dataesr/scientific-paragraphs-categorization huggingface dataset:
a b s t r a c t Habitat loss and degradation due to global agriculture land use is a major threat to biodiversity. Identifying agricultural management practices that mitigate these impacts is urgently needed. Thousands of experiments have been conducted worldwide in the last decades to compare the impacts of various agricultural management practices on biodiversity. The magnitudes of difference in biodiversity responses between pairs of agricultural practices, i. e. effect sizes, have now been synthesised in a growing number of meta-analyses. Yet, each meta-analysis generally focuses on a specific type of farming practice and on specific taxonomic groups, or a single region. Meta-analyses could furthermore yield different or sometimes opposite results for the similar research questions. Gathering all the effect sizes in one single dataset helps to critically assess and weigh the available evidence across all studied practices, taxonomic groups and geographical areas, and provide stakeholders a solid base to better inform their decisions.
Here, we present a comprehensive dataset of 200 published meta-analyses gathering 1885 effect sizes based on more than 14 0 0 0 primary studies. We detail the effect of 8 main individual field practices (e. g. pest and disease management, amendment and fertilisation), 3 agricultural systems (e. g. organic farming, conservation agriculture) and 2 landscape level interventions (i. e. landscape complexity, land-use change). Our dataset covers numerous taxonomic groups over 14 phyla, including animals (e. g. birds, insects), microorganisms (e. g. fungi, bacteria), plants (e. g. trees, weeds). The dataset presented provides a resource to support decision-makers, farmers, and conservation ecologists alike for managing agricultural land for biodiversity. © 2023 Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.
g. Intervention type, Biodiversity group, Vote-counting effect…). -Table "Glossary & coding": lists the entries in the table "Effect sizes and qualitative data" for the columns related to the Intervention and mode of comparison, the biodiversity Outcome and the metrics of the effect sizes, and gives a definition for each item when appropriate. -Table "Test-list": in 39 rows, lists the meta-analyses used to make a preliminary test of the performance of the literature search; in 5 columns their metadata. How the data were acquired Following a systematic review procedure, we performed a search in four scientific databases to retrieve all relevant peer-reviewed meta-analyses quantifying the effects of agricultural management practices in croplands on biodiversity. We obtained a total of 6709 references from which 2555 duplicates were removed.
Two screening steps were carried out: (i) title and abstract, to exclude articles that clearly failed to meet our inclusion criteria, (ii) full-text of all remaining articles to further exclude non-relevant articles. Article eligibility was based on pre-defined inclusion and exclusion criteria reflecting the objectives of our study. Finally, within each retained meta-analysis, we identified the effect sizes that present the effects of an agricultural management practice on biodiversity and we manually extracted their related qualitative data (e. g. , Intervention, Comparator, Moderator(s), Biodiversity Outcomes).
Data format Raw Description of data collection After the two-steps screening process, 200 meta-analyses met our inclusion criteria and were retained for subsequent analysis. Data were extracted and archived in an Excel spreadsheet. The selection process was undertaken by two curators, and the consistency of their decision was assessed with a Kappa test. The retained meta-analyses were characterised by their bibliographic metadata (e. g. publication title, authors, etc. ). The effect of agricultural management practices on biodiversity were characterised by: (1) the type of intervention (e. g. individual practices such as fertilization or tillage, agricultural systems such as organic agriculture, etc. ) and their comparators (e. g. natural habitat, conventional agriculture, etc. ), (2) the included study characteristics or effect moderators (e.
Value of the Data (1) We provide an up-to-date standardised catalogue of existing meta-analyses evaluating the effects of all agricultural management practices on biodiversity. (2) The database contains effect size qualitative data providing information on the impacts of a large range of farming practices on terrestrial biodiversity across the globe. (3) The database can be used to build an evidence map of the effects of various agricultural management practices on terrestrial biodiversity in croplands, highlighting knowledge gaps and knowledge clusters in terms of available evidence syntheses. (4) The structure of the different tables included in our databases facilitates the inclusion of the results of future relevant meta-analyses. (5)  The structure of our database also allows for inclusion of quantitative data for each effect size, which may be analysed in a second-order analysis.
