dataesr/scientific-paragraphs-categorization huggingface dataset:
This is indeed challenging for the Mediterranean Sea, where results of comprehensive OA studies are still sparse and not easily accessible and/or scalable. In addition to the remarkable discrepancy of some OA research within the different Mediterranean countries, a clear and straightforward identification of gaps in the various OA research aspects has not been previously addressed. This paper focuses on the much needed assessment of the current state of OA research progress, geographic discrepancies and gaps in this regional sea, based on the OA-ICC bibliometric data and on the responses of researchers from the Mediterranean OA sphere collected via a survey. Overall, this paper i) summarizes the state of art of OA research in the Mediterranean Sea, ii) identifies various aspects of ongoing OA research in the Mediterranean, iii) discusses main knowledge gaps and, iv) highlights agreed-upon recommendations that might serve as a roadmap for the community in the context of the UN Ocean Decade.
The paper is structured into 9 sections. Section 2 describes the database and the survey used in this study. Section 3 provides an overview of the Mediterranean OA research, including themes and geographic distribution of OA studies. Sections 4, 5, 6 and 7 highlight the status quo of OA in terms of chemistry, modeling and predictions, effects of OA on key species, and on the connections between organisms-ecosystems and ecosystem services, respectively. Section 8 discusses the socio-economical consequences of OA. In section 9, specific aspects related to OA policy and legislation are analyzed. In section 10, specific recommendations are identified for every OA theme in response to the identified gaps of knowledge. Finally, section 11 provides a long-term perspective of the needed actions for the understanding and management of OA among the Mediterranean countries.
Methodology Using the OA-ICC database (Bibliographic databases|Ocean Acidification (news-ocean acidification-icc. org)/ https://www. iaea. org/services/oa-icc) via Zotero (Zotero | Groups > OA-ICC), 534 articles (Tables  S1, S2 ), compiled by December 2021, on ocean acidification in the Mediterranean Sea were collected (out of a total of 8955 articles worldwide). The OA-ICC database keywords were used to filter the geographic location, date and type of the publication, studied organisms, process and parameters, and the applied methodology (see Microsoft Word -UserInstructions for the OA-ICC Bibliographic Database 2021 final. docx (news-oceanacidification-icc. org)). Afterwards, the selected 534 articles were carefully reviewed to confirm the OA relevance of the study, the specific region of interest, including the type of basin and sub-basin (i.
Trends in OA-related publications This paper is based on the analysis of a total of 534 entries (i. e. , 'items', see below) retrieved from the OA-ICC database. These included: 86% peer-reviewed journal articles, 4. 5% PhD theses, 4. 5% book chapters, 3% conference papers, and 2% other documents (i. e. , gray-literature reports, etc. ) (Fig. 2A ). This reflects the fact that the scientific community is more interested in producing scientific material (peer-reviewed articles), while communication products dedicated to disseminate OA results to a broader audience, such as conference papers, books, reports and targeted policy-briefs, are not a priority. On average, around 1 to 4 OA items were published annually before 2008. After this year, the number increased to 20 OA items per year, mainly consisting of book chapters, in parallel with the launching of the European Project on Ocean Acidification  (EPOCA project -2008 (EPOCA project - -2012) )  and the publication of the CIESM report containing multiple book chapters tackling the topic of OA  (CIESM, 2008).
These two sub-basins are then followed by studies conducted in the Adriatic (8%), Aegean (7%), Levantine (4%), Alboran (3%), Ionian (1. 5%), and the Algero-Provencal (1%) sub-basins. The reason why the Algero-Provencal Sub-basin is the least studied can be explained by the almost non-existing OA items produced in the neighboring Mediterranean countries with a total of three in Tunisia, two in Algeria, and zero OA items from Morocco (Figure  3 ). More than 90% of all OA-related studies were performed by scientists that come from the Northern region of the Mediterranean (Figure  4 ). Thus, current OA conclusions are predominantly derived from European institutions. In fact, there were only a total of seven joint OA studies between North and South (N-S) Mediterranean countries (~1%) found in the database, reflecting a significant lack of collaboration.
