Abstract
This review article explores the state of DNA barcoding of macroalgae in the Mediterranean Sea. Data from the Barcode of Life Data System (BOLD) were utilised in conjunction with a thorough bibliographic review. Our findings indicate that from around 1124 records of algae in the Mediterranean Sea, only 114 species have been barcoded. We thus conclude that there are insufficient macroalgal genetic data from the Mediterranean and that this area would greatly benefit from studies involving DNA barcoding. Such research would contribute to resolving numerous questions about macroalgal systematics in the area and address queries related to biogeography, especially those concerned with non-indigenous species. It could also possibly result in the development and application of better, cost-effective biodiversity monitoring programmes emanating from UN conventions and EU Directives. One possible way of achieving this is to construct DNA libraries via sequencing and barcoding, subsequently enabling better cost-effective biodiversity monitoring through environmental DNA metabarcoding.
Introduction
This review delves into the current state of DNA barcoding of macroalgae with a focus on the Mediterranean Sea. To this end, data from the Barcode of Life Data System (BOLD) were researched and a literature review was conducted. The study concludes that there is a lack of genetic data for these organisms. This article discusses the steps required for improving DNA-based methods in this region.
Algae including macrophytes and phytoplankton are the base of marine food webs, provide oxygen to aquatic environments and humans, can be used as biological indicators, and are a potential food source which is underutilised in most parts of the world (Wolf 2012). However, in direct contrast to their importance in marine ecosystems (Brodie et al. 2017), only 10% of algal species are believed to have been described so far (De Vargas et al. 2015).
Macroalgae include mainly three phylogenetic lineages. Green and red algae originated about 1.5 billion years ago after the endosymbiotic event that resulted in the first photosynthetic eukaryotes (Yoon et al. 2004), (Rodríguez-Ezpeleta et al. 2005). On the other hand, the brown algae evolved after a secondary endosymbiotic event that occurred around 1.3 billion years ago, involving a unicellular red alga engulfed by a heterokont protist that was only distantly related to the green and red algae (Yoon et al. 2004), (Ševčíková et al. 2015). Complex multicellularity is thought to have evolved independently in the three large groups (Cock et al. 2010).
The term “seaweed” has traditionally been used to define the macroscopic, multicellular marine algae (Wolf 2012). However, these may also exist as microscopic representatives since all macroalgae are unicellular at some point in their life cycle, as zygotes or spores, which could also be temporarily planktonic (Amsler and Searles 1980).
The term Mediterranean is derived from the Latin Mare medi terraneum, meaning “sea in the middle of the land” (Coll et al. 2010). It is the largest and deepest semi-enclosed sea in the world. The Mediterranean is connected to the Atlantic Ocean through the Strait of Gibraltar, to the Sea of Marmara and the Black Sea through the Dardanelles and to the Red Sea and the Indian Ocean through the Suez Canal (Coll et al. 2010).
The Mediterranean has a large area classified as deep sea with high salinity (37.5–39.5) and homothermy from around 300 to 500 m to the bottom of 12.8–13.5°C in the western basin and 13.5–15.5°C in the eastern basin (Coll et al. 2010). It also has a geological history that included segregation from the other seas causing the Messinian Salinity Crisis around 5.96 million years ago, which brought about major changes in climate, salinity and sea level (Coll et al. 2010).
Although Mediterranean species evolved from ancestors originating from the Atlantic following the Messinian Salinity Crisis, both temperate and subtropical organisms have managed to survive in the Mediterranean’s wide range of hydrology and climate. However, the Mediterranean Sea also has a high level of endemism (Coll et al. 2010), (Pascual et al. 2017) and is considered a biodiversity hotspot.
DNA barcoding involves the identification of DNA sequences by checking their identity against a publicly-accessible database. DNA barcoding has revolutionised classical species identification, biogeographical and population studies. The concept of DNA barcoding is based upon sequencing one or a few relatively short loci, which are standardised among a large number of taxa, and which provide an unambiguous identification of the given taxon – similar to barcoding a commercial product (Hebert et al. 2003). It is meant to be rapid, relatively inexpensive and applicable using generic methodology and equipment – thus, at least in principle, empowering non-specialists or even non-scientists to obtain a correct identification of any previously identified taxon (including groups for which very few taxonomic experts exist worldwide).
So far, barcoded taxa span across almost all known eukaryotic phyla, including groups which are morphologically difficult to identify such as the oomycete genus Pythium (Robideau et al. 2011), dinoflagellates (Stern et al. 2012) or the ciliate Tetrahymena (Kher et al. 2011). However, large gaps in coverage still exist, both geographically and phylogenetically.
DNA barcoding provides many benefits including identification at a faster speed especially for routine monitoring and also for the discovery of new algal species which could be flagged faster (Saunders and Kucera 2010). In addition, it enables the identification of algal species at different life stages and even degraded DNA samples from museum specimens (Comtet et al. 2015). DNA barcoding has often highlighted the occurrence of cryptic taxa (McIvor et al. 2001), (Andreakis et al. 2007) and the detection of morphologically cryptic algae at new locations (Provan et al. 2005), (Uwai et al. 2006). This is important since cryptic species are frequently produced by recent speciation or convergent evolution, and these species are impossible to differentiate morphologically; however, they are genetically distinct (Cianciola et al. 2010). Moreover, diverse genetic populations which are indistinguishable morphologically may have a different invasive range (e.g. Voisin et al. 2005). Crypticisms could refer to cryptic size (i.e. microscopic species), cryptic stages that may be unknown microscopic forms of a known macroalgal species, cryptic morphology (i.e. where morphology is hardly discernible) or cryptic species (i.e. different species of similar morphologies) (Peters et al. 2015). Morphological identification is problematic in the case of crypticisms and thus DNA barcoding is necessary to reveal them. For example, as indicated in Zanolla et al. (2018), genetic studies have revealed two cryptic lineages in Asparagopsis armata Harvey and six in Asparagopsis taxiformis (Delile) Trevisan (Andreakis et al. 2004, 2016, Dijoux et al. 2014) and, due to their cryptogenic status, their native ranges remain unknown (Dijoux et al. 2014). Cryptogenic species are species with an undefined origin which are often small and/or cryptic, belong to taxonomic groups which have not been studied extensively and are frequently described taxonomically by diverse names in different new areas (Carlton 2009, Mineur et al. 2012).
