dataesr/scientific-paragraphs-categorization huggingface dataset:
1  for details) were installed along each elevation gradient, with an average of 200 m elevation difference, resulting in 99 plots in total (Supplementary material Appendix 1 Fig. A1 ). Each plot included a subplot for collecting plant data and three subplots for soil sampling (Fig. 1 ). Plant and soil data were always sampled in the same year. All plant species in the subplot were identified by professional botanists. For the analyses, we only kept the 44 species for which we had sufficient presences (i. e. > 20 plots, Supplementary material Appendix 2 Table  A1 ) to build reliable species distribution models  (Guisan et al. 2017). For each plant species, we selected plant height and three leaf traits, the ratio between leaf carbon and nitrogen contents (leaf C/N), leaf dry matter content (LDMC) and specific leaf area (SLA) because they represent important aspects of plant nutrition and light competition  (Cornelissen et al.
Climatic data To best characterize species' climatic niches, we calculated a set of bioclimatic variables as averages over 1988-2018. More specifically, we used solar radiation and growing degree days (GDD, Choler 2018) to represent the average length and intensity of the growing season, intensity of freezing events (freezing degree days, FDD, Choler 2018) and water stress (climatic water deficit, CWD) in each plot (see Table  1  for details). These variables were calculated from the SAFRAN-SURFEX/ISBA-Crocus-MEPRA reanalysis  (Durand et al. 2009 , Vannier 2012). This corresponds to a version almost identical to the dataset made available by  Vernay et al. (2019). The last corrections made before the publication of this dataset are sufficiently minor to not modify the conclusions of this study.
In this paper, trait T was averaged at the species level. The interaction between the mean trait T and the environmental variable X c provides information on how far the species is from its environmental optimum, and in that sense 'modulates' species probability of presence along environmental gradients. A positive trait-environment interaction (i. e. positive β c,t ) means that a species s with high trait value T s has a high probability of presence (high P s,i ) for a site i with high values of the considered environmental variable X c,i. The four hierarchical GLMMs were fitted using a binomial response and a logit link. Species identity was included as a random effect, for which both slope and intercept were allowed to vary. Parameters of all models were estimated by Laplace approximation of maximum likelihood using the 'Bobyga' optimiser of the 'lmer' function (package 'lme4' in R, R Core Team).
Acknowledgements -The research presented here belongs to the  ORCHAMP monitoring program. We thank the few dozens of interns, field assistants, park managers and rangers for their help conducting the field observations and the lab measurements. In particular, we thank  Cindy Arnoldi  for her help with the laboratory work on the AEEM technical platform. The S2M data were provided by  Météo-France -CNRS, CNRM Centre d'Etudes de la Neige , through AERIS. We thank  Samuel Morin  ( Centre d'Etudes de la Neige ) for interactions with respect to meteorological data and indicators. Funding -Part of the funding came from the  French Agence Nationale de la Recherche (ANR)  through the  GlobNets  ( ANR-16-CE02-0009 ) and the  GAMBAS  ( ANR-18-CE02-0025 ) projects and from two ' Investissement d'Avenir' grants (Trajectories :  ANR-15-IDEX-02 ; Montane: OSUG@2020:  ANR-10-LAB-56 ).
We also received support from the  Lautaret Garden-UMS 3370  ( Univ. Grenoble Alpes, CNRS, SAJF, 38000 Grenoble, France ), member of  AnaEE-France  ( ANR-11-INBS-0001AnaEE -Services, Investissements d'Avenir frame)  and of the  LTSER Zone Atelier Alpes , a member of the eLTER-Europe network. Authors contributions -CMA, TM and WT conceptualized the overall study and analyses. WT, AS, CMA, TM and the  ORCHAMP Consortium  collected the soil data. LB carried out the plant trait measurements. JR run the database and formatted the climate data. GP, LB and AS carried out the lab work for exoenzymatic activities and soil properties. CMA run all statistical analyses. CMA, TM and WT led the interpretation of the results with strong support from GB, PC, JP and AF. CMA led the writing with contributions of all authors.
