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A dearth of data: fitting parasitoids into ecological networks

2021; Elsevier BV; Volume: 37; Issue: 10 Linguagem: Inglês

10.1016/j.pt.2021.04.012

1471-5007

Kirsten E. Miller, Andrew Polaszek, Darren M. Evans,

Nematode management and characterization studies

Parasitoids are key ecosystem service providers within sustainable agriculture and integrated pest-management strategies due to their function as biocontrol agents.There is a dearth of data regarding how parasitoids fit within wider communities of interacting species, but such information is essential for the successful implementation of conservation biological control in open-field agroecosystems.DNA barcoding is a useful tool for the establishment of host–parasitoid interactions (and many other associations) and can enable the rapid and relatively cost-effective construction of ecological networks.Ecological networks, specifically multilayer ecological networks constructed using DNA-based methods, can significantly aid our understanding of how land management can influence multiple ecosystem services and lead to enhanced agricultural sustainability. Studying parasitoids can provide insights into global diversity estimates, climate change impacts, and agroecosystem service provision. However, this potential remains largely untapped due to a lack of data on how parasitoids interact with other organisms. Ecological networks are a useful tool for studying and exploiting the impacts of parasitoids, but their construction is hindered by the magnitude of undescribed parasitoid species, a sparse knowledge of host ranges, and an under-representation of parasitoids within DNA-barcode databases (we estimate <5% have a barcode). Here, we advocate the use of DNA metabarcoding to construct the host–parasitoid component of multilayer networks. While the incorporation of parasitoids into network-based analyses has far ranging applications, we focus on its potential for assessing ecosystem service provision within agroecosystems. Studying parasitoids can provide insights into global diversity estimates, climate change impacts, and agroecosystem service provision. However, this potential remains largely untapped due to a lack of data on how parasitoids interact with other organisms. Ecological networks are a useful tool for studying and exploiting the impacts of parasitoids, but their construction is hindered by the magnitude of undescribed parasitoid species, a sparse knowledge of host ranges, and an under-representation of parasitoids within DNA-barcode databases (we estimate <5% have a barcode). Here, we advocate the use of DNA metabarcoding to construct the host–parasitoid component of multilayer networks. While the incorporation of parasitoids into network-based analyses has far ranging applications, we focus on its potential for assessing ecosystem service provision within agroecosystems. Host–parasitoid dynamics have been a major focus of ecological and evolutionary study since the early 20th century due to the essential role parasitoids play within ecological communities, and their function as biocontrol agents (Box 1). The economic value of natural pest control is estimated to be $4.5 billion annually in the USA alone [1.Losey J.E. Vaughn M. The economic value of ecological services provided by insects.Bioscience. 2006; 56: 311-323Crossref Scopus (1038) Google Scholar]. With recent bans on insect pesticides, as well as the prevalence of insecticide resistance, usage of biocontrol agents is likely to increase in the near future [2.Shields M.W. et al.History, current situation and challenges for conservation biological control.Biol. Control. 2019; 131: 25-35Crossref Scopus (40) Google Scholar]. Release of parasitoids to control pests in closed systems, such as the widespread use of Encarsia formosa to control greenhouse whitefly (Trialeurodes vaporariorum) [3.Bale J.S. et al.Biological control and sustainable food production.Philos. Trans. R. Soc. B Biol. Sci. 2008; 363: 761-776Crossref PubMed Scopus (345) Google Scholar], is standard agricultural practice, and a range of parasitoid species are commercially available. Similarly, parasitoids have successfully been used in classical biological control (see Glossary). Anagyrus lopezi has been spectacularly successful against cassava mealybug in Africa, with savings estimated between US$8 billion and US$20 billion over 40 years [4.Zeddies J. et al.Economics of biological control of cassava mealybug in Africa.Agric. Econ. 2001; 24: 209-219Crossref Google Scholar]. Parasitoids are increasingly being discussed in the context of conservation biological control (CBC) [2.Shields M.W. et al.History, current situation and challenges for conservation biological control.Biol. Control. 