DNA metabarcoding combines the concepts of DNA barcoding with that of Next Generation Sequencing (NGS) and involves High-Throughput Sequencing (HTS) methods applied directly to an environmental sample without the prerequisite of isolation of individuals (Porter and Hajibabaei 2018). The importance of environmental DNA (eDNA) metabarcoding is that it simultaneously characterises the composition of species in environmental DNA, and is thus contributing significantly to taxonomic, ecological and biogeochemical studies (Deagle et al. 2014).
eDNA consists of genetic material which is present in samples such as sediment, air and water, including whole cells, cell debris, different life stages such as propagules, whole microorganisms and/or extracellular DNA (Ficetola et al. 2008). Community DNA also relies on metabarcoding; however, it is distinguished as “bulk organismal samples”. The difference to eDNA is blurred in this case, especially when whole microorganisms are captured in eDNA samples (Ruppert et al. 2019). eDNA metabarcoding is useful in detecting invasive species rapidly, toxic species (such as species that cause Harmful Algal Blooms), the presence of endemic species which are threatened, rare species or species which are traditionally difficult to detect (Ruppert et al. 2019). Algal metabarcoding could target single cells, planktonic life stages, or cell debris from macrophytes to answer ecological questions or to address policy requirements. The application of eDNA metabarcoding to the marine environment has adopted many collection procedures which include sampling of sediments, water, specimens and biofilms (Ruppert et al. 2019). Koziol et al. (2019) compared the results obtained from different substrates including marine sediment, surface water, settlement-plates and planktonic tows and concluded that there was a significant variation between taxa observed in the different substrates. Thus, they concluded that a single substrate underestimates eukaryotic diversity and that future studies should use more than one substrate, as well as choosing the substrate in conjunction with the taxa of interest carefully. Sampling for eDNA metabarcoding from different substrates is preferably carried out on the same day, as in the study by Koziol et al. (2019), where water was sampled in sterile bottles, filtered and stored at −20°C. On the other hand, sediment was obtained through a Van-Veen grab and plankton was captured in planktonic tows and concentrated in 50–70 ml sterile containers that were stored at −20°C.
For DNA metabarcoding to be successful, it must be ensured that the barcode locus chosen with given primers is suitable for all target species and that the barcode is sufficiently variable to allow distinction between species (Hebert et al. 2003). The barcode also needs to be flanked by two conserved regions.
On the other hand, the Germling Emergence Method, which involves the incubation of a sample of substratum in a herbivore-free and nutrient-rich environment, facilitates the subsequent isolation of developing germlings (Peters et al. 2015). This, coupled with DNA barcoding, provides opportunities to study morphology and life cycles of macroalgae in parallel with genetic analysis (Fredericq et al. 2014).
With regard to the Mediterranean Sea, the study of genetic diversity is particularly important since geographically enclosed ecosystems are at a higher risk of species loss and genetic erosion in view of dispersal and range shift barriers (Buonomo et al. 2018). In fact, Buonomo et al. (2018) concluded that the coverage of Carpodesmia tamariscifolia (Hudson) Orellana & Sansón, Carpodesmia amentacea (C. Agardh) Orellana & Sansón and Cystoseira compressa (Esper) Gerloff & Nizamuddin would substantially decrease under two different climate change models, especially in the Mediterranean region where C. amentacea would be especially at risk of extinction (Buonomo et al. 2018). The genetic data of the aforementioned species showed that the populations were differentiated with low inter-population connectivity; thus, the loss of any population could cause a permanent loss of genetic variability (Buonomo et al. 2018). Since species of the genus Cystoseira sensu lato have a particular ecological niche and function in the Mediterranean, their loss could have wider negative implications (Buonomo et al. 2018).
Traditionally, studies of Mediterranean macroalgae have involved their morphology. A comprehensive review of macroalgal identification through morphology of macroscopic and microscopic features is provided in Cormaci et al. (2012), (2014), (2017) and Rodríguez-Prieto et al. (2013). To date, the major shortcomings of these works is that they are not connected to genetic information and there is no English translation. Moreover, AlgaeBase (Guiry and Guiry 2019) provides a database which is constantly being updated to reflect the latest algal biodiversity-related research.
Established checklists or lists of macroalgae based on morphology exist for many Mediterranean countries such as Algeria (Ould-Ahmed et al. 2013), Cyprus (Taşkın et al. 2013), (Tsiamis et al. 2014b), Egypt (Shabaka 2018), France (e.g. Thau Lagoon: Verlaque 2001), Greece (Tsiamis et al. 2013), (2014a), Israel (Einav and Israel 2008), Italy (Furnari et al. 2003, 2010), Malta (Cormaci et al. 1997), Morocco (Benhissoune et al. 2001), (2002a),b, 2003), Spain (e.g. Catalonia: Ballesteros 1990), Turkey (Taşkın 2008) and regions such as the eastern Adriatic coast (Antolić et al. 2001), (2010), (2011), (2013). Moreover, a few checklists of the Mediterranean as a whole are available, such as those by Gallardo et al. (1993) for Chlorophyta, Ribera et al. (1992) for Phaeophyceae and Gómez Garreta et al. (2001) for Rhodophyta. However, such checklists need to be updated regularly in view of new discoveries and the introduction of non-indigenous species.
Mediterranean European Union (EU) Member States are legally bound to adhere to the EU Water Framework Directive (WFD) and EU Marine Strategy Framework Directive (MSFD) in order to protect aquatic ecosystems. In addition, the UN “Convention for the Protection of the Marine Environment and the Coastal Region of the Mediterranean”, also referred to as the “Barcelona Convention”, which is one of the Regional Sea Conventions, applies not only to EU Member States, but also includes countries from north Africa and the eastern Mediterranean. The overall aim of the Barcelona Convention is to protect and improve the marine and coastal environment of the Mediterranean.
There have been extensive morphological studies in the Mediterranean in view of ecological indices utilised for the EU WFD, EU MSFD and the UN Barcelona Convention. These monitoring programmes have been time-consuming, as well as costing EU Member States millions of Euros. One of the shortcomings of the MSFD is that it studies specific taxa whereas the choice of taxa should ideally be based on a study of all taxa including “microbial community interactions”, which are currently not considered under the MSFD (Bourlat et al. 2013). Such shortcomings could be overcome through the inclusion of DNA-based methods. The greatest costs of including DNA-based methods in monitoring programmes would be in the setting up of the reference library, however this could be offset by future benefits and savings (Bourlat et al. 2013).
