dataesr/scientific-paragraphs-categorization huggingface dataset:
The percentages were performed by calculating the report of abundance of each OTU per site on the sum of OTUs abundances per site. To test the homogeneity of dispersion of the different communities against PHP concentration and plant species identity, beta-dispersion analyses were performed on the Bray-Curtis matrices using the "betadisper" function in the "vegan" package. To reveal which fungal or bacterial taxa were affected by the contamination levels or plant species, Kruskal-Wallis tests or ANOVA were performed on the abundances at class level and on the most abundant thirty OTUs of fungi and bacteria in both soil and root datasets. Non-metric multidimensional scalings (NMDS) were performed to visualize the effects of contamination levels, plant species and biotopes on community composition.
Soil Microbial Diversity versus Root Microbial Diversity After quality filtering and standardizing the number of sequences in the different datasets, the soil 16S rRNA gene dataset allowed us to retrieve a total of 23085 reads (855 per sample) which were assigned to 4083 OTUs, while a total of 25839 reads (957 per sample) were obtained from the soil ITS dataset and were assigned to 215 OTUs. For the root 16S rRNA gene dataset, we retrieved a total of 2403 reads (89 per samples) which were assigned to 820 OTUs, while a total of 13716 reads (508 per samples) were obtained from the root ITS dataset and assigned to 188 OTUs. Rarefaction curves showed that the sampling effort for fungi was close to saturation for all samples with Good's coverage values ranging between 0. 97 and 0. 99 for soil fungi, and 0.
94 and 0. 98 for root fungi. Contrary to fungi coverage, the sampling efforts were relatively low yielded for bacteria, in particular for the samples related to L. europaeus and P. balsamifera. The Good's coverage values of bacteria ranged between: 0. 68 and 0. 81 for soil bacteria, and between 0. 22 and 0. 70 for root bacteria (Figure  1  and Supplementary Table  S1 ). The Shannon diversity indices and Chao richness estimators (Figures  2A, B ) of bacterial OTU were significantly higher in soils than in roots (Wilcoxon test, P < 0. 0001). For fungi, Wilcoxon test on Shannon diversity indices showed no significant difference 2 https://github. com in fungal diversity between soil and root datasets (P = 0. 15). However, a significant difference in OTU richness was observed between the soil and root samples for fungi (Student's t-test, P = 0.
05), with Chao values higher in the soil samples (mean Chao value = 68. 66) compared to root samples (mean Chao value = 59. 58) (Figures  2A, B ). PERMANOVA analyses showed that the bacterial and fungal community structures present in soil were significantly different from the communities found in association with plant roots (P < 0. 001). For both bacteria and fungi, NMDS ordinations showed a clear change in community structures across the two habitats (stress value = 0. 14 and 0. 22, respectively) (Figures  3A, 4A ). BLAST searches of the bacterial 16S rRNA gene sequences showed that, at class or phylum level, the bacterial profiles were similar in soil and root datasets. The most abundant bacterial taxa identified in the two datasets belong to classes Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and phyla Actinobacteria and Acidobacteria.
However, except for Alphaproteobacteria and Betaproteobacteria, which were found approximately in similar proportions in soil and root datasets (36 and 33% for Alphaproteobacteria, and 14 and 12% for Betaproteobacteria), the proportions of the other most abundant bacterial groups were different between the soil and root datasets. Acidobacteria and Deltaproteobacteria had higher proportions in the soil dataset (11 and 5. 25%, respectively) compared to the root dataset (4 and 3. 41%, respectively) (Student's t-test, P < 0. 05) while, the percentages of Actinobacteria (represented mainly by Streptomyces) and Gammaproteobacteria were higher in the root dataset (19 and 14%, respectively) than in the soil dataset (6 and 9%, respectively) (Wilcoxon test, P < 0. 0001 for Actinobacteria and Student's t-test = 0.
15 for Gammaproteobacteria) (Supplementary Figures  S2A, B ). In addition, within each bacterial group, abundances and taxonomic affiliations of the most OTUs found in the root dataset were different from those found in the soil dataset (Supplementary Figures  S3A, B ). For instance, in the root dataset, the OTUs related to Alphaproteobacteria were represented mainly by Bradyrhizobium, Skermanella, Sphingobium, and Hoeflea, while in the soil dataset, Alphaproteobacteria were dominated by Sphingomonas, Skermanella, and Dongia. Similarly, Betaproteobacteria were represented mainly by Ideonella, Duganella, and Limnobacter in the root dataset, while they were dominated by Caenimonas, Burkholderiales, and Ferrovum in the soil dataset. For the ITS sequences, BLAST searches showed that almost all fungal taxa identified in the soil dataset were also identified in the root dataset.
