dataesr/scientific-paragraphs-categorization huggingface dataset:
SRS Basics for Measuring Precipitation Satellite-based precipitation measurements with advanced infrared (IR), passive microwave (MW) and radar (SAR) sensors provide a complementary alternative to in-situ records  [62, 270]  as they give a full spatial and temporal coverage with a good accuracy (Table  8 )  [271] [272] [273] [274] [275] [276] [277] [278]. Yet, despite the growing collection of satellite-based rainfall measurement datasets providing near-real-time estimates  [63] , only a few high-resolution satellite-based products providing historical data at the daily time-step with real-time or near-real-time updates for the African continent are publicly available (Table  8 ). To improve the accuracy of rainfall estimations, the merging of satellite and gauge measurements have been designed, thus maximizing the benefits of each data type  [279, 280].
It is a performing product for depicting inter-annual variations but offers a coarser spatial resolution (Table  8 ) which could be detrimental when studying animal movement at the landscape scale. Since 2019, the GPM IMERG v06 algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000-2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014-present). Therefore, the GPM IMERG v06 now offers 20 years of data coverage and can potentially be of interest regarding animal movement studies at the landscape scale regarding its spatial resolution of 0. 1 • and its broad coverage (Table  8 ). The products that combine thermal infrared and passive microwave imagery such as RFE or CHIRPS (Table  8 ), perform comparatively well and outperform products which are only based on thermal infrared imagery such as TARCAT (Table  8 )  [272].
Funding: This research was funded by  I-SITE MUSE  ( Montpellier Unversité d'Excellence )-https://muse. edu. umontpellier. fr/en/muse-isite/through the  French National Research Agency (ANR)  under the " Investissements d'avenir" program , grant number  ANR-16-IDEX-0006. Acknowledgments: This work is part of a thesis integrated into the TEMPO (TElédétection et Modélisation sPatiale de la mObilité animale)-https://tempo. cirad. fr/enand is implemented under the framework of the  Research Platform "Production and Conservation in Partnership "-www. rp-pcp. org.
foo. org. au/priorities/surfacewaves/. Tier 1 priorities 1) EnhancEd and updatEd nEarshorE and coastal bathymEtry. Capturing an enhanced and updated Australian nearshore and coastal bathymetry is a clear priority for the wave research community, recognizing the importance of this key parameter to how waves transform as they propagate toward and across the shoreface. Challenges remain in identifying and developing appropriate technologies that can provide cost-effective, accurate measurements of nearshore and coastal bathymetry. This is particularly the case in shallower areas where the bathymetry needs be surveyed on a regular basis, as it can undergo rapid changes. Another challenge relates to use of a common datum to enable "stitching" of terrestrial and marine elevation databases, which has been an outstanding issue for littoral zone research.
Seamless bathymetry/topography datasets (e. g. ,  Wilson and Power 2018)  are not typically compiled despite the fact that they are vital for most coastal modeling applications. Meeting such challenges, and delivery of a high-accuracy national coastal elevation dataset, spanning 50 m below to 20 m above mean sea level, has relevance well beyond that E451 of the waves community, significantly improving our ability to model other hazards such as tsunamis and storm surges, as well as providing information to improve marine management including marine planning, monitoring, research, and emergency response. The importance of improving available topographic and bathymetric elevation data to underpin adaptation planning in Australia has been recognized for some time  (COAG 2006)  and its prioritization here again recognizes this.
4) ImprovEd data accEss and sharIng. As noted above, the publicly available Australian wave buoy network is operated by a number of different agencies, some state-based, some federal, and some provided by private industry. As a consequence, it can be a cumbersome and timeconsuming process to acquire a set of wave observations for any national-or regional-scale study. There is clearly a need for national integration of, and easy access to, existing historical wind and wave observations. The pursuit of this is an ongoing process, with numerous technical and political challenges. Recent positive steps have been undertaken by the Integrated Marine Observing System (IMOS) Australian Ocean Data Network (portal. aodn. org. au), which has commenced the process of seeing the public buoy data, in both real-time and delayed mode, being made more accessible to the Australian marine data user community.
