dataesr/scientific-paragraphs-categorization huggingface dataset:
The snowpack initialization is described in  Fettweis et al. (2005)  and a spatial resolution of 25 km is used here. SISVAT does not include a 3-D ice sheet model and consequently Greenland maintains a fixed height and extent through the simulations. Since the version of  Fettweis et al. (2011b)  used in the MAR future projections made for ICE2SEA  (Rae et al. , 2012) , a new tundra/ice mask based on the Greenland land surface classification mask from Jason Box (http://bprc. osu. edu/wiki/Jason Box Datasets) is prescribed and the smoothing of the  Bamber et al. (2001)  based topography is reduced by a factor 2 in the CMIP5 forced MAR simulations. According to  Vernon et al. (2012) , this alters a little the MAR results, as discussed in the Supplement. The ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis-forced MAR simulations over 1980-2010 are used to homogenise the results by crosscalibration when values of SMB components are given at the scale of the whole ice sheet, since results using different ice sheet masks and topographies are presented here.
, 2006 (Fettweis et al. , , 2011b) )  and the albedo  (Lefebre et al. , 2003; Fettweis et al. , 2005). In its validation, MAR is comparable to another RCM: RACMO2 (Regional Atmospheric Climate Model)  (van Angelen et al. , 2012a; Lenaerts et al. , 2012). Its simulations will be compared later to show that the trends in our future projections are independent of the used RCM. That is why, in a lack of SMB observations at the scale of the whole ice sheet, the ERA-Interim forced MAR (referenced as MAR ERA-Interim hereafter) simulation is used as the reference run in this manuscript. For computing future projections, we force MAR with 6-hly outputs (temperature, wind, humidity and surface pressure) from four GCMs (BCC-CSM1-   1 ). The version of ECHAM5 and HadCM3 is intermediate between the one used in the CMIP3 and CMIP5 databases  (Rae et al.
Evaluation over current climate The aim of this section is to evaluate the ability of the CMIP5 GCMs to simulate the present-day climate (general circulation) over Greenland in respect to the  ERA-Interim over 1980 -1999. Moreover, the present-day SMB (1980 -1999)  simulated by MAR, forced by three chosen GCMs from the CMIP5 database, are going to be compared to MAR ERA-Interim. The MAR simulations performed in the framework of the ICE2SEA project are also evaluated for comparison, whereas they are not used in our future SLR projections. Only the period 1980-1999 covered by ERA-Interim and used by the IPCC (2007) as the reference period over current climate is investigated here. Comparisons on longer time periods are available in the Supplement. An evaluation only over 20 years could be litigious because the ERA-Interim based time series could contain significant decadal variability affecting the general circulation over Greenland, which cannot be temporally matched to the decadal variability in the GCM forced simulations.
But, the mean climate over Greenland, a balance of changes in sea-ice, thermohaline circulation and path of the storm tracks, depends of the ability of the GCM to simulate the climate on the global scale. Thus, the regional climate may be correct due to a compensation of model errors, and therefore, the amplitude of its projected warming could be questionable. Such a possibility is reduced through the use of a model ensemble, here, those available from the CMIP5 database are used. The surface conditions (except the SST and SIC, which are used as forcing) simulated by the forcing GCM do not considerably vary the results of the RCM but the general atmospheric circulation in the RCM, is fully induced by the GCMbased boundaries forcing. Therefore, GCM atmospheric biases in temperature, humidity and wind with respect to the ERA-Interim at the RCM boundaries, affect results in its integration domain.
However, the mean pattern of the general circulation is the same in summer and in winter. That is why, an evaluation at the annual scale is enough here. The evaluation of the variables listed above is enough to explain most of the differences between MAR forced by the GCMs and MAR ERA-Interim. Biases in SST and SIC have less impact on the MAR results  (Hanna et al. , 2009) , and the specific humidity in the free atmosphere from GCMs compare generally well with the one from ERA-Interim (not shown here). The CMIP5 GCMs used here (CanESM2, MIROC5 and NorESM1-M) have been selected among the most suitable GCMs from the CMIP5 database (with 6-h outputs available at the model levels) in respect to their ability to simulate the summer free-atmosphere (at 700 hPa) temperature and the general circulation over Greenland at 500hPa (see the Supplement).
