CSIRO PUBLISHING Review Marine and Freshwater Research, 2017, 68, 1788–1802 https://doi.org/10.1071/MF16304 A DNA barcode database of Australia’s freshwater macroinvertebrate fauna M. E. Carew A, S. J. Nichols B, J. Batovska C, R. St Clair D,G, N. P. Murphy E, M. J. Blacket C and M. E. Shackleton F,H A School of BioSciences, The University of Melbourne, Parkville, Vic. 3010, Australia. Institute for Applied Ecology, University of Canberra, Bruce, ACT 2601, Australia. C Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Vic. 3083, Australia. D Environment Protection Authority, 200 Victoria Street, Carlton, Vic. 3053, Australia. E Department of Ecology, Environment, and Evolution, La Trobe University, Bundoora, Vic. 3086, Australia. F Murray–Darling Freshwater Research Centre, La Trobe University, 133 McKoy Street, Wodonga, Vic. 3690, Australia. G Present address: Museums Victoria, GPO Box 666, Melbourne, Vic. 3001, Australia. H Corresponding author. Email: [email protected] B Abstract. Macroinvertebrates are widely used for monitoring freshwater ecosystems. In most monitoring programs, identifications take substantial time and expense. Methods that improve the speed, accuracy and cost-effectiveness of macroinvertebrate identification would benefit such programs. Increasingly, DNA barcodes are being used to provide accurate species-level identifications and have the potential to change how macroinvertebrates are routinely identified. Herein we discuss the need for DNA barcodes of freshwater macroinvertebrates with particular reference to Australia. We examine the use of DNA barcodes for species identification and compare DNA barcoding efforts of macroinvertebrates from Australia with those globally. We consider the role of high-throughput sequencing of DNA barcodes in freshwater bioassessment and its potential use in biosurveillance. Finally, we outline a strategy for developing a comprehensive national DNA barcode database for Australian freshwater macroinvertebrates and present the initial efforts in creating this database. Additional keywords: Barcode of Life Data System, BOLD, biological monitoring, biosecurity, DNA sequences, national database. Received 7 September 2016, accepted 6 January 2017, published online 30 March 2017 Introduction Macroinvertebrates are important components of freshwater ecosystems, providing an integral link between primary producers and higher trophic level predators (Wallace and Webster 1996). Consequently, macroinvertebrates have often been used to understand in-stream processes, such as food web dynamics, and for testing ecological hypotheses (e.g. Thompson and Townsend 1999; Lake et al. 2007). The response of macroinvertebrates to environmental change, their ubiquity, and the relative ease with which macroinvertebrates can be collected have made them ideal biological indicators of river health (Rosenberg and Resh 1993). Worldwide, freshwater macroinvertebrates are used for assessing human impacts, such as changes to hydrology, habitat degradation, eutrophication and contamination with toxicants, as well as for monitoring the Journal compilation Ó CSIRO 2017 success of remedial actions to improve water quality, biodiversity and ecological condition (i.e. Chessman et al. 1997; Bonada et al. 2006; Sudduth and Meyer 2006; Walsh 2006). Freshwater biological assessment, or ‘bioassessment’, using macroinvertebrates provides an integrated overview of the prevailing conditions and ‘health’ of waterways over time. The benefits of using bioassessment are in the monitoring and evaluation of the large expenditures made in catchments. It enables those responsible for management decisions to determine whether their policies, interventions and investments are achieving the intended ecological objectives (Nichols et al. 2017). Describing and interpreting changes in the composition of macroinvertebrate communities has been a particular focus for bioassessment and biomonitoring worldwide (e.g. Reynoldson et al. 1995; Barbour et al. 1999; www.publish.csiro.au/journals/mfr DNA database of Australia’s macroinvertebrates Australian and New Zealand Environment and Conservation Council & Agriculture and Resource Management Council of Australia and New Zealand 2000; Norris et al. 2007; Jones et al. 2010). Stein et al. (2014) reported that biological monitoring is conducted on ,19 500 sites annually in the US and, from these, some 1.3–1.7 107 organisms are identified. The UK has had a long-running bioassessment program that started in the early 1970s (Wright et al. 2000; Jones et al. 2010). Other countries have also developed, or are in the process of developing, broadscale bioassessment programs, including South Africa (Dickens and Graham 2002), Thailand (Boonsoong et al. 2009), the Hindu Kush–Himalayan Region (Stubauer et al. 2010) and in East Africa (Masese et al. 2013). Australia has invested heavily in river bioassessment, primarily based on communities of freshwater macroinvertebrates, producing large datasets using standard assessment techniques, such as the AUStralian RIVer Assessment System (AUSRIVAS; Simpson and Norris 2000). The National River Health Program, which ran from 1994 to 2000 (Davies 2000), delivered a nationwide assessment of river health (Norris et al. 2001) and saw biological monitoring conducted in over 6000 sites. The Murray and Mitta Rivers Biological Monitoring Program, conducted by the Murray–Darling Basin Authority, has been running at various capacities for over 35 years and processes more than 25 000 organisms each year (Cook and Hawking 2014). Similarly, the Environment Protection Authority Victoria have undertaken biological monitoring of Victorian rivers since 1990 (Metzeling 2002) and currently identifies ,25 000 specimens each year. Increasingly, the focus on river health in Australia is on the allocation of environmental flows under the Murray–Darling Basin Plan, aimed at maintaining riverine communities. For example, between 2008 and 2024 the Australian Government plans to spend almost A$13 billion to increase flows in the Murray–Darling river system (http://www. environment.gov.au/resource/infrastructure-investment-murraydarling-basin, accessed 17 August 2016). Biological monitoring will continue to be essential for assessing the ecological