151
of DNA from an entire sample, without the need of picking
out single individuals, like larvae or other targeted groups of
mesozooplankton. It is based on the New Generation
Sequencing (NGS) technology, where millions of short
sequences (reads) are produced allowing to screen entire
genomes or transcriptomes in order to obtain a higher resolution of spatio-temporal patterns of species distribution
(Bucklin et al. 2016). This technique is becoming increasingly available as sequencing is getting cheaper. Commonly
used genetic markers for metabarcoding are 16S, 18S and
28S, while COI is not often used as it requires specific primers (Deagle et al. 2014). So far, metabarcoding has mainly
been used for microorganism research, however, it might
also be used for monitoring of zooplankton for which the
dynamic changes may not be detected with other tools. It is
now also possible to obtain DNA from environmental samples (environmental DNA, eDNA), like water or soil, without prior isolation of target organisms, as they continuously
expel DNA into their surroundings from where it can be collected (Thomsen and Willerslev 2015). This approach can
provide information about the presence and type of organisms which were in a particular location in the recent past,
like fishes or whales (Sigsgaard et al. 2016). Metagenomics
represent an even more advanced method, for which entire
genomes present in environmental samples are analyzed. Yet
it is mostly applied on microorganisms, since not enough reference genomes exist for metazoans (Wooley et al. 2010).
Nevertheless, as mentioned at the beginning of this chapter,
databases are growing at an enormous speed and new
genomes are published every day, what means that analyses
of metagenomes of different ecological groups will become
possible in the nearest future. Metagenomics significantly
exceeds beyond species identification, in biodiversity
research it allows for investigation of uncultured microbial
populations. It is a very powerful tool, which enables exploration of metabolic diversity, isolation and identification of
enzymes and it may be an effective way to produce novel
bioresources (Kodzius and Gojobori 2015).
Currently, the analysis of high-throughput sequence
(NGS) data requires an in-depth knowledge in bioinformatics. Moreover, the obtained results are rather qualitative than
quantitative, e.g. based on presence/absence of DNA in a
sample, however, this is currently being improved.
Nonetheless, until these methods are not optimized for converting number of sequences into abundances of organisms
in the field, the best method remains the integrative taxonomic approach, which combines molecular with morphological data.
In conclusion, molecular data are a promising tool for
detecting the influence and consequences of global warming
on different communities. Standard molecular methods have
been successfully applied in Arctic research and their fast
development will render analysis even more feasible and
cost-effective. The use of DNA barcoding should be emphasized for long-time monitoring studies. Considering the
opportunity of acquiring fast results, however, caution should
be taken with regard to the choice of an adequate molecular
marker, a careful analysis of the data and if possible, the
application of an integrative approach by supporting these
results with morphological analyses.
Knowledge on the existing biodiversity is the baseline for
many studies, e.g. on ecological and physiological aspects.
In order to investigate the future of the Arctic ecosystems,
further research should focus on combining data obtained
from biodiversity assessments with modelling and experiments, in which molecular tools can be used as well.
Appendix
This article is related to the YOUMARES 8 conference session no. 8: “Polar Ecosystems in the Age of Climate Change”.
The original Call for Abstracts and the abstracts of the presentations within this session can be found in the appendix
“Conference Sessions and Abstracts”, chapter “9 Polar
Ecosystems in the Age of Climate Change”, of this book.
References
ACIA (Arctic Climate Impact Assessment) (2004) Impacts of warming
Arctic. Cambridge University Press, Cambridge
Ambrose WG Jr, Renaud PE (1995) Benthic response to water column
productivity patterns: evidence for benthic-pelagic coupling in the
Northeast Water Polynya. J Geophys Res 100:4411–4421
Appeltans W, Ahyong ST, Anderson G et al (2012) The magnitude of
global marine species diversity. Curr Biol 22:2189–2202
Berge J, Johnsen G, Nilsen F et al (2005) Ocean temperature oscillations enable reappearance of blue mussel Mytilus edulis in Svalbard
after 1000 years of absence. Mar Ecol Prog Ser 303:167–175
Berge J, Renaud PE, Darnis G et al (2015) In the dark: a review of
ecosystem processes during the Arctic polar night. Prog Oceanogr
139:258–271
Bickford D, Lohman DJ, Sodhi NS et al (2006) Cryptic species as a window on diversity and conservation. Trends Ecol Evol 22(3):148–155
Błachowiak-Samołyk K, Søreide J, Kwasniewski S et al (2008)
Hydrodynamic control of mesozooplankton abundance and biomass in northern Svalbard waters (79–81°N). Deep Sea Res Part 2
55:2210–2224
Bluhm BA, Gradinger RR, Schnack-Schiel SB (2009a) Sea ice meioand macrofauna. In: Thomas DN, Dieckmann GS (eds) Sea ice, 2nd
edn. Wiley-Blackwell, Oxford, pp 357–393
Bluhm BA, Iken SL, Mincks BI et al (2009b) Community structure of
epibenthic megafauna in the Chukchi Sea. Aquatic Biol 7:269–293
Bluhm BA, Gebruk AV, Gradinger R et al (2011a) Arctic marine biodiversity: an update of species richness and examples of biodiversity
change. Oceanography 24(3):232–248
Bluhm BA, Gradinger R, Hopcroft RR (2011b) Editorial – Arctic
Ocean diversity: synthesis. Mar Biodivers 41:1–4
Breines R, Ursvik A, Nymark M et al (2008) Complete mitochondrial
genome sequences of the Arctic Ocean codfishes Arctogadus glacialis and Boreogadus saida reveal oriL and tRNA gene duplications.
