dataesr/scientific-paragraphs-categorization huggingface dataset:
Material & methods: Database choice: We used AMF SSU rRNA gene sequences from MaarjAM database, the largest global database of AMF gene sequences  [13]. Ideally, our analyses would be based on several independent genomic regions, however such data has yet to be acquired. In fungi, the usual barcode is the ITS region. However, for several reasons the SSU rRNA gene has been preferred over the ITS in AMF  [35] , and therefore ITS data on AMF are currently less common. In addition, we confirmed using the dataset of  [35]  that the ITS are very difficult to align, preventing us from reconstructing a robust phylogeny to investigate AMF evolutionary history (Supplementary Fig. 1 ).
Virtual taxa phylogenetic reconstruction: We reconstructed several Bayesian phylogenetic trees of the 384 VT from the corresponding representative sequences available in the MaarjAM database  [13]  updated in June 2019 (Supplementary Methods 1). We used the full length (1,700 base pairs) SSU rRNA gene sequences from  [17]  to better align the VT sequences using MAFFT  [36]. We selected the 520 base pair central variable region of the VT aligned sequences and performed a Bayesian phylogenetic reconstruction using BEAST2  [37]. We obtained a consensus VT tree and selected 12 trees equally spaced in 4 independent Bayesian chains to account for phylogenetic uncertainty in the subsequent diversification analyses, hereafter referred to as the VT replicate trees. We set the crown root age at 505 Myr  [20] , which is coherent with fossil data and previous dated molecular phylogenies  [8, 10].
6 We considered several ways to delineate AMF species based on the SSU rRNA gene. In addition to the VT species proxy, we delineated AMF de novo into evolutionary units (EUs) using 5 different thresholds of sequence similarity ranging from 97 to 99% and a monophyly criterion. We gathered 36,411 AMF sequences of the SSU rRNA gene from MaarjAM, mainly amplified by the primer pair NS31-AML2 (variable region)  [38, 39]  (dataset 1, Supplementary Table  1 ), corresponding to 27,728 haplotypes. We first built a phylogenetic tree of these haplotypes and then applied to this tree our own algorithm (Rpackage RPANDA  [40, 41] ) that traverses the tree from the root to the tips, at every node computes the average similarity of all sequences descending from the node, and collapses the sequences into a single EU if their sequence dissimilarity is lower than a given threshold (Supplementary Methods 2).
For each clade, we reconstructed Bayesian phylogenetic trees of haplotypes following the procedure described in Supplementary Methods 1. We then ran GMYC analyses (splits R-package  [44] ) on each of these trees and evaluated the support of the GMYC model compared to a preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted October 30, 2020. ; https://doi. org/10. 1101/2020. 07. 28. 224790 doi: bioRxiv preprint 7 null model in which all tips are assumed to be different species, using a likelihood ratio test (LRT). If the LRT supports the GMYC model, different SSU haplotypes belong to the same AMF species, i. e. the SSU rRNA gene has time to accumulate substitutions between AMF speciation events.
Diversification analyses: We estimated lineage-specific diversification rates using ClaDS, a Bayesian diversification model that accounts for rate heterogeneity by modeling small rate shifts at speciation events  [46]. At each speciation event, the descending lineages inherit new speciation rates sampled from a log-normal distribution with an expected value log  [α×λ]  (where λ represents the parental speciation rate and α is a trend parameter) and a standard deviation σ. We considered the model with constant turnover ε (i. e. constant ratio between extinction and speciation rates; ClaDS2) and ran a newly developed ClaDS algorithm based on data augmentation techniques which enables us to estimate mean rates through time (https://github. com/OdileMaliet/ClaDS_Julia). We ran ClaDS2 with 3 independent chains, checked their convergence using a Gelman-Rubin diagnostic criterion  [47] , and recorded lineage-specific speciation rates.