effectiveness of such restoration measures. Currently, most monitoring efforts either only apply a coarse taxonomic resolution, thereby possibly overlooking subtle changes in community responses, or identify to the lowest possible taxonomic level but with increased error around identifications and reduced sample sites (Stribling et al. 2008; Haase et al. 2010). Species identifications are often time-consuming, costly and require specialised taxonomic knowledge and robust taxonomic keys (Schmidt-Kloiber and Nijboer 2004; Pilgrim et al. 2011). However, there is an increasing push to use species in freshwater bioassessment, because species responses to particular stressors could be used to provide diagnostic monitoring (Baird and Hajibabaei 2012). For example, species-level identification of Chironomidae has shown associations with specific environmental characteristics and types of pollution (Pettigrove and Hoffmann 2005; Carew et al. 2007b). Furthermore, using macroinvertebrate families relies on the assumption that traits associated with species within a family are phylogenetically conserved (i.e. all species within a family respond in a similar way to their environment; Blomberg et al. 2003; Buchwalter et al. 2008; Carew et al. 2011b). This may be true for some taxa (e.g. some Ephemeropteran families), but it is not Marine and Freshwater Research 1789 true for others (e.g. genera or species within the Chironomidae) where finer taxonomic levels are required to understand environmental responses (Carew et al. 2011b). DNA barcodes are short, standard amplified fragments of DNA that can be used to delineate taxa. Although different DNA barcode regions exist (e.g. Hollingsworth et al. 2009; Schoch et al. 2012), the DNA barcode proposed by Hebert et al. (2003a), which uses a partial DNA sequence from the mitochondrial cytochrome oxidase I (COI) gene, is the most widely used for animals. This DNA barcode could provide broad-scale species identification of macroinvertebrates for bioassessment and be used to develop low-cost tools for routine identification. DNA barcodes for macroinvertebrates could also be beneficial for biosurveillance, because they can provide accurate species identifications of exotic pest species, as well as exotic and indigenous disease vectors. The creation of DNA barcodes for macroinvertebrates falls within the global effort of the International Barcode of Life (iBOL; http://www.ibol.org/, accessed 6 February 2017) to create a universal DNA barcode database of life (see the Barcode of Life Database (BOLD); http://www. boldsystems.org/, accessed 6 February 2017). A focus on developing a national DNA barcode database of freshwater macroinvertebrates would directly feed into this global initiative and facilitate rapid, accurate and easy identification of species. Herein we review the state of DNA barcoding of freshwater macroinvertebrates with particular attention to Australia. First, we examine the use of DNA barcodes for species identification and compare Australian DNA barcode efforts for freshwater macroinvertebrate families with those globally. We highlight where DNA barcodes are lacking and where new efforts could be concentrated. We examine the application of DNA barcodes in freshwater bioassessment, but also consider their potential use in biosecurity and public health. We evaluate the need for a coordinated approach to developing a comprehensive national DNA barcode database for Australian freshwater macroinvertebrates and outline what is needed. Finally, we introduce the Aquatic Invertebrates of Australia (AIA) database, which is currently available on BOLD and embodies an early development towards fulfilling these needs. DNA barcoding DNA barcodes for species identification DNA barcodes have been shown to be highly effective for performing species identification in morphologically distinct species and also for detecting cryptic diversity (Hebert et al. 2003b, 2004; Jackson et al. 2014). Typically, .90% of species can be distinguished with DNA (Hebert et al. 2003a; Meyer and Paulay 2005; Virgilio et al. 2010). However, some taxa can present problems for DNA barcoding, and this needs consideration when undertaking DNA barcoding for species identification (Moritz and Cicero 2004; DeSalle et al. 2005; Will et al. 2005; Meier et al. 2008; DeWalt 2011). In some species, there is intra- and interspecific variation overlap, and thus the sequences show no ‘barcoding gap’ and different species cannot be distinguished (Kaila and Ståhls 2006). DNA barcodes can also become dissociated from species by processes such as incomplete mitochondrial lineage sorting or infection by endosymbionts, like Wolbachia (Shaw 2002; Smith et al. 2006; 1790 Marine and Freshwater Research Whitworth et al. 2007; Trewick 2008; Alexander et al. 2009; Dai et al. 2012). Alternatively, if a single species has high intraspecific variability in DNA barcodes, it can create confusion as to where species boundaries exist, especially where there is incomplete geographical sampling or varying rates of evolution between congeneric species (Meier et al. 2006; Elias et al. 2007). When developing DNA barcode databases, integrated approaches that combine morphology and other information are useful for ensuring that DNA barcodes are correctly linked to species (Moritz and Cicero 2004; DeSalle et al. 2005; Will et al. 2005; Meier et al. 2008; Ferri et al. 2009; DeWalt 2011; Srivathsan and Meier 2012). Furthermore, incongruity between morphology and DNA barcodes should be followed up with other information, such as DNA sequences from nuclear DNA regions (see Carew and Hoffmann 2015). Here we outline the importance of considering morphological information by linking DNA barcodes to voucher specimens. DNA barcodes linked to vouchered reference specimens The openness of databases, such as BOLD, is a liability and an asset. Greater access for people to contribute data results in greater accumulation of taxonomic errors, but also increases the likelihood of detecting those errors as more users scrutinise data. For most taxonomic groups, the extent of misidentified DNA sequences in databases has not been appropriately investigated. However, studies such as those of Bridge et al. (2003) and Nilsson et al. (2006) have