dataesr/scientific-paragraphs-categorization huggingface dataset:
Testing the reliability of improved scenarios using climate model simulations A second test of the realism of the improved LULC scenarios is to examine whether incorporating LULC changes improves the realism of the simulated climate when compared to palaeoclimate reconstructions (Fig. 8 ). The mid-Holocene (6000 years ago, 6 ka BP) is an ideal candidate for such a test because benchmark datasets of quantitative climate reconstructions are available (e. g. Bartlein et al. , 2011) , the interval has been a focus through multiple phases of PMIP, control simulations with no LULC have already been run, and evaluation of these simulations has identified regions where there are major discrepancies between simulated and reconstructed climates e. g. the observed expansion of Northern Hemisphere monsoons, climate changes over Europe, the magnitude of high-latitude warming, and wetter conditions in central Eurasia  (Mauri et al.
The real strength of the revised LULC scenarios is to provide boundary conditions for transient climate model simulations. The CMIP6-PMIP4 simulation of 850-1850 CE (past1000) already incorporates LULC changes as a forcing  (Jungclaus et al. , 2017)  based on a harmonized dataset that provides LULC changes from 850 through 2015 CE  (Hurtt et al. , 2017) , which in turn draws on output from the HYDE3. 2 scenario  (Klein Goldewijk et al. , 2017a). The past1000 protocol  (Jungclaus et al. , 2017)  acknowledges that this default land use dataset is at the lower end of the spread in estimates of early agricultural area indicated by other LULC scenarios and recommends that modelling groups run additional sensitivity experiments using alternative maximum and minimum scenarios. The revised LULC scenarios created by LandCover6k could be used as an alternative to these maximum and minimum scenarios.
LULC changes should be implemented by imposing crop and pasture area through time as specified in the revised LULC scenarios; elsewhere, the simulated vegetation should be active. It will be necessary to run the Holocene transient climate simulation in two steps. A first simulation (holotrans_LULC) should be run using prescribed atmospheric CO 2 concentration, even though the carbon cycle is fully interactive, because this will establish the consistency of the carbon cycle in the land surface model. However, once this is done it will be possible to rerun the simulations with interactive CO 2 emissions. Table  3  provides a summary of the proposed ESM simulations. Unlike the situation for the mid-Holocene, wherein there is a global climate benchmark dataset  (Bartlein et al. , 2011)  which provides reconstructions of multiple bioclimatic variables of seasonal temperature and moisture, the opportunities for quantitative evaluation of the holotrans simulated climate are more limited.
Seasonal temperature reconstructions are available for Europe  (Davis et al. , 2003)  and North America  (Viau et al. , 2006; Viau and Gajewski, 2009). Although there is a new global dataset that provides global temperature reconstructions for the Holocene  (Kaufman et al. , 2020) , it is based on only 472 terrestrial records worldwide, and the results for zonally averaged temperature changes are therefore likely to be more robust than the regional details. There are also time series reconstructions for individual sites outside these two regions (e. g. Nakagawa et al. , 2002; Wilmshurst et al. , 2007; Ortega-Rosas et al. , 2008). Furthermore, the simulated time course of CO 2 emissions can be compared to the ice core records. The CMIP6-PMIP4 midHolocene simulations are stylized experiments lacking several potential forcings (in addition to LULC), including changes in atmospheric dust loading, solar irradiance, and volcanic forcing.
, 2016; Tierney et al. , 2017; Messori et al. , 2019). Although continuous reconstructions of dust loading through the Holocene are not available, it would be possible to use estimates for particular time slices  (Egerer et al. , 2018)  to test the sensitivity to this forcing. LandCover6k has developed a scheme for using archaeological information to improve existing scenarios of LULC changes during the Holocene, specifically by using archaeological data to provide better estimates of regional population changes through time, better information on the date of the initiation of agriculture in a region, more regionally specific information about the type of land use, and more nuanced information about land use per capita than currently implemented in the LULC scenarios generated by HYDE and KK10.