We also used CoMET, its equivalent piecewise-constant model in a Bayesian framework (TESS R-package  [49, 50] ). We chose the Bayesian priors according to maximum likelihood estimates from TreePar, disallowed mass extinction events, and ran the MCMC chains until convergence (minimum effective sample sizes of 500). We also fitted a series of time-dependent and environment-dependent birth-death diversification models using RPANDA  [40, 51]  to test the influence of temperature, pCO2, and land plant fossil diversity on AMF diversification. For the time-dependent models, we considered models with constant or exponential variation of speciation rates through time and null or constant extinction rates (fit_bd function). Models with constant rates correspond to the null hypothesis of clock-like speciation, whereas the exponential variation ensures positive rates and are an approximation of diversity dependence, a process often invoked during radiations  [52].
The influence of pCO2  [54]  and of land plant fossil diversity was tested starting from 400 Myr ago, as these environmental data are not available for more ancient times. For these analyses we sliced the phylogenies at 400 and preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted October 30, 2020. ; https://doi. org/10. 1101/2020. 07. 28. 224790 doi: bioRxiv preprint 9 200 Myr ago, and applied the diversification models to the sliced sub-trees larger than 50 tips. Estimates of land plant diversity were obtained using all available Embryophyta fossils from the Paleobiology database (https://paleobiodb. org) and using the shareholder quorum subsampling method (Supplementary Methods 4;  [55] ).
Testing for potential drivers of AMF diversification: To further investigate the potential factors driving AMF diversification, we assessed the relationship between lineage-specific estimates of present-day speciation rates and characteristics of each AMF taxonomic unit, i. e. VT or EUs. First, we characterized AMF niche width using a set of 10 abiotic and biotic variables recorded in MaarjAM database for each AMF unit. In short, among a curated dataset containing AMF sequences occurring only in natural ecosystems (dataset 2;  [26] ), for each AMF unit, we reported the number of continents, ecosystems, climatic zones, biogeographic realms, habitats, and biomes where it was sampled, as well as its number of plant partners, their phylogenetic diversity, and its centrality in the plant-fungus bipartite network, and performed a principal component analysis (PCA; Supplementary Methods 5).
To test the effect of latitude, we associated each AMF unit with its set of latitudes and used similar MCMCglmm with an additional random effect corresponding to the AMF unit. To account for inhomogeneous sampling along the latitudinal gradient, we re-ran the model on jackknifed datasets (we re-sampled 1,000 interactions per slice of latitude of twenty degrees). Similarly, we tested the effect of climatic zone and habitat on the speciation rates. Finally, to test the effect of dispersal capacity, we assessed the relationship between spore size and speciation rate for the few (n=32) VT that contain sequences of morphologically characterized AMF isolates  [57]. We gathered measures of their average spore length  [57]  and tested their relationship with speciation rate by using a phylogenetic generalized least square regression (PGLS).
05; Supplementary Fig. 2 ); on average, there are 10 SSU haplotypes for one AMF unit delineated using GMYC (Supplementary Table  5 ). Rarefaction curves as well as Bayesian and Chao2 estimates of diversity suggested that more than 90% of the total diversity of AMF is represented in our dataset regardless of the delineation threshold (Fig. 1b , Supplementary Tables  2, 5 , & 6; Supplementary Note 2), which is consistent with the proportion of new AMF units detected in recent studies  [59]. The reconstructed Bayesian phylogenetic trees based on VT and EU species delineations did not yield high support for the nodes separating the main AMF orders; yet, they had similar topologies and branching times of the internal nodes overall (Fig. 2 , Supplementary Figs. 3). As expected, finer delineations resulted in an increase in the number of nodes close to the present (Supplementary Figs.
Acknowledgment: The authors acknowledge  C. Strullu-Derrien ,  M. Elias ,  D. de Vienne ,  A. Vogler ,  J. -Y. Dubuisson ,  C. Quince ,  S. -K. Sepp , and  M. Chase  for helpful discussions. They also thank  L. Aristide ,  S. Lambert ,  J. Clavel ,  I. Quintero ,  I. Overcast , and  G. Sommeria  for comments on an early version of the manuscript and  David Marsh  for English editing. BPL acknowledges  B. Robira ,  F. Foutel-Rodier ,  F. Duchenne ,  E. Faure ,  E. Kerdoncuff ,  R. Petrolli , and  G. Collobert  for useful discussions and  C. Fruciano  and  E. Lewitus  for providing codes. This work was supported by a doctoral fellowship from the  École Normale Supérieure de Paris  attributed to BPL and the  École Doctorale FIRE -Programme Bettencourt. MÖ was supported by the  European Regional Development Fund (Centre of Excellence EcolChange)  and  University of Tartu  ( PLTOM20903 ).
