dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , higher than 50 Mg C ha -1 , such as grasslands and forests). In these soils, the required additional C inputs will have to be higher according to Century, raising concern about a compensation for CO 2 emissions through improved SOC stocks at a global scale. This result depends on the quality of the simulated carbon inputs (i. e. , the predicted metabolic : structural ratio) and does not take into account any notion of soil saturation. Before applying this trend to calculate the required C inputs from current SOC stocks, we should extend the database to cover different pedoclimatic regions and different ecosystems of the world. Moreover, inaccuracies in simulations outcomes, such as those found in this study, need to be reduced. As discussed in Sect. 4. 2. 3, a better representation of C input dynamics and management practices could improve the simulation of SOC stocks.
Acknowledgements. We acknowledge  Mancomunidad de la Comarca de Pamplona  for maintenance and access to Arazuri site data. Research grant  RTA2017-00088-C03-01  was received from the  Instituto Nacional de Investigación Agraria y Alimentaria, INIA (Spanish Agency). We acknowledge  Margaret Glendining , curator of the electronic Rothamsted Archive (e-RA), for providing the Broadbalk data. The Colmar and Feucherolles field experiments form part of the SOERE-PRO (network of long-term experiments dedicated to the study of impacts of organic waste product recycling) certified by ALLENVI ( Alliance Nationale de Recherche pour l'Environnement)  and integrated as a service of the " Investment for future" infrastructure AnaEE-France , overseen by the  French National Research Agency  ( ANR-11-INBS-0001 ).
Code availability. The code used for simulations is publicly available and can be accessed at https://doi. org/10. 5281/zenodo. 5036268  (Bruni, 2021). Data availability. Data used for this work were provided by the data owners of the different long-term experiments. Please contact them to get access to the data (see Table  2 ). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-3981-2021-supplement.
Recent developments including the Clumondo global land use models  (Van Asselen & Verburg, 2013)  and ISIMIP2b global time-series climate model data  (Frieler et al. , 2017 ; https://www. isimip. org/proto col/#isimip2b) now allow such projections worldwide (e. g. ,  Woolway & Merchant, 2019). These new model output data provide opportunities to disentangle and explore the combined effects of projected land use and climate changes on lake ecosystems. To test lake ecosystem responses to increasingly severe environmental change, we used long-term observations from 14 European and 15 North American lake basins to project mid-21st century phytoplankton (including cyanobacteria) and in particular cyanobacteria abundance based on a range of land use and climate scenarios. We then applied our projections of phytoplankton and cyanobacterial responses to more than 1500 lakes, each within 160 km of one of the 29 focal lake basins and with similar morphometric characteristics, to compare the responses across different combinations of land use and climate scenarios.
, 2017). Samples were integrated across depth using integrated samplers or by combining separate samples from discrete depths across the epilimnion and hypolimnion. The samples for each lake have been counted under a microscope using consistent methods over the entire sampling duration and have been counted by the same taxonomist whenever possible. Exceptions are Windermere with various sample processors in early years, the Oneida Lake, for which the sample processor changed in 1996  (Idrisi et al. , 2016) , and lakes Geneva, Annecy, and Bourget, for which sample processors changed in 2007. The data include measures of phytoplankton and cyanobacteria abundances, which are representative for large-scale changes. To allow comparisons across the focal lake basins, abundance data of individual phytoplankton and cyanobacteria taxa from each sampling date were converted to total abundance (cells L -1 ).
For more detail about the sampling methods and sample processing for the different lakes see the metadata description in the Forest Ecosystem Monitoring Cooperative (FEMC) repository that contains the Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages (GEISHA) database (https://www. uvm. edu/femc/data/archi ve/proje ct/geish a-storm blitz. fr) and the references listed in Table  S1.
, 2017). For the sustainability scenario, demands were included for tons of crop production, livestock units (goats and sheep), built-up area, and tons of carbon; middle of the road and regional rivalry scenarios included demands for tons of crop production, livestock units (goats and sheep), and built-up area  (O'Neill et al. , 2017). These three modeled scenarios assume an increasing demand for cropland production resulting from a combination of cropland expansion and cropland intensification  (Eitelberg et al. , 2015). The Clumondo data are classified at a spatial resolution of 5 arc minutes (~9. 25 km), where pixels represent the dominant land use types. For a comprehensive coverage of our study area, we merged the 24 original sub-categories into five distinct categories of cropland, urban, forest, grassland, and bare.
