dataesr/scientific-paragraphs-categorization huggingface dataset:
Nitrogen-fixing forbs are protein-rich compared to C 4 grasses and thus nutritionally valuable for both grazers and browsers  (60). For example, Indigofera spp. were eaten by all 17 species in Laikipia and were among the top foods overall there and in Serengeti (Datasets S1-S10). The extensive use of forbs by savanna grazers highlights the functional importance of this often overlooked growth form  (60, 75, 76). Generality of Food Plant Partitioning in Space and Time. In support of our second prediction, we detected interspecific differences in diet composition at the assemblage level, between pairs of grazers and browsers, among close relatives, and, indeed, in 97% of all 723 pairwise comparisons. This typically resulted in discrete clusters of samples in ordinations, but even when such clusters overlapped, intraspecific variation helped to separate species' average diets.
But these herbivores also ate different species: The tree Euclea divinorum was >20% RRA for giraffe and kudu but just 1% for dik-dik; the tree Senegalia mellifera was >20% RRA for giraffe and dik-dik but just 1% for kudu; and the small shrubs Melhania ovata and Plicosepalus sagittifolius were 9% RRA for dik-dik but 0% for giraffe and kudu (Datasets S1-S4). Similarly, zebra, wildebeest, and Thomson's gazelle in We included all bouts at repeatedly sampled sites, owing to substantial within-site variability in the identity of species sampled and their mean grass RRA (Fig. 2  and SI Appendix, Fig. S5 ); in general, the across-site trends are also qualitatively evident within sites. Serengeti all ate the grass Digitaria macroblephara (18%, 25%, and 5% RRA in May-June 2018), as per the grazing succession model  (35).
Yet, the low-lying annual forbs Euphorbia inaequilatera and Monsonia angustifolia dominated gazelle diets (61% RRA) but were just 9% RRA for wildebeest and 0% for zebra; the grasses Themeda triandra and Sporobolus fimbriatus together were 15% RRA for zebra but just 4% for wildebeest; and a half-dozen forb species (Indigofera, Euphorbia, Pentanisia, Monsonia, and Phyllanthus spp. ) were 29% RRA for wildebeest but just 4% for zebra (Datasets S5-S10). Species at similar positions on the grazer-browser spectrum  (18)  thus have multiple nonredundant paths to dietary differentiation, perhaps explaining why interspecific differences in sward height and patch size selection are not always clear-cut  (46, 47). The consistency of plant partitioning probably reflects interspecific competition in both modern  (22, 35, 36)  and evolutionary time  (24, 37, 77) , although diet data alone cannot prove it  (78).
At Addo, extracellular DNA was extracted in the field (98) and transferred to Universit e Grenoble Alpes. For all samples, the P6 loop of the trnL(UAA) intron (  58 ) was amplified by PCR, purified, and sequenced on an Illumina HiSeq, but protocols and data processing differed for Addo vs. other sites (SI Appendix, Text S1). We curated sequence data using OBITools  (99). We performed taxonomic assignment using both local reference databases (from Laikipia, Serengeti, and Gorongosa) and a global reference database from the European Molecular Biology Laboratory (Addo sequences were assigned only to a local database). Unique sequences retained after filtering and accounting for ≥1% RRA per sample were considered molecular operational taxonomic units (mOTUs, "taxa"). We generated one sample × mOTU matrix per sampling bout to calculate the RRA of each mOTU per sample.
To assess relationships between rainfall and the mean grass RRA of species and assemblages, we extracted daily rainfall from the Climate Hazards Group InfraRed Precipitation with Station data (101) as a raster file with gridded 0. 25°resolution, using heavy-Rain (102); for each bout, we calculated the centroid of sample collection locations and used that point to calculate total rainfall during the 90 d before the onset of sampling. To analyze dietary niche differences, we calculated the Bray-Curtis compositional dissimilarity index between each pair of samples in each bout and contrasted interspecific vs. intraspecific dissimilarity at each site. We visualized these patterns using NMDS for each site and bout, as well as for subsets of species in each of the eight best-sampled sites (excluding Hwange and Kruger) to contrast 1) the species with the two highest and two lowest mean grass RRA values and 2) closely related sympatric species.
