dataesr/scientific-paragraphs-categorization huggingface dataset:
2013, this issue). Tie-points used for aligning paleoclimate records were based on the rapid transitions between interstadial and stadial events. These are the most abrupt and clearly definable events in each record  (Hughen et al. 2006; Bard et al. 2013, this issue) , minimizing the potential for error from misidentification of events, or due to significant time lags between regions. Points were selected midway through the abrupt transitions to avoid artifacts from differences in resolution between records. From 0 to 10. 5 cal kBP, Marine13 is based on the ocean-atmosphere box diffusion model  (Oeschger et al. 1975)  with parameters set as in Marine04 and Marine09  (Hughen et al. 2004; Reimer et al. 2009). a Bahamas speleothem calendar ages were erroneously entered into the database and random walk model with their associated 2, not 1, uncertainties.
This error serves to reduce the weighting of this data in the calibration curve by a small amount. The much greater 14 C age uncertainties were correctly incorporated. b Speleothem data younger than 14 cal kBP not included in the IntCal13 curve. Table  3  New and updated marine records, age ranges, and marine reservoir corrections included in the IntCal13 curve >13. 9 cal kBP and Marine13 curve >10. 5 cal kBP. Reservoir ages were calculated from the 14 C difference of the overlap with the tree rings. The greater of the calculated uncertainty and ±100 14 C yr was used for all marine records <13. 9 cal kBP, whereas an increased uncertainty of ±200 14 C yr was used for >13. 9 cal kBP. A modeled comparison of the new and the existing marine records with Lake Suigetsu (Bronk Ramsey et al. 2012, supplemental online material) suggests that the variability of the reservoir ages for the period 14-45 cal kBP may have been underestimated in IntCal09/Marine09.
Accordingly, the reservoir age uncertainty for all of the marine records beyond the tree-ring overlap was increased to ±200 14 C yr. For the Iberian Margin and for the Barbados corals and the Cariaco Basin, this additional uncertainty encompasses most of the modeled uncertainty at 2 standard deviations. For the Pacific corals, the modeled comparison suggests that ±200 14 C yr remains an underestimation for the Last Glacial Maximum; however, these results are preliminary and may change in the future. More marine data are needed, as well as efforts to strengthen the Lake Suigetsu varve chronology, especially beyond 40 cal kBP, to confirm the estimated reservoir age uncertainty. In the absence of confirming evidence, ±200 14 C yr has nevertheless been used.
2013 , this issue) and from 14 C data from the Hulu Cave speleothem  (Southon et al. 2012 ) and the Lake Suigetsu plant macrofossils  (Bronk Ramsey et al. 2012). It is beyond the scope of this paper to interpret the complexities of the impact of the Heinrich events on marine 14 C outside the main ice-rafting belts, but it is interesting to note that the Cariaco Basin reservoir age offset correlates with H1b observed in ice-rafted debris off the coast of Portugal  (Bard et al. 2000). The discrepancies observed during both the YD and HS1 are tightly constrained and the 14 C data have thus been removed from the Cariaco and Barbados data set prior to incorporation into IntCal13/Marine13. While the YD and HS1 discrepancies in the Cariaco Basin and Barbados records suggest that a similar pattern might be expected during the other Heinrich stadials (possibly due to shutdown of the Atlantic Meridional Overturning Circulation;  McManus et al.
IntCal13 and the Estimate of the Random Walk Variance The IntCal13 curve estimate was obtained from the output of 2 different specially written computer implementations of the random walk model  (Niu et al. 2013, this issue). The implementation used to construct the tree-ring (i. e. younger) part of the curve allows for uncertainty on the variance of the random walk and makes use of all of the IntCal13 tree-ring data (including those that rely on wigglematching for their calendar age estimates). The implementation used to construct the older part of the curve accommodates data with a range of calendar age error structures, but assumes a fixed vari-ance on the random walk, which is taken to be the mean of the posterior distribution estimated when deriving the tree-ring part of the curve. The estimate for the older part of the curve is based on data from archives other than tree rings (i.
