dataesr/scientific-paragraphs-categorization huggingface dataset:
While trends in water levels are readily apparent from gages along major rivers, understanding their impacts on ecological and biogeochemical processes and human communities requires quantifying how higher water levels translate into commensurate increases in flooding extent and duration, as well as hydrological connectivity of seasonally inundated floodplain areas, which have not yet been analyzed. Here, we use hydrological-hydrodynamic models (MGB (Siqueira et al 2018) and CaMa-Flood  (Yamazaki et al 2011) ) and long-term satellite-derived datasets (see section 2), supported by in-situ observations, to analyze for the first time a 40 year record of floodplain inundation patterns in relation to basinwide rainfall changes.
Thus, the lower river reaches, mainly downstream of the Negro-Amazon river confluence, are suitable for the use of GSWO. In upper reaches, inundated forests are more common, although floodplain lakes bordered with non-forest vegetation are present and are used to assess long-term changes. Besides being in agreement with the other inundation datasets and the increasing trend for water levels across the Amazon River (figure  3 ), the ability of GSWO to represent open water inundation changes was considered satisfactory given its correlation with the in-situ water levels in the Amazon River at Óbidos gage (figure S5(a)), which shows the high agreement between annual maxima of in-situ water level measurements and annual maxima of inundation area (R = 0. 81, P < 0. 001). The GSWO version used here is provided at a monthly basis and converted to annual maximum and minimum maps to reduce the uncertainties related to lack of images due to cloud-cover, especially during wet season.
(Tapley et al 2004 , Landerer et al 2020) , based on the JPL RL06M Mascon solution, were used to investigate the impact of extremely wet years on seasonal water storage in the central Amazon. Given its short-term data availability (since 2003) and coarse resolution (∼300 km), no long-term trend analysis was performed, but the GRACE data were used to infer large-scale water storage patterns. In-situ observations of river water levels were obtained from the Brazil's National Water Agency for all gages in the Brazilian Amazon basin, in addition to in-situ data from the Peru's SENAMHI for the Tamshiyacu gage on the Amazon River close to the city of Iquitos. Because of delayed data availability, at the time of this study some gages did not yet have data available for the 2021 extreme flood event that occurred in central Amazon (Espinoza et al 2022); the gages which data covered the 2021 flood are highlighted in figure  1(a).
Trend analysis Maximum and minimum annual inundation trends obtained from  MGB (1980 MGB ( -2014)) ,  CaMa-Flood (1980 -2014)  and  GSWO datasets (1984 GSWO datasets ( -2020) )  were analyzed individually without any type of merging, and spatially mapped with the rank-based nonparametric Mann Kendall trend test (Kendall and Gibbons 1975) at the product resolution (500 m for MGB and CaMa-Flood and 30 m for GSWO), i. e. for each pixel the trend was assessed considering the entire inundation time series. The long-term changes in annual maximum and minimum flooded areas, as well as annual amplitudes (i. e. annual maxima minus minima), for the Amazon mainstem floodplain were obtained by applying a linear fit to the annual series, and then computing the change between the inundation in the first and last years of the adjusted line.
The year 2021 was not considered since it was not available in GSWO or with the hydrological models. In the case of the models, data were only available until 2014 because the model forcings (i. e. rainfall and runoff) did not extend to more recent years. Although updated versions of the models are available until present time with different model forcings than those used in the consolidated versions, the new runs do not satisfactorily represent the water level trends across central Amazon due to unsatisfactory bias correction, and thus are unsuitable to assess inundation trends  (Yamazaki, pers. comm. ; Brêda, pers. comm. ). Given the unsuitability of GSWO for forested floodplains and issues with cloud cover, only MGB and CaMa-Flood were used to estimate the long-term changes in inundation extent for the entire Amazon mainstem floodplain.