Currently, the MSFD indicators sparsely consider DNA-based methods. Recent literature is pointing towards the integration of DNA barcoding and DNA metabarcoding in MSFD indices which would result in more representative (Bourlat et al. 2013) and less costly (Aylagas et al. 2016) monitoring programmes. Table 1 (adapted from Aylagas et al. 2016) shows which tools could possibly be applied for each MSFD descriptor.
Genomic tools and their possible use with respect to Marine Strategy Framework Directive (MSFD) descriptors (source: Aylagas et al. 2016).
| Genomic tool | Application to monitoring | MSFD descriptors |
|---|---|---|
| Barcoding and Metabarcoding | Community taxonomic characterisation | D1, D2, D4, D5, D6 |
| Metagenomics | Community metabolic potential characterisation | D1, D2, D4, D5, D6 |
| Metatranscriptomics | Community metabolic activity characterisation | D1, D2, D4, D5, D6 |
| Microarrays | Metabolic activity characterisation and high-throughput species detection and quantification and gene expression quantification | D2, D5 |
| qPCR | Low-throughput species detection and quantification and gene expression quantification | D2, D5 |
| SNP genotyping | Connectivity assessment and assignment of individuals to populations | D1, D3 |
Some Mediterranean macroalgae, such as coralline algae, have received a lot of attention from a conservation point of view, despite sparse DNA sequence data which are still insufficient to define species diversity in detail (Pezzolesi et al. 2019). Pezzolesi et al. (2019) show how this is in contrast to other regions where genetic studies have been undertaken, such as the Pacific Ocean, Indo-Pacific Ocean, western Indian Ocean, Subarctic and Boreal Atlantic, Atlantic and Caribbean, among others. The lack of sequence data for the Mediterranean has also been acknowledged for the genus Ulva (Miladi et al. 2018), which on the other hand, has been well-studied morphologically (Cormaci et al. 2014).
The present review article is in agreement with the above inferences by Pezzolesi et al. (2019) and Miladi et al. (2018). In fact, both a thorough bibliographic search and the analysis of BOLD data suggest that DNA barcodes for macroalgae are underrepresented in the Mediterranean Sea.
The reason for the underrepresentation of DNA barcodes in the Mediterranean cannot be attributed to it being a species-poor region. In fact, the Mediterranean is considered species-rich based on morphological data, and it was estimated that there are ca. 277 brown, 657 red and 190 green algae in the Mediterranean Sea, making up 17.3%, 10.6% and 7.6% of the world’s taxa, respectively (Coll et al. 2010).
DNA barcoding
An ideal DNA barcode contains significant species-level genetic variability, it possesses conserved sites for developing polymerase chain reaction (PCR) primers, and is of an adequate size (approximately 600–1000 base pairs) to facilitate DNA sequencing (CBOL Plant Working Group 2009). A 600 base pair (bp) DNA sequence of the 5′-end of the mitochondrial cytochrome c oxidase subunit 1 gene (cox1, COI or COI-5P) in general fits the aforementioned description and was thus accepted as the universal species-level barcode for animals (Kress and Erickson 2012). In fact, the mitochondrial COI together with the plastid large subunit of ribulose-1,5-bisphosphate carboxylase (rbcL) are the preferred loci in DNA barcoding of brown and red macroalgae (Saunders and McDevit 2013), (Peters et al. 2015) since they discriminate well between species. The preference for the COI biomarker is due to its length which is not too short to hinder detection of species variation, nor is it too long (i.e. when sequencing a longer gene broken into fragments by multiple overlapping primer pairs) to make it expensive and time consuming (Minicante et al. 2014). COI is effective at discriminating even closely related species since it is highly variable, particularly in the third codon position (Hebert et al. 2003). However, COI is not suitable for barcoding green algae due to the presence of introns in this lineage (Pombert et al. 2004, 2006, Saunders and Kucera 2010). As a result, this review is divided into two sections: brown and red algal genes are discussed together, while green algal genes are in a separate section.
COI and rbcL barcodes have been used to identify species and to address the taxonomy and classification of the phylum Rhodophyta (Robba et al. 2006), (Saunders and McDevit 2013) including for example the genera Hypnea (Manghisi et al. 2011), (Wolf et al. 2011), Grateloupia (Wolf et al. 2014), (Yang and Kim 2015), Pachymeniopsis (Yang and Kim 2015), Kintokiocolax (Yang and Kim 2015), Kallymenia (Vergés et al. 2014), Pyropia (Minicante et al. 2014), Gracilaria (Sfriso et al. 2010), Rueness 2010), Polysiphonia, Plumaria, Ptilota, Antithamnion, “Heterosiphonia” (Rueness 2010), Gelidium, Gracilariopsis, Chondracanthus, Solieria (Mineur et al. 2012), Porphyra (Milstein et al. 2012), Felicinia (Manghisi et al. 2014), Agardhiella (Manghisi et al. 2010), Laurencia (Machin-Sanchez et al. 2014), Laurenciella (Machin-Sanchez et al. 2014), Sebdenia (Küpper et al. 2019) and Nemalion (Le Gall and Saunders 2010).
The plastid-encoded intergenic RuBisCO spacer is also well suited to be used for red and brown algal barcoding (Brodie et al. 1998), (Robba et al. 2006), (Ni-Ni-Win et al. 2011). The RuBisCO spacer has been useful in identification of species (Stache-Crain et al. 1997) even though it is less sensitive than COI; it is also sometimes not long enough for phylogenetic analysis (Robba et al. 2006) and may, like the internal transcribed spacer region of the ribosomal cistron ITS, contain indels, which make sequences difficult to align. However, the small size of the non-coding regions of the rbcL-rbcS spacer region makes them a good marker to amplify from old material (Wolf 2012).
Some other genes which are used in red and brown algae include the D2/D3 variable domains of the nuclear ribosomal large subunit (LSU-D2/D3) which is mostly used as a secondary DNA barcode (Bittner et al. 2008), (Saunders and McDevit 2012), (Manghisi et al. 2014), the mitochondrial spacer between the cytochrome oxidase subunit 2 and subunit 3 genes (cox 2-3), which has been found useful in red algal population studies since it is more variable (Wolf 2012), and the universal plastid amplicon (UPA), which has less resolution than COI; however, it is useful in separating certain genera such as in the Florideophyceae (Milstein et al. 2012).
