dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018)  over the years 1959-2019. Precipitation data was then extracted from the Eddy Covariance tower coordinates using the terra package  (Hijmans 2021)  in R (R Core Development Team, 2020) to estimate a monthly total precipitation for June, July, and August for each of the sites included in this study (Tables  S1 ). We did not use the precipitation collected by the meteorological sensors installed in the tower sites given the gaps in the site-level dataset. The other environmental variables used in this study were collected at the towers' sites. The median difference between the total precipitation and the total ET in each site was estimated to evaluate the PPT-ET in each study site during each time of the season (June, July, and August as shown in Fig. S2c ). The median was used as it is less affected by outliers.
squaredGLMM" function within the "MuMIn" package in R  (Nakagawa et al. , 2013; Johnson et al. , 2014). Given the very similar results between the partial correlation and the linear mixed modelling, we only included the results of the partial correlation and linear regression analyses in Table  1 , and Table  S2. To maximize the dataset for each analysis we included all available time periods for the variables regressed in Fig. 2 , but only selected the 2004-2019 period for the Maximum Covariance Analysis (MCA) analysis to include a time period where most sites had data available. The MCA was performed on two fields (e. g. anomalies in NEE and anomalies in snowmelt timing, see Fig. 3 , Figs. S3-5); the columns of the two fields are spatial locations (each site was retained as a unit of variation of this analysis) and rows are temporal measurements.
The first pair of singular vectors are the phase-space directions when projected that have the largest possible cross-covariance. The singular vectors describe the patterns in the anomalies that are linearly correlated. We used the time series of the first singular value decomposition (SVD) mode to visualize the parts of the datasets that vary together and report the squared covariance fraction (SCF) with the MCA (Fig. 3 , Fig. S3-5 ). Given the limited length of the dataset we did not discuss the long-term changes in the reported anomalies. However, the MCA allowed us to evaluate the influence of snowmelt timing on the carbon balance over time at different times of the growing season. All analyses were carried out in R version 4. 0. 5 (R Core Team 2021). Table  1  Significance (P) and Pearson's correlation coefficient (r) of the relationships between the indicated monthly median standardized anomalies for June, July, and August retaining site as unit of variation.
Acknowledgements The complete list of funding bodies that supported this study is included in the SI Appendix. DZ, WCO, XX, and DAL acknowledge support from the  Office of Polar Programs of the National Science Foundation (NSF)  (award number  1204263 , and  1702797 ) with additional logistical support funded by the  NSF Office of Polar Programs , from the  NASA Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) , an Earth Ventures (EV-1) investigation, under contract with the  National Aeronautics and Space Administration , and from the  NASA ABoVE  ( NNX15AT74A ;  NNX16AF94A ) Program. The Alaskan sites are located on land owned by the  Ukpeagvik Inupiat Corporation (UIC). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No.
Data Availability The eddy covariance data from RU-Che, RU-Cok, and GL-ZaH (previously named DK-ZaH), CA-DL1, were obtained from the European Fluxes Database (http://www. europe-fluxdata. eu/home), from the Ameriflux Database (http://ameriflux. lbl. gov/), with some updated versions provided directly by the principal investigators of each site (e. g. the data from GL-ZaH are also available on: https://data. ge-m. dk). The data from US-ICh and US-ICs are stored in the http://aon. iab. uaf. edu/data_access. US-Bes, US-Atq, US-Ivo are stored in the Arctic Data Center (Donatella Zona. 2019. Greenhouse gas flux measurements at the zero curtain, North Slope, Alaska, 2012-2019. Arctic Data Center. doi:10. 18739/A2X34MS1B).
Over the fall 2018 deployment period, we used the same pressure transducer and optical-backscatter turbidity mooring setup in the South River channel 2. 4 km from the mouth (ch_OBS S ; Figure  1 ). We also deployed a bottom mounted Aquadopp acoustic Doppler current profiler (2 MHz) in the North River 0. 8 km from the inlet (ch_U N ). It provided velocity data at 20 cm increments from 0. 6 m above bottom to 1 m below the water surface. We used significant wave heights from the Boston wave buoy located ∼20 km north of the estuary (NDBC buoy 44013) as a proxy for wave activity on the seaward side of the inlet and North River discharge at USGS gauge 01105730 (Figure  1 ) as proxy for freshwater input to the North and South Rivers. North River discharge is a reasonable proxy for temporal variation in South River discharge given their spatial proximity.
