dataesr/scientific-paragraphs-categorization huggingface dataset:
These models confirmed the existence of a seasonal trend in both countries, with a recurrent peak of cases during summer (Figure  5 , panel E and F). The same models disclosed a progressive increase in the number of cases of anti-NMDAR encephalitis diagnosed in France over the study interval (2008-2018) (Figure  5 , panel E) that was not observed in Greece, where the yearly number of cases remained relatively constant from 2010 to 2019 (Figure  5 , panel F). Based on the observation that the incidence of anti-NMDAR encephalitis seemed to increase during warm months, we used the French dataset to perform temperature analyses. Tlag-he impact of temperature on the incidence of anti-NMDAR encephalitis was assessed using a lag-response association model. Both in the overall dataset (n = 328) and the subset of patients from the Île-de-France region (n = 115), which was the region with more cases, we observed a significant association between hot temperatures and the crude incidence of anti-NMDAR encephalitis in the overall dataset, RR 1.
2 [1. 02-1. 4, p = 0. 03, and also in Île-de-France, RR 1. 15 [1. 02-1. 55] p = 0. 04 (Figure  6 , panel A and B), providing additional evidence in support of this association. This model was used to estimate the quantitative impact of temperature on the incidence of anti-NMDAR encephalitis. We found that high temperatures accounted for approximately 15% (CI 0. 7-27%) of cases of anti-NMDAR encephalitis. The Greek dataset was not analyzed using this approach due to the limited number of patients. Note that in both models, the grey area is slightly above the horizontal line (RR = 1), showing a significant association. RR = relative risk.
Consistently with temperature analysis, we observed that anti-NMDAR encephalitis displayed a seasonal pattern, with a higher number of cases during warm months. It should be noted that in the Greek dataset, with a limited number of cases, there was a great number of cases during June, July and August (Figure  5D ). A similar observation was previously reported in a small study conducted in the United States on pediatric patients  [15] ; however, multivariate analyses or seasonal modelling were not included. Although, a higher peak of cases during summer could simply reflect higher seasonal temperatures, other factors that have not been taken into account in the present study, such as recurrent viral epidemics, might also be implicated. Some studies on infectious encephalitis, including herpes simplex virus encephalitis, conducted in western countries have pinpointed a higher incidence of hospital admissions during summer  [99, 100] , while others have failed to disclose any significant seasonal pattern  [101].
The differences in age composition rendered crude incidence estimates not directly comparable between populations. Data on the incidence of anti-NMDAR encephalitis were unavailable for many world countries, mainly in the southern hemisphere, and completely missing for Africa. Therefore, our results should be interpreted with caution when considering the data of southern hemisphere, including the association between NMDAR encephalitis incidence and ultraviolet radiation exposure and the mean annual temperature. The impact of factors such as nutrition and lifestyle could not be assessed in our metaanalysis, although these likely represent important elements, as demonstrated for other autoimmune disorders  [102]. However, we used the Socio-Demographic Index (SDI) similar factors. This is a summary measure of a geography's socio-demographic development.
Funding: This work has been developed within the  BETPSY  project, supported by a public grant overseen by the  French National Research Agency (ANR)  (reference  ANR-18-RHUS-0012 ). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests Tropical forests face increasing climate risk  1, 2  , yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, Ψ 50 ) and hydraulic safety margins (for example, HSM 50 ) are important predictors of drought-induced mortality risk  [3] [4] [5]  , little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters Ψ 50 and HSM 50 vary markedly across the Amazon and are related to average long-term rainfall characteristics.
Here, we present a pan-Amazon dataset of plant hydraulic properties (Ψ 50 , HSM 50 and Ψ dry ), following a fully standardized methodology. Our dataset includes hydraulic traits (HTs) from 129 species across 11 forest plots in the eastern, central eastern and southern Amazon. Our sampling spans the entire Amazonian precipitation space ranging from ecotonal forests at the biome edges with long dry season length (DSL) to ever-wet aseasonal forests (Fig. 1 , Extended Data Fig. 1  and Supplementary Table  1 ). In each site, sampling effort was concentrated on adult dominant canopy and subcanopy species (Supplementary Table  2 ). For each species at each site, we constructed xylem embolism Article vulnerability curves (describing the reduction in hydraulic conductivity with declining water potential), from which we determined Ψ 50 and Ψ 88 and also measured midday leaf water potential during the peak of the dry season (Ψ dry ; Extended Data Fig.
