Carew etal 2017

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
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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;
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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
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Number of DNA barcode BINs
(a) 1 000 000
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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.
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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
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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).
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