For example, all sub-categories that described urban land use (i. e. , peri-urban or villages and urban areas) were considered to form the category "urban" (see Table  S2  for details on sub-categories). For each of the 29 focal basins, we calculated the proportion of each land use category in its upstream watershed area. The land use proportions were calculated for Clumondo land use data from the year 2000 (i. e. , to be used for the sample data and the baseline period) and the three land use scenarios (i. e. , to be used for the future period).
Modeled time-series data were extracted for the grid point situated closest to the center of each lake. To account for the uncertainty stemming from climate scenarios or models, we used four climate model projections-GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5 from the CLM45 impact model for the historic period  and two defined periods of temporal baseline  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)  and horizon 2050 (2044-2056) under two climate scenarios of RCP 2. 6 and RCP 8. 5. The RCP 8. 5 is the most aggressive scenario that might be very unlikely; however, this scenario is reported to be consistent with the historical total cumulative CO 2 emissions, thus serving a useful tool for quantifying climate risks over the midterm time horizons  (Schwalm et al.
, 2020). These two scenarios (RCP 2. 6 and 8. 5) and all four available model projections (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) span a range of potential future global radiative forcing from anthropogenic impacts. Furthermore, using the least severe (RCP 2. 6) and the most severe (RCP 8. 5) scenarios allowed us to cover the whole range of possible variability in lake phytoplankton and cyanobacteria responses to climate change. To fit the statistical relationships between the biological sample data and climate data, we needed to add the climate data from the observation period  (i. e. , 1971-2016)  to the biological data set. We extracted the time-series daily mean data for the 10 climate variables according to the historic period , and averaged the values across the four model projections (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5).
In our study, each forest comprised 2000 trees and each tree used one-third of the variables to predict unknown data. The RF node size was set to "5," which is appropriate for regression models. We used the R-package of randomForestSRC (version 3. 6. 2) for training the model and performing predictions (R Development Core Team, 2016). The statistical relationships were set up by linking the abundance data to the predictor variables (Table  S2 ). The predictor variables consist of 10 climate predictors, five land use categories, and nine fixed lake attributes variables. To give equal weighting to all predictor variables and prevent strong relative influences for predictors with significantly large values, we normalized the predictor variables without creating a distortion in the final results.
| Projected phytoplankton and cyanobacteria changes in 1567 lakes from focal regions To assess the regional effects of land use and climate changes on phytoplankton and cyanobacteria, we selected 3942 freshwater lakes within a 160 km buffer around the 29 focal lake basins. The 160 km distance is within the expected dispersal distance for phytoplankton taxa; thus, the 29 lake basins and the 3942 lakes across the focal regions should have comparable phytoplankton communities  (Kristiansen, 1996). We excluded 2375 of the 3942 lakes that were within the 160 km buffer because they fell outside of the observed range of physical attributes (elevation, watershed area, lake area, total volume, mean discharge, and mean depth) of the 29 focal lake basins. We calculated the proportion of upstream land use data and gathered climate data and lake attributes (from the Hydrolakes attribute table;  Messager et al.
ACK N OWLED G M ENTS This study was funded through the "LimnoSCenES" project (Research on social-ecological scenarios for biodiversity and ecosystem service changes in north temperate freshwater ecosystems over the next half century; AD 91/22-1) supported by the network programming and funding research on biodiversity and ecosystem services across European countries and territories (BiodivERsA) and the Belmont Forum. RA was funded by the MANTEL-ITN project within the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement no. 722518. Data collection for Acton Lake was supported by US National Science Foundation grants 9318452, 9726877, 0235755, 0743192, and 1255159. Windermere monitoring is currently supported by Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.