Most fungal taxa found in the two datasets belong to the classes Dothideomycetes, Sordariomycetes, Agaricomycetes, Chytridiomycetes, and Glomeromycetes. Dothideomycetes, Agaricomycetes, and Chytridiomycetes were found in similar proportions in the root and soil datasets. Dothideomycetes represented 23 and 24% of the OTUs in soil and root datasets, respectively. Agaricomycetes represented 10 and 7% of OTUs in the soil and root datasets, respectively. Similarly, Chytridiomycetes represented 5 and 4% of OTUs in the root and soil datasets, respectively. However, the proportions of  Sordariomycetes and Glomeromycetes were clearly different between soil and root samples (Wilcoxon test, P ≤ 0. 0001). Sordariomycetes were found as the most dominant taxa in the soil dataset, with 37% of OTUs, whereas they represented only 5% of the root dataset.
Glomeromycetes represented 20% of the root dataset, but only 1% of the fungal sequences in the soil dataset (Supplementary Figures  S2C, D ). At the genera level, abundances and taxonomic affiliation of the fungal OTU found in roots were also different from those found in soil. For example, Dothideomycetes were found at similar proportions in both soil and roots habitats (23% in soil and 24% in roots), however, the Dothideomycetes genera found in soil and roots were different. Leptosphaeria was the most dominant genus of Dothideomycetes found in roots (37%), while unclassified Pleosporales was the most dominant Dothideomycetes found in soil (42%) and Leptosphaeria represent only 10% (Supplementary Figures  S4A, B ).
PHP and Plant Species Identity Effects on Soil and Root Bacterial Diversity ANOVA tests revealed a nearly significant effect of the contamination concentration on Shannon diversity indices of bacteria in the soil dataset (P = 0. 077), with a slightly higher diversity in the LC site than in HC, and MC sites. On the other hand, there was clearly no effect of contamination on the bacterial diversity indices in the root dataset (Table  1  and Figure 2C ). However, when comparing the shifts in Shannon diversity indices by plant species, a highly significant effect of plants species identity on bacterial diversity was observed in the root dataset (P < 0. 001), with a higher diversity of bacteria in L. europaeus and P. balsamifera than in S. canadensis. In contrast, there was no effect of plant species identity on the bacterial diversity in the soil dataset (Table  1  and Figure 2D ).
PERMANOVA analysis revealed that the community structure of bacteria was significantly affected by the contamination level for both soil and root datasets (P = 0. 01 and 0. 034, respectively). However, a significant effect of plant species identity on the bacterial community structure was observed only on the root dataset (P = 0. 001), while no effect of plant species was observed on bacteria in the soil dataset (P = 0. 42) (Table  1 ). The variations in diversity and community composition of bacteria were corroborated by the non-metric multidimensional scaling (NMDS) plots in both soil and root datasets. In NMDS plots performed across contamination concentrations, a clear separation of the bacterial communities was observed between the LC and HC sites in both soil and root datasets (stress value = 0.
15 and 0. 22, respectively) (Figures  5A, B ). However, the NMDS plot performed across plant species showed that the bacterial communities were determined by plant species identity only in the root dataset, where bacteria from S. canadensis roots grouped apart those from the other plant species (stress value = 0. 15 and 0. 22, respectively) (Figure  6A, B). The bolded values are significant at P < 0. 05. §P-values calculated using Kruskal-Wallis tests. For each group, n = 9. Kruskal-Wallis tests performed on the soil bacterial OTUs showed clearly that the contamination level had a stronger effect on the bacterial composition than plant species (Supplementary Table  S2 ). Among the most abundant 30 OTUs, 17 were significantly affected by the contamination concentrations, two by plant species, and two by both factors, while no effect was found on the remaining nine OTUs.
PHP and Plant Species Identity Effects on Soil and Root Fungal Diversity ANOVA tests showed that there was no effect of contamination on the Shannon diversity indices of fungi in either soil (P = 0. 424) or root (P = 0. 714) datasets (Table  1  and Figure 2C ). However, there was a highly significant effect of plant species identity on the fungal diversity in roots (P < 0. 001). Tukey's range test showed that the divergence in root fungal diversity has occurred in L. europaeus, which showed the highest diversity compared with P. balsamifera and S. canadensis. No effect of plant species identity on the fungal diversity was found in soil (Table  1  and Figure 2D ). PERMANOVA analysis showed that contamination had a significant effect on the community structure of fungi in both soil and root samples (P = 0.