Open water lateral connectivity Open water lateral connectivity and flood duration changes were computed over the  1985-1996 and 2009-2020  decades with the GSWO dataset for the lower Amazon floodplain between Manaus and Gurupá, which is characterized by an abundance of large lakes (Sippel et al 1992). To estimate connectivity changes, we used the methodology presented by  Trigg et al (2013). This analysis estimates, for a given floodplain area, the degree of connectivity across a given direction, measured as the number of pixel pairs connected across a given distance. The direction adopted for each analyzed area refers to the longest dimension of the floodplain unit, e. g. west-east for the Curuai floodplain (figure  3(b) ). For the flood duration changes, long-term maps (i. e. estimation of how many days per year a given pixel was, on average, inundated) were computed for the decades  1985-1996 and 2009-2020, and  subtracted from each other.
Along the lower mainstem region (at the Óbidos gage station), where the largest increase is observed, the average maximum water level has risen by 87 cm (13% of the mean annual amplitude of ∼6. 90 m, computed for the period  2009-2021) between 1985-1996 and 2009-2021. These periods refer to the earliest and latest multi-year periods available from the GSWO dataset. In contrast to maximum water levels, annual minima along the central Amazon have remained unchanged over the past 40 years (figure  S2 ). The decrease in rainfall in parts of the southern basin coincides with a negative trend of annual minimum discharge documented in the upper Madeira River (at the Porto Velho gage station), which includes the extensive Llanos de Moxos wetlands along the Mamoré River (Espinoza et al 2019b). Additional evidence for temporal changes in floodplain water storage, although over a shorter period, is provided by the total water storage variation along the central Amazon floodplains estimated from gravity field variations by the GRACE and GRACE-FO satellite missions since 2002 (figure  1(d) ).
Understanding how these changes may interact either synergistically or antagonistically is important for planning dams and navigation channels as well as regulating deforestation. Furthermore, although our estimates based on hydrologic-hydrodynamic modeling considered a natural basin scenario, without the effects of anthropogenic impacts such as dams  (Chaudhari and Pokhrel 2022, Flecker et al 2022) , the impacts of rainfall changes on inundation extent reported here are well aligned with evidence from other, rainfallindependent remote sensing datasets. It is important to stress that such models have simplifications such as time-constant and rectangular river channel cross sections, which are necessary to be assumed due to scarcity of detailed, in situ data to be used in parameterization. However, their ability to simulate large-scale inundation extent and rainfall-runoff processes has been thoroughly investigated and is considered satisfactory  (Yamazaki et al 2012 , Paiva et al 2013 , Siqueira et al 2018 , Wongchuig et al 2019 , Fleischmann et al 2022).
Conclusions The Amazon River basin has been facing multiple extreme events in the last decade. Here, we used two hydrological models and multiple remote sensing datasets to investigate the changes in the inundation dynamics of the Amazon since 1980. Hydrological models show that since then the maximum inundation extent of the central Amazon floodplain has increased by 26%, or ∼25 000 km 2 -an area equivalent to the size of Belgium. The Landsat-based GSWO dataset revealed increases in flooding duration and river-floodplain connectivity in the lower Amazon reaches, where multiple open water areas are common. Such changes have multiple consequences for floodplain ecosystems and the people that rely on them. Governments from local to national, as well as international organizations, must strengthen mitigation measures, particularly for vulnerable populations across the Amazon river-floodplain system.
Acknowledgments The work was part of the  SABERES  project funded by the  BNP Paribas Foundation  as part of its ' Climate & Biodiversity Initiative' 2019 program , led by  Dr Marie-Paule Bonnet. The authors acknowledge  Dr David Kaplan ,  Dr David da Motta Marques ,  Dr Naziano Filizola  and  Dr Fernando Jaramillo  for their collaboration on an earlier version of this manuscript. Funding  CNPq  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil ) Grant  141161/2017-5  (ASF)  CNES  ( SWOT-ST  project  SWOT for SOUTH AMERICA ) Grant  6018-4500066497  (FP,  MPB )  CNES   SWOT-ST  project  SWOT Wetlands Hydrology Monitoring (FP ,  MPB) IRD Groupement De Recherche International (GDRI) SCaHyLab (FP)   NASA IDS  Grant  NNX17AK49G  (JMM)  US National Science Foundation (Division of Environmental Biology)  Grant  1753856  (JMM)  NSF -Belmont Forum Award   1852113  (LC)  ANR  and  IRD  Grant  ANR-18-MPGA-0008  ( AMANECER-MOPGA  project) (SWC,  JCE )  JSPS KAKENHI  Grant  21H05002  (DY, MR)  BNP Paribas Foundation  ( SABERES Project -Climate & Biodiversity Initiative ).