2 ) to compute hydraulic safety margins (HSM 50 = Ψ dry -Ψ 50 ). Collectively, the species sampled encom- pass a wide array of life-history strategies  37  and represent about 24% of total Amazon tree biomass, excluding palms  38  (Extended Data Fig. 3 ). We use this dataset to assess basin-wide biogeographic variation in embolism resistance and vulnerability to hydraulic failure. Finally, we take advantage of standardized long-term inventory plots distributed across the Amazon  39  , within which our sites are nested, to test whether these traits predict Amazonian species distribution and long-term aboveground biomass (AGB) accumulation (that is, the forest AGB carbon sink).
Many species in the driest sites have negative HSM 50 (Fig. 2b ), suggesting that (1) they may be adapted to cope with seasonal exceedance of HSM 50 and (2) mortality thresholds in these regions may be associated with higher conductance losses; for example, HSM 88 as has been reported in experimental studies  5, 42. Although our results point to a very strong control of background climate (MCWD) in driving variation in hydraulic properties across the Amazon, we note that other factors governing water availability locally are also probably important, including topography-associated variation in water table depth  28, 43. The relationship between community mean HSM 50 and MCWD is unlikely to be driven by differences in leaf phenology across sites. Within our dataset, we find that deciduous species have lower HSM 50 than do semideciduous and evergreen species (Extended Data Fig.
Although there are inevitable uncertainties (for example, precise determination of minimum water potential requires continuous measurements  58  and other portions of the tree hydraulic pathway may show different sensitivities to water stress  59, 60  ), the fully standardized dataset allows direct comparison of the drought vulnerability of forests across the Amazon. We find that central eastern forests that have informed most of our current understanding of Amazon drought impacts are the least vulnerable to drought, possibly due to the periodic occurrences of El Niño/Southern Oscillation events and high climate variability creating a selection pressure for more drought-adapted taxa  61, 62. Of all sites considered in this study, the Tapajós site located close to one of the Amazon ecosystemscale drought experiments has the most resistant Ψ 50 and the most positive HSM 50 , suggesting that upscaling of drought sensitivity inferred from these forests to the whole biome may underestimate Amazonian sensitivity to climate change.
Site description We assemble a pan-Amazon dataset of key HTs (Ψ 50 , HSM 50 and Ψ dry ), including 129 species distributed across 11 forest sites (Fig. 1  and Extended Data Fig. 1 ). The sites are old-growth lowland forests (less than 400 m of elevation), with no evidence of significant human disturbance, located in western, central eastern and southern Amazon. They were specifically chosen to span the full Amazonian precipitation gradient and to encompass the principal axes of species composition in the Amazon. The MAP varied from around 1,390 to around 3,170 mm yr -1 and mean MCWD varied from -640 to -15 mm across sites. Summary information for all sites can be found in Supplementary Tables  1  and 2.
Wood density and leaf mass per area We combined published and new field measurements of LMA and wood density to understand the power of these traits relative to HTs in predicting forest carbon balance. Stem wood density data (WD stem ) were obtained from the Global Wood Density database  65, 66  and calculated as species mean values. We measured wood density at branch level (WD branch ) using a water displacement method  90. In this method, branch segments of about 25 mm length and 12 mm diameter were first cut and debarked. Samples were then placed in a recipient with filtered water to rehydrate for 24 h and subsequently weighed with a three-decimal scale. After this, the sample was oven-dried for 48-72 h at 70 °C and the dry weight measured with a balance. Wood density was then expressed as the ratio of wood dry mass and wood fresh volume (g cm  -3  ).
