dataesr/scientific-paragraphs-categorization huggingface dataset:
Reconciliation of double codes. To consolidate multiple responses into a single entry for each article, we used a script in R that followed a series of if/then statements (see protocol on the Nature Protocol Exchange, https://doi. org/10. 21203/ rs. 3. pex-1242/v1). A final database was compiled with a single line entry for each article. All the articles were assigned to IPCC regions based on the countries identified during coding. The final database contained 1,682 articles and 70 columns (70 data points for each article). Synthesis. Geographical mapping. We used 'geoparsers' to classify documents based on their geographic focus. Geoparsers refer to algorithms that can extract geographical place names from text, based on dictionary methods or pretrained models. We employed geoparsers to determine the country of affiliation for the first author of the paper, as well as to identify which countries or places within countries are mentioned in the abstracts.
Acknowledgements We thank the following individuals for contributions to various stages of this initiative:  C. Abbey ,  C. Alarconr ,  S. Arowolo ,  K. Christopher ,  R. Cremades ,  E. Cremin ,  K. Dave ,  S. Davis ,  D. Die ,  S. D'haen ,  S. Gruza ,  T. Harrison ,  D. Heinrich ,  F. I. Hoefsloot ,  M. Hothman ,  K. Hou ,  J. Kumar ,  R. Lama ,  A. Mahanti ,  C. McOmber ,  A. Mukjerji ,  N. Nnebe ,  M. North ,  C. Ofeogbu ,  H. Panchal ,  S. Pandey ,  A. Pasha ,  J. Pathak ,  P. Shrestha ,  D. Singini ,  A. Srinidhi ,  C. Thangata ,  V. Thimmaiah ,  A. Welles ,  K. Wroten ,  A. Yue  and  K. Zhu. We thank  T. Leuchtefeld  and the  SysRev team  for extensive technical and design support in partnering with this initiative. This work was supported by the following funding grants:  Agence Française de Développement  (A.
K. M. ),  UK Government Foreign, Commonwealth & Development Office  and the  International Development Research Centre, Ottawa ,  Canada  ( 109419-001 , N. P. S. ),  Agence Nationale pour la Recherche (French National Research Agency ,  ANR-10-LABX-14-01 , A. K. M. ),  Agence Nationale pour la Recherche (French National Research Agency ,  ANR-15-CE03-0003 , A. K. M. ),  Studienstiftung des Deutsches Volkes (P. N. S. ), UK Economic and Social Research Council  ( ESRC) Doctoral Training Partnership  ( ES/P000622/1 , project reference  2098296 , T. L. ),  National Science Foundation, Directorate for Geosciences  (no. 1935961 , E. A. G. ),  Social Science and Humanities Research Council (SSHRC)  ( 756-2021-0037 , E. K. G. ), China Scholarship Council ,  Australian National University-Climate Change Institute Supplementary Scholarship (Y.
Data availability Our a priori methodological protocol is registered (06-12-2019) and available via the OSF website  142  : https://osf. io/ps6xj. We prepared a series of detailed methods to accompany this paper via the Nature Protocol Exchange, which include: Part 1-Introduction and overview of methods (https://doi. org/10. 21203/rs. 3. pex-1240/ v1) 131 , Part 2-Screening Protocol (https://doi. org/10. 21203/rs. 3. pex-1241/v1)  132 , and Part 3-Coding protocol (https://doi. org/10. 21203/rs. 3. pex-1242/v1)  133. The data presented in this manuscript included survey extraction of information on adaptation from peer-reviewed articles.
ACKNOWLEDGEMENTS Special thanks are due to  Dávid Rédei  ( Nankai University, China ) for valuable consultations. This research was partially supported by the  Hungarian Ministry for Innovation and Technology  within the framework of the  Higher Education Institutional Excellence Program  ( NKFIH-1159-6/2019 ) in the scope of plant breeding and plant protection research of  Szent István University , the  Pannon Breeding Program  ( GINOP 2. 2. 1-152017-00042 ), and the  NKFIH OTKA  grant  FK131550.
In total, we found records for 28 spider mite species in Portugal, comprising nine Bryobiinae and 19 Tetranychinae, and including new national records for Stigmaeopsis nanjingensis and Eotetranychus tiliarium. Additionally, we record a new exotic mite species for the mainland, Eotetranychus lewisi, which was found in two localities in the Algarve District on leaves of Euphorbia pulcherrima. This is the first record for continental Europe of an established population in outdoor conditions of this regulated quarantine pest. We also comment on the presence of seven species not reported by international taxonomic databases but already recorded from Portugal: Aplonobia histricina, Eotetranychus rubiphilus, Schizonobia sycophanta, Tetranychus kanzawai and Tetranycopsis horridus (at a national level), and Oligonychus perseae and Panonychus citri (for the mainland).
