dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006). In addition to SNOWPACK, Alpine3D includes a preprocessing and interpolation module for meteorological fields  (Bavay and Egger, 2014) , a module computing the spatially distributed radiations as affected by topography  (Helbig et al. , 2009) , an optional snow transport model  (Groot Zwaaftink et al. , 2011) , and an optional runoff model  (Zappa et al. , 2003; Comola et al. , 2015). The interpolated or provided spatial meteorological fields drive the energy and mass balance of the surface snowpack, computed by SNOWPACK. The canopy module and its new features described hereafter can run within Alpine3D. They are included in the SNOW-PACK model version 3. 2. 1 (revisions from 741) which is available under GPL license and upon creation of an account at https://models. slf. ch/.
The description here uses LAI; the direct throughfall fraction can be set to zero if LAI is provided as a stand-scale average including canopy gaps of moderate size (up to ∼ 1 m). These three model parameters intend to describe differences between forest stands without further tuning. The consideration of the thermal inertia of the forest stand in the one-layer version with heat mass (1LHM) and the twolayer version (2LHM) imposes the use of an additional input parameter, the mean stand basal area B (m 2 m -2 ). The different parameters used by the SNOWPACK canopy module are listed in Table  1 , distinguishing between the ones to be provided by users according to forest specificities, and the ones internal to the model. The idea behind the two-layer version of the canopy module is to capture the thermal contrast between two distinct compartments of the canopy: -the upper (or outer) canopy compartment (leaves or needles) which is most directly exposed to the atmosphere; -the lower (or inner) canopy compartment (twigs, branches, trunks, some leaves), for which energy and mass fluxes have already been altered by the upper canopy compartment.
25 , (20) respectively. These variables have no influence on the onedimensional simulations presented here, but are used to estimate the contribution of long-wave and shortwave radiation from surrounding terrain when the SNOWPACK model is used within the distributed Alpine3D model. Finally, the radiation fluxes calculated by the canopy module are only applied to the fraction of the surface covered by the canopy, assumed to be the complement of the direct throughfall parameter: (1 -c f ). An exception to that occurs for direct shortwave radiation which is collimated in the solar direction: when sun is not at the zenith, the sun beams are not parallel to the tree trunks and the projected surface occupied by the canopy along their trajectory is higher than (1 -c f ). This higher fraction of canopy shading (1 -c f,dir ) is derived following  Gryning et al.
Alptal site The first data set is from the Alptal forest site (47 • 03 N, 8 • 43 E; Erlenbach sub-catchment, Switzerland; site 1012 in the Fig. 1  of  Stähli et al. , 2006)  that served as test site for the SnowMIP intercomparison study  (Rutter et al. , 2009)  and builds on a long tradition of snow and meteorological investigations (e. g. ,  Stähli et al. , 2006 Stähli et al. , , 2009)). The site features an ∼ 11 • west-orientated slope at 1185 m a. s. l. and is dominated by Norway spruce (85 %) and silver fir (15 %), with a basal area of 41 m 2 ha -1 and a maximum height of typically 25 m. The site LAI (including slope corrections and corrections for clumping) ranges from 3. 4 to 4. 6 with a mean value of 3. 9 m 2 m -2  (Stähli et al. , 2009). At this site, the SNOWPACK model is run using meteorological data derived from observations: -downward shortwave and long-wave radiation measured on a 35 m high mast above the canopy: a heated, non-ventilated CNR1 from Kipp and Zonen (  2002 ) comprising two pyranometers CM3 (for SW) and two pyrgeometers CG3 (for LW); -precipitation measured by a heated gauge placed at 25 m height on the high mast: the highest trees providing sheltering similar to a fence; -wind speed recorded by a cup anemometer at 35 m on the mast; -air temperature measured at 35 m by a ventilated thermo-hygrometer Thygan (Meteolabor) also integrating a dew point hygrometer; -relative air humidity at 35 m height, derived from the air temperature and dew point.
98), whereas the air temperature is much colder and modeled canopy temperature closely follows the air temperature signal. The precipitation record (Fig. 2b ) features almost continuous snowfall over that period. It is hence suspected that the measured radiation originates from snow at temperature -Snow depth, snow density, and snow water equivalent (SWE) that were measured below the canopy on a weekly basis, at 1 m intervals along a 30 m transect adjacent to the trajectory of the radiometer carrier. More details of the exact procedure are available in  Stähli et al. (2009). We use the spatial average of the measurements to come up with stand-representative values. Meteorological and validation data are available for four consecutive winter seasons between 2003 and 2007.