Acanthamoeba isolates and environmental and clinical samples. A total of 33 isolates of amoebae of the genus Acanthamoeba were tested. This encompassed 20 Acanthamoeba reference strains, of which 12 were strains whose draft genome sequences were available in the NCBI GenBank sequence database. They were ordered from the DSMZ biological resource center (https://www. dsmz. de/). No strain was available for A. castellanii ATCC 50370 or A. pearcei. Thirteen additional Acanthamoeba strains were isolated in our laboratory from environmental or clinical samples, in 5 and 8 cases, respectively (Table  1 ).
Design of PCR primer systems. In a previous work, a prediction of ORFs was performed using the Prodigal program for the draft genome sequences of A. polyphaga ATCC 30872 and 13 other Acanthamoeba species  25, 26. A search for homologous sequences in the NCBI GenBank non redundant protein sequence database (nr) was then performed with the BLASTp program (https://blast. ncbi. nlm. nih. gov) using non-redundant scaffolds of A. polyphaga ATCC 30872 as queries. The sequences of genes larger than 300 nucleotides predicted from the draft genome sequences of the 14 Acanthamoeba species were used in the subsequent analyses that consisted in a BLASTp all-against-all search with protein sequences predicted for each of these genes  25. We delineated the core genome of these amoebae and then extracted the nucleotide sequences of these genes that were found to be present in all 14 Acanthamoeba species.
Thereafter, we performed with the BLASTn program systematic pairwise comparisons between these genes  27. Only genes for which none of these pairwise comparisons resulted in 100% nucleotide identity, i. e. , their sequence differed in each of the 14 draft genome sequences, were further examined. For each of these genes, nucleotide sequences were aligned using the MUSCLE software  28. Thereafter, nucleotide alignements were screened using the SVARAP tool  29  in order to identify areas (i) conserved in the sequences from the 14 different species, where universal PCR primers or probes could hybridize reliably, and (ii) that flank a region variable enough to enable sequence-based discrimination between Acanthamoeba species (i. e. , with nucleotide identities < 99%). Additionaly, four primer systems were designed using the same method to target the complete 18S rDNA of reference strains Acanthamoeba lugdunensis ATCC 50240, A.
DNA was sequenced using the Sanger sequencing method on an automatic sequencer (ABI-3130 XL genetic analyser; Applied Biosystems) with the BigDye Terminator v1. 1 sequencing kit (Applied Biosystems). The sequencing data were analysed with the Chromas Pro 1. 7. 1 software (Technelysium Pty, Ltd. , Tewantin, Queensland, Australia).
Phylogenetic analyses. Sequence alignments were performed using the Muscle program, and phylogenetic trees were constructed with the MEGA software using a Neighbor-Joining method, with a Maximum Composite Likelihood substitution model (considering both transitional and transversional substitutions), uniform rates, homogeneous patterns among lineages and pairwise deletion of gaps  31. The alanine-tRNA ligase sequence of the amoeba Dictyostelium discoideum strain AX4 available in the NCBI GenBank database (accession number: NC_007089. 4) was used as an outgroup for these phylogenetic analyses. Overall, the identification of Acanthamoeba species was based on the two following criteria: nucleotide similarity <100% in pairwise comparisons and bootstrap threeshold <90% in phylogenetic analyses.
Acknowledgements This work was supported by the  French Government  under the  "Investments for the Future" program  managed by the  National Agency for Research (ANR) ,  Méditerranée-Infection 10-IAHU-03 , and was also supported by  Région Provence-Alpes-Côte d' Azur and European funding FEDER PRIMMI (Fonds Européen de Développement Régional -Plateformes de Recherche et d'Innovation Mutualisées Méditerranée Infection). Nisrine Chelkha  was financially supported through a grant from the  Infectiopole Sud foundation.