Articles dealing with water exchanges between the Mediterranean Sea and the adjacent oceanic compartments (Atlantic Ocean, Marmara Sea and Red Sea), as well as between the Mediterranean sub-basins, correspond to 15% of the working dataset, with a special focus on the Strait of Gibraltar (12%). To constrain the carbonate chemistry in seawater, two out of four carbonate system parameters should be measured: total alkalinity (A T ), pH, partial pressure of CO 2 (pCO 2 ) and/or total dissolved inorganic carbon (C T )  (Touratier and Goyet, 2011; Hassoun et al. , 2015; Krasakopoulou et al. , 2017). The vast majority of research dealing with OA chemistry includes two different combinations of the seawater carbonate system measurable parameters (pH and A T : 34%, C T and A T : 26%). In 16% of the scrutinized studies, three out of the four widely-measured carbonate system parameters were measured to quantify the CO 2 -carbonate system (Figure  6 ).
, 2015; Flecha et al. , 2019) ]. However, datasets originating from these time-series stations have fueled only one-fourth of the published articles. Highfrequency measurements of the seawater carbon chemistry provided by sensors on moored observatories, volunteer observing ships, floats and gliders seem to be relatively scarce in the Mediterranean contributing to around 10% of journal articles in the database, which possibly reveal two major challenges: 1-the high cost, and 2-the complexity of use, thus preventing the widespread utilization of in situ instrumentations  (Byrne et al. , 2010). In most of these studies, only pCO 2 in surface waters was measured  (Hood and Merlivat, 2001; Turk et al. , 2010; Turk et al. , 2013; Merlivat et al. , 2018) , whilst in two studies the pH variability was recorded with a SENSORLAB-pH system located at a fixed mooring  (Gonzaĺez-Davila et al.
, 2016)  and with an ISFET sensor on an underwater glider  (Hemming et al. , 2017). In another study, both SAMI-pH and SAMI-CO 2 Submersible Autonomous Moored Instruments were used  (Flecha et al. , 2015). The survey results validated the database analysis. Around 70% of respondents measure the carbonate system parameters in the following order: pH (86%), A T (77%), C T (49%), pCO 2 (43%), followed by carbonate ions (23%). Moreover, more than 75% of the respondents encounter problems in: (1) securing Certified Reference Materials (CRMs) mostly from the USA Dickson's lab (> 85%), (2) assessing the quality of the carbonate system measurements mainly due to limited or no funds (64%), (3) shipment (18%), and other reasons such as limited CRMs production, difficulty in paying in foreign currency, and institutional administrative issues.
Although 78% of survey respondents are able to share their OA chemistry data, only 30% are submitting it to the IOC-UNESCO for the reporting of the SDG indicator 14. 3. 1. , while only few respondents are uploading their data in different databases such as PANGAEA (via OA-ICC), GOA-ON data portal, SOCAT and GLODAP. This study therefore reveals a serious issue related to data access and availability within the OA research community. The measured parameters used to constrain the carbonate system in OA biogeochemical studies.
Acidification trends have been assessed also through model re-analysis. Cossarini et al. (2021)  focused on the reconstruction of changes of pH and spatio-temporal dynamics of the major carbonate system parameters in the Mediterranean Sea during the last 2 decades, as a part of the Copernicus model-based reanalysis of biogeochemical parameters in the Mediterranean, now available as Copernicus Marine Service product (Data | Copernicus Marine). In this study, model simulations are constrained by qualified available information through dataassimilation techniques, and the final result -named reanalysiscan be considered as the best possible spatio-temporal interpolation of available information, since it is performed by using all the physical and ecological knowledge on ecosystem dynamic that is embedded in the model.
More work needs to be done on these aspects, taking into consideration the cumulative effects of multiple stressors on organisms, food webs, and ecosystems  (Zunino et al. , 2021). Only 35% of the survey participants are modeling the carbonate system parameters with the following order: pH (69%), C T (50%), pCO 2 (50%), A T (44%), and carbonate ions (19%). Their models are predominantly with yearly time scale resolution (>80%), mainly one or three dimensional (40% each), with a resolution in the order of 5 km (50%) and rarely better than 1/16°(< 20%). To compare their model outputs, participants use mostly PANGAEA via OA-ICC (56%) and GOA-ON data portal (22%). Remarkably, only 15% are satisfied with the skill of their OA model outputs, while 54% of participants confirm that their models need to be improved.