Both these early-winter events were short in duration (1 tide cycle) and occurred during neap tides. Sensors were retrieved for the winter before capturing the post-event response during the spring tide following these December storms. We complement the 2020 marsh time series data (Figure  5 ) with seven seasonal sediment traps deployed over the same time intervals (Table  2 ). Integrated seasonal accumulation of clastic material from these traps was consistent with higher sedimentation rates on the marsh platform in the spring and   fall relative to the quiescent summer season. Spatial variability in sedimentation rates among the trap locations was influenced by their relative distances from the marsh edge, marsh elevation, and along-channel distance from the sediment source  (Temmerman et al.
Study population The Whitehall II study is an ongoing cohort study established in 1985-1988 among 10,308 persons (6895 men and 3413 women, aged 35-55 years) employed in London-based government departments  [20]. Written informed consent from participants and research ethics approvals were renewed at each contact; the most recent approval was from the University College London Hospital Committee on the Ethics of Human Research, reference number 85/0938. Since baseline, follow-up clinical examinations have taken place approximately every 4 to 5 years  (1991-1993, 1997-1999, 2002-2004, 2007-2009, 2012-2013, and 2015-2016). Data over the follow-up were also available using linkage to electronic health records of the UK National Health Service (NHS) for all but ten of the 10,308 participants recruited to the study.
Dementia Ascertainment of dementia was undertaken using linkage to Hospital Episode Statistics (HES), the Mental Health Services Data Set (MHSDS), and the mortality register using ICD-10 codes F00-F03, F05. 1, G30, and G31. HES contains clinical diagnoses from inpatient and outpatient clinical encounters in English acute generals hospitals and has sensitivity and specificity of 78. 0% and 92. 0%, respectively  [22]. MHSDS contains dementia diagnoses from inpatient, outpatient and community mental health services, including memory clinics, and the British national mortality register collects information about cause-specific mortality. Record linkage was available until 31st of March 2019, and the date of dementia was set at the first record of dementia diagnosis in any of these three databases.
Two, to examine the individual contribution of metabolites and rank them by their importance we examined change in the predictive accuracy of the score excluding one metabolite at a time from the risk score with the largest number of metabolites. Three, the role of the Apolipoprotein genotype was examined by adding ApoE e4 (yes/ no) status to the risk scores in participants with data on this measure and predictive accuracy was examined as in the main analyses. Four, we compared the predictive accuracy of the best-performance risk score in our analyses with two sets of metabolites previously identified in conventional rather than a machine learning approach in meta-analyses that included the Whitehall II cohort study  [11, 12]. Elastic net regression and GND test were performed using R software (version 4.
When cross-validation is used some authors have highlighted issues arising from the random partitioning of the dataset as results are inconsistent across the random samples  [19, 38, 39]. Previous studies on metabolites have not considered this source of inconsistency  [13, 40, 41]. We adopted an approach that allows circumvention of this limitation by repeating the cross-validation 100 times. Other studies using similar approaches, but are characterized by small sample sizes, cross-sectional design, or short follow-up-the AUC in Fig. 2  Observed and predicted rate of dementia per 1000 person-years (calibration-in-the-large) as a function of deciles of predictors (age, risk score 3, risk score 4 and risk score 5). VLDL, very low-density lipoproteins; HDL, high-density lipoprotein. The first and second decile were collapsed due to a small number of events in these deciles.
77 to 0. 88  [36, 42]. A recent study on 38 metabolites in 1440 Chinese participants, mean age 70. 7 years at baseline, used LASSO regression to identify 5 metabolites that predicted dementia (AUC 0. 72) over a 5-year follow-up  [13]. The authors used a cross-validation procedure for the estimation of AUC but the variation arising out of partitioning of the dataset was not considered. This was also the case for another study that found 10 plasma metabolites to predict a combined outcome of amnestic mild cognitive impairment or Alzheimer's disease with an AUC of 0. 827 in the discovery sample and 0. 77 in the validation sample  [41]. However, these findings were not replicated in three subsequent studies which reported AUC between 0. 395 and 0. 642  [14, 15, 38]. These inconsistencies highlight the need to address the variation due to partitioning the dataset in cross-validated machine learning models.
