1. No category

FEMS Microbiology Ecology Advance Access published March 24, 2015
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High diversity of protistan plankton communities in remote high mountain
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lakes in the European Alps and the Himalaya mountains
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Kammerlander1,2,
Hans-Werner
Breiner3,
4
Barbara
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Sommaruga1, Bettina Sonntag2 and Thorsten Stoeck3,*
Sabine
Filker3,
Ruben
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7
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Technikerstrasse 25, 6020 Innsbruck, Austria
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Universität Innsbruck, Institute of Ecology, Lake and Glacier Research Group,
Universität Innsbruck, Research Institute for Limnology, Mondsee, Ciliate Ecology
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and Taxonomy Group, Mondseestrasse 9, 5310 Mondsee, Austria
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3
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Building 14, 67663 Kaiserslautern, Germany
University of Kaiserslautern, Department of Ecology, Gottlieb-Daimler-Strasse
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Running title: protistan plankton in high mountain lakes
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Keywords: diversity, alpine lakes, next generation sequencing
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*correspondence:
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email: [email protected]
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phone:
+49-631-2052502
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fax:
+49-631-2052496
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Abstract
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We analyzed the genetic diversity (V4 region of the 18S rRNA) of planktonic
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microbial eukaryotes in four high mountain lakes including two remote biogeographic
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regions (Himalaya and the European Alps) and distinct habitat types (clear and
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glacier-fed turbid lakes). The recorded high genetic diversity in these lakes was far
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beyond of what is described from high mountain lake plankton. In total, we detected
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representatives from 66 families with the main taxon groups being Alveolata (55.0%
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OTUs97% ,
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Cryptophyta (4.0% OTUs97%), Chloroplastida (3.6% OTUs 97% ), and Fungi (1.7%
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OTUs97% ). Centrohelida, Choanomonada, Rhizaria, Katablepharidae, and Telonema
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were represented by <1% OTUs97%. Himalayan lakes harbored a higher plankton
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diversity compared to the Alpine lakes (Shannon index). Community structures were
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significantly different between lake types and biogeographic regions (Fisher exact
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test, p<0.01). Network analysis revealed that more families of the Chloroplastida (10
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vs. 5) and the Stramenopiles (14 vs. 8) were found in the Himalayan lakes than in the
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Alpine lakes and none of the fungal families was shared between them..
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Biogeographic aspects as well as ecological factors such as water turbidity may
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structure the microbial eukaryote plankton communities in such remote lakes.
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operational
taxonomic
units),
Stramenopiles
(34.0%
OTUs97%),
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Introduction
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In the last years, molecular tools have seized biodiversity studies and next generation
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sequencing approaches have become popular instruments to estimate protist
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diversity (Margulies et al., 2005). To date, a strong focus in microbial biodiversity
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research, including protists and fungi, has been on marine ecosystems (e.g.,
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Alexander et al., 2009; Stoeck et al., 2010; Edgcomb et al., 2011; Bik et al., 2012;
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Stock et al., 2012). Even though the diversity of freshwater microbial plankton is
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presumably much higher than in the marine environment (Logares et al., 2009;
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Auguet et al., 2010; Barberán & Casamayor, 2010; Barberán et al., 2011; Triadó-
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Margarit & Casamayor, 2012), only few freshwater lakes were examined so far by
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using such molecular tools (e.g., Chen et al., 2008; Lefèvre et al., 2008; Steele et al.,
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2011; Charvet et al., 2012a, b, 2014; Stoeck et al., 2014). Hitherto only very few
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sequence data were available from protistan plankton in high mountain lakes (e.g.
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Triadó-Margarit & Casamayor, 2012). Yet, these data emphasized high mountain
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lakes as diversity hotspots for (hitherto unknown) eukaryotic microbial plankton. Such
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a scarce knowledge on microbial eukaryote plankton in high mountain lakes is
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unsatisfying considering the ecological importance of these organisms in the energy
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and carbon transfer within aquatic food webs (e.g., Azam et al., 1983; Sherr & Sherr;
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1988; Weisse & Müller, 1998; Sonntag et al., 2006; Zingel et al., 2007).