To evaluate assemblage-level niche overlap, we calculated Pianka's index  (104)  for each pair of species based on their population-level average diets and then calculated the mean ±1 SE across all pairs using EcoSimR (105). We calculated weighted bipartite modularity of each network using the DIRT-LPAwb+ algorithm  (106) , selecting the maximum value from 10 iterations of the algorithm. We calculated nestedness as weighted NODF (107) in bipartite  (108). Data, Materials, and Software Availability. Raw and filtered sequencing data and R code are deposited in Dryad Digital Repository  (109).
ACKNOWLEDGMENTS. We thank the  Governments of Kenya, Malawi, Mozambique, South Africa, Tanzania, Zambia , and  Zimbabwe , and, specifically,  Mpala Research Centre, OI Jogi Conservancy, Malawi 's  Department of National Parks and Wildlife, Mozambique's National Directorate of Conservation Areas ,  South African National Parks, Ezemvelo KwaZulu Natal Wildlife , the  Tanzania Wildlife Research Institute, Zambia 's  Department of National Parks and Wildlife, and Zimbabwe Parks and Wildlife Management Authority. We thank  D. Druce ,  W. Mgoola , and  C. McBride  and their staff,  J. Montenoise ,  E. Sichali ,  M. Stalmans ,  Mariri Environment Centre staff , and park rangers for support and assistance. This research was funded by the  NSF  (grants  IOS-1656527 ,  DEB-1457697 , and  BCS-1461728 ), the  Cameron-Schrier and Greg Carr Foundations , the  High Meadows Environmental Institute 's  Grand Challenges program ,  Princeton University, National Geographic  (grant  NGS-52921R-18 ),  Agence Nationale de la Recherche  (grant  ANR-16-CE02-0001-01 ), the  National Research Foundation  (Grant  88167 ), the  PROTEA South Africa-France Science and Technology Cooperation (National Research Foundation  Grant  85062  and  Minist ere Franc¸ais des Affaires Etrang eres  Grant  2973PM ), the  Laboratory of Alpine Ecology (Grenoble, France) , and  Nelson Mandela University.
3. Community-level dietary dissimilarity of large herbivores in 10 savanna ecosystems. NMDS ordinations visualize dietary dissimilarity (Bray-Curtis metric) within and among species. Points correspond to individual fecal samples; points farther apart are more dissimilar. Ellipses show 1 SD. Stress value and PNAS 2022 Vol. 119 No. 35 e2204400119 https://doi. org/10. 1073/pnas. 2204400119 5 of 12 perMANOVA testing for significant dissimilarity among all species are shown in each panel. For repeatedly sampled sites, we chose one illustrative period from among those with the largest number of species and samples; data from all sampling bouts at these sites are shown together in SI Appendix, Fig. S6. Panels are ordered from northernmost (top left) to southernmost (bottom right) site. (A) Laikipia, Kenya, July 2016 (n = 149 samples, 10 species; wet season, 90-d rainfall 156 mm); (B) Serengeti, Tanzania, February-April 2018 (n = 129 samples, 8 species; wet season, 90-d rainfall 205 mm); (C) Nyika, Malawi, August 2017 (n = 137 samples, 6 species; dry season, 90-d rainfall 71 mm); (D) Niassa, Mozambique, August-September 2017 (n = 134 samples, 6 species; dry season, 90-d rainfall 0.
Summary data here are based on 3,547 fecal samples from 30 large-herbivore species represented by ≥10 samples per bout (of 3,928 total samples analyzed). Species are listed in order of increasing body mass [from panTHERIA (110) ]. Several sites were sampled repeatedly; we show the number of sites sampled, number of sampling bouts, and sample size for each species. For each species in each bout, we calculated the population-level mean RRA of grasses and legumes (rounded to integer percent values), along with population-level dietary richness and diversity; these data are shown as ranges spanning all sites and sampling bouts for each species (site-and bout-specific tables are in Datasets S1-S24). Dietary richness and Shannon diversity (here based on the complete set of samples collected in each bout, elsewhere rarefied to n = 10 for comparative analysis) were not calculated for Addo because methodological differences precluded comparable estimates with other sites (Methods); thus, two species sampled only in Addo (common duiker and bushpig) lack values for these metrics.
New information This paper describes the ZNIEFF national dataset, which comprises 1 013 25 synthesised data for various animal (38%), vegetal (59%) and fungus (2%) species in terrestrial and marine zones (the last download took place on 26 May 2020). A total of 19 842 sites throughout continental France as well as in the overseas departments and territories (Guadeloupe, Martinique, Mayotte, La Réunion, French Guiana, Saint-Martin, Saint-Barthélemy and Saint-Pierre-et-Miquelon) are included in the ZNIEFF dataset (last download: 26 May 2020). This dataset is now available in open access. All data were collected by skilled naturalists using professional protocols over almost 40 years. They consist mainly of observations of rare, threatened or endemic species, all validated by regional experts.