Acknowledgements We thank all of the participating civil service departments and their welfare, personnel, and establishment officers; the  British Occupational Health  and Safety Agency; the  British Council of Civil Service Unions ; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Whitehall II Study team comprises research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants and data entry staff, who make the study possible.
Funding The  Whitehall II study  is supported by grants from the  National Institute on Aging ,  NIH  ( R01AG056477 ,  RF1AG062553 );  UK Medical Research Council  ( R024227 ,  S011676 ); and the  Wellcome Trust  ( 221854/Z/20/Z ). Séverine Sabia is supported by the  French National Research Agency  ( ANR-19-CE36-0004-01 ). Mika Kivimäki  was supported by  NordForsk  ( 75021 ) and the  Academy of Finland  ( 311492 ). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript.
e. biomass, abundance) or traits  (Genung et al. , 2020; Lefcheck et al. , 2021; Ulrich et al. , 2021; Winfree et al. , 2015). We use the three-part 'Community Assembly' Price partition proposed by  Bannar-Martin et al. (2017)  to link temporal changes in plant biodiversity to aboveground biomass. Additionally, we quantify absolute species losses and gains. By following experimental plots through time, we partition cumulative species compositional change and the associated with a change in aboveground biomass into that of species losses, species gains and species that persist through time (Figure  1 ). To quantify how community compositional change induced by nutrient addition contributes to altered aboveground biomass, we used data from sites within the Nutrient Network, a globally distributed nutrient addition experiment, replicated across grassland sites (NutNet; http://www.
For this study, we used data from two unfenced treatments: unmanipulated control (ambient conditions) and full fertilization (NPK) treatments. Unfenced plots are grazed by wildlife or livestock according to site-specific conditions, in the presence of a consumer food web. Sites with measurements for a year prior to fertilization (year 0) and for at least 3 years with fertilization were included in this analysis. The mean length of experiments across all sites included in this analysis is 8 years, with the maximum being 13 years. This resulted in 59 sites meeting all criteria, situated on every continent except Antarctica (Table  S1 , Figures  S1  and S2 ).
, 2012). This relates the species cover to biomass for different functional groups (Figure  S3a ) and accounts for differences in the mass to cover relationships among different life forms. For example, broadleaf forbs will likely have a higher cover-to-mass relationship as their leaves are more horizontal. In sites and years where biomass was not sorted into functional groups, or in plots where samples of functional groups were not matched between the cover and biomass data (e. g. a legume recorded in cover measurements but not in biomass samples), total live biomass values were used to estimate per species biomass. In these cases, the cover of each species relative to the whole plot was multiplied by the total live biomass for the plot  (Axmanová et al. , 2012; Hautier et al. , 2014; Isbell et al.
Quantifying components of change To quantify changes in species and biomass through time, we compared the composition of each plot in the year before fertilization (year 0, t0) to itself at every subsequent time-step measured annually (comparison, year n, tn) using the R package priceTools (Bannar-Martin et al. , 2017) (Figure  1 ). We used two approaches to quantify community change. First, we used an ecological adaptation of the Price equation  (Bannar-Martin et al. , 2017; Fox & Kerr, 2012)  to partition overall biomass changes into those associated with species losses, species gains and persistent species between two samples in time in every plot (Figure  1 ). Specifically, we use the 'Community Assembly' 3-part partition approach suggested by Bannar-Martin et al. , 2017 (Figure  1 , Box 1).
