dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, we implement a mechanistic hydraulic architecture modeling of the water transport in the continuum from soil to atmosphere in the ORCHIDEE-CAN model. We refer to this New Hydraulic Architecture module as "NHA" that is, ORCHIDEE-CAN-NHA. We describe three developments and their evaluation against field measurements for control and experimental throughfall conditions, in aspects of soil and plant water variables, and biometric variables such as tree growth and mortality, at the Amazon tropical forest site of Caxiuanã  (Fisher et al. , 2006; Meir et al. , 2018). Firstly, we describe the development of the new hydraulic architecture model. We then carry out site-level simulations and evaluate the model performance in aspects of seasonal variability in transpiration, soil moisture and productivity against experimental control and drought observations.
The starting point: ORCHIDEE-CAN r2290 The model version taken as the starting point for development in this study is ORCHIDEE-CAN (r2290), a branch of the ORCHIDEE land surface model. ORCHIDEE is a physical process-based model, which can simulate the energy, water and carbon fluxes between land surfaces and the atmosphere. The SECHIBA module corresponds to faster processes, such as the exchange of water and energy as well as photosynthesis between land and the atmosphere in time intervals of 30 min. The carbon module (STOMATE) simulates soil processes (soil decomposition, heterotrophic respiration, soil organic carbon dynamics) at the 30 min time step and vegetation carbon cycle processes at daily intervals, including carbon allocation, vegetation mortality and recruitment, phenology and litter fall.
In the model, the HY-BRD1 function from Minpack package in Fortran is used, which seeks a zero of N nonlinear equations in N variables. The evaluated function is the difference between water supply and water demand at each organ level. This function iteratively minimizes the absolute value of the evaluated function. The initial estimate of the solution vector is quite important and comes from the water potential at the last time step. For example, the initial estimate for leaf water potential at time step t that will be used in the formula is the stem water potential at time step t -1.
The meteorological data every 30 min are measured using an automatic weather station located at the top (51. 5 m) of a tower 1 km from the experimental plot. The simulation was run offline without coupling with a climate model. Two former model versions and our new developments are integrated as below. We compared ORCHIDEE-CAN-RS and ORCHIDEE-CAN-NHA to see the improvements brought by the new hydraulic architecture. It should be noted that all three of these simulations are realized through several flags to switch on/off some functionality: 1. ORCHIDEE-CAN with the original simple hydraulic module setup, 2. ORCHIDEE-CAN-RS, which adds a new dynamic soil-root scheme on top of (1), 3. ORCHIDEE-CAN-NHA, with the new mechanistic hydraulics on top of (2).
Statistical tools We used the R programming environment and statistical packages (version 3. 5. 0; R Core Team, 2019) for all data processing and analysis. Package ncdf4 v1. 17  (Pierce, 2019)  is used to handle files in NetCDF format from model outputs. Package fields v10. 3  (Nychka D, 2020)  is used in water potential plotting. 3 Results.
Model improvements by new parameterizations of hydraulic transport The original ORCHIDEE-CAN model included a limit from transpiration supply based on water transport and resis- The vertical axis is for the index of 20 tree cohorts represented in the model, a larger index indicating taller trees (see Table  S4  for tree height and diameter in each cohort). Tree mortality fraction per year is calculated by totaling the number of dead trees in each year and dividing it by the tree density on the first day of each year. tances along a water potential gradient  (Naudts et al. , 2015). Nonetheless, the constant value assumed for leaf , the lack of a dynamic simulation of stem and root and conductivities limit the mechanistic basis of the approach. To make a step forward, the new hydraulic module presented here tracks the water flow continuum from the soil to the atmosphere.
Additionally, in this study we have only calibrated the new hydraulic architecture against observations from one drought experiment site. It should be noted that the hydrological parameters are quite sensitive in aspect of drought response and are also uncertain. Expanding this method to other drought experiment sites is required to generalize the model performance. For example, this future work could address the extent to which the drought of 2005 and 2010 affected forest dynamics in western Amazonia. Largescale mortality observations and more comprehensive mortality benchmarking datasets are also required to evaluate the hydraulic architecture in the process-based model  (Adams et al. , 2013; Allen et al. , 2010). Regarding the parameterization of the model at the regional and global scales, here we focus on the tree mortality submodel to clarify the issue of parameter uncertainties.
