dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , "Cabinet of Curiosities") rather than belonging to the local available aquatic biodiversity. The few algae were also excluded they are not portrayed with the same fidelity as the fauna. The size of specimens was not retained because their relative lengths are not necessarily respected in the paintings. Every taxon was characterized by its biogeographical status and habitat group using the Ocean Biodiversity Information System (OBIS) database and the French Freshwater fish guide  (Keith et al. 2020)  with the following nomenclature: Biogeographical Status (European; Atlantic and North Sea; Mediterranean; Introduced); https://www. ecologyandsociety. org/vol26/iss4/art26/ and Habitat Group (Continental; Migratory and Pelagic Amphihaline; Sea; Sea-Benthic; Sea-Demersal; Sea-Pelagic). When these data were available, trophic level, maximum size, weight, and lifespan were determined for fishes using FishBase (https://www.
General distribution of the taxa across the paintings For regions and periods alike, we analyzed the distribution of taxa in two steps: (i) first, main distribution trends were highlighted using multivariate analyses and then, (ii) the relationship between these trends and the biological and biogeographical characteristics of the species were analyzed using statistical tests and comparisons of proportions. In order to highlight the distribution of taxa across the paintings, we used the non-normalized principal component analysis (PCA) available in FactoMineR  (Lê et al. 2008). PCA is a dimensionalityreduction method that aims to determine the principal components of a data set, based on the variance of combined variables. We used this method herein to visualize how well the principal components fit the variables of interest (region and period), and thus to identify a pattern in the representation of taxa across space and time.
General distribution of paintings in space and time The 73 paintings have been attributed to 41 artists who were active in two regions, the Atlantic Ocean-North Sea and the Mediterranean Sea (Fig. 2a ) during three periods (the 16th, 17th, and 18th centuries (Fig. 2b ). Further to the distribution of the artists by city within the two regions, we identified two schools of art: the Flemish School (related to the Atlantic and North Sea) and the Italian School (related to the Mediterranean). We partitioned the 73 paintings as follows (Table  1 ): those representing Atlantic-North Sea species (n = 44), and those depicting Mediterranean species (n = 29). They date from the 16th (n = 23), the 17th (n = 28), and the 18th (n = 22) centuries. Note that the dataset is rather unbalanced regarding the geographic origin of the paintings.
Identified groups In order to identify the most representative species of the geographical and temporal distribution of the different taxa, we performed a first PCA using all the taxa of the dataset (explained variance: Axis 1 = 10. 82 %, Axis 2 = 7. 90%). We then selected the 20 taxa with the highest explained variance of the previous PCA (80% of the explained variance) and performed a second nonnormalized PCA by using these 20 selected taxa (explained variance: Axis 1 = 20. 09 %, Axis 2 = 15. 98%, Fig. 5a ). Axis 1 was mainly explained by taxa represented in (i) Mediterranean paintings from the 18th century, and (ii) Atlantic-North Sea paintings from the 16th and 17th centuries (99% of the explained variance). This suggests that the abundance of Atlantic-North Sea taxa decreased from the 17th.
Acknowledgments: Taxa identification was carried out by Mireille and  Jean-Georges Harmelin ,  Sébastien Biton ,  Marine Briand ,  Mélanie Ourgaud , and  Sandrine Ruitton  from the  Mediterranean Institute of Oceanography (Marseille, France) ,  Laurence Le Diréach ,  Patrick Astruc ,  Adrien Goujard , and  Elodie Rouanet  from the  "Groupement d'Intérêt Scientifique" (GIS) POSIDONIE (Marseille, France) ,  Gaël Denys  and  Caroline Mougne  from the  National Museum of Natural History (Paris, France) ,  Catherine Dupont  from the  CNRS, UMR CReAAH (Rennes, France) , and  Yves Gruet  (retired), from the  University of Nantes (France). We would like to thank the fine arts and archeology museums, which responded positively to our request for the complete identification of the species in our selection of paintings:  Musée du Louvre de Paris, France, Musée d'Orsay de Paris, France, Musée des Beaux-Arts de Rouen, France, Musée Saint-Loup de Troyes, France, Musée des Beaux-Arts de Caen, France, Musée d'Art et d'Archéologie de Senlis, France, Musée Condé de Chantilly, France, Musée de Boulognesur-Mer, France, Musée Quentovic d'Etaples, France, Musée Sandelin de St-Omer, France, Musée d'Archéologie d'Antibes, France, Musée dArt Moderne André Malraux du Havre, France ,  Musée Picasso de Paris, France ,  Museu Nacional d'Art de Catalunya, Spain ,  Museo Bellas Artes de Sevilla, Spain, Museu de Belles Arts de Valencia, Spain, Gallerie dell'Accademia di Venezia, Italia , the  Rhode Island School of Design Museum, USA , and the  Metropolitan Museum of Art of New York, USA.
