dataesr/scientific-paragraphs-categorization huggingface dataset:
used satellite evaporation to calibrate the SWAT distributed model through a composite criterion and got a closer representation of actual evaporation and less equifinality in parameter determination; Mostafaie et al. (2018) performed a multi-objective calibration using NSE for streamflow and total water storage https://doi. org/10. 5194/hess-2021-413 Preprint. Discussion started: 16 August 2021 c Author(s) 2021. CC BY 4. 0 License. from GRACE satellite data; Milzow et al. (2011) combined several satellite datasets -surface soil moisture, radar altimetry and total water storage -to calibrate a semi-distributed model in a catchment which few streamflow measurements through a composition of nine criteria; Demirel et al. (2019) explored different combinations of objective functions, computed on several satellite products measuring soil moisture and water storage, to calibrate a conceptual model, with little gain on the streamflow simulation performance; Dembélé et al.
Genomic DNA was extracted from the polycarbonate filters using the DNA extraction kit NucleoSpin Plant II from (Macherey-Nagel, Hoerdt, France). The V4 region of the 18S rDNA gene, a marker gene conserved but highly variable across the taxa of the protistan community, was amplified using PCR, performed with a taq polymerase (Phusion High-Fidelity PCR Master Mix with GC Buffer) and eukaryote-specific primers  45. Library preparation was performed following the protocols of the NucleoSpin Gel and PCR Clean-up kits (Macherey-Nagel, Hoerdt, France). Sequencing was performed at the Genotoul platform using Illumina Mi-Seq (http://get. genot oul. fr/). Bioinformatics consisted of a sequence quality filter using built-in modules of USEARCH  46  , singletons removal as performed in reference  47  , and sequence taxonomic annotation with the PR 2 database  48.
Using Swarm2  49  , similar variants of the amplified marker gene were clustered together to define Operational Taxonomic Unit (OTU), i. e. the species unit of DNA-barcoding. OTUs were annotated with the taxonomy of their most abundant variant. OTUs annotated to metazoan and pluri-cellular plants were removed. Further details about our DNA barcoding approach can be found in reference  43  , where bioinformatics have been performed on a larger dataset to improve error detection and OTUs clustering. Our dataset contains 33 060 OTUs accounting for 3. 5 × 10 6 reads. Many OTUs were annotated with the same taxonomy, corresponding to 1028 unique taxonomic references. The taxonomic composition of this dataset can be investigated in Supplementary Fig. S1.
Eukaryotic phytoplankton diversity. All analyses were performed with the R software  50  and the specific packages mentioned. Selection of OTUs representing eukaryotic phytoplankton was carried out by coupling our barcoding dataset with a trait database (available at www. seano e. org/data/00405 /51662 /)  43. The database is On the map, the sampling stations (dots and names) are superimposed on the corresponding temperature (background color) and chlorophyll a (isoligns). Each map was assembled in R 50 using raster images estimated with satellite data ( source: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Ocean color Data; NASA OB. DAAC, Greenbelt, MD, USA. Accessed 2017/10/17). Vertical profiles of fluorescence (green, μg/L), temperature (black, °C), and PAR (yellow, W/m 2 ) were measured on board with a CTD probe and a fluorescence sensor.
Values of the down-cast were averaged every 5 m, variability comes from the triplicated casts. NOX (nitrate + nitrite, red, µM) were measured a posteriori from water sampled at three depths (a single time). Dashed horizontal lines represent the sampling depth for surface and, when present, DCM samples. a custom trait annotation of the taxonomic references given to our OTUs (see DNA-barcoding section). After a literature review, we were able to identify 362 taxonomic references (from 1028) with an obligatory phototrophic lifestyle that represented eukaryotic phytoplankton  51. We then created a sub-dataset of the 10,597 OTUs and 1. 5 × 10 6 reads that were annotated to these 362 taxonomic references (representing respectively 32% OTUs and 45% reads in the initial dataset). This sub-dataset was used to study patterns of phytoplankton diversity.