2019; 131: 25-35Crossref Scopus (40) Google Scholar,3.Bale J.S. et al.Biological control and sustainable food production.Philos. Trans. R. Soc. B Biol. Sci. 2008; 363: 761-776Crossref PubMed Scopus (345) Google Scholar,5.115th Congress Agriculture Improvement Act of 2018.https://www.congress.gov/bill/115th-congress/house-bill/2Date: 2018Google Scholar]. However, the focus of host–parasitoid research mostly concerns direct interactions between agricultural pests and their parasitoids (Figure 1A ). Relatively little is known about interactions between parasitoids and non-pest hosts (i.e., complete host ranges), other parasitoids, and predators (Figure 1B,C) [6.Stiling P. Biological control not on target.Biol. Invasions. 2004; 6: 151-159Crossref Scopus (30) Google Scholar] occurring within agricultural and natural systems. There is a growing realisation that these 'non-target' interactions can influence the utility of parasitoids as natural biological control agents via indirect effects, that is, interactions acting between two species that are mediated by one or more additional species (Figure 2) [7.Frost C.M. et al.Apparent competition drives community-wide parasitism rates and changes in host abundance across ecosystem boundaries.Nat. Commun. 2016; 7: 12644Crossref PubMed Scopus (39) Google Scholar, 8.Cronin J.T. Shared parasitoids in a metacommunity: Indirect interactions inhibit herbivore membership in local communities.Ecology. 2007; 88: 2977-2990Crossref PubMed Scopus (20) Google Scholar, 9.Sanders D. van Veen F.J.F. Indirect commensalism promotes persistence of secondary consumer species.Biol. Lett. 2012; 8: 960-963Crossref PubMed Scopus (20) Google Scholar]. The impacts of these indirect interactions have been shown in experimental and field settings; for example, Sanders and van Veen (2012) found the absence of one parasitoid species can lead to the extinction of another via competitive exclusion between their two host species [9.Sanders D. van Veen F.J.F. Indirect commensalism promotes persistence of secondary consumer species.Biol. Lett. 2012; 8: 960-963Crossref PubMed Scopus (20) Google Scholar], and Cronin (2007) demonstrated that two hosts can impact one another's population via shared parasitoids [8.Cronin J.T. Shared parasitoids in a metacommunity: Indirect interactions inhibit herbivore membership in local communities.Ecology. 2007; 88: 2977-2990Crossref PubMed Scopus (20) Google Scholar], an indirect effect called 'apparent competition' which has long been hypothesised to impact the dynamics of pest populations [10.Holt R.D. Bonsall M.B. Apparent competition.Annu. Rev. Ecol. Evol. Syst. 2017; 48: 447-471Crossref Scopus (123) Google Scholar].Box 1What are parasitoids?Insect parasitoids comprise a large number of species and are defined by their larval feeding strategy – that is, they feed exclusively on an arthropod host, almost exclusively leading to its death [23.Godfray H.C.J. Parasitoids: Behavioural and Evolutionary Ecology.1st edn. Princeton University Press, 1994Crossref Google Scholar]. They are mostly found within the orders Hymenoptera, Diptera, and Strepsiptera, but of these, Hymenopteran parasitoids are the best studied. Parasitoids are of vital importance for biocontrol [68.LaSalle J. Parasitic Hymenoptera, biological control and biodiversity.in: LaSalle J. Gauld I.D. Hymenoptera and Biodiversity. CAB International, 1993: 197-215Google Scholar], there is mounting recognition of their role in pollination services [69.Zemenick A.T. et al.A network approach reveals parasitoid wasps to be generalized nectar foragers.Arthropod Plant Interact. 2019; 13: 239-251Crossref Scopus (10) Google Scholar], and they have been successfully used as bioindicators for the wider health of ecosystems [18.Anderson A. et al.The potential of parasitoid Hymenoptera as bioindicators of arthropod diversity in agricultural grasslands.J. Appl. Ecol. 2011; 48: 382-390Crossref Scopus (41) Google Scholar].Insect parasitoids display a variety of life-history strategies leading to obscure parasitoid complexes. Askew and Shaw (1986) [70.Askew R.R. Shaw M.R. Parasitoid communities: their size, structure and development.in: Waage J. Greathead D. Insect Parasitoids, 13th Symposium of Royal Entomological Society of London. Academic Press, 1986: 225-264Google Scholar] grouped parasitoids into two types based upon whether the host is killed or paralysed during oviposition (idiobiosis) or whether the host can continue to develop after oviposition (koinobiosis). Idiobiont parasitoids are often (though not always) ectoparasitic, that is, their larvae do not develop within the host but are external to it. These species are generally believed to display a greater host range than koinobiont parasitoids, which are typically endoparasitic [23.Godfray H.C.J. Parasitoids: Behavioural and Evolutionary Ecology.1st edn. Princeton University Press, 1994Crossref Google Scholar]. Further, parasitoids can display primary parasitism, in which a single parasitoid directly attacks a single host; superparasitism, in which multiple individuals of the same parasitoid species attack the same host