Farm-level outputs are aggregated to the regional level by averaging total output of each farm type, weighted by its relative area in the region. In addition to representing the high heterogeneity of multiple farm types and geographic sectors, SYNERGY can also represent multiple farm activities. Crop activities are defined as the combination of a crop and the rotation it belongs to, which determines the levels of inputs described in cropping and fertilization modules. Livestock activities are defined as the combination of an animal and its feed ration, which determines (i) the levels of input use described in the feeding module and (ii) milk and meat yields described in the livestock module. The model represents many crop and livestock activities, making it possible to represent both widespread and alternative technologies (see Section 3).
SYNERGY's main originality lies in its ability to represent farm-to-farm exchanges of intermediate products (manure and crops), which occur on a local market (i. e. , intra-sector or intra-region). SYNERGY is composed of several modules that detail crop and livestock activities, as well as their impacts on N efficiency and potential losses of N (Fig. 1 ). It generates four types of indicators: (i) structural (e. g. , crop areas, numbers of animals), (ii) technical (e. g. , protein self-sufficiency, application of N   2017 )). Crop activities are described in cropping and fertilization modules, while livestock activities are described in livestock and feeding modules. fertilizers) (iii) economic (e. g. , regional profit, farm income, farm-tofarm exchanges) and (iv) environmental (i. e. , N efficiency and potential losses of N).
Cropping module The cropping module sets the area of each crop activity for a farm type. Since SYNERGY is a static annual model, rotations are represented by combining different crops with constraints of crop share, which corresponds to each crop's minimum return period. The minimum return period is the maximum frequency of return of the same crop on the same plot over time. The cropping module also sets the outlets of crop production: kept on the farm, sold on the world market or sold on the local market. These local exchanges of crops can take place at the regional level i. e. , between farms that are located in the same sector or in different sectors. The non-linear cost function considers costs of crop production (excluding fertilizer costs) and makes it possible to calibrate the model for the crop areas observed during a reference period (see Section 2.
Diversity of farms and activities Three farm types were considered in the region: dairy cow, pig and crop. Dairy production had 20 potential rations, each differing in the main forage (i. e. , forage maize, forage grass or both) and in the N-rich feed (soybean meal, peas, faba beans or dehydrated alfalfa). Regardless of the main forage, soybean-based rations were the basic rations used, based on regional references (IDELE -Inosys, 2018). Legume-based rations were alternative ones created by replacing soybean meal with legumes (here, pea, faba bean or dehydrated alfalfa) using INRAtion® software  (INRA, 2003). If legumes could not replace all soybean meal due to nutritional constraints, some rapeseed meal was added. Appendix B lists examples of compositions of the dairy rations used. Pig production had two potential rations, each differing in the N-rich feed (soybean meal or a mixture of pea and faba bean) and calculated using Porfal® software  (IFIP, 2018).
Data and calibration specifications SYNERGY was calibrated for each farm type at the sector level. Animal numbers and areas of each farm type were calibrated using data from the most recent agricultural census in France (French Ministry of Agriculture, 2018b). Due to the PMP technique used, all crop areas were set to non-zero values to be able to evolve in the scenarios. Thus, for each legume studied (i. e. , peas, faba beans and dehydrated alfalfa), the initial area was arbitrarily set at 0. 5% of the area of each farm in each sector, which initialized the total legume share at 1. 5% in each farm in each sector. Input and output prices were based on mean regional or national data for the reference years 2013-2017 (French Ministry of Agriculture, 2018a; La Dépêche -Le Petit Meunier, 2018).
Acknowledgements. The authors thank all of their peers in the diverse formal and informal networks who helpedand still helpto develop these ideas and advance the large and growing community. Especially noteworthy are the inspiring exchanges with colleagues in the extended networks of the  OpenNESS  project, the  IPBES Assessments , the  Ecosystem Service Partnership (ESP) ,  SwedBio ,  Future Earth  and the  Program on Ecosystem Change and Society (PECS). The personal, real-life exchanges between scientists, policy-makers and practitioners in concrete local contexts have been instrumental in developing and pursuing plural valuation. Author contributions. SJ and NZC compiled and edited the final paper and facilitated the collaborative writing process. PB, DG, LG and UP convened, organized and led the Oaxaca workshop that generated the information on which the paper is based.