While the final global datasets are still in production, fast-track priority products have been created and their impact on current scenarios is being tested. Although the work of LandCover6k will provide more solid knowledge about anthropogenic modification of the landscape, some information will inevitably be missing and some key regions will be poorly covered. There will still be large uncertainties associated with revised LULC scenarios, even though these will be based on more solid evidence than the existing LULC scenarios. Documenting the uncertainties in the archaeological inputs and their impacts on the revised scenarios is an important goal of the LandCover6k project. We propose using the information about the uncertainties in the archaeological data sources to generate multiple LULC scenarios comparable to the "low-end" and "high-end" scenarios used for e.
Acknowledgements. LandCover6k is a working group of the  Past Global Changes (PAGES)  programme, which in turn received support from the  Swiss Academy of Sciences. We thank  PAGES  for their support for this activity. The land use group also received funding under the  Holocene Global Landuse International Focus Group of INQUA. SPH  acknowledges funding from the  European Research Council  for "GC2. 0:Unlocking the past for a clearer future". Marie-José Gaillard  thanks the  Swedish Strategic Research Area MERGE  ( Modelling the Regional and Global Earth System ) and  Linnaeus University's faculty of Health and Life Sciences (Kalmar, Sweden)  for financial support. We thank  Joy Singarayer  for providing the climate model outputs that were used to generate Fig. 8 and  Guangqi Li  for assistance in producing this figure.
BDS was funded by  ERC   H2020-MSCA-IF-2015. The dataset for Fig. 5 was generated from the " Cultivating Societies: Assessing the Evidence for Agriculture in Neolithic Ireland " project, supported by the  Heritage Council, Ireland , under the  INSTAR programme   2008-2010. Financial support. This research has been supported by the  European Research Council  (grant nos. 694481  and  701329 ) and the  Heritage Council, Ireland  (grant no. 16682 ). Review statement. This paper was edited by  Richard Neale  and reviewed by two anonymous referees.
Code and data availability. The data used for Fig. 1  are publicly available. The HYDE3. 2 data can be downloaded from: https://doi. org/10. 17026/dans-25g-gez3 (Klein Goldewijk, 2017). The KK10 data can be downloaded from: https://doi. org/10. 1594/PANGAEA. 871369 (Kaplan  and  Krumhardt, 2011). The code and data used to generate Fig. 1  are available from: https://github. com/jedokaplan/ALCC_comparison_ figure  (Kaplan, 2020). The data and code used to generate Fig. 3  are available from: https://github. com/mavdlind/GMD (Vander Linden, 2020). The data and code used to generate Fig. 5  are available from: https://doi. org/10. 5281/zenodo. 3625226 (Lewin and Whitehouse, 2020. The European pollen-based reconstructions used in Fig. 7  are available at: https://doi. org/10. 1594/PANGAEA. 897303 (Gaillard, 2019).
After ampli cation, PCR products were checked in 2% agarose gel to determine the success of ampli cation and the relative intensity of bands. Multiple samples were pooled together (e. g. , 100 samples) in equal proportions based on their molecular weight and DNA concentrations. Pooled samples were puri ed using calibrated Ampure XP beads. Then the pooled and puri ed PCR products were used to prepare a DNA library by following Illumina TruSeq DNA library preparation protocol. Sequencing was performed at the Molecular Research LP (MR DNA; Texas, USA) on a MiSeq™ System following the manufacturer's guidelines. Amplicon sequence data were sorted by the sample and demultiplexed using demux for QIIME 2 (version 2018. 11; Bolyen et al. 2018 ). Sequences were screened for quality, trimmed at 450 bp after removal of primer sequences and assigned as amplicon sequence variants (ASVs)  [55]  using DADA2  [56].
Taxonomy of the ASVs was determined using a pre-trained, naїve Bayes classi er  [57]  and the q2-feature-classi er plugin  [58]. The classi er was trained on the target 480 bp region of sequences in the Greengenes 13_8 99% database. ASV clusters were arranged in a phylogenetic tree using FastTree  [59, 60]  and visualised using Interactive Tree of Life  [61]. The feature table, metadata and taxonomic classi cations were exported from QIIME 2 in. biom format  [62] , and the rooted phylogenetic tree was exported in. nwk format.