It might be expected that atmospheric 14 C variability would have been higher during the Last Glacial Maximum (LGM) than during the Holocene because the lower geomagnetic field was not as effective a shield from solar cosmic-ray modulation  (Bard et al. 1990; Mazaud et al. 1991) ; also, lower atmospheric CO 2 concentrations may have resulted in a more sensitive carbon reservoir  (Muscheler et al. 2004 ). However, an investigation of the random walk variance using a floating tree-ring series from Japan, 14 C wiggle-matched to IntCal09 at 23,290-23,678 cal BP (van der Plicht et al. 2012), did not indicate an increase in the variability of atmospheric 14 C across 300 yr during this period. Thus, although this does not constitute proof that atmospheric 14 C variability during deglacial and glacial times was the same as during the Holocene, we use, for lack of suitable data, the variability in atmospheric 14 C levels obtained from the tree-ring data in IntCal13 for the full curve.
Marine13 The Marine13 curve is composed of 3 sections. From 0-10. 5 cal kBP, the marine atmosphere ocean box model  (Oeschger et al. 1975; Stuiver and Braziunas 1993)  was used with the IntCal13 curve as the input to create a global marine surface ocean curve (marmod13). We used the same parameter set as deployed in Marine04 and Marine09  (Hughen et al. 2004; Reimer et al. 2009 ). Although more sophisticated models have been shown to produce higher 14 C production rates for the Holocene from the IntCal09 curve  (Roth and Joos 2013) , the marmod13 model fits well with marine data from the IntCal database (Figure  3 ). From 10. 5-13. 9 cal kBP, the marine data include the varved Cariaco Basin data set, without the 12. 55-12. 9 cal kBP interval, and several coral data sets used in Marine09  (Reimer et al.
2009). For this section, we used the same code that was used to estimate the older part of IntCal13, but ran it only on marine data. Again, to create a smooth connection between the 2 estimates, in the interval 10. 5-11 cal kBP the final estimate of the curve was obtained using a weighted average of the output from the atmosphere-ocean box model and marine-only run of the code. These weights were chosen to vary linearly along this interval so that at 11 cal kBP only the marine-only output contributed to the curve while at 10. 5 cal kBP only the atmosphere-ocean box model was weighted. For the oldest portion of the Marine13 curve, all of the available data (marine, speleothem, and terrestrial) were used. Since there is more uncertainty in the calendar timescales of the tie-pointed records, the uncertainty of the 14 C measurement increases for old samples, and the marine reservoir corrections are also uncertain, we simply used the IntCal13 curve offset by 405 14 C yr.
RESULTS AND DISCUSSION The IntCal13 curve plotted as 1-standard deviation envelopes over the full range of the 14  C dating method (0-50 cal kBP) in intervals of 2000 yr, with all the data points and uncertainties shown, is given in the online Supplemental Material (Figures  S1-S25 ). They represent the "state of the art" in the ongoing community effort to develop 14 C calibration over the full range of the method. Comparisons of the contributing IntCal data sets allow a discussion of any systematic differences between the different data sets (reservoir age, dead carbon fraction, calendar age) and their variability with time, location, and climate, as well as our understanding of their causes. A comparison of all the IntCal data in Figure  1a  shows that for the period 10-27 cal kBP, there is a general data agreement within a bandwidth of ~500 yr, while the RWM calibration envelope gradually widens with increasing age from about 100 to 200 yr.
Effect of Covariance in Calendar Age Estimation The IntCal13 database contains 4 data sets where, within each data set, the estimates of the calendar ages are interdependent. Within each of the data sets, the level of dependence between any 2 estimates is expressed mathematically by a covariance matrix whose diagonal elements represent the variances of individual estimates; the coefficients of correlation between the observations are readily obtained from this matrix. What impact adding this additional uncertainty information has on the estimates of the curve depends largely on the magnitude and extent of these correlations. High correlations between substantial numbers of data points in a data set mean that the relevant calendar age estimates can only vary when the others do too, and thus considerably restricts the range of plausible calendar age estimates.
Of the 4 relevant data sets, Cariaco Basin, Iberian Margin, and  Pakistan Margin show moderate levels of internal correlation, but because the Lake Suigetsu record is a varve chronology, it has very high correlations between its calendar age estimates. Since Suigetsu is also an extremely long record, with calendar age estimates used for the covariance calculation from 11,271 to 52,824 cal BP, and also has small calendar age uncertainties relative to other data sets in the database, it has a substantial impact on the final estimate of the calibration curve. For this reason, in places, IntCal13 follows the Suigetsu record very closely even when it differs from other records (Figure  4a, b ).