Norunda site The second data set is from the Norunda forest site (60 • 05 N, 17 • 28 E), located in a quite level region about 30 km north of Uppsala, Sweden, at 45 m a. s. l. Since June 1994 it has been equipped with meteorological instruments which were complemented by biomass thermometers in June and July 1995. The forest stand is composed of Scots pine (61 %), Norway spruce (34 %), and birch (5 %) with a stand LAI between 4 and 5 m 2 m -2 , a mean basal area of ∼ 34. 7 m 2 ha -1 , and a maximum tree height of ∼ 28 m. At this site, SNOWPACK is driven by observed meteorological variables: -Downwelling LW and SW radiation are measured by a combination of a ventilated CM21 pyranometer (Kipp and Zonen) placed at 102 m above ground at the top of a Fluxnet tower (http://fluxnet. ornl. gov/site/730) and a ventilated LXV055 net radiometer placed at 68 m on the same tower.
-Air temperature is recorded at 37 m height above ground by a copper-constantan thermocouple placed in the ventilated radiation shields. -Air humidity is measured at 28 m by a HP100 TST probe (Robotronic). -Wind speed is recorded at 37 m by a sonic anemometer. -Precipitation data were unfortunately not available at the site. We therefore made use of precipitation data recorded at the Uppsala Aut WMO station (WMO no. 2-462) openly provided by the Swedish Meteorological and Hydrological Institute (SMHI; http: //opendata-catalog. smhi. se/explore/). This station is 26 km away from the Norunda site and the nearest station in operation at the time of the measurements used here. The specificity of the Norunda site lies in the continuous measurement, over a summer, of the biomass temperature at different heights and depths within the trunks and branches of the dominant tree species: pines and spruces.
They were complemented by a detailed calculation of tree-level and stand-level biomass heat storage, which builds a unique data set to evaluate a physics-based canopy model with heat mass. The details of the tree temperature measurements and heat storage calculations can be found in  Lindroth et al. (2010). In the present study we make use pine trunk temperature at 1. 5 m height, which has been measured close to the trunk surface (1 cm deep within the bark). Indeed, we are mostly interested in the ability of the model to reproduce the trunk surface temperature which generates the thermal emission of the trunk layer. We also provide an assessment of the canopy energy balance modeled by SNOWPACK by comparing the stand-scale modeled biomass storage flux to the one inferred from observations by  Lindroth et al.
Norunda: tree temperature and biomass storage flux At the Norunda site, SNOWPACK is run using the Alptal calibration from 2003 to 2007, and a canopy basal area and areal heat mass derived from local data (Sect. 3). The difference in latitudes (hence in solar angle), tree species (mostly Scots Pine at Norunda), and context (Alpine winter vs. boreal summer) between both sites constitutes a huge challenge and an excellent benchmark to test one desired feature of a physically based model, e. g. , its transferability to different climate and ecosystem types. We here specify that SNOW-PACK includes all the necessary features to be used as a soilvegetation-atmosphere transfer (SVAT) model in the absence of a snow cover: a soil water balance, a surface and canopy energy balance, and a temperature diffusion scheme in the soil.
Finally, the combination of thermal sheltering of the lowermost canopy layer and its thermal inertia delays the tree trunk cooling (warming) at evening (morning) times, improving the temporal correlation with observations. Heat fluxes to canopy elements are a substantial, though not dominant, component of the canopy energy balance  (Lindroth et al. , 2010, their Fig. 6 ): they can amount to ∼ 7 % of the daily net radiation received by the canopy. To assess the consistency of the SNOWPACK canopy module, we compare the modeled canopy heat fluxes to the ones derived by  Lindroth et al. (2010)  from field measurements and extrapolated at the stand scale. Note that 1LnoHM, having no heat mass, does not consider any such fluxes. Both the 1LHM and 2LHM versions overestimate the daily amplitude of biomass heat fluxes with respect to observations, with an increased bias for 1LHM (Fig.
