dataesr/scientific-paragraphs-categorization huggingface dataset:
Adding zero yield data In order to take into account climate conditions preventing soybean cultivation and leading to zero yields, the yield dataset was expanded by adding grid-cells located in climate zones known to be environmentally unsuitable for crop production, like deserts and arctic areas. Six climate zones from the last version of the Köppen-Geiger climate classification (available at http://koeppen-geiger. vu-wien. ac. at/present. htm 72 ) were selected, and 67 grid-cells were selected at random in each selected climate zone, so that added zero yield values represented 20% of the final dataset. The six selected climate zones are described in Table  S9 , and a map showing locations of added grid-cells is available in Figure  S23. The final dataset includes 30,337 yield data values.
Assessing model transferability in time and space The model predictive ability is first assessed using a bootstrap approach with 25 out-of-bag samples generated by bootstrap, using the train() function of the caret R package. However, recent articles have highlighted the importance of rigorous cross-validation strategies to ensure that the predictive capacity of a given algorithm is evaluated on data as independent as possible from the data used to train that algorithm  21. Here, we run two cross-validation strategies to assess transferability of the above algorithms in time and space. Transferability in time was assessed by splitting the dataset into two periods in order to assess the ability of each algorithm to predict a period of time different from the one used for the training: 1981-1995, and 1996-2010.
In a first step, each algorithm was fitted on 1981-1995 to predict 1996-2010. In a second step, each algorithm was fitted on 1996-2010 to predict 1981-1995. Transferability in space was assessed using a five-step procedure implemented for each algorithm in turn: (i) select a gridcell at random in the yield database (excluding the zero yield cells), (ii) define 7 buffer zones of different sizes (radius) around the selected grid-cell (radius values are 100 km, 500 km, 1000 km, 1500 km, 2000 km, 2500 km, and 3000 km), (iii) for each buffer zone, remove grid-cells within the buffer zone and fit the algorithm on the rest of the dataset (including added zero yield grid-cells) -note that to avoid any confusion with the size of the training dataset, the training dataset was composed of 700 grid-cells selected at random outside the buffer zone, (iv) predict the 30 years of yield for the grid-cell selected at step (i) considering each buffer zone in turn, (v) compute average error of prediction over years for the selected grid-cell considering each buffer zone in turn.
This procedure is repeated over 10 grid-cells selected at random in each country in order to estimate transferability in space for various degree of spatial proximity between the training and test datasets. Assessing transferability in space is key here because of the low number of grid-cells located in Europe in the historical soybean yield dataset (Figure  S21 ). Therefore, making projections of soybean yield in Europe will necessarily imply some degree of transferability in space of the algorithm. In both cases (transferability in space and time), predictive ability was measured by computing the root mean square error of prediction (RMSEP, t ha -1 ), and Nash-Sutcliffe model efficiency (MEF, unitless). An efficiency of 1 corresponds to a perfect match of modeled to observed data, an efficiency of 0 indicates that predictions are as accurate as the mean of observed data, whereas an efficiency lower than zero occurs when the observed mean is a better predictor than the tested algorithm.
The algorithm showing best transferability in time and space (i. e. the lowest RMSEP and highest efficiency) among ANN, RF, GAM, and MLR is used for projections of soybean yield in Europe under current and future climate. yields, hereafter referred to as the "GGCMI phase 1 dataset", and (ii) the AgMIP global fast-track climate impact assessment dataset  79  for future yields under different climate change scenarios, hereafter referred to as the "AgMIP fast-track dataset". The AgMIP fast-track dataset was obtained using the on-line tool described by Villoria et al. 80. Both datasets provide annual simulated yields for a number of crops including soybean, simulated with different Global Gridded Crop Models. In the GGCMI phase 1 dataset (historical period), 14 Global Gridded Crop Models, up to 11 weather datasets, 3 management harmonization levels, and purely rainfed and fully irrigated conditions were used in the simulations.