Our results did not qualitatively change when only sites with experiments running for a minimum number of years (i. e. all years, ≥3, ≥6 or ≥ 10 years) were included (Figure  S7 ), and we present results using a minimum of 3 years in the main text. For Bayesian inferences and estimates of uncertainty, all models described above were fitted using the Hamiltonian Monte Carlo (HMC) sampler Stan  (Carpenter et al. , 2017)  and coded using the 'brms' package  (Bürkner, 2018)  in the R for Statistical Computing and Graphics environment (v. 4. 0. 2; (R Core Development Team, 2019)). All models were fitted with four chains and varying iterations (Supplementary Information). We report the 95% Credible Intervals (hereafter CI) around the absolute average total change after 13 years, and the F I G U R E 2 The absolute average total change in the number of species (a, b) and live aboveground biomass (c-e).
AC K NOW L E DGE M E N T S This work was generated using data from the Nutrient Network (http://www. nutnet. org) experiment, funded and administered at the site-scale by individual researchers (Table  S1 ). Coordination and data management have been supported by funding to E. Borer and E. Seabloom from the National Science Foundation Research Coordination Network (NSF-DEB-1042132) and Long-Term Ecological Research (NSF-DEB-1234162 & DEB-1831944 to Cedar Creek LTER) programs and the University of Minnesota's Institute on the Environment (DG-0001-13). We gratefully acknowledge the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation  (DFG-FZT 118, 202548816). We thank Christian Krause and the UFZ administrative and support staff of the High-Performance Computing Cluster EVE, a joint effort of the Helmholtz Centre for Environmental Research (UFZ) and iDiv, for access to, and support associated with, EVE.
5 ). Architecture VII was the most likely for all anteaters but M. tridactyla (IX), for all pangolins except for S. gigantea (VIII), as well as for O. afer. Static allometry was detected for all species but P. maximus and P. cristatus. Apart from these species, results reported below relate to the allometry-corrected data (Table  3 ). Static allometry did not affect the number of retrieved modules except in S. temminckii, which presented a therian six-module architecture with a zygomatic arch (V; Table  3  and Additional file 3: Table  S4 ). Allometric-correction also changed the modular architecture of O. afer, but without changing the number of modules. Posterior to static allometry correction, the most likely architecture for O. afer was the seven-module VIII, which contrasts with architecture VII in presenting a complete zygomatic arch and palatine modules (Fig.
Although contrasting, the results from EMMLi and Z CR are not contradictory, as the two tests perform a model choice based on two different criteria  [71, 73]. However, unlike maximum-likelihood, CR effect sizes were not statistically different between modular architectures with six and seven modules (Table  5  and Additional file 3: Table  S6 ). These apparently more conservative results obtained with Z CR might be explained by two factors, one associated with the dataset, and the other related to how Z CR determines the favoured modularity hypothesis  [72, 139]. Firstly, the differences between architectures III to X, varying between six to seven modules, try to capture significant changes in covariance associated with a few landmarks, instead of large modules (see Additional file 3: Table  S2 ; in Z CR analyses, architecture III = architecture IV, as 'NAs' are interpreted as a module).
g. ,  [69, 78] ). In all our taxa, landmarks delimiting the anterior edge of the nasals were found to consistently cluster with anterior maxillary landmarks (Fig. 3  and Additional file 2: Fig. S3 ). M. tridactyla and P. cristatus (Aardwolf ) displayed a small naso-frontal module and an intact molarpalate region (IX; Table  3 ), but such architecture selection should, however, be taken with caution. In our dataset, the nasal module consists of only three landmarks, two of them bilateral (#39/50). Therefore, pronounced variance on a single landmark may have a disproportionate impact on the estimated likelihood of the modular architecture. The hierarchical clustering resulting from interlandmark distance correlations (Fig. 3A  and Additional file 2: Fig. S3 ) shows that, in P. tetradactyla, the naso-frontal cluster is closely associated with the remaining molar-palate landmarks (unlike in M.
tridactyla; Fig. 3B ). Curiously, P. tetradactyla was found to fit a seven-module architecture with a naso-palatine module (VII; Table  4 ). When nasofrontal bearing architectures (IX, X) were removed from the analyses, the maximum likelihood approach recovered a naso-palatine module in both M. tridactyla and P. cristatus (VII; Table  3 ). Overall, our results showed that the use of exploratory methods is a good complement to a priori hypothesis testing. These methods allow us to reveal patterns that are not predicted in developmental, functional, or even physiological hypotheses  [78]. Specifically, the presence of a naso-palatine module recovered from EDMA clusters was validated by two confirmatory methods (maximum-likelihood and covariance ratio). Our two-pronged approach enabled us to propose a new modular architecture that could be tested in other mammalian datasets.