revealed upwards of 20% error in taxonomic annotations on databases of fungi DNA. Incorrect identification can lead to ‘error cascades’ (Bortolus 2008), which can be considerable and costly. For example, incorrect identification of a mealy bug resulted in 12 years of collecting and introducing the wrong natural enemies of the coffee mealy bug as biological controls (Debach 1960; de Moraes 1987). Similarly, freshwater mussel species used to inform conservation decisions are commonly misidentified, with misidentification rates range up to 56% (Shea et al. 2011). Shea et al. (2011) concluded that such errors have the potential to bias estimates of population status and trends in these organisms. Misidentification can be common in medicinal plants (Bennett and Balick 2014) and bacteria (Janda and Abbott 2002). Because DNA barcodes are designed to be the primary source for identifying organisms, it is especially important that the data they contain are accurate. Given that incorrect taxonomic annotations are likely to persist (Nilsson et al. 2006), it is important to recognise and reduce them through well-preserved and curated vouchers or ‘reference’ specimens. The importance of voucher specimens in biological studies is well recognised (see Huber 1998). Wellpreserved and curated voucher specimens enable past studies to be re-examined, and may resolve identification issues (Huber 1998). The need for suitable voucher specimens means that DNA extraction methods used for DNA barcoding such specimens need to maintain their taxonomic integrity (e.g. Rowley et al. 2007; Castalanelli et al. 2010; Porco et al. 2010). Alongside appropriate voucher material, there must also be appropriate taxonomic expertise, because incorrect identification of the voucher specimen represents a major, basal error. In open-access databases, taxonomic errors can easily arise from M. E. Carew et al. contributors adding taxonomic annotations to DNA sequences for taxa for which they lack taxonomic expertise. Over time, taxonomic errors may be discovered as more voucher specimens of a species become available for comparison, but where this does not occur taxonomic misidentifications may remain unnoticed. For this reason, creating DNA barcodes from multiple individuals of a species is preferable. This not only provides assurance of a specimen’s identity, but can assist in delineating species because it enables intraspecific variation in DNA barcodes to be characterised (Hajibabaei et al. 2005; Ekrem et al. 2007). Indeed, some databases, such as the Reference DNA Barcode Database of BOLD, include only validated DNA barcodes from species represented by at least three individuals (Virgilio et al. 2010). DNA barcodes for macroinvertebrate species identification Coordinated approaches to DNA barcode databases have been enormously successful for large datasets, whether based on geographical regions (e.g. the Costa Rica barcode project; http://www.ibol.org/costa-rica/, accessed 6 February 2017) or particular orders or families (FishBOL; http://www.fishbol.org/, 6 February 2017). In many cases, DNA barcode databases may be well developed because of specific projects or initiatives. For macroinvertebrates, this is best illustrated in North America (e.g. Baird et al. 2011; Hajibabaei et al. 2011; Sweeney et al. 2011; Jackson et al. 2014), including for specific families (e.g. Ball et al. 2005; Zhou et al. 2009, 2011; Kim et al. 2012). Similarly, there are large-scale projects aimed at DNA barcoding thousands of species from invertebrate orders such as the Oligochaeta (Vivien et al. 2015), Ephemeroptera (Webb et al. 2012), Coleoptera (Pentinsaari et al. 2014) and Hemiptera (Gwiazdowski et al. 2015). Coordinated initiatives such as these enable researchers to easily see where gaps in the data exist, thus providing direction for future effort and reducing duplication of effort. Such initiatives foster greater collaboration by bringing together researchers from different disciplines or geographical areas, and this collaborative approach means that DNA barcode databases can be rapidly populated. Currently, there are over 519 125 sequences available on BOLD from ,22 458 species from macroinvertebrate families with aquatic life stages (http:// www.boldsystems.org/). However, with an estimated 107 730 freshwater invertebrate species worldwide (Balian et al. 2008), this represents only a fraction of the true macroinvertebrate species diversity. Globally, research efforts show positive progress towards comprehensive DNA barcode databases for macroinvertebrate families from some taxonomic groups (Fig. 1a). For example, over half the estimated species of the freshwater taxa in Gastropoda and Hirundinea have DNA barcodes on BOLD, but these groups also have comparatively low estimates of species diversity. Trichoptera, Amphipoda and Bivalvia are also well covered, with approximately one-third of the estimated freshwater species represented with DNA barcodes on BOLD (Fig. 1a). DNA barcodes can be produced for known species or ‘named’ species. Alternatively, DNA barcodes may be produced from individuals that have not been identified to species level or belong to groups with cryptic diversity. To deal with DNA database of Australia’s macroinvertebrates Marine and Freshwater Research Number of DNA barcode BINs (a) 1 000 000 1791 Estimated number of species Number of species with DNA barcodes 100 000 10 000 1000 100 Cnidaria Coleoptera Decapoda Diptera Ephemeroptera Gastropoda Hemiptera Hirudinea Mecoptera Nematoda Nematomorpha Nemertea Neuroptera Odonata Oligochaeta Platyhelminthes Plecoptera Porifera Trichoptera Cnidaria Coleoptera Decapoda Diptera Ephemeroptera Gastropoda Hemiptera Hirudinea Mecoptera Nematoda Nematomorpha Nemertea Neuroptera Odonata Oligochaeta Platyhelminthes Plecoptera Porifera Trichoptera Amphipoda Amphipoda Bivalvia Acarina log scale 1 Acarina 10 (b) 100 000 10 000 1000 100 1 Bivalvia 10 Order Fig. 1. DNA barcodes available for freshwater macroinvertebrate orders (a) globally and (b) within Australia, showing the number of barcode index numbers (BINs) and named species with DNA barcodes (http://www.boldsystems.org/), which is compared with the estimated number for each order (taken from the Global Biodiversity Information