DiversityScanner”, a very recent approach in which insect specimens from bulk samples are identified and processed by a machine (Wührl et al. 2022). However, fractioning samples also comes with some costs, such as doubling of sample containers and labels, which need to be kept track of. Some specimens might get damaged, as, for example, antenna of Ichneumonoidea and Diptera, especially in Nematocera. Therefore, benefits and costs of the fractioning method need to be considered before implementing it into the workflow of mass sample handling.

Malaise traps are ideal to catch Hymenoptera and Diptera (Geiger et al. 2016). Globally, 16 out of the 20 most commonly found insect families can be found in Malaise trap samples (Srivathsan et al. 2023), of which six Hymenoptera families are also found in this study. These samples are, therefore, a ‘bonanza’ for the diversity of these dark taxa (Zhang 2011, Forbes et al. 2018). Most Malaise trap monitoring approaches focus solely on insect biomass as it is cheap and easy to implement (Ssymank et al. 2018). Elbrecht et al. (2021) described the restrictions of metabarcoding approaches as small taxa often remain undetected. Further, samples are mechanically homogenised and end up as “biodiversity soup”, unable to be identified morphologically later on. Depending on the study goal, metabarcoding might be the choice for qualitative analysis of diversity as discussed in Elbrecht et al. (2017a). For quantitative assessments and especially for taxonomic approaches, it is, however, essential to retain intact specimens in order to preserve their morphological diversity.

In the light of biodiversity loss, collections of natural history museums are highly relevant as they can function as windows to the past and contribute significant data for the documentation of species declines (Shaffer et al. 1998). Scientists at natural history museums publish in the fields of systematics, evolution and nature conservation. As taxonomic experts are rare and descriptions and identifications of some species groups are inadequate, proper taxonomic work, including describing species and preserving voucher specimens in collections, is more important than ever. In this context, insect mass samples from monitoring projects are of high potential because they often contain new species even in supposedly well-studied regions (Moser et al. 2023).

We hope that our results provide an incentive for Hymenopterists to further investigate the morphological diversity associated with the study of the ‘black gold’. Automating parts of the sorting process, allows taxonomists to allocate their time towards much-needed taxonomic research. As a result, the fractionator has the potential to help overcome the taxonomic impediment.

We want to thank Tanja Schweizer for setting up and developing the adapted protocol of the fractionator and the insect monitoring team of SMNS consisting of Ingo Wendt, Raffaele Gamba, Sonia Bigalk, Sebastian Görn and Michael Haas for their enormous work in collecting and curating the samples. The Malaise trap sample was taken from an ongoing biodiversity monitoring initiative coordinated by the LUBW (Landesanstalt für Umwelt Baden-Württemberg) and funded by the state government of Baden-Württemberg within the “Sonderprogramm zur Stärkung der biologischen Vielfalt”. In this context, we want to thank the members of the Entomological Society Krefeld for setting up the Malaise trap.

Funding for Maura Haas-Renninger was provided by the Ministry of Science and Art of Baden-Württemberg through a graduate scholarship from the State Graduate Sponsorship Program. Funding for Marina Moser was provided by the Bundesministerium für Bildung und Forschung, Berlin, Germany, within the project "German Barcodeof Life III: Dark Taxa" (FKZ 16LI1901C).