dataesr/scientific-paragraphs-categorization huggingface dataset:
5%; z test: p = 0. 00083). A complex pattern of genomic introgressions has recently been highlighted between relict and non-relict IP strains showing that their genome is rather a mosaic of the two groups  43. We used this published dataset to estimate the probability of being relict around FLM in the IP strains according to the causal polymorphism. Interestingly, IP accessions diverge in their probability to be relict specifically around FLM according to SNP28958 and not their relict group (Fig. 6 ). It is also noteworthy that Can-0 belongs to an independent relict group  41. Furthermore, the FLM-Can allele is also found in the 11 native Madeiran lines that are even more divergent than the relict groups identified in the first place, likely because they had less chance of admixture with nonrelict strains  44.
Source data are provided in Supplementary Table  2. b Variations in leaf physiology associated with FLM variants compared to the worldwide leaf economics spectrum, i. e. , the relationship between log 10 -transformed A mass and LMA. Gray points represent all plant species from the GLOPNET dataset  12  (n = 701). Green points represent only herbaceous species from the GLOPNET dataset (n = 124). Black circles represent natural accessions of A. thaliana  14  (n = 340). Blue circles and triangles represent flm-3/Col-0 and the rHIF099-Can/Col plant groups measured in this study, respectively (n = 32 per genotype). Regressions have been estimated with standard major axis (SMA). b Boxplots represent leaf production rate with or without vernalization in Iberian Peninsula strains categorized according to their genotype at position 28,958,437 bp.
Source data underlying Fig. 5a  are provided as a Source Data file. data is also bound by the physiological constraints underlined by LES, and (2) whether FLM might contribute to phenotypic variation within the LES, using comparisons between our dataset and the generic interspecific GLOPNET database  12  , as well as the global variation measured among A. thaliana natural accessions  14. Our results revealed that FLM functional variation is constrained by a relationship between photosynthetic rate per unit of leaf dry mass and leaf mass per area, similarly to what is observed in the inter-and intraspecific datasets. However, slopes differed between A. thaliana (in our FLM dataset and among natural accessions) and interspecific measurements (in all or only herbaceous GLOPNET species). This suggests that the physiological constraints described by the LES might at least slightly differ between species, which can be explained by important evolutionary transitions and metabolic differences between plant species, such as deciduous-evergreen, herbaceous-woody, and annual-perennial.
The phenotypic values can be found in Supplementary Data 5. The growth room is set at an 8 h short days photoperiod (230 μmol m -2 s -1 ) with days at 21 °C/65%RH and nights at 17 °C/65% RH. Growth-related traits are extracted from daily images after segmentation (PRA = Projected Rosette Area = cumulative growth; RER = Rosette Expansion Rate = relative growth rate), as well as parameters describing the rosette color expressed in the HSV (Hue/Saturation/Value) scale. Leaf temperature was measured on the Phenoscope with FLIR infrared cameras targeting the whole rosette as well as a wet paper control for room temperature. Normalized leaf temperature was calculated by subtracting room temperature to the average rosette temperature. The growth chambers used for the experiments carried out at 16 and 23 °C was set at 16 h long day photoperiod of 150 μmol m -2 s -1 , 65% RH.
For the in vitro culture, seeds were surface-sterilized for 10 min in 70% EtOH, 0. 1% TritonX-100, followed by one wash with 95% EtOH for another 10 min. Sterile seeds were then resuspended in a 0. 1% agar solution and stratified in the dark at 4 °C for 3 days. Ten seeds per square Petri dishes (120 mm) containing typical Arabidopsis media  74  were placed on one side and the Petri dishes set vertically. Plants were grown for 12 days in a culture room (21 °C, 16 h light/8 h dark cycle). QTL mapping. QTL mapping was performed using Multiple QTL Mapping algorithm implemented in the R/qtl package  75, 76. At first, genotypic missing data were augmented. Then, one marker every three markers were selected as cofactors and significant ones were selected through backward elimination (backward selection of cofactors).