Subsequent manuscripts, book chapters, academic theses and meeting abstracts were mainly published in national publications with limited distribution, making the information on the Portuguese tetranychid fauna difficult to access for international researchers. Consequently, this knowledge has been poorly disseminated, so that some species known to be present for Portugal are not recorded in international reference taxonomic databases, such as Spider Mites Web  (Migeon and Dorkeld 2020)  and Fauna Europaea  (Bolland 2020). Our objective is to complete the information on the diversity of Tetranychidae mites in Portugal, by compiling an updated list of the species known to occur in the country and synthesizing the most relevant information on their national distribution, main hosts, and damages.
2007; Mirza et al. 2020 ) and primers COI-F (5 ′ TTYGAYCCWAGAGGAGGAGG3 ′ ) and COI-R (5 ′ AAACCTARAAAATGTTGWGG3 ′ )  (Matsuda et al. 2013)  were used for the PCR. PCR reactions were performed in a 50 µl final volume using the AccuStart II Taq DNA Polymerase kit (Quanta Biosciences) and the PCR cycling conditions as reported previously by  Mirza et al. (2020). PCR products were cleaned with ExoSAP reagent (Thermo Fisher Scientific) and directly sequenced in both directions using the same primers at INIAV Sequencing Laboratory (Lisbon, Portugal) on a DNA analyser ABI PRISM 3730xl (Applied Biosystems). Sequences were compared with those available in the GenBank database using the BLAST homology search. For the taxonomic nomenclature of plants, we followed the criteria of Flora Iberica  (Castroviejo, 2012).
Molecular analysis endorsed the morphological identification: BLAST analysis of the sequences of ITS2 region (447 bp) were 99,76% and 99,55% identical to the only two E. lewisi sequences (JF774172 and EU007694, respectively) available in the GenBank. The new sequences were deposited to GenBank database (NCBI) under the accession numbers MW524100 to MW524105. For the COI region (530 bp), the BLAST analysis showed that this primer combination amplified mtDNA of the mite, but in the absence of COI region sequences for this species in the GenBank, it is not possible to determine homology. The sequences were deposited to GenBank database (NCBI) under the accession numbers MW538643 to MW538648. Voucher specimens mounted in slides are deposited in the INIAV acarological collection, reference numbers SV-19-00673, SV-19-02071 and SV-20-01735.
Discussion Overall, we compiled a total of 28 species of tetranychids for the Portuguese fauna, including several species which have been previously reported but are generally overlooked in international taxonomic databases (A. histricina, E. rubiphilus, O. perseae, P. citri, S. sycophanta, T. horridus, T. kanzawai), two species which constitute new national records (E. tiliarium, S. nanjingensis) and one new record for the mainland (E. lewisi). Four and eight species occur on the Azores archipelago and Madeira Island, respectively. Spider mites have not been reported from Porto Santo, the second inhabited Island of the Madeira Archipelago and this could be due to the absence of sampling and/or to lower availability of suitable host plants in the small, semi-desert ecosystem on the island.
Acknowledgements We would like to thank the  Portuguese NPPO, Direção-Geral de Alimentação e Veterinária (DGAV) , in the person of  Engº Nuno Barbosa , for providing data on the location and host of E. lewisi in continental Portugal. Thanks also to  Marina Cardoso  and  Márcia Santos  ( INIAV ) for assistance in obtaining bibliographic references. We also want to acknowledge  Dr. Hugh Evans  ( Forestry Commission, UK ) for reviewing the English language, and to three anonymous reviewers for the useful comments and suggestions. Work was partially funded by  Fundação para a Ciência e a Tecnologia  through the  R&D Unit UIDB/04551/2020 GREEN-IT -Bioresources for Sustainability.
