dataesr/scientific-paragraphs-categorization huggingface dataset:
However, most of the latest phylogenetic studies found the following anchiornithine species: Anchiornis huxleyi, Aurornis xui, Caihong juji, Eosinopteryx brevipenna, Jianianhualong tengi, and Xiaotingia zhengi, to form a clade that is either sister to Troodontidae or included in Troodontidae  99, 135, 136, 141. These anchiornithine taxa are all from the Late Jurassic to Early Cretaceous, hence the diversification and diversity dynamics in the Late Cretaceous are not altered whether they are included or excluded from the troodontid dataset. We included as many specimens as possible in each family dataset. Despite a low sample size of fossil occurrences, we still included the Ankylosauridae because the family was an important component of Late Cretaceous ecosystems and their taxonomy has been well studied  53, [142] [143] [144] [145].
Overall, we retained six family datasets containing at least 100 occurrences and 10 species, and groups spanning the entire Late Cretaceous while being important elements of the ecosystems. To examine whether carnivorous and herbivorous dinosaurs had similar diversification dynamics over time, these six families were divided into two datasets based on their diets (carnivorous versus herbivorous). The carnivorous dinosaurs (Theropoda) included the three families Dromaeosauridae, Troodontidae and Tyrannosauridae, and the herbivorous dinosaurs (Ornithischia) comprised the three families Ankylosauridae, Ceratopsidae and Hadrosauridae. Finally, we assembled a ninth dataset including all six families in order to estimate a general macroevolutionary trend for the Cretaceous dinosaurs. It is important to mention that because of long-standing historical collection biases, there is an abundance of Laurasian (North American and Asian) taxa relative to Gondwanan taxa in all datasets (a common problem previously encountered and discussed  11, 13  ).
Hence, it should be borne in mind that our results are dominated by Laurasian dinosaur diversity trends and might not exactly reveal global diversification dynamics. All taxa included (genus and species) and analysed in this study, along with their temporal information (period, epoch, stage, and absolute age), geographic provenance (country and province/state) and when possible the type material information (specimen number) are listed in the Source data files (see Data availability). Assignment of specimen (taxon) age. Each taxon was first placed in a geological period, epoch, stage, and geological formation (temporal categories) as accurately as possible and using the latest geological timescale  146. Each taxon in the six families was then assigned a minimum age and maximum age that correspond to the upper and lower boundaries of the respective geological strata, respectively.
There is also strong evidence supporting the Nemegt Formation as being early Maastrichtian in age or even the possibility that it is synchronous with the Djadokhta as late Campanian in age  148. In all cases, our decision to regard these formations as late Campanian and Maastrichtian in age, respectively, should maximize the differences between the two time bins, making it more likely that they exhibit significant diversification dynamics. Final fossil datasets. In total, we were able to assemble a dataset of dinosaur fossils comprising 1636 occurrences, which represent 247 Cretaceous dinosaur species (Dataset 1). The whole dataset splits up into a carnivorous dinosaur dataset including 763 occurrences and 105 species (Dataset 2), and into an herbivorous dinosaur dataset comprising 873 occurrences and 142 species (Dataset 3).
The sixfamily fossil datasets contain: 184 (125 originally from Paleobiology Database) occurrences and 32 species for Ankylosauridae (Dataset 4), 329 (335) occurrences and 49 species for Ceratopsidae (Dataset 5), 288 (492) occurrences and 51 species for Dromaeosauridae (Dataset 6), 360 (388) occurrences and 61 species for Hadrosauridae (Dataset 7), 175 (172) occurrences and 37 species for Troodontidae (Dataset 8), and 300 (206) occurrences and 17 species for Tyrannosauridae (Dataset 9). All nine fossil-occurrence datasets are available as data files (Datasets 1-9, see Data availability). Modelling the evolutionary dynamics of speciation and extinction. We carried out analyses of the fossil datasets based on the Bayesian framework implemented in the program PyRate  55, 149. We analysed the fossil datasets under time-varying birth-death models to estimate simultaneously for each clade (i) the parameters of the preservation process, (ii) the times of speciation (Ts) and extinction (Te) of each species, (iii) the speciation and extinction rates and their variation through time, and (iv) the number and magnitude of shifts in speciation and extinction rates.
