dataesr/scientific-paragraphs-categorization huggingface dataset:
As an alternative mapping approach, we used an independent tree-based classification and regression trees (CART) model 77 (Supplementary Fig. 15 ). This approach was used to test whether model performance depends on model type. If the two models (random forest and CART) show similar results, this indicates that predictions are not biased by model selection. Using the same independent 'Tallo' dataset  40  used for testing the robustness of the random forest model, the CART model had an explanatory power of 0. 46, which is similar to the R 2 BC of 0. 47 of the random forest model. When directly comparing both models, the CART model showed 87% similarity (R 2 BC ) with the random forest model. This suggests that our maps and predictions do not depend on the type of model, and we report the random forest model results throughout the main text since it showed slightly higher accuracy  55.
Cross-validation using external data. We tested for the performance and correlation between the predictions of the area-based and individual-based random forest models. When using the same independent 'Tallo' dataset  40  for testing the robustness of the random forest models, the tree-based and area-based models had an explanatory power of 46% and 47%, respectively. When directly comparing both models, the area-based model showed 89% similarity (R 2 BC ) with the individual-based model, showing that both metrics result in similar predictions of leaf-type variation. To further evaluate the performance of our models, we additionally compared the model predictions with satellite-derived leaf type estimates from annual land cover maps from the ESA CCI LC  41. We used land cover layers for the years 2000, 2005, 2010 and 2015, each with a spatial resolution of 300 m × 300 m.
To generate global layers of soil features, the Soil Grids dataset relies on machine learning models informed by global, spatially explicit information on various climate variables. This global interpolation of soil information using climate data may thus lead to an overestimation of the covariation between climate and soil layers while reducing small-scale heterogeneity in soil features. To assess whether this potential caveat affects our results, we used point-level soil measurements from the WOSIS dataset, including clay content, silt content, pH and sand content. To spatially match this dataset with the full GFBi dataset containing more than 1. 1 million plots across the globe  55  , we used the 'geosphere' 78 R package. We then selected the nearest soil observation that fell within 250 m or 1,000 m of the centre of each forest plot.
Letter https://doi. org/10. 1038/s41477-023-01543-5 Interpolation vs extrapolation in model predictions. To evaluate how well our training dataset represents the full multivariate environmental covariate space, we performed a principal-component-analysis-based approach following refs. 76,79. We projected the covariate composite into the same space using the centreing values, scaling values and eigenvectors from the principal component analysis of the training data. We created the convex hulls for each of the bivariate combinations from the top principal components and classified whether each pixel falls in or outside each of these convex hulls. We used 24 principal components with 276 combinations for all covariates for the sampling dataset. This analysis showed that 99. 2% of land pixels (778,975,911 of 785,150,461) cover at least 90% of the environmental variables present in our training data locations (Supplementary Fig.
We then computed the variance inflation factors (VIFs) across all 21 principal components using the R package HH  82. All VIFs were lower than 4, suggesting sufficient independence among covariates. The principal components were then used as predictors in random forest models with different combinations of hyperparameters (that is, 1 to 12 samples per split and a minimum sample size at the end of the nodes of 1 to 10), and we selected the ten best models with the highest coefficient of determining variation (R 2 ). Variable importance was determined by calculating the relative influence of each variable, expressed by the variable's attributed reduction in squared error. To quantify the importance of individual environmental factors, we used the same combinations of hyperparameters. Based on R 2 , the ten best random forest models were again chosen to explore the relative importance of each factor.
Forest types and their future climate envelopes To assess the extent and distribution of future changes in forest leaf-type climate envelopes, we projected our leaf-type models into the future using CMIP6 climate models for the time interval of 2071-2100 and three emission scenarios (SSP1-RCP2. 6, SSP3-RCP7 and SSP5-RCP8. 5) 50. The CMIP6 climate models were extracted following the ISIMIP3b protocol and included five models (gfdl-esm4, ukesm1-0-II, mpi-esm1-2-hr, ipsl-cm6a-lr and mri-esm2-0). We projected the global forest leaf-type distribution for each emission scenario on the basis of each of the five climate models using our random forest models. To do so, we used our 100 random forest models of present leaf-type distributions and replaced the climate variables ('bioclim' variables from the CHELSA dataset, marked with hashtags in Supplementary Fig.