KB, AB, JC, SD, EG, SL, BML, VM, JM, HM, BM, AN, PO, JO, AP, AR, NS, MV and AHPR contributed to the generation of knowledge, information and perspectives during the plenaries and breakout sessions of the workshop, produced base material and reports of these sessions and discussions and co-authored by writing, revising, amending and validating all sections of the paper. Financial support. The authors wish to thank the Sida-funded SwedBio programme at the  Stockholm Resilience Centre , the  Programme for Ecosystem Change and Society of Future Earth , the  Gordon and Betty Moore University  through sub-grant  GBMF5433  to the  Basque Centre for Climate Change (bc3)  for supporting the undertaking of the workshop that led to this paper and the  EQUIVAL  project and the work of UP, PB and NZC, the  Future Earth Montreal Global Hub , the  Institute of Ecosystem and Sustainability Research at the Autonomous National University of Mexico  and the  Division of Science Policy and Capacity-Building  ( SC/PCB ) of the  United Nations Educational, Scientific and Cultural Organization (UNESCO) , CISEN V, the  ValuES  project from  GIZ  supported by the  BMUB  and ecoSERVICES from  Future Earth  for providing support and financial resources.
Soil Analyses Fresh soil samples were sieved upon sampling and the >5 mm fraction was kept in plastic ziplock bags and stored for 8-10 weeks at 4 • C until analyses. The activity of four soil enzymes linked to the cycles of phosphorus (alkaline phosphatase), sulphur (arylsulphatase), carbon (β-glucosidase), and nitrogen (urease), and the hydrolysis of fluorescein diacetate (FDA, considered a proxy for the hydrolytic activity of the soil) were determined in fresh soil subsamples  [39, 40]. Soil enzyme activities were determined using colorimetric methods as indicated in  [41]. The values of soil enzyme activities were expressed on an oven-dried soil basis. Soil subsamples were air-dried and sieved at 2 mm. Water retention data were determined on a pressure plate apparatus for two water potentials (-10 and -15,800 kPa)  [42].
One plant per species was excavated to estimate rooting depth. Moreover, branches or shoots of 1-3 plants per species were collected, kept in sealed plastic bags, and transported to Monggis Substation (Kinabalu Park), where they were processed the day of the sampling. One healthy leaf per plant was selected and put below a glass layer and photographed with a digital camera. The camera was placed in a fixed support to guarantee its orthogonal position with respect to leaves. In some cases, the leaf was cut in several fragments to ensure the correct estimation of leaf area. All leaf photographs included a ruler to allow the estimation of leaf area. Leaf area was obtained from the digital photographs using ImageJ software  [46]. After photographing, each leaf was cleaned with tap water, rinsed with deionized water, and put in a paper envelope.
Data Analysis The soil dataset, including soil pH, soil water retention capacity (AWS, Wfc, Wwp), P-Olsen, total soil C and N, soil CEC, soil plant-available (exchangeable) Ca, K, and Mg, Ca:Mg molar ratio, DTPA-extractable Ni, pseudototal concentrations of P, S, Mn, Ni, Cr, and Co, and enzyme activities was analyzed using principal component analysis (PCA). PCA was based on a correlation matrix in order to account for differences in metrics among variables and no further standardization was applied. Differences in soil variables between type of degraded sites (FIRE vs. MINE) were further assessed by nested ANOVA analyses, with factors 'Type' and 'Site' nested within 'Type'. Plant species cover in each plot was used to compute Shannon's H diversity index. Differences in plant communities between FIRE and MINE sites were further assessed by non-metric multidimensional scaling (NMDS) using Bray-Curtis distances (hereafter referred as taxonomic NMDS), and constraining solution to only two dimensions.