Data and materials availability All data used in this research are freely available, and described in the Materials and Methods section. In-situ river water level and discharge data from Brazilian National Water Agency are available at www. snirh. gov. br/hidroweb/, and from Peru's SENAMHI at www. senamhi. gob. pe/?p=pronosticometeorologico. Documentation and outputs for the MGB and CaMa-Flood models are available at www. ufrgs. br/lsh/ and http://hydro. iis. u-tokyo. acjp/ ∼yamadai/cama-flood, respectively, and GSWO data are available at Google Earth Engine (more details at https://global-surface-water. appspot. com). CHIRPS rainfall is available at www. chc. ucsb. edu/data/chirps. GRACE data from JPL were obtained at https:// podaac-tools. jpl. nasa. gov/drive/files/allData/tellus/ L3/mascon/RL06/JPL/v02/CRI/netcdf.
First, by using a total station, we mapped in detail each site to retrieve, with a precision of a few centimeters, the location and altitude of each microatoll, relative to the total station base. We used the GPS location of the total station and of sampled microatolls with WGS 84 coordinates to obtain its absolute spatial location. We also measured the tide at different times of the day to compare our data with the recorded tide and estimated an absolute value of altitude above the 2018 mean sea-level. For each microatoll, we measured the external ring (actual HLS for living corals), the center, and when possible, other visible rings and the substrate on which the coral developed (i. e. , the base of the coral). The 2018 HLS at the scale of the study area and that of a single microatoll are provided with mean precision of ±3 and ±1 cm, respectively.
Climatic Source of the RSL Signal: Insight From Tide Gauge Data To better understand the RSL changes and their sources, we analyzed the tidal records available around the southern Ryukyus (Figure  12 ). We selected five tide gauge stations from Permanent Service for Mean Sea-Level (PSMSL) data between Taiwan in the south and Naze on the Amami island in the north (locations in Figure  1 ; https://www. psmsl. org/data/obtaining/). The length of the record ranges between 38 years for Taiwan to 56 years for Naze. All tidegraphs show mean relative submergence along the Ryukyu arc and in Taiwan. We calculated the longterm trend in RSL rate for each tide record by linear regression. The obtained value varies from 0. 4 ± 0. 1 to 3. 0 ± 0. 7 mm/yr in Taiwan and in Ishigaki, respectively. A first drop of 10 cm occurred in 1968 and was recorded by the Naze, Naha and Taiwan tide gauges.
In 1979, a large drop of 7 cm in amplitude is observed by the Taiwan tidegraph and is also observable with a smaller amplitude at other sites. In 1984 the Ishigaki tide gauge documented a large drop of 7 cm in amplitude, which was also observed in Naha and Okinawa, and a few years later in Naze and Taiwan. We noted another main drop in 1993 in all tide gauge records, with an amplitude ranging     6 ). Orange circles and dashed lines are sea-level drops documented by our coral microatolls. Bold lines correspond to die-downs observed in the Highest Level of Survival curve of our microatolls. Gray area indicates the period of specific relative sea level pattern observed during events 8/9. 1. Tide data are from PSMSL. GPS data are GNSS data obtained from Geospatial Information Authority of Japan (GSI).
For the calculation of annual uplift (vertical velocity) by GNSS, we used the GNSS sites data of GEONET in the Ryukyu Islands which is operated by the GSI. We employed the vertical component of daily solutions of F3 (final) at the sites for the analysis. After removing the artificial height change due to the renewal of the equipment, the linear approximation was performed by the least squares method to obtain the secular change. In columns 4, 5, and 6, positive values indicate rising RSL, uplift and rising absolute sea-level, respectively. We do not use uncertainty as it is a first-order estimation. between 2 and 7 cm. In 2002, a small drop of a few centimeters is observed mostly in the central Ryukyu stations. In 2013, the Ishigaki tide gauge recorded a large drop of 10 cm, which was also recorded by the Okinawa, Naha, and Naze tide stations.