Oneida Lake monitoring is supported by Cornell University and New York State Department of Environmental Conservation. Data for Lakes Annecy, Bourget and Geneva were collected as part of the long-term monitoring programs conducted by the Observatory of alpine LAkes (OLA, © SOERE OLA-IS, AnaEE-France, INRAE of Thonon-les-Bains) and co-funded by the INRAE and SILA, CISALB, CIPEL, respectively. Cheney Reservoir data collection was funded by the U. S. Geological Survey Cooperative Matching Funds Program and the City of Wichita, Kansas. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U. S. Government. Compilation.
DATA AVA I L A B I L I T Y S TAT E M E N T Data that support the findings of this study have been deposited in https://zenodo. org/recor d/55018 51#. YT3xT y0ethF. USGS waterquality data are available through the National Water Information System at: http://doi. org/10. 5066/F7P55KJN. USGS phytoplankton.
In the present study, the role of TDE was evaluated for induction of oxidative stress in mice. In this context, biochemical and histological markers in the livers and kidneys were analyzed. The liver and kidneys were selected since they are the first targeted organs by toxic damage. Hepatocytes contain a large number of metabolizing enzymes capable of biotransformating xenobiotic into less toxic or active metabolites. The liver is a lipid peroxidation site therefore considered a hallmark for oxidative stress tests. The kidneys perform the same functions along with filtration  [38]. Histological analysis provides the most reliable data on the type of alterations induced by pollutants in tissues. Extremely toxic nature of TDE was evident from the histopathological alterations recorded in mice following chronic exposure.
Although no death was recorded during the 90-day exposure period, severe damage to the liver and kidney was induced by hazardous textile wastewater. These findings are generally consistent with other studies [14,  15, 20, 39]. These authors confirm that exposure to textile effluents leads to a disruption in the histo-architecture of kidney and liver tissues in rats and mice. The histological alterations observed in our study may be due to increased cellular formation of oxidative stress via the creation of an imbalance between the production of reactive oxygen species and cellular antioxidant capacities  [40]. There is no universal marker for oxidative stress status. A critical point of standard biomarkers is the insufficient data on the mechanism of action of pollutants present in environmental samples using a single standard biomarker  [41].
, 2016). The foraminifera dataset of this paper will be used for the comparison between sites and with the other benthic components. Since our study is part of a large multidisciplinary project, more stations and samples were investigated for other purposes, but samples of the three benthic components were only collected at two sites. Therefore, a complete data set that allow the comparison among the three faunal components is only available from these two sites: one within an active pockmark and another one located away from pockmarks. The main goals of our study were to: 1) characterize and compare macrofauna, metazoan meiofauna and foraminifera benthic communities in the referred two sites; 2) discuss the effect of environmental constraints on the distribution and diversity of the three aforementioned benthic components; and 3) evaluate the reliability of key most dominant taxa (i.
Seepage exploration and study sites The study area was selected based on previous samples of bivalves usually associated with cold seeps (Bathymodiolinae shells and living Vesicomyidae) and collected during the MIRIKI cruise (2009) (P. Bouchet, pers. comm. ). Our sampling sites (Fig. 1b ) were chosen based on geophysical data (seismics, bathymetry and seafloor backscatter data and acoustic imagery of the water column) of the Ptolemée and Pamela-Moz01 cruises  (Jorry, 2014; Olu, 2014)  and seabed inspection with the deep-towed camera Scampi to locate cold seeps (e. g. reduced sediment, bivalves, microbial mats)  (Olu, 2014; Dupré et al. , 2019). Multibeam echosounder surveys were conducted during these marine expeditions with the use of a EM122 ship-borne multibeam echosounder operated at 12 kHz.
Seafloor bathymetry (Figs. 1b  and 1c ) and acoustic imagery of the water column (Fig. 1c ) were acquired offshore northwestern Madagascar in the Majunga Basin. Given the impedance contrast between the ambient seawater and gas bubbles, water column echosounder data record acoustic anomalies caused by the presence of gas bubbles, providing thus crucial information on potential active seeping sites  (Dupré et al. 2015). Two sites located on Betsiboka and Mahahavy Sud slopes (ca. 30 km apart) were chosen for faunal sampling with samples collected inside an active pockmark and away from another pockmark field. Nevertheless, all three faunal components were only analyzed at two sampling points (Table  1 ; Fig. 1 ). At Site 1 on the Mahavavy Sud slope, samples were specifically collected within an active pockmark (600 m of diameter), at c.