individual; multiparasitism, in which multiple parasitoid species attack the same host individual; or hyperparasitism, in which one parasitoid will attack the larva of another parasitoid within a nonparasitoid host, as well as variations around each of these life histories [71.Quicke D.L.J. The Braconid and Ichneumonid Parasitoid Wasps: Biology, Systematics, Evolution and Ecology. John Wiley, 2015Google Scholar]. Due to this complexity, the study of parasitoid interactions is fraught with difficulties, but molecular approaches have the potential to overcome many of these.Figure 2The importance of a whole-ecosystem approach: indirect effects and multiple habitat types.Show full caption(A) Example of a multilayer network combining data from plant–pollinator, plant–herbivore, and herbivore–parasitoid networks within four farmland landscape habitat types: woodland, hedgerow, grassland, and crops. (B–D) The progression of species and interactions considered for primary (direct) interactions with a crop plant species (B), secondary (indirect) interactions with a crop plant species (C), and tertiary (indirect) interactions with the same crop plant species (D). (E–G) The effect of removing non-crop habitats on direct and indirect interactions: hedgerow (E), woodland (F), and grassland (G). Considering whole communities across multiple habitat types reveals that crop plants display many 'hidden' interactions.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Insect parasitoids comprise a large number of species and are defined by their larval feeding strategy – that is, they feed exclusively on an arthropod host, almost exclusively leading to its death [23.Godfray H.C.J. Parasitoids: Behavioural and Evolutionary Ecology.1st edn. Princeton University Press, 1994Crossref Google Scholar]. They are mostly found within the orders Hymenoptera, Diptera, and Strepsiptera, but of these, Hymenopteran parasitoids are the best studied. Parasitoids are of vital importance for biocontrol [68.LaSalle J. Parasitic Hymenoptera, biological control and biodiversity.in: LaSalle J. Gauld I.D. Hymenoptera and Biodiversity. CAB International, 1993: 197-215Google Scholar], there is mounting recognition of their role in pollination services [69.Zemenick A.T. et al.A network approach reveals parasitoid wasps to be generalized nectar foragers.Arthropod Plant Interact. 2019; 13: 239-251Crossref Scopus (10) Google Scholar], and they have been successfully used as bioindicators for the wider health of ecosystems [18.Anderson A. et al.The potential of parasitoid Hymenoptera as bioindicators of arthropod diversity in agricultural grasslands.J. Appl. Ecol. 2011; 48: 382-390Crossref Scopus (41) Google Scholar]. Insect parasitoids display a variety of life-history strategies leading to obscure parasitoid complexes. Askew and Shaw (1986) [70.Askew R.R. Shaw M.R. Parasitoid communities: their size, structure and development.in: Waage J. Greathead D. Insect Parasitoids, 13th Symposium of Royal Entomological Society of London. Academic Press, 1986: 225-264Google Scholar] grouped parasitoids into two types based upon whether the host is killed or paralysed during oviposition (idiobiosis) or whether the host can continue to develop after oviposition (koinobiosis). Idiobiont parasitoids are often (though not always) ectoparasitic, that is, their larvae do not develop within the host but are external to it. These species are generally believed to display a greater host range than koinobiont parasitoids, which are typically endoparasitic [23.Godfray H.C.J. Parasitoids: Behavioural and Evolutionary Ecology.1st edn. Princeton University Press, 1994Crossref Google Scholar]. Further, parasitoids can display primary parasitism, in which a single parasitoid directly attacks a single host; superparasitism, in which multiple individuals of the same parasitoid species attack the same host individual; multiparasitism, in which multiple parasitoid species attack the same host individual; or hyperparasitism, in which one parasitoid will attack the larva of another parasitoid within a nonparasitoid host, as well as variations around each of these life histories [71.Quicke D.L.J. The Braconid and Ichneumonid Parasitoid Wasps: Biology, Systematics, Evolution and Ecology. John Wiley, 2015Google Scholar]. Due to this complexity, the study of parasitoid interactions is fraught with difficulties, but molecular approaches have the potential to overcome many of these. (A) Example of a multilayer network combining data from plant–pollinator, plant–herbivore, and herbivore–parasitoid networks within four farmland landscape habitat types: woodland, hedgerow, grassland, and crops. (B–D) The progression of species and interactions considered for primary (direct) interactions with a crop plant species (B), secondary (indirect) interactions with a crop plant species (C), and tertiary (indirect) interactions with the same crop plant species (D). (E–G) The effect of removing non-crop habitats