Objective The objective of this study was to collect and describe the available evidence in terms of meta-analyses dealing with the impacts of agricultural management practices on terrestrial biodiversity at the global scale. We present the precise methodology used to conduct the review and extract qualitative data, following recent guidelines and standards in order to increase transparency, reproducibility and FAIR principles. Our rigorous reporting methods and dataset structure should allow for the process of updating i. e. including new references (rows) and/or new descriptors of each reference (columns).
Search string(s) The search string was built during a scoping exercise in Web of Science (see §4. 2. 3) for use in publication databases, we present it in Table  2. Search terms, languages and restrictions. Sensitivity is favoured over specificity. Sensitivity implies that the emphasis of the search procedure is put on collecting the largest selection of relevant articles at the risk of also obtaining a high number of non-relevant articles (hence increasing the duration of the screening steps). We thus firstly built a relevant keywords listreflecting the PICO-C components -that we then used for building an initial search string list. Main differences between search strings and refining. We initially wrote a search string to question WoS and Scopus search engines that share similar search equation writing rules.
Publication databases searched, search engines, and supplementary searches The following four multidisciplinary databases and search engines were used for the literature search: 1. Web of Science (WoS) Core Collection (producer: Clarivate Analytics, USA), coverage is from the year 1900 to the present day. Multidisciplinary, peer-reviewed. url: https://www. webofscience. com/wos/ 2. Scopus (producer: Elsevier, The Netherlands). Coverage is from the year 1800 to the present day. Broad, multidisciplinary peer reviewed. url: https://www. scopus. com/search/form. uri 3. Ovid (producer: Wolters Kluwer group, USA), coverage is from the year 1946 to the present day. Broad, multidisciplinary, peer reviewed. url: https://ovidsp. ovid. com/ Ovid aggregated results from two databases: (i) Cab Abstracts (producer: CABI), emphasising international agricultural literature, and (ii) Agricola (producer: Department of Agriculture, USA), indexing a wide variety of agricultural and allied fields literature.
ab. Google Scholar * * (meta analysis OR "quantitative review") AND (crop * OR agr * OR land * ) AND (system OR practice OR fertil * OR intensi * OR till * OR ecol * OR complex * OR amend * OR diversifi * ) AND (biodiversity OR species OR population * OR communit * OR biota OR richness) 4. Google Scholar (producer: Google, USA). Broad, multidisciplinary peer reviewed and grey literature. We focused on the first 400 search results, thus doubling the threshold suggested by Haddaway et al. [6]  organised by relevance. url: https://scholar. google. com/ Google Scholar search was performed using the free Publish or Perish software (producer: Anne-Wil Harzing, www. harzing. com). We performed WoS, Scopus and Ovid searches through our CIRAD institutional access. We completed additional bibliographical searches as following: 5.
Managing duplicates To identify duplicates between publication sources, we used the function find_duplicates (arguments: match variable "title", match function "stringdist", method "lv") in the revtools R package  [9]. This function scans the closely-written records titles and produces a variable that we added in the records table in a new column. We used R for Windows v. 4. 1. 0 (R Core Team, 2021) and RStudio Desktop v. 1. 4. 1717 (RStudio Team, 2021). We then manually verified the records table to validate (or not) the flagged duplicates by revtools.
Data extraction & coding The first step of data extraction consisted in extracting the metadata (title, authors, date…) of the articles and was carried out alongside reference extraction and management during the screening process. This step allowed us to fill the table "Included studies" and "Excluded studies". The second step consisted in extracting -within each included article -each effect size representing a relevant Intervention * Outcome combination, which allowed us to fill the table "Effect size and qualitative data". Each effect size was qualified and quantified with data describing notably the intervention, the comparator, the moderators, the biodiversity outcome (taxon and metric). Moderators are qualitative or quantitative information on cofactors that may influence the Outcome; they may concern for example the geographical extent of the meta-analysis, the climate concerned by the effect size, the crop species, the soil parameters, etc.
