dataesr/scientific-paragraphs-categorization huggingface dataset:
2012; Hickey et al. 2012). GBS has attracted great interest for the application of GS in plant breeding. Poland et al. (2012)  recently reported that they successfully applied GBS to a large set of advanced wheat lines from the semi-arid wheat breeding program of CIMMYT and predicted breeding values with 0. 28 to 0. 45 prediction accuracy for grain yield (GY), an improvement of 0. 1 to 0. 2 over another marker platform. The authors concluded that its low cost makes GBS a good method for GS. The extensive LD of most wheat populations probably aids the accuracy of imputation of missing genotype data. However, the accuracy of genomic predictions based on imputed data may be quite different in other species, such as maize, with much less extensive LD. Although GBS appears promising as a genotyping method to use in GS, currently, no studies have been performed that evaluate the use of GBS data for prediction accuracy in diverse maize breeding populations.
Broad-sense heritability of the combined analyses across environments was calculated as h 2 ¼ s 2 g =ðs 2 g þ s 2 ge e þ s 2 e e • r Þ, where s 2 g , s 2 ge , and s 2 e are the genotype, genotype • environment interaction, and error variance components, respectively, and e and r are the number of environments and replicates within each environment included in the corresponding analyses, respectively. Phenotypic data for the 296 inbred lines (experiment 2): These lines represent a wide range of germplasm from the global maize breeding program of CIMMYT and were derived from different breeding subpopulations. The two trait combinations considered in this study were GY (kg/ha) evaluated in four severe drought stress (SS) trials and five well-watered (WW; ample rainfall) trials. All 296 lines were included in all the trials and pre-adjusted based on estimates of block and plot effects derived from the linear model, as described for data in experiment 1; broad-sense heritability of the combined analyses across environments was calculated as explained in experiment 1.
Because of extensive paralogy in maize, SNPs were then further filtered based on homozygosity in inbred lines, the biparental error correction plug-in in Tassel GBS Discovery Pipeline, which was applied to eliminate SNP with high errors rates (2m • E = 0. 01), and a minimum median r 2 (parameter: 2mnPLD = 0. 5) for LD with markers in the local genome region across biparental families. And, finally, only those that segregate in the mapping populations were used in this study. Source code and the Tassel GBS discovery pipeline are available at www. maizegenetics. net and SourceForge Tassel project (http://sourceforge. net/projects/tassel/). Tassel was also used for computing pairwise measures of LD (r 2 ) between loci for each data set (experiments 1 and 2) and for each chromosome within the data sets  (Bradbury et al.
The posterior distributions of all unknowns in both models were obtained using MCMC techniques. We used the Bayesian implementation of RKHS described by de los Campos et al. (2010) with the kernels and prior probability distributions described in  González-Camacho et al. (2012). Notation of the different models: Concerning the notation of the models, we appended to either GBLUP or RKHS subscripts that denote the imputation and haplotype methods used. No subscripts were used for nonimputed GBS data. For experiments 1 and 2 data sets, the default marker imputation method (1000 flanking markers) is denoted by the subscript IM (GBLUP IM , RKHS IM ); for the haplotypebased method, we used H as the subscript (GBLUP H , RKHS H ). For experiment 1, the pedigree model denoted as P is fitted as described in Appendix A and when combined pedigree and marker information is used with nonimputed GBS, the notation is PGBLUP and PRKHS.
Prediction accuracy using GBS data Successful prediction accuracies in models using GBS data in wheat were recently reported  (Poland et al. 2012). In this study, we provide the first extensive evaluation of genomic-enabled prediction using GBS on real maize phenotypic data. We considered two maize data sets: three traits were evaluated in experiment 1 and one trait was evaluated under three environmental conditions in experiment 2. Importantly, these two data sets have very different genetic structures and, therefore, represent very different prediction problems. Experiment 1 comprises full-sib and other related families, whereas experiment 2 includes a diverse panel of lines from the global maize breeding program of CIMMYT, some of which are distantly related.
