Comprobación

The typical traditional approach to assess nematode communities was through the use of a light microscope to identify a portion of the nematodes present based on their morphological characteristics. A large body of literature has been using, and continues to use, this approach, and admittedly, there is currently probably no better or more efficient method. However, this approach is highly time- and energy-demanding and requires considerable taxonomic expertise, which is often not or insufficiently available (Taberlet et al., 2012). Because a typical meiofauna sample of intertidal sediments contains hundreds, if not more, of nematodes, it is usually not feasible to identify them all, and different ‘schools’ follow different approaches with respect to the question whether to identify a fixed number of specimens per sample, or rather a fixed proportion of the specimens in a sample (Barbour and Gerritsen, 1996; Giere, 2009). Both approaches have their advantages and drawbacks. Because of the time-consuming nature of the identification work, it is not uncommon to see the nematode/meiofauna results of interdisciplinary projects lag one or two years behind the results of microbiologists and macrobenthologists. Moreover, the classical identification approach not only has limits in terms of numbers of samples that can be processed and specimens per sample that can be identified; it is now clear that the existence of cryptic species, i.e. species that cannot be differentiated unambiguously based on morphological characters alone, is widespread in marine nematodes (Bhadury et al., 2006; Derycke et al., 2005, 2008a, 2010). Such cryptic diversity cannot be uncovered using traditional approaches alone. Hence, DNA-based approaches such as meta-barcoding, are gaining increasing attention in the study of the diversity and community composition of nematodes (e.g. Creer et al., 2010; Fonseca et al., 2010; Fonseca et al., 2017).

Meta-barcoding can be an economically attractive alternative to classical approaches; it can produce millions of sequences of bulk samples at once after a relatively simple and rapid sample processing

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procedure, and thus alleviate both fundamental limitations of traditional morphological methods mentioned above (time-consuming and unable to detect cryptic species) by offering rapid and reliable identifiers (operational taxonomic units, OTUs) that are independent of taxonomic expertise. Meta- barcoding combines DNA taxonomy with high-throughput DNA sequencing (Ji et al., 2013). The former offers powerful and reliable identification due to its consideration of invisible morphological characteristics, while the latter allows the analysis of bulk samples or at least of bulk extracts (for instance a sample of nematodes collected from a sieve after repeated sample decantation), and hence of hundreds of nematodes, at once. It has already proven its potential for ecological research on communities of small-size organisms that are difficult to identify, such as meiofauna in marine sediments (Carugati et al., 2015; Chariton et al., 2015; Fonseca et al., 2014).

However, metabarcoding has not arrived at the point yet where it can render classical microscopical work redundant. This mainly has three reasons. First, our ability to put species names on OTUs depends on the quality of the reference database. There are currently several thousands of nematode species for which sequences of the 18S ribosomal RNA gene are available (Quast et al., 2012). Hence, in most evolutionary lineages of the phylum, a sequence can be assigned at least to a family, often to a genus, and regularly to a species. However, not all sequences in GenBank stem from reliably identified nematodes. More importantly, the 18S rRNA gene in nematodes has a poor identification resolution at the species level (Powers, 2004; Hebert et al., 2003); hence, species-level diversity in general, and cryptic diversity in particular, is unlikely to be adequately assessed based on 18S sequences. For any other target gene that would allow a higher identification resolution, such as the mitochondrial cytochrome oxidase subunit I gene (CO I) (Hebert et al., 2003; Derycke et al., 2005), databases contain sequences from at most a few hundreds of nematode species and do not offer a complete coverage of all major evolutionary lineages (Mitreva et al., 2011). This problem can only be resolved through a concerted effort of ‘classical’ and DNA taxonomists, aiming to substantially increase the extent and the quality/reliability of the reference database. Therefore, a combination of classical and DNA-based approaches is still direly needed (Rzeznik-Orignac et al., 2017).

A second major problem with metabarcoding is that, while it should theoretically be able to detect all species present in a sample, neither of the two most commonly used marker genes, 18S and CO I, is easily amplified from all nematodes species. Amplification success depends, among others, on the primers used and on the specific partition of the target gene (Derycke et al., 2010), and there tends to be a significant minority of species whose sequences are not amplified by the primer sets which we commonly use in our lab.

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Finally, probably the main issue with metabarcoding of multicellular organisms like nematodes, is that there are currently no sufficiently reliable ways of quantifying the relative abundances of species in a sample (Elbrecht and Leese, 2015), because gene copy numbers differ between species, between specimens of a species, and even between cells of an individual (Schrider and Hahn, 2010; Katju and Bergthorsson, 2013). Hence, even if we overcome the above problems and manage to detect all species present in a sample, what we end up with is a richness estimate and a species/OTU list, but not a reliable quantitative assessment of community composition. During the course of this PhD, I metabarcoded nematode communities from part of the sampling locations of chapter 2 and determined environmental drivers of the metabarcoding-based community composition to compare these with what we found based on the classical approach of chapter 2. Unfortunately, time did not permit me to include these results in this PhD, but I can nevertheless say that the results are encouraging for the future use of metabarcoding. If this would prove to be more generally true, it would definitely increase our ability to deal with much larger numbers of samples, and hence to produce more powerful statistical analyses of the relationships between community composition and diversity on the one hand, and potential environmental drivers on the other.