This precipitation estimation will be used in the next section to estimate GrIS SMB changes from GCMs outputs. It is obvious that precipitation changes simulated by RCMs and GCMs can be very different locally but at the scale of the whole ice sheet, they compare relatively well, as shown in Fig. 6d. To estimate the snowfall over GrIS from GCMs, we select the GCM pixels located over the region covering Greenland (20-70 • W and 60-85 • N) and having an altitude of at least 1000 m a. s. l using the orography (variable OROG in the CMIP5 database) of each GCM. Since snowfall (variable PRSN) is given in kg m 2 s -1 in the CMIP5 database, we convert these values to GT yr -1 by using a constant factor 365×24×600×k, where k is a parameter fixed to 1. 6, to achieve the best comparison of the GCM-based versus RCM-based snowfall changes.
Conclusions 21st century projections of the Greenland ice sheet SMB were carried out using the regional climate model MAR forced by climate change scenarios RCP 4. 5 and RCP 8. 5 and driven by the output of three GCMs from the CMIP5 database. The GCMs have been chosen for their ability to simulate the current climate (general circula-tion at 500 hPa and JJA temperature at 700 hPa) over Greenland with respect to the ERA-Interim reanalysis. Most of the differences between MAR forced by ERA-Interim (which we consider here as the best representation of the current SMB) and forced by CMIP5 GCMs over 1980-1999 are smaller than the uncertainty related to the estimate of the present-day GrIS SMB. Therefore, we can consider that MAR, when forced by these three CMIP5 models (CanESM2, MIROC5 and NorESM1-M), is able to realistically simulate the current SMB of the GrIS.
Acknowledgements. Xavier Fettweis is a postdoctoral researcher of the  Belgian National Fund for Scientific Research. For their roles in producing, coordinating, and making available the CMIP5 model output, we acknowledge the climate modelling groups (listed in Table 1 of this paper), the  World Climate Research Programme 's (WCRP) Working Group on Coupled Modelling (WGCM), and the  Global Organization for Earth System Science Portals (GO-ESSP). We thank  Jamie Rae  for having provided the ECHAM5 and HadCM3 outputs,  Jonathan Gregory  for his helpful comments and both reviewers for their very useful and constructive reviews which greatly improved our paper. Finally, this work was partly supported by funding from the  ICE2SEA  programme from the  European Union   7th Framework Programme  ( FP7 ), grant number  226375  (ice2sea contribution number  098 ).
(This table is available in its entirety in a machine-readable form in the online journal. A portion is shown here for guidance regarding its form and content. ) consistent with previous results even though the SEDS measurements were made using different software and methods and in significantly more crowded mosaics. Given the use of different photometric software with different parameterizations and the different depths of the data sets being compared, we regard the agreement between SEDS and the preexisting surveys as excellent.
03 and 0. 36 ± 0. 03 nW m -2 sr -1 at 3. 6 and 4. 5 μm, respectively. The total contributions to the CIB of detected IRAC sources are therefore 5. 6 ± 1. 0 and 4. 4 ± 0. 8 nW m -2 sr -1 at 3. 6 and 4. 5 μm, respectively, down to 26 mag. Our results agree with  Helgason et al. (2012) , whose "default" construction estimated total contributions from IRAC sources to the CIB of 4. 9 +1. 7  -0. 7 and 3. 3 +1. 2 -0. 5 nW m -2 sr -1 at 3. 6 and 4. 5 μm, respectively, where the ranges bracket the two extremes of the faint-end LF extrapolation consistent with the  Helgason et al. (2012)  LF database. The SEDS CIB measurement is significantly smaller than that estimated in the three DIRBE "Dark Spots" at 3. 6 μm by  Levenson & Wright (2008). This may be because the "Dark Spots" estimate is based on an extrapolation to faint levels of relatively bright counts having a significantly steeper slope (∼0.
e. nematodes in sediments  [6] ), "group-specific" primers will be effective. Alternatively, when the goal is to obtain a comprehensive analysis of samples containing species from numerous phyla (as most environmental samples do), primers should target a locus found universally across all animals or plants. Despite the inherent difficulty of designing versatile primers (also referred to as broad-range or universal primers), several sets are readily available to amplify nuclear and mitochondrial gene fragments across animals. For example, there are primers to amplify short fragments of the nuclear 18S and 28S ribosomal markers  [22, 23] , but these regions evolve slowly and may underestimate diversity  [24] [25] [26] [27]. Versatile primers have also recently become available to target a short fragment of the mitochondrial 12S gene  [28] , a region with high rates of molecular evolution suitable for species delineation and identification, but taxonomic reference databases are currently highly limited for this marker.