, Lincoln, NE, USA) connected to a gas analyzer system (LI-6400XT; Li-Cor). A mass was determined at steady state at 180 µmol m -2 s -1 PPFD, 20 °C) and at 390 ppm reference CO 2. After gas exchange measurement, rosettes were cut, and then leaves were separated and scanned individually for measurements of leaf area (ImageJ 1. 43C). Leaf blades were then oven dried at 65 °C for 72 h, and their dry mass (DM) was determined. Leaf dry mass per area (LMA, g m -2 ) was calculated as total blade DM divided by total blade area. The raw data can be found in Supplementary Table  2. Cloning procedures. FLM genomic fragments from Col-0 and Can-0 were amplified using Phusion high-fidelity Taq polymerase (Finnzymes, http://www. thermoscientificbio. com/finnzymes/) with a couple of primers including Gateway sites and flanking positions -2219 to +4737 relative to the FLM start codon of Col-0 annotated in TAIR10 (Supplementary Table  3 ).
"p < 0. 1; "*"p < 0. 05; "**"p < 0. 01; "***"p < 0. 001) in Figs. 1a-c , 2a, 3, 4a and Supplementary Figs. 1B, 4, 5 or with the Tukey HSD test without adjustment for multiple comparisons, the different letters representing groups at p < 0. 01 in Fig. 1d  and Supplementary Fig. 3A. Linear regressions between A mass and LMA were examined with standard major axis (SMA), using the package smatr in R  79. Physiological traits (i. e. , LMA and A mass ) across Arabidopsis accessions were obtained from  14  , while physiological traits from interspecific data were obtained from the GLOPNET database  12. FLM datasets (in the Col-0 as well as in the rHIF099 background) were orthogonally projected on the intraspecific Arabidopsis LES regression to estimate the contribution of variation at FLM to the species-wide LES range (flm-3/WT or FLM-Can/FLM-Col LES range expressed in percentage of the 95%-confidence interval of the 340 accessions LES range).
All analyses were performed in R 3. 2. 3 (Team RC 2014). Trait-climate relationships were modeled with linear mixed-effect models, using the lmekin function from the coxme package in R. Relatedness matrix between the IP accessions was included as covariate in the model. Relatedness matrix was calculated from whole-genome sequences  41  with the function--make-rel from PLINK  80. Yann Serrand for the supervision of the Phenoscope, and Lilian Dahuron for expert care of our plants. We also would like to thank Markus Schmid (MPI Developmental Biology, Tübingen, Germany; Umea Plant Science Center, Umea, Sweden) who provided seeds of the flm-3 mutant; Carlos Alonso-Blanco for sharing his experience in sequence diversity analyses; Cheng-Ruei Lee (National Taiwan University) for sharing his analysis concerning relict accessions; and Moises Exposito-Alonso for his advice regarding traits and genotype associations with climatic variables.
Acknowledgements This work was supported by funding from the  European Commission   Framework Program 7 ,  ERC Starting Grant  ' DECODE'/ERC-2009-StG-243359  to O. L. This work has benefited from the support of IJPB's Plant Observatory technological platforms. The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). F. V. was supported by  INRAE ,  CNRS , and the  Agreenskills fellowship program  (grant agreement #  3215 , F. V. ), which has received funding from the  EU 's  Seventh Framework Program  under the agreement No. FP7-609398. C. V. was supported by the  European Research Council (ERC)   Starting Grant Project  " Ecophysiological and biophysical constraints on domestication in crop plants " (ERC-StG-2014-639706-CONSTRAINTS). We thank.
r 2 : 6 NATURE 26 Fig. 4 Allelic variation at FLM modulates important traits of the leaf economics spectrum. a Boxplots representing the difference in leaf dry mass per area (LMA, g m -2) and photosynthetic rate by unit of dry mass (A mass , nmol CO 2 g -1 s -1 ) between the two allelic lines of rHIF099 as well as between flm-3 and Col-0. Source data are provided in Supplementary Table2. b Variations in leaf physiology associated with FLM variants compared to the worldwide leaf economics spectrum, i. e. , the relationship between log 10 -transformed A mass and LMA. Gray points represent all plant species from the GLOPNET dataset 12  (n = 701). Green points represent only herbaceous species from the GLOPNET dataset (n = 124). Black circles represent natural accessions of A. thaliana 14  (n = 340).