Facility; http://www. gbif.org/). Lepidoptera were excluded because of the many terrestrial species. these taxonomic limitations, DNA barcodes are assigned barcode index numbers (BINs), which group DNA barcodes into potential ‘species’ groups based on DNA sequence similarity (Ratnasingham and Hebert 2013; Fig. 1a). For example, few of the Acarina (mites) in BOLD are identified to species level, most likely because of taxonomic limitations and difficulties associated with preparing and accurately identifying mites, but the order is represented by 3121 BINs (http://www.boldsystems. org/). High diversity in other freshwater orders, such as the Coleoptera and Diptera (which are estimated to contain over 110 000 and 64 000 freshwater species respectively), can slow the progress of DNA barcoding because of the sheer volume of barcodes required to gain comprehensive coverage. High species diversity can also be associated with the presence of many undescribed species, which can impede efforts to link DNA barcodes with taxonomic descriptions and names. For Coleoptera, there are more species named than BINs assigned in BOLD because of many single species DNA barcodes (BINs can only be assigned if there are multiple similar individuals with DNA barcodes; Fig. 1a). Despite high species diversity, DNA barcoding projects targeting Coleoptera show that DNA barcoding is able to identify .98% of species with highly structured interspecific variation (Monaghan et al. 2005; Pentinsaari et al. 2014). In contrast, it is less clear whether DNA barcoding is highly accurate for Diptera. Meier et al. (2006) stated that ,70% of Diptera were identified accurately with DNA barcodes. However, Virgilio et al. (2010) suggested that the use of singleton DNA barcodes contributed to this high error. The existence of species complexes in some Dipteran families has also been shown to reduce the success of DNA barcoding (e.g. Jiang et al. 2014). Only ongoing effort to DNA barcode species, and compare these with morphological characters and other information, will determine how well DNA barcodes will delineate species within the various taxonomic groups. 1792 Marine and Freshwater Research DNA barcodes for Australian macroinvertebrates In Australia, a comprehensive DNA barcode database is lacking for the freshwater macroinvertebrate fauna. DNA barcodes are available for a limited number of Australian taxa, but most of Australia’s macroinvertebrates have not been sequenced. Currently, there are over 6600 sequences available on BOLD from ,1200 species from macroinvertebrate families with aquatic life stages in Australia (http://www.boldsystems.org/). However, estimates of Australian macroinvertebrate diversity exceed 21 000 species (Global Biodiversity information Facility; http://www.gbif.org/, accessed August 2015). DNA barcoding of Australian freshwater macroinvertebrates has focused on particular groups or regions, such as the Trichoptera (e.g. Shackleton and Webb 2014) and Chironomidae (e.g. Carew et al. 2007a, 2011a, 2013; Carew and Hoffmann 2015), and some groups associated with human and animal health, such as Culicidae (Batovska et al. 2016) and Ceratopogonidae (Bellis et al. 2015). The most taxonomically diverse DNA barcoding study of freshwater macroinvertebrates in Australia to date was undertaken by Shackleton and Rees (2016), in which DNA barcodes were created for 227 individuals from 11 orders collected throughout south-eastern Australia. This effort has produced DNA barcoding coverage for many common freshwater macroinvertebrates in temperate Australia, but similarly broad coverage is lacking in Western Australia, Tasmania, and northern Australia, where endemic taxa are likely to exist and, in some regions, there is likely to be high levels of species diversity (see Oliver et al. 2014). In addition, because the COI gene is often used for systematic (e.g. Webb and Suter 2010; Mynott et al. 2011; Mynott 2015) and phylogeographic (e.g. Baker et al. 2004; Page et al. 2005; Cook et al. 2006; Murphy et al. 2009) studies, DNA barcode information is available for many additional taxa. Within the Australian fauna, the groups best represented by DNA barcodes include the Amphipoda and Decapoda, for which one-third of the estimated species are sequenced (Fig. 1b), although the presence of cryptic species, especially in Amphipoda (see Murphy et al. 2009), means the total number of species is likely to be vastly underestimated. DNA barcodes for over 100 species in Coleoptera, Diptera, Ephemeroptera and Trichoptera are also currently available (Fig. 1b). Within Coleoptera, DNA barcodes are largely biased to Carabidae, Staphylinidae and Elmidae, and in Diptera they are biases for Chironomidae and Culicidae. Most other macroinvertebrate families have few DNA barcodes available (Table 1), with common families in Coleoptera, Diptera, Hemiptera and Odonata poorly represented by DNA barcodes relative to their diversity. For example, no DNA barcodes from Australia are available on BOLD for diverse families such as the Hydraenidae and Tabanidae. Gaining better coverage of these groups will be essential in future DNA barcoding efforts. Using DNA barcodes in environmental monitoring and surveillance DNA barcodes for detecting species Compiling an accurate reference DNA barcode database can be costly and slow for the large-scale production of DNA barcodes. However, accurate DNA barcodes are critical for applying a M. E. Carew et al. molecular approach to routine detection of species in environmental samples. Once DNA barcodes are accurately linked to species, this will allow researchers from different laboratories and institutions to easily recognise species by their DNA barcodes (Hebert et al. 2013). New molecular tools based on high-throughput sequencing (HTS) of individuals or bulk analysis of DNA barcodes from mixed macroinvertebrate samples (metabarcoding; Hajibabaei et al. 2011; Gibson et al. 2014; Shokralla et al. 2015) could be used to rapidly detect species for bioassessment. Furthermore, rare, invasive or medically significant species could be detected through environmental (e) DNA analysis (Ficetola et al. 2008; Foote et al. 2012; Jerde et al. 2011). Here, we discuss these techniques in more detail. DNA barcodes in freshwater