, 2012). Red curve: vertical deformation rate promoted by backslip along the dislocation (fault 1). Dashed black curve: vertical deformation rate (mm/yr) promoted by backslip along fault 2 between 0 and 60 km using theoretical faults tangential to megathrust kink  (Kanda & Simons, 2012). (b) Dark gray area: vertical deformation rate promoted by backslip with seismic coupling between 15% and 40% along fault 1 between 0 and 35 km. Note. For the calculation of annual uplift (vertical velocity) by GNSS, we used the GNSS sites data of GEONET in the Ryukyu Islands which is operated by the Geospatial Information Authority of Japan (GSI). We employed the vertical component of the daily solution of F3 (final) at the sites for the analysis. After removing the artificial height change due to the renewal of the equipment, the linear approximation was performed with the least squares method to obtain the secular change.
Acknowledgments The authors are grateful to all the people who helped in the sampling and the analysis of the coral slices. A special thanks to  H. Carton  for the fruitful proofreading and advices on the manuscript. The corals were sliced thank to the  Milano Industry Co. Ltd (Ogyu Manufacturing Inc. ). The slices were X-rayed with the kind help of  Prof. M. Mochizuki  from the  Department of Veterinary Medical Sciences , the  University of Tokyo , and CT-scanned with the contribution of  K. Seike 's team from  National Institute of Advanced Industrial Science and Technology (AIST). We are also very grateful to  J. Weil-Accardo  for her helpful advices on the coral analysis process. We are grateful to the reviewers  O. Oncken  and  J. Zachariasen  for constructive remarks that helped improve the manuscript.
Field work and coral analyses were funded by the  JSPS KAKENHI  Grant  JP16H01838. U/Th dating was supported by Grants from the  Science Vanguard Research Program  of the  Ministry of Science and Technology, Taiwan ,  ROC  ( 110-2123-M-002-009 ), the  Higher Education Sprout Project of the Ministry of Education, Taiwan, ROC  ( 110L901001  and  110L8907 ), and the  National Taiwan University  ( 109L8926 ) to C. -C. S. This work was supported by the  Institut National des Sciences de l'Univers, Centre National de la Recherche Scientifique  ( CNRS-INSU Alea program   2019 ), the  Institut de Physique du Globe de Paris  ( BQR IPGP 2018 ), the  Ministère de l'enseignement supérieur, de la Recherche et de l'Innovation and the Ecole Doctorale UPC-IPGP. This is IPGP contribution 4277.
STRATEGIES THAT SHOULD BE IMPLEMENTED FOR A SUCCESSFUL OUTCOME OF A MULTI-OMICS STUDY Due to their nature, -omics data are intrinsically both highly variable and noisy, leading to several issues when trying to compare or reproduce them. When planning an experiment, several steps should be considered: experimental design and challenges, individual -omics datasets, integration and data issues, and biological knowledge. Here, we present a summary of the workshop discussions in the context of these key themes.
Metadata collection Another important aspect that should be considered is to record the metadata associated with samples  (Ryan, Schloter, Berg, Kinkel et al. , 2021)  this reference is the one published in Environmental microbiology. This is essential when an -omics protocol is applied, since metadata are not often standardized between biological systems or methods and are often mis-annotated or ambiguous, generating an overly complex environment for sample reanalysis in the event of novel methods and bioinformatic techniques  (Kasmanas et al. , 2021). Several metadata standards are already available from the NCBI and the Genomes OnLine Database (GOLD), while consortia such as the International Human Microbiome Standards promote standard operating procedures in human microbiome research. Several consortia have already proposed "gold standards" that should be employed.
An example is given from the area of agricultural microbiome research  (Dundore-Arias et al. , 2020). A lack of complete or comprehensive metadata attributable to an experiment or even the absence of entire experimental datasets (including nucleic acid and protein sequence, metabolites, spectra and images) from public repositories also makes it extremely difficult for the scientific community to reuse valuable data for meta-analyses or comparative studies ("Overcoming hurdles. ," 2017). Some onus must also be placed upon the scientific journals, such as obliging authors to publish both their datasets and associated metadata in accordance with predefined standards, depending on the field. This effort will encourage researchers to preserve their data in repositories and by enabling its reuse, increase its value to microbiome research  (Hu et al.