The preservation process infers the individual origination and extinction times of each taxon based on all fossil occurrences and on an estimated preservation rate, denoted q, and expressed as expected occurrences per taxon per Myr. To cross-validate rate inferences, we applied two birth-death models that estimate rate variations and infer shifts of diversification as well as the Ts and Te for each species. We first performed the classical BDMCMC (-A 2 option) and secondly its recently enhanced version through the reversible-jump MCMC (RJMCMC) algorithm (-A 4 option). For each of the nine datasets, we ran PyRate for 5 million MCMC generations for the family datasets and 10 million MCMC generations for the whole, carnivorous and herbivorous dinosaurs datasets. All analyses were set with the best-fit preservation process after comparing (-PPmodeltest option) the homogeneous Poisson process (-mHPP option), the non-homogeneous Poisson process (default option), and the time-variable Poisson process (-q option).
The time-variable Poisson process estimated a preservation rate for each geological stage. We also accounted for varying preservation rates across taxa using the Gamma model (-mG option), that is, with gamma-distributed rate heterogeneity  55. We monitored chain mixing and effective sample sizes by examining the log files in Tracer 1. 7. 1  150  after excluding the first 10% of the samples as the burn-in period. We then combined the posterior estimates of the origination and extinction rates across all replicates to generate rates-through-time plots (origination, extinction, and net diversification). We replicated all the analyses on ten randomized datasets of each clade and calculated estimates of times of speciation and times of extinction as the mean of the posterior samples from each replicate.
Thus, we obtained ten posterior estimates of the Ts and Te for all species and we estimated the past diversity dynamics by calculating the number of living taxa at every point in time based on the Ts and Te. For all the subsequent analyses, we used the estimated Ts and Te for all species, which avoids re-modelling preservation and re-estimating times of speciation and extinction, therefore focusing exclusively on the estimation of the birth-death parameters for specific models. This procedure reduced drastically the computational burden, while still allowing us to account for the preservation process and the uncertainties associated with the fossil ages. We analysed the datasets at the global scale, and did not analyse them at the regional scale given that in most cases (e. g. Ankylosauridae and Tyrannosauridae) there are a low number of species and/or small regional sample sizes  53, 143, 151.
Robustness of evolutionary dynamics inference. The robustness of PyRate has been thoroughly evaluated using simulations that reflect commonly observed diversity dynamics  37, 55, 56. Datasets were simulated under a range of potential biases, including violations of the sampling assumptions, variable preservation rates, and incomplete taxon sampling. Simulation results showed that the dynamics of speciation and extinction rates, including sudden rate changes and mass extinction, are correctly estimated under a wide range of conditions, such as low levels of preservation (down to 1-3 fossil occurrences per species on average), severely incomplete taxon sampling (up to 80% missing), and high proportion of singletons (exceeding 30% of the taxa in some cases). It is known that the strongest bias in birth-death rate estimates is caused by incomplete data because missing lineages alter the distribution of taxa; an effect notably pervasive in phylogeny-based models.
However, in the case of PyRate, simulations confirm the absence of consistent biases due to the incompleteness of the data in the fossil record. Incomplete taxon sampling appears to have a less problematic effect on the estimation of speciation and extinction rates because, in contrast to molecular phylogenies, removing a random set of taxa does not affect the observed occurrences of other lineages  55. In addition, it has recently been shown that the RJMCMC model is very accurate for estimating speciation and extinction rates, and is able to recover sudden extinction events regardless the biases in the fossil dataset  37. Finally, the RJMCMC model in PyRate estimates diversification dynamics more accurately than traditional approaches such as the boundary-crossing and three-time methods  37.
We have nonetheless assessed the robustness of the diversification and diversity dynamics as inferred in PyRate at the global scale by analysing the fossil dataset at the hemisphere scale. We divided the global dataset into a New World and an Old World dataset to estimate whether we recovered similar dynamics of speciation, extinction and species diversity. We repeated the PyRate analyses with the BDMCMC model (as explained previously) and compared the hemisphere-scale patterns to the global patterns (Supplementary Fig. 4 ). Selection of potential drivers of diversification dynamics. Sakamoto et al. 10  could not identify a causal mechanism for the speciation downturn in dinosaurs, although they proposed that variations of sea level were an important driver as often proposed  18, 40, 71. To identify putative mechanisms of Mesozoic dinosaurian demise, we examined the correlation between a series of past environmental variables and speciation/extinction rates over the entire Cretaceous.