The mitochondrial Cytochrome c Oxidase I gene (COI) has been adopted as the standard 'taxon barcode' for most animal groups  [29]  and is by far the most represented in public reference libraries. As of January 2013, the Barcode of Life Database included COI sequences from >1,800,000 specimens belonging to >160,000 species collected among all phyla across all ecosystems. However, versatile primers are only available to amplify the barcoding region of 658 bp  [30, 31]  and are known to be poorly conserved across nematodes  [6, 26] , gastropods  [31]  and echinoderms  [32]  among others. A single attempt was made at designing a versatile primer to amplify a shorter "mini-barcode" COI region  [17] , but it has received limited use due to large numbers of mismatches in the priming site that affects its efficiency across a broad range of taxa  [33].
Primer design We aimed to design a versatile PCR primer within the 658 bp COI barcoding region which could be used in combination with a published primer commonly used for DNA barcoding (i. e. LCO1490 or HCO2198  [30] ) to target a short DNA fragment. The Moorea BIOCODE project provided an alignment of 6643 COI sequences belonging to ~3877 marine taxa, mostly coral reef associated species (up to five specimens per morphospecies) spanning 17 animal phyla (sequences available in BOLD, projects MBMIA, MBMIB and MBFA). The information content [entropy h(x)] at each position of the alignment was plotted using BioEdit  [38]  to locate more conserved regions within the 658 bp COI barcoding fragment (Figure  1 ). A site with limited variation was located between positions 320 and 345 of the 658 bp COI region (Figure  1 ).
Analysis of sequence data A diagram of the bioinformatics pipeline is provided in Figure  2. Denoising "Standard flowgram file" (. sff ) is the standard output of 454 platforms. It contains bases, quality and strength of the signal for each read. We used the program Mothur  [45]  to extract the flowgram data (. flow file) and sort reads as follow: 1) we partitioned flowgrams per sample based on barcodes and MIDs, 2) we discarded reads with more than two mismatches in the primer sequence, 3) we discarded reads with less than 200 flows (including primers and barcode), 4) we discarded or trimmed flowgrams based on standard thresholds for signal intensity (as suggested in  [46] ). Following this initial quality filtering, we conducted additional denoising of flowgrams using a mothur implementation of Pyronoise  [46]  that uses an expectation-maximization algorithm to adjust flowgrams and translates them to DNA sequences (command shhh.
Alignment to reference barcode database and removal of non-functional sequences We used amino acid translations to align sequences to the BIOCODE reference dataset using MACSE v1. 00  [47]. Quality-filtered sequences were sequentially aligned and added to the reference dataset using the option "enrichAlignment". This alignment strategy is only reasonable because the studied COI fragment is highly conserved at the amino acid levels. To further optimize computing time, sequences were split into subsets containing 500 sequences that were aligned in parallel thanks to a computer farm and then progressively merged into a single final alignment using the option "alignTwoProfiles". MACSE can detect and quantify interruptions in open reading frames due to: (1) nucleotide substitutions that result in stop codons and (2) insertion or deletion of nucleotides (non multiples of three) that induce frameshifts.
Sequences with stop codons are likely bacterial sequences, pseudogenes or chimeric sequences. On the other hand, frameshifts may be caused by sequencing errors that are frequent with the 454 platform  [48]. MACSE can also detect and quantify insertions and deletions that do not lead to interruptions in open reading frames. COI is relatively conserved and indels are relatively uncommon. For example, only 0. 9% of the sequences in BIOCODE dataset (including platyhelminthes, gastropods and isopods) display a deletion of one codon in their COI sequence, and none of the sequences in the BIOCODE dataset have codon insertions. As a result, we decided to keep all sequences from the 454 dataset which satisfied the following criteria: no stop codons, no frame shifts, no insertions and less than four deletions.