In the AgMIP fast-track dataset (future scenarios), 7 Global Gridded Crop Models, 4 RCPs, 5 Global Circulation Models, and two options for the effect of CO2 fertilization (with and without) were used in the simulations. The comparison with the outputs of our RF model was performed using a subset of those simulations. Selected simulations were chosen to maximize consistency with the input variables used in our RF model, and to rely as much as possible on the same crop models over the historical period and future scenarios considered. The resulting subset of selected simulations is presented in Table  S10  for the GGCMI phase 1 dataset, and in Table  S11  for the AgMIP fast-track dataset. Then, the median simulated yield over years, crop models, and (when relevant) GCMs was computed and mapped, in the same way as described above for the RF model outputs.
Potential N-fertilizer savings from soybean expansion Soybean is an N2-fixing crop which is usually not fertilized with nitrogen. Recent data collected in a farm survey in France (588 farmers) indicate that only 5% of soybean fields received mineral-N fertilization (average rate 45 kgN ha -1 ) in 2016 (www. terresinovia. fr, unpublished data). Thus N-fertilizer savings are generated when soybean replaces an N-fertilized crop like wheat. To estimate potential N-fertilizer savings if soybean production area expanded enough to reach self-sufficiency, we used published global maps of crop-specific N-fertilizer rate and harvested area for wheat, barley, maize, potato, rapeseed, sugarbeet, and sunflower (Figure  S18  and S19)  37, 38. These maps report data for around the year 2000, but represent the most detailed spatially-explicit crop-specific dataset on N-fertilization to date.
Tables Table  1. Performances assessment of the tested machine learning algorithms. Soybean yield was expressed as a function 5 climate variables (daily minimum and maximum temperatures, rainfall, solar radiation, and vapour pressure) calculated monthly for a 7-month growing season, plus the irrigated fraction in a grid-cell, making a total of 36 predictors. The whole dataset contains 30,337 yield observations from 1981 to 2010. The model predictive ability is first assessed using 25 out-of-bag samples generated by bootstrap. Then, transferability in time is assessed by fitting the algorithms on a period of time different than the predicted period, and transferability in space is assessed by ensuring a minimum spatial distance between training and test datasets, with seven minimal distances ranging from 100 km to 3000 km considered successively.
20mperature changes associated with a decrease (Group 1) or a decrease (Group 2) in projected soybean yield under RCP 4. 5 by mid-century relative to historical climate. Reported temperature values represent mean values for each group of grid-cells. Group 1: yield decrease (projected yield change < -0. 3 t ha -1 ), Group 2: yield increase (projected yield change > + 0. 3 t ha - 1 ). Groups are the same than those in the Linear Discriminant Analysis presented in Figure3, but for clarity, only Group 1 and Group 2 are presented here (see TableS6for a description of all groups and other climate variable means by group). The GRASP dataset20is used for historical climate, and the median over height Global Circulation Models 22 is shown for RCP 4. 5 by midcentury. Yield projections are performed with the Random Forest algorithm presented in Table1and FigureS2.
Acknowledgements This work was supported by the  CLAND convergence institute  ( 16-CONV-0003 ) funded by the  French National Research Agency (ANR) , by the  ACCAF INRA meta-program  ( COMPROMISE  project,  COMPROMISE_MP-P10177 ), and by the  LegValue  project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  N°727672. T. I. was partly supported by the  Environment Research and Technology Development Fund  ( S-14 ) of the  Environmental Restoration and Conservation Agency of Japan  and  Grant-in-Aid for Scientific Research  ( 16KT0036 ,  17K07984  and  18H02317 ) of  JSPS.
Methods Soybean yield, irrigated fraction, and climate data Soybean yields used in this paper are from the global dataset of historical yields updated version  18, 19. This includes grid-wise soybean yields worldwide with the grid size of 1. 125 degree, which covers the period 1981-2010. Yield values reported in this dataset result from the combination of national-scale yield statistics from the FAO, global crop calendars and harvested areas, and satellite-derived net primary production values. Therefore, the grid-cell yields are estimated values resulting from the combination of several sources of information. In this dataset, the soybean harvested areas are those of the year c. a. 2000, and are kept constant (Figure  S21 ). The geographical coverage of soybean harvested area found in  18  is relatively limited compared to other datasets  38  because in some parts of the world the crop calendar used to generate grid-cell yield estimates  65  is missing.