Due to the complexity of studying whole communities, only ~11% of the studies (42 in total) have tackled the community-level (Figure  7 ), with more than half (57%) of community studies implemented in field settings and the majority (45% of community level studies) in field vents. Laboratory settings were used in 14% of the studies, while 26% of the studies used a review of existing literature to unravel the effects of OA on communities. The results based on the OA-ICC database are in consonance with the survey outcomes, showing that 40% of participants are doing only field biological observations, 20% are doing only experimental biological studies, and 40% are using both biological approaches. Although field biological observations can realistically reflect the feedback of organisms and ecosystems towards OA, there are significant obstacles related to limited-funding needed to sustain long-term observations FIGURE 8 Biological study approaches used to derive the biological responses towards OA.
Hassoun et al. 10. 3389/fmars. 2022. 892670 Frontiers in Marine Science frontiersin. org (81%), the necessity of long-term monitoring to derive conclusions (69%), and the difficulty in distinguishing the main drivers of change (41%). Whereas for experimental studies, difficulties are mainly attributed to sustaining long-term experiments (66%), the complexity of factors that should be taken into consideration (47%), and the absence of unified methodologies (25%), according to the Mediterranean OA community (survey; Table  S6 ). The main studied biological groups are algae (36%), phytoplankton (36%), mollusks (36%), zooplankton (32%) and corals (27%) with lesser to non-existent interest in other groups, in harmony with the results deduced from the database analysis. Moreover, the main studied processes are the abundance (73%) of organisms, followed by community composition (40%), primary production (37%), calcification and growth (37%), then mortality (33%), morphology (33%) and photosynthesis (30%), with less interest in other biological processes.
To assess OA effects on ecosystems' functions, the community uses various types of data, including chlorophyll a concentration, phytoplankton abundance and structure, occurrence of Harmful Algal Blooms (HABs), concentrations of biotoxins, stock growth, mortality/growth/metabolism, aquaculture and fisheries. Data are either produced through research or collected from public databases (PANGAEA via OA-ICC and GOA-ON data portal, 37. 5% each, and EMODnet portal, 12. 5%). Just a minority of researchers (26%) provides an economic valuation for the ES through cost-benefit, transfer-value and contingent analysis, and sometimes via establishing collaborative works between ecologists and socio-economists.
Data availability To date, too few scientists consistently share their OArelated data on open data portals (section 4). Thus, significant efforts should be channeled towards pushing institutions to produce FAIR data (which meet principles of Findability, Accessibility, Interoperability, and Reusability) at national and regional levels. To quantify and monitor OA trends and variability, the community needs high quality, consistent ocean biogeochemical data that can be archived in a state-of-the-art data resource. This will make data submission and retrieval machine-compatible and efficient  (Tanhua et al. , 2021). Similarly to the IAEA's OA-ICC biological database, a biogeochemical database would be helpful to gather all OA-related data in one platform that can be used by biogeochemists and modelers for comparisons and future projections (e.
Acknowledgments The IAEA is grateful for the support provided to its Marine Environment Laboratories by the  Government of the Principality of Monaco. This is a contribution of the  IAEA's Ocean Acidification International Coordination Centre (OA-ICC ), a project generously funded through the  Peaceful Uses Initiative  of the IAEA. The authors would like to thank the  OA-ICC  for supporting the OA Med-Hub in expanding its activities and visibility. MG and PZ acknowledge funding from  MINECO  ( BIOCAL  Project  -PID2020-113526RB-I00.
Moreover, the importance and implications of the accumulation of ARGs in soils have fostered multiple investigations aiming to understand the environmental factors controlling their abundance and diversity from local to global scales  [11, 12]. In recent years, information on soil ARGs has increasingly become available at reference databases such as RefSoil+  [13]. However, there are still major unknowns associated with the global distributions of soil ARGs and with the environmental drivers of their abundance and diversity. For example, despite the recognized importance and risks of increased antimicrobial resistance under global change  [5] , we still lack global atlas with the distributions of soil ARG diversity and abundance. This information is lacking even for the most dominant individual ARGs found across global soils.
