dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research paper was only possible through the help and support of the  Federal Environmental Agency, Dessau, Germany , the  Meteorological Synthesizing Centre -East (MSC-E), Moscow, Russia and West (MSC-W), Oslo, Norway , and the  ICP Vegetation Programme Coordination Centre, Centre for Ecology & Hydrology, Bangor, UK. We thank the  United Kingdom Department for Environment, Food  and Rural Affairs (Defra; contract  AQ0810  and  AQ0833 ), the  UNECE (Trust Fund)  and the  Natural Environment Research Council (NERC)  for funding the  ICP Vegetation Programme Coordination Centre.
18] Mean 0. 19 [0. 11] 0. 28 [0. 26] 0. 23 [0. 23] 0. 30 [0. 25] 0. 14 [0. 13] 0. 23 [0. 18] 0. 34 [0. 28] 0. 23 [0. 18] 0. 24 [0. 21] 0. 20 [0. 13] Explanation: n = number of observations in training dataset; countries or regions with less than 30 observations in training or test dataset (Kosovo, Denmark(Faroe Islands), Belgium, Ukraine (Donetsk) and Italy (Bolzano) were excluded; Pseudo R 2 >= 0. 4 are depicted in bold; For example: 0. 19 = Arithmetic mean of Pseudo R-Squareds; (0. 15) = Standard deviation of Pseudo R 2 ; Mean = Pseudo R 2 averaged over all rows / columns (median is displayed in box brackets); ---= No Data.
| Experimental design At each of 31 sites (30 European and 1 Canadian), 15 grassland communities comprising 4 monocultures and 11 four-species mixtures of four forage species were sown at two seed densities (Table  S1. 1 in Appendix S1;  Kirwan et al. (2014)  for full details of species used, sowing and management). There were 30 experimental plots per site, and data from 930 plots were analysed. The four species selected at each site represent four distinct functional types based on combining two functional traits, "method of nitrogen acquisition"  (Nyfeler et al. , 2011)  and "pattern of temporal development"  (Finn et al. , 2013). Functional types were: fastestablishing, N 2 -fixing legume (L F ); fast-establishing, non-N 2 -fixing grass (G F ); temporally persistent, N 2 -fixing legume (L P ); and temporally persistent, non-N 2 -fixing grass (G P ).
During the years of the experiments, plots were not weeded. The first year of data analysed was based on the first whole production year after the year of sowing. The biomass (t DM ha -1 ) of each sown species and weeds was calculated annually for each plot. In monocultures, biomass from species in the sown species pool other than the sown monoculture was included in weed biomass. Plots were surrounded by guard rows to inhibit invasion from adjacent plots  (Kirwan et al. , 2014). In some of our systems, we can find both exotic and non-exotic invaders. However, the identification and quantification of individual invading species was not part of our study and therefore no precise reference to "exotic" can be made.
| Modelling weed biomass variation within a site To address Question 4, we conducted a separate repeated measures analysis with community and site fixed to provide an estimate of the within-site standard deviation of response for each community (see Table  S2. 4 in Appendix S2. 2). Analyses were mainly carried out using sas/stat software (9. 3; SAS Institute Inc. , Cary, NC, USA) and r (R Core Team, 2014).
ACKNOWLEDGEMENTS We thank the many colleagues who have assisted this work. We thank  M. Coll  for her early contribution. Co-ordination of this project was supported by the  EU Commission  through  COST Action 852 'Quality legume -based forage systems for contrasting environments'. M. T. S. , R. L. and A. R. were supported by the  Spanish Ministry of the Economy and Competitiveness through projects CARBOAGROPAS  ( CGL2006-13555-C03-01/BOS ) and  BIOGEI  ( CGL2013-49142-C2-1-R ) and the  Ministry of the Environment through OPS  ( 209/PC08/3-08. 2 ). L. K. was supported by an award from  Science Foundation Ireland  ( 09/RFP/EOB2546 ). A. L. , J. A. F. , J. C. and M. S. were partly supported by the  EU   FP7 project 'AnimalChange'  under grant agreement no. 266018.
Sampling sites Eight sites were studied in Georgia, including sites representing both large and isolated Alnus stands located from 0 to 1,800 m elevation. Three sites were located in the Colchis, considered as a refugium (PV, PP, PF). Five sites were located in the recolonized zone, three in central Georgia near the water separation line that splits Western and Eastern Georgia (BA, BO and SV), and two in Eastern Georgia at the Caucasus footstep (TS and TR; Fig. 1A , Table  S1 of Appendix S1 ). Climatic data (19 variables) were retrieved from the Worldclim database (http://www. worldclim. org) through R raster package  (Hijmans & Van Etten, 2012).