The rbcL marker is also used in green algal studies, but it is not the best marker in this respect. Saunders and Kucera (2010) studied the applicability of rbcL, plastid elongation factor Tu gene (tufA), UPA, LSU-D2/D3 and ITS in many genera to identify the best barcode marker for green algae. They concluded that the plastid elongation factor tufA is applicable as the standard marker for all green algae except for the identification of Cladophoraceae (Saunders and McDevit 2013). It has been used for genera such as Caulerpa (Fama’ et al. 2002), (Aplikioti et al. 2016) and Ulva (Wolf et al. 2012), (Lawton et al. 2013), (Minicante et al. 2014), (Miladi et al. 2018), which are difficult to identify morphologically. However, rbcL (Wolf et al. 2012), (Saunders and McDevit 2013) and ITS (Lawton et al. 2013) are other options for species-level markers in Ulva.
ITS and the nuclear ribosomal 18S subunit (nrSSU) are the preferred genes for Cladophora species discrimination (Hayakawa et al. 2012), (Taylor et al. 2017) which has been a challenge for DNA barcoding since many biomarkers have not been successful in this genus. ITS is useful in green algal genera and has been used for studying molecular ecology and evolution as well as phylogeny (Hayden and Waaland 2004).
Generally, the choice of barcode locus will depend on which genus is being studied, as well as the question being addressed. This review provides an overview of a list of primers (Table 2) that are currently in use by scientists and that could be applied to regions where DNA barcoding is relatively new.
Some of the primers presently used for barcoding in marine macroalgae.
| Gene | Primer name | Sequence | Reference | Length (circa bp) | |
|---|---|---|---|---|---|
| Brown algae | |||||
| COI | GazF2 | CCAACCAYAAAGATATWGGTAC | Lane et al. (2007) | 650 | |
| GazR2 | GGATGACCAAARAACCAAAA | Lane et al. (2007) | |||
| rbcL+Rubisco spacer | rbcLP2F or rbcL40DF | GAWCGRACTCGAWTWAAAAGTG | Kawai et al. (2007) | >1400 | |
| rbcS139R | AGACCCCATAATTCCCAATA | Peters and Ramírez (2001), Peters et al. (2010) | |||
| rbcL+Rubisco spacer | rbcL1273F | GTGCGACAGCTAACCGTG | Peters et al. (2010) | 500 | |
| rbcS139R | As above | Peters et al. (2010) | |||
| Other rbcL primers | rbcL3F | GGCACCGGAGAATCTATATG | Peters and Ramírez (2001) | ||
| rbcL77F | TGGGNTAYTGGGATGCTGA | Yang et al. (2014) | |||
| rbcL461F | CTTACTTAAAAACTTTCCAAGG | Peters and Ramírez (2001) | |||
| rbcLRH3F | TTAAYTCTCARCCDTTYATGCG | Hanyuda et al. (2004) | |||
| rbcl952R | CATACGCATCCATTTACA | Kawai et al. (2007) | |||
| P1F | GKGTWATTTGTAARTGGATGCG | Kawai et al. (2007) | |||
| Green algae | |||||
| tufA | tufAF | TGAAACAGAAMAWCGTCATTATGC | Fama’ et al. (2002), Nielsen et al. (2013) | 900 | |
| tufAR | CCTTCNCGAATMGCRAAWCGC | Fama’ et al. (2002), Nielsen et al. (2013) | |||
| ITS1+5.8S+ITS2 | CladoITS-9Fshort | GTCGCTCCTACCGATTGGGTGTG | Hayakawa et al. (2012), Taylor et al. (2017) | 945-1100 | |
| CladoITS-7R | TCCCTTTTCGCTCGCCGTTACTA | Hayakawa et al. (2012), Taylor et al. (2017) | |||
| tufA | tufGF4 | GGNGCNGCNCAAATGGAYGG | Saunders and Kucera (2010) | 800 | |
| tufAR | As above | Saunders and Kucera (2010) | |||
| rbcL-3P | GrbcLFi | TCTCARCCWTTYATGCGTTGG | Saunders and Kucera (2010) | 740 | |
| 1385R | AATTCAAATTTAATTTCTTTCC | Saunders and Kucera (2010) | |||
| Red algae | |||||
| COI | GazF1 | TCAACAAATCATAAAGATATTGG | Saunders (2005) | 665 | |
| GazR1 | ACTTCTGGATGTCCAAAAAAYCA | Saunders (2005) | |||
| COI | GazF2 | CCAACCAYAAAGATATWGGTAC (same as above - for browns) | Saunders (2005) | 665 | |
| DumR1 | AAAAAYCARAATAAATGTTGA | Saunders (2005) | |||
| COI | GWSFn | TCAACAAAYCAYAAAGATATYGG | Le Gall and Saunders (2010), Saunders and McDevit (2012) | 665 | |
| GWSRx | ACTTCTGGRTGICCRAARAAYCA | Saunders and McDevit (2012) | |||
| COI | GHalF | TCAACAAATCATAAAGATATYGG | Saunders 2008, Iha et al. 2015 | ||
| GWSFn (could be used instead of GHalF) | As above | Le Gall and Saunders (2010), Guimarães et al. (2019) | 665 | ||
| Cox1R1 | GTATACATATGATGHGCTCAA | Saunders 2008, Guimarães et al. (2019) | |||
| rbcL | F753 | GGAAGATATGTATGAAAGAGC | Freshwater and Rueness 1994 as cited in Freshwater et al. (2017) | 700 | |
| RrbcSstart | GTTCCTTGTGTTAATCTCAC | Freshwater and Rueness (1994), Freshwater et al. (2017) | |||
| rbcL | F7 | AACTCTGTAGAACGNACAAG | Gavio and Fredericq (2002) | F7, R753, F645 and RbcSStart combined give 1400 bp | |
| R753 | GCTCTTTCATACATATCTTCC | Freshwater and Rueness (1994) | 1400 | ||
| rbcL | F645 | ATGCGTTGGAAAGAAAGATTCT | Lin et al. (2001) as cited in Bustamante et al. (2013) | ||
| RbcSStart | As above | Lin et al. (2001) as cited in Bustamante et al. (2013) | |||
| rbcL | F2 | TGTCTAACTCTGTAGAACAACGGA | Dıaz-Tapia et al. (2018) | ||
| R1452 | TGGAGTTTCYACRAAGTCAGCTGT | Dıaz-Tapia et al. (2018) | |||
| rbcL | F7 | As above | Gavio and Fredericq (2002), Dıaz-Tapia et al. (2018) | 1400 | |
| RbcSStart | As above | As above | |||
| rbcL | F57 | GTAATTCCATATGCTAAAATGGG | Saunders and Moore (2013) | 1350 | |
| rbcLrvNEW | ACATTTGCTGTTGGAGTYTC | Saunders and Moore (2013) | |||
| rbcL | F7 | As above | As above | 800 | |
| R893 | GAATAAGTTGARTTWCCIGCAC | Stuercke and Freshwater (2008) | |||
| rbcL | CfD | CCRTTYATGCGTTGGAGAGA | Hamsher et al. (2011), Saunders and McDevit 2012 | 748 (diatoms) | |
| DPrbcL7 | AARCAACCTTGTGTAAGTCT | Saunders and McDevit (2012) | |||
| Cox2-3 | cox2F | GTACCWTCTTTDRGRRKDAAATGTGATGC | Zuccarello et al. (1999) | 350-400 | |
| cox3R | GGATCTACWAGATGRAAWGGATGTC | Zuccarello et al. (1999) | |||