The international wave satellite remote sensing (SRS) community has exerted considerable effort, particularly via the GlobWave program, which ended in 2014, in providing calibrated and validated SRS wave data streams, and now the ESA Seastate CCI program. Continued effort, however, is required. The IMOS SRS Surface Waves subfacility is addressing this challenge, to strengthen Australian capability in wave remote sensing, support calibration/ validation efforts in the Australian/Southern Hemisphere region, and facilitate uptake of data among local users. The accessibility and sharing of bathymetry data are also an issue. This has been challenging in the past due to the absence of a nationally coordinated program. AusSeabed (ausseabed. gov. au) is a national initiative established to facilitate the collaborative collection and update of seabed mapping data within Australia's maritime jurisdiction and provides access to various existing bathymetric datasets.
Acknowledgments. The Integrated Marine Observing System (IMOS) office is thanked for providing access to their SurveyMonkey registration. We acknowledge  Chris Yetsko ,  Stuart Anstee ,  Paul Taylor ,  Jan Flynn ,  Grant Elliott , and  Matthew Zed  for their contributions to the prioritization process. Three anonymous reviewers are also thanked for their positive and constructive comments on the manuscript. The following organizations are acknowledged for their contributions to publication fees: the  Forum for Operational Oceanography (FOO) , the  Bureau of Meteorology, CSIRO , the  University of Melbourne , the  University of New South Wales , and the  University of Western Australia.
anguilla and A. rostrata), with a particularly high proportion of hybrids in Iceland  15, [19] [20] [21] [22]. However, while these Atlantic species have so far received most of the scientific attention, the greatest concentration of Anguilla species is present in the tropical Indo-Pacific, where 11 species occur and may partially spawn at the same locations  23, 24. A locally high frequency of hybrids between two species occurring in this region (A. marmorata and A. megastoma) has been suggested by microsatellite markers and small datasets of species-diagnostic single-nucleotide polymorphisms (SNPs)  18  ; however, the pervasiveness of hybridization across all tropical eel species, the degree to which hybridization leads to introgression in these species, and the mechanisms maintaining species boundaries have so far remained poorly known.
Results Extensive sampling. Collected in 14 field expeditions over the course of 17 years, our dataset included 456 individuals from 14 localities covering the distribution of anguillid eels in the tropical Indo-Pacific (Fig. 1a , Supplementary Table  1 ). Whenever possible, eels were tentatively identified morphologically in the field. Restriction-site-associated DNA (RAD) sequencing for all 456 individuals resulted in a comprehensive dataset of 704,480 RAD loci with a mean of 253. 4 bp per locus and up to 1,518,299 SNPs, depending on quality-filtering options (Supplementary Fig. 1 ). RAD sequencing reads mapping to the mitochondrial genome unambiguously assigned all individuals to one of the seven tropical eel species A. marmorata, A. megastoma, A. obscura, A. luzonensis, A. bicolor, A. interioris, and A.
mossambica, in agreement with our morphological assessment that indicated that the remaining four Indo-Pacific Anguilla species A. celebesensis, A. bengalensis, A. borneensis, and A. reinhardtii were not included in our dataset (Supplementary Fig. 2 ). For those individuals for which sufficient morphological information was available (n = 161, restricted to A. marmorata, A. megastoma, A. obscura, and A. interioris), predorsal length without head length (PDH) and distance between the origin of the dorsal fin and the anus (AD), size-standardized by total length (TL)  25  , showed species-specific clusters, even though these were not fully separated from each other (Fig. 1b , Supplementary Fig. 3 ). This diagnosis was further supported by principal component analysis (PCA) of seven morphological characters (Supplementary Fig.
4 ), which are both presumed to have a single spawning area in the western South Pacific  18, 30. Deep divergences among tropical eel species. To analyze genomic variation among tropical eel species, we used a dataset of 155,896 SNPs derived from RAD sequencing (Supplementary Fig. 1 ) for PCA (Fig. 1c , Supplementary Fig. 5 ). With few exceptions (see next section), the 430 individuals grouped according to species, and the seven species included in our dataset formed largely well-separated clusters. Pairwise nuclear genetic distances between species ranged from 0. 0053 to 0. 0116 (uncorrected p-distance; excluding individuals with intermediate genotypes) and were largest for A. mossambica, followed by A. megastoma (Supplementary Table  4 ). We further investigated the relationships among tropical eels species and their divergence times by applying Bayesian phylogenetic inference to genomewide SNPs  31  , using the multispecies coalescent model implemented in the software SNAPP  32.