This program is conceptually similar to the Key Biodiversity Areas (KBA) initiative developed on the international level (IUCN 2016), but with a more regional and national scale of issues. The ZNIEFF inventory is included in the National Inventory of Natural Heritage (INPN) (Pon cet 2013) as part of the national information system for sharing observational data on biodiversity in France (Système d'Information sur la Nature et les Paysages, SINP). The ZNIEFF species dataset is therefore now available in open access in both national (INPN-OpenObs) and global (GBIF) biodiversity web platforms.
General description Purpose: The inclusion of a site in the ZNIEFF inventory is based on the presence of a species or associations of species of high ecological value (named 'determinant species' in the ZNIEFF terminology). The presence of at least one determinant species makes it possible to create a ZNIEFF. The data on remarkable species and habitats in the area are collected, analysed and synthesised  (Horellou et al. 2017). There are two types of ZNIEFF. Type I ZNIEFFs are ecologically homogeneous areas, defined by the presence of species, associations of species or habitats that are rare, remarkable or characteristic of the regional natural heritage. Type II ZNIEFFs usually correspond to larger areas that integrate functional and landscaped natural zones. Type I ZNIEFFs can be included in type II ZNIEFFs  (Horellou et al.
Data related to other (non determinant) species are also collected during field surveys and generally included in the dataset, given that it is useful for understanding the global ecological functioning of the ZNIEFF or to anticipate global anthropogenic threats or more local impacts (e. g. extension of the range of species due to climate change, urbanization). Only data concerning species (not habitats) will be presented in this datapaper, as habitat data do not yet benefit from viewing and downloading tools on the national level. There is a particular case of species with 'confidential distribution'. This concerns a limited number of species in a given region that are particularly 'sensitive', i. e. subject to harmful human activity, and for which the availability of data is likely to increase the likelihood of the harmful activity occurring  (Chapman and Grafton 2008).
This may concern species which are threatened, rare or and of high heritage interest and for which the dissemination of information represents (in the regional context) a risk of targeted destruction. This dissemination of information could also seriously affect the conservation status of the population of a species or the habitat that shelters it. The confidentiality of a species must however remain exceptional and is assessed on a case-by-case basis on the regional level by the scientific council. The corresponding data are excluded from the free diffusion dataset on the INPN website and the GBIF portal.
Project description Personnel: In order to guarantee the consistency of the information, data is collected using a common framework  (Horellou et al. 2017 ) defined jointly by a scientific and technical coordination team based in the French National Museum of Natural History (UMS PatriNat) and the Ecology Ministry. The observations are transmitted by the wide-ranging naturalist network in France, including independent professional and amateur naturalists, associations for nature studies and protection, public institutions, contractors (private sector), learned societies, etc. The Regional Scientific Councils for Natural Heritage (CSRPN) validate the local inventories in close conjunction with the regional services of the Ecology Ministry and their scientific secretariat 'ZNIEFF' before transmission to the MNHN for validation to ensure its technical and scientific consistency.
Sampling methods Sampling description: Sampling is focused on areas of high biodiversity identified on the regional scale. These zones (19 842 registered as ZNIEFFs on May 2020; Table  1 ) are pre-identified based on the knowledge of field naturalists, often through the primary study of vegetation, but occasionally also on abiotic characteristics (e. g. a moor on a serpentine substrate). The subsequent inventory of various taxa on these sites helps to confirm (or not confirm) their value for conservation. ZNIEFF data come mainly from direct field observations, but can also rely on literature sources, collections and other databases. A great variety of sampling methods and protocols are used to collect data, depending on the environment and the taxon concerned.
Quality control: The dataset presented in this publication is managed by UMS PatriNat (OFB/CNRS/MNHN), the unit also responsible for the National Inventory of Natural Heritage (INPN). The INPN is part of the SINP, i. e. the French national information system for sharing observational data on biodiversity. This information system guarantees the traceability of data, authorship and normalized standards of data and metadata. Before integration and dissemination on the INPN website, a series of validation checks are systematically performed  (Jomier et al. 2019). The first category of checks consists of validating compliance with standard data and metadata formats, i. e. all mandatory fields completed, use of required formats, repositories (including the geographic and taxonomic repositoires), classifications and lists of values.