We specifically thank  Philippe Costamagna , Director of  Musée Fesch d'Ajaccio, France , and  Annick Lemarrec , librarian for the same museum, for specific information on the painter  Alessandro Di Pesci. We finally Ecology and Society 26(4): 26 https://www. ecologyandsociety. org/vol26/iss4/art26/thank the  State Hermitage Museum of St-Petersburg, Russia , the  Rhode Island School of Design Museum, USA , and the  Musée Maritime de l'Ile Tatihou, France , for their permission to publish paintings from their collections. The study was funded by the  Fond Epicurien de Provence , by the  French ECCOREV (Continental Ecosystems and Environmental Risks) Research Federation , by the  Excellence Initiative of Aix-Marseille University -A*Midex , a  French "Investissements d'Avenir programme "  AMX-19-IET-012  through the  Mediterranean Institute for the Environmental Transition ITEM , by the  Rhône Mediterranean Corsica Water Agency  ( Pierre Boissery, Agence de l'Eau Rhône-Mediterranée-Corse ), and by the  Fondation de France  through the  programme "Quels littoraux pour demain ?.
For more details, see  Togola et al. (2014). Mass spectrometry was performed on a triple quadripole (Quattro Premier XE, Waters) in Multiple Reaction Monitoring (MRM) fitted with an electrospray ionisation (ESI) interface and controlled by MassLynx software. Mass spectrometry conditions were previously described  (Togola et al. , 2014). Briefly, positive and negative polarity modes were used simultaneously during the same analytical run. The limits of quantification for DZ and OXA were evaluated at 4 µg kg -1 of fresh soil. The half-life dissipation time (DT50) for DZ and OXA was calculated with a first-order kinetic model.
Concerning ITS2 gene for fungi, a portion of the ITS2 gene was amplified using the specific primer set w404 CTTTCCCTACACGACGCTCTTCCGATCTTGTGARTCATCG AATCTTTG/w40 GGAGTTCAGACGTGTGCTCTTCCGATCT TCCTCCGCTTATTGATATGC  (Hadziavdic et al. , 2014). PCR reactions were performed using the AccuStart II PCR ToughMix kit, followed by cleaning (HighPrep PCR beads, Mokascience), and pooled triplicates were submitted for sequencing on Illumina MiSeq instrument at GeT-PlaGe (Auzeville, France). Fastq files were uploaded to the Galaxy web platform (Sygenae), and the public server at usegalaxy. org was used to analyze the data, with the FROGS analysis platform  (Escudié et al. , 2017). For 16S rRNA gene sequences, affiliations were obtained using the Silva 138_16S database. For ITS2, Unite_Fungi_8. 0_18112018 reference database was used for OTUs affiliation.
The affiliation tables and sequence counts can be found in Supplementary Tables  1, 2. The sizes of reads for R1 and R2 (forward and reverse) were 250 bp. For trimming, the FROGS Pre-process merging, denoising, and dereplication tool was used (Galaxy Version r3. 0-3. 0). Mismatch rate was set to 0. 1 (maximum rate of mismatch in the overlap region of R1 and R2 in consensus sequences), and merge calculations were performed using Flash software for an expected amplicon size of 413 bp (min 340 bp and max 450 bp) for 16S rRNA genes, and 389 bp (min 267 bp and max 511 bp) for highly variable ITS2 region extracted using the FROGS ITSx tool (Galaxy Version r3. 0-1. 0). Then, clustering was performed using FROGS Clustering swarm amplicon sequence clustering tool (Galaxy Version r3. 0-1. 4) with denoising.
The aggregation distance clustering was set to a maximum of 3 bp for inclusion into clusters. To remove the PCR chimera in each sample, the FROGS Remove chimera tool was used (Galaxy Version r3. 0-7. 0). This tool is based on the VSEARCH tool for chimera detection and removal  (Rognes et al. , 2016). Alpha diversity indices were calculated from the NGS data. The first index corresponds to the observed number of OTUs in data from 16S rRNA gene and ITS2 region sequencing. The Chao1 index estimates the unobserved OTUs by correcting values with OTUs that are observed only one or two times in data (correction for the rarest OTUs). The Shannon and Inverse Simpson indices give an indication of the specific richness in the samples, by linking the number of species with the specific abundance of these species in the different samples.