Alnus phylogenetic position and genetic diversity One cm 2 of leaf was ground to extract DNA using the Wizard genomic DNA purification kit (Promega, Charbonnières les Bains, France) as in  Rochet et al. (2011). The Internal Transcribed Spacer (ITS) of the plant nuclear ribosomal DNA was amplified using ITS1P (5 -TTATCATTTAGAGGAAGGAG-3 )-ITS4 primers  (White et al. , 1990)  for each tree, following conditions of  Rochet et al. (2011). The chloroplast gene mat K was amplified for one tree per population using the primer pairs Matk_1R_KIM and MatK_3F_KIM  (Dunning & Savolainen, 2010)  and sequenced following conditions of  Rochet et al. (2011). Sequences were manually corrected, and deposited in Genbank under accession numbers KX897895-KX897935 for ITS and KX897936-KX897944 for matK ). Reference sequences of European Alnus species were downloaded from Genbank Alignments were created with MUSCLE  (Edgar, 2004).
Phylogenetic diversity of Frankia and comparison with European strains The DNA was extracted from Frankia root nodules and three genes were amplified and sequenced: dnaA, ftsZ and pgk, with primers specific to Frankia as in Pozzi et al. (2015). Sequences were submitted to the EMBL (European Nucleotide Archive) under accession numbers LT616989-LT617015 (dnaA), LT617016-LT617048 (ftsZ ), LT599862, LT599864, LT599865 and LT599870-LT599890 (pgk). To test if Frankia sequences were phylogenetically more diverse in Georgia as compared to Europe, sequences from  Pozzi et al. (2015) , including strains isolated from A. glutinosa in Europe, were used as a reference. Alignments for each marker were built using MUSCLE and concatenated. A phylogeny was computed following the same methods as for the plant phylogeny.
Phylogenetic diversity of ectomycorrhizal fungi and comparison with European sequences DNA extraction was performed on each EM root tip, using the Wizard genomic DNA purification kit (Promega, Charbonnières les Bains, France) as described in  Rochet et al. (2011). The fungal ITS region was amplified using fungal universal primers ITS-1F/ITS-4  (Gardes & Bruns, 1993; White et al. , 1990). PCR conditions were the same as in  Rochet et al. (2011). Amplification products were sequenced by the MilleGen company (Labège, France). Sequences were manually corrected using 4Peaks 1. 7. 1  (Griekspoor & Groothuis, 2017)  and deposited in Genbank (accession numbers KX897613-KX897894). Fungal sequences were compared to Genbank and UNITE http://unite. ut. ee/  (Kõljalg et al. , 2005)  databases using the BLAST algorithm  (Altschul et al.
, 1990) , which allowed the identification of fungal genera. To compare our sequences with MOTUs previously detected in Europe, all sequences produced recently from Alnus roots in Europe  (Põlme et al. , 2013; Roy et al. , 2013)  were downloaded. Sequences were aligned separately for each genus with MUSCLE (http://www. drive5. com/muscle/) (Edgar, 2004) , and groups of sequences more than 97% similar were delineated with MOTHUR  (Schloss et al. , 2009) , a threshold used in previous publications on Alnus and commonly accepted to delineate fungal MOTUs  (Nilsson et al. , 2008). Sequences that had between 93 and 97% similarity with Genbank or Unite sequences were identified from the BLAST result at the genus level only (e. g. , ''genus sp. ''). Sequences that had no close similarity (>97%) with Genbank or Unite sequence were considered as putatively endemic to Georgia.
For the specialist genus Alnicola, reference sequences produced from European specimens were downloaded from Genbank  (Moreau, Peintner & Gardes, 2006; Rochet et al. , 2011)  and aligned with the sequences from the present study. Based on the alignment and phylogeny, computed using methods described above, Alnicola MOTUs were identified to the species level, and rare ITS variants were detected from the alignment.