Statistical analysis The exported feature table and phylogenetic tree were imported into R version 3. 5. 2 (R Core Team 2019) and stored as a phyloseq object [63] for downstream analyses. All ASVs not assigned to phylum were ltered from the data, and those designated as chloroplasts or cyanobacteria were removed and stored as a separate object for further analysis. Samples were rare ed to minimum sampling depth for diversity analyses; however, non-rare ed data were used for multivariate modelling  [64, 65]. Multivariate generalised linear models were used to test for signi cant differences in bacterial communities among host sh species, feeding behaviour and location along intestines using mvabund in R [66, 67]. Bacterial taxa were grouped by class when examining microbiome changes along the length of the intestinal tract.
, Seriola dumerili [80], Scophthalmus maximus [81, 82], Sparus aurata [83], Solea quinqueradiata [84] and Solea senegalensis [85]. Although identi ed as Vibrio harveyi in the GreenGenes database, GenBank revealed there was high similarity of these sequences to other members of the Harveyi clade, such as Vibrio owensii [80]. There are thought to be up to 11 species of Vibrio belonging to this clade [86], most of which are pathogens of sh, shrimp and coral [87-89].
Studying the spatial and temporal distribution of surface water resources is critical, especially in highly populated areas and in regions under climate change pressure. There is an increasing number of satellite Earth observations that can provide information to monitor surface water at global scale. However, mapping surface waters at local and regional scales is still a challenge for numerous reasons (insufficient spatial resolution, vegetation or cloud opacity, limited time-frequency or time-record, information content of the instrument, lack in global retrieval method, interpretability of results, etc. ). In this paper, we use 17 years of the MODIS (MODerate-resolution Imaging Spectro-radiometer) observations at a 8-day resolution. This satellite dataset is combined with ground expertise to analyse the evolution of surface waters at the Cambodia/Vietnam border in the Upper Mekong Delta.
The coupling of these methods appears particularly interesting for characterising dynamics at mesoscale, especially in a transboundary region where field-based hydrological data can be politically sensitive and data sharing mechanisms fraught with challenges. There is an increasing number of Earth satellites and resulting datasets monitoring surface waters. However, mapping surface water is still a challenge  [7]. It is difficult to provide products with the accuracy required for a large range of applications (e. g. , agriculture, disaster management, and hydrology). Each satellite observation has its own advantages and drawbacks (insufficient spatial resolution, vegetation or cloud opacity, limited time-frequency or time-record, information content of the instrument, lack in global retrieval method, and so forth).
However, no long-term SAR dataset exists so far, and other datasets are required in order to measure long-term changes. Several methods were proposed to monitor surface water using VISible (VIS) and Near-InfraRed (NIR) images. Reference  [14]  applied a threshold on NIR reflectances of the NOAA/AVHRR observations to delineate lakes. Reference  [15]  used positive values of the Normalised Difference Water Index (NDWI) to classify water bodies. Reference  [16]  detected surface water by identifying the positive values of the Modification of Normalised Difference Water Index (MNDWI). Detailed lake morphometry over basins with large water level fluctuations can also be obtained from MODIS satellite observations  [17]. Reference  [18]  produced a monthly-mean climatology of the water extent from 2000 to 2004 with a spatial resolution of 500 m, on a 8-day temporal resolution (by compositing all the clear data available during these eight days), using VIS/NIR Terra/MODIS data.
Mapping paddy rice agriculture in South and Southeast Asia can be done by using multi-temporal MODIS images  [19]. Reference  [20]  produced a new Global Surface Water Explorer (GSWE) dataset from Landsat imagery using a 32-year record, allowing for a description of the trends of surface waters and their occurrence. However, with 85% to 95% cloud cover during the wet season over the Mekong Delta  [21] , remote sensing methods derived from VIS/NIR images present some evident limitations. Vegetation is also an issue, as it makes the water detection difficult or impossible under canopy  [7]. Reference  [22]  explored the limits of Landsat imagery for the monitoring of small water bodies with rapid changes. Reference  [23]  investigates the retrieval of remotely sensed hydrological variables like water level and their corresponding drought indices (i.
e. , TRMM Standardized Precipitation Index (TRMM-SPI) and GRACE Drought Severity Index (GRACE-DSI)) over the Mekong and the Yangtze Rivers Basins. The Global Inundation Extent from Multi-Satellites (GIEMS) database combines satellite observations in the VIS, NIR, and passive/active microwaves at coarse resolution  [24] , and GIEMS-D3 at high spatial resolution  [25] , over a  15-year period (1993-2007). In Reference  [7] , it was shown that GIEMS-D3 is a good solution to obtain inundation at the global scale, with high spatial resolution, over cloud-covered and vegetated areas. This is a perfect complement to optical databases (e. g. , Landsat) that highly underestimate surface waters over some tropical regions. However, the high spatial resolution of GIEMS-D3 is not a native satellite information but rather an indirect information from topography.