Sampling quality was evaluated by rarefaction curves (read vs. OTU number; rarecurve function of R package "vegan" 52 ; Supplementary Fig. S2 ), and an estimate of richness saturation was calculated using the R package "iNEXT"  53. Eukaryotic phytoplankton richness (number of OTUs per sample or alpha-diversity) was computed for each of our 184 distinct samples, corresponding to distinct season (3), station (5), depth (2 when the DCM was sampled), size-fraction (3) and triplicate. Phytoplankton richness was first analyzed using boxplots across seasons, stations and size-fractions. Secondly, replicates and depth samples were merged and phytoplankton richness was computed again to represent the total phytoplankton richness across seasons and stations (15 samples). All statistical tests were performed on the dataset made of 184 samples.
Patterns of phytoplankton richness were tested with the Kruskal-Wallis test, a non-parametric one-way analysis of variance. Differences in community composition (occurrence and proportions of OTUs across samples) were tested with a Permutational Multivariate Analysis of Variance using the Bray-Curtis distance (PERMANOVA; adonis function of R package "vegan")  52. The spatial structure of phytoplankton diversity was studied by focusing on the occurrence of OTUs in each station within a season. OTUs were categorized in 'shared' , when they occurred in more than one station within a season, or to 'specific to station X' , when occurring only in station X. Phytoplankton richness was also studied according to its abundance. Rank abundance curves were built by calculating the total read number of OTUs in each season.
Community assembly of eukaryotic phytoplankton. To describe the spatio-temporal structure of eukaryotic phytoplankton, the number of OTUs shared between stations, within and across seasons, was computed with the R package "vegan" (betadiver function, metric α)  52. To represent this metric, from now on called OTU-connectivity, we computed a network (with the R package "igraph"  54  ) where nodes represent stations and edges width represent the number of OTUs shared between two stations. Surface and DCM samples were distinguished in this analysis. We tested if the sampling effect between seasons (different phytoplankton richness across seasons) affected OTU-connectivity by comparing the observed OTU-connectivity with the OTU-connectivity in a sub-dataset with a curated number of OTUs by season (see the experimental procedure in Supplementary Fig.
S3 ). The correlation between the connectivity matrices (pairwise comparison of the number of OTUs shared between samples) of the original and the curated dataset were studied with the Spearman rank correlation. Further analyses of the connectivity network can be found in Supplementary Figure  S4. To quantify the ecological processes that constrained phytoplankton community patterns across the Ushant tidal front we followed the approach of reference  55. The method has already been reviewed  37  , further details are given in Supplementary Figure  S5. Briefly, the method is based on the comparison of observed community turnovers (shift in composition across samples), phylogenetic turnovers (shifts in composition weighted by phylogenetic similarity between taxa) and turnovers expected by chance (in null-models), in order to estimate whether the differences between pairs of communities are explained by Dispersal, Selection, or Ecological Drift  37.
This method uses the estimation of species occurrence across samples and a trait-table describing these species, to compute functional richness. Using our trait database  43  , we generated a sub-dataset of 287 eukaryotic phytoplankton taxonomic references (out of 362) that were well annotated with the following morphological and trophic traits (12): SizeMin, SizeMax, Cell Cover, Cell Shape, Presence of Spicule, Cell Symmetry, Cell Polarity, Coloniality, Motility, Ingestion method, Symbiosis type and Resting Stage during the life cycle. These 287 taxonomic references constituted a sub-dataset of 7106 OTUs and 1. 2 × 10 6 reads (representing respectively 67% of OTUs and 80% of reads in the original phytoplankton dataset). To compare taxonomic and functional richness at large, OTUs occurrence was merged across seasons (3) and stations (5).
Functional richness was then computed based on (a) this sub-dataset (7106 OTUs/15 samples) transformed in a presenceabsence table, and (b) a trait table describing the 7106 OTUs of the sub-dataset (12 traits). Functional richness was computed with built-in R functions using Gower distance and Principal Coordinate Analysis (PCoA)  61. Finally, to investigate the ecological strategies of phytoplankton displayed across the Iroise Sea, we computed Gower's Distance and the Complete Linkage's method of Hierarchical Clustering 62 on our trait table of 7106 OTUs and 12 traits (see this section). This approach identified 9 clusters of OTUs with similar trait combinations, thus representing ecological strategies (Supplementary Fig. S6 ). We focus on the distribution of these strategies in September by assuming that by the end of summer the accumulated selection processes might have narrowed the communities to few strategies.