Acknowledgements We thank the  Global Forest Biodiversity Initiative (GFBi)  for establishing the data standards and collaborative framework; TRY database managers and countless researchers who contributed open-access data. This work was supported by grants to C. M. Z. from the  Ambizione fellowship  ( PZ00P3_193646 ), L. M. from the  China Scholarship Council , and T. W. C. from DOB Ecology and the  Bernina Foundation. O. B. acknowledges funding from the  Romania National Council for Higher Education ,  CNFIS , project number  CNFIS-FDI-2023-F-0579. We thank the  French National Forest Inventory  (NFI campaigns; raw data 2005 and subsequent annual surveys were downloaded by GFBI at https://inventaire-forestier. ign. fr/spip. php?rubrique159, accessed on 1 January 2015) and the Italian Forest Inventory (NFI campaigns; raw data 2005 and subsequent surveys were downloaded by GFBI at https://inventarioforestale.
We also thank the  National Council for Science and Technology Development of Brazil (CNPq)  for support to the  Cerrado/Amazonia Transition Long-Term Ecology Project  ( PELD/441244/2016-5 ). J. D. was funded by the  Czech Science Foundation  (project no. 21-26883S ). We are grateful to all the ministries and agencies from the  Government of Spain  and to all the people that supported the collection, compilation, and coordination of forest inventory data, also including the  Spanish National Forest Inventories. Sergio de Miguel  benefitted from a Serra-Húnter fellowship provided by the  Government of Catalonia (Generalitat de Catalunya). J. C. S. considers this work a contribution to Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by the  Danish National Research Foundation  (grant  DNRF173 ) and his  VILLUM Investigator  project ' Biodiversity Dynamics in a Changing World ', funded by  VILLUM FONDEN  (grant  16549 ).
The exploratory plots of FunDivEUROPE were established through funding from the  European Union   Seventh Framework Programme   FP7/2007-2013  under Grant  265171. Plot data collection from the Brazilian Atlantic Forest was supported by the  State of São Paulo Research Foundation/FAPESP  ( BIOTA  Grants  2003/12595-7 ;  2012/51509-8  and  2012/51872-5 ) and by the  Brazilian National Research Council/CNPq  ( PELD  Grant  403710/2012-0 ). H. V was funded by  FCT -Portuguese Foundation for Science and Technology , project  UIDB/04033/2020  and  ICNF-Instituto da Conservação da Natureza, Portugal ,  National Forest Inventory. Financial support for the Monafor network in Mexico came from many projects, including the  National Forestry Commission (CONAFOR), Council of Science and Technology of the State of Durango (COCYTED) , the  Natural Environment Research Council, UK (NERC ;  NE/T011084/1 ), and local support of  Ejidos and Comunidades.
Data availability Tree occurrence data from the Global Forest Biodiversity initiative (GFBi) is available upon request via Science-I (https://science-i. org) or the GFBi website (https://www. gfbiinitiative. org/). Information on leaf habit (evergreen vs deciduous) and leaf form (broadleaved vs needle-leaved) came from the TRY database (https://www. try-db. org). Additional, leaf-type data came from the Tallo dataset (https:// zenodo. org/record/6637599). Plot-level soil information came from the World Soil Information Service (WOSIS) dataset (https://www. isric. org/explore/wosis).
MOTIVATING EXAMPLE The case study that motivated the development of this model was a camera trap dataset from the long-term monitoring program COAT (Climate-ecological Observatory for Arctic Tundra,  Ims et al. 2013). The camera trapping program targets small rodents and their small mustelid predators. Small rodents (grey-sided vole Myodes rufocanus, tundra vole Microtus oeconomus and Norwegian lemming Lemmus lemmus) constitute prey, while small mustelids (stoat Mustela erminea and least weasel Mustela nivalis) constitute predators. Rodents and mustelids have for decades been known to exhibit a predator-prey interaction  (Hanski et al. 1991) , which is one of the main hypotheses for the population cycles of rodents  (Krebs 2013). However, estimating the prevalence and strength of that interaction has proved difficult, as reliable data-especially on mustelids-have previously been lacking  (King & Powell 2006).