It was noticed that over anthropized and very flat regions such as the Mekong Delta, GIEMS-D3 could be misled by the topography information, and that downscaling is difficult over these areas  [25]. Since we are interested here by long-term and recent changes in the hydrological system at the Cambodia/Vietnam border, we chose to use the MODIS data that is available over 2001-2017. This VIS/IR data has also the advantage to be available on a 8-day compositing temporal resolution, which is important for considerations such as detecting the start or the end of the flood season. The methodology presented in Reference  [18]  is being used to process the 17-year record  [26]. Section 2 presents the spatial domain with the six zones studied here, the MODIS surface water estimates, and some preliminary analysis.
• Zone 3V: This zone constitutes part of what is commonly known as the "plain of reeds". Located in Dong Thap province, the plain of reeds is one of the last areas of the Mekong Delta to have been reclaimed for agricultural purposes due to sociopolitical and ecological challenges  [29]. Most of the zone is protected by low-dykes built in the 1990s (also called August dykes because they allow delaying the flood to harvest the summer-autumn crop) and farmers generally engage in double rice cultivation. As can be seen in Table  1 , there are thousands of pixels in each one of the six zones. These pixels are no independent from each other so we do not have thousands of independent pixels. However, the results obtained in the following appear to be statistically significant. In particular, retrieval errors from the MODIS database (e.
As we have one MOD09A1 estimate every eight days, in the following and for simplicity purpose, we will sometimes call "week" the 8-day time step of the MODIS dataset. MODIS/Terra satellite observations are distributed free of charge for research and educational purposes from the NASA EarthData Hub at https://search. earthdata. nasa. gov/search. Reference  [18]  introduced a methodology using MODIS observations to detect surface water over the Lower Mekong Basin for the 2000-2004 period. It was then applied in Reference  [26]  to obtain a 17-year long time record (2001-2017) period, which we apply to the transboundary Upper Mekong Delta in this paper. This methodology uses low values of water indices to detect the presence of surface water extent. Here, a summary of the methodology is briefly presented but other details should be searched for in Reference  [18].
First, the Enhanced Vegetation Index (EVI, Equation (  1 )), the Land Surface Water Index (LSWI, Equation (  2 )), and the Different Value between EVI and LSWI (DVEL, Equation (  3 )) are calculated at the pixel level from the original atmospherically corrected surface reflectance data derived from the MOD09A1 8-day composite products. EV I = 2. 5 × N IR -RED N IR + 6 × RED -7. 5 × BLUE + 1 ( 1 ) LSW I = N IR -MIR N IR + MIR (2) DVEL = EV I -LSW I, (3) where RED, NIR, BLUE, and MIR are MODIS surface reflectances in the Visible band 1 (RED: 620-670 nm), NIR band 2 (NIR: 841-876 nm), Visible band 3 (BLUE: 459-479 nm), and MIR band 6 (MIR: 1628-1652 nm), respectively. Cloud-covered pixels (where surface reflectances the blue band ≥ 0. 2) are filled using a linear interpolation before a simple weight function is applied to smooth all water indices  [26].
Preliminary Analysis of the MODIS Database Figure  3  represents the probability of the mixed (left), water (middle) and total (right) states during the 2001-2017 MODIS record, over the Lower Mekong Basin. Most of the pixels are inundated at least once a year so the probability of the mixed state is not zero for most of the domain. The Tonle Sap is permanently in the water state, meaning that it is 100% covered by water during all the time steps of the long-term record. These three maps provide the overall surface water statistics in the whole Lower Mekong Basin but no long-term trend information is provided here as this statistics mixes what happened in the beginning and in the end of the time record. Figure  4  represents the time evolution from 2001 to 2017 of the number of mixed, water and total (mixed + water) pixels over the six zones represented in Figure  1  and described in Table  1.