The second category of checks consists of validating the consistency (i. e. the absence of logical incompatibility) within the data, within the metadata and between the data and the metadata. As an example, the observation start date must be less than or equal to the observation end date. ZNIEFF data are updated two times a year. The INPN provides the data directly to the GBIF portal in order to make the data available on the international level. All taxa are identified by experienced naturalists prior to the validation by the regional scientific councils (CSRPN). The dataset producers are responsible for the reliability of the identification.
The map of each ZNIEFF can be accessed independently (e. g. https://inpn. mnhn. fr/zone/ znieff/930012348). Geographic data can be downloaded in the form of GIS layers (3 available formats) and webservices (WMS and WFS): Type I -continental ZNIEFF: https://inpn. mnhn. fr/telechargement/cartes-et-informationgeographique/inv/znieff1 Type II -continental ZNIEFF: https://inpn. mnhn. fr/telechargement/cartes-et-informationgeographique/inv/znieff2 Type I -marine ZNIEFF: https://inpn. mnhn. fr/telechargement/cartes-et-informationgeographique/inv/znieff1_mer Type II -marine ZNIEFF: https://inpn. mnhn. fr/telechargement/cartes-et-informationgeographique/inv/znieff2_mer Coordinates: ;.
gbif. org/dataset/0e61f8fe-7d25-4f81-ada7-d970bbb2c6d6). More than 35 000 taxa (species and subspecies) have been inventoried in ZNIEFFs to date, which represents approximately 19% of the number of species currently listed in France  (INPN 2019). The taxa targeted by the ZNIEFF inventory usually have high conservation value on the national and supra-national levels (taxa which are geographically localized, endemic, at the limit of the range, threatened on the regional, national or international level, etc. ). The taxonomic groups with the highest number of taxa inventoried are, in decreasing order, angiosperms (13 312), insects (8 823), fungi (3 033), birds (1 503) and fish (1 128) (Table  2, Figs 2, 3, 4 ). These groups have a significant percentage of endemic or subendemic taxa and species assessed as threatened according to the national Red List (Table  2 ).
Three taxonomic groups have more than 100 000 records, namely angiosperms (559 081), birds (153 047) and insects (144 237) (Fig. 2 ). Among the group of insects, the orders Lepidoptera, Odonata and Coleoptera are the most represented in the dataset (Fig. 3 ). Five groups have more than 10 000 data (mammals, pteridophytes, fungi, amphibians, fish and mosses) (Fig. 4 ). The remaining groups have less than 1 000 records (Fig. 5 ).
Temporal coverage Notes: The data span the years 1757 to 2019. The ZNIEFF inventory was officially launched in 1982, but data prior to this date have been taken into account to justify the ecological value of some areas. There were two inventory phases (i. e. 'generations' according to the ZNIEFF terminology). The first generation took place between 1982 and 1995 while the second generation lasted from 1995 to 2014  (Hore llou et al. 2017). These phases corresponded to important methodological changes. Since 2017, the ZNIEFF inventory is continuously updated (no more 'generations'). The effort put into field surveys and data collection in regional information systems was very important until 2010 (Fig. 6 ). Following this tremendous collection effort, the subsequent sampling effort on the regional level became more qualitative, mainly dedicated to determinant species and taking into account the territorial specificities in terms of natural habitats and naturalistic networks.
Despite the drop observed in Fig. 6 , new data are incorporated into the dataset every year, with national validation taking place twice a year. The full scientific name of the family in which the taxon is classified amateur naturalists, associations for nature studies and protection, public institutions, contractors (private sector), learned societies, etc. We also wish to thank DREAL/DEAL/ DRIEE IDF/DAF, ZNIEFF managers on the regional level and all the Regional Scientific Councils (CSRPN) whose role is essential to guarantee the scientific validity of the ZNIEFFs. The Ecology Ministry is here thanked for its support and monitoring for almost 40 years. Finally, we thank Jean Ichter, who helped on the data quality section, and Mathieu Clair, Nicolas Boulain, Benjamin Ledentec, Sophie Pamerlon and Sylvain Morin from the "Data" and "IT applications" teams of UMS PatriNat (OFB/CNRS/MNHN) and GBIF France.