The camera trap monitoring was started in the autumn of 2015 and consisted of 4 blocks, with 11 camera trap sites within each block. In the summer of 2018, the monitoring was expanded by 4 blocks, containing 12 camera trap sites each, to make up a total of 8 blocks. We here aim to investigate the strength of the interaction between rodents and mustelids using this camera trapping dataset. The spatial hierarchy in the sampling design leads to spatial dependencies in the data that need to be accounted for. However, it also provides an opportunity to investigate the predator-prey interaction on two nested spatial scales. The case study is viewed as an inspiration for the following model framework, which is nonetheless more general.
Model Fitting The models were analyzed in a Bayesian framework using the JAGS software  (Plummer 2003)  with the jagsUI package  (Kellner 2015)  in R v4. 0. 3 (R Core Team 2020). We note that the model is a hidden Markov model and could also be fitted in a maximum likelihood framework using the Forward algorithm  (McClintock et al. 2020). Convergence was assessed by having a R < 1. 1 for all key parameters  (Gelman et al. 2013 ) and from graphical investigation of traceplots.
To investigate how contrasting scenarios affected bias of parameters, we simulated data where both species had low (lo), medium (mo) and high (ho) average occupancy probability (with medium detection probability). In addition we simulated data where both species had low (ld), medium (md) and high (hd) detection probabilities (with medium occupancy probability, for more details, see Appendix S1, Section 2). For each scenario, we simulated and analyzed 50 replicate datasets. We specified Uniform(0, 1) prior distributions for the detection, colonization and extinction probabilities and Uniform(0, 0. 5) prior distributions for the initial occupancy probabilities. The MCMC algorithm was run with an adaptation phase (initial phase where the Bayesian sampler can adapt to increase efficiency) of 1000 iterations.
EMPIRICAL CASE STUDY Working with camera trap data requires to associate species identity to the photographs taken by cameras. For this we used the automatic classification model of  Tabak et al. (2019)  implemented in the MLWIC R-package  (Tabak et al. 2018 , see Appendix S1 Section 3 for more details). As there were particularly few observations of least weasel and Norwegian lemming, the analysis could not be conducted at the species level. However, as both mustelid species prey on all rodent species present in tundra, rodents and mustelids were considered functional groups. As rodents are known to exhibit rapid local scale colonization-extinction dynamics  (Andreassen & Ims 2001)  we here define a primary occasion as one week (i. e. 7 days) and secondary occasions as the days within that week.
While mustelids have slower demographic processes than small rodents, they are assumed to show a spatially aggregative response (movement towards prey-rich areas) on a short time scale (i. e. , changing foraging grounds from one week to the next,  Hellstedt & Henttonen, 2006). The resulting dataset describes, for each day, if each functional group was detected or not. We combined the data for the two functional groups in a multi-state occupancy dataset with 4 states (U = none of the species are observed, A = only small rodents observed, B = only small mustelids observed or AB = both small rodents and small mustelids are observed). We included all weeks from the beginning of the monitoring in 2015 to the summer of 2021, resulting in a total of 304 weeks. Note that the different blocks were established at different times.
Hence, since more than half of the sites were only observed for the last 47 weeks this resulted in missing data in the earlier years. Since mustelid observations mostly occurred in the snowbed habitat, we chose to focus only on these sites, reducing the number of sites to 5-6 per block. This study system exhibits strong seasonality that needs to be accounted for. However, there is a lack of detailed environmental data representative of the seasonality in this area. Therefore, we used temperature measurements from the camera traps to estimate the onset of winter (defined as the first day after summer with a daily mean temperature < 0) and the time of snow melt (defined as the first day in spring with daily mean > 0) for each camera trap individually. Such partitioning of the year will account for most of the seasonal changes affecting detection, as the winter period is mostly snow-covered, forcing small mammals into the subnivean space at the bottom of the snow pack, while the summer season is mostly snow-free.