Prediction accuracy with family data In experiment 1, results show that combining GBS with pedigree data yields consistent gains in prediction accuracy, relative to pedigreebased predictions. These gains ranged from modest (2-5%) to substantial (10-24%), depending on the trait (regardless of whether GBS genotypes were imputed) and on the statistical method used to incorporate GBS data (either GBLUP or RKHS). Our results are in agreement with previous reports of consistent gains in prediction accuracy when pedigree was combined with genomic information derived from hybridization-based genotyping methods in maize and wheat (e. g. ,  de los Campos et al. 2009; Crossa et al. 2010; Burgueño et al. 2012; Heslot et al. 2012). The fact that combining GBS and pedigree data leads to higher prediction accuracy than using pedigree data alone indicates that GBS data capture information beyond what can be captured by the pedigree only, which in the case of experiment 1 reduces to the mean of the family.
Unlike pedigree information, the use of marker data allows capturing within-family differences and, to some extent, bridging the signal of genetic similarity detected by genetic markers, which might have been overlooked by pedigree information. It should be mentioned that prediction from the pedigree GBLUP model operates not only within family means but also between families by capturing family structure. Furthermore, GBLUP and RKHS captured population structure and substructures (because of different parental relationships), as well as within and between family means, because regressing the phenotypic values on all marker values is equivalent to regressing phenotypes on all marker-derived principal components (de los  Campos et al. 2010; Janss et al. 2012). Models using GBS data with no pedigree information were systematically outperformed by those combining pedigree and GBS data.
Prediction accuracy in a diverse panel Experiment 2 comprises a collection of maize lines with no close family relationships. However, these lines have different degrees of genetic similarity that could be described as a combination of population structure, substructure, and cryptic relationships, all of which are captured by markers and consequently accounted for in genomic regressions such as GBLUP  (Janss et al. , 2012)  or RKHS (de los  Campos et al. 2010). In this experiment, pedigree information is not available; therefore, comparing marker vs. pedigree prediction is not possible. However, our results can be compared with those of others who used this data set for genomic-enabled prediction using SNP arrays (e. g. ,  Crossa et al. 2010; González-Camacho et al. 2012; Windhausen et al.
2012). In our study, prediction accuracy using GBS data for GY under WW conditions ranged from 0. 544 to 0. 624, depending on the model and depending on whether GBS data were imputed. Windhausen et al. (2012)  used 55,000 SNPs and experiment 2 data with the GBLUP model, and achieved a prediction accuracy of 0. 50 for GY in optimum environments. Crossa et al. (2010) , using experiment 2 with only 1148 SNPs, reported predictions of approximately 0. 51 for GY-WW for RKHS,  Bayesian-LASSO, and GBLUP; however, González-Camacho et al. (2012) , using experiment 2 data with 55,000 SNPs, obtained predictions of approximately 0. 55 for GY-WW using linear and nonlinear models. Regardless of the prediction model, GBS information seemingly performs better than other genotyping platforms with or without imputation for traits under WW conditions.
In experiment 2, the prediction accuracy of GY under stressed conditions ranged from 0. 373 to 0. 4541, depending on whether GBS data were imputed and depending on the statistical model used. These correlations are considerably lower than those obtained under WW conditions and are consistent with previously reported results for this trait under these conditions. For instance, using this same data set but only 1,148 SNPs,  Crossa et al. (2010)  found prediction accuracies ranging from 0. 42 to 0. 45 for GY-SS. Part of the differences across studies may be attributable to the specifics of the validation designs used; however, the similarities of results across traits, environmental conditions, and studies allow us to conclude that, at a minimum, prediction accuracy with GBS-imputed  data are on par with that of 55,000 SNPs, with the advantage that GBS data are less expensive.
Parametric vs. semi-parametric methods In our study, we compared two ways of incorporating GBS data into models: parametric method (GBLUP) and semi-parametric method (RKHS). In general, no single method appeared to be clearly superior. Overall, in models using GBS data only (i. e. , without pedigree information), RKHS performed slightly better than GBLUP, with the only exception of GY under WW conditions in experiment 2. However, when GBS data were combined with pedigree information, prediction accuracy increased and the difference between the methods (GBLUP vs. RKHS) diminished. Overall, our results are in agreement with those of other authors who reported only small differences in the predictive performance of different statistical methods  (Heslot et al. 2012). However, still other authors  (Crossa et al.