Acknowledgements This project received funding from the  Marie Curie International Outgoing Fellow  under the European Union's  Seventh Framework Programme  (project  BIOMME , agreement No. 627684 ) and a  PICS grant  from the  CNRS  (project  PASTA ) to F. L. C. ; from the  NERC  grant  NE/ I027630/1  and  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (agreement No. 788203 ) to M. J. B. ; from the  NSERC  grant  2017-04715  to P. J. C. The analyses benefited from the  Montpellier Bioinformatics Biodiversity (MBB)  platform services.
Methods Foliation is determined by parallel alignment of glaucophane, lawsonite, and white mica, and compositional layering of minerals. The lineation of the samples is defined by the shape preferred orientations of the lawsonite and glaucophane  [38] , and thin sections were made in the x-z plane (x: parallel to the lineation; z: normal to the foliation). The thin sections were polished with diamond paste and colloidal silica (0. 06 μm) and coated with carbon to avoid charging in the scanning electron microscope. Electron backscatter diffraction (EBSD) analysis, utilized to measure the CPOs of lawsonite and glaucophane, was performed in the x-z plane of the samples utilizing Aztec software (Version 4. 3, Oxford Instruments, Abingdon, UK) with a symmetry detector attached to a field emission scanning electron microscope (JSM 7100F, JEOL, Tokyo, Japan) at the School of Earth and Environmental Sciences at Seoul National University, Korea.
Methods Foliation is determined by parallel alignment of glaucophane, lawsonite, and white mica, and compositional layering of minerals. The lineation of the samples is defined by the shape preferred orientations of the lawsonite and glaucophane  [38] , and thin sections were made in the x-z plane (x: parallel to the lineation; z: normal to the foliation). The thin sections were polished with diamond paste and colloidal silica (0. 06 µm) and coated with carbon to avoid charging in the scanning electron microscope. Electron backscatter diffraction (EBSD) analysis, utilized to measure the CPOs of lawsonite and glaucophane, was performed in the x-z plane of the samples utilizing Aztec software (Version 4. 3, Oxford Instruments, Abingdon, UK) with a symmetry detector attached to a field emission scanning electron microscope (JSM 7100F, JEOL, Tokyo, Japan) at the School of Earth and Environmental Sciences at Seoul National University, Korea.
The system was operated at a 20 kV acceleration voltage and a 25 mm working distance. The lawsonite and glaucophane EBSD patterns were automatically indexed by mapping the sample with step sizes ranging from ~1. 2-3. 0 µm, which were at least 10 times smaller than the average grain size of the samples. To prevent misinterpretation of the original data, the raw EBSD data was postcleaned utilizing Aztec software (Oxford Instruments) in three steps: (1) wild spikes with a pixel size were removed, (2) zero solution pixels that neighbor six pixels with solutions were eliminated, and (3) Step 1 was repeated. The procedure to remove twins in lawsonite for plotting the CPOs is described as follows: Twin boundaries having a [001] rotation axis with a 67 • misorientation angle and a 5 • deviation were automatically disregarded as grain boundaries in EBSD orientation maps using Channel 5 software (Version 5.
To calculate the seismic velocity and anisotropy in the absence of twins in lawsonite, narrow twin areas in lawsonite grains were removed manually in EBSD orientation maps using HKL Channel 5 Tango software. The choice of narrow twin removal in lawsonite was consistent for all samples. All point-per-grain crystallographic orientation data of lawsonite and glaucophane were used for accurate seismic properties with and without twins  [39]. The seismic properties of the whole rocks were calculated utilizing the normalized volume fraction of the major minerals as lawsonite and glaucophane (Table  1 ). The normalized volume proportions of lawsonite and glaucophane were based on the mineral fractions from the large area EBSD mapping. Minor minerals such as garnet, omphacite, titanite, quartz, phengite, and epidote were ignored in the calculations.