Therefore, more experiment data manifesting the tree tolerance should be well included to constrain the drought exposure threshold uncertainties in our model framework. Towards the enrichment of parameters for the regional simulation, generally, three means can be resorted to for the benefit of such realizations. The first one can be embedding the plant trait database like TRY  (Kattge et al. , 2020)  into our process-based model, although the records are still limited in aspect of hydraulic traits. The second solution can be the optimization of hydraulic parameters using e. g. , Markov chain Monte Carlo methodology with measurements or remotesensing products as constraints like the retrieval of traits in Y. Liu et al. (2021)  or other data-assimilation systems like ORCHIDAS. Here, the data quality of constraint is highly important as the error can be accumulated.
Conclusion Our study proposes a new mechanistic hydraulic architecture module, ORCHIDEE-CAN-NHA, which simulates a dynamic xylem cavitation indicator of percentage loss of conductance (PLC) through modeling the water flow in the soil-root-stem-leaf continuum and water charge from storage. The model was calibrated against observations from the Caxiuanã throughfall exclusion field experiment in the eastern Amazon, during 2001-2008, with regard to the seasonal variability in transpiration, soil moisture and productivity. Besides the improvement of hydraulic architecture, we also built a relationship between PLC and tree mortality rate via two empirical parameters, drought exposure duration, which determines the mortality frequency and the mortality fraction in each day once exceeding the exposure.
Code availability. The ORCHIDEE-CAN-NHA model (r7236) code used in this study is deposited at https: //forge. ipsl. jussieu. fr/orchidee/browser/branches/publications/ ORCHIDEE_CAN_NHA (last access: 17 June 2021) and archived at https://doi. org/10. 14768/8C2D06FB-0020-4BC5-A831-C876F5FBBFE9  (Yao, 2021a). The detailed code used to reproduce the analysis and figures is publicly available at https://doi. org/10. 5281/zenodo. 5721245 (Yao, 2021b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-15-7809-2022-supplement.
Once every 3 months, the internal flow meter was itself manually checked and-if needed-recalibrated with an independent external volumetric flow meter. A clean time series was generated from raw Tekran data with special software developed at the Institute of Environmental Geosciences (see https:// gmos. aeris-data. fr/, last access: 11 September 2021). During this automated process, a flag ("valid," "warning," or "invalid") was assigned to each TGM observation based on over 40 criteria linked to all phases concerning the instrument calibration and the calculation of TGM concentration  (D'Amore et al. , 2015). Based on this flagged data set, the site manager generated the final quality-insured and quality-controlled data set under consideration of field notes, logbooks, and site characteristics  (Magand & Dommergue, 2022).
HYSPLIT Back Trajectories to Estimate Air Mass History We estimated long-range transport to CHC and adjacent regions on the seasonal timescale with HYSPLIT ("Hybrid Single-Particle Lagrangian Integrated Trajectory") back trajectories  (Stein et al. , 2015) , using the HYSPLIT model in conjunction with the R library "splitr" (an R interface to HYSPLIT, https://github. com/ rich-iannone/splitr, last access: 7 February 2022). We used the Global Data Assimilation System (GDAS1) as meteorological input due to its global and temporal coverage and manageable computational cost. Back trajectories were launched hourly from nine arrival points on a regular 0. 5° × 0. 5° (lat × lon) grid centered on CHC (see grid points in Figure  S1  of Supporting Information S1). The starting altitude for all trajectories was 5,245 m asl.
Comparison to CARIBIC Data Making use of the CHC TGM increase in 2015, we estimated the seasonality of TGM in the UTPC. To validate this remotely reconstructed seasonality, we compared it to aircraft TGM observations in the SH tropical UT. We used IAGOS CARIBIC data taken on civil aircraft flights between June 2005 and March 2016, selecting only flights connecting to destinations in SH South America (36 individual flights). To constrain TGM seasonality in the SH UT as close to the UTPC as possible, we selected only CARIBIC TGM measured at altitudes above 4,000 m asl. , within latitudes 25°S-5°S (the threshold latitude for NH air mass intrusion at 15°S  ± 10°), and longitudes -50° to -30°. We then summarized by month of the year to maximize available data and to focus on the mean TGM seasonality.