On a daily basis, the air heat-storage term reacts more rapidly to solar heating than the biomass heat-storage flux. The air heat-storage flux is not specifically accounted for in SNOW-PACK. However, the increased correlation coefficient and reduced RMSE, obtained when the SNOWPACK canopy heat flux is compared to the sum of estimated air and biomass heat fluxes, indicate that the canopy module produces a bulk representation of the observed fluxes. Such a result should be confirmed against further observational data sets.
Discussion Our results show that the new features implemented in the SNOWPACK canopy module, especially the two-layer scheme, improve the representation of the radiation budget at the sub-canopy level. The importance of accounting for the canopy temperature and for contrasts between different canopy elements has often been underlined  (Sicart et al. , 2004; Pomeroy et al. , 2009). Pomeroy et al. (2009)  compared three sub-canopy thermal irradiance models based on measurements of (i) air temperature only; (ii) air, trunk and needles temperatures; and (iii) air temperature and empirical shortwave-long-wave conversion function by the canopy. The two latter formulations exhibited distinctively better per-formances than the first one in terms of mean bias and RMSE in both the uniform and discontinuous stands investigated.
Acknowledgements. We thank the  CRYOS team  and  IRSTEA  for the funding of a one and a half year visiting scientist position at  EPFL  and for fruitful lab discussions. Our special thanks also go to  Mathias Bavay  ( SLF Davos ) for efficient communication about the code, careful code cleaning, and successful merging of new developments. We additionally want to acknowledge  Charles Fierz ,  Manfred Stähli , and  David Moeser  for data and viewpoint sharing. Finally, our two reviewers' supportive and insightful comments helped much to refine the focus and quality of the paper, we are grateful to them. Edited by:  J. Fyke.
Field Data To check the quality of this approximation for G -1 , we tested it using real data. Phenotypic data included 11,626,576 records for final score on 7,093,380 cows, with 10,709,878 animals in the pedigree provided by Holstein Association USA Inc. (Brattleboro, VT). Final score is a weighted linear combination of 5 major breakdown score for type traits in dairy cattle, and was chosen for this study because of availability of records. Genotypes on 42,503 SNP markers were available for 569,404 animals. However, to have comparisons with the regular ssGBLUP where direct inversion of G is used, analyses involved only 100,000 of the genotyped animals, which is the limitation of ssGBLUP for the available computer. Thus, genotypes were considered for all 23,174 bulls with progeny information, all 27,215 cows with records (hereafter termed "cows"), and additionally 49,611 young animals.
Analyses Initially, GEBV were calculated using the regular ssGBLUP, which applies direct inversion for G. Second, GEBV were calculated using APY to obtain G -1 recursively (G -1 APY ) with several different definitions for proven animals: only sires; sires and cows; only cows; and sires with >5 progeny including sons and daughters. Third, previous analyses were repeated with "proven" animals randomly sampled from the group of all 100k genotyped animals in sets of 2k, 5k, 10k, 15k, and 20k animals; the sampling was replicated 4 times. Evaluations for final score were done using a single trait model as described in  Tsuruta et al. (2002). All analyses were conducted with blup90iod2 (http:// nce. ads. uga. edu/wiki/BLUPmanual) program with modifications as in  Aguilar et al. (2011). The quality of approximations was assessed by correlations between GEBV for the almost 50k young animals obtained from ssGBLUP using direct inversion of full G (regular ss-GBLUP) and ssGBLUP using approximated G -1 from the APY algorithm.
ACKNOWLEDGMENTS We gratefully acknowledge the very helpful comments by the two anonymous reviewers and insightful suggestions by section editor  Jennie Pryce. This research was supported by grants from  Zoetis  ( Florham Park, NJ), Cobb-Vantress Inc. (Siloam Springs, AR ),  Smithfield Premium Genetics (Rose Hill, NC), American Angus Association (St. Joseph, MO) ,  Holstein Association USA (Brattleboro, VT) ,  Pig Improvement Company  (Hendersonville, TN), and by  Agriculture and Food Research Initiative Competitive  Grants no. 2015-67015-22936  from the  US Department of Agriculture's National Institute of Food and Agriculture (Washington, DC). A. L. thanks  INRA metaprogram SelGen and projects X-Gen and GenSSeq.