We present the principal entries available in our database for the intervention type, the taxonomic group and the biodiversity metric, for each effect size, in Fig. 5. Each of the meta-analysis presents mean effect sizes but also factors that may influence the value of these effect sizes' Outcome. In the case of meta-analyses, such factors are called Moderators. In our dataset, we noted and provided the qualitative characteristics of each mean effect size and we extracted all moderators presented in each meta-analysis. Thus, Moderators that are present in our dataset are the ones presented by the authors of each meta-analysis. When coding our database, we harmonized the wording among Moderators and grouped them by topic for clarity. When subgroup analyses were presented by a meta-analysis, we avoided extraction of redundant effect sizes by extracting in priority: effect sizes with the most detailed biodiversity information (e.
Acknowledgments This research is a product of the " Agri-TE " group funded by the synthesis center  CESAB  of the  French Foundation for Research on Biodiversity  (FRB; www. fondationbiodiversite. fr). The  Agri-TE  project ( ID 2002-016  ) was funded through  LabEx AGRO 2011-LABX-002  (under  I-Site Muse framework ) coordinated by  Agropolis Fondation. Within the Agri-TE group, authors are thankful to (by alphabetical order):  A. Avadi ,  N. Casajus ,  A. Chaudhary ,  G. Damour ,  N. Estrada-Carmona ,  S. K. Jones ,  D. Makowski ,  M. Mitchell  and  R. Seppelt , for their involvement as members of the project's scientific committee.
Data availability A global database to quantify the impacts of agricultural management practices on terrestrial biodiversity (Original data) (Dataverse) CRediT Author Statement Jonathan Bonfanti: Conceptualization, Methodology, Investigation, Data curation, Writingoriginal draft; Joseph Langridge: Validation, Writing -review & editing; Damien Beillouin: Conceptualization, Methodology, Investigation, Data curation, Writing -review & editing, Supervision, Funding acquisition.
Data source locationRaw data from meta-analyses: the list of all retrieved references from searches performed is provided. All data extracted from retained articles in accordance with our systematic review protocol are provided in Excel spreadsheets. Dataset (Excel spreadsheets) are hosted on the online repository "CIRAD Dataverse" by research unit CIRAD, UPR HortSys, F-34398 Montpellier, France. Data accessibility Repository name: CIRAD Dataverse Data identification number: Bonfanti, Jonathan; Beillouin, Damien, 2023, "A global database to quantify the impacts of agricultural management practices on terrestrial biodiversity," https://doi. org/10. 18167/DVN1/RIRTOT , CIRAD Dataverse, V3, UNF:6:UOGjnVxAQPIZxRTMtaOV3w == [fileUNF] Direct URL to data: https://doi. org/10. 18167/DVN1/RIRTOT ).
Fecal sample collection To study the effects that brood size may have on the nestling gut microbiome and its links to individual nestling body mass, survival to fledging and apparent juvenile survival, we used a subset of data from a larger experiment (Cossin-Sevrin et al. , unpublished data). In this subset, we use individuals from which fecal samples were collected on day 7 after hatching and analyzed for microbiome diversity and composition (C = 23 nestlings/15 nests, COU = 22/13, E = 23/15, R = 24/16) We aimed to collect two samples (one from original and one from foster nestlings) per nest. Fecal samples from the nestlings were collected gently by stimulating the cloaca with the collection tube. Samples were collected straight into a sterile 1. 5 ml Eppendorf tube to avoid possible contamination of the sample.
DNA extraction and sequencing We chose two samples per nest for DNA extraction, yet in such a way that both fledged and not-fledged nestlings would be included in the dataset. DNA was extracted from nestling fecal samples using the Qiagen QIAamp PowerFecal Pro DNA Kit (Qiagen; Germany) following the manufacturer's protocols. Additionally, we included negative (RNAse and DNAse free ddH 2 O) controls to control for contamination during DNA extraction and additional controls to confirm successful amplification during PCR. A short fragment of hypervariable V4 region in the 16S rRNA gene was amplified using the purified DNA samples as template with the following primers: 515F_Parada (5'-GTG YCA GCMGCC GCG GTAA-3') and 806R_Apprill (5'-GGA CTA CNVGGG TWT CTAAT-3')  [77, 78]. PCRs were performed in a total volume of 12 µL using MyTaq RedMix DNA polymerase (Meridian Bioscience; Cincinnati, OH, USA).