bioassessment A substantial component of time and expense in bioassessment programs is taken up by identifications (Marshall et al. 2006). Emerging molecular technologies and approaches can help improve the efficiency of bioassessment and meet the challenges of current and future large-scale sampling programs (Dafforn et al. 2016). Bioassessment practitioners need costeffective tools for biomonitoring so that limited funds can be targeted efficiently to improve biodiversity and ecosystem condition (Nichols et al. 2017). Sequencing of DNA barcodes has largely been completed using the Sanger method (Sanger et al. 1977), but new HTS methods have been shown to increase the output and reduce the cost of producing large-scale DNA barcodes for individual specimens (Shokralla et al. 2015). Sanger DNA sequencing is ideal for small projects, but large-scale projects should consider the benefits of these new technologies. Using HTS, it is now feasible to sequence the DNA barcodes of many individuals across multiple species and sites in a single sequencing run, effectively enabling detection of all taxa at a sampling site (Hajibabaei et al. 2011; Yu et al. 2012; Shokralla et al. 2014). Earlier studies relied on Roche 454 pyrosequencing (Roche, Basel, Switzerland), but the Illumnia MiSeq platform (Illumina Inc., San Diego, CA, USA) is currently more widely used because of its higher output and lower run costs (e.g. Gibson et al. 2014, 2015; Shokralla et al. 2015). Furthermore, because of the large amount of data produced by HTS, multiple DNA barcode markers can be simultaneously sequenced, thereby increasing the taxonomic resolution of the barcoding method (Gibson et al. 2014). However, unlike Sanger DNA sequencing, read lengths are generally restricted to less than 300 bp, meaning that full-length DNA barcodes require sequencing using multiple primer sets (see Shokralla et al. 2015). Other HTS technologies often have lower outputs or shorter read lengths than needed for barcoding (Shokralla et al. 2012). However, future advances in HTS are likely to further reduce cost and increase output and read length (e.g. Feng et al. 2015). Current HTS of DNA barcodes allows freshwater biomonitoring to move beyond assessments of the presence or absence of individuals (e.g. Carew et al. 2013; Zimmermann et al. 2014; Saito et al. 2015) and abundance. For example, DNA sequencing of multiple individuals per site will allow genetic diversity metrics to be compared across species. This can provide insight into demographic processes occurring at sites (e.g. population DNA database of Australia’s macroinvertebrates Marine and Freshwater Research 1793 Table 1. DNA barcoding of selected macroinvertebrate families in Australia taken from the Barcode of Life Database (BOLD) system, ver. 3, website (http://www.boldsystems.org/) Common families were taken from a sustainable rivers audit (available at http://www.mdba.gov.au/sustainable-rivers-audit/assets/pdf/SRA_Report_1_ tech_full.pdf, accessed 3 June 2016). BINs, barcode index numbers Order Acarina Amphipoda Bivalvia Coleoptera Decapoda Diptera Ephemeroptera Gastropoda Hemiptera Hirudinea Lepidoptera Family Trombidiformes Chiltoniidae Eusiridae Paramelitidae Corbiculidae Sphaeriidae Dytiscidae Elmidae Gyrinidae Haliplidae Hydraenidae Hydrophilidae Ptilodactylidae Psephenidae Scirtidae StaphylinidaeA Atyidae Hymenosomatidae Palaemonidae Parastacidae Athericidae Blephariceridae Ceratopogonidae Chironomidae Culicidae Dixidae Ephydridae MuscidaeA Sciomyzidae Simuliidae Tabanidae Tipulidae Ameletopsidae Baetidae Caenidae Coloburiscidae Leptophlebiidae Oniscigastridae Planorbidae Thiaridae Corixidae Gelastocoridae Gerridae Hebridae Hydrometridae Mesoveliidae Naucoridae Nepidae Notonectidae Pleidae Veliidae Glossiphoniidae PyralidaeA DNA barcodes on BOLD systems Total number of sequences Assigned BINs Named species sequenced 55 250 0 1 0 8 24 81 0 0 0 2 0 1 1 44 170 0 63 65 0 0 379 544 370 0 4 13 0 75 0 6 2 4 5 4 98 0 3 0 3 0 25 1 5 9 0 0 0 9 4 1 2245 23 0 0 1 0 2 8 19 0 0 0 1 0 0 1 31 9 0 4 32 0 0 26 68 50 0 1 2 0 12 0 4 1 3 5 1 8 0 3 0 1 0 1 0 0 0 0 0 0 1 0 1 582 2 14 0 1 0 2 13 11 0 0 0 0 0 0 1 29 28 0 5 21 0 0 15 54 35 0 0 1 0 13 0 0 0 1 2 1 4 0 3 0 1 0 4 0 1 1 0 0 0 2 2 1 638 Estimated species diversity 516 8 9 46 4 11 185 150 25 5 212 175 15 15 70 1600 17 1 12 116 12 25 174 615 275 11 85 180 11 38 243 704 1 13 5 3 54 3 50 8 31 23 32 7 8 2 8 10 40 4 39 10 48 (Continued) 1794 Marine and Freshwater Research M. E. Carew et al. Table 1. (Continued) Order Odonata Plecoptera Trichoptera Family Aeshnidae Coenagrionidae Corduliidae Diphlebiidae Gomphidae Isostictidae Lestidae Libellulidae Synlestidae Austroperlidae Eustheniidae Gripopterygidae Notonemouridae Atriplectidae Calamoceratidae Calocidae Conoesucidae Ecnomidae Glossosomatidae Helicophidae Helicopsychidae Hydrobiosidae Hydropsychidae Hydroptilidae Leptoceridae Limnephilidae Odontoceridae Philopotamidae Philorheithridae Polycentropodidae Tasimiidae DNA barcodes on BOLD systems Total number of sequences Assigned BINs Named species sequenced 0 8 0 0 1 4 3 0 5 0 0 35 4 6 42 239 129 134 34 28 40 221 268 62 278 172 26 146 105 35 25 0 5 0 0 1 2 2 0 1 0 0 3 3 2 9 28 33 47 9 8 23 48 55 19 53 33 16 33 42 18 5 0 2 0 0 0 2 3 0 3 0 0 27 2 2 9 11 14 33 4 4 27 36 23 3 40 22 2 14 13 4 6 Estimated species diversity 45 30 53 5 38 14 14 55 7 15 9 143 29 2 10 20 23 56 23 7 14 67 32 146 180 2 5 48 15 18 7 A Not all species have an aquatic life stage. recovery or crash) and, because most monitoring is undertaken over multiple seasons, it can reveal changes in population demography over time. Sequencing entire communities will enable the creation of phylogenetic trees for individual sites, and the use of community phylogenetic metrics to increase the dimensions of diversity measured by traditional metrics (e.g. Andújar et al. 2015). In addition, generating DNA sequence data across sites allows preliminary information on population structures of multiple species to be determined, and the dispersal abilities of species and connectivity among populations to be elucidated. Such information would be invaluable for monitoring habitat restoration. Moreover, by comparing population structure patterns among species, comparative phylogeographic patterns can