dataesr/scientific-paragraphs-categorization huggingface dataset:
Microbial Community Structure and Taxonomic Changes Induced by Treatments Beta diversity analysis indicated that microhabitat was the main factor that influenced the gammaproteobacterial community structure (p = 0. 001; Table  1 ). This factor explained 43. 9% (as shown by PERMANOVA) of total gammaproteobacterial community variation as indicated by separated clustering according to the microhabitat (Figure  4A ; Table  1 ). Sampling sites also influenced the gammaproteobacterial community structure (p = 0. 003) but only explained a small variation of the community (3. 7%). To test if the variation of gammaproteobacterial communities in each microhabitat is influenced by soil amendment treatment and/or farm, deepening analysis by separating datasets from each microhabitat was performed. Principal coordinate analysis (PCoA) plots indicated that the gammaproteobacterial communities of banana plants showed significant grouping in rhizo-and endosphere by the different farms (  Figures 4B, C ; Table  1 ).
Similarly,  Wemheuer et al. (2020)  showed the stability of bacterial communities in cocoa agroforests, while fungal communities changed  (Wemheuer et al. , 2020). Taken together, we here, confirmed former observations and moreover, increased our knowledge of the complex banana agroecosystem. Microbiome management in the soil will be of significant importance to maintain healthy agricultural soils, not only in banana farming. Even though gammaproteobacterial communities were shown to be stable under multiple conditions, future investigations including the whole bacterial as well as multi-kingdom communities should give a better look into soil amendments impact on the banana-associated microbial community. Organic amendments applied over a longer period also deserve attention. accession number(s) can be found below: https://www.
Plain Language Summary The ice in the Arctic Ocean is not continuous: it is broken into individual pieces of ice (floes). As the winds and ocean currents continually move these ice floes, they get piled up together or pushed away from each other, forming regions of increased ice thickness (ridges) or regions of open water (leads). These leads and ridges (ice deformations) are important features of the Arctic pack ice because they control the amount of energy that can be exchanged between the atmosphere and the ocean. Current climate models cannot simulate individual ice floes and their deformations. Instead, various methods are used to represent the movement and deformation of the Arctic sea-ice cover. The goal of the Sea Ice Rheology Experiment (SIREx) is to compare these different methods and evaluate the ability of a large number of sea-ice models to reproduce observed sea-ice deformations from satellite imagery.
We have however verified, using a subset of the participating models, that the Lagrangian deformation statistics presented below are robust to using higher-frequency output for integrating the Lagrangian trajectories (i. e. , hourly means vs. daily means) for both low-and high-resolution model runs (not shown). Note also that most model output were provided as daily means, but some groups provided daily snapshots at an instant in time such as noon. The deformation statistics are also robust to the choice of temporal averaging of the model output (i. e. , snapshots or daily means-not shown). The simulated deformation statistics are compared with those derived from the RGPS Lagrangian motion data set. The RGPS Lagrangian motion data set is given as a list of trajectories (time and position) for a 10 × 10 km grid that is initialized at the beginning of the winter season over the central Arctic Ocean for different satellite passes (i.
e. , streams), tracked using sequential SAR images  (Kwok, 1998). The nominal spatio-temporal resolution of the RGPS Lagrangian data set is T* = 3 days and L* = 10 km, however sampling of the RGPS Lagrangian data set is non-uniform given that trajectories are not always updated on the same days or at the same times or can be missing if the tracking on the SAR images was unsuccessful. For this reason, a pre-processing of  JFNK: Jacobian-Free Newton-Krylov solver) or the number of elastic iterations performed to solve the EVP/EAP equations. The grid spacing is given by the mean horizontal grid spacing within the Arctic Ocean. For unstructured grids it refers to the mean node spacing. Run labels including the symbols ↓P or ↑S indicate that the VP elliptical yield curve was modified by reducing the ice strength in isotropic compression, or by increasing the ice shear strength.
g. ,  Madec et al. , 2019). Trajectories are integrated until 31 March, unless they drift to within 100 km of the model landmask in which case they are terminated. When the Lagrangian integration is done, the hourly model trajectories are sampled at the beginning of the same regular 3-day intervals as for the RGPS Weighted-Average data set. In the case of data gaps in the RGPS data set, we remove the corresponding simulated trajectory to minimize the effects of the different spatio-temporal coverage on the deformation statistics. Note that the neXtSIM trajectories used in this article were also initialized with the same RGPS cell positions on 1 January 1997 and 2008 and are sampled and masked to the corresponding RGPS coverage following the same constraints as described above for other model trajectories.