Although parametric linear regression imposes a linear relationship between markers and phenotype, nonparametric models do not impose any assumptions on the phenotype-genotype relationship, therefore making it possible to capture interactions among loci. The results of this study using GBS in two maize data sets show that linear regression and nonparametric models provided similar prediction correlations for most trait-environment combinations, except for GY under drought conditions (GY-SS), in which GBLUP predictions were lower than those from RKHS. However, this trend changed under WW conditions (GY-WW), in which both models performed very similarly in terms of prediction accuracy.
CONCLUSIONS Our results indicate that prediction of the performance of inbred maize lines using GBS data combined with pedigree information outperforms the prediction accuracy of pedigree-based methods and, at a minimum, can yield prediction accuracies comparable to those obtained with low-density to mid-density SNP arrays. The relatively low cost of GBS data further makes GBS a competitive alternative for improving the efficiency of breeding programs. Regarding the models, we found no clear differences in the prediction accuracy of the two models; however, even though RKHS was slightly better than GBLUP when using GBS alone, both models performed similarly with pedigree information. Dealing with missing genotypes poses an important challenge when using GBS data, and in this study we propose several ways of confronting that challenge.
ACKNOWLEDGMENTS We thank all national programs in Africa and Mexico that performed the experiments and collected the data analyzed in this study. We acknowledge the financial support provided to the  Drought Tolerance Maize for Africa project , to the  Water Effective Maize for Africa  project, and to Cornell-CIMMYT by the  Bill and Melinda Gates Foundation , and by the  Howard Buffet Foundation. National Institutes of Health  also provided financial support (grants  R01GM099992-01A1  and  R01GM101219-01  to G. d. l. C. and P. P. ).
However, this is counterbalanced by the higher probability of absorption between scattering events in snow with large B, because the path in the grains is longer. In short, the increase of photons paths within the grains for larger B is counterbalanced by the shortening of total path lengths in the snowpack due to lower g G. This explains the limited sensitivity of albedo to the shape of the grains. The low sensitivity was noticed experimentally by  Domine et al. (2006)    studied snow samples with various crystal shapes and confirmed later by  Gallet et al. (2009). They demonstrate indeed that the reflectance of snow samples of known SSA  (Domine et al. , 2001)  is well reproduced using DISORT-Mie code  (Wiscombe, 1980; Stamnes et al. , 1988)  (i. e. for a medium of independent spheres). In this paper, we exploit the sensitivity of light e-folding depth to grain shape to estimate the parameter B using measurements of reflectance, density and irradiance in the snowpack, and TARTES model.
Acknowledgements. We wish to thank  A. A. Kokhanovsky  and two anonymous reviewers for their comments. This study was supported by the  ANR MONISNOW programme. We are grateful to the  French Polar Institute (IPEV)  for the logistic support at Concordia station in Antarctica through the  CALVA and NiteDC programmes. J. L. F. and M. D. K. thank  NERC  for support through grants  NE/F0004796/1 ,  NE/F010788  and  NERC FSF  for support through grants  555. 0608 ,  584. 0609  and support from  Royal Holloway Earth Sciences  research strategy fund awards. Edited by:  M. Schneebeli The publication of this article is financed by  CNRS-INSU.
, 2011). In order to increase our understanding of mPWP climate, and better integrate proxy-based studies with dynamical modelling, the Pliocene Model Intercomparison Project (PlioMIP) was initiated  (Haywood et al. , 2010 (Haywood et al. , , 2011)). The aim of PlioMIP is to simulate the mPWP in a suite of atmosphere general circulation models and coupled atmosphereocean general circulation models, with the same boundary conditions  (Haywood et al. , 2010 (Haywood et al. , , 2011) )  applied. Currently fourteen modelling groups participate in the PlioMIP  (Haywood et al. , 2013a) , of which eight  (Chan et al. , 2011; Bragg et al. , 2012; Chandler et al. , 2013; Contoux et al. , 2012; Kamae and Ueda, 2012; Rosenbloom et al. , 2013; Stepanek and Lohmann, 2012; Zhang et al. , 2012)  have completed coupled simulations and submitted data to the PlioMIP database (Table  1 ).