on direct and indirect interactions: hedgerow (E), woodland (F), and grassland (G). Considering whole communities across multiple habitat types reveals that crop plants display many 'hidden' interactions. The rise in development and application of ecological networks (Box 2) over the past two decades is indicative of their utility for analysing complex questions in ecology and evolution [11.Delmas E. et al.Analysing ecological networks of species interactions.Biol. Rev. 2019; 94: 16-36Crossref Scopus (170) Google Scholar]. Their application to host–parasitoid systems has provided valuable insights into impacts of anthropogenic drivers of ecosystem change beyond agricultural systems, such as habitat modification within tropical forests [12.Tylianakis J.M. et al.Habitat modification alters the structure of tropical host–parasitoid food webs.Nature. 2007; 445: 202-205Crossref PubMed Scopus (619) Google Scholar] and the effect of climate change within arctic communities [13.Kankaanpää T. et al.Parasitoids indicate major climate-induced shifts in arctic communities.Glob. Chang. Biol. 2020; 26: 6276-6295Crossref PubMed Scopus (10) Google Scholar]. But the use of networks to link species, habitats, and ecosystem services within an agricultural context could facilitate decision-making at both the landscape and local scales. The construction of ecological networks that reflect the specificity and frequency of interactions within natural systems enables the quantification of direct and indirect effects on population trends, attack rates, and ecosystem services like pollination and biological control [7.Frost C.M. et al.Apparent competition drives community-wide parasitism rates and changes in host abundance across ecosystem boundaries.Nat. Commun. 2016; 7: 12644Crossref PubMed Scopus (39) Google Scholar,11.Delmas E. et al.Analysing ecological networks of species interactions.Biol. Rev. 2019; 94: 16-36Crossref Scopus (170) Google Scholar,12.Tylianakis J.M. et al.Habitat modification alters the structure of tropical host–parasitoid food webs.Nature. 2007; 445: 202-205Crossref PubMed Scopus (619) Google Scholar,14.Bohan D.A. Networking our way to better ecosystem service provision.Trends Ecol. Evol. 2016; 31: 105-115Abstract Full Text Full Text PDF PubMed Scopus (49) Google Scholar], and could provide a paradigm shift in the holistic management of agroecosystems. For example, knowing the complete host range of a parasitoid species can inform how its population can be bolstered by the presence of non-pest hosts [7.Frost C.M. et al.Apparent competition drives community-wide parasitism rates and changes in host abundance across ecosystem boundaries.Nat. Commun. 2016; 7: 12644Crossref PubMed Scopus (39) Google Scholar] or predict how it might impact native communities through non-target effects if introduced as a biocontrol agent [15.Henneman M.L. Memmott J. Infiltration of a Hawaiian community by introduced biological control agents.Science. 2001; 293: 1314-1316Crossref PubMed Scopus (232) Google Scholar], and understanding which parasitoids share host species enables us to study competition and how this reduces overall pest control function [16.Cusumano A. et al.Interspecific competition/facilitation among insect parasitoids.Curr. Opin. Insect Sci. 2016; 14: 12-16Crossref PubMed Scopus (41) Google Scholar]. The study of hosts and parasitoids with no known impacts upon agricultural systems could not only benefit ecosystem services within cropland via unknown indirect effects, but can also help to uncover the impacts of climate [13.Kankaanpää T. et al.Parasitoids indicate major climate-induced shifts in arctic communities.Glob. Chang. Biol. 2020; 26: 6276-6295Crossref PubMed Scopus (10) Google Scholar] and land use change [17.Grass I. et al.Past and potential future effects of habitat fragmentation on structure and stability of plant–pollinator and host–parasitoid networks.Nat. Ecol. Evol. 2018; 2: 1408-1417Crossref PubMed Scopus (50) Google Scholar,18.Anderson A. et al.The potential of parasitoid Hymenoptera as bioindicators of arthropod diversity in agricultural grasslands.J. Appl. Ecol. 2011; 48: 382-390Crossref Scopus (41) Google Scholar] upon ecological communities in the context of conservation.Box 2Ecological networksEcological networks characterise the interactions that occur between coexisting species such as predation, herbivory, pollination, and parasitism (Figure I). They deepen our understanding of community structure by revealing the strength and nature of the connections between organisms. This information can enhance our ability to anticipate and mitigate the effects of both anthropogenic and natural environmental change on biological communities.Ecological networks, in this context, comprise nodes (species) connected by links (interactions). These links can simply represent the presence of an interaction (qualitative networks) or the relative strength of an interaction (quantitative networks). Link