Several benchmark tests exist and LES results are sometimes compared to DNS models, which normally provide exact numerical flow fields, to evaluate the predictive capability of particle dispersion in more complex turbulent flows. For instance, an international benchmark  [227]  was proposed for LES Eulerian-Lagrangian simulations in a particle-laden channel flow, gathering a large database, and was compared to DNS results. Marchioli  [228]  recently presented a review of LES in turbulent dispersed flows, which was complementary to other reviews and guidelines  [157, 197]. Objectives at least should be able to model physical mechanisms such as erosion or deposition, settling, saltation of aerosols or solid particles in bounded flows, migration in shear layers and boundary layers, preferential concentration and turbophoresis.
, 2019). Chambers are useful especially in remote, sparsely measured treeless tundra where they can capture the entire ecosystem CO 2 balance and directly measure NEE and ER  (Sørensen et al. , 2019). Thus, a compilation of both EC and chamber flux measurements and the comparison of available modeling techniques is clearly required to ensure accurate CO 2 flux estimates from existing data and models. Here, we synthesize annual and growing season CO 2 fluxes from EC and chamber measurements across the high-latitude terrestrial tundra and boreal region. We then use this new database to upscale annual average ecosystem CO 2 fluxes at relatively high spatial resolution (1 km 2 ) across the high-latitude domain using several statistical models. We compare our new database of in situ CO 2 fluxes to past tundra syntheses  (Belshe et al.
| Collection of CO 2 flux data Our study area was defined by the high-latitude tundra and boreal biomes (>45°N) based on global ecoregions (20. 6 × 10 6 km 2 ; Figure  1 ;  Dinerstein et al. , 2017). We first conducted a literature survey to identify existing EC and chamber-based terrestrial CO 2 flux observations of GPP, ER, and NEE over annual and growing season periods across the domain. Potential sites were identified from previous studies  (Ichii et al. , 2017; Marushchak et al. , 2013; McCallum et al. , 2013; Watts et al. , 2014)  and prior synthesis efforts  (Belshe et al. , 2013; McGuire et al. , 2012; Virkkala et al. , 2018). We augmented the resulting site list using a Web of Science search with key words ("tundra" or "boreal" or "arctic") and ("CO 2 flux" or "CO 2 exchange" or "CO 2 budget").
Additionally, a community call was solicited through a CO 2 flux synthesis workshop  (Parmentier et al. , 2019) , whereby investigators contributed their most current unpublished data. Additional EC data were downloaded from FLUXNET2015  (Pastorello et al. , 2020). The compiled dataset represents all natural terrestrial vegetation types (categorized by needle-or broadleaf forest, shrubland, grassland, wetland, and sparse vegetation) present in the high-latitude region. We included studies and sites with NEE, GPP, and ER estimates over a full growing season or calendar year (i. e. , cumulative flux). Growing season flux measurements are provided by EC and chambers. Non-growing season flux measurements include a variety of methods in addition to EC and chambers (e. g. , a gas diffusion method by  Björkman et al.
, 2010 , soda lime by  Welker et al. , 2004 , or an empirical model by  Vogel et al. , 2009). Growing season length and measurement period were defined in multiple ways at individual sites. To allow inter-site comparison, we filtered out measurements that did not represent the entire growing season and standardized the remaining measurements (see Supplementary Text Section 1. 1 and a similar approach in  Belshe et al. , 2013). From this filtered dataset, we calculated average growing season daily flux rates based on the reported measurement length and standardized the fluxes based on a common growing season length. The final list of sites having representative annual or growing season measurements is provided in Table  S1 , sites that were excluded from our analysis are in Table  S2. The resulting dataset included 148 sites with CO 2 fluxes from 1990 to 2015 from variable measurement periods (Figure  1 ).
e. , CO 2 sink) and  Belshe et al. (2013)  we also included data from the boreal biome, additional tundra sites, and wetlands (not synthesized in  Belshe et al. , 2013; Figure   S1 ). Similar to  McGuire et al. (2012)  and  Belshe et al. (2013) , our database primarily represents undisturbed environments. However, it also includes measurements from ca. 10 sites that have experienced high natural, anthropogenic or anthropogenically induced disturbances, such as permafrost thaw  (Bäckstrand et al. , 2010; Cassidy et al. , 2016; Trucco et al. , 2012) , fires  (Iwata et al. , 2011; Ueyama et al. , 2019) , insect outbreaks  (Heliasz et al. , 2011; López-Blanco et al. , 2017; Lund et al. , 2017) , or extensive harvesting practices  (Coursolle et al. , 2012; Machimura et al. , 2005). Throughout the text, positive numbers for NEE indicate net CO 2 loss to the atmosphere (i.