The third set of priors was specified to be skewed towards our ecological expectations, by using either a Beta(2, 4) or Beta(4, 2) depending on the expected relationships in a predator-prey system. Detection probabilities were given a Normal(-0. 5, 1) prior distribution on the logit scale (see Appendix S1, Section 4). The prior sensitivity analysis was also used to investigate identifiability by additionally estimating prior-posterior overlap  (Gimenez et al. 2004). To assess the goodness of fit we calculated a Bayesian p-value ( p Bayes ) as described by  Kéry & Royle (2020)  (see details in Appendix S1, Section 4) and performed graphical predictive checks. To reach convergence with the field dataset we needed to run the model for 20,000 iterations, with 5000 steps of adaptation. In addition we discarded the first 1000 iterations as burn-in and used a thinning of 20.
Our real-world case study incorporates some of the classical empirical challenges that ecologists have to deal with. First, the dataset has a high proportion of missing data and detection probability differs between the functional groups. Second, the dataset comes from an ecosystem with strong seasonal and inter-annual (i. e. cyclic) variability, which likely affects the detection, colonization and extinction probabilities. Third, we use functional groups instead of species, potentially adding some unexplained variability in the data. Moreover, we treated blocks as if they were identical, which is likely to be an oversimplification. Even without addressing these challenges specifically in the model (with the exception of a seasonal covariate on detection probability), the model seems to be able to identify most parameters.
ACKNOWLEDGEMENTS We thank  H. Böhner  for her invaluable help in classifying the camera trap images,  M. Murphy ,  L. E. Støvern ,  I. Jensvoll  and  S. Killengreen  for their help installing the camera trap monitoring design,  M. L. Dahle  and  J. E. Knutsen  for their help checking cameras and  J. P. Mellard  for language corrections. We thank the reviewers for constructive comments that considerably improved the manuscript. This work is a contribution from COAT ( Climate-ecological Observatory for Arctic Tundra , www. coat. no), supported by the  RCN  (project nr. 245638 ), and  COAT-Tools+ , supported by  Tromsø Research Foundation  and  UiT. OG was funded by the  French National Research Agency  (Grant  ANR-16-CE02-0007 ).
Sunflower is a hybrid crop that is considered moderately drought-tolerant and adapted to new cropping systems required for the agro-ecological transition. Here, we studied the impact of hybridity status (hybrids vs. inbred lines) on the responses to drought at the molecular and ecophysiological level exploiting publicly available datasets. Eco-physiological traits and leaf proteomes were measured in eight inbred lines and their sixteen hybrids grown in the high-throughput phenotyping platform Phenotoul-Heliaphen. Hybrids and parental lines showed different growth strategies: hybrids grew faster in the absence of water constraint and arrested their growth more abruptly than inbred lines when subjected to water deficit. We identified 471 differentially accumulated proteins, of which 256 were regulated by drought.
Proteomics Full methods for sample preparation, mass spectrometry, protein identification and quantification are described in Balliau et al. [33]  and in the data paper describing the data used in the present paper  [34]. In brief, leaf proteins were extracted using the TCAacetone protocol  [35]. Protein digestion and LC-MS/MS were performed as described in Balliau et al. [33]. Peptides were analyzed by LC-MS/MS using a Qexactive mass spectrometer (Thermo, Waltham, MA, USA). Protein identification was performed by searching the Heliagen database  [36]  using the X!Tandem search engine  [37]. Data filtering and protein inference were performed using X!TandemPipeline 3. 3. 4 [38]. Only proteins with at least two different peptides with an e-value < 0. 01 and a protein e-value < 10 -5 were considered (other parameters are given in Table  S10 ).
Statistical Analysis, Modelling and Data Integration Data analysis was performed in the R environment (cran. r-project. org (accessed on 10 July 2023)). The effects of genotype (G, n = 24), treatment (T, n = 2) and genotype-treatment interaction (GxT) were tested by analysis of variance (ANOVA) for all eco-physiological traits and quantified proteins. For proteins, a Bonferroni correction for multiple comparisons was used (1211 proteins). Proteins were considered to have a significant effect when the corrected probability was <0. 01 and the fold change between treatments (WD/WW) or between the highest and the lowest genotype was greater than 1. 3 or lower than 1. 3-1. This fold change threshold was used in all subsequent tests on proteins. All subsequent tests on protein levels were performed using the protein set selected here.
