dataesr/scientific-paragraphs-categorization huggingface dataset:
The Jacobian is  B2 m g q g q q q q q 15 15 15 , The first entry is ⎡ ⎣ ⎢ ⎤ ⎦ ⎥ ( ) ( ) ( ) ( ) b b b b = ¶ ¶ ¶ ¶ ¶ ¶ ¶ ¶ ¶ ¶ ¶ ¶ ⎡ ⎣ ⎤ ⎦ ( ) ( ) ( ) ( ) f f f f = l l l l l J q q q q 0 1. ⎜ ⎟ ⎛ ⎝ ⎞ ⎠ ( ) ( ) ( ) ( ) ( ) ( ) å f f b f f ¶D ¶ = ¶ + ¶ - ¶ ¶ + ¶ + ¶ - ¶ ¶ l l l = m q q q q q q q q q 15 15. B3 The covariance matrix used is the same one that is output from the fPCA analysis code. The fPCA templates and a fitting routine can be used by installing snlcpy, a Python package located at https://github. com/laldoroty/snlcpy (Aldoroty et al. 2022).
L. A. acknowledges support by the  NASA Texas Space Grant Consortium  and  NSF  grant  AST-1613455. They also thank  D'arcy Kenworthy  for discussing SALT3, and  Yaswant Devarakonda  for discussing statistical methods. L. A. , L. W. , J. Y. , and N. S. acknowledge the support of the  Mitchell Institute for Fundamental Physics and Astrophysics. P. H. was supported by  National Science Foundation (NSF)  grant  AST-1715133. We also thank the  Gordon & Betty Moore Foundation  for their support. This work was supported in part by the Director,  Office of Science, Office of High Energy Physics of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. Support in France was provided by  CNRS/ IN2P3 ,  CNRS/INSU , and  PNC  and  French  state funds managed by the  National Research Agency  within the  Investissements d'Avenir program  under grant reference numbers  ANR-10-LABX-0066 ,  ANR-11-IDEX-0004-02  and  ANR-11-IDEX-0007.
Across the globe, tree species are under high anthropogenic pressure. Risks of extinction are notably more severe for species with restricted ranges and distinct evolutionary histories. Here, we use a global dataset covering 41,835 species (65. 1% of known tree species) to assess the spatial pattern of tree species' phylogenetic endemism, its macroecological drivers, and how future pressures may affect the conservation status of the identified hotspots. We found that low-to-mid latitudes host most endemism hotspots, with current climate being the strongest driver, and climatic stability across thousands to millions of years back in time as a major co-determinant. These hotspots are mostly located outside of protected areas and face relatively high land-use change and future climate change pressure.
This dynamic leads to substantial species turnover, and reduced chance for immigration, thereby could contribute to the formation of neo-endemism centers  34, 37, 38, 47, 49, 50. Up to now, an explicit test of these hypotheses for global tree PE centers is missing, limiting our understanding of the vulnerability of tree PE hotspots, areas of long-term importance for tree survival and diversification, to current human  7, 51  and future climate change threats  52, 53  , and our ability to develop efficient conservation planning. Here, we use a recently compiled global tree distribution dataset  54  , covering 41,835 species or 65. 1% of the known tree species globally  55  , to map tree PE and explore its macroecological drivers, its conservation status, and exposure to future pressures. We analysed the relative roles of present climate conditions and past climate variability at various time scales in shaping global tree PE patterns.
g. , as also seen between subsets of trees such as Australian eucalypts  29  and acacias  25. The similarity in PE hotspots for trees to those for other taxonomic groups indicate the utility of tree-targeted protection activities to preserve much of biodiversity and entire ecosystems in many settings (Fig. 5 )  7  , albeit likely less so in drier and colder biomes, where trees are less diverse or play less important ecological roles. Improving our understanding of where the phylogenetic rarity of trees is concentrated, how much land use pressure these phylogenetic endemism hotspots are exposed to, and whether they will be maintained under future climate changes is indispensable for efficient conservation of Earth's tree species. We addressed these questions using a comprehensive occurrence dataset and phylogenetic data on 41,835 tree species.
Tree species and their range maps We used the world tree species list  75  and species range maps compiled by  7, 54. The world tree species checklist (GlobalTreeSearch, GTS, v. 1. 6  75  ,) was used to extract the global tree species list for the current study. Tree species included in the GTS are based on the definition by the IUCN's Global Tree Specialist Group (GTSG), i. e. , "a woody plant with usually a single stem growing to a height of at least two meters, or if multi-stemmed, then at least one vertical stem five centimeters in diameter at breast height"  75. The Taxonomic Name Resolution Service (TNRS) online tool  76  was used to remove synonyms and to taxonomically standardize the list. The occurrence records of the selected species were collated from five widely used and publicly accessible databases, namely: the Global Biodiversity Information Facility (GBIF; the derived dataset summarizing the used occurrence records from 2070 datasets in GBIF can be viewed and accessed via https://doi.