| LSU | T01N | GATGACCCGCTGAATTTAAG | Saunders and Moore (2013) | 2700 | |
| T15 | TGATAGGAAGAGCCGACATCGA | Saunders and Moore (2013) | |||
| LSU | T04 | GCAGGACGGTGGCCATGGAAGT | Saunders and Moore (2013) | 1500 | |
| T15 | As above | Saunders and Moore (2013) | |||
| UPA | p23SrV_f1 | GGACAGAAAGACCCTATGAA | Saunders and Moore (2013) | 370 | |
| p23SnewR | TCAGCCTGTTATCCCTAGA | Saunders and Moore (2013) | |||
Metabarcoding
The success of metabarcoding depends on the choice of primers and often involves a trade-off between taxonomic resolution and species amplification (Zhang et al. 2018). Often, however, studies use just one marker in metabarcoding (Hebert et al. 2003), (Leray et al. 2013), (Schmidt et al. 2013). In contrast, Zhang et al. (2018) used a mock zooplankton community to validate their methods in metabarcoding and concluded that the use of multiplexed markers increased species detection. The use of a single organelle marker could sometimes cause errors in species identification in view of interspecific mitochondrial introgressions (Meyer and Paulay 2005); thus both uniparentally and biparentally inherited DNA is recommended (Taberlet et al. 2012). Zhang et al. (2018) used the mitochondrial COI gene and nuclear 18S in a single Illumina run since the multiple markers approach can increase detection limits as well as decrease amplification biases. Such studies that test both taxonomic identification accuracy and species detection using multiple primers and markers against a mock community have been scarce so far (Zhang et al. 2018). Oliveira et al. (2018) describe how algal systematics can be advanced through HTS technologies and how species discoveries can be augmented through metabarcoding. Metabarcoding has the potential to help in better estimation of algal biodiversity and distribution of species even if they have not been formally described yet (Oliveira et al. 2018). However, metabarcoding currently cannot be used to characterise new species (Oliveira et al. 2018).
There are two main uses of metabarcoding in ecology and each has its challenges. One involves inferring all species present within an environmental sample and the other comprises checking the absence or presence of a particular set of species of interest (Ficetola et al. 2015). In order to overcome certain shortcomings and challenges of metabarcoding, such as false positives, Ficetola et al. (2015) studied the implications of replicates and how to reduce errors through the number of replicates. However, the challenges associated with metabarcoding can be overcome and, in another study, Ji et al. (2013) compared metabarcoding and standard taxonomy for two conservation issues and they made similar policy decisions. The same study also acknowledged how, in comparison to the classical taxonomical method, metabarcoding was taxonomically more comprehensive, quicker, did not rely on taxonomic experts and was more auditable, which is important in the case of dispute resolution. A possible limitation of metabarcoding is the lack of data in reference libraries since one can end up with sequences that do not identify with a species name due to a lack of morphological data and information related to life cycles (Danovaro et al. 2016). Moreover, incorrect (and non-updated) identifications in databases may also cause problems.
The benefits of metabarcoding outweigh its shortcomings and thus it is becoming increasingly recognised in biodiversity monitoring programmes (Taberlet et al. 2012), (Baird and Hajibabaei 2012), (Bohmann et al. 2014), (Thomsen and Willerslev 2015), (Aylagas et al. 2016). In the microalgal realm, metabarcoding has revealed a high, unexpected diversity of dinoflagellates in aquatic environments (Stern et al. 2010). Moreover, some studies have also started testing the applicability of DNA methods to the EU Marine Strategy Framework Directive (MSFD) with results showing that metabarcoding could provide a more cost-effective and holistic environmental status assessment in European directives (Aylagas et al. 2016), Aylagas 2017).
Genus and species delimitation in macroalgae
The issue of what comprises a species and what criteria should be used to delineate them has been debated extensively (Leliaert and De Clerck 2017). Identification based solely on morphological features has been problematic for many macroalgae, especially for morphologically simple species and in species that have recently diverged, including complexes of cryptic species (Leliaert and De Clerck 2017).
Saunders and McDevit (2012) attribute these challenges in macroalgal identification to simple and convergent morphologies as well as to phenotypic plasticity and variations of heteromorphic generations within life histories in macroalgae. These problems are encountered in the identification of most macroalgae (Saunders 2005, 2008). Notwithstanding the efforts taken to identify the red algae morphologically and via reproductive structures, many inaccuracies have nonetheless occurred in classification. Likewise, brown algae have been challenging to classify at all levels of taxonomic hierarchy due to their homoplasy (Silberfeld et al. 2014). The same applies for green algae. Genera such as Ulva are among the most problematic as they do not have many distinctive features, and as changes in morphology can be induced by environmental factors such as salinity or bacterial metabolites (Provasoli and Pintner 1980, Blomster et al. 1998, Marshall et al. 2006 as cited in Miladi et al. 2018).
