dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016b). Beyond the culture flasks of the laboratory, this result is also observed in datasets from the geological record: A global decrease in surface ocean CO 2 is believed to explain for instance why the magnitude of the vital effects of coccoliths extracted from marine sediments has increased since the Late Miocene  (Bolton and Stoll, 2013; Hermoso et al. ,  https://doi. org/10. 5194/cp-2021-76 Preprint. Discussion started: 28 June 2021 c Author(s) 2021. CC BY 4. 0 License. (Jin et al. , 2018) , and tentatively linked to changes in surface ocean CO 2  (Hermoso, 2016). Using the inverse approach, attempts at deriving Eocene-Oligocene CO 2 concentrations from the vital effects of fossil coccoliths using transfer functions from culture data were successful in reproducing the pCO 2 curves obtained using the alkenone and boron paleobarometers  (Tremblin et al.
9 µmol. kg -  at core MD95-2037). However, the rise from 7. 3 to 9. 2 µmol. kg -  represents a 25% relative increase in CO 2 over the deglaciation in a very carbon-limited ocean, and is thus likely to trigger a strong response in coccolith metabolic rates. This is evident in Fig. 5 , which shows that the sensitivity of coccolith differential vital effects to the measured changes in CO 2 in our study is twice as large as the one derived from the datasets of Bolton and Stoll 2013 for the Late Miocene on similar coccolith size fractions 340 (see Table  S1  for details on the datasets used). The largest change in carbon and oxygen differential vital effects occurs at the end of MIS 6, from the onset to the peak of Heinrich Event 11 centred around 132 ka. In the glacial (G) ocean, during the Last Interglacial (LIG) and the Glacial Inception (GI), differential vital effects display a variability that does not seem to correspond to a change in CO 2 levels (Fig.
Financial support M. H. acknowledges financial support from the  French Agence Nationale de la Recherche (ANR) -Project  " CARCLIM"  under reference  ANR-17-CE01-0004-01. Part of this work was also supported by the  Mission pour l'Interdisciplinarité of the French Centre National de la Recherche Scientifique (CNRS)  in the framework of the  Défi ISOTOP with grant awarded to Fabrice Minoletti -Project  " COCCOTOP ".
Sampling design and environmental data To examine the spatial and temporal variations in species abundance of G. niger and its environmental drivers in the Marchica lagoon, data on monthly abundances was extracted from a scientific monitoring of the fish fauna in the lagoon between October 2015 and September 2016. The monitoring was carried out according to an optimized network of 20 stations (S1-S20) covering the whole lagoon (Fig. 1 ). The collecting gear was a purse seine of about 110 m in length and 11 m in height, with a mesh size of 6 mm. The catches were expressed by units of effort corresponding to the surface sampled: the deployment of the seine describes a circle on the surface of the water with an estimated radius of 16 meters allowing to deduce the encircled surface equivalent to 800 m².
Environmental parameters were measured at each station. Water temperature (T), salinity (S) and conductivity (Cond, mS/cm) were measured in situ using a conductivity meter ''Cond 315i/SET'', and depth (D) was recorded using an LCD Digital Sounder (HONDEX PS-7). The water pH was measured in the laboratory from water samples collected in the field using a pH meter ''IONOMETER-EUTECHINSTRUMENTS-CYBERSCAN-PH-510''. The assay of suspended matter (SM, mg/l) in water samples was performed in the laboratory by the 0. 45-μm membrane filtration method. The percentage of vegetation cover (VC: combined seagrass and macroalgae) and the nature of the substratum expressed as mean grain size (MGS, um) were also derived from the literature  (Najih et al. , 2016 , Najih et al. , 2017). Data were obtained by projection of the fish sampling points on the vegetation cover map and on the map of distribution of surface sediment facies.
Distance-based linear modelling (DISTLM) was performed to identify the key environmental drivers of the black goby's distribution pattern in the Marchica lagoon. The best overall model was selected using the BEST selection procedure with the Akaike Information Criterion (AIC) in order to reveal the significant variables influencing the observed patterns in spatial abundance  (Akaike, 1973; Anderson et al. , 2008). A distance-based redundancy analysis (dbRDA,  Legendre & Anderson, 1999; McArdle & Anderson, 2001)  was used to illustrate graphically the results. The draftsman plots, and the associated correlation matrix between all pairs of variables, were examined for evidence of collinearity  (Clarke et al. , 2014). All the multivariate analyses were performed using the PRIMER 6 software  (Clarke & Gorley, 2005).
