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ACKNOWLEDGEMENTS C RESULTS I Temporal shifts in the diversity and population structure of marine microbial diversity: understanding the ecosystem functioning of Sub-Antarctic island systems

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M ICROBIAL COMMUNITY ANALYSIS

Temporal shifts in the diversity and population structure of marine microbial

diversity: understanding the ecosystem functioning of Sub-Antarctic island systems

R.A. Dorrington

1

, G.F. Matcher

1

, A. Mendes

1

and I. Ansorge

2

1

1Department of Biochemistry, Microbiology and Biotechnology, Rhodes University, Grahamstown, South Africa,

2

Department of Oceanographyt, University of Cape Town, Rondebosch, South AfricaAfrica

Figure 2. Relative abundances of the dominant phyla in surface water sampled in April or July 2012.

DNA and RNA was isolated from biomass using the AllPrep DNA/RNA extraction kit (Qiagen) followed by PCR (Total) or RT-PCR (Active) amplification of the 16S rRNA hypervariable regions V4/V5 for bacteria and archaea and V4 for eukaryotes. After curation of 454-pyrosequencing data (removal of chimeras, reads <200 bp and reads containing ambiguous nucleotide or a homopolymeric stretch >7), a total of 67 522 reads representing 326294 bacterial, 15174 archaeal and 16254 eukaryotic reads were classified using the Ribosomal Database Project and Silva databases. Other = reads assigned to minor phyla.

I NTRODUCTION

C ONCLUSIONS

Antarctica

P HYSICO -C HEMICAL C HARACTERISTICS

RESULTS

The Prince Edward Islands are two Sub-Antarctic islands, straddled between the Sub- Antarctic (SAF) and Antarctic Polar Fronts (APF) within the Antarctic Circumpolar Current (ACC). The region is characterized by relatively simple, terrestrial and marine ecosystems with significant endemism across a broad range of taxa. The Prince Edward islands are important breeding grounds for millions of marine top predators and primary productivity of the terrestrial and marine environments is tightly connected and very sensitive to climatic and oceanographic perturbations, particularly latitudinal shifts in the ACC (Figure 1). Recent investigations have demonstrated that an extensive eddy train extends along the ACC, into the island vicinity. These eddies, originating from the Antarctic (cold, cyclonic eddy) or north of the SAF (warm eddy) exert a significant biological influence, transporting physical and biological characteristics typical of the Antarctic and Subtropics into the island region. Our interest lies in understanding the mechanisms of microbial nutrient cycling, the interconnectedness between the marine and terrestrial ecosystems of the Prince Edward islands and their response to climate change. In this study we investigate the influence of these eddies on the microbial communities off the northeast coast of Prince Edward island.

Figure 1: Map of the Southern Ocean (left) showing the position of the Prince Edward Islands in relation to the ACC, downstream of an area of high oceanographic variability. The variability (represented by solid black lines) in the position of both the SAF and APF within the island vicinity plotted using 10 years of satellite data, is shown (right).

July Active April Active July Total

April Total Bacteroidetes

Alphaproteobacteria Gammaproteobacteria Other Proteobacteria Cyanobacteria

Deferribacteres Verrucomicrobia Other

Unclassified

July Active April Active July Total April Total

Crenarchaeota Euryarchaeota Unclassified

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

July Active April Active July Total April Total

Alveolata

Haptophyceae Metazoa

Viridiplantae Stramenophiles Other

Unclassified

Ba cteri a Ac rhaea Euk ary ota

O PERATIONAL TAXONOMIC UNITS

April 2012

A Sea Surface Height B Nutrient Analysis

Figure 2: (A) Satellite data highlighting the eddy field close to the islands.

In April, a negative anomaly assumed to be a cold eddy, typical of the Antarctic, was observed close to the island region (top) while a positive anomaly (warm eddy) was present in July. (B) Nutrient analysis. Five litres of water was collected at a depth of 5 m, filtered through a 100 μm filter after which microbial biomass was collected on a 0.2 μm filter and the filtrate used for nutrient analysis.

July 2012

o April: Cold eddy surrounds islands, relatively high nitrate concentrations o July: Warm eddy, reduced nitrates but increased level of silicates

o Bacteria: Alphaproteobacteria were numerically dominant, but

Gammaproteobacteria are metabolically dominant, particularly in July o Achaea: Euryarchaeota were dominant in the April samples while

Crenarchaeota dominated the July samples

o Eukaryota: Alveolata and Metazoa dominant in both seasons

Bacteria

Archaea

Eukaryota

o Distinct oceanographic features – cold versus warm eddy in April and July o Shifts in nitrate and silicate concentrations

o Low archaeal diversity, numerically dominant = metabolically active

o Relatively high bacterial diversity. Numerically dominant species are not the most metabolically active OTUs

o Unicellular eukaryotes mostly phytoplankton but July samples include Euphausia sp. larvae

o Distinct shifts in dominant species of bacteria, archaea and eukaryotes between April and July2012

ACKNOWLEDGEMENTS

OTU Classification April July 1 Pelagibacter ubique Dormant Dormant 2 Verrucomicrobiaceae Active - 11 Cyanobacterium Active - 13 Cyanobacterium Active Active 17, 20 Colwellia spp - Active 19 Pseudoalteromonas sp - Active

OTU Classification April July

1 Dinoflagellate (Phytoplankton) Active Active 2 Dinoflagellate (Phytoplankton) Dormant Dormant 3 Oithona similis (Copipod) Dormant - 5 Prasinophyceae (green alga) Active -

6 Dinoflagellate Active Active

16 Euphausia sp (Krill) - Active 17 Bathycoccus prasino (green alga) - Active

OTU Classification April July

1 Euryarchaeote (Thermoplasmatales) Active Active 2 Euryarchaeote (Thermoplasmatales) Active - 3 Crenarchaeota (Marine Group I) Active Active 4 Euryarchaeote (Thermoplasmatales) Active Active 5 Crenarchaeota (Marine Group I) - Active

Figure 4: Comparative analysis of the numerically (total) and metabolically (active) dominant OTUs in the water column. Curated dataset was used to determine OTUs using the Mothur software. Subsampling was done on a random basis in order to negate biases due to uneven sizes of data sets and OTUs represented by a single sequence read were removed from the data sets. OTUs were determined at a distance value of 0.03.

FUNDING

South African National Antarctic Programme Rhodes University Sandisa Imbewu Programme LOGISTICAL SUPPORT

South African Department of Environmental Affairs

SAMPLE COLLECTION

Dr Margaux Noyon, Dept Zoology, Rhodes University Captain Gavin Syndercombe and the crew of the SA Agulhas

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