Dashed lines are the least squares fit to Equation 16 used to derive the degree of multi-fractality μ, the degree of heterogeneity C 1 , and the fluctuation exponent H. The solid black lines, dark gray, and light gray shaded areas are the mean, standard deviation, and min/max for the entire RGPS data set. Model results are separated with low-resolution runs in top panels, and high-resolution runs in bottom panels. 16 ) for the spatial structure function β(q), and for the temporal structure function α(q), for runs in 1997 and RGPS inter-annual variability (boxplots). Dashed areas represent parameters outside the valid range predicted by the multi-fractal formalism. Model results are separated with low-resolution runs in top panels, and high-resolution runs in bottom panels. BOUCHAT ET AL.
Data Availability Statement All published code and data products originating from the SIREx is organized in the SIREx data collection: https://zenodo. org/communities/sirex/: this includes the model output in netCDF format (Bouchat et al. , 2022a), the model Lagrangian trajectories and deformation data  (Bouchat et al. , 2022b) , and the RGPS Lagrangian composite deformation data (Bouchat and Hutter, 2022), as well as the LKF data  (Hutter et al. , 2022b).
All photographs were analyzed using the photoQuad software (version 1. 4) as in  Trygonis and Sini (2012) , and the percent cover of sessile organisms was assessed. Each photograph was calibrated for size and pixels-segmented to the highest level possible (segments ignored < 10 pixels). PhotoQuad's image segmentation is based on the SRM algorithm developed by  Nock and Nielsen (2004). The photograph's size was usually comprised between 3x10 6 pixels to 9x10 6 pixels for a surface area typically around 125 cm 2. For top sides, three main categories describing the settled organisms were identified: (i) Algal turf, (ii) CCA, and (iii) empty (i. e. , tile surface devoid of any recognizable benthic organisms). For bottom tile sides, one more category was assessed, namely: (iv) suspension-feeders (i.
Statistical Analyses and Data Presentation The database for the map (Figure  1 ) was collected from map tiles at www. georep. nc ( © Georep contributors) and customized in QGIS  (version 3. 4. 14). Statistical analyses were conducted and figures were produced using RStudio (R Development Core Team, version 4. 1. 0, 2021), including the packages: "ggplot2", "stats", "dunn. test", "FSA". Homogeneity of variance was tested using the Levene test and the normality of variance distribution was tested using the Shapiro-Wilk test and checked graphically with a Q-Q plot. When data did not conform to normality or homogeneity of variance, we used the non-parametric Wilcoxon test or the Kruskal-Wallis followed by the Dunn's multiple comparisons test (Bonferroni-adjusted). First, for each site separately we used two factors Wilcoxon test (two groups) for each of the three categories of organisms (CCA, Turf, and Empty) settled on the top sides of tiles to test for statistical differences first after two months of incubation (i.
Values were calculated from data collected during August 2017, June 2018 and January 2019 at station M1, and during 3-year long-term monitoring at B3 and R2 considering both diel and seasonal fluctuations, and compiling several thousands of measurements  (Camp et al. , 2017; Maggioni et al. , 2021). The temperature at station M1 was only occasionally recorded during the 2-year study period, therefore not presented. family, with recruits who equally settled at all three stations in terms of relative abundance (14. 85%, 17. 39%, and 16. 83% at B3, R2, and M1, respectively). Only a few Poritidae were found at B3 and M1 (17. 39%, and 6. 52%), and none at M1 which exhibit more unknown genera.