strength in host–parasitoid networks is typically established using the frequency of association. That is, the proportion of individual larvae that have been parasitized by a given parasitoid species. Networks comprising different interaction types are traditionally studied independently, but there is a growing realisation that this approach limits predictive ability. Because the ecological and evolutionary dynamics of a community are dependent upon all interaction types present at a given time, combining multiple 'subnetworks' of, for example, plant–pollinator or host–parasitoid into a single 'multiplex' network can reveal greater insights into the relationship between species interactions and community composition, with a growing toolbox for analyses drawn from advances in complexity science. Ecological networks characterise the interactions that occur between coexisting species such as predation, herbivory, pollination, and parasitism (Figure I). They deepen our understanding of community structure by revealing the strength and nature of the connections between organisms. This information can enhance our ability to anticipate and mitigate the effects of both anthropogenic and natural environmental change on biological communities. Ecological networks, in this context, comprise nodes (species) connected by links (interactions). These links can simply represent the presence of an interaction (qualitative networks) or the relative strength of an interaction (quantitative networks). Link strength in host–parasitoid networks is typically established using the frequency of association. That is, the proportion of individual larvae that have been parasitized by a given parasitoid species. Networks comprising different interaction types are traditionally studied independently, but there is a growing realisation that this approach limits predictive ability. Because the ecological and evolutionary dynamics of a community are dependent upon all interaction types present at a given time, combining multiple 'subnetworks' of, for example, plant–pollinator or host–parasitoid into a single 'multiplex' network can reveal greater insights into the relationship between species interactions and community composition, with a growing toolbox for analyses drawn from advances in complexity science. A deeper understanding of parasitoid interactions at the community level is therefore required before CBC can be fully incorporated into viable integrated pest management (IPM). This is of particular importance in light of contemporary policy directives promoting the use of CBC in sustainable agricultural practices globally [5.115th Congress Agriculture Improvement Act of 2018.https://www.congress.gov/bill/115th-congress/house-bill/2Date: 2018Google Scholar,19.European Union Directive 2009/128/EC of the European Parliament and of the Council of 21 October 2009.Establishing a Framework for Community Action to Achieve the Sustainable Use of Pesticides. 2009Google Scholar]. DNA barcoding (Box 3) can reduce the time and cost of constructing host–parasitoid networks by aiding the identification of parasitoid species and enabling the establishment of trophic links between parasitoid and host [20.Kitson J.J.N. et al.Detecting host–parasitoid interactions in an invasive Lepidopteran using nested tagging DNA metabarcoding.Mol. Ecol. 2019; 28: 471-483Crossref PubMed Scopus (37) Google Scholar]. It can also help to overcome the bias of traditional rearing approaches and enable the characterisation of difficult-to-observe interactions [21.Wirta H.K. et al.Complementary molecular information changes our perception of food web structure.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: 1885-1890Crossref PubMed Scopus (99) Google Scholar]. With the development of new high-throughput sequencing approaches, plus the increased availability and reduced cost of these tools as the field progresses, their utility is continuing to grow [22.Evans D.M. Kitson J.N. Molecular ecology as a tool for understanding pollination and other plant–insect interactions.Curr. Opin. Insect Sci. 2020; 38: 26-33Crossref PubMed Scopus (10) Google Scholar]. Yet, the benefit of molecular taxonomy for host–parasitoid research is significantly lessened by a poor representation of parasitoid species within global barcode repositories relative to global parasitoid diversity.Box 3DNA (meta)barcodingDNA barcoding, that is, the use of a genetic 'barcode' to identify an unknown species, is a popular molecular tool used across the globe [72.Valentini A. et al.DNA barcoding for ecologists.Trends Ecol. Evol. 2009; 24: 110-117Abstract Full Text Full Text PDF PubMed Scopus (690) Google Scholar]. In metazoa, this barcode is usually a 658 bp length of the COI gene dubbed 'the Folmer region' [73.Folmer O. et al.DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates.Mol. Mar. Biol. Biotechnol. 