In the present study, we used the identification dataset to species level generated by  Fontanier et al. (2016)  to cluster the specimens in the main foraminifera groups: hyaline, agglutinated, porcelaneous and softshell foraminifera (see the referred publication for detailed information on sampling procedure and identification of alive specimens). Oxygen profiles were measured ex situ using Clark-type electrodes as described in  Pastor et al. (2011). Organic Carbon (OC) was measured on freeze-dried and crushed sediment after removal of carbonates with 2 M HCl using an automatic ThermoFinnigan EA1112 Series Flash elemental analyzer. Total sulfur was measured on the same powdered samples using a LECO CNS-2000 auto-analyzer. On-board measurements of H2S were based off the Cline method  (Cline 1969; Grasshoff et al.
Faunal data and statistical analysis Macrofauna, metazoan meiofauna and foraminifera community descriptors were: (1) richness, (2) abundance and (3) taxonomic composition. We used the same community descriptors for Polychaeta, Nematoda and hyaline foraminifera, considering only the uppermost 0-1 cm of the vertical profile because it is the single layer for which we obtained a complete dataset and which allows a comparable study among the three groups (cores for macrofauna were sliced at different depths than those for meiofauna). Richness was measured as the number of high-taxonomic-level taxa of the macrofaunal, metazoan meiofaunal and foraminifera communities. In addition, we used the number of families for Polychaeta, the number of genera for Nematoda, and the number of species for hyaline foraminifera.
Abundance was measured as the number of individuals in a core sample and densities were calculated as the number of individuals per surface area (1 m 2 for macrofauna and Polychaeta, 10 cm 2 for metazoan meiofauna, foraminifera, Nematoda and hyaline foraminifera). Statistics were based on pseudoreplicates, which explain larger spatial variance of richness and abundance than true replicates do according to  Montagna et al. (2017). Hence, analyses of each benthic component were performed using the faunistic data from all cores collected at each site, considering them as independent units. Differences in taxa richness and abundance were described along the vertical profile within each site (intra-site study) for macrofauna (0-1, 1-3, 3-5, 5-10, 10-15 cm), metazoan meiofauna (0-1, 1-2, 2-3, 3-4, 4-5 cm) and foraminifera (0-1,  1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 cm).
1b ), using the site as a discrete explanatory variable (i. e. inter-site study), including the variables of "sediment depth" and "core" as random factors. Similarly, GLMs were performed considering only the 0-1 cm layer for the following taxa: family for Polychaeta, genera for Nematoda and species for hyaline foraminifera. GLMs and GLMMs were conducted using the 'glm  ', 'lmer' and 'glmer' functions implemented in R (Zuur et al. , 2007). Polychaeta, Nematoda, and hyaline foraminifera diversities were measured using the Shannon-Wiener diversity index (H', log-base e) with the Pielou index (J) for evenness, using the 'diversity' function included in the R package vegan v. 2. 2-1  (Oksanen et al. , 2015). Differences in community composition were tested using Ružička matrix with a permutational analysis of variance models (PERMANOVA).
Ružička index were calculated using the 'beta' function of the R package vegan v. 2. 2-1  (Oksanen et al. , 2015) , and PERMANOVA was performed using the 'adonis' function included in the R package vegan v. 2. 2-1  (Oksanen et al. , 2015). To visualize community structure variations between sites in macrofauna, metazoan meiofauna, and foraminifera as well as in Polychaeta, Nematoda, and hyaline foraminifera, we conducted a Principal Component Analysis (PCA) on abundance using the 'rda' function of the R package vegan v. 2. 2-1  (Oksanen et al. , 2015). Abundance data were transformed (Hellinger distance) using the 'decostand' function of vegan 2. 5-5 package  (Oksanen et al. , 2018) , because this distance gives a lower weight to dominant taxa and does not consider double absence as an indicator of similarity between samples  (Legendre and Gallagher, 2001).