be generated (e.g. Fujisawa et al. 2015), enabling management of freshwater systems to take into account largeand small-scale biogeographic patterns. Using HTS to process mixed macroinvertebrate samples allows large-scale and rapid detection of multiple species from diverse taxonomic backgrounds (e.g. Zhou et al. 2013; Gibson et al. 2014, 2015; Kermarrec et al. 2014; Vivien et al. 2016) without the need to separate individuals. This method is termed ‘metabarcoding’ or ‘environmental barcoding’. Metabarcoding is best suited for detecting species presence in samples rather than for estimating abundance (Elbrecht and Leese 2015). Rather than full DNA barcodes, single or multiple shorter DNA barcodes are sequenced using highly universal, often degenerate, polymerase chain reaction (PCR) primers within the chosen DNA barcode region (e.g. Hajibabaei et al. 2012; Clarke et al. 2014; Brandon-Mong et al. 2015) to ensure that species diversity in samples has been adequately captured. Although the COI DNA barcoding region is mostly used (Carew et al. 2013; Gibson et al. 2014; Brandon-Mong et al. 2015), other regions can be suitable for insects (Kocher et al. 2017). Currently, macroinvertebrate samples processed using metabarcoding detect approximately 90% of species present in samples (Hajibabaei et al. 2011, 2012; Gibson et al. 2014). Taxa missed during these studies tend to be species represented by single individuals or those that do not amplify with the PCR primers selected (e.g. Elbrecht and Leese 2015). However, metabarcoding can also amplify DNA barcodes from egg masses, tissue DNA database of Australia’s macroinvertebrates fragments or gut contents present in macroinvertebrate samples (Hajibabaei et al. 2011; Gibson et al. 2014). Species detected from these sources are generally uncommon compared with the taxa present in samples and are unlikely to present major issues for metabarcoding (e.g. Hajibabaei et al. 2011). Metabarcoding provides an unprecedented potential to investigate distributions of species along environmental gradients and to consider temporal variation in species distributions using sites that have been visited on multiple occasions. It enables new species-based bioassessment tools to be developed that incorporate species responses to environmental changes but also provide a rapid and cost-effective means for detecting species during these assessments (Baird and Hajibabaei 2012). DNA barcodes in freshwater biosurveillance The freshwater environment is host to a variety of macroinvertebrates that pose biosecurity or health risks to environments and people. Invertebrates such as mosquitoes, midges, horse flies and black flies are capable of transmitting viruses and parasites, and are thus vectors for debilitating diseases such as Dengue fever, filariasis, Japanese encephalitis and Ross River fever (Likens 2010). These insects breed in and around water, making molecular surveillance a useful way to measure vector incidence and determine public health risk. Consistent monitoring and knowledge of breeding sites can help guide source reduction with the development of location-specific control measures (Regis et al. 2008). In addition to disease vectors, there are freshwater pests that pose agricultural threats. For example, the golden apple snail (Pomacea canaliculata) and the rice water weevil (Lissorhoptrus oryzophilus) are exotic pests that would severely affect Australia’s A$288 million per annum rice industry if they were to become established in Australia (Plant Health Australia 2009). Regular surveillance of freshwater environments needs to be undertaken to ensure these agricultural pests are quickly detected and eradicated. Advances in molecular biological monitoring techniques would greatly improve freshwater biosecurity surveillance. For example, using DNA barcodes detected from eDNA would be particularly useful (see Ficetola et al. 2008; Jerde et al. 2011; Mächler et al. 2014). Testing eDNA obtained from water samples for the presence of macroinvertebrate species that pose a biosecurity or public health threat can enable them to be detected without the need to capture whole specimens. However, these efforts still rely on the initial development of a DNA barcode database based on accurately identified specimens. What is needed to create a DNA barcode database for Australian macroinvertebrates? Better coordination through a single ‘geographically specific’ database Currently, DNA barcoding projects in Australia are uncoordinated, which has resulted in numerous examples of species with multiple DNA barcodes from multiple projects while other taxonomic groups are entirely missed. Better coordination of projects and direction of resources is key to creating a comprehensive Australian macroinvertebrate DNA barcode database. This could be achieved through a database with a single Marine and Freshwater Research 1795 point of entry that allows users to access Australian data. It would allow those planning a DNA barcode project to examine what DNA barcodes are available in their region or for given taxa. Current DNA sequence databases, such as GenBank (Benson et al. 2009), do not always include geographical information, making it difficult to obtain comprehensive datasets for specific regions. However, BOLD is an open-access system where DNA barcode data can be organised into discrete projects based on taxonomic group or geographical region. These range from specific taxon datasets occurring in a limited geographical area of interest to datasets containing multiple taxa over large areas (continent or country). The BOLD platform also allows users to download DNA barcode data from user-defined geographical boundaries, making it easy to retrieve data from a geographic area such as Australia. Quality of specimen identification A major component lacking from all current DNA sequence databases is a measure of the reliability or accuracy of the identifications given to DNA-barcoded specimens. A measure of accuracy of identifications can give users an indication of whether DNA barcodes are suitable as a reference for species identification or whether they should be treated with caution. Currently, there are DNA barcodes found in BOLD with identical BINs (Ratnasingham and Hebert 2013) but