If the evidence is IDA, then the property was directly observed for a live isolate by one of the authors or an expert mentioned in the acknowledgements Fig. 1  Phylogenetic tree highlighting the position of Bacillus dielmoensis strain FF4 T relative to the most closely related type strains within the genus Bacillus. The strains and their corresponding GenBank accession numbers for 16S rRNA genes are (type = T ) and in parenthesis, we indicated GA if the genome is available or GNA if the genome is not available in NCBI web site: Bacillus fumarioli strain CIP 106910 T (GNA), B. dielmoensis strain FF4 T , B. vireti LMG 21834 (GA : ALAN00000000), B. novalis strain IDA3307 (GNA), B. senegalensis strain RS8 (GNA), B. drentensis strain WN575 (GNA), B. arbutinivorans strain rif200874 (GNA), B. bataviensis strain LMG 21833 (GA: AJLS00000000), B.
dielmoensis strain FF4 T exhibited the phenotypic differences detailed in Additional file 1: Table  S1. Matrix-assisted laser-desorption/ionization time-of-flight (MALDI-TOF) MS protein analysis was performed using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany), as previously reported  [16]. The scores previously established by Bruker Daltonics allowing validating or not the identification of species compared to the database of the instrument were applied. Briefly, a score ≥ 2 with a species with a validly published name provided allows the identification at the species level; a score ≥ 1. 7 and < 2 allows the identification at the genus level; and a score < 1. 7 does not allow any identification. We performed 12 distinct deposits from 12 isolated colonies of strain FF4 T. Two microliters of matrix solution (saturated solution of alpha-cyano-4-hydroxycinnamic acid) in 50 % acetonitrile and 2.
5 % trifluoroacetic-acid were distributed on each smear and submitted at air drying for five minutes. Then, the spectra from the 12 different colonies were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the main Fig. 4  Reference mass spectrum from Bacillus dielmoensis strain FF4 T. Spectra from 12 individual colonies were compared and a reference spectrum was generated spectra of 6,252 bacterial spectra including 199 spectra from 104 Bacillus species. Scores ranged from 1. 1 to 1. 3 were obtained for the strain FF4 T , suggesting that this isolate was not a member of any known species. The reference mass spectrum from strain FF4 T was incremented in our database (Fig. 4 ). The gel view highlighted spectrum differences with other Bacillus species (Fig.
Genome project history The organism was selected for sequencing on the basis of its 16S rRNA similarity, phylogenetic position and phenotypic differences with other members of the genus Bacillus, which support that Bacillus dielmoensis strain FF4 T likely represents a new bacterial species. Besides, this strain is part of a study aiming to characterize the skin flora of healthy Senegalese people. Currently, there are more of 270 sequenced genomes of Bacillus species  [2]. The strain FF4 T is the first genome of B. dielmoensis sp. nov. GenBank accession number is CCAD000000000. It consists of 75 contigs. Table  2  shows the project information and its association with MIGS version 2. 0 compliance  [17]. Associated MIGS records are detailed in Additional file 2: Table  S2.
Genome annotation Open Reading Frame prediction of the B. dielmoensis FF4 T genome was performed using Prodigal  [18]  with default parameters. We excluded the predicted ORFs if they spanned a sequencing gap region. Functional assessment of protein sequences was carried out by comparing them with sequences in the GenBank  [19]  and Clusters of Orthologous Groups (COG) databases using BLASTP. The tRNAs, rRNAs, signal peptides and transmembrane helices were identified using tRNAscan-SE 1. 21  [20] , RNAmmer  [21] , SignalP  [22]  and TMHMM  [23] , respectively. Artemis  [24]  was used for data management whereas DNA Plotter  [25]  was used for visualization of genomic features. In house perl and bash scripts were used to automate these routine tasks. ORFans were sequences which have no homology in a given database i.
If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05. PHAST was used to identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids  [26]. To estimate the nucleotide sequence similarity at the genome level between B. dielmoensis and other members of the genus Bacillus (Table  3 , Fig. 6 ), orthologous proteins were detected using the Proteinortho software  [27]  (with the parameters: e-value 1e-5, 30 % percentage of identity, 50 % coverage and algebraic connectivity of 50 %) and genomes compared two by two. After fetching the corresponding nucleotide sequences of orthologous proteins each pair of genomes, we determined the mean percentage of nucleotide sequence identity using the Needleman-Wunsch global alignment algorithm.