Observations and climate models are combined to identify an anthropogenic warming signature in the upper ocean heat content (OHC) changes since 1971. We apply a new detection and attribution analysis developed by Ribes et al. that uses a symmetric treatment of the magnitude and the pattern of the climate response to each radiative forcing. A first estimate of the OHC response to natural, anthropogenic, greenhouse gas, and other forcings is derived from a large ensemble of CMIP5 simulations. Observational datasets from historical reconstructions are then used to constrain this estimate. A spatiotemporal observational mask is applied to compare simulations with actual observations and to overcome reconstruction biases. Results on the 0-700-m layer from 1971 to 2005 show that the global OHC would have increased since 1971 by 2.
(2012)  datasets are used for this study. Those datasets were constructed using temperature and salinity observations issued from the World Ocean Database. Main differences between both datasets lie in the interpolation techniques and instrumental corrections for biases in expendable and mechanical bathythermograph data. The observed OHC anomalies for the upper 700 m used for this study are the ensemble mean of the two reconstructions. Reconstructed fields are provided globally and over the full ocean depth, although in situ observations are sparse and unevenly distributed with a bias toward the Northern Hemisphere and the upper ocean (the vast majority of observations are above 700-m depth;  Rhein et al. 2013; Abraham et al. 2013). In particular there are very few observations in the Southern Ocean before the year 2000  (Durack et al.
2014). Different techniques are used to fill areas presenting few or no observations. Ishii et al. (2017)  and  Levitus et al. (2012)  use objective interpolation techniques to carry out the infilling, which bias the temperature anomaly toward zero in data-sparse regions  (Gregory et al. 2004; Gleckler et al. 2012). This affects the depiction of OHC content changes and variability at the global scale and in regions with sparse data  (Gregory et al. 2004; AchutaRao et al. 2007 ). To remove this bias and ensure the accuracy of the D&A analysis, we apply an observational mask that restricts the data to areas and periods with a sufficient number of available observations. We build the observational mask using the data distribution field (dd) available in the World Ocean Atlas. For a given grid cell and a given year, the entire 0-700-m water column is considered as observed when at least 10 observations are present along the water column.
To avoid seasonal biases, an additional criterion retains a given grid cell and a given year only if observations cover at least two trimesters. We also choose to focus over the period 1971-2005 because the data coverage becomes more global in 1971  (Rhein et al. 2013; Abraham et al. 2013). Figure  1  shows the total and subsampled yearly mean OHC anomalies. As expected, the subsampling induces a larger variability in particular at the times of the sparsest sampling  [i. e. , before 1971 and between 1996 and 2003  when the coverage of the global ocean was below 40%;  Abraham et al. (2013) ]. The subsampled dataset also exhibits a larger trend over the period 1971-2005. This is not surprising as the infilling of nonobserved areas with null trends in  Ishii et al. (2017)  and  Levitus et al.
NAT runs cover the same period but only the natural forcing varies with time. GHG runs also cover this period, with varying GHG concentrations while other forcings are kept constant. The inclusion of other attribution experiments (e. g. , anthropogenic forcings only, aerosol forcing only) was prevented by the limited number of runs available in CMIP5. However, by FIG. 1. Yearly mean time series of ocean heat content anomalies for the total reconstructed dataset (continuous line) and for the dataset subsampled with the observational mask (dashed line) as derived from the World Ocean Atlas (see text). assuming that the response to a combination of forcings is equal to the sum of the responses to individual forcings, we can infer the response of OHC to other combinations of forcings. The additivity assumption has been verified for the dynamical sea level by  Slangen et al.
c. Calculation of the ocean heat content trends To compute OHC anomalies, monthly 3D temperature and salinity fields from 0-to 700-m depth are averaged annually and masked over marginal seas and lakes (such as the Mediterranean Sea, Red Sea, Black Sea, Caspian Sea, Baltic Sea, Persian Gulf, Hudson Bay, and Great Lakes) for each dataset. OHC anomalies are computed from the annual mean temperature fields and a climatology of the salinity fields, following the 1980 UNESCO International Equation of State (IES80). The OHC is calculated on each layer and each cell of the reconstruction/model grid and vertically integrated. Anomalies are obtained by subtracting the 1971-2000 climatology for each grid cell. For observations, temperature and salinity fields were first converted into potential temperature fields [using the methodology of  Bryden (1973) ] before the computation of OHC anomalies.