conflicting species identifications. Species that belong to cryptic complexes, those that have multiple synonyms or unresolved taxonomic status, are undescribed or are misidentified are at particular risk of creating confusion for DNA barcode database users. For example, a species of Leptoceridae from the genus Triplectides has three names in BOLD: T. XZspAU2, T. australis and T. magnus. This makes it difficult for those using the database to know whether the species has multiple synonyms, if the taxonomic status is unresolved or whether some specimens have been misidentified. There are many other instances where there is ambiguity in assigning species names to DNA barcodes in BOLD. Incorporating ‘tags’ to specimens indicating whether they had been identified by a taxonomic expert would be highly beneficial. The BOLD platform can allow tagging of data. We propose DNA barcodes could be tagged ‘Gold’ if identification has been undertaken by a taxonomic expert and the specimen has been vouchered and deposited in an appropriate facility (i.e. museum or other recognised reference collection) with collection details on the sampling location. Ideally, all data would be gold-standard. However, we recognise that this would not be feasible for a large portion of data collected and that those data are still a valuable resource. For this reason, specimen data that do not meet these standards would still be included in a database but remain untagged. If accurate identification is critical to a research project, users can choose to use only the gold-standard data. In BOLD, this is simply a matter of searching for the word ‘Gold’ under the ‘Tags’ option of the search tool. The adoption of a ‘Gold’ label system provides a step-up in reference specimen data quality compared with the current system on BOLD, which relies solely on whether a specimen has been provided with a species name to determine whether it should be included as a reference sequence in a BOLD identification search. 1796 Marine and Freshwater Research Allow multiple DNA sequences for specimens or species A national DNA barcode database also needs to allow for inclusion of multiple sequences from a species, and ideally be able to include other DNA sequences from outside the DNA barcode region. The BOLD platform currently accepts up to 150 sequence markers, including the major DNA barcode markers. This would provide more markers than necessary for programs requiring DNA-based identification, but allows for the collection of gene markers that could be used for other purposes, such as resolving species status and phylogenetic or population– genetic studies. Targeting macroinvertebrate families and geographical regions for DNA barcoding Continued development of a national DNA barcode database will require many more DNA barcodes from specific geographic regions and taxonomic groups. More DNA barcodes from Western Australia, Tasmania and northern Australia are needed. We suggest that taxa from common and diverse families with few or no DNA barcodes would be a useful start for gaining better taxonomic coverage. In particular, this could include families in Diptera, Coleoptera, Hemiptera and Odonata, which are currently under-represented (see Table 1) but are also common and widespread. Further inclusion of non-insect taxa with poor coverage or those that remain difficult to identify below Order level, such as Platyhelminthes, Acarina and native Oligochaeta, would facilitate finer-level identification of these groups and has the potential to enable these groups to be widely used at species level in biological monitoring. Targeting DNA barcoding of specific families that contain taxa known to differ in their environmental responses may be of benefit to bioassessment practitioners (see Table 2). Sample preservation To help facilitate the development of a national DNA barcode database, the dual preservation of material for DNA amplification and taxonomic work should be considered. Preservation of material for DNA barcoding is often done by freezing fresh material in a generous amount of absolute ethanol that is periodically replaced (Hajibabaei et al. 2005). This method generally allows for the amplification of DNA barcodes (and other genetic markers) many years after samples have been collected. In contrast, macroinvertebrates collected for morphological identification in Australia, usually during freshwater bioassessment surveys, are routinely stored in 70% ethanol at room temperature (e.g. Chessman 1995; Marchant and Hehir 2002; Walsh 2006). Although short-term storage under these conditions can often result in successful amplification of DNA barcodes (Stein et al. 2013), the long-term storage of macroinvertebrates in 70% ethanol at room temperature can result in severe DNA degradation (Hajibabaei et al. 2005; Zimmermann et al. 2008; Baird et al. 2011), particularly when coupled with the use of formalin (Baird et al. 2011). This can be further exacerbated by insufficient ethanol being added when samples are collected, evaporation of ethanol over time or when too many individuals are stored together. In the future, macroinvertebrate collections made during freshwater bioassessment should be considered for their M. E. Carew et al. Table 2. Selected Australian macroinvertebrate families with variable genus level Stream Invertebrate Grade Average Level (SIGNAL) biotic indices Australian genera SIGNAL scores data are scores for the 10-most sensitive to the 1-most tolerant (from Chessman et al. 2007) Order Family Australian genera SIGNAL scores Coleoptera Dytiscidae Elmidae Atyidae Simuliidae Subfamily Chironominae Subfamily Orthocladiinae Subfamily Tanypodinae Tipulidae Baetidae Leptophlebiidae Corixidae Libellulidae Hydrophilidae Hydropsychidae Hydroptilidae Leptoceridae 2 to 10 6 to 8 5 to 10 4 to 6 3 to 9 3 to 9 5 to 8 3 to 8 7 to 10 8 to 10 5 to 9 1 to 7 5 to 8 5 to 8 3 to 8 6 to 8 Decopoda Diptera Ephemeroptera Hemiptera Odonata Trichoptera potential to produce DNA barcodes. Several small changes to existing protocols in bioassessment laboratories could be used to better preserve material for DNA barcoding. For example, ensuring adequate .80% ethanol is added at the time of collection with cold storage at ,48C until processing or identification