Insights from the genome sequence Today there are more than 277 sequenced genomes of Bacillus species (finished and draft) available in genomic databases  [8]. Here, we have compared B. dielmoensis  As it has been suggested in the literature that the G + C content deviation is at most 1 % within species, these data are an additional argument for the creation of a new taxon  [30]. Figure  6  shows the comparison of gene distribution into COG categories of B. dielmoensis with other finished genomes mentioned above. Table  3  presents the numbers of orthologous genes between genome pairs. Table  7  summarizes the AGIOS and dDDH values between the studied genomes. The AGIOS values ranged from 63. 25 to 73. 22 % at the interspecies level, between B. dielmoensis and other species, but was of 95.
2 μm (range 2. 6 to 5. 8). Optimal growth is observed aerobically, weak growth occurs under anaerobic conditions. Growth occurs between 30 and 45 °C, with optimal growth occurring at 37 °C. A catalase activity is present but not oxidase activity. A positive reaction is obtained only for esculin ferric citrate. Positive reactions are observed for esterase, esterase lipase, alkaline phosphatase, naphthol-AS-BI-phosphohydrolase, acid phosphatase, β-  The G + C content of the genome is 40. 8 %. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers HG315676 and CCAD000000 000, respectively. The type strain FF4 T (= CSUR P3026 = DSM 27844) was isolated from the skin of a healthy female in Dielmo, Senegal.
Spatial patterns of beta diversity are a major focus of ecology. They can be especially valuable in conservation planning. In this study, we used a generalized dissimilarity modeling approach to analyze and predict the spatial patterns of beta diversity for commercially exploited, demersal marine species assemblages along the Tunisian coasts. For this study, we used a presence/absence dataset which included information on 174 species (invertebrates and fishes) and 9 environmental variables. We first performed the modeling analyses and assessed beta diversity using the turnover component of the Jaccard's dissimilarity index. We then performed nonmetric multidimensional scaling to map predicted beta diversity. To delineate the biogeographical regions, we used fuzzy cluster analysis. Finally, we also identified a set of indicator species which characterized the species assemblages in each identified biogeographical region.
Generating predictions of beta diversity within unsurveyed areas across a region of interest is allowed by the generalized dissimilarity models (GDM). First proposed by  Ferrier et al. (2007)   [7] , GDM depend on a statistical technique to analyze and predict spatial patterns of beta diversity that relates biological distance to distance in environmental space. This approach is an extension of matrix regression and is specifically designed to accommodate the nonlinearity commonly encountered in large-scale ecological datasets  [7]. These models can be used to meet various conservation planning requirements, including bioregionalization, survey gap analyses and climate change impact assessments  [7]. Although the evidence suggests that it is more challenging to base conservation assessments using beta diversity, relatively little attention has been given to the application of marine beta diversity values.
Species data Since 1998, the National Institute of Marine Sciences and Technologies (INSTM) has regularly monitored Tunisian marine biodiversity and made their data (presence/absence data) freely available through the Ocean Biogeographic Information System (OBIS; http://www. iobis. org/). We collated data on 174 species from OBIS (accessed on December 2014; 133 fishes, 18 cephalopods, 22 decapods and one stomatopod; S1 Table ). For each species, we obtained trawl data, completed between 1998 and 2005, with central point georeferenced positions and the associated presence/absence information. The INSTM dataset we used belongs to a standardized and validated database (see  Hattab et al. (2013)   [27] ) for more details about the survey data protocol) that have been already used in previous studies (e.
Environmental data We selected nine variables to predict beta diversity that have previously been shown to strongly influence species distribution patterns in coastal environments  [31, 32]  and particularly for local scale  [27]. These included climate variables (sea surface temperature (SST) and sea surface salinity (SSS)), local-scale habitat variables (bathymetry, bathymetric slope and aspect) and spatial predictor (distance to shore). For SST (2002-2010; MODIS) and SSS (1955-2006; NOAA's World Ocean Atlas), we used monthly climatology data to derive annual mean values. We also identified the annual range for each 12 month period using the maximum and minimum monthly values, a metric that can be used to represent seasonality. We utilize the full temporal extent of the climate datasets in making these climatologies to better reflect regional disparities.