Patterns of phytoplankton richness. In this section, we investigated patterns of eukaryotic phytoplankton richness using the total number of OTUs as a proxy. Rarefaction analyses indicated that reaching near saturation for the eukaryotic phytoplankton of the Iroise sea's (13,659 OTUs) would require between 500 and 600 samples following our strategy (Supplementary Fig. S2 ). With 10,597 OTUs, our study did not saturated diversity but represents the diversity patterns of the most abundant share of the eukaryotic phytoplankton communities sampled. We detail the non-extrapolated richness retrieved in our original dataset. The variability of eukaryotic phytoplankton richness (i. e. the number of phytoplankton OTUs in a sample) in the Iroise Sea throughout 2015 is presented in Fig. 3a  (for all 184 samples considered independently).
In July, the stations nearest to the front (F and O2) were richer than the others (above 2000 phytoplankton OTUs in comparison with values lower than 1700 OTUs in other stations), while in September, station F showed the highest phytoplankton richness (1999 OTUs in comparisons with < 1100 OTUs elsewhere). Consequently, richness was significantly higher at station F over summer (P < 0. 005, Kruskal-Wallis test based on the complete dataset of 184 samples), highlighting that the front represented a hotspot of phytoplankton diversity. To investigate the composition of this hotspot, OTUs were sorted according to their occurrence in each station by season (Fig. 3b ). Across July and September, when the front was established, Station F showed a significantly higher richness of 'shared' OTUs (P < 0.
01, Kruskal-Wallis test based on the summer-only dataset, 140 samples). When cumulated, its proportion of 'specific' OTUs reached 50% of phytoplankton richness in September (station F: 997 'Specific' + 1002 'Shared' OTUs), the highest value across stations and seasons (Fig. 3b ). Next, the 'shared' OTUs constituted the larger part of the 'abundant' community (OTUs > 0. 1% of the total read number of phytoplankton OTUs by season; Fig. 3c ). 'Specific' OTUs were present in the 'low abundance' community (0. 1-0. 01%) and numerous in the 'rare' community (< 0. 01%; Fig. 3c ). This indicated that the most abundant species had a larger dispersal (and detection potential), while the distribution of rarer taxa was sparser and spatially structured. The numerous 'specific' OTUs found at Station F in September thus had low abundances.
The rank abundances also highlighted that the shrinking of phytoplankton richness across seasons was mostly due to the rare community (with 4092, 3262 and 2462 of rare OTUs respectively in March, July and September; Fig. 3c ). Phytoplankton turnovers, dominant ecological processes and environmental drivers. As a preliminary step to this section, we tested if OTU-connectivity (Fig. 4a ) was influenced by the decreasing number of OTUs by season observed in Fig. 3 , a potential bias in our analysis. The observed diversity patterns were conserved in a dataset with a curated number of OTUs by season (Spearman Rank Correlation of 0. 99; Supplementary Fig. S3 ), indicating that OTU-connectivity was not influenced by the varying number of OTUs retrieved in each season, we thus detail the observed patterns.
4a ), and followed the same selection process over time. The pairwise community comparisons where selection was relaxed (i. e. where |ßNTI| < 2) were all (6) within the micro-plankton and 4 explaining the pairwise differences between phytoplankton communities sampled in the Iroise Sea, the edge color represent the dominant ecological processes between the communities of each station as estimated in the approach of reference  55. On the figure, we put emphasis on processes different from Homogenous Selection, the process dominating 5541 out of the 5551 pairwise community comparisons in our dataset. The edge width linking 'C1 in July' and 'F in September' was doubled because an ecological process other than Homogenous Selection appeared more than once (across replicates of the micro-plankton).
Phytoplankton functional diversity. Using a trait database adapted to our DNA barcoding dataset, we first tested if the hotspot of phytoplankton diversity also corresponded to a higher number of ecological strategies. Secondly, we investigated if the observed selection pressures resulted in distinct sets of ecological strategies across the front by the end of summer. Functional richness, ranging between 0 and 1, was high (> 0. 7) supposing that all stations and seasons presented several distinct phytoplankton strategies (Fig. 6a ). Contrary to taxonomic richness, the functional richness of phytoplankton did not show its highest values in March (between 0. 79 and 0. 83) but in July and September (ranging between 0. 87 and 0. 88). These high values were observed in the offshore and frontal area in July (O1, O2, F) but only at the most offshore station O1 in September (Fig.