, 2013). Frequent practice is to discard unannotated microbes or unannotated molecules, focusing on the subset of microbes or molecules that can be matched to an existing database. Alternate strategies do not lump unknowns, hypotheticals, and "others" together but treat them as yet-uncharacterized entities that can be identified by a single sequence, enabling future association of function with pure culture and genomic data from more and more microorganisms. Whenever possible, machine learning protocols should be applied to predicting the function of such uncharacterized sequences, with the stated goal of increasing understanding and enlarging the existing databases. Moreover, applying an extensive and systematic collection of microbe maps and molecules across different biological environments can improve our understanding of microbiome structure and function.
This same approach also improves existing databases by reducing the proportion of unknown molecules/proteins/genomes. In vitro studies (protein expression at single cell or single-cell sequencing)  (Pineda et al. , 2020)  and microbe interaction studies can help decipher the unknown function/potential of the microbes and may be a valuable tool to enlarge databases. The use of cell culture from microbial communities remains a challenge due to the interactions that often become too complex when communities grow beyond two or three members. However, the application of model-based identification of key microbial members that can co-grow to produce a specific outcome would be a valuable contribution to databases  (Kessell et al. , 2020). It should be mentioned that several repositories that are currently available contain hundreds of thousands of samples (just to cite few: QIITA https://qiita.
org/protocolsand-standards/)  (Nannipieri et al. , 2019)  that could be extended to other -omics technologies to enable comparability and integrability of datasets from different studies. Notably, a range of different -omics technologies, including transcriptomics, proteomics, and metabolomics, were applied to study soil microbiomes  (Biswas & Sarkar, 2018)  in the past. However, many studies applied one technology only, resulting in limited insight into the complexity of soil microbiomes. The need to go beyond taxonomic and onedimensional functional studies was elaborated in several excellent reviews  (Jansson & Hofmockel, 2018; Mishra et al. , 2022; Nannipieri et al. , 2019)  and will thus not be discussed further here. Rather, we will describe some recent success stories that showcase how the integration of multiple-omics approaches contributed to a better understanding of the soil microbiomes and their function.
However, as of yet, metaproteomic analysis is still unable to achieve the information content or sampling depth of sequencing-based technologies. Indeed, highly abundant and stably expressed conserved proteins typically dominate in metaproteomic datasets, resulting in artificially stable profiles being generated  (Ferrer et al. , 2013). Despite this challenge, it should be noted that current limitations can be addressed by refining protein preparation protocols  (Xiong et al. , 2015)  and improved identification  (Tanca et al. , 2016). Another -omic approach, metabolomics, involving the direct measurement of metabolic outcomes, is likely to offer greater sensitivity in resolving the functional microbiome. Indeed, quantitative methods, such as proton nuclear magnetic resonance (NMR) analyses, are able to capture some of the most important, high-abundance microbial metabolites, such as short-chain fatty acids.
While advanced metabolomic methods allow the resolution of thousands of metabolic features from human microbiome samples, current limitations relate to the very large fraction of unknown metabolites that can be generated (more than 90% of measured features may be unknown) even when comparing metabolomic data to comprehensive databases, as well as the challenges of relating specific metabolic features to their microbial provenance. Advances in computational mass spectrometry, de novo reconstruction, and modeling of metabolic networks have the potential to address some of these limitations and help understand the functional microbiome. A final -omic approach of relevance is culturomics. Notably, in the past, when traditional culture methods were employed, it was estimated that only 10%-25% of the gut microbiota could be isolated and identified  (Jandhyala et al.
DATA INTEGRATION: CURRENT APPROACHES AND REMAINING HURDLES Complex cellular functions depend on the interplay between genes, transcripts, proteins, metabolites, and other biological molecules. Because of this complexity, if a biological system is to be understood with the greatest accuracy and detail, the methods used must try, as much as is possible, to carry out a simultaneous and integrated 15414337, 0, Downloaded from https://ift. onlinelibrary. wiley. com/doi/10. 1111/1541-4337. 13103 by CochraneAustria, Wiley Online Library on [13/01/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License analysis of these molecules. This type of multi-omic data integration offers a powerful way to view a cellular system but can be fraught with challenges  (Jiang et al.