Tax4Fun data were also generated from FROGS platform, and potential metabolic pathways were recovered by comparison of the 16S rRNA gene sequences with the KEGG database  (Aßhauer et al. , 2015). For the 19 samples, only 1. 79-2. 91% of OTUs could be mapped to KEGG organisms. All 16S rRNA gene sequences and ITS2 sequences were submitted to European Nucleotide Archive (ENA) and are available under accession numbers ERS7290419 (SAMEA9567684) to ERS7290437 (SAMEA9567702). The whole project is referenced under accession number PRJEB47338.
24) and NAD(P)Hquinone oxidoreductase subunit 6 (EC:1. 6. 5. 3), suggesting that once biodegradation is completed, the soil returns to its original functions. If tax4fun provides a means to further exploit 16S rRNA genes dataset and draw hypotheses on the functional capabilities of microbial communities, results should be taken with caution as they remain only predictive and depend on functional information available from public genome databases. Other functions decreased compared to the native soil (Table  2 ), but to a lower extent. Indeed, no function decreased lower than 100% compared to the native soil, and only 0. 2-3. 2% were decreased up to 50% compared to the native soil. This highlights that soil functions were conserved after benzodiazepines addition.
FUNDING We thank European Commission for financial support through the H2020 AquaNES EU project under agreement number no. 689450. The authors also gratefully acknowledge the financial support provided to the PIVOTS project by the Centre -Val de Loire Region  (ARD 2020 program and CPER 2015 -2020)  and the French Ministry of Higher Education and Research (CPER 2015-2020 and public service subsidy to BRGM). Support from the European Union via the European Regional Development Fund is also acknowledged. Finally, the authors wish to thank BRGM for the financial support on the internal TOFIDEM project and funds received for open access publication.
Statistical Analyses Data were statistically analyzed by the MIXED procedure of SAS program (SAS version 9. 0, SAS Inst. Inc. , Cary, NC, USA) according to the model: Yijkl = μ + CTi + LSj +Pk + Al + eijkl (1) where Yijkl = individual observation of measured traits, DMY (kg), MMY (kg), TME (min), TMD (min), MMD (min), AMF (kg/min), PMF (kg/min), μ = overall mean, CTi = the fixed effect of milk flow type (i = 1 to 3), LSj = the fixed effect of lactation stage (j = 1 to 3), Pk = fixed effect of parity (k = 1, 2), Al = the random effect of the animal (k = 1 to 32) and eijkl = random error. Significant differences between least square means were determined with.
Statistical Analyses Data were statistically analyzed by the MIXED procedure of SAS program (SAS version 9. 0, SAS Inst. Inc. , Cary, NC, USA) according to the model: Yijkl = µ + CTi + LSj +Pk + Al + eijkl (1) where Yijkl = individual observation of measured traits, DMY (kg), MMY (kg), TME (min), TMD (min), MMD (min), AMF (kg/min), PMF (kg/min), µ = overall mean, CTi = the fixed effect of milk flow type (i = 1 to 3), LSj = the fixed effect of lactation stage (j = 1 to 3), Pk = fixed effect of parity (k = 1, 2), Al = the random effect of the animal (k = 1 to 32) and eijkl = random error. Significant differences between least square means were determined with the PDIFF (Pairewise Difference by least significant difference) test. Significance was declared at p < 0. 05, otherwise stated. The χ 2 test was used to evaluate group differences of bimodality trait and curve type.
The Transport Reaction Model The Mediterranean Sea biogeochemical model OGSTM-BFMv5 is based on the software detailed in  [22, 23]  and is off-line coupled to the 1/16 • version of the Mediterranean Copernicus Marine Environmental Monitoring Service (CMEMS, http://marine. copernicus. eu) physical system  [24]. The physical forcing data needed to compute the transport due to water mass circulation include the 3D horizontal and vertical current velocities, vertical eddy diffusivity, potential temperature, and salinity and 2D surface data for wind stress. These datasets were produced by the CMEMS Mediterranean Monitoring and Forecasting (MED-MFC) system. The horizontal resolution is approximately 6 km and there are 72 vertical levels with 3 m resolution at the surface, reducing to 300 m at the deeper levels.