Comparison between EM fungi communities from Colchis and Hyrcan refugia Only sequences of EM fungi were compared because our sequencing strategy for Frankia differs from  Põlme et al. (2014). To compare EM communities between Colchis and the Hyrcanian forests, sequences produced by  Põlme et al. (2013, sampling  sites positioned on Fig. 1B ) were downloaded from UNITE. Sampling strategy for EM root tips (5-6 trees sampled per site) and sequencing methods (Sanger sequencing of individual root tips, using the ITS marker) were sufficiently similar to be compared in a single analysis. Fungal sequences produced from mycorrhizae of Alnus subcordata CA Mey. and A. glutinosa in Iran, and A. orientalis Decne. in Turkey, were aligned with sequences from the present study, and MOTUs were delineated for each genus as previously described.
DISCUSSION Our first objective was to document the diversity of symbiont communities associated with Alnus glutinosa ssp. barbata in Georgia, especially in the Colchis floodplain, a glacial and Tertiary refugium for alders. In all, 29 EM MOTUs and 12 Frankia MOTUs were uncovered, and Chao 1 estimates showed that twice as many species may occur there. For Frankia, the common European lineages were detected, together with five new lineages, which is an exceptional pattern considering the extensive worldwide Frankia database  (Pozzi et al. , 2015). Georgia proved to be a region of high Frankia diversity, probably of ancient origin as suggested by the long branches supporting Caucasian lineages (Fig. 2 ). For EM fungi, most Georgian MOTUs (24 out of 29) were already reported from Europe, on roots of A.
ACKNOWLEDGEMENTS The authors are deeply grateful to  Professor George Nakhutsrishvili  and  Dr. Angelina Jorjadze  at the  Tbilisi Botanical Garden and Institute of Botany , and to  Joël Bastenaire  and  Adeline Latrille  at the  Institut Français de Géorgie  and  Lela Gogava , for their helpful support in Georgia. We thank  Arndt Hampe ,  Corinne Vacher  and  Jason Hoeksema  for interesting discussions and feedback. We deeply thank our anonymous referee,  Dee Carter  and  Lee Taylor  for their useful comments that contributed to improve this paper.
DNA Deposition The following information was supplied regarding the deposition of DNA sequences: Plant sequences are deposited at Genbank (KX897895-KX897935 for ITS and KX897936-KX897944 for matK). Frankia Sequences were submitted to the EMBL (European Nucleotide Archive) under accession numbers LT616989-LT617015 (dnaA), LT617016-LT617048 (ftsZ), LT599862, LT599864, LT599865 and LT599870-LT599890 (pgk). Fungal sequences are deposited at Genbank under accession numbers KX897613-KX897894.
Sensitivity of K leaf to ABA for 10 offspring genotypes of the Syrah 3 Grenache population and the parents. K leaf was measured on detached leaves fed with artificial sap containing variable ABA concentration. Each panel (A-L) represents a genotype, and genotypes are ordered from the highest (A) to the lowest (L) sensitivity of K leaf to ABA. K leaf was measured according to the EFM. Mean 6 SE for n = 5 leaves taken from two to three plants measured at each concentration for each genotype. Fitting of linear regression models was performed on semi-ln transformed data (Supplemental Fig. S3 ) and is represented here by the lines in the nontransformed coordinates. concentration made it possible to predict changes in K leaf (% reduction K leaf ) induced for each genotype by the water deficit scenario.
Statistical Analyses Statistical analyses were performed with R (R Development Core Team, 2012). The Kruskal-Wallis test at the 5% alpha level was used for comparison of means. Semi-ln transformations were used to fit linear models to K leaf response to ABA and extract the slopes. Regression slopes were compared by analysis of covariance. Tests for significant differences among all pairs of slopes were further achieved using R compSlopes package with the FDR correction method to adjust P values and protect against false-positive results. Modeling the Possible Influence of Down-Regulation of K leaf by ABA on (An)isohydric Behavior Two simulations were performed with all parameters maintained identical in both cases apart from K leaf sensitivity to ABA and K leaf max , values of which were chosen based on results obtained on detached leaves of representative isohydric and anisohydric genotypes.
ACKNOWLEDGMENTS We acknowledge  Philippe Hamard ,  Philippe Péchier , and  Victorien Taudou  for their technical support in plant preparation, high-throughput experiments, and measurements on detached leaves. We are also grateful to  Wendy Sullivan  for helping with the HPFM experiments and to  Annette Boettcher  for running ABA analysis with the LCMS. In memory of our dear colleague and friend  Eric Lebon.