Based on these results, we reconstructed the form of TGM seasonality in the UTPC, as shown in Figure  7a. To validate this remotely reconstructed UTPC TGM seasonality, we compared it to in situ TGM measurements in the SH tropical UT, as provided by CARIBIC (see Section 2. 7). Although no CARIBIC data is available for the SH Pacific, some data is provided further eastwards, for the eastern part of South America and the Atlantic Ocean. Considering the prevailing strong westerlies in the SH tropical UT (see Figure  5d ), we can assume certain comparability between the seasonality of TGM in the UTPC and further eastward. We find that the reconstructed UTPC seasonality based on CHC TGM (Figure  7a ) agrees well with the seasonality of CARIBIC TGM (Figure  7b ) in the selected region of the SH tropics (longitude: -50° to -30°, latitude: 25°S-5°S).
Data Availability Statement Chacaltaya L1 TGM data (https://doi. org/10. 25326/347) are freely available  (Magand & Dommergue, 2022)  at https://gmos. aeris-data. fr/ from a GMOS-FR data portal coordinated by IGE (Institut des Géosciences de l'Environnement-Grenoble, France; technical PI: Olivier Magand) with the support of the French national AERIS-SEDOO partners, data and services center for the atmosphere (last access: 11 September 2021). Chacaltaya aethalometer data on equivalent BC is freely available under https://www. gaw-wdca. org/Browse-Obtain-Data (last access: 10 September 2021). Data on the ENSO indices ONI, SOI, and MEI are freely available from NOAA, under https://psl. noaa. gov/enso/dashboard. html (last access: 10 September 2021). Chacaltaya carbon monoxide data will be made available upon acceptance at Velarde (2022, https://doi.
We first use these assemblies to resolve longstanding questions regarding the phylogeny and divergence times within this group. We then assess protein-coding and structural evolution across all seven genomes. This allows us novel insight into the rates of each type of evolutionary divergence in this clade, and also provides the power to test fundamental hypotheses on the relationship between structural and coding evolution. Lastly, in order to facilitate the use of these genomes as a resource for the communities of Drosophila biologists and ecological geneticists, we present a public database of the assemblies and annotations (cactusflybase. arizona. edu). Details of the strains used for genome sequencing can be found in Table  1. Genome assembly and annotation statistics can be found in Table  2.
Genome assembly and annotation Genome size was consistent across the three new D. mojavensis genomes, with both D. arizonae exhibiting larger genomes and D. navojoa slightly smaller. There is no evidence that these evolutionary patterns in genome size are driven by expansions of repeats or TEs, as these did not display such consistent trends between the species. Our genome assemblies confirmed previous findings on fixed chromosomal inversions between these species and populations (Fig. 2 ; Supplemental Figs. S1, S2), with a single inversion occurring at the base Supplemental Fig. S3 ), but this pattern was not ubiquitous. To facilitate further study of these species, we have deposited the assemblies and annotations in a new public database at cactusflybase. arizona. edu. Users can download fasta and gff files directly, view annotations and underlying RNA-seq data via JBrowse  (Buels et al.
Phylogenomics and divergence time estimation Both the topology of the phylogeny as well as the divergence time estimates differed when using nuclear (Fig. 4 ) versus mitochondrial genes (Supplemental Fig. S4 ). The nuclear derived phylogeny placed the four D. mojavensis populations in a single clade, with the two D. arizonae populations as a sibling clade, and D. navojoa Although the timing of divergence within the D. arizonae/D. mojavensis clade was identical between the two datasets, the mitochondrial phylogeny gave a twice as old split of D. navojoa from this group.
Discussion The assembly of these seven de novo genomes represents one of the largest and most contiguous genomic datasets in any Drosophila clade. To date, we can identify 21 genomes that have been assembled at or near chromosome level in Drosophila (https://www. ncbi. nlm. nih. gov/genome/browse#!/overview/Drosophila). However, these are heavily clustered in clades closely related to D. melanogaster and D. pseudoobscura. While there has been a clear logic to concentrating sequencing effort on a small handful of clades, the establishment of generalizable patterns of genomic evolution requires that analyses be replicated across taxa. Our genomes add to this endeavor. Furthermore, the inclusion of separate conspecific populations of two species allows for deeper resolution in the calculation of fundamental evolutionary patterns.