(A) SGI1 and SGI1-HKL variant group. SGI1 has already been reported to be inserted at the 3=-end of the trmE gene upstream of the following chromosomal (Chr) genes: the int2 gene (retron phage gene) in S. Typhimurium DT104, the yidY gene of S. enterica, and the hipB gene of Proteus mirabilis. The SGI1-H, -K, and -L groups were previously reported to be inserted at the 3=-end of the trmE gene upstream of the following chromosomal genes: the yidY gene in S. enterica, the hipB gene in P. mirabilis, and the permease gene in Morganella morganii. *, the 5=-end of S044 is deleted in SGI1-K variants. (B) SGI1-LK1 variant. The SGI1-LK1 variant is inserted at the 3=-end of the trmE gene upstream of the hipB gene of P. mirabilis. It was 61. 07 kb long (GenBank accession number MH734354), and like the SGI1-HKL variants, the backbone includes an IS1359 element, with one end at the same position in traN as in the SGI1-HKL group.
It was resistant to fluoroquinolones, and appropriate S83I and S80I alterations in gyrA and parC genes, respectively (29), were detected. A 35. 41-kb SGI1-HKL variant found at the 3=-end of the chromosomal trmE gene of Pm144BOUSA (GenBank accession number MN167852) was assembled from four contigs of the draft genome. The backbone had the alteration characteristic of the SGI1-HKL group, and the 9. 57-kb integron was located at the same position as in SGI1. The integron was bounded by IRi on the left-hand side and an In4-type integron that contained the dfrA15 cassette (like SGI1-L) on the right-hand side, with the inverted repeat of the transposon (IRt) at the right end (Fig. 3 ). Parts of the SGI1-K integron (blue) containing the IS26 composite transposon with a partial tnpR gene of Tn5393 and a partial Tn2 with the bla TEM-1b gene was also present.
The reads were assembled using SPAdes  (34)  via the PATRIC Web-based platform (https://www. patricbrc. org). The contigs of the Pm294MATLI and the Pm144BOUSA draft genomes were analyzed with the Basic Local Alignment Search Tool (BLAST) (https://blast. ncbi. nlm. nih. gov/Blast. cgi). They were aligned to the complete sequence of SGI1 (GenBank accession number AF261825) to retrieve the backbone of the SGI1 variant and to the P. mirabilis HI4320 genome (GenBank accession number AM942759) to exclude the contigs belonging to the genome of P. mirabilis. ResFinder (https://cge. cbs. dtu. dk/services/ResFinder/) was used to identify contigs with acquired antimicrobial resistance genes. PCR linkage between genes belonging to nonrepeated genetic elements and Sanger sequencing were performed to close the gaps and to check the single nucleotide polymorphisms (SNPs) not present in SGI1, as previously reported  (33).
To ensure the correct assembly of segments flanked by IS26, all possible combinations were examined, and a long elongation step (7 min) was done during PCR. Analysis of the SGI1-HKL group. The sequences of the new variants were compared to the complete sequences of SGI1-H (SGI1-PmSCO) (GenBank accession number JX121639), SGI1-K (GenBank accession number AY463797), SGI1-L (GenBank accession number LT630458), and SGI1-PmGUE (GenBank accession number JX121641) using BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi), followed by detailed manual inspection of the outputs. Mobilization experiments. The plasmid pEA409TEM24 (GenBank accession number MG764534), originally present in Enterobacter aerogenes EA409 (5) and harboring the resistance genes bla TEM-24 , aacA4, dfrA1, aadA1, and sul1, was used for the mobilization assay of SGI1-LK1.
The transfer frequency of SGI1-LK1 was calculated as the number of transconjugants per donor. To check for the presence of SGI1-LK1 and the IncC plasmid in the transconjugants, antibiotic susceptibility tests were performed in accordance with Clinical and Laboratory Standards Institute (CLSI) guidelines, and PCRs were performed as previously described  (6) , with the same primers. For P. mirabilis Pm294MATLI, the presence of the sgiA gene (S026) was screened to confirm the presence of the SGI1-LK1 variant using the same primers as the ones previously reported  (27). Accession number(s). The sequences of SGI1-LK1 and SGI1-LK2 were submitted to GenBank under accession numbers MH734354 and MN167852, respectively.