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Likewise, microscopy studies on protist diversity in high mountain lakes are
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scarce (Félip et al., 1999; Straškrabová et al., 1999; Wille et al., 1999; Sonntag et al.,
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2011). The difficulties that occur in protist investigation by microscopy are their small
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sizes, few morphological characters available to identify especially flagellated species
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and low abundance particularly in high mountain lakes. Though, as protists include
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manifold groups that have specific demands on their environment concerning their
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nutrition or temperature, there is a need to identify them as exact as possible to
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receive an overall picture of the food web interactions. To overcome such
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inconveniences,
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pyrosequencing (Margulies et al., 2005) are promising techniques to unravel the
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hidden diversity of microbes in environmental samples (Sogin et al., 2006; Caron et
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al., 2012). One major strength of NGS is the depth of sequencing which allows
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elucidating the local “rare biosphere” (Sogin et al., 2006), a seed-bank of low
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abundant taxa that may play a pivotal role in ecosystem response to environmental
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changes as well as in ecosystem stability and function(ing) (Pedrós-Alió, 2007;
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Dawson & Hagen, 2009; Stoeck & Epstein, 2009).
next
generation
sequencing
(NGS)
approaches
such
as
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Indeed, specifically in high mountain lakes all organisms are confronted with
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short growing seasons, low food availability or high incident solar radiation increasing
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with altitude (Sommaruga, 2001; Rose et al., 2009). Characteristically, high mountain
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lakes in the European Alps can be very transparent or turbid dependent on the
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connection to a glacier. The lower the particle load as well as the concentration of
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chromophoric dissolved organic matter (CDOM), the more transparent are such
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lakes. Lake transparency is crucial for the organisms as it is closely correlated to the
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depth penetration not only of photosynthetically active radiation but also of ultraviolet
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radiation (UVR). For example, in a clear high mountain lake the potentially harmful
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wavelengths of the ultraviolet-B radiation can reach down to the lake bottom and
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influence growth rates of protists or the vertical distribution of phyto- and zooplankton
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(Morris et al., 1995; Laurion et al., 2000; Sommaruga & Augustin, 2006; Hylander et
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al., 2011; Sommaruga & Kandolf, 2014). As for clear high mountain lakes, several
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surveys have concentrated on the effects of UVR onto the planktonic community and
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their key players, practically no study is available from turbid glacial lakes
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(Sommaruga & Kandolf, 2014).
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In our study, we sampled four different lakes, including two biogeographic regions
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(Austrian Central Alps and Himalaya, Nepal) and two lake types (clear vs. glacier-fed
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turbid lakes) using massively parallel tag sequencing (pyrosequencing) of the
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hypervariable V4 region of the small subunit ribosomal DNA to determine the
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plankton diversity. The reasoning for these habitat choices is among others to
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maximize the extent of diversity that can be identified from different mountain ranges
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and different lake types. Our data shows a much higher diversity of fungal and
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protistan plankton as known from previous diversity studies in high mountain lakes,
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with ca. 60% of our detected sequences showing a high genetic divergence to
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deposited sequence data.
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Materials and Methods
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Study sites, sampling and analyses
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Faselfad lakes
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The study site Faselfad (FAS) is located in the western Austrian Central Alps and
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comprises a group of six adjacent lakes situated between 2,263 and 2,620 m above
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sea level (a.s.l.). All six lakes originate from one glacier, the ‘Faselfadferner’, and
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mainly differ in altitude and water transparency. Out of these, we selected one clear
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(FAS 4) and one glacier-fed turbid lake (FAS 3) (Fig. 1, Table 1). FAS 3 is located
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approximately 300 m below the glacier and fed by melt water enriched with high
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particle loads, so-called ‘glacial flour’ derived directly from the glacier and partially by
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water from the catchment. The clear lake FAS 4 has already lost its connectivity to
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the glacier and is fed by seepage from its catchment (Sommaruga & Kandolf, 2014).
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On 29th August 2011, water samples were collected with a 5-L Schindler-
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Patalas sampler at the deepest point of each lake from an inflatable boat. Mixed
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water samples (10 L) in a ratio of 1:1:1 were taken from the uppermost meters (0 m,
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1 m, 2 m) and 1 m above and below the chlorophyll a (chl a) maximum. This
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sampling strategy was based on previous samplings along vertical depth gradients
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indicating that most of the taxa were found around the chl a maximum
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(Kammerlander et al. unpubl.). Prior to sampling, the chl a maximum in the water
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column was detected with a Backscat I-Fluorometer (Haardt, model 1101.1,
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excitation 380 – 540 nm, emission 685 nm). Further, subsamples were taken for the
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analysis of abiotic parameters, i.e., turbidity, conductivity, pH, total phosphorus (TP),
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dissolved organic carbon (DOC), nitrate (NO3-N), and ammonium (NH4-N). These
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chemical parameters were measured in the laboratory of the Institute of Ecology at
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the University of Innsbruck as described in Sommaruga-Wögrath et al. (1997). The
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DOC analyses were measured with a high temperature catalytic oxidation method
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(Shimadzu TOC - VCPH - total organic carbon analyzer). For details see Laurion et al.