Environmental information The coordinates and ecosystem type (grassland, shrubland, forest, or cropland) of each location surveyed were recorded in situ. Information on the total annual temperature and precipitation (BIO1 and 12), as well as on their variability (BIO4 and 15), was obtained from the World-Clim v2 database at 1 km resolution  [21]  (https:// www. world clim. org/ data/ biocl im. html). The climatic variables included here are calculated as explained in  [22]  and are based on highly standardized, well-accepted, and longterm used climatic variables  [21, 22]. Soil pH was measured with a pH meter, in a 1:2. 5 mass: volume soil and water suspension. Soil fine texture (% of fine fractions: clay + silt) and the concentration of soil total organic C and total N were measured using standardized methods  [22, 23].
2 ; also see Supplementary Table  3  for further details on the predictors used and Supplementary Tables  4 5 for all direct associations considered). We grouped the different categories of predictors (climate, soil properties, vegetation, and MGEs) in the same box for graphical simplicity. But please note that these boxes do not represent latent variables (Supplementary Fig. 2 ). Variables within these boxes are allowed to covary, with the exception of elevation and spatial dissimilarity, which constituted our degree of freedom (Supplementary Fig. 2 ). The most globally distributed vegetation types in our database (forests, shrublands, and grasslands) were included in our SEM as categorical variables with two levels: 1 (a given vegetation type) and 0 (remaining vegetation types). Since some of the variables introduced were not normally distributed, the probability that a path coefficient differs from zero was tested using bootstrap tests  [27].
Global mapping Mapping of the proportion and richness of soil ARGs was done using a random forest algorithm  [28]. Model fitting and prediction were done using ArcGIS Pro (ESRI, Redlands, California, USA). Random forest algorithms create hundreds of ensembles of decision trees to create a model that can then be used for prediction  [28, 29]. For each decision tree, a random set of points generated from the training data (here two-thirds of the dataset) is used to calculate a prediction that then contributes to the overall outcome. To ensure compatibility across datasets and variables, we calculated all maps at a spatial resolution of 0. 25°. We considered as predictors the following: climate (mean annual temperature; mean annual precipitation; temperature seasonality; precipitation seasonality), land-use type (forests; grasslands; cropland; shrublands), and vegetation cover, elevation, and soil variables (soil fine texture; carbon content; pH and C:N ratio).
When generating our global maps, information on soil properties was collected from SoilGrids (https:// soilg rids. org) and resampled to 0. 25° by using the mean value of all pixels contained in each cell. The same applied to elevation  [21]  and vegetation cover  [30]. While our global survey covers most of the variability in environmental conditions found on Earth, we left out of our predictions locations of the planet with high uncertainty in our database (areas in gray in Supplementary Fig. 1 ). Our models returned an R 2 of 0. 92 (for richness) and 0. 86 (for relative abundance). Further cross-validation of model fit was done with a randomized set of 10% of the samples. The resulting correlations between predicted and observed richness and proportion of soil ARGs were highly significant in both cases (P < 0.
Results First, we investigated the proportion and richness of ARGs in soils worldwide. Using a histogram, we could infer that while most surveyed soils showed relatively low proportions of ARGs, only a few soils had relatively high proportions of topsoil ARGs (Fig. 2A ). Intermediate levels of ARG richness (40-60 out of 285 ARGs) were common across the soils surveyed (Fig. 2A ). We also found that multidrug resistance genes, and efflux pump machineries, were the most dominant types of ARGs in soils globally (Fig. 3A ). We further identified, for the first time, dominant ARGs that were abundant (within the top 20% most abundant ARGs) and ubiquitous (occurred at > 50% of sites and were present in >2/3 of the global biomes   2. Temp. , temperate. Trop. , tropical. We grouped our data to ensure high enough resolution for all ecosystem sub-types in Supplementary Table  2 ) across global soils.