To analyze the response of up-and downregulated proteins, hybridity status x treatment (HxT) combinations were compared using a one-way ANOVA followed by a post-hoc Tukey test. HxT combinations were considered to be significantly different if the Tukey p-value was p < 0. 05. A cluster analysis was performed using the K-means method. Principal component analyses (PCA) and partial least square (PLS) regressions were performed using the mixOmics package v6. 18. 1  [43].
Acknowledgments: Data were produced with funding from the  French National Research Agency  ( ANR SUNRISE ANR-11-BTBR-0005 ). This research used the  PHENOME-EMPHASIS facility Phenotoul-Heliaphen (Phenome- ANR-11-INBS-0012 ) and was part of the  French Laboratory of Excellence project  " TULIP"  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). Proteomics analyses were performed on the PAPPSO platform (http://pappso. inra. fr(accessed on  10 July 2023 )) which is funded by  INRA  (http://www. inra. fr(accessed on  10 July 2023 )), the  Ile-de-France regional council  (https: //www. iledefrance. fr/education-recherche(accessed on  10 July 2023 )),  IBiSA  (https://www. ibisa. net(accessed on  10 July 2023 )) and  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
In this paper, we extend Paper I's analysis to the entire map of the CARMA-NRO Orion Survey to understand how CMFs depend on the cloud environment. The map covers a wider area of Orion A than the NRO 45 m data of  Nakamura et al. (2019)  used in Paper II. We present the core catalog of Orion A and reveal the physical properties of the cores. A difference from the analysis in Paper I is that we use the updated H 2 column density map with 18″ angular resolution, which is a factor of 2 better than the previous map of  Kong et al. (2018). We also use the young stellar object (YSO) catalog of the Vienna survey in Orion (VISION) with the European Southern Observatory's Visible and Infrared Survey Telescope for Astronomy (VISTA;  Meingast et al. 2016) , which is more complete compared to the catalog of the Herschel Orion protostar survey  (Furlan et al.
Observations and Data 2. 1. C 18 O (J = 1-0) Data We used the C 18 O (J = 1-0, 109. 782182 GHz) data, which cover the 1°× 2°area of the Orion A molecular cloud. In Figure  1 , we show the observed area. The map contains the OMC-1/2/3/4/5, L1641N, V380 Ori, and L1641C regions. The map used in the present paper is slightly larger than that of Paper I and  Kong et al. (2018) , which do not include the L1641C region. We obtained the data by combining the CARMA interferometer and the NRO 45 m single-dish telescope data as described in the dense core survey in the ONC region (Paper I). Details of the procedure are described in detail in  Kong et al. (2018). The angular and velocity resolutions are the same as in the previous map, corresponding to an angular resolution of 8″ and a velocity resolution of 0.
Core Identification In our previous papers, we confirmed that the C 18 O integrated intensity emission is proportional to the H 2 column density in the range of a few ×10 22 to a few ×10 23 cm -2 (Papers I, II). Therefore, we consider that the C 18 O emission reasonably traces the structure in Orion A in a dynamic range of its column density. We identified the dense cores in the PPV space using the C 18 O (J = 1-0) data cube. We applied astrodendro to the CARMA-NRO C 18 O (J = 1-0) data cube. The algorithm identifies the hierarchical structure of the data and the hierarchies are named as leaf, branch, and trunk similar to the structure of a tree. A leaf corresponds to a peak and two leaves are combined into one branch. A branch can also be made by a combination of one leaf and one branch or two branches.
Conclusions We conducted a dense core survey toward the Orion A molecular cloud with the CARMA and NRO 45 m combined C 18 O (J = 1-0) data and summarize the core properties in Table  9. We list the main results and discussions as follows. 1. We applied astrodendro to the observed PPV data and identified 2341 cores after applying several additional selection criteria as described in Section 3. 1. Using the Class 0/I objects in the protostar catalog from a Vienna  IMF. Although there are no clear differences in the turnover mass of CMF among the four subregions, the CMF in OMC-1/2/3 obviously has a shallower slope at the high-mass end than do the other subregions. However, massive cores (10 M e ) are only located in OMC-1/2/3. 5. Except for several massive cores in OMC-1, the freefall time is almost independent of the core mass.