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(2000) and Sommaruga & Augustin (2006). For nucleic acid extractions, triplicate
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water samples were collected in clean plastic carboys and 2-3 liters each were drawn
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onto Durapore membranes (0.65 µm, 47 mm, Millipore) using a peristaltic pump.
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Filters were frozen immediately in liquid nitrogen. Samples were stored at -20°C until
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DNA extraction.
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Himalaya lakes
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One glacier-fed turbid (HL 5) and one clear (HL 15) lake were sampled in the
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Khumbu Valley region (Nepal) close to Mount Everest on 8th and 9th October 2004
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(Fig. 1, Table 1). For more details on this study site see Tartari et al. (1998) and
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Sommaruga & Casamayor (2009).
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Samples were collected from a boat and integrated over the water column.
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Filters for nucleic acid extraction were prepared as described above and transported
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to Innsbruck in liquid nitrogen.
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Pyrosequencing
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DNA isolation and construction of pyro-amplicon libraries
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DNA was isolated directly from the Durapore membranes using Qiagen’s AllPrep kit
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according to the manufacturer’s instructions. The samples (filters) were extracted and
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pooled. From these extracts, the hypervariable V4-region of the 18S rRNA gene was
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amplified using the eukaryote specific primer pair TAReukV4F and TAReukREV (5'-
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ACTTTCGTTCTTGATYRA-3', Stoeck et al., 2010) yielding ca. 500 basepair (bp)
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fragments. To distinguish the different samples in downstream processes, the V4
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forward primer was tagged with specific ten-bp identifiers (MIDs) at the 5´-end. The
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PCR protocol followed the description of Stoeck et al. (2010). To minimize PCR-bias,
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we ran three individual reactions per sample. The resulting PCR products were
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purified (MinElute PCR purification kit, Qiagen, Germany) and pooled prior to
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sequencing. The V4-DNA amplicon libraries were sequenced on 1/2 of a
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PicoTiterPlate with a Roche FLX GS20 sequencer and the Titanium chemistry (FAS
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lakes: EnGenCore, SC, USA; HL lakes: LGC Genomics Berlin, Germany).
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V4-amplicon data processing
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Amplicons were denoised with the software Acacia (Bragg et al., 2012;
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http://sourceforge.net/projects/acaciaerrorcorr/). For further data cleaning, including
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chimera checking and data analyses, we used the software package QIIME
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(Caporaso et al., 2010). After quality filtering, only reads with exact barcodes and
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primers, a quality score >25, unambiguous nucleotides, and a minimum length of 300
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bp were kept. The remaining sequences were then checked for chimeras and
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clustered at different threshold levels (90, 95, 96, 97, 98, 99, and 100%) using the
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OTUpipe script (Edgar et al., 2011) implemented in QIIME. The OTUpipe script was
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done with the following adjustments: -m usearch –l –reference_chimera_detection –j
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1 –s 0.xx –word_length yy. The word length value was calculated according to an
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equation given in Edgar et al. (2011). For our pyro-amplicons a value of 64 was used.
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For taxonomic assignments, one representative sequence (longest) from each OTU
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was extracted and analyzed with the software package JAguc (Nebel et al., 2011a)
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and GenBank´s nr nucleotide database release 187 as reference database. JAguc
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employs BLASTn searches, with algorithm parameters adjusted for short reads (-m 7
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-r 5 -q -4 -G 8 -E 6 -b 50). Using a custom Java-based script, the output files from
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QIIME´s OTUpipe and JAguc were merged. Non-target OTUs (metazoans and
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embryophytes) were excluded and the resulting file converted into a biom-file, which
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was then used as a basis for statistical and network analyses.
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Statistical and network analyses
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Rarefaction profiles and Shannon index (alpha-diversity), as well as Chao-Jaccard
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beta-diversity were calculated in QIIME. For this purpose, data were normalized and
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resampled 1000 times to account for uneven sample sizes (Logares et al., 2012).
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UPGMA-clustering was used to construct Chao-Jaccard distance dendrograms.
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A table including the number of observed OTUs (only amplicons were considered
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that were at least 95% similar to database entries) across each sample and their
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taxonomic assignment (rank: family) was generated and subjected to QIIME for
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calculation of a network data file. Cytoscape (Cline et al., 2007) was used to visualize
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and analyze shared and exclusive families in samples. Nomenclature follows Adl et
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al. (2012). We created a network graph with an “edge-weighted spring embedded
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layout” where every dot represented a taxonomic family, which was colored
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according
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http://qiime.org/tutorials/making_cytoscape_networks.html.
to
its
phylogenetic
affiliation.