Genera of macroalgae with well-known challenges in morphology-based identification include Ectocarpus (Leliaert and De Clerck 2017), (Montecinos et al. 2017), Desmarestia (Yang et al. 2014), Gracilaria (Kim et al. 2010), Ulvella (Nielsen et al. 2013), Fucus (Kucera 2010), Porphyra/Pyropia and relatives (Kucera 2010, Koh and Kim 2018), Ulva (Kucera 2010, Silva et al. 2013), Grateloupia (Gavio and Fredericq 2002) and Cladophora (Hayakawa et al. 2012), (Taylor et al. 2017) among others. For example, Ulva species have been underestimated in the Mediterranean, as a result of cryptic species within this group (Wolf et al. 2012). Additionally, new introductions of Ulva species could also go unnoticed because many species are similar morphologically (Melton et al. 2016). Species of Cystoseira are also challenging due to their morphological adaptability to different ecological conditions (Ercegović 1959), so that difficulties arise in distinguishing whether the morphological variation is a different species or whether it is an adaptation (Rožić et al. 2012). Similar issues are also found in the genus Sargassum (Mattio and Payri 2011), (Amaral-Zettler et al. 2017). Cladophora is challenging due to the lack of absolute discontinuities (John and Maggs 1997), and morphological changes associated with algal age and a response to environmental conditions (van den Hoek 1982). Genetically identical species may also exhibit different morphologies when grown in the same conditions, as with Acetabularia acetabulum (Linnaeus) P.C. Silva (Nishimura and Mandoli 1992).
In view of the above challenges in resolving morphological identification of macroalgae, DNA barcoding has become increasingly important in this field. However, despite obvious advantages, criticism of DNA barcoding-based approaches relates especially to species discovery and delimitation, especially those using a single gene (Taylor and Harris 2012). There are evolutionary differences between mitochondrial and nuclear DNA, which means that reliance upon a single gene such as COI will not necessarily reflect nuclear divergence and implies that a genetic divergence taken from only one part of the genome is not an accurate representation of divergence/speciation (Humphries and Winker 2011).
Although single-locus data are effective for a quick identification of species, they are not very effective for species delimitation (Leliaert et al. 2014). In fact, Leliaert and De Clerck (2017) discuss how combining multi-locus data coupled with model-based species delimitation has become increasingly popular.
Metabarcoding suffers the same limitations as DNA barcoding and cannot rely on a single organellar marker since it cannot detect cases of recent hybridisation and organelle introgression (Taberlet et al. 2012). Montecinos et al. (2017), using a nuclear (ITS1) and a mitochondrial (COI) marker, conducted a study on Ectocarpus spp. that revealed introgressions among species. Interspecific plastid and mitochondrial introgressions are prevalent in algal genera and species that are evolutionarily young and could result in identification errors (Taberlet et al. 2012), (Pawlowski et al. 2018). Thus, using multiplexed biomarkers of uniparentally (COI) and biparentally inherited DNA (nrRNA genes) is recommended.
DNA barcoding data from the Mediterranean Sea
According to a “Taxonomy” search conducted in BOLD (12th September 2018), there are 6,182,866 barcoded specimens of animals, plants, fungi and protists from all over the world. BOLD Systems is an online data storage and analysis platform that also mines in public data from GenBank that meet certain criteria. This means that it will mine any data uploaded to an International Nucleotide Sequence Database Collaboration (INSDC) institution which includes GenBank at the National Center for Biotechnology Information (NCBI) together with the DNA DataBank of Japan (DDBJ) and the European Nucleotide Archive (ENA). These three databases exchange data daily (National Center for Biotechnology Information 2011). Presently, NCBI has no reliable way to gather statistics, including terms such as “Mediterranean”, since there is no controlled vocabulary for the collection location (NCBI 2017, personal communication). The advantage of BOLD is that it is a workbench that combines morphological, genetic and distributional data (Ratnasingham and Hebert 2007), even though this is still not comprehensive.
By using the BOLD workbench, the present study focused on the macroalgae of the Mediterranean Sea by creating a polygon around this area (Figure 1) which mined all data referring to GPS coordinates located within the region.
Polygon used to delineate the Mediterranean Sea whilst searching for records in the Barcode of Life Data System (BOLD).
Source: http://www.boldsystems.org/
As a result, it was discovered that, in the case of the Rhodophyta, there were only 148 specimens and 117 sequences in the area of interest as compared to a total of 37,316 macroalgal specimens with sequences worldwide.
Results for the Chlorophyta and Phaeophyceae were similar to that for the Rhodophyta and show that, in general, macroalgal DNA barcodes for Mediterranean specimens are underrepresented (Table 3). In fact, there were only 15 specimens and 18 sequences for green macroalgae in the Mediterranean compared to a total of 8614 specimens with sequences world-wide, and 23 specimens/sequences for brown algae compared to a total of 7764 worldwide. Another search on the Workbench included a country search, for example, “Spain” or “Italy”, which also gave sparse results. Therefore, notwithstanding that there are records that lack a GPS reference, it is evident that very little has been published on DNA barcoding of macroalgae in the Mediterranean.
Data obtained from Barcode of Life Data System (BOLD) for macroalgae in the Mediterranean Sea.
| Taxon | No. of specimens | No. of sequences | Details | No. of sequences recorded globally |
|---|---|---|---|---|
| Rhodophyta (Division) | 148 | 117 | Florideophyceae (class): 113 Bangiophyceae (class): 35 | 37,316 |
| Chlorophyta (Division) | 15 | 18 | Ulvophyceae (class): 13 Trebouxiophyceae (class): 2 | 8614 |
| Phaeophyceae (Class) | 23 | 23 | Dictyotales (order): 12 Ectocarpales (order): 5 Sphacelariales (order): 4 Cutleriales (order): 2 | 7764 |
As such, data searches on databases, including BOLD, need to be treated with caution since they do not capture macroalgae that lack GPS or other location data. Taking this disadvantage into consideration, we searched the Taxonomy page on BOLD (Figure 2) and compared other geographical locations, with the result that the above inference was further reinforced. When including other areas outside the Mediterranean, Canada, Australia, the US and France were found to be the leading four countries to report DNA barcodes of Rhodophytes.
Rhodophyta data by country from Taxonomy page on the Barcode of Life Data System (BOLD).
Source: http://www.boldsystems.org/index.php/TaxBrowser_Home
DNA barcodes that are not georeferenced are a shortcoming within databases. BOLD requires GPS coordinates for the DNA barcode to gain formal status (Ratnasingham and Hebert 2007). For researchers about to embark on sampling for DNA barcoding, the use of the free smartphone application “DNA Barcoding Assistant” could facilitate the provision of GPS data (Santschi et al. 2013). In fact, the DNA Barcoding Assistant was created for students in order to streamline, standardise and simplify the data collection process since it provides an intuitive interface for users to compile records which contain provisional taxonomic identification, digital images, temporal and geospatial data and other collection event details (Santschi et al. 2013). Therefore, using this application also provides a check-list during sampling which ensures that no data are overlooked.