Acknowledgements This work was financed by the  French Government  under the  programme 'Investissements d' Avenir: Oceanomics  (Grant  ANR-11-BTBR-0008 ), by the  project of the initiative Ecosphere Continentale et COtière  ( EC2CO ) of the  Institut National des Sciences de l'Univers/Centre National de la Recherche Scientifique  ( INSU/CNRS ): POHEM (2016). This research was carried out within the framework of  Pierre Ramond 's PhD, co-funded by  Ifremer and Region Bretagne (Allocation de REcherche Doctorale (ARED)  fellowship). We thank all members of the M2BIPAT consortia, as wells as the cruise members who contributed to samples' collection with the Albert Lucas vessel from the  French Oceanographic fleet. We also thank the  Genotoul platform  for samples' sequencing (https ://www. genotoul.
As opposed to other methods (including boundarycrossers and three-timers), which are prone to edge effects and tend to flatten the extinction estimates, especially during mass extinctions, PyRate recovers the dynamics of speciation and extinction rates, including sudden rate changes and mass extinctions  37. The Bayesian framework of PyRate also allows testing hypotheses related to the drivers of diversification  [56] [57] [58]. Yet, this approach has never been used to address questions on dinosaur diversification. Here we compile and analyse a dataset of dinosaur fossils comprising over 1600 occurrences at a global scale and stage-or formation-level for dinosaur ages, which spans the Cretaceous Period and represents 247 dinosaur species of the most speciose and well-documented dinosaur families that composed the Late and end-Cretaceous ecosystems, namely the ornithischians Ankylosauridae, Ceratopsidae, and Hadrosauridae, and the theropods Dromaeosauridae, Troodontidae, and Tyrannosauridae.
In addition, spatial biases can hamper the estimation of diversity trajectories  38. In dinosaurs, the North American fossil record is much better documented than the Eurasian counterpart  3, 11, 61  such that the diversity decline could be regional and not global (bearing in mind that our dataset is mostly Laurasian). We addressed this issue by estimating the diversification and diversity dynamics of New and Old World dinosaurs independently. Despite spatial heterogeneity in macroevolutionary dynamics, both the New and Old World dinosaurs show a pattern of diversity decline before the K/Pg boundary (Supplementary Fig. 4 ). The palaeodiversity of New World dinosaurs complies with the global dinosaur diversity in that the decline started in the Campanian (~76 Ma), while the Old World dinosaurs show a delayed declining phase initiated at the Campanian-Maastrichtian boundary (~72 Ma).
Overall, our results provide evidence that dinosaur biodiversity was declining prior to the asteroid impact. Analysing the six dinosaur families as a whole assumes that diversification rates and their dynamics were homogeneous for all clades. However, rate heterogeneity is a common phenomenon in macroevolution  62, 63  and clade-specific evolutionary dynamics could be expected in dinosaurs  10, 13, 14, 16. We therefore repeated the analyses for each family independently. As for the whole dataset, we find evidence for significant temporal changes in both speciation and extinction rates for all families except Troodontidae (Fig. 3 ; Supplementary Figs. 5-10). As expected, these results further support the idea that rate heterogeneity is rampant in dinosaurs. Analyses recovered two patterns of diversification among the tested clades: (1) a decrease in speciation rate toward the K/Pg boundary coupled with constant extinction so that speciation became lower than extinction, which occurred in hadrosaurs and tyrannosaurs (Fig.
Surprisingly, the MCDD analyses also estimate several cases of positive inter-clade interactions mostly driven by Ankylosauridae (and Dromaeosauridae in North America). We estimate that increasing ankylosaur diversity improved the speciation rates of Ceratopsidae and Hadrosauridae by 11. 9% and 4. 9%, respectively, (Fig. 5 ; Supplementary Table  2 ), and, unexpectedly, it also reduced the extinction rate of Dromaeosauridae by 16. 6%. It is well recognized that large bodied herbivorous dinosaurs such as ceratopsians, hadrosaurs and sauropods likely had gregarious behaviour. Evidence of gregarious behaviour exists for some Asian ankylosaurs (Pinacosaurus, Talarurus) at juvenile stages  [110] [111] [112]. However, ankylosaur data point to a solitary adult life with efficient anti-predator defence system.