For
more
details
see
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Additionally, Fishers exact tests (Fisher, 1922) were run to test the nonrandom
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independency of the datasets among the lakes, i.e., the null hypothesis was that the
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taxon distribution was equal in all lakes. For this statistical analysis, we used the
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vegan package of R.
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Results
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Lake characteristics
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The turbidity in FAS 3 was 16-fold higher than in FAS 4 (Table 1). Though turbidity
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was not directly measured in HL 5 and HL 15, the optical appearance of both lakes
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was similar to the according turbid and clear FAS lakes (Sommaruga pers. obs., Fig.
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1). The nitrate (NO3-N) values were generally higher in the Alpine lakes than in the
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Himalayan ones (mean NO3-N of Alpine lakes vs. HL lakes: ~145 vs. HL 5: 31 µg L-
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1
).
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In the clear lakes, conductivity and DOC concentrations were higher than in
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the turbid lakes and the concentrations of TP and the pH were lower in the clear
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lakes, with the highest TP concentration (mean TP: 8.8 µg L-1) observed in FAS 3
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(Table 1).
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Overview of V4 amplicon data
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After quality check, 226,267 sequences in total with >300 basepairs (bp) length
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(mainly between 350 – 450 bp) were used for the taxonomic assignments (Table 2;
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Fig. S1). Our target organisms were eukaryotic unicellular organisms and fungi
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checked at least at the family level. Non-target sequences (1.4%), unassigned
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sequences (0.9%) and singletons/ doubletons were removed finally resulting in
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219,155 sequences, and grouping into 1,804 operational taxonomic units (OTUs)
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called at 97% sequence similarity. Different cluster thresholds (90 - 100%) were
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applied (Fig. S2) and the rarefaction curves (Fig. S3) showed that we obtained
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saturated sampling profiles for OTUs called at 97% sequence similarity.
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Approximately 60% of the target sequences had a best BLAST hit with >95%
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sequence similarity to a deposited sequence of a described taxon (Fig. S4),
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indicating that a relatively large proportion of the data points to an as yet
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unsequenced novel diversity in high mountain lakes.
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Plankton diversity and partitioning of diversity
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The main groups detected in all lakes belonged to the alveolates (55.0% of total
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OTUs97% ), the stramenopiles (34.0% of total OTUs97%), the cryptophytes (4.0% of
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total OTUs97%), the chloroplastids (3.6% of total OTUs97%), and the fungi (1.7% of
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total OTUs97%). The contribution of the phyla Centrohelida, Choanomonada, Rhizaria,
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Katablepharidae, and Telonema was <1% of the total OTUs97%. The number of total
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sequences followed the same ranking except for the fungi, which represented <1% of
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the total sequences (Fig. 2).
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In the turbid FAS 3, OTUs were assigned to 34 different taxonomic families,
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and in FAS 4, we detected 28 different families (Fig. 3, Table S1). Interestingly, in the
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HL lakes, the clear HL 15 harbored a larger number of families (n=39) as HL 5
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(n=35). Accordingly, Shannon diversity in both HL lakes was higher than in the FAS
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lakes, and the clear HL 15 was more diverse than HL 5 (Fig. 4).
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Differences in community composition between the turbid and the clear lakes
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in both geographic regions were confirmed by significant Fishers exact tests
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(p<0.01). In addition, communities between the HL and the Alpine lakes were
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significantly different (Fishers exact tests, p<0.01). Partitioning of diversity (Chao-
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Jaccard) shows that the two FAS lakes were more similar to each other regarding
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their protistan plankton communities than to either of the two HL lakes. Furthermore,
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the Chao-Jaccard distance among the FAS lakes was smaller than the distance
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among the HL lakes. This applies to both, analyses conducted with OTUs 97%
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obtained from all four lakes (Fig. 5) as well as to taxonomic families observed in the
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four samples (Fig. S5).
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In comparison to the FAS lakes, the network analysis shows that the HL lakes
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had notably more families of the Chloroplastida (10 vs. 5) and the Stramenopiles (14
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vs. 8), whereas alveolate (18 vs. 16) and fungal families (5 in both cases) were in the
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same order of magnitude (Fig. 3). None of the fungal families was shared between
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the two geographic regions. The HL lakes additionally harbored one cercozoan, one
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telonemid, and one choanomonad family that were not detected in the FAS lakes. On
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the other hand, one acanthoid family (Centrohelida) was exclusively detected in the
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FAS lakes.