, 2013; Fabricius et al. , 2017; Jorissen et al. , 2021). Our findings seem to provide an alternative scenario using a natural extreme environment where coral recruits survived. In fact, although the extreme conditions reduced the CCA cover in Bourake, after two years of exposure we observed a 3-fold increase in coral recruits when compared with the stations R2 and M1. Recruitment rates recorded in this study are difficult to compare with the literature because values are quite variable. For instance, in French Polynesia, it varies from 0 to 10. 8 recruits tile -1  (Gleason, 1996; Adjeroud et al. , 2007; Bramanti and Edmunds, 2016) , and it was less in the Virgin Islands (average of 3 recruits tile - 1 ;  Edmunds, 2021)  and in Palau (0. 3 to 3. 9 recruits tile -1 ;  Victor, 2008) , while no data is available for New Caledonia.
ACKNOWLEDGMENTS We wish to express our thanks to the captains of the IRD vessels and the diving technical staff for their fieldwork assistance. We thank  Dr. Mehdi Adjeroud  for providing us with the tiles used and for scientific advice on coral recruitment. We thank  Dr. Claude Payri  and  Dr. Christopher Cornwall  for their help and expertise regarding the assessment of organisms percentage cover and for CCA taxonomy and health review. We also thank the  Province Sud of New-Caledonia  for sample collection permits (no. 3413-2019 ). We are indebted to  Federica Maggioni  for environmental data sharing. We thank  Yuri Artioli  for his help throughout the reviewing and editing process of this article.
FUNDING This study was supported by the  French grant scheme Fonds Pacifique  (no. 1976 ; project  SuperCoraux ). CT received a PhD fellowship from the  University of Western Brittany (UBO)  and the  Brittany region (France). This study has also received financial support from the  CNRS  through the MITI interdisciplinary programs and was supported by  ISblue  project , Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ) and cofunded by a grant from the  French government  under the program " Investissements d'Avenir ". Funding for FB was provided by  KAUST  baseline research funding and award  FCC/1/1973-49-01.
A detailed image of the deformation will help to constrain the structure and evolution of the margin prior and coeval with the continental rifting and answer some of the remaining questions: Does the stretching and the thinning of the continental crust occur over a narrow or broad region? Is the rifting smooth or stepped? Is the existent of the differential extension between the crustal layers from the early stages or only during the late stages of the rift evolution? What are the interactions between mechanical stretching and thinning of the crust, and magma intrusions (e. g. Keir et al. , 2013) ? In order to resolve the high-resolution crustal structure of the western margin of the Afar Depression for the first time, we analyze data from 24 temporary broadband stations distributed along two densely sampled profiles going from the western plateau to central and northern Afar.
1). For central Afar we used 15 stations (S001, S003 to S009, S011 to S014, S017, S018 and S020) deployed along a ~225 km-long profile spaced at ~20 km at the ends and ~10 km in the middle of the profile  (Doubre et al. , 2021, Fig. 1). For improved continuity of the southern profile, we added data from FINE station installed from March 2007 to November 2009  (Ebinger, 2007). All stations recorded continuous data with a sampling rate of 100 Hz except FINE, which recorded continuous data at 50 Hz. For the receiver function analysis, the IRIS earthquake catalog was searched for events with magnitude, Mb ≥ 5. 5, occurring within an epicentral distance range of 30 • -95 • from the center of the network. To increase the azimuthal coverage, regional events between 20 • and 30 • were also included (e.
g. Park and Levin, 2001; Salmon et al. , 2011, Fig. 2a ), with a specific treatment detailed in section 3. 2. 1. In total, waveforms from 186 earthquakes including 11 regional events were selected. The data were manually inspected and rated based on the signal-tonoise ratio (S/N; the ratio between the amplitude of the signal at Parrival time to the maximum amplitude of the 5 s pre-arrival noise window), where only low-noise traces (S/N > 4) with a clear P wave arrival recorded at each station, were kept for further analysis. This process resulted in the selection of ~126 earthquakes (including 11 regional events) for receiver function construction and processing (Fig. 2a ). After removing the mean and the first-order trends from the selected waveforms, the data were filtered using a zero-phase Butterworth bandpass filter with corner frequencies of 0.