For CAX, we used published data by ref. 91 for the control plot. The other six plots are part of the RAINFOR network  39  , having been established by and/or monitored by RAINFOR partners (Supplementary Table  5 ). Plot data for these analyses were curated and obtained via the ForestPlots. net database  92, 93  , for which standard quality control procedures are applied. We only included plots in the analysis that lacked a history of recent anthropogenic disturbance. For all forest dynamics analyses we excluded KEN plots because of a fire event that occurred in the region in 2004  68  and may still be affecting biomass stocks and dynamics. Following previous studies  15, 94  , plots smaller than 0. 5 ha that were up to 1 km apart from each other were combined and treated as a single plot (for example, TAP-54, TAP-55, TAP-56 and TAP-57 treated as TAP-02, the plot we used to represent TAP).
For each plot, we only included pre-2015 El Nino censuses and selected the census start date to be as consistent as possible across plots. For this we excluded pre-2000 measurements, apart from TAP plot for which censuses were available only from 1983 to 1995. For other plots, we used the earliest census available for this plot if data collection started after 2000 (VCR-02 plot, for example, which starts in 2003). We tried to ensure that biomass dynamics metrics used in the analyses represented at least 10 yr of total monitoring time per plot. If application of the 2000 start date for a given plot resulted in fewer than 10 yr of monitoring, we also included the census date immediately before 2000 (99 for BNT-02 plot, which we used to represent MAN) to ensure at least 10 yr of monitoring (Supplementary Table  5 ).
The monitoring time used for the plots included in the analysis was on average 12. 3 (s. d. = 2. 5) yr. In RAINFOR plots, all live individuals of more than 10 cm in diameter at breast height (DBH) are repeatedly measured over time, using standardized protocols, with species identified and careful records kept of trees that die or recruit from one census to the next. AGB for each census per plot was computed using the ref. 63 equation for moist forests on the basis of tree diameter, wood density and height. As local height data were often unavailable, a Weibull equation with regionally varying coefficients was used to estimate height following ref. 11. Species-level wood density values from the Global Wood Density database  65, 66  were used to compute AGB, AGWP and AGB MORT. For each census, biomass values were calculated for all dicotyledonous trees in the plots above the 10 cm DBH cut-off and summed to give total stand-level biomass stocks.
As AGB varies across sites it is useful to account for this when comparing sites. We therefore also computed relative ΔAGB (ΔAGB/AGB), relative AGWP and relative AGB MORT by dividing absolute values by the time-weighted mean standing woody biomass across censuses per plot. Both absolute and relative values are presented in the Extended Data Figs. 7, 8 and 10 and Supplementary Table  4 ). We computed the annual instantaneous stem mortality rate (% y -1 ) following ref. 67:       A B (ln( ) -ln( )) census interval × 100 (4) in which A is the number of stems per ha in the beginning of the census interval and B is the number of stems per ha that survived throughout the census interval. Owing to the sensitivity of these rates to census interval effects, we standardized them to a common census interval, following ref.
96. For all calculations above (AGB, AGWP, AGB MORT and stem mortality) we used the BiomasaFP R package  97. We calculated the time-weighted mean values of all these absolute and relative parameters (AGB, AGWP, AGB MORT and stem mortality) to have one value per plot. We then calculated woody biomass residence time (τ w ) as the ratio of the time-weighted mean standing woody biomass and the time-weighted mean annual biomass mortality  52. To test whether relationships between HSM 50 and forest dynamics at plot level apply over landscape scales and to account for the influence of within-and among-plot stochasticity in dynamics, we also we used mean forest values of forest dynamics metrics across groups of plots (clusters) in the same landscape with similar structure and composition to plots sampled for hydraulic measurements (Supplementary Tables  5  and 6 ).