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The FAS lakes shared 24 families (Fig. 3), most of them assigned to the Alveolata
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(12 families) and Stramenopiles (6 families). Interestingly, fungi (5 families) were
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exclusively detected in FAS 3. In total, ten families were unique to FAS 3 and only
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four to the clear one.
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In total, 52 different families were detected in the two HL lakes, 22 of which
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were shared. The only choanomonad family was exclusively found in the turbid HL 5.
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Additionally, HL 5 harbored only three unique alveolate families, whereas in the clear
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HL 15 seven unique alveolate families were found. An overview about the families
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detected in each lake is given as supplementary information (Table S1).
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Discussion
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The few available studies focusing on microbial eukaryote plankton diversity in high
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mountain lakes have exposed only the tip of the iceberg of an as yet undiscovered
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microbial diversity in this extreme freshwater ecosystem (Félip et al., 1999;
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Straškrabová et al., 1999; Wille et al., 1999; Sonntag et al., 2011; Triadó-Margarit &
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Casamayor, 2012). In our study, we overall discovered 3,252 molecular operational
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taxonomic units called at 97% sequence similarity (OTUs97%, Table 2), blasting with
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66 distinct taxonomic families. This exceeds previous investigations by far. However,
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we here note that translating molecular OTUs into taxonomical hierarchies such as
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morphospecies is very difficult (if not even impossible at this time). This is mainly
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because the genetic variability in taxonomic marker genes is not always
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taxonomically informative (Caron et al., 2009; Nebel et al., 2011b; Dunthorn et al.,
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2012) and the accuracy of taxonomic assignments can be unsatisfying, specifically
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for short sequence fragments (Stoeck et al., 2014). For further reasons, discussed in
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detail previously, we refer to Caron et al. (2009), Nebel et al. (2011b) and Stoeck et
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al. (2014). Therefore, to compare our molecular data with previous studies, we here
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discuss our taxonomically assigned OTUs preferably on a higher-taxonomic level,
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namely the family-rank, which is a relatively solid and reliable assignment rank for
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short V4 tags in microbial eukaryotes (Stoeck et al., 2010; Dunthorn et al., 2012).
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The major taxon groups detected in the four lakes corroborate with those
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found in the gene-based study of Triadó-Margarit & Casamayor (2012) for lakes
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located in the Central Pyrenees, Spain. The authors found that most of the
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sequences belonged to the stramenopiles, alveolates and cryptophytes, but also
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sequences of opisthokonts (fungi), chloroplastids, rhizaria, and a few katablepharids,
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euglenozoans and Telonema were identified. Although different molecular techniques
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have been applied in the latter survey, our study supports the taxonomic pattern
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(except for euglenozoans) as we also found that most of the sequences belong to the
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stramenopiles, alveolates and cryptophytes (Fig. 1).
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In the Himalaya lakes (HL, Fig. 2 and 3), we detected that >90% of the target
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sequences belong to the alveolates (>80% of which were Dinophyceae) and
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stramenopiles (>70% chrysophytes, i.e., Chrysophyceae and Synurophyceae). In the
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Alpine lakes, Chrysophyceae and Dinophyceae are key algal groups (Tolotti et al.,
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2009) belonging to the most abundant taxa (e.g., Rott, 1988). They can be indicators
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for environmental changes such as acidification or nutrient availability (Tolotti et al.,
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2003). Therefore, a regular (molecular-based) time-series survey of these taxa may
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be important for environmental assessments and management processes in high
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mountain lakes, which are extremely sensitive to environmental changes (e.g.,
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Rogora et al., 2013; Modenutti et al., 2013; Catalan et al., 2009). Studies like the one
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presented here set the baseline and benchmark for such monitoring processes.
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Dinophyceae as well as Chrysophyceae seem well adapted to this extreme cold and
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nutrient-limited habitat type. This is coincident with their wide distribution even in the
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high arctic (e.g., Charvet et al., 2012a, b) and in glacial ice (García-Descalzo et al.,
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2013). Survival strategies of Dinophyceae and Chrysophyceae are, for example,
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mixotrophy enabling them to adapt to low food and light supply by changing their
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main nutrition mode from heterotrophy to phototrophy (Stoecker et al., 1999, 2009;
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Holen & Boraas, 2009; Charvet et al., 2012a; McMinn & Martin, 2013). Furthermore,
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specialized life stages such as resting stages or cysts guarantee the survival under
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harsh environmental conditions. For example, nutrient shortage or low temperature
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are well known to induce cyst (resting stage) formation in many protists including
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dinoflagellates and chrysophytes, but also in ciliates (Kristiansen, 1996; Foissner,
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2006, 2008; Mertens et al., 2012). For instance, ~24% of the freshwater
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dinoflagellates produce cysts (Mertens et al., 2012) and siliceous cysts are also a
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substantial part of the chrysophyte life cycle (e.g., Sandgren, 1991).