At present, if we give credence to all of the records, we are unable to unambiguously determine this fine structure for earlier periods. Figure  5  The tree-ring portion of the IntCal13 curve from 0-14,000 cal BP compared to IntCal09. The addition since IntCal09 of several new data sets, and the extension of existing ones, has greatly improved the database for the construction of IntCal13 and Marine13 for the period beyond the treering calibration. The resulting spread of data points likely retains some systematic differences not solely attributable to noise, particularly with respect to potentially varying reservoir and DCF corrections. These unquantified errors are handled by the inclusion of an additive parameter in the RWM algorithm  (Niu et al. 2013, this issue). The resulting IntCal13 calibration envelope is somewhat narrower and much smoother than IntCal09, and centennial and millennial-scale fluctuations in 14 C concentration in the temporal structure of the individual records are largely removed.
ACKNOWLEDGMENTS Funding for this work was provided in part by the  Natural Environment Research Council ,  UK grant   NE/I01666X/1  and  NE/E019129/1. The  US National Science Foundation  supported contributions by some of the coauthors. Travel expenses for 2 members to attend Working Group meetings of the IntCal Oversight Committee were funded by  IGBP -PAGES  ( International Geosphere-Biosphere Programme -Past Global Changes ) Project. Additional support for the working group meetings was provided by the  Collège de France , the 14 CHRONO Centre, QUB and by individual IWG members. We appreciate the constructive review by  Tim Jull  and the many useful discussions with  Bill Austin ,  Maarten Blaauw ,  Raimund Muscheler ,  Jesper Olsen , and many others.
METHODS We selected all European breeding bird species for which global breeding distribution data were available, a total of 405 species. We obtained data on the global breeding distribution of these species from a global database of avian species distributions  (Birdlife International and Natureserve, 2012). These data represent the best available information on the global breeding distribution of this avian assemblage. We converted polygons to species presence and absence on a 10-arcmin resolution grid. We used the entire global distribution of each species in order to avoid missing portions of the species realized climate niche. We focused on 'The Complete Birds of the Western Palearctic' CD-ROM  (Perrins & Ogilvie, 1998)  as the data source for the habitat and trophic niches of these avian species because of its extensive treatment and literature review of habitat use, foraging behaviour, foraging location, food items and nesting characteristics.
We included in the analysis a number of axes from each niche ordination sufficient to describe a uniform threshold percentage (90%) of the total ecological variation in each niche. Thus, the number of ordination axes varied among niches, but we needed to ensure that any differences among niches during phylogenetic comparative analysis were not due to consideration of arbitrary and variable percentages of ecological variation among niches. Use of a standard proportion of ecological variation for each set of variables enabled us to make consistent and comparable estimates of variability for the analysis of ecological divergence of species in terms of the three niche components. All ecological ordinations were conducted in R (R Development Core Team, 2013) with the package ade4  (Dray & Dufour, 2007).
Items described exclusively as being taken only 'rarely' , 'occasionally' , or presented as a unique observation for a species, led to a score of 0 being assigned to the type of food item under consideration (see the Species received a score of 1 on each foraging behaviour attributed to the species, unless the species was described as 'rarely' performing a behaviour. The nine substrate variables received a score of 1 if specified in the description of foraging behaviours for species. Three variables (nocturnal, crepuscular and diurnal) described the daily period of foraging activity and were scored as 1 if the species was reported as being active (or 'occasionally active') during that period. When information existed regarding variation in trophic variables between the breeding and non-breeding seasons, only data concerning the breeding season (e.
Climatic niche We estimated the breeding distribution of each species by determining both resident and breeding areas from digital maps from the Birdlife International database of bird distributions and gridding them to 10-arcmin resolution  (Birdlife International and Natureserve, 2012). Climate variables at 10-arcmin resolution were obtained from the WorldClim database  (Hijmans et al. , 2005 ; http://www. worldclim. com). We used climate variables for the months of April to September as a period when European species would be on the nest and/or fledging chicks. The variables captured range, central tendency and variability of temperature and precipitation during the 6-month period (Table  S1 in Appendix S1 ). This span of months should capture much intra-and interspecific variation in timing of reproduction throughout the European range of the focal species, but excludes the mid-winter period that is irrelevant to the breeding conditions of almost all species except some large, northern owls  (Perrins & Ogilvie, 1998).