Instead, given the results from the MBD and MCDD models, we hypothesize a lack of evolutionary novelty (e. g. morphological disparity, niche packing  96, 120, 121  ) or adaptation to a changing environment (e. g. few angiosperms in herbivorous diets  92  ) for the non-avian dinosaurs in the last 10 Myr before the asteroid impact. Limitations. Here we propose an explanation for variations and decline of dinosaur diversity through time, which not only relies on both abiotic (temperature) and biotic (herbivorous dinosaur diversity) factors, but also on intrinsic species age effect on extinction. However, our study comes with issues either related to the dataset or the analytical approaches. First, it is important to recall that our results hold for six species-rich families of the Cretaceous that are well represented in the fossil record.
This does not represent a complete picture of the global diversification dynamics for all dinosaurs, but this study is a step forward in our understanding of the causes of dinosaur extinction. As new fossil discoveries and descriptions are continuously being made  66, 122  , assembling and analysing new large global fossil datasets will improve our understanding of the diversification of non-avian dinosaurs. Second, as with any process-based model PyRate makes assumptions about the processes generating the evolutionary history of a clade. These assumptions can violate real evolutionary processes. For instance, we did not explicitly take into account the geographic bias in sampling fossils while modelling diversification rates. Third, our results depend on the choice and availability of environmental and biological variables used as diversification drivers.
Based on these selective criteria, the following dinosaur families were retained in the analyses: Ankylosauridae (armoured dinosaurs), Ceratopsidae (horned dinosaurs), Dromaeosauridae (feathered theropod dinosaurs), Hadrosauridae (duck-billed dinosaurs), Troodontidae (bird-like theropod dinosaurs), and Tyrannosauridae (tyrant dinosaurs). Although other groups were surveyed for inclusion in the dataset, such as the theropod clades Abelisauroidae, Allosauroidea, Megalosauroidea, and also ornithischian clades Nodosauridae and Pachycephalosauridae, they could not be analysed for two main reasons. Fossil occurrences in the Campanian-Maastrichtian are not currently available in quantity to assemble a large dataset, almost entirely due to poor fossil sampling. Taxonomic confusion remains for the status of many taxa, including the family itself (e.
g. Pachycephalosauridae  [123] [124] [125]  ), which results in a small dataset (130 occurrences in the case of Pachycephalosauridae) likely due to the difficulty of assigning some taxa to a given higher-level group. Birds were not included in the dataset, because we chose to focus specifically on non-avian dinosaurs, and more importantly, because of severe sampling biases that limit the number of Campanian-Maastrichtian lineages with too few occurrences that could be used (resulting in a reduced dataset). However, we included two nonavian dinosaur clades (Dromaeosauridae, Troodontidae) that are included with birds in the clade Paraves  126. Fossil occurrences. Fossil occurrences for each family were initially retrieved using the Paleobiology Database through the FossilWorks website (fossilworks.
org/; regularly accessed between November 11, 2015 and May 2, 2020). The literature was thoroughly screened to check and clean each occurrence one by one but also to include additional occurrences. We decided to include in the dataset the recently described and valid taxa (species or genus) but only known from a single specimen and thus lacking multiple occurrences (e. g. Zuul crurivastator Arbour & Evans  127  for which ROM 75860 is the single known specimen of this ankylosaur species; Dynamoterror dynastes McDonald, Wolfe & Dooley  128  for which UMNH VP 28348 is the single known specimen of this tyrant species), despite the possibility to increase singletons. The literature search was done especially to clarify and correct the taxonomy based on the latest advances in dinosaur systematics and discoveries.
Because the databases also include records of fragmentary specimens that have been assigned to various species in the literature (sometimes, regardless of whether or not that assignment is well-supported or based on solid evidence), each entry was scrutinized using the museum specimen number to track its assignment in the literature. Only specimens that could confidently be assigned to a species were included and we were able to identify 1555 unique specimen numbers out of 1636 total occurrences (81 occurrences had no specimen numbers). All dubious or poorly justified material was removed to avoid biases in diversity or age of first/last appearance and singletons. In cases of conflict, two approaches were followed depending on whether the conflict was 'hard' or 'soft': either there is a soft conflict meaning that there is a taxonomic consensus about the assignment of material according to several studies and the occurrence can be retained (e.