Acknowledgment We thank the  INRA ECOSERV Meta-program  for his financial support, and the  SERVICESCALES  project. The contribution of IGR was partly funded by  ECOPOTENTIAL  project (Contract No. 641762 ) funded under the  Horizon 2020 Programme  of the  European Commission. We thank the experts at the  Tour du Valat  for their open exchanges, in particular  Damien Cohez ,  Lisa Ernoul ,  Hugo Fontes ,  Patrick Grillas ,  Philippe Lambret ,  François Mesleard ,  Anthony Olivier  and  Olivier Pineau.
Funding: This research was supported by the  Public Service of Wallonia, Department of Energy  and  Sustainable Building  in the framework of the  SMARTWATER  project. IDAEA-CSIC is a Centre of Excellence Severo Ochoa  ( Spanish Ministry of Science and Innovation , Project  CEX2018-000794-S ). E. P. was also funded by the  Barcelona City Council through the Award for Scientific Research into Urban Challenges in the City of Barcelona 2020  ( 20S08708 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
To avoid staining of double-stranded RNA, 50 μg/mL of RNAse (Fluka, Buchs, Switzerland) was also added to the suspension. After a 5-minute incubation period, the samples were analyzed in a Partec CyFlow Space flow cytometer (Sysmex Partec, Görlitz, Germany) equipped with a green solid-state laser (Cobolt Samba 532 nm, operating at 30 MW; Cobolt, Stockholm, Sweden) to measure the relative fluorescence of stained nuclei. Results were obtained using Partec FloMax software (v. 2. 9; Sysmex Partec, Görlitz, Germany). About 5,000-5,500 nuclei were examined in each sample. The DNA-ploidy level was inferred as the position of the sample G1 peak relative to that of the internal reference standard. The genome size of each individual (in picograms; sensu  Greilhuber et al. 2005)  was obtained using the following equation: 2C nuclear DNA content ¼ ðIndividual G1 peak mean= Reference-standard G1 peak meanÞ Â Reference-standard genome size.
Seed Traits and Germination Assessment Subsamples of 100 fresh seeds from each tree of all sampled populations were used to analyze seed weight and morphological traits. First, each 100-seed subsample was weighed (Weight). Then, the projected area (Area), curved length (Clength), and length-to-width ratio (Length/Width) of individual seeds were obtained using WinSEEDLE 2019 software. The relationship between seed weight and seed size was expressed as the seed weight-to-area ratio (Weight/Area), calculated by dividing mean seed weight by mean seed area. Morphological seed traits were analyzed for all populations except the Morocco population, which had an insufficient total number of seeds for subsampling. A 31-day germination trial was conducted in April 2018 in a greenhouse with an air-cooling system to prevent air temperatures exceeding 26 C and a periodic fog watering system to prevent seed desiccation.
Climatic Variables Climatic data series from the 30 years previous to seed collection  were obtained for each of the 12 populations from the closest 0. 5 Â 0. 5 grid point of the Climate Research Unit's global gridded database (CRU TS4. 03;  Harris et al. 2014). Monthly rainfall and maximum and minimum temperature data were used to calculate various climatic variables (Table  S1 ), including annual variables and variables for the spring months of March, April, and May (MAM) when germination of our target species would most likely occur across their natural geographic ranges  (McVean 1955a). The altitude of each sampled tree was obtained from the European Digital Elevation Model (v 1. 1; https://land. copernicus. eu/imagery-in-situ/eu-dem/eu-dem-v1. 1, last access: 29 April 2020), and the mean altitude of each population (except the Moroccan population, for which only the altitude of the middle point of the transect was available) was calculated.
Linear mixed models were implemented using the nlme package  (Pinheiro et al. 2016). All statistical analyses were performed in R (v. 3. 5. 1; R Core Team 2018). Seed germination rates were calculated using the Kaplan-Meier estimate, and differences in germination success between species and between populations were examined using the log-rank test in the survival package  (Thearneau & Grambsch 2000; Therneau 2015 ). Pairwise differences were determined with the survminer package  (Kassambara et al. 2021 ). The germination success followed a beta distribution with continuous values on the interval [0, 1], which was confirmed by visual comparison between data and test distribution quantiles using the qqtest package  (Oldford 2016 ). Thus, beta regression was used to model germination success of A.