002 and P = 0. 006, respectively), while plant species identity had a significant effect in roots only (P = 0. 002) (Table  1 ). NMDS plots showed a clear separation between the community composition of the HC and LC sites, while the community of the MC site was intermediary in both soil and root datasets (Figures  5C, D ). NMDS plots across plant species showed differences in community structure in roots only, where a distinct grouping of the fungal communities was found between L. europaeus and P. balsamifera, with the community of S. canadensis being intermediate (Figures  6C, D ). The Kruskal-Wallis tests confirmed that soil fungi were more affected by the contamination levels than by plants species identity, while a similar amount of root fungi were affected by either contamination and plants species (Supplementary Table  S2 ).
, 2016). In the present study, Alphaproteobacteria was the most dominant bacterial class in both soil and root datasets (36% in soil and 33% in roots), while Sordariomycetes was the most dominant fungal group in soil (37%) and Dothideomycetes was the most dominant in roots (24%). Here, Gammaproteobacteria represented only 9% of OTUs in soil and 14% in roots, while Pezizomycetes formed only 1. 3% of OTUs in roots and 0. 8% in soil (Supplementary Figure  S5 ). The pezizomycete OTU was represented by Sphaerosporella brunnea, an ectomycorrhizal species that we previously found abundant in willow roots grown in the same contaminated site  (Bell et al. , 2014). However, here we did not find any significant effect of plant species on this fungal OTU (Supplementary Table  S2 ). Differences in community structures between the AMF spore-associated microbiomes and the rhizospheric and root microbiome were also found at the genus level  (Iffis et al.
The high abundance of Alphaproteobacteria and Actinobacteria in the HC site may be related to their PHP tolerance and/or their ability to degrade PHP compounds. Several studies carried out on the microbial communities in PHP contaminated sites showed that Alphaproteobacteria and Actinobacteria were often found in higher abundances in soils containing high amounts of organic contaminants  (Greer et al. , 2010; Bell et al. , 2014; Yergeau et al. , 2014; Pagé et al. , 2015). Furthermore, Sphingomonas (the most dominant Alphaproteobacteria in the soil dataset), Bradyrhizobium (the most dominant Alphaproteobacteria in the root dataset), and Streptomyces (the most dominant Actinobacteria in soil and roots) were shown to degrade a range of recalcitrant PAH compounds, such as phenanthrene, pyrene, and naphthalene  (Rentz et al.
, 2008; Qu and Spain, 2011; Balachandran et al. , 2012; Bourguignon et al. , 2014). The presence of Gammaproteobacteria in similar abundances in the LC and HC sites, both in soil and root datasets, may be related to the large spectrum of activities of the species belonging to this class. For example, in PHP contaminated soils, Pseudomonas (the most dominant genus of Gammaproteobacteria in roots) was shown to degrade a range of PAH compounds such as phenanthrene, alkane, and naphthalene  (Ma et al. , 2006; Ní Chadhain and Zylstra, 2010; Sun et al. , 2014). On the other hand, in agricultural soils, Pseudomonas taxa are known as potential plant growth-promoting bacteria able to establish a symbiotic association with plant roots and to play an important role in plant growth, nitrogen fixation and phosphate solubilization  (Rodrìguez and Fraga, 1999; Desnoues et al.
, 2008; Ferrari et al. , 2011; Harms et al. , 2011; Stefani et al. , 2015). For example, Alternaria and Cladosporium, which were detected here both in soil and root datasets, have been shown to degrade crude oil and a variety of its derivative products such as phenanthrene, benzo[a]pyrene, fluoranthene, and anthracene  (Giraud et al. , 2001; Potin et al. , 2004; Mohsenzadeh et al. , 2012; Ameen et al. , 2016). Our results also showed that the OTU richness of bacteria and fungi were significantly decreased in root samples in comparison to the soil samples. Generally, the microbial diversity was shown to increase in rhizospheric soils compared to the different plant compartments (roots, stem, or leaves)  (Xu et al. , 2012; Turner et al. , 2013a; Edwards et al. , 2015). The increase of microbial richness in soils compared to roots may be related to the difference in environmental conditions and nutrient bioavailability in the two ecological niches (soil versus roots).