Viral Sequences Treatment and Analysis A total of 70,552,236 raw paired reads were obtained and trimmed, filtered, mapped and assembled into contigs/scaffolds using CLCgenomics7. Trimming parameters allowed the selection of sequences with a minimum length of 50 nucleotides and a quality above 0. 05, resulting in 70,124,886 trimmed paired-reads. After assembly, the 828,771 contigs, with an average length of 596 bp (range 17 bp-117,982 bp), were aligned (BLASTx) against the nr database using DIAMOND  [13]. The taxonomical annotations were then determined with MEGAN (LCA top percent = 0. 1)  [14]. Contigs assigned as bacteria or eukaryotes as well as non-assigned contigs were discarded and only the contigs assigned as viruses were analyzed (107,657 contigs). Viral sequences assigned as unknown viruses or unknown environmental marine viruses (45.
Acknowledgments: We thank the  Montpellier University of Excellence  (I-site  MUSE; Project "The MOME ") and the  EC2CO program  for their financial support. We are grateful to  Antoinette Adingra ,  Justin Amendé ,  Nadège Kouadio ,  Philippe Cecchi ,  Aurélie Guillou  and the  Reunion Fishing Club of Saint Gilles les Bains , for their assistance during sampling. We thank  Alexia Bordigoni  and  Felana Harilanto Andrianjakarivony  for their help during the laboratory work and analyses of the data.
-Chromosomal analysis Chromosomes were arranged in order of decreasing size and based on the ratio of the chromosomes arm lengths (r = long arm/short arm), according to the classification proposed by  Levan et al. (1964). The chromosomes were classified as m = metacentric (r = 1-1. 7), sm = submetacentric (r = 1. 7-3), st = subtelocentric (r = 3-7), and a = acrocentric (r > 7); they were organized using Adobe Photoshop® 21. 1. 1 and measured using Image Pro Plus®. Ideograms of the NOR-bearing chromosome/chromosomes (i. e. , graphical representation of the chromosomes concerning the rDNA clusters) of the ant species were then designed with the Easy Idio software  (Diniz and Xavier, 2006). For the fluorochrome staining and FISH 18S rDNA technique, 30 metaphases from at least three individuals of each species were analyzed.
Acknowledgments We would like to thank  Dr. Cléa S. F. Mariano  for kindly providing Acromyrmex echinatior samples,  Dr. Jacques H. C. Delabie  for species identification, and  Dr. Luiz Fernando Gomes  for his valuable assistance in the laboratory with Odontomachus bauri chromosome preparations. We also acknowledge  Laboratório de Biologia Molecular de Insetos of the Universidade Federal de Viçosa (UFV)  for technical support. GAT thanks the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  for the scholarships granted. Financial support for this study was provided by " Investissement d'Avenir " grants managed by the  French Agence Nationale de la Recherche  ( DRIIHM ref. ANR-11-LABX-0010  and CEBA, ref.
e. large CV of total biomass within each local community) and provides a spatial insurance effect  (Loreau et al. 2003 , Wang and Loreau 2014 , 2016 ; Fig. 1C-D ). There have been few empirical examples of the mechanisms influencing metacommunity variability  (Wang et al. 2019 , 2021 , Wilcox et al. 2017) , partly due to a lack of theoretical development and long-term, broad-scale community datasets  (Oliver et al. 2010 , Donohue et al. 2013 , Wang and Loreau 2014 , 2016). The few empirical examples exploring such mechanisms have highlighted the importance of the taxonomic diversity among local communities (i. e. beta diversity), which has the potential to reduce spatial aggregate synchrony and decrease temporal variability at the metacommunity scale. However, all of these prior studies solely focused on aggregate variability.
Here we present a similar approach to partitioning compositional metacommunity variability based on the Hellinger distance. For a given metacommunity, we assume each local community is sampled in a similar way, such that m communities (or sites) are sampled over n time steps. During each survey, the biomass of s species is recorded. The data can be summarized as a community array X, where X itj represents the biomass of species j in community i at time t. The metacommunity corresponds to the broadest spatial scale and is defined by 1) the total metacommunity biomass obtained by summing the biomass across all communities and species and 2) a n × s time-by-species matrix containing the regional biomass of each species over time obtained by summing the biomass across all communities (Table  2 ).
Case study: understory macroalgal communities To illustrate the aggregate and compositional variability across space and time with empirical data, we focused on understory macroalgal communities inhabiting shallow rocky reefs off the coast of Santa Barbara, California, USA. From late July to early August of each year, the abundance of 55 macroalgae were recorded at 36 fixed 80 m 2 plots distributed across 11 shallow (4-12 m depth) rocky reefs. Abundances were converted to biomass density (g decalcified dry mass m -2 ) using species-specific allometries  (Harrer et al. 2013 , Reed 2018). Data were collected annually from 2004 to 2017 as part of the Santa Barbara Coastal Long Term Ecological Research program (<http://sbc. lternet. edu>; Reed 2018) and are publicly available on the EDI Data Portal (<https://doi.