Polar Biol 31:1245–1252
Arctic Ocean Biodiversity and DNA Barcoding – A Climate Change Perspective
of DNA from an entire sample, without the need of picking
out single individuals, like larvae or other targeted groups of
mesozooplankton. It is based on the New Generation
Sequencing (NGS) technology, where millions of short
sequences (reads) are produced allowing to screen entire
genomes or transcriptomes in order to obtain a higher resolution of spatio-temporal patterns of species distribution
(Bucklin et al. 2016). This technique is becoming increasingly available as sequencing is getting cheaper. Commonly
used genetic markers for metabarcoding are 16S, 18S and
28S, while COI is not often used as it requires specific primers (Deagle et al. 2014). So far, metabarcoding has mainly
been used for microorganism research, however, it might
also be used for monitoring of zooplankton for which the
dynamic changes may not be detected with other tools. It is
now also possible to obtain DNA from environmental samples (environmental DNA, eDNA), like water or soil, without prior isolation of target organisms, as they continuously
expel DNA into their surroundings from where it can be collected (Thomsen and Willerslev 2015). This approach can
provide information about the presence and type of organisms which were in a particular location in the recent past,
like fishes or whales (Sigsgaard et al. 2016). Metagenomics
represent an even more advanced method, for which entire
genomes present in environmental samples are analyzed. Yet
it is mostly applied on microorganisms, since not enough reference genomes exist for metazoans (Wooley et al. 2010).
Nevertheless, as mentioned at the beginning of this chapter,
databases are growing at an enormous speed and new
genomes are published every day, what means that analyses
of metagenomes of different ecological groups will become
possible in the nearest future. Metagenomics significantly
exceeds beyond species identification, in biodiversity
research it allows for investigation of uncultured microbial
populations. It is a very powerful tool, which enables exploration of metabolic diversity, isolation and identification of
enzymes and it may be an effective way to produce novel
bioresources (Kodzius and Gojobori 2015).
Currently, the analysis of high-throughput sequence
(NGS) data requires an in-depth knowledge in bioinformatics. Moreover, the obtained results are rather qualitative than
quantitative, e.g. based on presence/absence of DNA in a
sample, however, this is currently being improved.
Nonetheless, until these methods are not optimized for converting number of sequences into abundances of organisms
in the field, the best method remains the integrative taxonomic approach, which combines molecular with morphological data.
In conclusion, molecular data are a promising tool for
detecting the influence and consequences of global warming
on different communities. Standard molecular methods have
been successfully applied in Arctic research and their fast
development will render analysis even more feasible and
cost-effective. The use of DNA barcoding should be emphasized for long-time monitoring studies. Considering the
opportunity of acquiring fast results, however, caution should
be taken with regard to the choice of an adequate molecular
marker, a careful analysis of the data and if possible, the
application of an integrative approach by supporting these
results with morphological analyses.
Knowledge on the existing biodiversity is the baseline for
many studies, e.g. on ecological and physiological aspects.
In order to investigate the future of the Arctic ecosystems,
further research should focus on combining data obtained
from biodiversity assessments with modelling and experiments, in which molecular tools can be used as well.
Appendix
This article is related to the YOUMARES 8 conference session no. 8: “Polar Ecosystems in the Age of Climate Change”.
The original Call for Abstracts and the abstracts of the presentations within this session can be found in the appendix
“Conference Sessions and Abstracts”, chapter “9 Polar
Ecosystems in the Age of Climate Change”, of this book.
References
ACIA (Arctic Climate Impact Assessment) (2004) Impacts of warming
Arctic. Cambridge University Press, Cambridge
Ambrose WG Jr, Renaud PE (1995) Benthic response to water column
productivity patterns: evidence for benthic-pelagic coupling in the
Northeast Water Polynya. J Geophys Res 100:4411–4421
Appeltans W, Ahyong ST, Anderson G et al (2012) The magnitude of
global marine species diversity. Curr Biol 22:2189–2202
Berge J, Johnsen G, Nilsen F et al (2005) Ocean temperature oscillations enable reappearance of blue mussel Mytilus edulis in Svalbard
after 1000 years of absence. Mar Ecol Prog Ser 303:167–175
Berge J, Renaud PE, Darnis G et al (2015) In the dark: a review of
ecosystem processes during the Arctic polar night. Prog Oceanogr
139:258–271
Bickford D, Lohman DJ, Sodhi NS et al (2006) Cryptic species as a window on diversity and conservation. Trends Ecol Evol 22(3):148–155
Błachowiak-Samołyk K, Søreide J, Kwasniewski S et al (2008)
Hydrodynamic control of mesozooplankton abundance and biomass in northern Svalbard waters (79–81°N). Deep Sea Res Part 2
55:2210–2224
Bluhm BA, Gradinger RR, Schnack-Schiel SB (2009a) Sea ice meioand macrofauna. In: Thomas DN, Dieckmann GS (eds) Sea ice, 2nd
edn. Wiley-Blackwell, Oxford, pp 357–393
Bluhm BA, Iken SL, Mincks BI et al (2009b) Community structure of
epibenthic megafauna in the Chukchi Sea. Aquatic Biol 7:269–293
Bluhm BA, Gebruk AV, Gradinger R et al (2011a) Arctic marine biodiversity: an update of species richness and examples of biodiversity
change. Oceanography 24(3):232–248
Bluhm BA, Gradinger R, Hopcroft RR (2011b) Editorial – Arctic
Ocean diversity: synthesis. Mar Biodivers 41:1–4
Breines R, Ursvik A, Nymark M et al (2008) Complete mitochondrial
genome sequences of the Arctic Ocean codfishes Arctogadus glacialis and Boreogadus saida reveal oriL and tRNA gene duplications.
Polar Biol 31:1245–1252
Arctic Ocean Biodiversity and DNA Barcoding – A Climate Change Perspective