2007 ). These studies also presented variable divergence times; nuclear studies  (Machado et al. 2007; Smith et al. 2012)  found the initial divergence within D. mojavensis to have occurred ~250,000 years ago with further divergence between 100,000 and 150,000 years ago, the mitochondrial study found node ages more than twice as old. Once again, our mitochondrial and nuclear data cleanly recover these differences. Our mitochondrial and nuclear analyses also showed major differences in the relationships and divergence times across species. The biggest among these is the finding of paraphyly in D. arizonae in our mitochondrial dataset. Although one analysis in previous mitochondrial work  (Reed et al. 2007 ) also failed to support D. arizonae as a clade, it differed in which population grouped with D.
mojavensis. Here, our patterns of syntenic divergence align with the nuclear dataset in grouping the two D. arizonae populations as sibling taxa. These consistent differences between mitochondrial and nuclear datasets could reflect a different demographic history for the mitochondrial genome, as previously suggested  (Reed et al. 2007 ). However, we consider this discrepancy to be more likely due to noise, given the nearly 10,000-fold difference in genes analyzed (12,218 vs. 13) in the nuclear genome compared to the mitochondrial genome. Thus, our data best support the topology of the earlier nuclear phylogeny of  Machado et al. (2007) , with divergence times broadly in agreement with both previous nuclear studies  (Machado et al. 2007; Smith et al. 2012). Our results regarding divergence times between species significantly contradicted estimates from earlier work.
2007; Rampasso et al. 2017 ), appear to be too ancient to have played a role here. Descriptions of the rates of sequence and expression evolution have served as foundational patterns of evolutionary genomics for decades. However, limited data relating to rates of accumulation of structural genomic variation have been published. Chakraborty et al. (2021)  found that 15% of sequence did not align between D. simulans and D. melanogaster, which are diverged by about 3 million years, and noted that this was over twice the percentage of sequence variation between these species. Long et al. (2018)  estimate a rate of 50 structural mutations per Mb per million years within D. melanogaster, which, given an average variant size of around 25 kb in their dataset, amounts to approximately 13% divergence per million years.
Is this a fast or slow rate of structural divergence? Do structural variants accumulate more rapidly in highly speciose taxa, or those undergoing adaptive radiations? To begin to address these questions within our dataset, we asked what factors might explain local variation in synteny within the D. mojavensis group. The best predictor of syntenic divergence was chromosome. Although results varied slightly depending on the evolutionary distance, the dot chromosome (Muller element F) diverged most rapidly, followed by Muller elements A, B, and E. In nearly all comparisons, Muller elements C and D maintained the greatest collinearity. It is unlikely that this heterogeneity can be explained by a single factor. For Muller element F, although there is some evidence for relaxed constraint in D. mojavensis  (Allan and Matzkin 2019) , it is more likely that our results are explained by a genus-wide propensity for this chromosome to accumulate repeats and TEs, which has been attributed to a unique chromatin structure for this chromosome  (Riddle and Elgin 2018).
2021) , meaning that mutations near these genes are more likely to be maintained. Second, the causality could be reversed, and structural changes to genes could directly cause subsequent bouts of reduced constraint and relaxed selection. In many cases, this could be explained as a result of sub-or neofunctionalization following gene duplication. However, in our dataset, molecular evolution was only assessed for single copy orthologs across all seven genomes. Thus, the relevant duplications would have occurred prior to the common ancestor of these species, and would not register as structural variants in this dataset. A more likely possibility is that structural changes resulting in alterations to gene regulation alter protein function, which subsequently leads to a relaxation of purifying selection on amino acid sequences.