1994; 3: 294-299PubMed Google Scholar]. Taxonomic classification of sequences involves their comparison with a reference database containing sequences of the same locus derived from morphologically identified specimens. The most commonly used of these databases is Barcode of Life (BOLD) [31.Ratnasingham S. Hebert P.D.N. The barcode of life data system.Mol. Ecol. Notes. 2007; 7: 355-364Crossref PubMed Scopus (4168) Google Scholar]. In order to confidently describe insect communities and enable comparability between sampling efforts in different parts of the globe, international reference databases such as BOLD need to be well populated with morphologically described species.Metabarcoding is a common biomonitoring tool used for determining species composition in environmental samples [74.Hebert P.D.N. et al.Counting animal species with DNA barcodes: Canadian insects.Phil. Trans. R. Soc. B. 2016; 37120150333Crossref PubMed Scopus (183) Google Scholar]. It is the parallel amplification and subsequent parallel sequencing of barcodes from multiple organisms simultaneously in order to rapidly characterise the approximate species richness and composition of a mixed sample. Unlike barcoding, which classically utilises Sanger sequencing to generate individual barcode sequences (though this is changing [75.Srivathsan A. et al.A MinIONTM-based pipeline for fast and cost-effective DNA barcoding.Mol. Ecol. Resour. 2018; 18: 1035-1049Crossref Scopus (52) Google Scholar]), metabarcoding requires high-throughput sequencing platforms such as Illumina®, Pacific Biosciences®, or Oxford Nanopore Technologies®.While the standard COI barcoding region is 658 bp in length, the maximum sequence that can be generated by, for example, Illumina sequencing, is realistically 550 bp when taking account of read overlap and quality control. Since the standard COI barcode region is therefore too long to be fully sequenced, many studies utilise mini-barcodes that typically vary between 130 and 500 bp in length [76.Elbrecht V. Leese F. Validation and development of COI metabarcoding primers for freshwater macroinvertebrate bioassessment.Front. Environ. Sci. 2017; 5: 11Google Scholar]. It should be noted that their reduced length means that mini-barcodes do not provide as much genetic information, which can impact their ability to resolve species differences [77.Meusnier I. et al.A universal DNA mini-barcode for biodiversity analysis.BMC Genom. 2008; 9: 214Crossref PubMed Scopus (448) Google Scholar].Once sequences are generated, similar sequences are typically clustered into OTUs, or filtered to create amplicon sequence variants (ASVs) [78.Callahan B.J. et al.Exact sequence variants should replace operational taxonomic units in marker-gene data analysis.ISME J. 2017; 11: 2639-2643Crossref PubMed Scopus (1089) Google Scholar], before being linked to sequences within a reference database that have an assigned taxonomic identity [79.Altschul S.F. et al.Basic local alignment search tool.J. Mol. Biol. 1990; 215: 403-410Crossref PubMed Scopus (67299) Google Scholar]. A variety of algorithms exist for the purpose of reducing noise, and revealing which species are present, and their efficacy varies depending upon the composition of species assemblages. For example, some clustering algorithms rely on the percentage of sequence similarity with a relatively arbitrary cut off, usually leading to inaccuracies in the assemblage descriptions. However, (meta)barcoding remains a useful tool for the rapid identification of species and, in some cases, is the only option available. DNA barcoding, that is, the use of a genetic 'barcode' to identify an unknown species, is a popular molecular tool used across the globe [72.Valentini A. et al.DNA barcoding for ecologists.Trends Ecol. Evol. 2009; 24: 110-117Abstract Full Text Full Text PDF PubMed Scopus (690) Google Scholar]. In metazoa, this barcode is usually a 658 bp length of the COI gene dubbed 'the Folmer region' [73.Folmer O. et al.DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates.Mol. Mar. Biol. Biotechnol. 1994; 3: 294-299PubMed Google Scholar]. Taxonomic classification of sequences involves their comparison with a reference database containing sequences of the same locus derived from morphologically identified specimens. The most commonly used of these databases is Barcode of Life (BOLD) [31.Ratnasingham S. Hebert P.D.N. The barcode of life data system.Mol. Ecol. Notes. 2007; 7: 355-364Crossref PubMed Scopus (4168) Google Scholar]. In order to confidently describe insect communities and enable comparability between sampling efforts in different parts of the globe, international reference databases such as BOLD need to be well populated with morphologically described species. Metabarcoding is a common biomonitoring tool used for determining species composition in environmental samples [74.Hebert P.D.N. et al.Counting animal species with DNA barcodes: Canadian insects.Phil. Trans. R. Soc. B. 2016; 37120150333Crossref PubMed Scopus (183) Google Scholar]. It is the parallel