Data Analysis Statistical analyses were performed using R 3. 6. 1. [55]  and Rstudio 1. 2. 1335  [56]. To assess the impact of urbanization on the activity of bees, the response variables were the number of interactions realized, respectively, by (i) exotic bees except honeybees (i. e. , exotic wild bees), (ii) honeybees, and (iii) native bees on the experimental exotic plants. Honeybees were analyzed separately due to their sociality. Indeed, a honeybee colony can reach 20,000 to 60,000 workers, which is a hundredfold the abundance of a solitary bee population  [31]. Thus, their numerical dominance could have either induced a significant response at the level of the community of exotic bees or buffered a significant response from the solitary bees. The explanatory variables tested were the context (urban vs.
Plant-Pollinator Networks Plant-pollinator networks were obtained using the package Bipartite  [57] , implemented in R 3. 6. 1. [55]  and Rstudio 1. 2. 1335  [56]. To build the plant-pollinator networks, data were transformed and then pooled to obtain one network for the urban context and one network for the natural context. First, data were transformed according to the following equation: x i × n i 1000 , with x i being the number of interactions realized by a bee species on a plant species in a site during one session and n i being the number of flowers of this plant species. Then, these values were averaged for each landscape context (i. e. , urban and natural) to obtain the networks. Two network topology indices were calculated for each network. First, the specialization index (i. e. , d') was calculated.
Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015-2018) of SMAP datasets in combination with two approaches-Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process-and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at ~36 km resolution for the period from 2010 to 2018.
We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04. 4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics.
For example, the ESA's ECV SM, better known as CCI (Climate Change Initiative) SM product, which merged 11 active and passive microwave satellite sensors, covers a 40-year period between November 1978 and June 2018 with a daily temporal resolution (European Space Agency, 2012). The CCI dataset is producing an updated SM product once or twice a year. In this situation, even if an index can be defined, it cannot be developed into an operational tool. For example,  Carrão et al. (2016)  developed an empirical standardized SM index for agricultural drought assessment based on CCI SM dataset product, but the index has not led to near-real-time applications. The Copernicus Climate Change Service (C3S) is an online service developed by ESA's CCI SM project as an R&D product to provide key indicators on climate change including the global SM gridded data from 1978 to present on a near-real-time (minimum 10 days and maximum 20 days after sensing) basis (C3S, 2020).
, 2018; JPL, 2020). However, the short period of SMAP and its global revisit frequency (every 2-3 days) pose a statistical challenge for meaningful assessment of its indices. Sadri et al. (2018)  developed a near-real-time soil moisture index monitor for the Contiguous U. S. (CONUS) using SMAP at near-surface (~36 km resolution) and an assimilated version of it at root zone (~9 km resolution) depths. To overcome the shortterm of SMAP record, data was integrated with the simulated SM data from VIC LSM using the Bayesian conditional process. For the global scale, the ability of VIC to simulate SM is not sufficient globally  (Sadri et al. , 2018)  and hence it cannot be used. Bindlish et al. (2018)  demonstrated the use of reprocessed constant-angle SMOS brightness temperatures in the SMAP SM retrieval algorithm to develop a consistent multi-satellite product.
This approach, in practice, has become a very time-consuming task since the brightness temperatures from the two missions are not consistent and have a bias of about 2. 7 K over land with respect to each other. Furthermore, the two missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the SM products. Therefore, no integrated SMOS and SMAP dataset has become available. In this paper, we use statistical approaches to integrate SMAP and SMOS  (Kerr et al. , 2010 (Kerr et al. , , 2016) )  as one dataset (we refer to the integrated SMOS and SMAP SM dataset as SMOS/SMAP from this point on). The SMOS/SMAP is consistent with the SMAP data but, with an increased global revisit frequency (1-day) and a period of record from 2010 to the present that would be unachievable by either one of the satellites alone.