marmorata, A. megastoma, and A. obscura (N50 between 54,849 bp and 64,770 bp; Supplementary Table  5 ), and extracted orthologs of the markers used in the studies of Musilova et al. 39  and Rabosky et al. 38. The use of these combined datasets together with age calibrations from the two studies also had little effect on age estimates (Supplementary Fig. 6 ). Thus, all our analyses of divergence times point to an age of the clade formed by A. marmorata, A. megastoma, A. obscura, A. luzonensis, A. bicolor, and A. interioris roughly on the order of 10 Ma. High frequency of contemporary hybridization. Despite their divergence times up to around 10 Ma, our genomic dataset revealed ongoing hybridization in multiple pairs of tropical eel species. Analyses of genomic variation with PCA revealed a number of individuals with genotypes intermediate to the main clusters formed by the seven species (Figs.
All of the putative hybrids were confirmed by the ancestry paintings, showing that our dataset includes 20 hybrids between A. marmorata and A. megastoma, 3 hybrids between A. marmorata and A. obscura, 1 hybrid between A. megastoma and A. obscura, and 1 hybrid between A. marmorata and A. interioris (Fig. 2e-h , Supplementary Figs. 9-13, Supplementary Table  7 ). The frequency of  (according to mitochondrial sequence data). k Histogram of the relative morphological similarities between hybrids and the maternal species, measured as the relative proximity to the mean maternal phenotypes, compared to the proximity to the mean paternal phenotype. l Comparison of the proportions of hybrids' genomes derived from the maternal species and the similarity to the mean maternal species' phenotype. The dotted line indicates a significant positive correlation between the two measures (two-tailed t test; t = 3.
1, p = 0. 008, R 2 = 0. 381). PC, principal component; mito. , mitochondrial genome; AD, distance between the dorsal fin and the anus; PDH, predorsal length without head; TL, total length. hybrids in our dataset is thus 5. 8% overall and up to 22. 5% at the hybridization hotspot of Gaua, Vanuatu 18 (Supplementary Fig. 14 , Supplementary Table  8 ). This high frequency is remarkable, given that most animal species produce hybrids at a frequency far below 1%  1, 43. The heterozygosities of the hybrids are clearly bimodal with a peak near 1 and another around 0. 5 (Fig. 2i , Supplementary Table  7 ), supporting the presence of both first-generation hybrids and backcrossed second-generation hybrids. Using the mitochondrial genomes of hybrids as an indicator of their maternal species, we quantified the proportions of their nuclear genomes derived from the maternal species, f m,genome , based on their genotypes at the fixed sites used for ancestry painting.
16 ). Similar to the distribution of f m,genome values (Fig. 2j ), the distribution of f m,morphology values (Fig. 2k ) also has three peaks centered close to 0. 25, 0. 5, and 0. 75, and the two values were correlated (Fig. 2l ). In contrast to their intermediate sizestandardized overall morphology, hybrids in some cases had certain transgressive characters, exceeding the range of the parental phenotypes  44, 45  (Supplementary Figs. 17, 18). This was the case for VAG13071 and VAG12044, two F1 hybrids between A. marmorata and A. megastoma that had the greatest total length among all sampled individuals (Supplementary Table  1 , Supplementary Fig. 17 ). Notably, we recorded no signals of hybridization with Anguilla species that were not included in our dataset. Anguilla celebesensis, A. bengalensis, A.
borneensis, and A. reinhardtii all occur in the Indo-Pacific and could be expected to hybridize with the sampled species. If our dataset had included hybrids between sampled and unsampled species, we could have identified these hybrids from their expected increased heterozygosity, outlier positions in PCA analyses, and separate clustering in our analyses with ADMIX-TURE and fineRADstructure. However, as hybridization with the unsampled species could be locally restricted away from the sampled localities, a more extensive sampling scheme might be required to assess its overall frequency. Signatures of past introgression. Multiple independent approaches revealed highly variable signatures of past introgression among species pairs of tropical eels. First, we found discordance between the Bayesian species tree based on the multispecies coalescent model (Fig.