Each DNA sequence obtained in both directions was cleaned and checked in MEGA X  (Kumar et al. , 2018)  to assemble the corresponding consensus sequence. The obtained sequences were aligned with the CLUSTAL W algorithm  (Thompson et al. , 1994)  and then each sequence was blasted  (Altschul et al. , 1990)  with the sequences of Gobius niger 16S available in NCBI GenBank to check for possible matches. To make sure that all of our 16S sequences, both the newly obtained Moroccan sequences, and those we include from other studies, cluster monophyletically, without representatives of other species, a phylogenetic tree was built including all other species of Gobius that are represented on GenBank by the targeted fragment of the 16S rRNA gene (see Supplementary material Table  S1 ). The phylogenetic tree was constructed with MEGA X using the Neighbor Joining algorithm and the Kimura 2-P substitution model with 1000 bootstrap replicates.
All sequences were confirmed to belong to G. niger (Supplementary material Figure  S1 ) and hence were included in a maximum parsimony analysis in the software package FITCHI  (Matschiner, 2016)  in order to visualize the genealogical relationships across the geographical range of G. niger. DNASP v6. 12. 03  (Rozas et al. , 2017)  was used to calculate molecular diversity indices: number of segregating sites (K), number of haplotypes (H), haplotype diversity (h) and nucleotide diversity (π). Moreover, Fu, Li's F and Tajima's neutrality tests were performed to check for possible selection or change in population demography. Pairwise FST values were calculated in ARLEQUIN v3. 5  (Excoffier & Lischer, 2010)  using 100 permutations to express the degree of genetic differentiation among sets of individuals.
Acknowledgements This research was supported by the  Special Research Fund of Hasselt University  ( BOF23BL07  to A. L. ;  BOF21PD01  to N. K. ;  BOF20TT06  to M. P. M. V. ;  BOF21INCENT09 ) and by research grant  1513419N  of the  Research Foundation -Flanders (FWO-Vlaanderen ). The sampling survey was undertaken in the framework of an international cooperation between  Le Conservatoire du Littoral, Agence de l'Eau Rhône-Méditerranée-Corse , the  Ecocean Society ,  University Mohammed V in Rabat, Institut National de Recherche Halieutique, Observatoire de la Marchica , and  Fondation Mohammed VI  pour l'environnement. M. Selfati  thanks the  "Agence de l'Eau Rhône Méditerranée Corse " and the  Ecocean Society  for their financial support. Authors are very grateful to fishers for providing Gobius niger samples from both the Marchica lagoon and the adjacent Mediterranean coast of Morocco.
At least 134 species have been identified as being impacted by marine litter in the Mediterranean Sea  [27]. MPs have consequently been observed in various species in the Mediterranean Sea  [28]  but only three species were studied in the Gulf of Lion (Clupea harengus, Sardina pilchardus, and Engraulis encrasicolus)  [31] [32] [33] [34] [35]. Thus, the aim of this study was to improve our knowledge on MP impacted fishes in the Gulf of Lion, one of the most productive Mediterranean areas. We examined the presence of MPs within 18 local and coastal fish species and assessed their concentrations and their qualitative properties such as their shapes and polymer composition. We further analyzed these data in the search for patterns between the level of MP contamination and traits of the life of the different species (i.
Polymer Analysis with Fourier Transform InfraRed Spectroscopy A total of 31 fibers and 4 fragments found in fish guts were randomly selected and analyzed with a Spectra-Tech IR-PLAN ® microscope (Spectra Tech Inc. , Oak Ridge, TN, USA) coupled to a Bomem ABB ® FTLA FTIR spectrometer (ABB, Zurich, Switzerland) in order to verify their plastic nature and to determine their polymeric composition  [42]. The identification of each polymer was performed by comparison with a self-collected spectrum database including polyethylene (PE), polypropylene (PP), polyamide (PA), polyvinyl chloride (PVC), polystyrene (PS), poly(ethylene-vinyl acetate) (PEVA), polyethylene terephthalate (PET), polyester (PES), polyurethane (PUT), acrylic (A) and some non-plastic materials such as cotton, wool, wood, paper, calcium carbonate, talcum powder and plant fibers.