In a second step, we compute the global (and regional, when necessary) subsampled averages and trends. In this case, a common observational mask, varying in time and space, is applied to all observational datasets and simulations. As a consequence, the spatial coverage might be different from one year to another. The spatial average is computed from subsampled OHC anomalies over the globe (and over different ocean basins when necessary). Finally, linear trends are computed over the period of interest: 1971-2005. The D&A method is applied to the subsampled OHC anomaly trends. As a consequence, the observational constraint obtained applies to the subsampled OHC trend, which is different from the global OHC trend. This result can be difficult to understand and to be exploited. Therefore, we translate the results obtained on subsampled OHC (sub-OHC) into a result applicable to global OHC (tot-OHC) using a linear regression model between sub-OHC and tot-OHC.
Acknowledgments. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provided coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. We also acknowledge the  CHAVANA  project and the  RTRA STAE Toulouse Fundation  for funding and supporting this research. Finally, the authors would like to thank  Chris E. Forest  and three anonymous reviewers for their constructive comments that enabled us to improve the manuscript.
g. fish stock assessments, which yield physical samples as by-catch in bottom trawls). Similarly, camera footage obtained by (un-)manned vehicles and other devices, originally designed for other ecological or geological purposes, is also used to quantify benthic pollution. In operational terms, different tools are employed at different depths  (Galgani et al 2013b). The paragraphs above underline the need for larger and more standardised datasets and procedures providing more information for more robust decision making on the protection and management of marine ecosystems. One major reason for focusing on seafloor marine macrolitter is that there are fewer studies on this litter class compared to studies on smaller size classes and on floating or beached debris. Marine litter seafloor research should move towards a harmonised monitoring approach at large spatial and temporal scales.
Marine litter: boosted scientific production and databases With the rising awareness of marine litter, an increasing number of studies have been undertaken spurring a surge of publications in the last decade (figure  2 ). Data on marine litter have been derived mostly from national programs for MSFD implementation, Regional seas monitoring programs, project-related surveys, and research activities at large. The number of studies specifically focussing on seafloor litter is, however, still rather minor. In 2019 it represented less than 1/4 of all studies on marine litter, microplastics excluded (figure 2(a)), and about 1/7 of all studies on marine litter, microplastics included (figure 2(b)). Furthermore, not all information is readily accessible, as at present there is no common reporting template for seafloor litter.
Furthermore, the disturbance of seafloor habitats by trawling is often considerable and therefore trawling to monitor and survey litter must deserve appropriate consideration. Some of the limitations of trawl surveys are difficult to overcome (e. g. the changing nature of the seafloor, the low-catchability of highly mobile lightweight litter) but there are many improvements that, if implemented, would lead to more robust datasets. They range from agreeing on using specific litter-catching gear following calibration tests; applying standardised protocols when trawling for litter, and also when counting, weighing and classifying litter catches; and reporting conventions. ICES (2011, 2012) did develop standardised monitoring protocols of bottom trawl surveys for the North and Baltic seas, and for the North-East Atlantic, which have been used since 2011, however trawl gear differs between marine regions, because of different substrate types and fish stocks.
ices. dk/?ref=2) and those in the EMODnet database (right column, www. emodnet-chemistry. eu/welcome). The overall design, mesh size and operational/technical details for each gear type could influence litter catchability. Most acronyms are spelt out in the corresponding row. Other acronyms are: GOC; GOV; SOV, from Soviet, only used by the Russian Federation between 1976 and 1982 in the NS-IBTS survey; TV; TVL; TVM; TVS. See also the list of acronyms at the end of the paper.
Alternatively, calibrated stereo camera systems can be used to provide accurate length and volume calculations throughout the visual field, without regard to orientation of the objects to the camera  (Dunlop et al 2015 , Boutros et al 2015). Stereo camera systems may be particularly useful in areas with highly uneven terrain where vehicle positioning is often suboptimal. Quantitative data can be collected using video transects or still images from ROVs. In video transects the area surveyed can be calculated with known or estimated transect widths (field of view) and lengths via transect start/end latitude and longitude  (Barry and Baxter 1993 , Adams et al 1995 , Lundsten et al 2009 , Kuhnz et al 2014). Again, the type of ROV determines the availability and accuracy of navigational data. Some ROVs do not provide this information and scientists are forced to use the ship's position for ROV transect start and end locations, which reduces the accuracy of transect length measurements as ROVs usually move off the theoretical track.