For this cluster-level analysis, we excluded white-sand forests and permanently water-logged swamp forests because they are extreme edaphic habitats, known to have a more limited and edaphically specialized tree flora  98. We also excluded forests lying within active floodplains of rivers because their flora is also distinctive and, like swamp forests, they have access to more water beyond that which is climatically determined. In total, we used data from 34 long-term monitoring plots (31. 37 ha of forest). For this analysis, we used cluster mean forest dynamic values (instead of plot cluster weighted mean, for example) because plot area and monitoring length did not vary considerably within clusters (Supplementary Table  5 ). To account for sampling effort variation across cluster of forest plot, we tested if the residuals of the relationship between relative ΔAGB and HSM 50 were related to cluster mean monitoring time (mean ± s.
05. To investigate if species biogeographical distributions are related to mean HT, we used SMA regressions with WDA as the response variable. Following Esquivel-Muelbert et al. 45  , we restricted our analysis to the western Amazon as these published WDA data are based entirely on species distributions within western Amazon, helping to control for the potentially confounding effects of differences in soil and forest dynamics across Amazonian regions. Our subsample for this analysis encompassed a total of 87 species distributed across aseasonal, intermediate DSL and ecotonal long DSL forests, with MAP across plots ranging from 1,390 to 3,170 mm. SMA regressions were performed using the smatr package  99  in R. Using our entire dataset across the Amazon, we evaluated whether HTs were better predictors of Amazon forest carbon balance than climatic factors or other leaf and wood traits.
More specifically, we performed bivariate SMA models to investigate relationships between HTs (Ψ 50 , Ψ dry and HSM 50 ), climatic data (MCWD, MAP, DSL and MAT) and other functional traits (LMA, WD stem and WD branch ) versus long-term ΔAGB at plot level. We computed basal area weighted mean LMA, WD branch and WD stem data  65, 66. To account for the influence of multiple testing, we applied a Bonferroni correction to P values for bivariate regressions. SMA models were further conducted to examine the relationship between HSM 50 versus absolute and relative values of AGB annual woody production, AGB annual mortality, stem mortality and residence time of woody biomass. Supplementary Table  5  presents summary information per plot and clusters. All presented analyses were performed in RStudio v.
1. 1. 423 (ref. 100). 5. Stem wood density data were extracted from the Global Wood Density database  65, 66. KEN plots were excluded from all forest dynamics analyses because of a fire event that occurred in the region in 2004  66  and may still be affecting biomass accrual. Regression lines show significant relationships using standard major axis (SMA) models after Bonferroni correction for multiples hypothesis testing. Supplementary Table  3  shows the results of the SMA models. 4 )  67  ; H) Residence time of woody biomass calculated as the ratio of mean standing biomass to mean biomass mortality rate  52. All these parameters were calculated for each census interval and we calculated time-weighted mean to have one value per plot. Each point indicates one forest plot. Significant linear relations are shown by regression lines (Standard major axis models).
Extended Data Extended Data Fig. 9  | Relationship between published pan-Amazonian species-level growth rates  37  and hydraulic safety margins. We restrict our analysis to the western Amazon, where there is the highest overlap of species between ours and Coelho de Souza et al. 37  dataset and to avoid biases due to soil/forest dynamic differences across Amazonian regions. Hydraulic safety margins for species occurring across multiple sites were averaged to yield one value per species. 4 )  67  ; H) Residence time of woody biomass calculated as the ratio of mean standing biomass to mean biomass mortality rate  52. All these parameters were calculated for each census interval and we calculated time-weighted mean to have one value per plot. Each point indicates one forest plot. Significant linear relations are shown by regression lines (Standard major axis models).
The shade area represents the 95% bootstrapped confidence interval. Information about each plot and the observation period is available on Supplementary Table  5. Supplementary Table  4  shows the results of the SMA models. -Wickham H (2022). modelr: Modelling Functions that Work with the Pipe. https://modelr. tidyverse. org, https://github. com/tidyverse/modelr. -Henry L, Wickham H (2022). purrr: Functional Programming Tools. http://purrr. tidyverse. org -Lopez-Gonzalez, G. , Sullivan, M. J. P. & Baker, T. R. BiomasaFP: Tools for analysing data downloaded from ForestPlots. net. R package version. 1(1), For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers.