, 2001; Xu et al. , 2012; Shakya et al. , 2013; Edwards et al. , 2015; Quiza et al. , 2015). Usually, the same phyla or classes of microorganisms were found in soils and roots, but their abundances varied between the two habitats. Moreover, differences in the taxonomic affiliations were often reported when the comparisons were carried out at genus or species rank  (Gottel et al. , 2011; Turner et al. , 2013a,b; Edwards et al. , 2015). In our study, most of the bacteria and fungi identified in high proportions in roots were already known to be endophytic, mycorrhizal or obligatory biotrophic microorganisms, establishing a symbiotic or pathogenic associations with plants. For example, Bradyrhizobium and Pseudomonas (the most dominant Alphaproteobacteria and Gammaproteobacteria found in the root dataset) are plant growth-promoting bacteria able to establish endosymbiotic associations with the roots of several plant species  (Fisher and Long, 1992; Kneip et al.
Send offprint requests to: iary. davidzon@lam. fr Based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under ESO programme ID 179. A-2005 and on data products produced by TERAPIX and the Cambridge Astronomy Survey Unit on behalf of the UltraVISTA consortium (http://ultravista. org/). Based on data produced by the SPLASH team from observations made with the Spitzer Space Telescope (http://splash. caltech. edu). We can distinguish between different modes of galaxy growth e. g. by comparing the SMF of galaxies divided by morphological types or environments (e. g. Bolzonella et al. 2010; Vulcani et al. 2011; Mortlock et al. 2014; Moffett et al. 2016; Davidzon et al. 2016). Moreover, their rate of stellar mass accretion changes as a function of z, being more vigorous at earlier epochs (e.
g. Moster et al. 2013; Henriques et al. 2015; Volonteri et al. 2015) , which at present have to combine miscellaneous datasets. To get a continuous view of galaxies' history, one has to combine low-z estimates (e. g. Fontana et al. 2004 Fontana et al. , 2006;; Pozzetti et al. 2010; Ilbert et al. 2010)  with SMFs derived at z > 3 (e. g. McLure et al. 2009; Caputi et al. 2011; Santini et al. 2012). Unfortunately linking them is not an easy task. In particular, samples at different redshift are built with heterogeneous photometry and selection effects. For instance at high-z, instead of using photometric redshifts, the widespread approach is based on the "drop-out" technique that selects Lyman-break galaxies (LBGs,  Steidel et al. 1996). Even when photometric redshifts are used across the whole redshift range, differences e.
With them, our collaboration built a catalogue of galaxies (COSMOS2015) from z = 0 to 6. The COSMOS2015 catalogue has been presented in  Laigle et al. (2016) , where we showed the gain in terms of large-number statistics (due to the large volume probed) and improved depth (reaching K s = 24. 7 and [3. 6µm] = 25. 5, at 3σ in 3 diameter aperture). The deeper exposure translates in a higher completeness of the sample down to lower stellar masses with respect to previous versions of the catalogue. In this paper we exploit the COSMOS2015 catalogue (together with exquisite ancillary data available in COSMOS) to derive a galaxy SMF up to z ∼ 6, i. e. when the universe was about 1 Gyr old. Following galaxy mass assembly across such a large time-span allows to identify crucial stages of galaxies' life, from the reionization era (see  Robertson et al.
2015) , through the "cosmic noon" at z ∼ 2  (Madau & Dickinson 2014) , until more recent epochs when many galaxies have become red and dead (e. g. Faber et al. 2007). We aim at juxtaposing these key moments to get a global picture, also separating populations of active (i. e. , star forming) and passive (quiescent) galaxies. We organise our work as follows. First, we describe the COSMOS2015 catalogue and the other datasets we use, with particular attention to sample completeness (Sect. 2). At z 2. 5 we rely on the original SED fitting estimates from  Laigle et al. (2016) , while at higher z we recompute photometric redshifts (z phot , Sect. 3) and stellar masses (M, Sect. 4) with an updated SED fitting set-up optimised for the 3 z 6 range. Since the novelty of this work is the analysis between z = 2.
Dataset A description of our dataset is summarised in Sect. 2. 1. Sect. 2. 2 offers a comprehensive discussion about its completeness as a function of flux in IRAC channel 1 (i. e. , at ∼ 3. 6 µm). Core of our analysis is the COSMOS2015 catalogue, recently published in  Laigle et al. (2016, L16  in the following); other COSMOS datasets provide additional information. A complete list of the surveys carried out by the collaboration can be found in the official COSMOS website. 1.