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The Shannon diversity in the Himalaya lakes exceeded that of the Faselfad
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lakes in the Austrian Alps (Fig. 4). For this high diversity in the Himalayan lakes
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several reasons could be taken into account, all of which, however, require in-depth
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investigations. One possibility is input from atmospheric transport or from the
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catchment through precipitation in the Himalaya region and/ or nutrient availability. In
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general, atmospheric transport of microorganisms by air and dust is widespread
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among bacteria, fungal spores, protist cysts and pollen (e.g., Foissner, 2006; Kellogg
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& Griffin, 2006). Nepal and especially the Khumbu Valley region are strongly
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influenced by monsoon rainfalls. Particularly, from August to September over 98% of
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the total annual precipitation falls in the Khumbu Valley (Lami et al., 2010) so that
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probably more taxa are ‘washed out’ from the atmosphere or the catchment area. For
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example, typical Chlamydomonaceae (Chloroplastida) such as Chlamydomonas
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nivalis (“Red Snow”) or Chloromonas nivalis live in snow and ice water and may be
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easily washed into a lake during snow and ice melting processes or rainfall (Hoham &
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Duval, 2001; Remias et al., 2010). Interestingly, Chlamydomonaceae (C. raudensis)
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are also found in permanently ice-covered polar lakes (Pocock et al., 2004; Bielewicz
341
et al., 2011). In addition, Zhang et al. (2007) reported a higher diversity of cultivable
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bacteria in glacial ice in the Himalaya region during the monsoon period. The authors
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showed evidence that this was associated with long transport of continental dust and
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marine air masses.
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Additionally, the diversity (Shannon diversity, Fig. 4) is higher in lakes with a
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lower nitrogen concentration (HL lakes; Table 1). Not surprisingly the nitrogen
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deposition and concentration is higher in the European Alps (Table 1) than in the
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Himalayan region because of increased anthropogenic input (e.g., Rogora et al.,
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2008). Consequently, nitrogen deposition can affect not only production and biomass
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of phytoplankton but also their taxonomic composition as reviewed in Slemmons et
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al. (2013). For example, certain taxa of the diatom family Fragilariaphycaea such as
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Asterionella formosa and Fragilaria crotonensis occurs in high abundances by
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nitrogen enrichment (e.g., Saros et al., 2005). In our study, the most abundant
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diatoms belong to this family and were predominantly found in the FAS lakes (Suppl.
355
Table 1) with higher nitrogen concentrations.
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Therefore, nutrients such nitrate/ammonium could have structured the biodiversity of
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low (HL lakes) and high (Alpine lakes) nitrogen deposited lakes, especially for the
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protist communities dominated by phytoplankton.
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In the Himalaya lakes as well as in the Faselfad lakes, protistan community
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structures were significantly different between the glacier-fed turbid lakes and the
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clear ones (Fig. 3 and 5). Less pronounced differences between the two FAS lakes
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compared to the differences between the two HL lakes may be attributed to the close
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vicinity of the FAS lakes, located in the same catchment area. Some years ago, the
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two FAS lakes were connected to the same glacier. This makes it reasonable to
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assume that they had a similar seed-community of fungi and protists before FAS 4
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lost connectivity to the glacier, giving rise to the evolution of a new plankton
367
community. In contrast, the two HL lakes are located in different catchment areas and
368
thus, these lakes may receive different input to maintain and support the
369
corresponding plankton community structures.
370
Possible explanations for differences in plankton community structures in
371
turbid and clear lakes are at hand: For example, in turbid high mountain lakes,
372
organisms are faced with high loads of suspended particles (‘glacial flour’) from
373
retreating glacier affecting for example growth rate of heterotrophic flagellates
374
(Sommaruga & Kandolf, 2014). Whereas, in clear alpine lakes, the potentially harmful
375
UV-B can reach the lake bottom (Sommaruga & Psenner, 1997; Laurion et al., 2000;
376
Sommaruga & Augustin, 2006) and protists in such transparent habitats developed
377
different strategies to protect themselves from high levels of incident solar radiation.