Sequence processing All statistical analyses were performed with R (v. 4. 11. 0; R Development Core Team 2021) unless otherwise stated. The demultiplexed Illumina sequence data was first processed with Cutadapt version 2. 7  [80]  to remove locus-specific primers from both R1 and R2 reads. Then, the DADA2 pipeline (v. 1. 24. 0; [81] ) was used to filter the reads based on quality, merge the paired-end (R1 and R2) reads, to define the single DNA sequences i. e. , Amplicon Sequence Variants (henceforward ASV), and to construct a 'seqtab'. Seqtab is a matrix also known as otutable or readtable: ASVs in columns, samples in rows, number of reads in each cell, using default parameter settings. In total, our seqtab consisted of 6,929,537 high-quality reads. Reads were assigned to taxa against the SILVA v132 reference database  [82]  resulting in 8658 ASVs.
To control for contamination, negative DNA extraction and PCR controls were used to identify contaminants (60 ASVs) using the decontam package (v. 1. 12;  [83] ) and all were removed from the dataset. Sequencing runs (replicate PCR's) were merged using the phyloseq package (v. 1. 32. 0) and non-bacterial sequences (mainly Chlorophyta) were removed from the data as they were not of interest in this study resulting in a total of 6566 ASVs (a total of 4,107,028 high-quality reads in all samples; mean per sample: 15,155. 085; mean range per sample: 0-97,264). Singleton reads were removed from the dataset by the DADA2 pipeline. Data was further analyzed with the phyloseq package (v. 1. 32. 0; [84] ), and the microbiome package (v. 1. 18. 0; [85] ) and visualized with the ggplot2 package (v. 3. 3.
6; [86] ). The final dataset contained 92 samples from great tit nestlings resulting in a total of 3,161,696 reads (mean per sample: 34,366. 261; mean range per sample 108 -189,300 reads), which belonged to 6,505 ASVs. The dataset was then rarefied for alpha diversity analyses at a depth of 5000, as this was where the rarefaction curves plateaued (see Additional file 2). The rarefied dataset contained 4,791 ASVs in 88 samples. For beta diversity, the unrarefied dataset was used after confirming that the beta diversity statistics were quantitatively similar for the rarefied and unrarefied datasets. Bacterial relative abundances were summarized at the phylum and genus level and plotted based on relative abundance for all phyla and genera. A Newick format phylogenetic tree with the UPGMA algorithm to cluster treatment groups together was used to visualize sample relatedness (see Additional file 3) and was constructed using the DECIPHER (v.
Nestling body mass First, to analyze whether brood size manipulation affected nestling body mass in the C, E, and R treatment groups, we ran two linear mixed-effects models with the lme4 package (v. 1. 1-29;  [91] ). In these models we used either body mass on day 7 or 14 as the dependent variable and brood size manipulation treatment, hatching date, body mass on day 2 post-hatch and original brood size as predicting variables. Hatching date is used as a predicting variable because it is known to affect nestling body mass during the breeding season  [92]  and there were differences in hatching date between the COU and other treatment groups (see Table  1 ). We included the interaction between original brood size and brood size manipulation treatment in both models as the effect of manipulation may depend on the original brood size.
Alpha diversity For alpha diversity analyses, which measures withinsample species diversity, we ran two linear mixed-effects models with the lme4 package (v. 1. 1-29;  [91] ) to measure if either brood size manipulation or manipulated brood size as a continuous variable were associated with gut microbiome diversity. We used two alpha diversity metrics: the Shannon Diversity Index, which measures the number of bacterial ASVs and their abundance evenness within a sample, and Chao1 Richness, which is an estimation of the number of different bacterial ASVs in a sample. Both metrics were used to check if alpha diversity results were consistent across different metrics. Each diversity index was used as the dependent variable at a time and either brood size manipulation treatment or manipulated brood size as a predicting variable.
Short-term survival To explore whether alpha diversity associated with survival to fledging (i. e. , short-term survival) and with apparent juvenile survival in Autumn 2020 (i. e. , midterm survival), we used generalized linear models with binomial model (v. 1. 1-29; lme4 package,  [91] ), and then tested the significance of factors with type 2 ANOVA from the car package (v. 3. 0-13;  [93] ). Type 2 ANOVA was used because the model did not contain interaction between predicting and there was no order between covariates, as they could not be ranked. Survival to fledging and recapture in Autumn 2020 were used as the binomial response variable (yes-no) in each model. Alpha diversity (Shannon Diversity Index and Chao1 Richness) was the main predicting variable, and weight on day 7 post-hatch (same time as sampling the fecal gut microbiome), hatching date and manipulated brood size were included as covariates in the model.