2018). Considering that it is much easier to measure environmental variation than species interactions and dispersal, it is generally easy to link aggregate and compositional responses to the degree of spatial autocorrelation in environmental variation. Indeed, sets of environmental variables can now be easily retrieved at local and regional scales. Satellite data are more than ever available to a broad audience  (Nguyen et al. 2018 , Bell et al. 2020)  and environmental data layers are easily accessible through open-access datasets such as Bio-Oracle in the marine realms  (Assis et al. 2018). Moreover, geographically extensive time-series data, such as those gathered by long-term ecological research programs, have recently reached the multi-decadal durations suitable for making inferences about metacommunity synchrony through time  (Clutton-Brock and Sheldon 2010, Edwards et al.
Catch and effort data Catch and effort data for EU purse seiners operating in the EEZ of Côte d'Ivoire from 2000 to 2019 were compiled and managed by the Tuna Observatory (Ob7) of the French National Research Institute for Sustainable Development (IRD, UMR MARBEC), and the Spanish Institute of Oceanography (IEO) for the French and the Spanish fleets respectively. The raw logbook data produced by the skippers were corrected by the T3 methodology regarding total catch per set (to account for the difference between reported catch at sea and landed catch) and species composition (based on port size sampling), see  Pallarés and Hallier (1997)  and  Duparc et al. , (2020) , to generate the level 1 logbook database used in this paper. The commercial size category was used as a discriminant factor at the maturity stage of bigeye and yellowfin tuna.
Given that free schools are detected at random at the surface of the sea, the unit of effort associated with this fishing mode was expressed as the searching time (i. e. , the time spent on the fishing ground less the duration of all setting operations). In contrast, many dFADs are not encountered randomly, specifically when they are equipped with a GPS buoy and continuously tracked remotely by the purse seiner. In such a case we used the number of dFADs sets as a measurement of the fishing effort. The data were then divided into eight categories according to the species, the maturity stage and the fishing mode. Only catch and effort data from sets conducted out in the study area were selected in this study.
The resulting SST-based coastal upwelling index is characterized by a seasonal signal with peaks in the first and third quarter of the year (Fig. 2 ) Fig. 2. Seasonal variations in the SST-based coastal upwelling index in the Ivorian EEZ We applied a vector-autoregressive spatio-temporal delta-generalized linear mixed model to the catch and effort data, using the R package VAST  (Thorson, 2019). Recently, VAST has been expanded to account for seasonal and inter-annual variability  (Thorson et al. , 2020a). This allows an understanding how species distribution and abundance varies within a year by month or season, and also within a month or season across years. It offers reasonable performance even when data are not fully available for one or more combinations of years and seasons, which is common in commercial catch data.
Statistical analyses We decomposed the abundance indices into intrinsic mode functions to extract their seasonal and inter-annual components using the Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) algorithm. The CEEMDAN algorithm belongs to the broad family of Empirical Mode Decomposition (EMD) algorithms  (Huang et al. , 1998). Torres et al. (2011)  introduced this algorithm as a variation of EEMD algorithm  (Wu and Huang, 2009)  that allows exact reconstruction of the original signal and better spectral separation of intrinsic mode functions. We used the package "Rlibeemd"  (Luukko et al. , 2016)  to decompose the eight abundance indices with the CEEMDAN algorithm. See the supplementary material S1 for more details on the CEEMDAN algorithm application. The seasonal and inter-annual components of the abundance indices estimated in this paper are extracted from the CEEMDAN intrinsic mode functions (IMFs).
We ensure identifiability for   ,   (), and   () by imposing a corner constraint on the season and year effects (i. e. () = 0 and   () = 0 for the first season and year ). This corner constraint is necessary for intercepts but not spatial terms because the season and year intercepts are treated as fixed effects, while the season and year spatial terms are treated as random effects. We note that this model structure imposes no constraints on the expected "shape" of seasonal variation; that is the model can capture unimodal or multimodal distribution of abundance across seasons within a year. Parameters are estimated using release 3. 7. 1 of the Vector Autoregressive Spatio-Temporal (VAST) package  (Thorson, 2019b) , which is publicly available online (https://github. com/ James-Thorson/VAST) and runs within the R statistical environment  (R Core Team, 2017).