We used annual  data for GDD3, FDD, WAB, and NDVI; the remaining predictors were considered to be static. All predictor datasets were masked to only include high-latitude tundra and boreal biomes  (Dinerstein et al. , 2017) , and to exclude permanent water bodies, urban areas, and croplands based on a land cover dataset developed by ESA (2017). We compared our annual ecosystem NEE predictions and budgets (see Section 2. 2. 1) with FLUXCOM, a global product derived from FLUXNET EC towers and machine learning at 0. 5° resolution  (Baldocchi et al. , 2001; Jung et al. , 2017; Tramontana et al. , 2016)  and an ensemble of global Earth system models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) at 1. 92 × 1. 5° resolution  (Taylor et al. , 2012)  (Supplementary Text Section 1. 3).
93-0. 94), so they can be assumed to reflect the same relationships with the predictors. To extrapolate across the study domain, we fit the models using the entire dataset to produce annual flux predictions and their ensembles that were subsequently averaged to 1990-2015 mean values. Because the ensemble predictions were among the most accurate and least uncertain predictions across all response variables, and because their use is generally recommended in predictive efforts  (Araújo & New, 2007) , our final flux maps and budgets were based on the flux ensemble. In addition to annual and growing season budgets, we also calculated a non-growing season budget (see Table  S4 ). We had different numbers of observations and sites available for each flux and model, and consequently observed and predicted ER and GPP fluxes and budgets do not sum up to NEE.
| Model fit, predictive performance and uncertainty To evaluate model fit, we predicted fluxes over the entire model training data. To assess the predictive performance of the models, we used a leave-one-site-out cross validation scheme in which each site was iteratively left out from the dataset, and the remaining data were used to predict fluxes for the excluded site  (Bodesheim et al. , 2018). For both model fit and predictive performance, we calculated bias as an average of the absolute error between prediction and actual observations, Pearson correlation (r) to determine the strength of the linear relationship between the observed and predicted fluxes, and root mean squared error (RMSE) to estimate the model error. We use the terms "observed" and "predicted" to distinguish between field measurements and model predictions but acknowledge that some of these observed values represent indirect estimates of fluxes (e.
g. , GPP). We evaluated the prediction uncertainty of all flux models and the budget uncertainty of annual and growing season NEE models using a repeated random resampling procedure  (Aalto et al. , 2018). Prediction uncertainty was calculated to characterize the spatial variability in flux predictions across the high-latitude region, whereas budget uncertainty quantified the range of potential NEE budget values. We used bootstrapping (fractional resampling with replacement based on LC classes) to subset the model training data into 200 different datasets, all of which had the same number of observations as the original flux data itself. These 200 datasets were then used to produce 200 individual predictions with all five statistical models and their ensemble for each flux and for each year from 1990 to 2015 to assess prediction uncertainty which was summarized using the prediction interval (PI; 95th percentile -5th percentile).
, 2016). Our models performed well when predicting to the same data that the models were trained with, but the models had challenges when tested against independent validation data. The predictive performance of our ensemble predictions was comparable to (annual GPP and ER) or less than (growing season GPP, ER, NEE, and annual NEE) that of in other global and regional upscaling studies  (Ichii et al. , 2017; Natali et al. , 2019; Peltola et al. , 2019; Tramontana et al. , 2016; Ueyama, Ichii, et al. , 2013). However, comparisons of when these measurements cover variable environmental conditions that can be captured by the predictors. For example, FLUXCOM models  (Jung et al. , 2017 (Jung et al. , , 2020;; Tramontana et al. , 2016)  might have had a higher predictive performance than our models because they use a global FLUXNET database  (Pastorello et al.