Acknowledgements. This paper was jointly supported by the  Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences  (Grant No. XDA05080803 ), the  National 973 Program of China  (Grant No. 2010CB950102 ), and the  Earth System Modeling (ESM)  project financed by  Statoil ,  Norway. Z. Zhang  and  K. H. Nisancioglu  acknowledge that the development of NorESM-L and experiments were supported by the  ESM project. A. M. Haywood  and  A. M. Dolan  acknowledge that the research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 278636. A. M. Dolan acknowledges the  UK Natural Environment Research Council  for the provision of a Doctoral Training.
A global pigment database consisting of 35 634 pigment suites measured by high performance liquid chromatography was assembled in support of the MARine Ecosytem DATa (MAREDAT) initiative. These data originate from 136 field surveys within the global ocean, were solicited from investigators and databases, compiled, and then quality controlled. Nearly one quarter of the data originates from the Laboratoire d'Océanographie de Villefranche (LOV), with an additional 17 % and 19 % stemming from the US JGOFS and LTER programs, respectively. The MAREDAT pigment database provides high quality measurements of the major taxonomic pigments including chlorophylls a and b, 19'-butanoyloxyfucoxanthin, 19'hexanoyloxyfucoxanthin, alloxanthin, divinyl chlorophyll a, fucoxanthin, lutein, peridinin, prasinoxanthin, violaxanthin and zeaxanthin, which may be used in varying combinations to estimate phytoplankton community composition.
, 2012)  or to different size classes of phytoplankton  (Ciotti and Bricaud, 2006; Uitz et al. , 2006; Hirata et al. , 2008 Hirata et al. , , 2011;; Kostadinov et al. , 2009; Brewin et al. , 2010)  can be quantified via remote sensing methods. However, at present these methods lack sufficient global validation datasets, and are restricted to the surface ocean. Depth resolved phytoplankton community structure can be determined in field samples through electron or light mi-croscopy, flow cytometry, genetic analysis or high performance liquid chromatography (HPLC). Microscopy and genomics allow for direct identification of algal species and morphology; however, these methods are time-consuming for large-scale surveys, and certain types of taxa or features (e. g. , flagella) may be lost or damaged depending on preservation and handling  (Havskum et al.
, 2006). In all cases, the pigment data need to be of high quality and consistent across all considered pigments. We have combined 136 independent field datasets totaling 35 634 quality-controlled HPLC pigment suites from the world ocean for the MARine Ecosytem DATa (MARE-DAT) initiative (Table  1 ). The largest fraction (22 %) was contributed by the Laboratoire d'Océanographie de Villefranche (LOV). Additional major contributions were made by the Palmer LTER project (19 %), the US JGOFS program (17 %), the collective US BATS and HOT time series (13 %), and the AMT cruises (14 %), with the remaining significant fraction coming from individual investigators and cruises (see Table  1 ). The data cover nearly two decades and extend over a wide range of trophic regimes from extremely oligotrophic to near-coastal eutrophic.
MAREDAT pigment data include both HPLC-derived chlorophyll a and accessory pigments (e. g. , chlorophyll b, 19'-butanoyloxyfucoxanthin, 19'hexanoyloxyfucoxanthin, alloxanthin, divinyl chlorophyll a, fucoxanthin, lutein, peridinin, prasinoxanthin, violaxanthin and zeaxanthin) in varying proportions within samples, and in varying locations, depths, and months among samples. This suggests the utility of MAREDAT's pigment database for assessing regional, vertical and seasonal scales of phytoplankton community structure and succession. The following sections describe the data distributions and quality control procedures pertaining to the MAREDAT pigment database. Overall, we intend for the data to be readily exploited by the observational, modeling, and remote sensing communities to deepen understanding of the temporal and spatial diversity of primary producers in the ocean, and the consequences for global carbon fixation.