Data Analysis The highest number of suspected plastics found in blank laboratory control filters (10 fibers) was subtracted to the initial sorting to correct overestimation due to laboratory contaminants. In addition, FTIR analysis was used to remove the false-positive particles, i. e. , non-plastic particles incorrectly sorted as "suspected plastic items" in our samples. An identified plastic ratio was calculated by dividing the number of particles with a real "plastic spectra" by the total number of suspected plastics. All MP results (contents and relative contents of shapes given in the following text, figures and tables) correspond to identified and spectrometrically verified MPs. Finally, mean concentrations of MPs (items fish -1 ) were estimated by dividing the corrected numbers of MP items by the number of individuals for each species.
Acknowledgments: The authors thank  Anthony Caro  and  Joaquim Claudet  ( CRIOBE ) for giving us intestinal tracts of all the fish species. We thank  Nùria Ferrer  from the " Centres Cientifics i Tecnologics " of the  University of Barcelona  for her technical support during FTIR analyses. This study was performed within the PhD of Mel Constant funded by the  "Ecole Doctorale   ED305 " of the  University of Perpignan Via Domitia.
It analyzes the economic morphology of both mechanisms and shows how they differ according to regulatory pressures for mitigation, organizational configuration of exchange, type of metric and robustness of the mechanism. Studying contrasting cases helps to reveal a central trade-off between metric complexity and economic mechanism flexibility; this trade-off is then discussed in terms of its consequences for public accountability. This paper draws on three sources of data. A first set of data was gathered in 2017 during two specific events. The first event was a oneweek conservation banking training course organized by and for environmental agency field officers in order to provide and discuss tool kits for conservation bank design and implementation. The second event was the 20th National Mitigation and Ecosystem Banking Conference held in Sacramento, California.
So does the equivalent credit. For instance, if habitat quality has improved by 75% after restoration, an 80-acre parcel of restored habitat going through certification is granted 60 credits. Habitat quantification tools are based on standardization and exhaustiveness. For instance, the Wyoming Greater Sage Grouse Habitat Conservation Tool (version 3) is a substantial 103-page document that proposes a "standardized scoring method"  (Quétier and Lavorel, 2011). Measurements are converted into scores attributed to multiple spatial and temporal scales to define habitat quality. They include four levels of spatial scale, three seasonal periods and a set of ten field attributes that are measured at varying times of the year. It also provides a detailed field data collection methodology to ensure equity and comparability in measurement.
Acknowledgements The author acknowledges, without implicating, helpful comments on earlier versions or presentations of this work by SKAT members at  Columbia University Department of Sociology, CAK  and LISIS colleagues as well as  Stefano Ponte  and  Steven Wolf. The author also wants to thank  Kevin Agblo  and  Peter Hamilton  for their help on editing. Finally, the JRS reviewers offered many valuable insights that are reflected in the final version of this article.
Sequencing was performed by Biofidal company (Lyon, France) and sequences (Bioproject number PRJEB27522) were blasted against the NCBI nr-database  [12]. DNA extracts from cave walls and collembola were used to amplify the V3-V4 region of bacterial 16S rRNA genes with primers 341F and 805R  [13] , the second fungal internal transcribed spacers (ITS2) with primers ITS3_KYO2 and ITS4  [14] , and (for cave wall DNA extracts only) the eukaryotic 18S rRNA genes with primers 18S_0067a_deg and NSR399  [15]. Illumina sequencing was implemented by Fasteris company (Geneva, Switzerland) from 1 µg of DNA extract, based on MiSeq paired-end chemistry (2 × 300 bp), with the goal of 70,000 paired reads per sample.
Bioinformatic Treatment of Illumina Sequence Data Paired-end reads were demultiplexed. The adaptors were removed, as well as all sequences whose primer-complementing regions displayed at least two mismatches with the primer sequences, using a proprietary Perl script from Fasteris company. The sequences were then merged using Fast Length Adjustment of Short reads (FLASh)  [16]  with a maximum of 10% mismatch in the overlapped region. Denoising was performed by removing all reads that did not display the expected 200-500 bp length or exhibited ambiguous base(s) (N). The sequences were dereplicated and clustered using SWARM  [17] , on the basis of a local clustering threshold level and an aggregation distance of 3 to identify operational taxonomic units (OTUs). The finer taxonomic level thus obtained was the genus or the species depending on the taxa.