5B  and D  and 6 ). Croplands did not show significantly different levels of ARG richness compared with most other biomes. However, we would like to stress that most croplands in the present dataset are from Asia, and that future studies would need to better address the global impact of agriculture on soil ARGs. Vegetation structure was also an important predictor of the community composition of individual ARGs (Fig. 6 ). Our findings indicate, therefore, that any land use promoting reforestation or deforestation  [47]  may have important consequences for the global management of soil ARGs. Our SEM analyses further identified a direct and significant positive association between soil ARG richness and temperature seasonality (Fig. 5B  and D ). These results were consistent when removing the subset of locations belonging to croplands (Supplementary Fig.
Conclusions Our results significantly advance our knowledge of the ecology, biogeography, and environmental factors associated with environmental soilborne ARGs. We generated the first atlas of the global distribution of soil ARGs, pinpointing the major global hotspots of soil ARGs, and providing a comprehensive identification of global drivers for their diversity and proportions. We further provide a key database including standardized information on soil ARGs for > 1000 globally distributed locations, which will be useful for soil microbial ecologists to test multiple hypotheses related to the ecology, biogeography, and evolution of soil microbiomes. Together, our results pave the way for an improved understanding on the ecology and global distribution of key microbial traits such as those associated with the topsoil antibiotic resistome and provide key insights to better manage the soil ARG pool and understand the future potential implications of soilborne ARGs for microbial warfare and human health.
Acknowledgements We thank the researchers involved in the CLIMIFUN, MUSGONET, and  BIODE-SERT  projects for collection of field data and soil samples. We thank  Drs. Cesar Plaza ,  Ana Rey , and  Asunción de los Ríos  for their valuable comments on a previous draft of our manuscript. We also thank  Melissa Martin  for revising the English of the manuscript and  Dr. Felipe Bastida  for his help with lab analyses. M. D. B. acknowledges support from the  Spanish Ministry of Science and Innovation  for the  I+D+i  project  PID2020-115813RA-I00  funded by  MCIN/ AEI/10. 13039/501100011033. M. D. B. is also supported by a project of the  Fondo Europeo de Desarrollo Regional (FEDER)  and the  Consejería de Trans-.
Funding This project received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant agreement 702057  (CLIMIFUN), a  Large Research Grant  from the  British Ecological Society  (agreement no. LRA17\1193 ; MUSGONET), and from the  European Research Council  ( ERC grant  agreement no. 647038 , BIODESERT). M. D. B. was also supported by a  Ramón y Cajal grant  ( RYC2018-025483-I ). M. D-B. also acknowledges support from the  Spanish Ministry of Science and Innovation  for the  I+D+i  project  PID2020-115813RA-I00  funded by  MCIN/.
Our framework further delivers a quantification of uncertainties through a confidence interval of the model prediction as well as the relative importance of each input variable. The proposed reconstruction method and resulting product are named OSnet for Ocean Stratification network. The paper is organized as follows. Section 2 introduces the datasets used as inputs and outputs of OSnet as well Ocean Sci. ,  18, 2022  https://doi. org/10. 5194/os-18-1221-2022 as the products used as a benchmark to evaluate the performance of our reconstruction. Section 3 presents the method composed of the neural network and the MLD adjustment. Section 4 evaluates the accuracy of OSnet predictions and presents property maps and sections. OSnet profiles are compared to a mooring; we also compare time series and an analysis of the relative importance of each input for each output.
Temperature and salinity in situ profiles We use the in situ temperature and salinity (T -S) vertical profiles sampled by Argo floats (Argo, 2022) and ships from the CMEMS quality-controlled Coriolis Ocean Dataset for Reanalysis (CORA) database  (Cabanes et al. , 2013; Szekely et al. , 2019). We keep only the profiles extending at least from 25 to 1000 m for the period 1993 to 2019, totaling 67 767 T -S profiles for the region 80 to 30 • W and 23 to 50 • N. All the profiles that do not reach 1000 m or start deeper than 25 m are discarded. Profiles are interpolated on an uneven vertical grid with 51 levels, with spacing increasing with depth (27 levels are within the first 100 m leading to a vertical resolution of 1 m in the upper levels and 450 m at 1000 m depth). Profiles without data at the surface are extrapolated by repeating the shallowest observation point.