VAST estimates fixed effects while approximating their marginal likelihood using the Laplace approximation  (Skaug and Fournier, 2006). The Laplace approximation is implemented in turn using R package TMB  (Kristensen et al. , 2016) , and computational efficiency is improved using automatic differentiation  (Fournier et al. , 2012)  and the SPDE approximation to spatial correlation matrices (and associated projection matrices) from R-INLA  (Lindgren and Rue, 2015). Standard errors are calculated using a generalization of the delta method  (Kass and Steffey, 1989) , and standard errors are available for predictions of local density after estimating all fixed and random effects: where density can then be visualized or further processed to yield derived statistics. For example, total abundance is calculated the area-weighted sum of density (, ) predicted at a fine spatial resolution: 𝐼(𝑡) = ∑ 𝑛 𝑠 𝑠=1 𝑎(𝑠)𝑑(𝑠, 𝑡) where   is the number of fine-scale predictions and () is the spatial area associated with each prediction.
Acknowledgements This project was co-funded by  IRD (ARTS funding)  and the " Observatoire des Ecosystèmes Pélagiques Tropicaux exploités" (Ob7)  from  IRD/MARBEC. We sincerely thank the contribution of the staff of the  Ob7  for providing data on the French fleet. The authors also thank  Lorelei Guery ,  Francis Marsac  and  Hervé Demarq  ( IRD-MARBEC ) for many helpful suggestions, advices and methodological tools regarding our analyses. Our gratitude also goes to the reviewers of this manuscript for their comments, corrections and suggestions for its improvement.
Conventional PCR and sequencing To better characterise these genes, positive qPCR samples were simultaneously tested using standard PCR. The purified PCR products were sequenced using specific primers and the BigDye Terminator® version 1. 1 cycle sequencing ready reaction mix (Applied Biosystems, Foster City, CA, USA). All primers used in this study have previously been described (Supplementary Table  S2 ). Sequencing was performed on Applied Biosystems 3130 platform (ABI PRISM, PE Applied Biosystems, USA). The sequences obtained were edited and assembled using Chromas Pro 1. 77 (Technelysium Pty Ltd, Australia) and were then aligned with reference genes from the ARGANNOT site (Gupta SK, 2014) and Genbank site. These sequences are available in GenBank at accession numbers from MT475739 to MT475760 (for mcr-1), MT475761 (for mcr-3) and MT475762 (for mcr-8).
CRediT authorship contribution statement Thi Loi Dao: contributed to experimental design and writing, contributed to interpretation, data analysis and statistics, conducted the laboratory technique, administered questionnaires, followed patients and collected samples. Van Thuan Hoang: contributed to interpretation, data analysis and statistics, conducted the laboratory technique, administered questionnaires, followed patients and collected samples. Amal Magmoun: administered questionnaires, followed patients and collected samples. Tran Duc Anh Ly: conducted the laboratory technique. Sophie Alexandra Baron: contributed to interpretation, data analysis and statistics, provided technical assistance, provided technical assistance. Linda Hadjadj: contributed to interpretation, data analysis and statistics, provided technical assistance.
Naomie Canard: conducted the laboratory technique, administered questionnaires, followed patients and collected samples, administered questionnaires, followed patients and collected samples. Tassadit Drali: administered questionnaires, followed patients and collected samples. Frédérique Gouriet: contributed to interpretation, data analysis and statistics, provided technical assistance. Didier Raoult: contributed to critically reviewing and editing the manuscript. Philippe Parola: contributed to critically reviewing and editing the manuscript. Pierre Marty: contributed to experimental design and writing. Jean-Marc Rolain: contributed to experimental design and writing. Philippe Gautret: contributed to experimental design and writing, coordinated the work.
Hemicellulose is a cellulose-like macromolecule predominantly represented in the dual form of xyloglucan (( ) = 29. 2%) and arabinoxylan (( ) = 25%)  (Hopkins, 2003)  in primary and secondary plant cell walls. In order to maintain the representativeness of these two biomolecules, the ( ) value assigned to hemicellulose was estimated at 27. 1% for our study. Protein content was determined by means of total nitrogenous content quantification. Proteins are made up of amino acids whose ( ) values lie within a range of 23 to 58%. The essential amino acids were therefore analyzed in each matrix separately. For matrix A-1, the composition in % by weight is available in  Péron et al. (2018)  and the ( ) for proteins was calculated at 37%. For matrices B-1 and B-2, the amino acid composition was determined by data available in the literature  (Muztar et al.