Chimeras were removed using VSEARCH  [18] , as well as singletons and low-abundance sequences so as to keep only OTUs representing at least 0. 005% of all sequences  [19]. Taxonomic OTU affiliation at phylum, class, or genus/species level (with a focus on genera) was performed using RDP Classifier  [20]  against (i) the 119 SILVA database  [21]  for bacteria, (ii) the 123 SILVA database for micro-eukaryotes, and (iii) the UNITE database for fungal ITS2  [22]. The procedure was implemented in the FROGS pipeline  [23]. The sequencing datasets containing merged paired-end reads have been deposited in EBI (reference PRJEB55380). For normalization, random resampling was carried out down to 6000 (bacteria), 26,674 (micro-eukaryotes) and 21,074 (fungi) sequences per sample, corresponding to the lowest sequence number per sample to enable comparisons between cave wall samples.
Statistical Analyses The efficacy of sampling was evaluated using rarefaction curves on raw data (Figure  S1 ). OTU richness was estimated with the Chao 1 index  [24]  while alpha-diversity, which combines both OTU richness and abundance, was estimated with both the Shannon's H'  [25]  and Simpson 1-D  [26]  indices, using Paleontological Statistics (PAST) software v3. 14  [27]. Analysis of variance (ANOVA) followed with Tukey's HSD tests were carried out to compare the number of OTUs or microbial diversity indices in/or outside dark zones (p < 0. 05). A comparison of the composition of the microbial communities was achieved using a non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarity matrix  [28]  after square-root transformation of data (to avoid over-dominance effects), using PAST v3.
Acknowledgments: We are very grateful to  B. Bigaï ,  D. Chapulliot  and  A. Dubost  ( Ecologie Microbienne ) and  L. Konecny-Dupré ,  B. Kaufmann  and  C. Douady  ( LEHNA ) for help and discussion,  S. Géraud ,  J. C. Portais ,  A. Rieu  and  M. Mauriac  ( DRAC Nouvelle Aquitaine ) for key information, guidance and help,  D. Henry-Lormelle  and its restorer team for technical help with Lascaux sampling, and  Lascaux Scientific Board  for helpful discussions.
, 2017; Kuhn et al. , 2019). Disregard for the importance of the ACM choice could hamper the reproducibility of the analysis, leading to difficulties for the monitoring of vegetation or even producing flawed results. The main objective of our study is to substantiate the uncertainty induced by atmospheric correction when using Sentinel-2 data acquired over tropical forests, and to provide guidelines for optical data preprocessing in the context of biodiversity monitoring. Our results will contribute to ensuring the comparability of tropical vegetation studies, and to assessing the reliability of spectral diversity metrics. To achieve this objective, we assessed the temporal stability of Sentinel-2 images and products obtained from four ACMs, and their pairwise agreement over a short period of time.
Satellite images Level-1C images (TOA reflectance) were downloaded from the Copernicus hub (https://scihub. copernicus. eu/) for the application of three of the four atmospheric correction algorithms tested here, while Level-2A images (BOA reflectance) corrected with the MAJA processing chain were directly downloaded from the Theia data hub (https://theia. cnes. fr/). Sentinel-2 acquisitions were selected based on visual analysis over the shortest possible time period in order to minimize cloud and haze cover, as well as changes in forest properties. We identified five Sentinel-2 images from tile 33NVE with low cloud cover acquired over a period of less than five weeks in the dry season, between January and February 2018.
Overview We identified a set of atmospheric correction methods to assess the stability and agreement of remotely sensed products dedicated to tropical forest functions and biodiversity monitoring. We computed vegetation indices (3. 3) and spectral diversity metrics (3. 4) from atmospherically corrected images produced from each ACM. We then assessed the temporal stability of reflectance and higher-level metrics for each ACM (4. 1). We also measured the agreement between ACM for the computation of vegetation indices and spectral diversity metrics (4. 2). Finally, we estimated the influence of the spatial scale of analysis of spectral diversity metrics, as their computation results from data driven analysis and may be sensitive to this factor (4. 3). A flowchart providing an overview of the developed methodology to compare temporal stability and consistency between ACMs is presented in Fig.
MAJA The MACCS-ATCOR Joint Algorithm (MAJA) combines an algorithm for atmospheric correction and cloud screening (MACCS) developed by CNES, and an atmospheric correction software (ATCOR) developed by DLR  (Lonjou et al. , 2016). MAJA is based on a multitemporal approach requiring a digital elevation model (DEM) for topographic correction, Ground Image Processing Parameters (GIPP) provided by ESA for each Sentinel-2 tile, and meteorological data when available. The latest version of MAJA includes data from Copernicus Atmosphere Monitoring Service (CAMS)  (Rouquié et al. , 2017).