We judged the alternative of directly taking only climate variation during nesting as being of too fine a scale for our purposes, likely to obscure broad geographical differences in climate and needing comprehensive observational data across large areas, elevations and times of year for all the species. These latter data were unavailable at the comprehensive level necessary to cover an analysis of all 405 species. The monthly averages of the climate variables we used were determined over a base period of the years 1951 to 2000. We estimated species climatic niches  (Austin et al. , 1990)  using the outlying mean index (OMI) analysis  (Dolédec et al. , 2000). Climatic niche position for each species along the two selected axes was extracted by projecting species centroids orthogonally to the ordination axes  (Thuiller et al.
Phylogenetic reconstruction We used the phylogenetic tree of European birds constructed for a previous study  (Thuiller et al. , 2011) , following a published pipeline  (Roquet et al. , 2013). To do so we retrieved from GenBank: 10 mitochondrial gene regions (12S, ATP6, ATP8, COII, COIII, ND1, ND3, ND4, ND5, ND6) plus six nuclear ones (28S, c-mos, c-myc, RAG1, RAG2, ZENK). The percentage of genetic sampling of species varied among regions (Table  S2  in Appendix S1). The sequences were aligned with several algorithms, including ClustalW  (Larkin et al. , 2007) , Kalign  (Lassmann & Sonnhammer, 2006) , mafft  (Katoh & Toh, 2010)  and muscle  (Edgar, 2004) , and checked by eye. The best alignment for each region was selected with mumsa  (Lassmann & Sonnhammer, 2006)  and depurated with TrimAl  (Capella-Gutierrez et al.
, 2009). Then, DNA matrices were concatenated to obtain a supermatrix. We conducted phylogenetic analyses using maximum likelihood within RaxML  (Stamatakis et al. , 2008) , applying the GTRCAT model with partitions defined for each region, and a prior tree constraint at the ordinal level for the birds dataset (based on  Hackett et al. , 2008). The inferred tree generally had a satisfying level of node support (for details on bootstrap and support analyses, refer to the supplementary materials of  Thuiller et al. , 2011). We kept 100 most likely trees for our analysis to account for the uncertainty associated with phylogenetic inference. We dated these trees by applying penalized likelihood with r8s  (Sanderson, 2003)  and fossils relevant for 14 clades (Table  S3  in Appendix S1). The best ML tree can be found in Treebase (http://treebase.
The estimation of these parameters and the test of their significance relative to 0 and 1 were performed based on likelihood optimization, using the motmot package in R  (Thomas & Freckleton, 2012). For phylogenetic signal, lambda was preferred to another metric, Blomberg's K, because a recent simulation study showed that lambda estimated the expected strength of the phylogenetic signal better than other metrics, and was insensitive to tree size and tree uncertainty  (Münkemüller et al. , 2012). We chose not to make inferences based on the third Pagel index, delta, which captures the timing (late versus recent) of ecological diversification. Calculation of this parameter often causes computation problems as it does not increase asymptotically with increasingly late character divergence, and hence becomes impossible to estimate when diversification occurs very late on the tree.
We accounted for phylogenetic uncertainty by repeating the calculations using the 100 most-likely phylogenetic trees. The deviation between observed disparity and Brownian simulations was quantified using the minimum discrimination index (MDI) statistic, which estimated the area between the curves of observed disparity and mean Brownian simulated disparity (as in  Harmon et al. , 2003). This analysis was repeated identically for the climatic, habitat and trophic niches. All these steps were performed using available functions in the package geiger  (Harmon et al. , 2008 , script available upon request). The comparisons of empirical and modelled disparity are robust to incomplete taxon sampling that generally occurs on assemblage trees because empirical disparity curves were compared with corresponding neutral (Brownian) expectations based on phylogenetic divergences among the species.