We describe the implementation of a Global Soil Moisture Index Monitor (GSMIM) between 55°S and 80°N with a time lag of about 7-8 days with a spatial resolution of ~36 km. Similar to the AFDM, in the GSMIM drought and pluvial conditions are reported as percentiles relative to long-term climatology. The system is an extension and improvement of its prototype system developed by  Sadri et al. (2018)  for the Contiguous U. S. Major differences to achieve this global system include: we integrated SMOS with SMAP datasets using Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process approaches and compared the two approaches to understand the efficiency of each method in defining a global drought index; we used the 4-parameter Beta distribution with an asymptotic approach but for extending the tails of the distribution, which guarantees that the value of the two bounding parameters will envelop both the smallest and largest observed values; and we did validation by comparing our results with the in-situ SM data, other indices, and SPI-1.
A key element of this study is the development of a web interface to view the daily SM index maps overlaid as a dynamic layer of Google Maps, which provides coordinate lookup, panning, zooming, and overlaying other geographical layers such as land use, land covers, and political borders. Users can access the archive of previous days, and download the maps. In Section 1, we looked at the importance of monitoring SM and having a global near-real-time agricultural drought index. The rest of this paper is organized as follows: Section 2 explains our datasets and the availability of passive and active microwave soil moisture retrievals. Section 3 explains our methodologies. Section 4 is about validating the results in the Canadian Prairies. In Section 5, we explain the results and discuss the findings.
, 2010). Detection of ice and snow is performed and added as flags. For SMOS L3SM both ascending (~6 am local time) and descending (~6 pm local time) orbits are processed separately  (Al Bitar et al. , 2017 ; Centre Aval de Traitement des Données SMOS, 2017) on a daily basis. Here, we use only the ascending orbits of SMOS L3SM (hereafter referred to as SMOS), in order to have consistency with the SPL3SMP data as well as smaller uncertainties and bias from the in-situ data  (Al Bitar et al. , 2017).
ESA-CCI merged SM Developed by the Vienna University of Technology (TU Wien), Dutch company VanderSat BV, and the Austrian Earth Observation Data Centre and hosted by ESA, the ESA-CCI merged SM is a quarter degree resolution global scale daily long-term SM record (provided in volumetric units m 3 /m 3 ). Version 4. 4 of the CCI product covers a 40 year period from 1978 to 2018 and is based on blending data from 11 active and passive microwave satellite sensors through the method described in  (Liu et al. , 2011; Wagner et al. , 2013)   (Chung et al. , 2019). SMOS has been integrated into Version 4. 04 but it is a version of SMOS estimated with AMSR-E/Aqua surface soil moisture (LPRM, V5). The way SMOS was integrated was to use a grid per grid change detection approach (scaled by either the ground site or model outputs and then spatially smoothed) to derive ASCAT SM maps on which AMSR LPRM data have been scaled to produce CCI.
SMAP, Aquarius, and MetOp-C missions are not considered in the v04. 4 and even in the current release (v04. 5) of the CCI SM product. The harmonization of CCI datasets aimed to take advantage of both types of microwave techniques but proved difficult owing to sensor degradation, drifts in calibration and algorithmic changes in the processing systems. In fact, one of the challenges in using the ESA's CCI product is guaranteeing consistency between the SM data retrieved from the different active and passive microwave instruments. In addition, it is noteworthy that CCI's product is not a near-real-time product. Albergel et al. (2013)  considered in-situ measurements from 2007 to 2010 from 196 stations of five networks in different countries (USA, Spain, France, China, and Australia) to evaluate the CCI product.
In-situ dataset The performance of active and passive soil moisture products is assessed by comparing them with station data. The in-situ candidate sites used in this study are from the International Soil Moisture Network (ISMN)  (Dorigo et al. , 2011a (Dorigo et al. , , 2012;; Ojo et al. , 2015; TUWIEN, 2019). Initiated by the SMOS project, ISMN plays a crucial role in globally assessing the quality of SM estimates from space-borne microwave sensors and LSMs. ISMN is the first, fully operational, and publicly available international initiative to globally host in-situ measurements of SM  (Karthikeyan et al. , 2017b)  from more than 500 stations spanning 18 different networks  (Dorigo et al. , 2011b; TUWIEN, 2019). The main limitation of the ISMN is that it does not cover the globe homogeneously.