Other ROVs, such as MBARI's ROVs Doc Ricketts and Ventana provide very accurate navigation from USBL sampled at 2 s intervals. Likewise, many ROVs cannot hold constant altitude, speed and camera focus, and these limitations affect the quality of the quantitative data that is collected. More advanced ROVs will have automated precision navigation capabilities. Motion blur, an artefact of poor camera quality, lighting and vehicle speed, also affects the ability to use the video data for quantitative analysis of seafloor litter. Photographs from still image cameras or video frame grabs can also be used as pseudo-quadrats if the area of the image can be calculated by using a calibrated laser system (see above), a pair of calibrated laser lines  (Dias et al 2015) , or by using camera altitude above the seafloor and logged camera parameters  (Durden et al 2016).
For example, MBARI uses Dorado (Sibenac et al 2002) AUVs to collect 1 m resolution multibeam sonar data with mission durations of up to 18 h. Most AUVs are outfitted with forward-looking sonars that allow for detection and avoidance of approaching obstacles (Martin et al 2013). Using downward or oblique facing cameras and strobe packages, AUVs can take overlapping images of the seafloor at set intervals matched with vehicle speed such that complete photographic coverage of a swath of seafloor can be achieved  (Singh et al 2004a , Martin et al 2013). These images can then be used individually as pseudo-quadrats, or with photogrammetry software to render georeferenced 3D reconstructions and mosaics of the seafloor  (Williams et al 2010 , Dunlop et al 2015 , Durden et al 2016). AUVs can operate in the most extreme environments, including great depths and even under polar ice  (Kunz et al 2008 , Wulff et al 2016).
Image handling: analysis, processing, annotation, management and tailoring The application of imaging and video technology to detect, identify and quantify different kinds of litter in marine environments leads to large amounts of image and video data that must be archived, analysed and evaluated. The basic issue of archiving is addressed by publicly funded international databases such as PANGAEA (www. pangaea. de)  or other national non-profit organisations. Different software systems for manual image and video annotation have been developed in the last 10 years, and most of them are available as open source. One example is the VARS for analysing video and images from a variety of platforms  (Schlining and Jacobsen Stout 2006). A GUI front end and a database backend make VARS data entry, retrieval and long-term storage possible (Schlining and Jacobsen Stout 2006).
For example, annotators may manually enter observations at the greatest detail, but they may wish to conduct a survey that utilises data at the material level because it is more meaningful to identify sources. This hierarchical system has, for instance, been employed by  Schlining et al (2013)  where litter was identified at a specific level (e. g. plastic crisp bag) but it could be queried for at a higher level (i. e. plastic, metal, wood, glass) so that various litter observations in MBARI's VARS database could be retrieved for further analysis at various levels of detail  (Schlining et al 2013). Since the annotation of litter in images and videos has started recently, no standards for annotation catalogues and best practice protocols have been defined yet. Thus, experiences and recommendations like the RecoMIA guidelines (Schoening et al 2016) should be considered to increase efficiency and effectiveness in future projects.
Development needs for image-based monitoring It is essential that image-based monitoring is harmonised so that results from different surveys made with different cameras can be compared. Despite the mentioned technological breakthroughs and software tools that can make the process of data annotation more efficient, other aspects of image interpretation have to be considered as well. Large-scale and temporal trend assessments of marine litter require agreed reporting units, validated data (i. e. image annotation accuracy) and a defined set of metadata and dictionaries (see above). These enable the use of harmonised datasets for data analysis and also inform about potential non-compatibility of data in order to avoid erroneous interpretations. Agreed coding and interoperability of databases both from the information technologies side and the content description including metadata sets are essential.