A) and D) xylem water potential at which 50% of the conductance is lost (Ψ 50 ); B) and E) Hydraulic safety margins related to Ψ 50 (HSM 50 = Ψ dry -Ψ 50 and C) and F) Minimum in situ leaf water potential observed in the dry season (Ψ dry). Dashed lines denote the mean value of each trait across all tree taxa in the dataset. Red line, the hydraulic safety margins equal to zero. Boxplots display the 25 th percentile, median and 75 th percentile. The vertical bars show the interquartile range times 1. 5 and datapoints beyond these bars are outliers. Sites are sorted by increasing water availability. Each point represents one species per site (N = 170 species) in the top panels and species mean (Ψ 50, HSM 50 ) and species minimum Ψ dry per leaf habit in the bottom panels (N = 136 species). Deciduous, semideciduous and evergreen species are represented by red, blue and green points, respectively.
68,66nce explained by individual predictors when using SMA models to predict plot-level relative ΔAGB, with ΔAGB calculated as (AGB end - AGB start )/ period of monitoring length/standing woody biomass. Climatic data (MAT, MAP and MCWD), HTs (Ψ 50 , Ψ dry and HSM 50 , defined as the difference between Ψ dry and Ψ 50 ) and other plant traits (stem and branch wood density and LMA) are indicated as red, blue, brown and green bars, respectively. Asterisk denotes statistically significant bivariate relationships after correcting for multiple hypothesis testing, using Bonferroni-corrected P < 0. 05. Stem wood density values were extracted from the Global Wood Density database65,66. Bivariate plots and statistics for all predictor variables considered are shown in Extended Data Fig. 6and Supplementary Table3.
Acknowledgements Data collection was largely funded by the  UK Natural Environment Research Council (NERC)  project  TREMOR  ( NE/N004655/1 ) to D. G. , E. G. and O. P. , with further funds from  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil  (CAPES, finance code  001 ) to J. V. T. and a  University of Leeds Climate Research Bursary Fund  to J. V. T. D. G. , E. G. and O. P. acknowledge further support from a  NERC -funded consortium award (ARBOLES,  NE/S011811/1 ). This paper is an outcome of J. V. T. 's doctoral thesis, which was sponsored by  CAPES  ( GDE 99999. 001293/2015-00 ). J. V. T. was previously supported by the  NERC -funded  ARBOLES  project ( NE/S011811/1 ) and is supported at present by the  Swedish Research Council Vetenskapsrådet  (grant no. 2019-03758  to R.
Data availability The pan-Amazonian HT dataset (Ψ 50 , Ψ dry and HSM 50 ) and branch wood density per species per site, as well as forest dynamic and climate data per plot presented in this study are available as a ForestPlots. net data package at https://forestplots. net/data-packages/Tavares-et-al-2023. Basal area weighted mean LMA is shown in Supplementary Table  2. Species stem wood density data were obtained from Global Wood Density database  65, 66. Species WDA data were extracted from ref. 45.
Data analysis All data analysis was performed using the open source R software version 3. 6. 3 and RStudio version 1. 1. 423 and packages dplyr (1. 0. 6) and tidyr (1. 1. 3) to manage datasets. Standardized major axis (SMA) regressions were perfumed using the smatr package (3. 4-8). We created and plotted the figures using ggplot2 (3. 3. 4) and egg (0. 4. 5). We used package modelr (0. 1. 8) and purrr (0. 3. 4) to create 95% bootstrapped confidence interval of best fit line from the Standard major axis model. We used the biomasaFP package to calculate forest dynamic parameters (i. e. , AGB, AGWP, AGBMORT and stem mortality). References: -R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project.
org/. -RStudio Team. RStudio: Integrated Development for R. Boston, MA: RStudio, Inc. (2016). Available at: http://www. rstudio. com/. -Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1. 0. 6. -Hadley Wickham (2021). tidyr: Tidy Messy Data. R package version 1. 1. 3.