ACKNOWLEDGEMENTS We thank two anonymous referees whose comments contributed to the improvement of earlier manuscript versions. This research received funding from the  European Research Council  under the European Community's  Seven Framework Programme FP7 /2007-2013 Grant Agreement no. 281422  (TEEMBIO). This work was also funded by a  French ANR  project  called EVORANGE  ( ANR-09-PEXT-011 ). P. B. P. and N. E. Z. acknowledge support from  EU  grant  ENVCT-2009-226544  'MOTIVE'. P. B. P. acknowledges support of  Swiss National Fund  grant  31003A_122433  'ENNIS'. R. W. was supported by  Swiss National Fund  grant  CRSII3_125240  'SPEED'. C. R. was supported by a  Fundación Ramón Areces  postdoctoral grant when she conducted the phylogenetic inference.
Very Large Genomes and Transposable Element Content Genome sizes vary among eukaryotes by nearly five orders of magnitude with the largest genomes, that is, those greater than 20 Gb, found in the diatoms, dinoflagellates, metazoans and green plants (Fig. 1 ). The number of records available, however, is highly skewed, with the metazoans, fungi and green plants being much more highly represented than other groups (Fig. 1 ). Genome sizes shown in this figure, therefore, may reflect only a fraction of actual genome size diversity, particularly in the less well examined groups. Clearly, the best estimates of TE content of a genome will be from the analysis of a fully sequenced and assembled genome. However, no very large genome has been sequenced yet, due chiefly to technical and financial restraints,  3  and search of the literature and databases suggests that there are no plans to fully sequence a very large genome.
The percentage of the genomes of Schistosoma mansoni (0. 38 Gb), S. japonica (0. 40 Gb), S. hematobium (0. 38 Gb), estimated to be repetitive were similar, 40% -47%. However, the TE composition varied. For S. janonica and S. mansoni only 20% of the genome was identified as TEs, 13% nonLTR-RTs and 6% LTR-RTs in S. janonica,  35  and in S. mansoni the inverse, 5% nonLTR-RTs, 15% LTR-RTs. 36 For S. hematobium 32% of the genome was estimated to be nonLTR-RTs and 11% LTR-RTs. 37 enome sizes in the Caudata range from 10 to 120 Gb, with a median of ~30 Gb (Fig. 2 ) and in the lungfish from ~40 to 129 Gb with a median of 51 Gb (Fig. 2 ). The genomes of six salamanders, with genome sizes ranging from ~14 to 50 Gb, were examined using next generation sequencing, representing 0. 07-1. 9% of the genomes.
30 However, the authors were only able to identify a tiny portion of the sequence (less than 1%) as transposable elements and for the tandem repeats were unable to find any homology to known repetitive elements nor to any domains in the Pfam database. 30 iatoms (Diatomophyceae) are one of the predominant contributors to global carbon fixation and account for 40% of the total primary production in the ocean. 31 As far as we can tell, no large diatom genomes have been sampled for TE content but two smaller genomes have been sequenced. The major TE component of both the Phaeodactylum tricornutum and Thalassiosira pseudonana genomes is LTR-RTs, as in plants, but with a much higher abundance in P. tricornutum. 32 ithin the metazoans very large genomes are found in the shrimps and amphipods (Malacostraca), flatworms volume 3 issue 2 In addition, TE activity is often repressed by host genome control mechanisms.
11 ost copies of TEs within a genome are inactive; copies may acquire stop codons or frameshifts or another TE may insert within the copy. Genome size is log (10) haploid genome size in Gb. Bars indicates min to max genome sizes for a particular group. Dots within the bars indicate median genome size. Figures to the right of the bar are the number of records for that group, the figure within brackets is the percentage of genomes within that group that are > 20 Gb. The blue shading indicates genomes larger than 20 Gb. Most of the data is taken from databases listed on DOGs database. 2 Other references are too numerous to list but can be obtained from the authors on request. Duplicate records were removed so that the minimum genome size listed was kept.
Acknowledgments This work was supported by  Centre National de la Recherche Scientifique  under the  program 'Action Thématique Incitative sur Programme'  awarded to  Didier Casane  from  2006-2009. The authors would also like to thank the two reviewers for their comments and improvements to the manuscript. seen in both the lungfish and the salamanders. The lungfish and salamanders, therefore, accommodate their large load of TEs because these TEs have accumulated gradually over a long period of time and have been subject to inactivation and decay.