The Biogeochemical Model The biogeochemical model here adopted (Biogeochemical Flux Model (BFM)  [25] ) has been used to study primary producers' biogeochemistry  [17] , alkalinity spatial and temporal variability  [26] , and CO 2 fluxes  [27] , and has been corroborated using in-situ data for operational purposes within CMEMS  [23]. A full description of the BFM model equations can be found in  [25]. The BFM model has been expanded in the present configuration adding CDOM dynamics by assuming that in DOC production only a fraction of the produced DOC is optically active. The carbon dynamics in the BFM phytoplankton module are determined by the rates of primary production, respiration, excretion, and predation. We assign for each production process of DOC the corresponding fraction of optically active molecules (CDOM): this is facilitated by the modular structure of BFM.
Dataset for Model Validation Rrs is presented in two forms: as derived in  [28]  valid just below the sea surface, so called Rrs(0-), and computed considering a further correction  [36, 37]  to derive Rrs(0+) that is valid above the sea surface. The latter is the most appropriate to be compared with satellite observations. In the following sections we keep both Rrs(0+) and Rrs(0-) to evidence the sensitivity of such correction in terms of Rrs determination.
Dataset for Model Validation Remote sensing reflectance is the main satellite product for ocean color applications as it represents the primary variable for the derivation of other bio-optical and biogeochemical products, such as chlorophyll a concentration (total Chl and phytoplankton functional types (PFTs)), diffuse attenuation coefficient for downwelling irradiance (Kd490), as well as inherent optical properties (IOPs), e. g. , phytoplankton light absorption aph(443), absorption of detrital and dissolved organic matter adg(443), and particulate optical backscattering bbp(443). To extend the existing time series beyond that provided by a single satellite sensor, the European Space Agency (ESA) has in recent years promoted the Ocean Color-Climate Change Initiative (OC-CCI), a multi-sensor, global, ocean-color product, mainly devoted to climate applications  [38] , that merges observations from four different sensors: SeaWiFS, MODIS, MERIS, and VIIRS.
Chlorophyll Spatial Distribution Global and regional biogeochemical models are usually validated with respect to satellite ocean color (OC) products. The present model can provide chlorophyll both as model phytoplankton physiological units (i. e. , state variable of the phytoplankton functional type) and/or as a derived quantity from Rrs output, by applying the same empirical formulas used by remote sensing. The model data comparison will be based on the model phytoplankton physiological unit  [17]  since it allows the direct investigation of phytoplankton dynamics and ecosystem functioning. While the model simulates specific chlorophyll for each of the four PFTs, we compared the total model chlorophyll with the corresponding satellite product. The dataset used for the comparison is the CMEMS satellite product that adopts a regional algorithm specifically calibrated for the Mediterranean Sea  [39, 44].
Clearly, the novel bio-optical model has the potential to investigate water masses with higher optical complexity, as the cases of the marginal Seas, however this would require a specific study that should consider additional factors such as terrestrial input of particulate substances, optically active DOM  [47] , and the effects of sea floor reflection. Bio-optical constituent accumulation and consumption is strongly correlated with seasonal cycling, an example is the excretion of CDOM modulated by primary production. Therefore, information related to average Rrs spectra, as shown in Figure  6 , can be augmented by analyzing its seasonally variability. In particular, considering that much of the bio-optical absorption takes place in the blue wavelengths, the 412 nm band is the most informative radiometric parameter available in the considered validation dataset.
In particular, in these algorithms the accumulation of chlorophyll is determined by a reduction of Rrs (at a given wavelength according to the specific algorithm) normalized versus a reference measured Rrs at 555 nm (a sort of blank) that is less affected by chlorophyll absorption. In our results we focus on Rrs at 412 nm because it is most interesting for CDOM dynamics and we observe similar dynamics: an increase in phytoplankton and CDOM results in an increase in absorption and a corresponding reduction in Rrs. Table  1. Validation metrics of Rrs and chlorophyll for the three experiments. Data and model are spatially aggregated for the year 2017 over the sub-basins shown in Figure  4. Metrics are temporal correlation for Rrs(0+) at 412 nm, 443 nm, and 490 nm, with respect to CMEMS Rrs ocean color (OC) product  [41] , and RMSD (units mgChl/m 3 ) with respect to CMEMS satellite chlorophyll product  [39].