We did not include brood size manipulation treatment in the survival models as not enough birds from each treatment group were recorded for fledging and juvenile survival. Moreover, we excluded random effects from this model as the model failed to converge. 65 nestlings fledged successfully, while 8 nestlings were found dead in nest boxes. For 15 nestlings we had no fledging record, so these were excluded from the survival to fledging analysis. In apparent juvenile survival, 19 birds out of 92 (with data on microbiome diversity) were recaptured as juveniles. For all analyses, the R package car (v. 3. 0-13;  [93] ) was used to test Variance Inflation Factors (VIFs) and the package DHARMa (v. 0. 4. 5; [94] ) to test model diagnostics for linear mixed-effects and generalized linear models.
Beta diversity For visualizing beta diversity, i. e. , the similarity or dissimilarity between the treatment group gut microbiomes, non-metric multidimensional scaling (NMDS) was used with three distance matrices: Bray-Curtis  [95] , weighted UniFrac, and unweighted UniFrac  [96]. Permutational multivariate analysis of variance (PERMANOVA) using the Euclidean distance matrix and 9999 permutations was tested with the R package vegan (adonis2 function; v. 2. 6-2;  [97] ) to investigate if any variables affected to the variation in gut microbiome composition. Nest of rearing was set as a blocking factor in the PERMANOVA to control for the non-desirable effects of the repeated sampling of foster siblings. The test for homogeneity of multivariate dispersions was used to measure the homogeneity of group dispersion values.
Moreover, there was no significant interaction between alpha diversity and manipulated brood size (χ2 = 1. 268, df = 1, p = 0. 260; see Additional file 9). However, apparent juvenile survival was negatively associated with hatching date (χ2 = 4. 654, df = 1, p = 0. 031; see Additional file 9). Additional analyses to check for the consistency of results were tested the following way: survival to fledging with nestlings from the COU group removed and apparent juvenile survival without the nestlings with no recorded survival for fledging (see methods). These results were quantitatively similar as in the whole dataset for both Shannon Diversity Index (survival to fledging: χ2 = 2. 285, df = 1, p = 0. 131; apparent juvenile survival: χ2 = 1. 515, df = 1, p = 0. 218; see Additional file 11) and Chao1 Richness (survival to fledging: χ2 = 0.
Acknowledgements We would like to thank  Charli Davies ,  Petri Papponen ,  Sari Viinikainen  and  Elina Virtanen  at the  University of Jyväskylä  for their help with bioinformatics (CD) laboratory optimizations (PP, SV and EV), as well as  Jorma Nurmi ,  Robin Cristofari ,  Natacha Garcin ,  Toni Laaksonen  at the  University of Turku  and volunteer bird ringers for their help in the field.
Funding Open Access funding provided by  University of Jyväskylä (JYU). This study was funded by the  Emil Aaltonen Foundation. NCS was supported by  EDUFI  Fellowship (Opetushallitus). AS was funded by the  Turku Collegium for Science and Medicine , who contributed to fund the field study. AS acknowledges funding from the  European Commission   Marie Sklodowska-Curie Postdoctoral Fellowship (#894963 ) at the time of writing.
For 4 samples, the resulting solution was then filtered through Anodisc™ filters for analysis, and for the last two samples, a densimetric separation was thirdly conducted using a NaI solution (ρ = 1. 65 g/cm 3 ) and a JAMSS unit  [31]  during 24 h to improve the MP recovery. The supernatant was also filtered through Anodisc™ filters for analysis. MP detection and quantification were performed over the entire filter with a µFTIR (Thermo Scientific Nicolet iN10, Waltham, MA, USA) using a transmission mode, a spectral range of 4000 to 1200 cm -1 and a pixel resolution of 25 × 25 µm. Maps were then analyzed using the SiMPle analysis free-to-use software developed by Aalborg University, Denmark, and the Alfred Wegener Institute, Germany (v. 1. 1. β, [32] ). This software allows a comparison of IR spectra with the reference database based on 1st and 2nd derivatives.