PCA Results Figure  5  represents the first four temporal base functions { f k = F(j, :); k = 1,. , 4}. The percentage of variance explained by each one is also indicated. Together, the first four components explain 81% of the variability in the MODIS dataset. Figure  6  represents the corresponding spatial components C(:, k) of the first four components. First component-The first component (representing 69. 3% of the surface water variability) is a low-frequency time evolution that includes the seasonal cycle and some inter-annual variabilities such as a strong decrease of the total inundated area in 2015 or a shorter inundation season in 2012. A general decreasing trend can be noticed. This component shows that there is a well-identified seasonal cycle, but also strong inter-annual variations.
Conclusions The MODIS flood database developed and used here enables the monitoring of seasonal, inter-annual, and long-term trend evolution of the surface water distribution in the floodplain of the Upper Mekong Delta at the Cambodia/Vietnam border. There are clear differences in the distribution of these changes between Cambodia and Vietnam, over the 2001-2017 record. In Cambodia, changes detected by visible/infrared observations can be misleading because some of them are related to deforestation and land reclamation instead of a change in water presence. In contrast, over Vietnam, very significative negative trends in the extent and duration of flooding can be observed. Climate change may explain some of these changes, in particular for precipitation, but they are mainly the result of land use and water management changes that have occurred over the last twenty years.
In Vietnam, notably, the construction of high dykes and drainage networks has induced a large decrease in the extent and duration of flooding so as to support triple rice cultivation. The 500 m spatial resolution of the MODIS data appears to be compatible with the heterogeneity observed in the field. Satellite data, especially when combined with ground truth expertise, can contribute to better monitoring and understanding hydrological dynamics at such mesoscale ( 100 × 100 km 2 ), hence inform water managers and policy makers involved in designing water control infrastructure projects to support agricultural intensification. Visible data are an essential long-term source of information but this type of observation (MODIS as well as Landsat and Sentinel-2 data) is limited by cloud cover and vegetation presence.
It is very important to keep these two limitations in mind when using such visible-based databases. Furthermore, satellite observations need to be complemented by ground-truth expertise in order to interpret the results correctly. Our results-and notably our ground knowledge-allows putting in perspective Pekel's analysis  [20]  which points to an increase in the frequency of flooding in the Upper Mekong Delta of Cambodia, which we attribute at least partly to deforestation and land reclamation rather than actual change in flood dynamics. This study also stresses that, for any long-term analysis, older satellite missions such as MODIS or Landsat will continue to be necessary: newer and more precise missions such as SWOT (to be launched in 2021) will not be able to describe the long-term evolution of continental hydrology due to climate change or water management.
Vegetation occultation would be less of a concern there. In this study, we show that the consequences of a national policy to control water (construction of dykes and drainage canals in Vietnam) can be monitored using global remote sensing database such as MODIS. The construction of water control infrastructures has had some advantages for agriculture allowing to have up to three rice harvests a year but it also brought some difficulties such as the demise of capture fisheries and the decrease of soil fertility due to a lack of silt brought by the natural flood, which made necessary the use of chemical fertilisers. The relevance of such policy is now being questioned and further analysis will be needed to measure its long-term impacts. This is particularly relevant as Cambodia tends to follow the path followed by Vietnam over a decade ago and invests significantly in water control infrastructure projects.
Metagenomic Identification of Bacterial Taxa Modulated by Presence of APL1 We taxonomically profiled the bacteria in A. stephensi midguts in the presence and absence of APL1 and antibiotics, and analyzed differential bacterial abundance. The V4 hypervariable region of the 16S ribosomal RNA (rRNA) gene was amplified and sequenced from DNA of A. stephensi midguts dissected from mosquitoes treated with dsAPL1, dsGFP, antibiotics, or no antibiotics, each in three biological replicates (sample list, Supplementary Table  S1 ). Bacterial operational taxonomic units (OTUs) were picked, taxonomically assigned, and counted using the SHAMAN workflow  (Quereda et al. , 2016) , which incorporates the program DESeq2  (Love et al. , 2014)  for analysis of quantitative metagenomics data and differential abundance using a multifactor generalized linear model.