Chimera removal We used the BIOCODE reference dataset to facilitate chimera detection implemented in UCHIME  [49]. Clustering sequences in operational taxonomic units (OTUs) Our dataset comprised sequences belonging to a diversity of taxonomic groups that are known to have dissimilar rates of COI evolution. This means that using a fixed sequence dissimilarity cutoff (i. e. 5%) for clustering OTUs may not result in accurate species delineations. Therefore, rather than using a conventional hierarchical clustering method, we ran CROP  [50] , a Bayesian clustering program that delineates OTUs based on the natural distribution of the data. The program uses user-defined lower and upper bound variance to generate clusters with different standard deviations. The settings used in CROP for clustering sequences in OTUs will determine our estimation of taxonomic diversity in each sample.
Ideally, each OTU should represent an evolutionary distinct unit. In order to optimize lower and upper bound values, we first use CROP to cluster sequences from the reference BIOCODE database using a variety of thresholds that in turn correspond to sequence dissimilarities (e. g. , lower and upper values of 3 and 4 correspond to sequence dissimilarities of between 6% and 8% respectively). The following paired lower and upper thresholds were tested because they are within the range of intra-and interspecific sequence dissimilarity reported in the literature for marine invertebrates  [2, 43, 51, 52] : -l 1. 5 -u 2. 5; -l 2. 5 -u 3. 5; -l 3. 0 -u 3. 75; -3. 0 -u 4. 0; -l 3. 25 -u 4. 25. We particularly examined the frequency of false positives (splitting of a taxon in two or more clusters because of deep intraspecific variation) and false negatives (lumping of two or more sister taxa together because of shallow interspecific divergence) in comprehensively sampled and diverse groups (i.
Taxonomic assignment of OTUs We performed BLAST searches  [53]  of representative sequences in the local BIOCODE database (implemented in Geneious) and in GENBANK. We considered species level match when sequence similarity was at least 98%  [2, 51, 52]. Whenever sequence similarity was lower than 98%, we used the Bayesian approach implemented in the Statistical Assignment Package (SAP,  [54] ) to assign the sequence to a higher taxonomic group. SAP retrieves GENBANK homologues for each query sequences and builds 10,000 unrooted phylogenetic trees. It then calculates the posterior probability for the query sequence to belong to a taxonomic group. Here we allowed SAP to download 50 GENBANK homologues at ≥70% sequence identity and we accepted assignments at a significance level of 95% (posterior probability).
Preliminary tests showed that the forward mlCOIintF primer used in combination with the reverse jgHCO2198 (Table  1 ) amplified the highest proportion of metazoan diversity tested herein (91% -Table  2 ). On the other hand, the reverse mlCOIintR primer performed poorly whether it was used with LCO1490, dgLCO1490 or jgLCO1490 (57%, 60% and 64% respectively -Table  2 ). Despite the degenerate sites in both mlCOIintF and jgHCO2198 primer sequences, particularly at the third codon position (Table  1 ), there was no evidence of non-specific binding (see single bands on agarose gel pictures in Additional file 2) using the touchdown PCR thermal profile. A total of 87% (250 of 285) of templates successfully amplified, among which 93% provided good quality sequences (GENBANK accession numbers KC706674-KC706906).
Pyrosequencing of fish gut contents We obtained a total of 93,973 flowgrams after initial denoising with Pyronoise. Alignments of sequences to the reference BIOCODE dataset using MACSE revealed 38,576 sequences (41%) with anomalies in their amino acid translation. Among them, 6407 sequences with a single frameshift but with no stop codons and no inserted or deleted codons were kept in the dataset, as we assumed they were the result of minor sequencing errors. All remaining 32,169 sequences, among which 2. 4% only had a stop codon, were discarded. UCHIME detected 522 potential chimeric sequences that were also removed to obtain a final dataset of 54,875 high quality reads. The number of reads per individual varied from 1219 to 8423 (mean ± SD = 3430 ± 1104), most likely as a result of differences in ligation efficiency during addition of MIDs due to the primer tag (Additional file 3).