org/ 10. 15468/dd. 4jvnmv), the public domain Botanical Information and Ecological Network v. 3 (BIEN; http://bien. nceas. ucsb. edu/bien/  14, 77  ), the Latin American Seasonally Dry Tropical Forest Floristic Network (DRYFLOR; http://www. dryflor. info/  78  ), the RAINBIO database (http:// rainbio. cesab. org/  79  ), and the Atlas of Living Australia (ALA; http:// www. ala. org. au/). The compiled occurrence data was assessed  54  and the high-quality records were then used to generate range maps based on the alpha hull algorithm  80  via the Alphahull package 81 in R (ver. 4. 1. 1  82  ). We further validated the range maps using three external independent continental and global datasets  30, [83] [84] [85] [86] [87] [88] [89] [90]  , and our observed tree diversity pattern fitted well to those obtained from each of the external datasets  7.
The estimated range maps of the 41,835 tree species were rasterized to 110 km equal-area grid cells (~1 degree at the Equator), a resolution commonly used in global diversity studies (e. g ref. 91), using the letsR package  92. See ref. 7 for detailed information on the range map estimations and three types of external validations. To reduce the potential bias of low-species cells, we only used cells with ≥ five species for further analyses.
Phylogeny We constructed a dated phylogenetic tree for tree species using the most comprehensive seed-plant phylogeny (the ALLMB tree  93  ). This megatree combines a backbone tree  94  , which was built using sequence data from public repositories (GenBank) to reflect deep relationships, with previous knowledge of phylogenetic relationships and species names from the Open Tree of Life (Open Tree of Life synthetic tree release 9. 1 and taxonomy version 3, https://tree. opentreeoflife. org/about/synthesis-release/v9. 1). As 5,791 species in our 54,020 tree species dataset were missing from the megatree, they were manually added according to its genus or family, a method widely applied in similar studies  7, 23, 48, 93. We then pruned the phylogeny to contain only species with distribution data (i.
Spatial phylogenetic diversity and endemism analyses We used the BIODIVERSE software (version 3. 1. 0)  97  in R using the "Biodiverse pipeline" (https://github. com/NunzioKnerr/biodiverse_ pipeline) to perform all the following metric calculations and randomization tests. Phylogenetic endemism (PE) was calculated as the sum of phylogenetic branch length spanned by species present in each of the 110 × 110 km grid cell, with each branch length divided by the global range size of its descendant clade  13. Here, PE is scaled to represent the proportion of variation within the tree represented by the given taxa in the grid cell and the total tree length  13, 21, 97. We then calculated relative phylogenetic endemism (RPE), which is the ratio of PE measured on the original phylogeny (PE original ) and estimated from a comparison phylogeny with equally distributed branch lengths (PE equal )  25.
Environmental variables To detect the potential drivers of global tree phylogenetic endemism, we compiled a series of environmental variables, including current climate, paleoclimate, and topographic heterogeneity (Table  S1 ). Climate, both present-day and paleoclimate, is generally assumed to be a vital predictor of species distribution and diversity patterns (e. g. ,  36, 38, 39, 49, 98, 99  ). We included two bioclimatic predictors commonly used in relevant studies: annual mean temperature (MAT) and annual precipitation (AP). Current climate variables were extracted from the CHELSA database (www. chelsa-climate. org) at a resolution of 30 arcseconds (~1 km at the Equator)  100  , averaging global climate data from the period 1970-2000. We selected two paleo-time periods spanning from ca.
11. 6-7. 2 Mya to ca. 21 kya, representing warmer or cooler climatic conditions compared to the present-day climate. Specifically, the late Miocene climate (11. 61-7. 25 Mya) was used to represent the warmer pre-glacial climate compared to the present day (hereafter Miocene)  45. The Last Glacial Maximum (LGM, ~21 kya) was used to present the extreme cooling of the Pleistocene glaciations 101. The LGM data was extracted from the CHELSA database at a resolution of 30 arcseconds 100. Mean values for all predictors were extracted for each grid cell at a 110 ×110 km resolution. The variable extractions and averaging were carried out in the letsR package  92. In addition to climate, topographic heterogeneity can also affect plant distributions  36, 83, [102] [103] [104] [105]  and is considered a universal driver of biological diversities  105, 106.
Human and future climate threats We used the human modification index  51  as a proxy of human activities. The human modification index was modelled with 13 most recent global-scale anthropogenic layers (with the median year of 2016) to account for the spatial extent, intensity, and co-occurrence of human activities, many of which show high direct or indirect impact on biodiversity 107. The index was extracted at 1 km 2. We further extracted future climate variables to calculate future climate anomalies. MAT and AP for 2071-2100 were obtained from the most-updated CHELSA dataset (V2: CMIP6 model, https://chelsaclimate. org/cmip6/) 100. For CHELSA V2: CMIP6 scenarios, there are only five general circulation models (GCM) available. For each model, three SSP and GCM combinations were provided, i.