HPLC analytical methods The majority of the HPLC data in the database were analyzed following standard protocols, with details provided in the original publications (Table  1 ). The method employed by the Laboratoire d'Océanographie de Villefranche has not been previously described in detail, and is therefore elaborated here. Generally, 2. 8 L of seawater were filtered onto GF/F Whatman 25 mm filters and frozen until analysis. Prior to 2002, samples were frozen in liquid nitrogen onboard ship, and then at -20 • C prior to analysis. After 2002, samples were frozen at -80 • C until analysis. All samples were extracted in 100 % methanol; however, four different analytical protocols (LOV-A, B, C and D) have been applied at the LOV since 1990. Method LOV-A is described in  Mantoura and Llewellyn (1983)  with gradient elution as in  Williams and Claustre (1991).
(2008)  and is characterized by its capacity for quantifying more than 26 pigments, and for its good resolution of the Zeax/Lut and 19Hex/Pras pairs. Before their submission to MAREDAT, the data underwent quality control procedures including comparison to CTD fluorescence data, detection and elimination of outliers and visual control of each vertical profile. Since 1999, the LOV analytical procedures have undergone regular unbiased evaluations in the framework of international intercomparison exercises ("round-robins") that comprise NASA's SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE) (see  Van Heukelem and Hooker, 2011). SeaHARRE-1 was associated with the PROSOPE project  (Hooker et al. , 2000) , SeaHARRE-2 with the Bencal cruise  (Hooker et al. , 2005)  and SeaHARRE-3 with the BIOSOPE project  (Hooker et al.
Quality control for the MAREDAT pigment database Marine HPLC measurements from samples taken at various depths and locations around the globe were collected from individual investigators and online data repositories for the individual field campaigns summarized in Table  1. Sampling years spanned from 1989 to 2008. The full suite of pigments available from each contribution was collected in order to conduct the quality control (QC) analysis. QC procedures followed the protocols of  Trees et al. (2000)  and  Uitz et al. (2006)  for data compilations involving contributions from multiple institutes, technicians, and methodologies. A total number of 40 536 discrete samples from 136 field surveys were initially compiled for MAREDAT. The geographic locations of the data that passed the full QC analysis are illustrated in Fig.
, 2012b)  to the cruise percentages of F Rat flags, which identified 8 outlying cruises, thus flagging an additional 589 samples (Table  1 ). Overall, 4902 samples were flagged in the QC process, representing 12. 1 % of the original collection. The resulting MAREDAT pigment database thus contains 35 634 high quality HPLC pigment measurement suites. There is a clear trend of increased frequency of flagged samples with increased sample depth, mostly driven by the increase in F TA flags with depth (Fig. 3a ). This is consistent with pigment concentrations decreasing to detection limits well below the base of the euphotic zone. The frequency of F Rat , the flag most closely related to HPLC analytical uncertainty  (Trees et al. , 2000) , occurs roughly in proportion to TCHLA, indicating that analytical fidelity is not specifically biased toward low or high chlorophyll levels except within the minimum TCHLA bin (Fig.
Properties of the quality-controlled MAREDAT pigment database The majority of the quality-controlled samples fall within the 0-75 m depth range, with the upper 20 m of the water column accounting for 34 % of the dataset. The number of Southern Hemisphere data is biased toward the polar region due to the collectively large contribution of samples from the Palmer Station LTER (Fig. 4a ; Table  1 ). The data distribution for the Southern Hemisphere is also skewed toward the month of January for this reason, as Antarctic waters were typically accessed for HPLC sample collection during austral summer. Samples are overall evenly distributed between the two decades of collection (Fig. 4b ). In terms of the seasonal distribution and range in TCHLA values, the MAREDAT pigment database contains good coverage for both hemispheres.
The higher variability in chlorophyll concentration in the Northern Hemisphere than Southern Hemisphere during each season as seen from ocean color observations  (Yoder et al. , 1993)  is also represented by the MARE-DAT chlorophyll data (Fig. 5a  and b ). This relatively lower annual chlorophyll variability in the Southern Hemisphere is due to large areas of Southern Ocean iron depletion limiting the spring phytoplankton blooms  (Behrenfeld and Kolber, 1999). To further assess the quality, range and representation of Chl a measurements in the MAREDAT pigment dataset, we compare TCHLA with remotely sensed chlorophyll. We extracted climatological mean, 9 km-resolution chlorophyll data from NASA's SeaWiFS archive for the years 2000-2007 (http://oceandata. sci. gsfc. nasa. gov/SeaWiFS/L3SMI/).