Individual rarefaction curves implemented in R, package VEGAN  [55]  indicate that additional sequencing would be required for further describing the gut contents of some fishes (curves do not reach a plateau, Additional file 4). The Bayesian clustering program CROP revealed a total of 337 OTUs. None were identified as bacteria or non-COI sequences from BLAST searches. Of these, 177 OTUs (52. 5%) were identified to the species level as they showed more than 98% sequence similarity with BIOCODE or GENBANK sequences (Figure  4A , Additional file 5). For the three fish species separately, 56. 9%, 50. 5% and 52. 9% of OTUs determined from N. savayensis, M. berndti and S. microstoma gut contents respectively had a species-level match. Three OTUs representing the DNA of the predatory fish species themselves (N.
savayensis: 1012 sequences, S. microstoma: 921 sequences; M. berndti: 3 sequences) were removed. Importantly, none of the 177 OTUs identified to the species level were assigned to the reference barcode of the same morphological species. Moreover, CROP was effective at discriminating closely related species, such as 12 species within the genus Alpheus (among which A. obesomanus and A. malleodigitus are sister species within the obesomanus species complex) (see Additional file 5 for more examples). The Bayesian assignment tool offered further taxonomic insights by confidently assigning 108 additional OTUs (32%) to a higher taxonomic level, and only 52 OTUs (15. 4%) remained unidentified. An alignment of all representative sequences is provided in Additional file 6 and all unique sequences were deposited in the Dryad Repository doi:10.
5061/ dryad. 6gd51). OTUs belonged to 14 phyla (Figure  4A ); Arthropoda, Chordata and Annelida were the most represented, with 175 OTUs (52. 4%), 42 OTUs (12. 6%) and 27 OTUs (8%) respectively. Species level matches were more prevalent among Chordata (88. 1%) and Arthropoda (64%), two macrofaunal groups particularly well sampled by the Moorea BIOCODE teams  [56]  (Figure  4A ). Moreover, taxonomic assignments were more prevalent for OTUs represented by a high number of sequences. For example, only 51. 8% of Arthropoda OTUs matched reference barcodes when they were represented by a single sequence, whereas 100% of OTUs represented by more than 1000 sequences were assigned to BIOCODE or GenBank specimen (1: 51. 8%; [2-9]: 41. 8%; : 75%; [100-999]: 83. 9%; >1000: 100%; Figure  4B ). Similarly, 27.
Amplification tests conducted across 284 templates showed no evidence for amplification of non-target loci (single PCR band of expected size). The touchdown PCR profile may have helped increase the Columns present the number of taxa for which the target region was successfully amplified. Amplification success was evaluated on agarose gels (pictures shown in Additional file 2). The total number of taxa used for each phylum is displayed in parentheses. probability of primer-template specificity with high annealing temperatures during the first PCR cycles. Nevertheless, we also experimented with PCR conditions such as 35 cycles at 48°C for selected samples without observing any evidence for non-selective amplification (data not presented). Amplification and pyrosequencing of the 313 bp COI fragment from fish gut contents represents a better test of the likelihood of this primer set to co-amplify contaminants.
Bacteria are particularly preponderant in gut and faecal samples  [43]  and can become problematic when misconstrued as prey items  [57]. We used a sequence analysis pipeline that takes advantage of the coding properties of the COI region to exclude dubious DNA fragments. As a result, 34. 2% of A) The number of OTUs per phylum is presented for all fish guts pooled together. OTUs were identified from BLASTn searches performed in the Moorea BIOCODE database and GENBANK. We considered a match to be at the species level when sequence similarity to a reference barcode was >98%. When sequence similarity was < 98%, we used the Bayesian assignment tool implemented in SAP to assign each OTU to a higher taxonomic group, accepting assignments at a significance level of 95% (posterior probability). B) The proportion of OTUs presented per abundance classes.
Abundance corresponds to the number of sequences per OTUs. sequences were removed from the dataset, among which 2. 4% which had a stop codon were potential bacteria. Most anomalies were not attributable to co-amplification of contaminants but rather base insertions causing frameshifts. Pyronoise (used as initial denoising) removes errors caused by incorrect interpretation of signal intensity during 454 pyrosequencing, therefore, numerous frameshifts may in fact be the result of nucleotide mis-incorporation during PCR amplification. Therefore, we highly recommend using a proofreading taq polymerase to generate amplicons with fewer errors in future metabarcoding analysis. DNA may also get damaged during digestion  [14]. Other types of environmental samples where animals are collected alive (i.