Genetic population structure was determined using individualbased Bayesian assignment tests, implemented in the program STRUCTURE v2. 3. 4  (Pritchard, Stephens, & Donnelly, 2000)  (Appendix S1 for STRUCTURE running procedures). The significance of genetic differences between populations and geographic regions (England vs. Iberia) was determined based on a multilocus analysis of molecular variance (AMOVA) implemented in the R package GSTUDIO  (Dyer, 2009). as implemented in the programs Bayescan  (Foll & Gaggiotti, 2008)  and  LOSITAN (FDist, Antao, Lopes, Lopes, Beja-Pereira, & Luikart, 2008) , were used to identify SNPs potentially under directional selection, or linked with genes under selection, based on higher levels of genetic differentiation among populations relative to expected neutral distributions (Appendix S1 for test parameters).
Allele frequencies of SNPs identified as outliers were correlated against environmental variables (maximum temperature and summer rainfall) using logistic regressions (glm function in R), as described by  Schoville, Widmer, Deschamps-Cottin, and Manel (2013). We carried out a genotype-environment association analysis to test for associations between allele frequencies and local environmental variables (maximum temperature and summer rainfall). We used the latent factor mixed model (LFMM) Frichot, Schoville, Bouchard, and Francois (2013) approach, implemented in the R package LEA  (Frichot & Franc ßois, 2015). We corrected for population structure through including the number of populations (K) identified by STRUCTURE assignment tests as latent factors in the models. We performed five LFMM repetition runs with 1,000,000 iterations and 500,000 iterations for burn-in.
| Adaptive genetic variation Bayescan identified 24 outlier SNPs potentially under selection. LOSITAN identified 224 SNPs as potentially under directional selection, which included 20 of the outlier SNPs also identified by Bayescan. Allele frequencies in 13 outlier SNPs were significantly correlated with either maximum temperatures (11 SNPs) or summer rainfall (10 SNPs). Significant correlations were also identified within Iberia between five SNPs and maximum temperatures and seven SNPs and summer rainfall (Table  S2 ). STRUCTURE assignment tests divided the full data set into two main genetic clusters, separating the English and Iberian samples. The Iberian cluster was further divided into two clusters, separating the two northern populations, Bizkaia and Girona (Fig. S2 ). Therefore, LFMM was run with three latent factors for the full data set and two for Iberia.
LFMM detected 93 outlier SNPs associated with maximum temperatures and 129 SNPs with summer rainfall across the study area. In the Iberia-only data set, 177 SNPs were associated with maximum temperatures and 278 with summer rainfall. We identified eight SNPs potentially associated with climate-adaptive genetic variation that were supported by all methods (Bayescan, LFMM and logistic regressions; Table  S3 ). The spatial distribution of genetic variation in SNPs identified as potentially climate-adaptive indicates a lower frequency of warm and dry adaptive alleles in the north and eastern Iberian populations (Bizkaia, Girona and Valencia; Fig. S3 ; Table  S4 ), and therefore, these populations were classified as having high adaptive sensitivity to climate change. Genetic differentiation in SNPs identified as potentially associated with climate-adaptive genetic variation was high overall (mean F st = 0.
3. We present a general sampling formula for community abundance data under this assumption. We show that for the few models for which an exact solution with zero-sum constraint is known, our independent species approach leads to very similar parameter estimates as the zero-sum models, for six frequently studied tropical forest community samples. 4. We show that our general sampling formula can be easily confronted to a much wider range of datasets (very large datasets, relative abundance data, presence-absence data, and sets of multiple samples) for a large class of models, including non-neutral ones. We provide an R package, called SADISA (Species Abundance Distributions under the Independent Species Assumption), to facilitate the use of the sampling formula.
In particular, we consider community models with protracted speciation, species-level density dependence, and species-specific dispersal rates, and datasets of very large size, relative abundance data, presence-absence data and sets of multiple samples. In each of these cases the independent species framework leads to a straightforward fitting procedure, illustrating its simplicity and versatility. We provide an R package called SADISA (Species Abundance Distributions under the Independent Species Assumption) to evaluate the new sampling formulas.
By applying this tuning, we obtain parameter estimates with the independent species approach that are almost identical to those obtained with the zero-sum approach, as we will show in the next section. For the case of dispersal-limited sampling, given by eqn (3), the same sampling formula applies for the entire local community or for a sample taken from the local community. This is due to a property called sampling invariance (see Appendix S2). It suffices to set the parameter q in accordance with the size of the dataset, whether it is an exhaustive census or a non-exhaustive sample. In particular, the sampling formula does not depend on the size of the local community from which the sample was taken. However, sampling invariance, and the associated flexibility in dealing with either census or sample data, does not hold generally, as we will illustrate in the next section.