would slow down the initial degradation of DNA. Using absolute (.95%) ethanol for long-term cold storage of sorted macroinvertebrate samples would be a better preservation method. Although these methods are unlikely to prevent all DNA degradation, they would likely allow a greater window of opportunity for archived samples of macroinvertebrates to be reliably used for DNA barcoding. Alternatively, other storage mediums, such as RNAlater (Ambion Inc., Austin, TX, USA) and propylene glycol, have been shown successful for DNA preservation (Vink et al. 2005; Moreau et al. 2013) and could be considered as alternatives to ethanol preservation. RNAlater preserves both DNA and RNA, allowing possible additional transcriptome-based applications; however, it is fairly expensive. Propylene glycol is economical, does not evaporate and is non-flammable (Moreau et al. 2013). It has also been shown to be as effective as absolute ethanol for short-term DNA preservation at ambient temperature at fairly low concentrations (.40%; Ferro and Park 2013). In addition, other preservatives, such as extraction lysis buffers (e.g. Dawson et al. 1998) and other commonly available solutions (e.g. Steininger et al. 2015), can be used for short-term DNA preservation. Preserved specimens, such as dry pinned adult insects (e.g. in reference collections), can provide good sources of DNA for prolonged periods of time (i.e. decades) and can be useful for establishing reference DNA barcodes (Vij et al. 1997; Hernández-Triana et al. 2014; Batovska et al. 2016). These preserved reference specimens can be sampled by removing a DNA database of Australia’s macroinvertebrates Marine and Freshwater Research Table 3. Project names and abbreviations of the Aquatic Invertebrates of Australia (AIA) database, currently available on the Barcode of Life Database (BOLD) (http://www.boldsystems.org/) Name Abbreviation Aquatic Invertebrates of Australia Aquatic Acarina of Australia Aquatic Coleoptera of Australia Aquatic Hemiptera of Australia Aquatic Springtails (Collembola) of Australia Australian Chironomidae Australian Megaloptera Australian Odonata Blephariceridae of Australia Crustacea of Australia Culicidae of Australia Ephemeroptera of Australia Plecoptera of Australia Simuliidae of Australia Tanyderidae of Australia Tipulomorpha of Australia Trichoptera of Australia AIA ACAIA COAIA HEAIA SPAIA CHAIA MEAIA ODAIA BLAIA CRAIA CUAIA EPAIA PLAIA SIAIA TAAIA TIAIA TRAIA 1797 into understanding population structures. Globally, the effort to DNA barcode freshwater macroinvertebrates has been uneven across taxa and countries. In Australia, only few reasonably common, abundant and ecologically important freshwater macroinvertebrate taxa have DNA barcodes, despite their importance in bioassessment. In order to create a comprehensive DNA barcode database for Australia’s freshwater macroinvertebrate fauna, a more coordinated approach between those undertaking DNA barcoding is needed to increase taxonomic and geographical coverage. A central and publicly accessible repository for DNA barcode data will facilitate greater coordination and collaboration. Coupling DNA barcoding efforts with ongoing biological monitoring programs will facilitate DNA barcoding for many taxa, especially if samples are adequately preserved. Herein we have presented a system for assessing the reliability of specimen identifications, an easily interpretable and openly accessible database, sequences linked to vouchered specimens and the ability to include multiple genetic markers. We make the first attempt at establishing such a national DNA barcode database for macroinvertebrates, in the AIA database. We have largely focused on Australia, but the methods and considerations in this review could be used worldwide. Acknowledgements small section (e.g. a leg) or preferably non-destructively, as alternative DNA extraction methods that result in retention of voucher material are available (Castalanelli et al. 2010; Porco et al. 2010; Krosch and Cranston 2012). Current progress in creating a DNA barcode database for Australian macroinvertebrates To address the need for a national DNA database of Australian freshwater macroinvertebrates that takes into account the factors mentioned above, we developed the AIA database within BOLD (http://www.barcodinglife.com/index.php/MAS_ Management_OpenProject?code=AIA, accessed 6 February 2017). The AIA currently contains 877 COI sequences from 16 taxonomic groups. The AIA is structured as an ‘umbrella’ project to include 16 subprojects. Most projects are specific to a taxonomic order. However, for ease of use, projects containing data on Diptera are divided mostly into families, with the exception of the Tipulomorpha project, which is a suborder project. Projects can be searched for by their name or their abbreviation (see Table 3). The data can be publicly accessed and those wanting to contribute data can request access to edit project data. We encourage those working on Australian freshwater macroinvertebrates to consider depositing their data into the AIA database. Access to deposit and edit data can be gained by contacting the corresponding author of this publication or the managers of the various subprojects as given on BOLD. Conclusions DNA barcoding macroinvertebrates is a burgeoning research area that can increase our knowledge of biodiversity in freshwater ecosystems and provide for cost-effective identification tools for bioassessment. DNA barcodes have practical applications for biosecurity and public health, and could be extended The authors thank Ary Hoffmann for his comments on this manuscript. The authors also thank Chris Davey, Gavin Rees, Julia Mynott, Philip Suter, Ivor Growns, Tapas Biswas, Jan Strugnell, Andrew Mitchell, Paul McInerney and Edward Tsyrlin who attended the first macroinvertebrate barcoding meeting on 30 October 2014 and provided the impetus for this publication. Work on this manuscript by S. J. Nichols was supported by a grant from the NSW Environmental Trust (project title: DNA-based Identification for Routine Aquatic Bio-assessment; http://www.environment.nsw.gov.au/ grants/2015-research.htm#dna, accessed 3 February 2017). References Alexander, L. 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