378
Avoidance of high levels of solar radiation during solar noon or the synthesis or
379
accumulation of photoprotective compounds and/or the presence of effective DNA
380
repair mechanisms can be found in various planktonic organisms (e.g., Tilzer, 1973;
381
Sommaruga & Psenner, 1997; Zagarese et al., 1997; Alonso et al., 2004; Sonntag et
382
al., 2007, 2011; Tartarotti et al., 2013).
383
Such selective mechanisms are most likely major evolutionary forces
384
governing shifts in plankton community structures when glacier-fed lakes turn into
385
clear lakes after loss of glacier connectivity. In the first step, some turbid-lake taxa
386
are eliminated from the original seed-community, when turbid lakes turn into clear
387
lakes. This is because these taxa have no physiological capabilities to survive in
388
clear UVR-flooded and warmer waters. In the second step, succession of new taxa
389
preferring clear-lake conditions complete the community shifts.
390
Not only because such high mountain lakes are important reservoirs of largely
391
unseen protistan diversity, but also to address the above discussed issue in detail,
392
more data as presented in here will be very valuable. The retreat of glaciers
393
worldwide is a given fact (e.g., Vaughan et al., 2013), resulting in the emergence of
394
numerous newborn glacial lakes or even the cut-off of lakes from glaciers. The latter
395
process again is associated with major changes in lake physicochemical conditions in
396
high mountain areas. Our study gives a first insight into microbial communities of
397
glacier-fed lakes and clear lakes, however, future studies are needed on the factors
398
that affect community structures and ecosystem function(ing). Future studies, among
399
others as presented here, will help to narrow the gap in this current knowledge.
400
401
Acknowledgments
402
We thank R. Psenner for identifying the Faselfad lakes as a natural experimental site
403
for research on climate change in alpine lakes. We thank J. Franzoi, G. Larsen, S.
404
Morales-Gomez, and C. Grubbauer for chemical analyses, as well as G. Kandolf and
405
P. Kirschner for help during field work. L. Bittner is acknowledged for introducing and
406
providing helpful comments and commands on QIIME and Cytoscape, D. Forster for
407
R-analyses. The study was financed by the Austrian Science Fund (FWF: P21013-
408
B03, BS and 24442_B26, RS), and by doctoral fellowships of the Austrian Academy
409
of Sciences (OEAW, DOC-fForte 22883, BK) and of the Leopold-Franzens University
410
Innsbruck
411
Forschungsgemeinschaft, grant STO414/3-2. The logistics for the field work and work
412
at the Pyramid Research Laboratory was supported by a project granted to RS by the
413
Committee on High Altitude Scientific and Technological Research (Ev-K²-CNR) in
414
collaboration with the Nepal Academy of Science and Technology, and thanks to
415
contributions from the Italian National Research Council and the Italian Ministry of
416
Foreign Affairs. We greatly appreciate the suggestions of two anonymous reviewers,
417
which helped to improve our manuscript.
(BK).
Financial
support
for
TS
came
from
the
Deutsche
418
419
Accession numbers
420
The sequence datasets have been submitted to the GenBank databases under
421
accession number SRP032772.
422
423
Conflict of interest
424
The authors declare that none of us has any competing commercial interests in
425
relation to the submitted work.
426
427
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Table and figure legends
702
Table 1. Geographic (latitude, longitude, altitude) and lake characteristics of the Alpine Faselfad lakes (FAS) and the Himalaya lakes
703
(HL): maximum depth (Zmax), area, abiotic parameters (min-max; mean) such as turbidity, conductivity, pH, total phosphorus (TP),
704
dissolved organic carbon (DOC), nitrate (NO3-N), ammonium (NH4-N). *values only available from surface water; n.d. not determined.
705
(Himalaya data from R. Sommaruga and Sommaruga & Casamayor, 2009; FAS data: for additional measured parameters see
706
Tartarotti et al., 2013).
Table 1.