S I N G L E S A M P L E S The most studied neutral community model, also known as Hubbell's model, combines point-mutation speciation and dispersal-limited sampling  (Hubbell 2001). To evaluate the zero-sum sampling formula, we follow the approach of  Etienne (2005). This involves an arbitrary-precision computation with Stirling numbers, using the computer algebra system PARI/GP. The evaluation of the independent species sampling formula, given by eqns (1-4), requires the computation of several one-dimensional integrals. Because the integrands are often sharply peaked, we use a dedicated numerical integration algorithm, which is included in the R package SADISA. We apply both sampling formulas to six datasets of tropical tree communities  (Volkov et al. 2005; Etienne & Haegeman 2011). The parameter estimates obtained with the zerosum and the independent species approach are very similar (Table  1 , rows ZSC and ISA).
Importantly, the likelihood values should not be compared, because they are not likelihoods for exactly the same data. The zero-sum approach assumes that the total number of individuals is given by the observed value, while the independent species approach treats this as additional data the probability of which is incorporated in the total likelihood. This explains why the zero-sum likelihood is systematically higher than the independent species likelihood (the log-likelihood is less negative, see Table  1 ). However, after conditioning the independent species likelihood on sample size (see Appendix S4), the zero-sum and independent species likelihood values almost coincide (Table  1 , rows ZSC and ISAC). Note that the parameter estimates are even closer than in the case without conditioning (except for the Sinharaja dataset).
Hence, at least for the community model and the datasets considered here, the zero-sum approach and the independent species approach give practically equivalent results. For two other speciation models, the zero-sum sampling formula for a single sample and single guild has been derived, assuming neutral dispersal-limited sampling. For randomfission speciation, the metacommunity abundance density q(x) is given by (see Appendix S3; compare with eqn (4)), Table  1. Fits for neutral model with point-mutation speciation and dispersal-limited sampling. We analysed six datasets of tropical tree communities  (Volkov et al. 2005; Etienne et al. 2007b; Etienne & Haegeman 2011 ), and we computed the maximum-likelihood fits for three model variants. The first variant, ZSC, imposes the zero-sum constraint, so that community size is invariant over time (results taken from  Etienne et al.
Dispersal number Zero-sum constraint Likelihood landscape for zero-sum and independent species approach. We consider the point-mutation speciation model with dispersal-limited sampling. We computed the zero-sum and independent-species likelihood as a function of metacommunity diversity h (x-axis) and dispersal number I (y-axis) for the BCI dataset. Warmer colours correspond to higher likelihood values. The white 9-mark indicates the maximum-likelihood parameters. The two likelihood functions are almost identical, up to a constant factor (the colour code is relative to the maximum log-likelihood value; for example, dark blue corresponds to log-likelihood values at least 40 units below the maximum). Similarly to the case of point mutation, we find that the zero-sum and independent species estimates are very close, both for the random-fission speciation model (Table  2 ) and for the per-species speciation model (Table  3 ).
The data does not contain signs of per-species speciation in the case without dispersal limitation, in agreement with  Etienne et al. (2007b). However, in the case with dispersal limitation, which has not been studied previously, there is strong evidence of per-species speciation in the Korup and Yasuni datasets. Hence, the selection between speciation models depends on whether or not dispersal limitation is taken into account. While this is an intriguing result, an analysis of its precise meaning is beyond the scope of this paper.
M U L T I P L E S A M P L E S The zero-sum analog of the multiple-samples sampling formula (7) has only been explored for the point-mutation Table  2. Fits for neutral model with random-fission speciation and dispersal-limited sampling. Same datasets as in Table  1. We consider two model variants: variant ZSC imposes the zero-sum constraint (results taken from Etienne & Haegeman 2011); variant ISA assumes independence between species. ZSC and ISA likelihoods are not comparable. In column DLL we compare the maximum log-likelihoods of the random-fission model with those of the point-mutation model, for the ZSC and the ISA variant   (2007b) , but results for model (DL, ZSC) have not been reported before. The maximum likelihood of the per-species speciation model is always larger than the corresponding point-mutation likelihood (column DLL), because point-mutation speciation is a special case of per-species speciation (case a = 0) ).