A search of the literature confirmed that the Mediterranean is underrepresented in molecular-systematic papers. Data from 121 papers were analysed, using search criteria which included the term “Mediterranean” and the names of countries such as “Italy”. Due to the dearth of Mediterranean sequences, data from papers published up through 2019 were analysed, of which only 65 of the 121 papers contained DNA barcodes of Mediterranean samples. Further analyses show that only 1126 (11.9%) of the 9438 barcodes reported in these papers were Mediterranean (Figure 3). The taxonomic coverage of these barcodes is also relatively small, with only 114 species (45 brown, 42 red, and 27 green) represented.
Total number of DNA barcodes obtained from the literature-based results of 121 papers.
It was also revealed, from the data analysed, that the majority of DNA barcodes were of algae collected in the USA, Canada, Korea, Australia, Chile and France (Figure 4), which, with the exception of Korea and Chile, was the same result obtained for searches of the BOLD dataset for Rhodophyta.
DNA barcoding by country obtained from the literature-based results of 121 papers: USA, Canada, Korea, Australia, Chile and France lead in the number of barcodes world-wide.
Another important point concerning Mediterranean data is that the majority (>75%) of DNA barcodes originate from samples collected in Italy, France, Croatia and Spain (Figure 5). This suggests that other Mediterranean countries such as Malta, Greece, Turkey and Cyprus are underrepresented and possibly non-existent when compared to the Mediterranean area in general.
DNA barcoding of macroalgae by Mediterranean country: Italy, France, Croatia, Spain and Greece lead in the literature-based results.
Our conclusion on the situation of sparse DNA data in the Mediterranean also holds true for full-genome sequencing of macroalgae. So far, only a few species of macroalgae have been genome sequenced. These include Ectocarpus sp. (Cock et al. 2010), as Ectocarpus siliculosus (Dillwyn) Lyngbye), Saccharina japonica (Areschoug) C.E. Lane, C. Mayes, Druehl & G.W. Saunders (Ye et al. 2015), Cladosiphon okamuranus Tokida (Nishitsuji et al. 2016), Nemacystus decipiens (Suringar) Kuckuck (Nishitsuji et al. 2019), Chondrus crispus Stackhouse (Collen et al. 2013), Pyropia yezoensis (Ueda) M.S. Hwang & H.G. Choi (Nakamura et al. 2013), Gracilariopsis chorda (Holmes) Ohmi (Lee et al. 2018), Porphyra umbilicalis Kützing (Brawley et al. 2017) and Ulva compressa Linnaeus (De Clerck et al. 2018). However, none of these are from the Mediterranean.
Genetic studies in the Mediterranean have focused on important foundation species such as members of Cystoseira (Draisma et al. 2010), (Buonomo et al. 2018), some other taxa of unique quality such as the genus Padina (Ni-Ni-Win et al. 2011), which is one of the only two genera of calcified Phaeophyceae (Herbert et al. 2016), and on the endangered endemic deep-water kelp Laminaria rodriguezii Bornet (Žuljević et al. 2016).
Most often, studies from the Mediterranean have been part of larger projects which took into consideration species from other seas, such as the studies on Nemaliales (Le Gall and Saunders 2010) and Ectocarpales (Peters et al. 2015). Yet, rarely, has there been research which focused specifically on the Mediterranean, such as the description of a new red algal genus Felicinia which is endemic to the Mediterranean (Manghisi et al. 2014).
DNA barcoding of macroalgae in the Mediterranean has been particularly popular in studies of alien species, such as those shown in Table 4. The reason for this could be that the Mediterranean has been described as being “under siege” by various anthropogenic pressures (Piroddi et al. 2017) and one of the major stressors associated with the Mediterranean is the infiltration of non-indigenous species. A key contributor to this stressor is the Suez Canal, which is an artificial connection to the Red Sea that was opened in 1869 and further expanded in 2015 (Galil et al. 2015). Many species that entered via the Suez Canal have established themselves and also reached the western Mediterranean, where they compete with local populations. A threat to human health is posed by some species that are venomous (Galil et al. 2015). Moreover, molecular studies have shown that gene flow has occurred in some species between populations of the Red Sea and the Mediterranean (Galil et al. 2015). Tsiamis et al. (2018) provided a synthesis of studies pertaining to non-indigenous species in Europe in relation to species inventories, pathways, gateways, trends, impacts and biological traits, as well as providing the first study on the native distribution range of such species. These authors concluded that the majority of the marine non-indigenous species in Europe (Mediterranean, NE Atlantic Ocean, Black, Baltic Sea), especially molluscs and fish, which are distributed in the Western and Central Indo-Pacific, are associated with the Suez Canal. On the other hand, non-indigenous macroalgal species from the temperate north-west Pacific have been introduced mostly through the importation of oyster spat into the western Mediterranean (Verlaque et al. 2015). Zenetos (2017) showed how the recent expansion of the Suez Canal did not result in the expected major increase in non-indigenous species; so far the reported increases were mostly fish.
List of DNA barcoding studies on non-indigenous macroalgal species in the Mediterranean Sea.