Shannon's index and NMDS were computed with functions diversity and metaMDS from the package vegan (ver 2. 5-1) for R (ver. 3. 4. 4) [48]. A community weighted mean (CWM) was computed for each functional trait and for each plot applying the following formula: CWM = Σ (p i × trait i ),  (2) , where p i is the relative contribution of species i to the total plant cover of the community and trait i is the trait value of species i  [21]. In the case of binary variables CWM indicates the frequency of the presence of a trait. In the case of ordinal variables, we kept the most common value in each plot. CWM has shown to be a reliable parameter that is not affected by differences in methods for the estimation of plant relative abundance or the trait values  [49]. CWMs were computed using the functions functcomp from the package FD (ver 1.
0-12) for R  [50]. The CWM data on the 15 studied plots were used to perform a second NMDS analysis (hereafter referred as functional NMDS) using Gower distance with the function metaMDS from vegan package  [48]. Solution was constrained to only two dimensions. Environmental factors (soil parameters, altitude, slope, aspect, time since disturbance) were fitted as vectors onto the taxonomic and functional NMDS ordinations using the function envfit from the package vegan  [48]. The correlation of the environmental vectors with the NMDS ordination and the p-value of that correlation were estimated by 1000 permutations. Only the environmental factors with p-values < 0. 05 were plotted onto the NMDS graphs. Differences in taxonomic diversity (Shannon's Index) between MINE and FIRE sites were compared by one-way ANOVAs.
Non-parametrical Mann-Whitney U tests were applied to compare CWM of binary and ordinal traits between MINE and FIRE plots, whereas one-way ANOVAs were used for the comparison of CWM of quantitative traits. When necessary, data were log-transformed to meet ANOVA assumptions. PCA, ANOVAs, and Mann-Whitney U tests were computed using SPSS (v. 15, SPSS Inc. , Chicago, IL, USA). Table  1. List of the plant functional traits assessed in the sampled species. For each trait we include the unit, the categories (for traits coded as binary or as ordinal variables), the associated ecological functions, and the interests for reclamation of degraded ultramafic habitats.
Plants with fixation of atmospheric N 2 were only present in MINE communities, whereas Ni hyperaccumulation was only present in FIRE plots, although low frequency of this trait made the differences between site types non-significant. Leaf traits (except Mn concentration) were similar between communities, whereas foliar Mn in MINE sites was two-fold higher than FIRE sites (Table  4 ). Compared to average values from the TRY database, our studied plant communities had lower SLA and lower foliar concentrations of N, P, Ca, and Mn, whereas foliar K were in similar ranges and foliar Mg was much higher. Table  4. CWM for 15 functional traits assessed in our study. For information on the type of variable, units and categories of each trait, please refer to Table  1. The second and the third columns present the CWM for each trait averaged for each type of site (MINE vs.
FIRE), followed by the standard deviation (in the case of binary or quantitative traits) or by the maximum and minimum (in the case of ordinal traits). The fourth column indicate the p-values. We considered significant differences when p < 0. 05. For comparative purposes, the last two columns present mean values of certain traits from the TRY database  [31]  and from an extensive study of ultramafic flora of Sabah  [32]. TRY is an international database on plant functional traits. In 2011, it contained almost 3 million trait data entries for around 69,000 plant species  [31] ; 2 average values of flora from several undisturbed ultramafic sites of Sabah. NMDS ordination of the studied plots on the basis of CWM showed that the FIRE and MINE plots partially overlap (Figure  3b ). FIRE plots occupy less area (i.
Functional Traits in Disturbed Ultramafic Habitats Plants must deal with extreme conditions in ultramafic soils, which are even harsher in human-disturbed soils  [3, 4, 11]. Our results on CWM for different functional traits allow the identification of main plant community adaptations to ultramafic stress. Despite the differences in soil conditions and taxonomic composition between MINE and FIRE sites, the CWM for most of the studied traits were similar in both types of disturbed areas. Moreover, the small differences we found were correlated to changes in soil properties (see 'functional NMDS'). In both types of disturbances, the plant communities had a conservative strategy (e. g. , slow-growing species that conserve resources)  [53]. As an example, CWM values of SLA are below the average SLA value in TRY database  [31] , and in the lower range of SLA values reported for tree species from tropical forest in Mount Kinabalu (SLA from 2.