Such an exclusion-and subsequent absence of a zygomatic arch module-in chewing placentals could be explained by two interacting factors: (1) an exclusion of the jugal from the landmarked structures (e. g. , this study;  [31] ) and (  2 ) genetically-underlied variation patterns that vary across taxa  [104]. Parr et al. (2016; [80] ) showed that the jugal bone could consist of an individual module in dogs. While this result highlights an artefact of a nonlandmarked jugal in our dataset, it also suggests that the dissociation observed between the zygomatic processes of the maxilla and the squamosal in P. cristatus could be due to the presence of an additional jugal module. On the other hand, a landmarked jugal bone would likely be integrated within the zygomatic arch module of P. maximus and O.
Morphological integration and allometry Previous studies highlighted the role of allometry as a major component of phenotypic integration (e. g. ,  [32, 103] ). Our results confirm that size variation is an important factor influencing integration magnitude in the mammal skull, and suggest that the impact of allometric growth is differentially distributed in the skull. As in other mammal clades (e. g. ,  [31, 32] ), the oro-nasal module of myrmecophagous species is more integrated than the remaining ones. The stronger integration of the oronasal module is explained by ontogenetic allometry and represents a pattern common to all mammals  [63, 64]. In our dataset, the strong correlation of this module could also result from its landmarks being spatially contiguous (except for P. cristatus). Nevertheless, the extreme rostrum elongation of the skull in myrmecophagous species was corroborated by the negative correlation between the oro-nasal and the molar-palate modules (sensu  [31] ; Fig.
Conclusions We showed that morphological changes in convergentlyevolved lineages did not radically change the modular patterns in the skull of myrmecophagous placentals. Our data fails to detect a convergent shift in cranial modularity and integration among myrmecophagous placentals. This confirms previous results suggesting that changes in the morphospace and adaptation to functionally demanding ecologies (e. g. , fossoriality) do not mandatorily impact structural integration  [122]. In the case of myrmecophagous placentals, the loss of covariance generated by tooth eruption (anteaters and pangolins) did not have a noticeable effect in either modularity or integration. On the other hand, the presence or absence of a zygomatic arch module, as well as its integration values, appear to be independent of the masticatory capabilities associated with each of the species in our dataset.
Biological sampling Skull digitization included specimens from the collections of the Natural History Museum (BMNH) in London (United Kingdom), the Museum für Naturkunde (MfN) in Berlin (Germany), the Muséum National d'Histoire Naturelle (MNHN) in Paris (France), the Institut des Sciences de l'Evolution in Montpellier (France), the Royal Museum for Central Africa (KMMA/RMAC) in Tervuren (Belgium), the American Museum of Natural History (AMNH) in New York (United States of America), the National Museum of Natural History (USNM) in Washington DC (United States of America), and the Museum of Vertebrate Zoology (MVZ) in Berkeley (United States of America). Our dataset is composed of cranial landmarks of 466 specimens from 13 extant species of myrmecophagous placentals (Additional file 1; Additional file 3: Table  S1 ).
2. 1. 0 R package  [132]. For each species, we tested the splitting of the tree in two to ten clusters. The preferred number of clusters was selected and we then determined the cluster composition with bootstrap resampling using the 'clusterboot' function in the fpc R package  [79, 133]. This function applied the Jaccard similarity coefficient  [134]  to compare landmark affiliation between bootstrap runs, in order to appraise the stability of each cluster. A cluster was considered unstable when the Jaccard coefficient was below the 0. 70 threshold  [133]. The number of clusters was reduced until all resulting units presented a Jaccard coefficient equal or superior to the defined threshold. A supplemental stability assessment was carried on using a less conservative threshold (0. 60) in order to understand the effect of threshold choice in the final number of clusters.