| Green algae | Red algae |
|---|---|
| Ulva ohnoi M. Hiraoka & S. Shimada (Minicante et al. 2014), (Miladi et al. 2018) | Pyropia yezoensis (Ueda) M.S. Hwang & H.G. Choi (Minicante et al. 2014) |
| Caulerpa cylindracea Sonder (Durand et al. 2002), (Verlaque et al. 2003), (Nuber et al. 2007), (Klein and Verlaque 2008) | Hypnea cornuta (Kützing) J. Agardh (Manghisi et al. 2011) |
| Caulerpa taxifolia (M. Vahl) C. Agardh (Jousson et al. 1998), (Fama’ et al. 2002) | Hypnea cervicornis J. Agardh (Wolf et al. 2011) |
| Caulerpa taxifolia var. distichophylla (Sonder) Verlaque, Huisman & Procaccini (Jongma et al. 2013), (Aplikioti et al. 2016) | Asparagopsis taxiformis (Delile) Trevisan (Andreakis et al. 2007) |
| Codium pulvinatum M.J. Wynne & R. Hoffman (Hoffman et al. 2017) | Agardhiella subulata (C. Agardh) Kraft & M.J. Wynne (Manghisi et al. 2010) |
Non-indigenous species are of concern in the Mediterranean since it is the most invaded region in the world (Klein and Verlaque 2008), (Zenetos et al. 2017), (Galil et al. 2018). In fact, some papers based on morphological studies (Balata et al. 2004), (Tsiamis et al. 2008), (Zenetos et al. 2009), (Occhipinti-Ambrogi et al. 2011), (Zenetos et al. 2018) have focused on establishing lists of non-indigenous species or invasive species as well as on their effects on the structure of macroalgal assemblages. Studies on invasive species are important as these play a noticeable role in the receiving ecosystem, including the replacement of keystone species, as well as possible economic impacts (Boudouresque and Verlaque 2002). Macroalgae represent a significant 40% of invasive species documented in the world’s oceans (Schaffelke et al. 2006). Excluding foraminifera, phytoplanktonic organisms, cryptogenic and questionable species that could be true aliens, there were 821 established multicellular non-indigenous species reported in the Mediterranean through early 2017 (Zenetos et al. 2017). Moreover, the 100 “Worst Invasives” of non-indigenous species in the Mediterranean have been identified in Streftaris and Zenetos (2006) due to their proliferation as well as their impact on indigenous populations. There are 19 species of macroalgae among these 100 invasive species, including ubiquitous species such as Caulerpa taxa (Figure 6).
Pinnate fronds of Caulerpa taxifolia var. distichophylla growing among the white flabellate blades of Padina sp.
Photo taken in Maltese waters in June 2017.
Conclusion
An extraordinary variation in the physiology, morphology, reproductive systems and genetic content of algae, a polyphyletic group of lineages, is presently recognised. Algae sustain ecosystems as primary producers of energy and also provide many biotechnological and commercial products which are increasingly being studied through “omics” approaches (Brodie et al. 2017). In this regard, DNA barcoding and metabarcoding are becoming increasingly important and both techniques are promising approaches when it comes to monitoring programmes for algae in the Mediterranean. A reference genetic library of DNA barcodes, coupled with taxonomic and georeferenced data is required to provide the basis for such mechanisms to be accurate. Additionally, the importance of biotic indices is expected to increase in the coming years due to their applicability in studies on the impacts of climate change (Brodie et al. 2017). In a study that compared the rate of discovery of new species over time, it appears that new species are still being discovered globally (De Clerck et al. 2013). Therefore, sequencing DNA barcodes of currently known species, as well as supporting the discovery and description of new species through DNA barcoding and taxonomy seems to be a good way forward in this field.
Apart from the underrepresentation of DNA barcoding in the Mediterranean, Salonen et al. (2019) also note the prevalence of unexplored environments or sparsely explored habitats such as sediments. The sediments contain eukaryotes that form diverse and complex assemblages (Kim et al. 2016) and DNA-based methods provide the potential to explore these. Therefore, exploring diverse substrates is also required.
This present review has revealed that there are major information gaps for macroalgal studies in the Mediterranean Sea, which need to be addressed for DNA-based methods such as metabarcoding to be successful. Thus, the focus for Mediterranean scientists should be on building an extensive DNA barcode library in order to facilitate easy identification of algae for ecological, legislative and commercial purposes. In this respect, unless the database library is given priority, the Mediterranean will be at a disadvantage with respect to other regions. Research in the Mediterranean area has so far focused on morphological studies, with the genetics of macroalgae being sparsely applied. In this respect, it should be easy to adopt methods applied in other regions as regards DNA extraction protocols together with amplification using well-known primers.
Subsequently, sequencing data must be uploaded to one of the databases which makes part of the International Nucleotide Sequence Database Collaboration (INSDC) institution. This study also reveals that the lack of georeferences of DNA barcodes is a major shortcoming of databases, therefore it is important that scientists provide the correct metadata including provisional taxonomic identification, digital images, temporal and geospatial data and other collection event details (Santschi et al. 2013).
Thus, the collection of algal genetic data from the Mediterranean region, its deposition through public DNA reference libraries and the funding of such research is imperative to help fill in the present knowledge gaps.
About the authors
Angela G. Bartolo is a PhD student at the University of Aberdeen in the division of Marine Biology. Her research addresses DNA barcoding in Malta. She holds a dual Master’s degree MSc/MS from the University of Malta and James Madison University (Virginia) where she focused her research on sea water quality through the use of ecological indicators. Angela also works on the EU Marine Strategy Framework Directive and Water Framework Directive at the Environment and Resources Authority in Malta.
Gabrielle Zammit is a member of the academic staff of the University of Malta. For the past 15 years, she has lectured and supervised students enrolled in various undergraduate and postgraduate degrees both in Malta and abroad. She has also carried out research in phycology in a number of European institutions and is especially interested in phototrophic biofilms and the systematics of Maltese cyanobacteria and algae, of which she has described genera and species that are new to science.
Akira F. Peters has a PhD from Konstanz University, Germany, is director of Bezhin Rosko (www.bezhinrosko.com) and lives near Roscoff in Brittany, France. He is honorary research fellow of Aberdeen University, Scotland, UK. Since 1980 he has worked on life histories, taxonomy, phylogenetics, ecology, pathology, genetics, development, cultivation and utilisation of macroalgae, principally on browns. His main techniques are isolation, purification and laboratory cultivation of seaweed microstages.
Frithjof C. Küpper, Chair in Marine Biodiversity at the University of Aberdeen, is studying the biodiversity and biochemistry of marine plants/algae. His research found that iodide serves as an inorganic antioxidant in kelp, the first known from a living system, impacting atmospheric and marine chemistry. A certified scientific diver, Frithjof has worked in the Mediterranean, South Atlantic (Ascension and Falklands), but also in the Antarctic and the Arctic for algal diversity-related projects.
Acknowledgments
The research work disclosed in this publication is partially funded by the ENDEAVOUR Scholarship Scheme (Malta)-Group B – National Funds. FCK would also like to thank the UK Natural Environment Research Council (grants NE/D521522/1, NE/J023094/1, Oceans 2025/WP 4.5), the TOTAL Foundation (Project “Diversity of brown algae in the Eastern Mediterranean”) and the Marine Alliance for Science and Technology for Scotland (MASTS) pooling initiative, which is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions. AFP was funded by the project IDEALG (France: ANR-10-BTBR-04).
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