This correction does not impact the seasonal variability of the signal but changes the model solutions from a condition of overestimation in the blue to an underestimation of Rrs. Moreover, it introduces an underestimation in the Rrs at 490 nm. The formula is based on solutions determined with the state-of-the-art Hydrolight software  [51] , and it would important to evaluate if specific corrections are needed for an optically anomalous area like the Mediterranean Sea and/or if the discrepancy in the Rrs results can be related to intrinsic approximations of the three-stream approach. An interesting aspect of the present simulations is that the explicit definition of different physiological processes in the Med-BFM biogeochemical model allows us to assign them specific optical properties (CDOM fraction) and to evaluate their impact on a measurable quantity such as Rrs.
III. 1. 3. Sampling and storage recommendations Sediment collection is the most important step when gathering representative data at each sampling site. An adapted sampling strategy must be considered, taking into account the selection of the sampling sites, the number of replicates, and the distance between samples and replicates. Studies on MP distribution (as a discontinuous variable), require the collection of at least five replicates at each location, with a minimum distance of 100 m between them  121, 122. Clean steel tools (washed and rinsed with Milli-Q water) are strongly recommended for sediment sampling. Sediments should be preserved in clean glass containers, placed at cold temperatures (around 4 ⁰C) and be rapidly transported back to the laboratory. Storage temperatures must be respected until analysis.
However, contact-analysis is one drawback of this mode, because the crystal interface may affect another particle when measured directly on the filter. On the other hand, single measurements are possible but require the sorting of particles (hand picking in general, with limitations of particle size). FTIR-imaging has been widely used and large databases are now available  135. An MCT detector (mercury cadmium telluride) was used in early study  110  , despite being size limited (150-200 µm) and very time consuming  136. An FPA detector (Focal Plane Array) was then developed for MP studies in the environment  133. Size constraints for the FPA detector are as small as 11 µm, but the method is still time consuming and generates very large amounts of data  83. For example, Bergmann et al. analyzed 166 mm² of filter area, and obtained results with 1.
Acknowledgements. This study has received funding from the  European Union 's  Horizon 2020 Research and Innovation program  under grant agreement No  634476  (project acronym TREASURE). IRTA thanks CERTA funding. Authors thank  Jaume Jaume  ( Semilla-Caib ), who supported the organization of the study in Mallorca Island;  Florence Garcia-Launay  and  Ludovic Brossard  ( PEGASE, INRAE, 35590 Saint-Gilles, France ) and  Alexandre Fonseca  ( Consortium Noir de Bigorre ) who participated in the organization of the French case study and realization of the focus-groups. E. A. Rivera-Toapanta  is a recipient of a doctoral fellowship awarded by the  INIA  ( Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria ). This work has been carried out within the framework of the  Doctorate Program in Food Sciences  of the  Autonomous University of Barcelona.
Study area The transboundary Tafna watershed is situated in the northwest of Algeria, and 30% of its surface area is situated in the  Hydriques ). (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000). These climate  The meteorological and hydrometric datasets (Figure  1  and.
). The validation step of the regional climate models is produced by their comparison to the observed datasets for the reference period (1981-2000). The SWAT components resulting from the ten climate models used in this study give an acceptable representation compared to those resulting from observed datasets. These results confirm the validation of the climate models used to study the tendency of SWAT components on the Tafna basin for the two months of January and July. This validation allows us to use the mean ensemble of the ten outputs obtained, a combination of eight GCMs and two RCMs, to study the climatic and hydrologic trends in the Tafna basin.
ACKNOWLEDGEMENTS The authors wish to thank  Algerian institutions  ( Agence Nationale des Ressources Hydriques, Office National de Météorologie ) for providing data series;  Amina Mami 's stay in France was made possible thanks to a scholarship from ' National Exceptional Program'  ( PNE-2017 ) awarded by the  Ministry of Higher Education and Scientific Research of Algeria. The laboratory of ecology environmental is gratefully acknowledged for its support of this research project and  Eleni Katragkou  from the  CORDEX Group. Amina Mami ,  Djilali Yebdri ,  Sabine Sauvage , and  José Miguel Sánchez-Pérez  conceived and designed the experiments; Amina Mami performed the experiments;  Amina Mami ,  Djilali Yebdri ,  Sabine Sauvage ,  Mélanie Raimonet , and  José Miguel Sánchez-Pérez  analyzed the results;  Amina Mami ,  Djilali Yebdri ,  Sabine Sauvage ,  Mélanie Raimonet , and  José Miguel Sánchez-Pérez  wrote the paper.