Our observation of frequent hybridization in four different species pairs, including two pairs involving A. megastoma with a divergence time around 10 Ma (Fig. 1d ), supports this conclusion in a natural system, indicating that prezygotic reproductive barriers may generally be weak in tropical eels. This interpretation is strengthened by the fact that the 25 hybrids in our dataset were sampled in five different years (Supplementary Table  7 ), suggesting that natural hybridization in tropical eels occurs continuously, rather than, for example, being the result of an environmental trigger that ephemerally caused spatially and temporally overlapping spawning  81. Moreover, the seven identified backcrosses demonstrate that hybrids, at least those between A. marmorata and A. megastoma, can successfully reproduce naturally, indicating that, just like prezygotic barriers, postzygotic barriers (in the form of a reduction of F1 fertility) are also incomplete in tropical eels, even after 10 million years of divergence.
Morphological variation was assessed with PCA in the program JMP v. 7. 0 (SAS Institute Inc. ; www. jmp. com) based on the ratios of PA, T, HL, TR, PD, PDH, and AD to TL; this analysis was performed for 161 individuals for which all measurements were available (100 A. marmorata, 30 A. megastoma, 30 A. obscura, and 1 A. interioris). Principal component scores were used to delimit "core" groups of putatively unadmixed individuals for the three species A. marmorata (73 individuals), A. megastoma (26 individuals), and A. obscura (26 individuals). In addition to PCA, we plotted the ratios of AD and PDH to TL, which were found to be particularly diagnostic for Anguilla species  84. Sequencing and quality filtering. Genomic DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen) as per the manufacturer's instructions, or using a standard phenol chloroform procedure  85.
DNA quality of each sample was evaluated on an agarose gel and quantified on a Qubit Fluorometer 2. 0 (Thermo Fisher Scientific). Double-digest restriction-site-associated DNA sequencing (ddRAD) was completed following Peterson et al. 86  with minor modifications; this protocol is described in Supplementary Note 2. Paired-end Illumina HiSeq 4000 sequencing was performed at Macrogen (Korea). Returned demultiplexed reads were processed using the software Stacks v. 2. 0-beta9 and v. 2. 2  87  , following the protocol described by Rochette and Catchen  88. In brief, the reads were checked for correct cut sites and adaptor sequences using the "process_radtags" tool and subsequently mapped against the European eel (A. anguilla) genome assembly  57  using BWA MEM v. 0. 7. 17  89. As this assembly does not include the mitochondrial genome, mitochondrial reads were identified by separately mapping against the A.
japonica mitochondrial genome (NCBI accession CM002536). Mapped reads were sorted and indexed using SAMtools v. 1. 4  90, 91. Species identification was verified for all individuals by comparing mitochondrial sequences with the NCBI Genbank database using BLAST v. 2. 7. 1  92. Individuals with low-quality sequence data (with a number of reads below 600,000, a number of mapped reads below 70%, or a proportion of singletons above 5%) were excluded (n = 26). Variants were called using the "gstacks" tool, requiring a minimum mapping quality of 20 and an insert size below 500. Called variants were exported to variant call format (VCF) and haplotype format using the "populations" tool, allowing maximally 20% missing data and an observed heterozygosity below 75%, returning 1,518,299 SNPs. The VCF file was further processed in two separate ways to generate suitable datasets for phylogenetic and population genetic analyses based on SNPs.
For phylogenetic analyses, the VCF file was filtered with BCFtools v. 1. 6  91 to mask genotypes if the per-sample read depth was below 5 or above 50 or if the genotype quality was below 30. Sites were excluded from the dataset if they appeared no longer polymorphic after the above modifications, if genotypes were missing for 130 or more of the 460 individuals (30%), or if their heterozygosity was above 50%. The resulting VCF file contained 619,353 SNPs (Supplementary Fig. 1 ). For analyses of genomic variation within and among species, filtering was done using VCFtools v. 0. 1. 14  93 and PLINK v. 1. 9  94. Sites were excluded if the mean read depth was above 50, the minor allele frequency was below 0. 02, or heterozygosity excess was supported with p < 0. 05 (rejecting the null hypothesis of no excess).
marmorata, A. megastoma, and A. obscura were error-corrected and trimmed for adapters with "merTrim" from the Celera Assembler software v. 8. 3  95  (downloaded from the Concurrent Version System repository on 21 June 2017) using a k-mer size of 22 and the Illumina adapters option  96. Celera Assembler was run with the following options: merThreshold = 0, merDistinct = 0. 9995, merTotal = 0. 995, unitigger = bogart, doOBT = 0, doToggle = 0; default settings were used for all other parameters. After assembly, the reads were mapped back to the assemblies using BWA MEM v. 0. 7. 12, and their consensus was recalled using Pilon v. 1. 22  97. The completeness of the three different assemblies was assessed with BUSCO v. 3. 0. 1  98  based on the vertebrate gene set. Mitochondrial genomes were assembled separately from reads mapping to mitochondrial queries with the iterative MITObim v.