Regarding historical climate data, the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP) was used  20. This dataset contains monthly average of five climatic variables relevant in explaining crop growth and yield: daily maximum and minimum air temperatures at 2m, daily precipitation, daily solar radiation, and daily vapor pressure. These variables are available for the period 1961-2010 at the same spatial resolution as yield data, i. e. a grid size of 1. 125 degree. Other meteorological forcing datasets are available  66  , but uncertainties associated with different datasets are small at monthly time scale. The SPAM2005 v3. 2 dataset (available at http://mapspam. info/) was used to retrieve irrigated soybean fraction in each grid-cell  67. This dataset provides the irrigated and rainfed harvested area for a set of crops (including soybean) at the global scale for around the year 2005, at a spatial resolution of 5 arc min (~0.
Comparison with process-based crop models The soybean yield projections performed with the RF model were compared to the outputs of the Agricultural Model Intercomparison and Improvement Project (AgMIP). AgMIP is a major international effort of coordinated agricultural modeling aiming at the improvement of crop models for assessing impacts of climate change and variability on agriculture  78. Crop models used in AgMIP are process-based while our RF model is data-driven and make use of a machine learning algorithm with no explicit representation of crop physiological processes. This major difference between the two approaches makes the comparison of their outputs very relevant to assessing the robustness of our results. To perform this comparison, we used two datasets from AgMIP: (i) the Global Gridded Crop Model Intercomparison phase 1 dataset  26.
The average pore size was measured from the confocal images after being analyzed by 2-D correlation analysis using ImageJ® software, as described by  Silva et al. (2018). The use of correlation function can be performed when there is a dependence between a physical parameter over an extension. Briefly, the images were adjusted to 16 bits, where each pixel had a greyscale ranging from 2 0 (white) to 216 (black). The proteins appear as white spots while the empty regions as black spots. The unclear patterns were calculated based on a spatial collection function. If the color of a pixel located at the coordinates r → = (x, y) is set as I( r → ), the function, C( δ → ), is defined by equation (3): C( δ → ) ≡ I( r → )I( r → + δ → ) I 2 -1 (3) where the result stands for the mean of coordinates over the whole image.
Statistical analysis All measurements were carried out in triplicate unless stated otherwise. The results were shown as the average value ± standard deviation (SD). The values given in the tables and figures are the means of triplicates, and error bars indicate the standard deviation. The values having different lower-case letters within each column are significantly different. The statistical analysis was performed by one-way ANOVA with Tukey's post-test with a level of significance of p < 0. 05 using GraphPad PRISM software.
The Neogastropoda (Mollusca, Gastropoda) encompass more than 15,000 described species of marine predators, including several model organisms in toxinology, embryology and physiology. However, their phylogenetic relationships remain mostly unresolved and their classification unstable. We took advantage of the many mitogenomes published in GenBank to produce a new molecular phylogeny of the neogastropods. We completed the taxon sampling by using an in-house bioinformatic pipeline to retrieve mitochondrial genes from 13 transcriptomes, corresponding to 5 families not represented in GenBank, for a final dataset of 113 taxa. Because mitogenomic data are prone to reconstruction artefacts, eight different evolutionary models were applied to reconstruct phylogenetic trees with IQTREE, RAxML and MrBayes.
If the over-parametrization of some models produced trees with aberrant internal long branches, the global topology of the trees remained stable over models and softwares, and several relationships were revealed or found supported here for the first time. However, even if our dataset encompasses 60% of the valid families of neogastropods, some key taxa are missing and should be added in the future before proposing a revision of the classification of the neogastropods. Our study also demonstrates that even complex models struggle to satisfactorily handle the evolutionary history of mitogenomes, still leading to long-branch attractions in phylogenetic trees. Other approaches, such as reduced-genome strategies, must be envisaged to fully resolve the neogastropod phylogeny.
Introduction Mitogenomes constitute one of the best compromises between informativeness and cost (both in terms of time and money) when it comes to select a suitable set of characters to resolve phylogenies  (Zaharias et al. , 2020). If it remains not informative enough for very deep relationships and sometimes prone to long-branch artefacts  (Schrödl & Stöger, 2014; Uribe et al. , 2019) , it has been widely used in various groups of organisms, and in particular in molluscs, where it helped to clarify relationships from the species level  (Abalde et al. , 2017)  to deeper relationships between lineages separated since several hundreds of MY  (Uribe et al. , 2019; Williams et al. , 2014). And even in an era when datasets based on transcriptomes or reduced genomes are becoming more and more common, new mitogenomes are being produced regularly.