Photometry One cornerstone of the COSMOS2015 photometry comes from the new Y, J, H, and K s images from the second data release (DR2) of the UltraVISTA survey  (McCracken et al. 2012) , along with the z ++ band from Suprime-Cam at Subaru  (Miyazaki et al. 2012 ). These images were added together to build a stacked detection image, as explained below. The catalogue also includes the broadband optical filters u * , B, V, r, i + , and 14 intermediate and narrow bands. In NIR, UltraVISTA is complemented by the y band images from the Hyper Suprime-Cam (HSC), as well as H and K s from WIRCam (at the Canada-France-Hawaii Telescope). The point-spread function (PSF) in each band from u * to K s is homogenised, so that the fraction of flux in a 3 diameter aperture suffers from band-to-band seeing variations by less than 5% (see  Fig.
4 in L16). Space-based facilities provided data in near-UV (from the GALEX satellite,  Zamojski et al. 2007)  and MIR (from IRAC on board the Spitzer Space Telescope), along with high-resolution optical images (ACS camera on board HST, see Sect. 3. 2). Galaxies with an X-ray counterpart from XMM  (Brusa et al. 2007)  or Chandra  (Marchesi et al. 2016 ) are excluded from the following analysis as their photometric redshifts, or the stellar mass estimates, would be likely corrupted by contamination of their active galactic nuclei (AGN). They represent less than 1% of the whole galaxy sample. The entire photometric baseline of COSMOS2015 is summarised in Table  1  of L16. Spitzer data represent another pillar of this catalogue, probing the whole COSMOS area at 3. 6-8. 0 µm, i. e. the wavelength range where the redshifted optical spectrum of z 3 galaxies is observed.
Such a crucial piece of information mainly comes from SPLASH but other surveys are also included, in particular the Spitzer-Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (S-CANDELS,  Ashby et al. 2015). Further details about how Spitzer/IRAC photometry was extracted and harmonised with the other datasets can be found in L16. Compared with the previous version of the catalogue  (Ilbert et al. 2013 ) the number of sources doubled because of the longer exposure time of the UltraVISTA DR2 in the so-called "Ultra-Deep" stripes (hereafter indicated with A UD , see Fig. 1 ). In that area of 0. 62 deg 2  we reach a 3σ limiting magnitude (in a 3 diameter aperture) K lim,UD = 24. 7, while in the remaining "Deep" area (dubbed A D = 1. 08 deg 2 ) the limit is K lim,D = 24. 0. The larger number of detected sources in DR2 is also due to the new χ 2 stacked image produced in L16.
Image stacking is a panchromatic approach for identification of galaxy sources, presented for the first time in  Szalay et al. (1999). With respect to the previous UltraVISTA (DR1) stacking, in L16 we co-add not only NIR images but also the deeper z ++ band, using the code SWarp  (Bertin et al. 2002). Pixels in the resulting zY JHK image are the weighted mean of the flux in each stacked filter. As a result, the catalogue contains ∼ 6 × 10 5 objects within 1. 5 deg 2 , 190 650 of them in A UD. In L16 we also show the good agreement of colour distributions, number counts, and clustering with other state-of-the-art surveys. For each entry of the COSMOS2015 catalogue we search for a counterpart in the four Spitzer-IRAC channels using the code IRACLEAN  (Hsieh et al. 2012 ). 2 The procedure is detailed in L16.
Photometric redshift and galaxy classification We estimate the z phot of COSMOS2015 sources by fitting synthetic spectral energy distributions (SEDs) to their multiwavelength photometry. The COSMOS2015 catalogue already provides photometric redshifts and other physical quantities (e. g. , galaxy stellar masses) derived through an SED fitting procedure detailed in L16. Here we follow the same approach, using the code LePhare  (Arnouts et al. 2002; Ilbert et al. 2006 ) but with a configuration optimised for high-z galaxies (Sect. 3. 1). The main reasons for a new SED fitting computation at z > 2. 5 are the following: -L16 explored the parameter space between z = 0 and 6, but to build an accurate PDF(z) for galaxies close to that upper limit one has to enlarge the redshift range. Therefore, we scan now a grid z = [0, 8] to select galaxies between z phot = 2.