Region
Lake
Latitude
Longitude
Altitude Zmax Area Turbidity & visual/ Conductivity
pH
TP
DOC
NO3-N
NH4-N
(µg L-1)
(µg L-1)
(µg L-1 )
optical appearance
(m a.s.l.) (m) (km²)
Alps
FAS 3 47°N 04’ 15’’ 10°E 13’ 15’’
FAS 4 47°N 04’ 27’’ 10°E 13’ 34’’
2,414
2,416
17.0
15.0
0.02
0.02
(NTU)
(µS cm-1)
(µg L-1)
(µg L-1)
5.71-11.80; 8.57
42.0-45.8;
7.2-8.8;
7.3-10.6; 220.0-318.0; 89.0-201.0; 1.0-4.0;
turbid
43.2
8.0
8.8
0.02-0.26; 0.17
48.8-51.5;
7.2-7.4;
1.7-5.7;
transparent
49.8
7.3
2.5
266.9
127.9
1.7
268.0-374.0; 158.0-165.0; 1.0-2.0;
305.1
162.3
1.1
Himalaya
HL 5 27°N 59’ 45’’ 86°E 49’ 24’’
HL 15 27°N 56’ 34’’ 86°E 47’ 40’’
707
708
709
710
5,400
5,160
4.0
4.8
0.01
0.01
n.d.
32.1-32.4;
7.2-7.2;
3.0-44.0; 270.0-830.0;
20.0-22.0;
5.0-7.0;
turbid
32.3*
7.2*
3.7*
550.0*
21.3*
5.7*
n.d.
57.3-57.5;
6.7-6.7;
0.0-1.0;
310.0-450.0;
40.0-42.0;
4.0-6.0;
transparent
57.4
6.7
0.7
363.3
40.7
6.7
710
711
Table 2. Number of sequences and OTUs generated after 454 data processing with 97% cluster threshold: target sequences =
712
eukaryotic unicellular organisms and fungi, checked at least at the family level. Non-targets = multicellular organisms such as higher
713
plants (Embryophyta) and Metazoa, prokaryotes and unassigned sequences.
714
Target sequences
Total
After QIIME
Target
Non-target
Unassigned
sequences
sequences
without singletons/
reads
quality-check
sequences
doubletons
Sequences 350,803
OTUs
226,267
221,011
219,155
3,241
2,015
3,252
3,073
1,804
118
61
Non-target
Unassigned
sequences
sequences
Target sequences
Total
After QIIME
Target
without singletons/
reads
quality-check
sequences
doubletons
Sequences 350,803
OTUs
226,267
221,011
219,155
3,241
2,015
3,252
3,073
1,804
118
61
715
716
717
Fig. 1. The studying sites Faselfad lakes (FAS) and Himalaya lakes (HL): FAS 3 is
718
located about 300 m below the glacier at 2,414 m a.s.l. and the clear lake FAS 4 at
719
2,416 m a.s.l. HL 5 (5,400 m a.s.l.) and HL 15 (5,160 m a.s.l.) are located in the
720
Khumbu Valley. Photos from T. Stoeck (FAS) and R. Sommaruga (HL).
721
722
723
723
724
725
726
Fig. 2. Distribution of the protistan and fungal communities among the lakes
727
(expressed in number of sequences in % per lake; Faselfad lakes, FAS; Himalaya
728
lakes, HL). Note that most of the sequences were assigned to the Alveolata (45.4%
729
of total sequences), Stramenopiles (45.2%), Cryptophyta (6.8%), and Chloroplastida
730
(1.5%). All other groups were <1% of the total sequences.
731
732
733
733
734
735
Fig. 3. Network of the protistan and fungal communities in the turbid FAS 3 and the
736
clear FAS 4 lake in the Austrian Alps and the turbid HL 5 and clear HL 15 lake in the
737
Himalaya using Cytoscape (Version 2.8.3). Each dot represents a taxonomic family,
738
which was coloured according to its phylogenetic affiliation; Faselfad lakes, FAS;
739
Himalaya lakes, HL.
740
741
742
742
743
744
745
Fig. 4. Shannon diversity (taxonomic rank: family) between the turbid FAS 3 and the
746
clear FAS 4 lakes in the Austrian Alps and the turbid HL 5 and clear HL 15 lakes in
747
the Himalaya. Only amplicons are considered that were at least 95% similar to
748
database entries; Faselfad lakes, FAS; Himalaya lakes, HL.
749
750
751
751
752
753
754
Fig. 5. UPGMA clustering of Chao-Jaccard beta-diversity based on operational
755
taxonomic units (OTUs) called at 97% sequence similarity (for details see methods).
756
Lakes from Alps in Austria (Faselfad, FAS) are more similar to each other than to
757
either of the two Himalayan lakes (HL) regarding protistan (incl. fungi) community
758
composition. Furthermore, similarity between the glacier-fed turbid and the clear lake
759
in the Alps (FAS 3 and FAS 4, respectively) was notably higher than similarity
760
between communities in the turbid and clear lakes in the Himalaya (HL 5 and HL 15,
761
respectively). The same pattern was observed when the analysis was conducted with
762
taxonomic families detected in the four lakes (Fig. S5).
763