Furthermore, in gastropods, the publication of a new mitogenome(s) provides often the opportunity to produce a new phylogeny by combining the newly produced mitogenome(s) with previously published ones (e. g. Barghi et al. , 2016; Choi et al. , 2021; Harasewych et al. , 2019; Huang et al. , 2021; Wang et al. , 2021). However, such publications generally limit the scope of the phylogeny to a few mitogenomes among those available in public databases, and the phylogenetic analyses conducted are often limited to one method, and do not test for various methods and/or models in order to more thoroughly reconstruct phylogenetic relationships. One of the groups that would benefit from such a more complete analysis are the neogastropods. This crown group of the caenogastropods is composed of more than 15,000 described species, mostly predators, and their evolutionary success has regularly been suspected to be linked to their capacity to produce various molecular compounds to capture their prey, such as anesthetics, anticoagulants and neurotoxins  (Bose et al.
, 2007; Fourdrilis et al. , 2018; Harasewych et al. , 2019; Machkour-M'Rabet et al. , 2021; Osca et al. , 2015; Wang et al. , 2017; Wang et al. , 2021) ; (ii) the monophyly of a group that includes all the neogastropods except the Volutoidea (Cancellariidae, marginellids, Volutidae), the Tonnoidea and the Ficoidea  (Abdelkrim et al. , 2018; Choi et al. , 2021; Cunha et al. , 2009; Fourdrilis et al. , 2018; Harasewych et al. , 2019; Machkour-M'Rabet et al. , 2021; Osca et al. , 2015; Uribe et al. , 2021; Wang et al. , 2017; Wang et al. , 2021). In order to further improve our understanding of the neogastropod phylogenetic relationships, we propose to take advantage of the many neogastropod mitogenomes available in GenBank (359 on the 28 th of January, 2022), complemented with several mitogenomes extracted from transcriptomic data obtained from GenBank or from other projects of our team (unpub.
Sampling All the neogastropod, tonnoidean and ficoidean mitogenomes from GenBank were downloaded on the 28 th of January, 2022. A selection of mitogenomes of Littorinimorpha, and in particular the most closely related linages to Neogastropoda  (Osca et al. , 2015)  were added as outgroups. Following  Osca et al. (2015) , Ifremeria nautilei (Provannidae) was used to root all the phylogenetic trees. The family, genus and species names were updated according to WoRMS. To complement the taxonomic sampling, mitochondrial gene sequences extracted from publicly available transcriptomes or transcriptomic data produced in the framework of other projects by our team were added to the dataset. The complete list of mitogenomes from GenBank and mitochondrial gene sequences extracted from transcriptomes is provided in the supplementary material 1.
Mitogenomes extracted from transcriptomic data To complete the taxonomic sampling, 13 partial mitogenomes were extracted from transcriptomes (see Supp. Mat. 1). We designed a reference-based approach with 1 to 4 mitogenomes used as reference for each transcriptome. A suite of custom python scripts to reproduce the pipeline is available at https://github. com/Hyperdiverseproject/Neo_mitogenomes/. First, a BLASTn  (Camacho et al. , 2009)  search of the transcriptomes against the reference mitogenomes was performed and the contig with the best hit (e-value < 1e-10) was retained. In parallel, a BLASTp search was also performed. Assembled contigs were translated from transcriptomes using ORFfinder  (Rombel et al. , 2002) , and translated genes were retrieved from reference mitogenomes directly from GenBank.
Subsampling and data cleaning Single mitochondrial genes (13 protein coding genes and ribosomal genes) were extracted from GenBank mitogenomes using AnnotationBustR  (Borstein & O'Meara, 2018). Given that some lineages (species, genera) are overrepresented in the GenBank mitogenome dataset, we first performed a phylogenetic analysis (Neighbor-Joining with MEGA -  Kumar et al. , 2016)  to confirm that conspecific and congeneric mitogenomes were clustering together, and then to select one mitogenome per genus, the one corresponding to the type species and/or with less missing data. One exception is the genus Turricula, for which two available (partial) mitogenomes do not cluster together: one (extracted from a transcriptome) cluster with the other Clavatulidae, the other (Turricula nelliae spurius, MK251986) within the Pseudomelatomidae.