Spectral diversity The effort of the scientific community towards wide-scale biodiversity monitoring has led to the production of several methods aiming at estimating ecological diversity from remotely sensed metrics  (Féret and Asner, 2014; Torresani et al. , 2019; Laliberté et al. , 2020; Marzialetti et al. , 2021). Most methods linking spectral reflectance to biodiversity stem from the Spectral Variation Hypothesis (SVH)  (Palmer et al. , 2002). Féret and Asner (2014)  developed a method based on the discretization of the spectral space with an unsupervised clustering algorithm, followed by a cluster inventory to produce diversity indices compatible with taxonomic diversity metrics derived from forest inventories. This method was adapted to Sentinel-2 images, and implemented in the R package biodivmapR  (Féret and Boissieu, 2020).
Acknowledgements This research was supported by the  Agence Nationale de la Recherche (France)  ( BioCop  project -ANR-17-CE32-0001 ) and the  TOSCA program  grant of the  French Space Agency (CNES)  ( HyperTropik/ HyperBIO  project). The authors thank the  Centre National d'Etudes Spatiales  and the THEIA data and service center for distributing L2A Sentinel-2 products produced with MAJA. The authors thank the  European Space Agency  for providing free and open access to Sen2Cor. The authors thank the developers of LaSRC. The authors thank  Hervé Poilvé  and  Airbus  for allowing using Overland for this study.
Why is a database on TreMs crucial for research? Borne by only a fraction of trees within forest stands, most of TreMs are, therefore, rare events. Still, actual TreM occurrence can differ, for example, due to stand development or age, thus being more common in unmanaged old-growth forests with high structural complexity as compared to young managed forest stands. In order to perform statistically sound analyses, the need for a large and standardised dataset is evident. Therefore, large standardised datasets are needed for performing statistically-sound analyses. Having available extensive number of trees individually observed not only across a wide range of forest types and biogeographical regions, but also a variety of management intensities (from old-growth forests to recently-harvested stands), makes this database a significant contribution to this field of research.
Geographic coverage Description: The network of marteloscope plots subject to this database is almost exclusively located in Europe. It is, however, open to include plots from institutions around the world recording data based on the collection protocol for tree-related microhabitats. So far, plots are included from the following European countries: Belgium, Bosnia and Herzegovina, Czech Republic, Denmark, France, Germany, Hungary, Ireland, Italy, Luxembourg, Poland, Serbia, Slovakia, Slovenia, Spain, Sweden and Switzerland. A few datasets are also from other world regions, namely Chile and Iran. Coordinates: -41. 64 and 69. 3 Latitude; -73. 92 and 57. 31 Longitude.
Additional information The 'spatially-explicit database of tree-related microhabitats (TreMs)' comprises data of 111 plots distributed across in 19 countries and total number 42,078 trees (Fig. 2 )  (Kraus et al. 2021). The individual plots are mainly located in public and community forests, but have been established also in church forests and privately-owned forests. They were selected by the forest owners, based on representing a particular forest management type. The number of plots differs widely between countries (Table  2 ). Each individual plot is described in more detail in an information sheet which can be accessed at: http://iplus. efi. int/. The plots in Bosnia-Herzegovina (1), Chile (3) and Iran (3) were set up to monitor TreMs occurrences only and have no further site description.
Data collection in all plots followed the agreed assessment protocol for TreMs as published in  (Kraus et al. 2016). TreMs surveys were conducted from the ground using binoculars, assuring good light conditions. TreMs recording in broadleaved forest stands were implemented without foliage during the winter months. Fig. 3  provides insight into the share of the most commonly recorded trees species by genus in the TreMs database. Most common are Fagus sylvatica (37. 3%), Pinus sylvestris (10. 9%), Picea abies (8. 7%), Carpinus betulus (7. 5%) and Quercus petraea (6. 3%). When looking at trees bearing at least one TreM, we find 16,233 entities. As an individual tree may host more than one TreM, the total number of recorded TreMs amounts to 34,228. Fig. 4  gives an overview on the total number of recorded trees by countries as compared to those bearing TreMs, while Fig.