The short-read Illumina data for D. navojoa is described in  Vanderlinde et al. (2019). Briefly, for all short-read data, we extracted DNA from a pool of ten adult males and ten adult females using Qiagen DNeasy Blood & Tissue Kits (Qiagen, Hilden, Germany), and we constructed the D. arizonae Chiapas library using KAPA LTP Library Preparation Kit (Roche, Basel, Switzerland) kits. It The assembly of the six de novo genomes largely followed the hybrid assembly strategy described in  Jaworski et al. (2020) , wherein a detailed description of sequencing and assembly methods can be found. Briefly, we used Platanus 1. 2. 4  (Kajitani et al. 2014 ) and DBG2OLC  (Ye et al. 2016)  to produce hybrid assemblies of the short-and long-read data. We also used Canu 1. 7  (Koren et al. 2017)  for long-read only assembly with the correctedErrorRate parameter set to 0.
We first polished the most current FlyBase assembly (version r1. 04) twice with Pilon 1. 23. We then manually scaffolded by aligning contigs from the existing Nanopore data  (Miller et al. 2018)  to the polished reference using Mauve and joining in Geneious Prime. We filled all N-gaps over 20kb with contigs from the Nanopore dataset. Lastly, we filled all N-gaps regardless of size if they occurred within 100bp of a putative CDS feature identified during annotation. Similar to the other assemblies, annotation revealed several indel errors in coding regions the CI genome, which we fixed manually. In addition, to filtering duplicate scaffolds with Minimap2 we also removed scaffolds that previously had a gene annotation in the 1. 04 release if those genes had strong BLAST hits to a gene on the chromosome scaffolds.
We analyzed both the completeness of our genome assemblies and our annotations by using BUSCO  (Seppey et al. 2019 ) to compare our own gene content against the most recent database of conserved single-copy dipteran genes (Diptera_odb10). We generated mitochondrial annotations by transferring existing annotations from the CI mitochondria to each of the other mitochondrial assemblies using Mauve. We used results from RepeatModeler above to calculate repeat content for each genome and BBMap stats (https://sourceforge. net/projects/bbmap/) to calculate GC content. To estimate transposable element (TE) content we used EDTA  (Ou et al. 2019) , which has been demonstrated to be effective in annotating nonmodel genomes  (Bell et al. 2022). We used custom bash scripts to calculate the percentage of GC, repeats, TEs, and genes in 100kb sliding windows overlapping by 50kb, and plotted these percentages for each genome using the R package circlize  (Gu 2014).
Phylogenomics and divergence time estimation We identified 12,218 single-copy orthologs across all seven genomes with OrthoFinder (Emms and Kelly 2019) using an iterative process. We first ran OrthoFinder under default parameters, separating singlecopy orthologs from the remaining genes. We then re-ran the software on the remaining genes using stricter parameters, and repeating this procedure twice. In this way, we were able to capture genes that may have duplicated recently but before the common ancestor of the three species, and therefore still useful for our analyses. We then performed codon-aware alignments of all single-copy orthologs using PRANK (Löytynoja 2014), and extracted fourfold degenerate sites from each alignment. We generated individual, unrooted gene trees using RAxML  (Stamatakis 2014) , and used these trees as input for consensus tree building using ASTRAL-III  (Zhang et al.
Analysis of structural genome evolution We aligned all seven genomes using NUCmer in order to identify breakpoints and visualize previously identified chromosomal inversions on Muller elements A, B, and E. We made figures of genome wide synteny using Dot (https://github. com/marianattestad/dot). Prior to analyzing structural variation quantitatively, we used these breakpoints to manually create "uninverted" chromosomes, wherein we forced all chromosomes to be homokaryotypic with CI. This allowed us to compare synteny inside and outside of major inversions in an unbiased manner. We re-ran NUCmer on the "uninverted" genome assemblies and used this output as input for identification of structural variation and syntenic genome regions using SyRI  (Goel et al. 2019). Using the CI genome as our template, we followed  Jiao and Schneeberger (2020)  in quantifying the percentage of syntenic sequence in 100kb regions of the genome over 50kb sliding windows using custom bash scripts.
Acknowledgments We thank  D. Kudrna  for his work to produce the PacBio sequences. We thank  N. Sage  for assistance with genome annotations. This work was supported by the  National Science Foundation  ( IOS-1557697  to L. M. M. ). We would like to dedicate this work to  Bill Heed ,  Marvin Wasserman  and  William Starmer  whose foundational work on this system has been tremendously impactful.