Given the morphological resemblance of Turricula nelliae with some members of Pseudomelatomidae (e. g. Comitas), we suspect here a misidentification. In any case, both mitogenomes were retained in the analyses. The resulting dataset comprised 114 taxa, including 10 outgroups. Each gene was then aligned using MAFFT v7. 490  (Katoh & Standley, 2013)  auto mode and phylogenetic trees were reconstructed with IQ-TREE v2. 1. 3 (Minh et al. , 2020)  with GTR+G in order to detect potential misalignments and contaminations. Indels in coding genes (when not a multiple of 3) were manually removed, as well as sequence fragments at the beginning or end of the alignment that were out of the gene ORF. Two gene fragments were also removed from the dataset: the first 600 nucleotides of the cox3 gene of JQ446041 (Concholepas concholepas) had no blast hit in GenBank; the second part of the cox1 of MW316798 (Aspa marginata) was obviously misaligned, leading to a very long branch.
Phylogenetic analyses Sequences from coding genes were translated from nucleotides to amino acids using the translateNT2AA program implemented in MACSE  (Ranwez et al. , 2011)  and then aligned with MAFFT using the G-INS-i algorithm. We use MAFFT E-INS-i for noncoding 12S and 16S. The program reportGapsAA2NT in MACSE was used to derive each nucleotide alignment from each MAFFT amino acid alignment. Finally, the 15 genes were concatenated in two ways: only nucleotides (all 15 genes) or amino acids for the 13 coding genes + 12S and 16S in nucleotides. We will refer to the two data sets as the NT (nucleotide) matrix or AA (amino acids + nucleotides) matrix. We performed a first round of analyses using IQ-TREE to determine the best fit model strategy for the NT matrix. In all cases, ModelFinder Plus (MFP) was used to select the best model of substitution  (Kalyaanamoorthy et al.
, 2017). The dataset was partitioned in eight different ways (Table  1 ): a single partition for the entire NT ("single-partition + MFP"), partitioning by gene with edge-proportional ("gene-partitioned + MFP") and edge-unlinked ("gene partitioned + MFP + edge-unlinked") partition models  (Chernomor et al. , 2016) , partitioning by codon (except for 12S and 16S) with edgeproportional ("codon partitioned + MFP") and edge-unlinked ("codon partitioned + MFP + edge-unlinked") partition models, partitioning by codon (except for 12S and 16S) with a selection of the best-fit partitioning scheme by merging partitions with edge-proportional ("codon partitioned + MFP + MERGE") and edge-unlinked ("codon partitioned + MFP + MERGE + edge-unlinked") partition models, by using the GHOST model with four classes, unlinked branch lengths, substitution rates and inferred base frequencies ("GHOST")  (Crotty et al.
Since there was no strong argument to support one or the other, we decided to run RAxML-ng and MrBayes on the NT matrix using both the codon partitioned + MFP and the codon partitioned + MFP + MERGE. The AA matrix data was run with the same strategy for IQ-TREE, RAxML and MrBayes; a gene partitioned approach was used and ModelFinder identified the best fit model for each partition. Unfortunately, the AA matrix did not work with 12S and 16S in IQ-TREE due to a software error mentioning running out of RAM (and reported to the IQ-TREE google group). For both the NT and AA matrix, when the model of substitution selected by ModelFinder did not exist in either RAxML or MrBayes, we converted the model into the equivalent model available in each respective software. RAxML was run using the "all-in-one" analysis flag implying the use of bootstopping criterion  (Pattengale et al.
Results The final dataset included sequences of 10 outgroups, 11 Tonnoidea (5 families), 1 Ficoidea (1 family) and 91 neogastropods (36 families) representatives. Among them, incomplete mitogenomes (between 8 and 13 genes each -Genbank accession numbers: available soon) were recovered from transcriptomes for 13 taxa, representing 10 families, including 5 (Colubrariidae, Olividae, Personidae, Pisaniidae, Turbinellidae) that were not represented in GenBank. However, 26 families of neogastropods and 4 families of Tonnoideans considered as valid in WoRMS are still not represented in our dataset. Eight trees were obtained and will be compared (Fig.