Conclusion We use the receiver function technique and teleseismic data recorded by a temporary broadband seismic array deployed along two profiles crossing the western margin of Afar to constrain the tectonic processes that are involved in the evolution of the rift. Our results provide constraints on the following characteristics of the crustal thickness and composition: (1) The crustal thickness beneath the western plateau is 37-43 km, and decreases eastward to its minimum ranges from 14 to 23 km beneath the central rift axis. The decrease in the crustal thickness is more pronounced beneath the foothills and marginal grabens, but the crust also thins elsewhere in discrete steps. This is consistent with jumps in strain localization during rifting, rather than progressive strain migration. The crustal thinning of northern Afar points towards a β of 2.
Acknowledgments Data used in this study were collected by the  AFAR17  project in Ethiopia. The instruments for the project were provided by the  French National Research Infrastructure SISMOB-RESIF  (http://www. resif. fr)and  SEIS-UK. RESIF is supported by the  French Ministry of Research and Higher Education. The facilities of SEIS-UK are supported by the  Natural Environment Research Council (NERC)  under agreement  R8/H10/64. Funding for fieldwork (deployments, data collection and maintenance) is from  Sorbonne Université, ISTEP, Institute des Sciences de la Terre de Paris. AA is supported by the  Sorbonne Université (ISTeP) , the  University of Strasbourg  ( FACE program ) and the  PAUSE program. DK is supported by  NERC  grantNE /L013932/1 , and  MIUR  grant  PRIN 2017P9AT72. CP and ALR acknowledge support by the  University of Pisa  grant  PRA_2018_19.
The main objective of this study was to assess the groundwater recharge and the potential of the SFR water transfer to increase groundwater availability in the Salgado watershed by channel transmission losses. In order to accomplish this goal, we developed an integrated modelling approach (WASA-MODFLOW) to improve estimations of the groundwater resources in this watershed, taking into account the spatio-temporal variability of in ltrated rainfall for aquifer recharge. Groundwater models, such as MODFLOW  (McDonald and Harbaugh 1984) , can be useful to assess scenarios of surface water and groundwater management. For instance, aquifer recharge by channel transmission losses occurring at streambeds and banks can be represented in MODFLOW using a built-in model package that calculates the exchange ux between streams and groundwater.
Groundwater model parameterization There are several open source codes available to model groundwater ow, such as FEFLOW, OpenGeoSys or AquiMod. For this study, the choice of MODFLOW was made because of its capability to simulate all considered subsurface ow processes, and to be consistent with existing studies in the area thus facilitating comparison with these studies. We used the Groundwater Modelling System (GMS) software package to set up MODFLOW. The model domain was designed to represent the Medium aquifer system. Its spatial discretization, a rectangular structured grid, which was required to apply the MODFLOW's nite volume technique, was selected to simulate the regional groundwater ow without imposing a computational burden. Thus, we set a xed grid size of 500 x 500 m. The resulting 3D grid (XYZ) had 208 cells in the X direction, 159 cells in the Y direction and 3 vertical layers, which was based on the main hydrogeological formations in the Medium aquifer system (Figure  2 ).
This interaction was represented in the model by the MODFLOW Recharge (RCH) package. The contribution of natural springs located at the cliff of the Araripe plateau to the recharge of the Medium aquifer was estimated using a database from COGERH and the National Department of Mineral Production (DNPM). The database lists 320 natural springs with data of annual water production and annual authorized water withdrawal. Information of pumping wells were retrieved from the database of the Brazilian Geological Survey (CPRM), called SIAGAS (http://siagasweb. cprm. gov. br/layout/pesquisa_complexa. php). Wells with no data concerning depth or pumping rate were excluded. In total, 532 wells were registered within the model domain (Figure  3 ). They were implemented in the model by the MODFLOW Well (WEL) package, using elevation, depth and pumping rate (m 3 /d).
Because screen depths were not available for these wells, either a default screen of 10 m was chosen or the wells were assigned automatically to the rst layer only ( rst 100 m) without using screens. The SIAGAS database also provided information about the type of groundwater use (public, domestic, irrigation, leisure or multiple uses), as well as the allowed hourly pumping rate. The daily pumping rate implemented in MODFLOW was based on Golder-Pivot (2005), Rede Cooperativa de Pesquisa (2007) and the World Bank (2011). For each well with a pumping rate inferior or equal to 50 m 3 /h, a daily pumping regime of 8 hours was assumed, corresponding to private wells for domestic water use. For wells with a pumping rate superior to 50 m 3 /h, a daily pumping regime of 20 hours was assumed, corresponding to public and irrigation wells, likely to be pumping water more intensively.