Usage licence: Other IP rights notes: This project is licensed under a Creative Commons Attribution -NonCommercial 4. 0 International Public License (CC BY-NC 4. 0). Data resources Data package title: Données d'occurrences Espèces issues de l'inventaire des ZNIEFF Resource link: https://doi. org/10. 15468/ikshke Alternative identifiers: http://ipt. gbif. fr/resource?r=inpn_inventaire_znieff Number of data sets: 1 Data set name: Données d'occurrences Espèces issues de l'inventaire des ZNIEFF Character set: Unicode (UTF-8) Download URL: http://ipt. gbif. fr/resource?r=inpn_inventaire_znieff Data format: Darwin Core Archive Description: The description of the ZNIEFF dataset for species is also available on the INPN website (https://inpn. mnhn. fr/espece/jeudonnees/7907). The ZNIEFF dataset can be downloaded from the INPN-OpenObs portal and downloaded from the GBIF portal (data as csv files or a Darwin Core Archive).
It is important to mention that the X, Y coordinates correspond with the centroid of the zones. date on which the resource was changed (YYYY) language In English and French (en | fr) datasetID The identifier for the dataset institutionCode The name in use by the institution having custody of the information referred to in the record basisOfRecord The specific nature of the data record informationWithheld The field indicates whether the taxon is regionally sensitive, i. e. the data should not be disseminated specifically to avoid harm to the taxon. This is a different level of sensitivity from that explained in the General description of the datapaper. This regional sensitivity implies blurred data. dataGeneralizations When the data is considered sensitive (cf. informationWithheld), this field indicates the blurring applied to the data.
1% formic acid and (B) acetonitrile + 0. 1% formic acid at a rate of 0. 45 mL/min. The gradient was: 1-min isocratic with 98% A, a 25-min linear decrease to 2% A, 5-min isocratic with 2% A and a 4-min equilibration time with 98% A. IMS-ToF-MS data-independent acquisition was carried out in positive (ESI+) and negative (ESI-) modes between 100 and 1000 Da (HDMS E mode). Samples were analyzed in randomized triplicates to minimize the intra-sequence variability caused by the instrument. Data were acquired and analyzed using UNIFI software (Waters). All detected features (i. e. , triplets of the m/z ratio, retention time and ion mobility drift time) were aligned across the samples and exported as a csv file for further processing (i. e. , visualization of HRMS fingerprints and multivariate statistical analyses) in R software (R Core Team, v.
Derivation of ethnicity variables is described in Additional file 1: Supplementary information S4. The overall sample size for MR analyses ranged from 2659 to 5085 for ALSPAC, 1566 to 2262 for BiB South Asians and 1339 to 1992 for BiB White Europeans. The sample sizes for confounder adjusted MV estimates were somewhat smaller due to missing confounder data (1884-3265 for ALSPAC, 325-449 for BiB South Asians and 442-604 for BiB White Europeans). To enable comparison between the confounder adjusted MV estimates from models that adjusted for different covariates, we fitted all the models for each outcome using an identical sample with non-missing data for all relevant variables.
Offspring anthropometric variables Offspring outcomes included BW and BMI at age 1 and 4 years (in ALSPAC and BiB), BMI at age 10 and 15 years (ALSPAC only) and fat mass index (FMI) at age 10, 12, 14, 16 and 18 years (ALSPAC only). The assessment of these outcomes is described in Additional file 1: Supplementary information S5 and Additional file 1: Supplementary information S6 and included extraction of measurements from routine data sources (birth records/notifications, child health records, primary care records and school nurse records), clinical measurement by research staff or UK Government National Child Measurement Programme (NCMP) staff and maternal/ offspring questionnaire responses. In ALSPAC, we calculated FMI as fat mass (kg) / height (m) 2 using fat mass measured by whole body dual-energy X-ray absorptiometry (DXA) (Additional file 1: Supplementary information S5).
We took a complete case approach and excluded individuals with any missing data; therefore, models one to three were fitted using identical samples. In sensitivity analyses, we adjusted all models for gestational age at delivery, and for 20 genetic principal components (PCs) which we calculated from genome-wide SNPs separately for each of the three samples (Additional file 1: Supplementary information S8), in order to adjust for ancestry. In BiB, we had to exclude a large number of individuals from the main MV models due to missing paternal BMI data. We therefore refitted models one and two without first excluding individuals with missing paternal BMI data (i. e. on larger samples), in order to explore potential selection bias.