1. 8 approach 99 based on the MIRA v. 4. 0. 2 assembler 100. Analysis of mitochondrial haplotypes. RAD-sequencing reads mapping to the mitochondrial genome were converted to FASTA format using SAMtools v. 1. 3, BCFtools, and Seqtk v. 1. 0 (https://github. com/lh3/seqtk). Sequences corresponding to regions 10,630-10,720 and 12,015-12,105 of the A. japonica mitochondrial genome were aligned with default settings in MAFFT v. 7. 397  101  and the two resulting alignments were concatenated. The genealogy of mitochondrial haplotypes was reconstructed based on the GTRCAT substitution model in RAxML v. 8. 2. 11  102  and used jointly with the concatenated alignment to produce a haplotype-genealogy graph with the software Fitchi v. 1. 1. 4  103. Species-tree inference. To estimate a time-calibrated species tree for the seven sampled Anguilla species, we applied the Bayesian molecular-clock approach of Stange et al.
31  to a subset of the dataset of 619,353 SNPs, containing data for the maximally five individuals per species with the lowest proportions of missing data (28 individuals in total: 1 A. mossambica, 3 A. interioris, 4 A. bicolor, and 5 of each remaining species). By employing the SNAPP v. 1. 3  32 package for the program BEAST 2 v. 2. 5. 0 104 , the approach of Stange et al. 31  integrates over all possible trees at each SNP and therefore allows accurate phylogenetic inference in the presence of incomplete lineage sorting. As the SNAPP model assumes a single rate of evolution for all substitution types, all SNAPP analyses were conducted separately for transitions and transversions. A maximum of 5000 SNPs was used in both cases to reduce run times of the computationally demanding SNAPP analyses.
After exploratory analyses unambiguously supported a position of A. mossambica outside of the other six sampled anguillid species, the root of the species tree was calibrated according to published estimates for the divergence time of A. mossambica. Specifically, we constrained this divergence to 13. 76 Ma (with a standard deviation of 0. 1 myr), as reported by Jacobsen et al. 14  based on mitochondrial genomes of 15 anguillid species and three outgroup species. A justification of this timeline is given in Supplementary Note 3. Five replicate Markov-chain Monte Carlo (MCMC) analyses were conducted and convergence was confirmed with effective sample sizes greater than 200, measured with the software Tracer v. 1. 7  105. The posterior distributions of run replicates were merged after discarding the first 10% of each MCMC as burn-in, and maximum-clade-credibility (MCC) trees with node heights set to mean age estimates were generated with TreeAnnotator  106.
megastoma, and A. mossambica were further investigated based on maximum likelihood, using the software IQ-TREE v. 1. 7-beta12  46 and the same 28 individuals as in SNAPP analyses. RAD loci were filtered to exclude those with completely missing sequences and those with fewer than 20 (19,276 loci) or more than 40 variable sites (1 locus). The resulting dataset contained sequences from 1360 loci with a total length of 393,708 bp and 0. 18% of missing data (Supplementary Fig. 1 ). The maximum-likelihood phylogeny was estimated from this set of loci with IQ-TREE's edge-linked proportional-partition model that automatically selects the best-fitting substitution model for each locus. Node support was estimated with three separate measures: 1000 ultrafast bootstrap-approximation replicates  107  and gene-and sitespecific concordance factors  47.
These two types of concordance factors quantify the percentage of loci and sites, respectively, that support a given branch, and thus are a useful complement to bootstrap-support values that are known to often overestimate confidence with phylogenomic data  108. The phylogenetic analyses with IQ-TREE were repeated with a set of 43 individuals that included 5 individuals from each of the four A. marmorata populations. Assessing genomic variation among and within species. Genome-wide variation was estimated based on the dataset of 155,896 SNPs, after excluding sites linked within 10-kb windows with R 2 > 0. 8 (Supplementary Fig. 1 ). We performed PCA using smartpca in EIGENSOFT v. 6. 0. 1  109  , including the function "lsqproject" to account for missing data, and through model-based clustering using ADMIX-TURE v.