The sum of these 532 pumping rates was the arti cial discharge of the Medium aquifer. Assuming that the yearly water demand remained constant during the MODFLOW simulation period (2010-2016), the wells represented an abstraction capacity of 7. 14 x10 7 m 3 of water per year. Streams were added to the groundwater model using the MODFLOW Stream ow Routing (SFR2) package. We adopted the simpli ed Manning equation, which required values of hydraulic conductivity of the streambed, width, sinuosity, roughness and value of incoming ow for each stream segment. The package automatically computes ow between the stream and the aquifer, including the channel transmission losses. Values for hydraulic conductivity of the streambed and stream geometry for each segment were retrieved from eld data (FUNCEME 2018).
For stream reaches, where no eld data was available, the values were inferred from the closest measured stream. We considered the reservoir water release as upstream input discharge for some stream reaches. The recharge to the Medium aquifer works as the exchange variable between the surface water model WASA and the groundwater model MODFLOW, i. e. the in ltrated rainfall. Recharge was implemented in the groundwater model as a recharge rate assigned to a polygon, which was provided by the GMS package. Then, the recharge rate was applied to the most upper active cell. The interaction with the Superior aquifer was also represented using the RCH package at the western boundary of the model as aforementioned. The estimated contribution from the Superior aquifer using RCH was added to the recharge calculated by WASA.
Hydrological model calibration For the three aforementioned sub-basins covering the area of the Medium aquifer, WASA was run from January 1990 to December 2016, using the rst ve years as model warm-up period. As the aim of the study was to model the regional groundwater ow over time periods of several years, the calibration of WASA primarily focused on the monthly time scale. Data available were daily discharge time series, measured at stream ow gauges located at the outlet of the sub-basins. Thus, the average river ow simulated by WASA was compared to the average river discharge at the stream ow gauge on a monthly basis. The model was then calibrated to t the simulations to the observations, by adjusting the saturated hydraulic conductivity and the porosity of the soil pro les in a manual trial-and-error process.
Potential of the São Francisco River transfer for the recharge of the Medium aquifer Once the integrated model was calibrated, CAC could be represented in the model, as a new channel with the SFR2 package of MODFLOW, releasing water to the main streams in the area. This new input of water to the streams might generate recharge into the Medium aquifer by channel transmission losses. By carrying out a scenario analysis, i. e. comparing simulations with and without CAC, the effects of this new water management system on the groundwater ow could be assessed. For the scenario analysis, we consider a context of a prolonged drought period, i. e. low groundwater recharge. Scenario analysis approach has been adopted to study the impact of human interventions on surface/groundwater resources in other drylands  (Bauer et al.
2006; Izady et al. 2015). The CAC route was retrieved from a COGERH shape le and used to create a new SFR2 segment. The channel elevations were estimated from the SRTM raster. The channel width was uniformly set to 20 m, based on design documents. The channel is concreted to preserve the ow of water, thus hydraulic conductivity of the streambed was set to 0. The design in ow of 30 m 3 /s was set at the entrance of the segment. An evaporation value of 1. 5 mm/d was also set to account for the evaporation at the water surface. No data regarding the potential amount of water released from the canal to the sub-basin streams were available. Thus, an arbitrary value of 0. 29 m³/s (5,000 m³/d) was set as in ow to each of the ve streams crossed by CAC, in order to preserve the ow to the next canal segments.
Here, the recharge was applied as a monthly varying stress entering the system to account for the intra-annual variability caused by distinct seasonality and the interannual variability caused by the highly variable annual precipitation. Groundwater ow was then simulated using a monthly time step from September 2012 to September 2016. As stated earlier, the dataset resulting from the steady state calibration in September 2011 was exported and used as the initial heads for the transient simulation of the groundwater ow in the Medium aquifer. The main issue was compensating the error introduced by the initial discrepancy in storage, which led to an enormous amount of water leaving the aquifer at the streams in the lowest elevation part of the model domain (NE part, downstream end of the main river network), where the groundwater level was simulated as being several meters above the streambed elevation.