Existing and potential protected areas The existing protected areas data were extracted from the November 2021 release of the World Database on Protected areas (WDPA) via the wdpar package 108,109. Following ref. 7, we extracted the protected areas from the WDPA database by selecting terrestrial areas belonging to IUCN protected area categories I to VI and having a status "designated", "inscribed", or "established", and areas not designated as UNESCO Man and Biosphere Reserves, and protected areas represented as points were excluded too. The final extracted protected areas were then resampled at the 110 × 110 km grid cell level. The three tree diversity priority areas were obtained from ref. 7. Using the same dataset here, we considered tree species' taxonomic, phylogenetic, and functional diversity simultaneously and applied a complementary analysis to identify priority tree species diversity areas for conservation.
05, Fig. S10 ). Thus, we only represented the results from SARs models in the text, even though the significance of some predictors varied between OLS and SAR models (Fig. S4 ). We performed a two-sample Fisher-Pitman permutation test (10,000 permutations) using the coin package 116 to compare differences in the variables used in the above models between hotspots and non-hotspot regions for each angiosperm and gymnosperm species. In addition, to represent the human and future climate threats on angiosperm and gymnosperm PE hotspots, k-sample Fisher-Pitman permutation test (10,000 permutations) was carried out, and multiple comparisons were further tested using the rcompanion package 117.
Acknowledgements J. -C. S. , W. Y. G. , and JMSD acknowledge support from the  Danish Council for Independent Research | Natural Sciences  (Grant  6108-00078B ) to the  TREECHANGE  project. J. -C. S. considers this work a contribution to his  VILLUM  Investigator project " Biodiversity Dynamics in a Changing World ", funded by  VILLUM FONDEN  (grant  16549 ), and  Center for Ecological Dynamics in a Novel Biosphere (ECONOVO) , funded by  Danish National Research Foundation  (grant  DNRF173 ). The BIEN working group was supported by the  National Center for Ecological Analysis and Synthesis , a center funded by  NSF   EF-0553768  at the  University of California , Santa Barbara , and the  State of California. Additional support for the BIEN working group was provided by  iPlant/CyVerse  via  NSF   DBI-0735191.
N. C. Herbarium ; and  GBIF ,  REMIB , and SpeciesLink. The staff at CyVerse provided critical computational assistance. We acknowledge the following herbaria that contributed species occurrence data to the BIEN database (the full names of the herbaria can be found at https://bien. nceas. ucsb. edu/bien/datacontributors/herbaria/): A, AAH, AAH, AAS, AAU, ABD, ABH, ABN, ABRN, ABS, ACAD, ACOR, AD, ADW, AFS, AIMS, AJOU, AK, AKPM, AKU, ALCB, ALTA, ALU, AMD, MADE, AMES, AMNH, AMO, ANA, ANGU, ANSM, ANSP, AQP, ARAN, ARIZ, ARM, AS, ASDM, ASU, ATCC, AU, AUT, AWH, B, BA, BAA, BAB, BABY, BACP, BAF, BAFC, BAI, BAJ, BAK, BAL,  BARC , BAS, BASBG, BBB, BBG, BBS, BC, BCB,  BCF ,  BCMEX ,  BCN ,  BCRU , BCW, BDD, BDK,  BEL , BEREA,  BERN , BEX, BFT, BG, BH,  BHCB ,  BHSC ,  BIO ,  BIRA ,  BIRM ,  BISH , BKF,  BLA , BM, BHM, BNRH,  BOCH , BOG, BOL,  BOLV , BON, BONN, BOON, BORH, BOTU, BOUM, BOZ,  BPI , BR, BREM,  BRI ,  BRIST ,  BRISTM ,  BRIT , BRIU, BRLU, BRM, BRNU, BRU, BRWK, BRY, BSB,  BSIP , BTN, BUF, BUL, BUT, C, CAM, CAMU, CAN, CANB, CAS,  CATIE , CAY, CBG, CBM,  CBS , CDBI, CEN,  CEPEC , CESJ,  CGE , CGG, CGMS,  CHAM ,  CHAP ,  CHAPA ,  CHAS ,  CHI ,  CHL ,  CHR ,  CHRB ,  CHSC ,  CICY ,  CIIDIR ,  CIMI ,  CINC ,  CLE ,  CLEMS ,  CLF , CLR, CM, CMC,  CMM ,  CMMEX ,  CNH ,  CNPO ,  CNS ,  COA ,  COAH ,  COCA ,  CODAGEM ,  COFC ,  COI ,  COL ,  COLO ,  CONC , COR,  CORD , CP, CPAP,  CPUN , CR,  CRAI ,  CRP , CS,  CSLA ,  CSU ,  CTES ,  CTESN ,  CU , CUVC, CUZ, CVRD, CYN, DAL, DAO, DAV, DBG, DBN,  DCR ,  DEE ,  DES ,  DFS ,  DFSM ,  DGS ,  DHM ,  DLF ,  DMFS ,  DMNH ,  DMU ,  DNA ,  DOR ,  DPU ,  DR, DS ,  DSM ,  DSY ,  DUE ,  DUKE ,  DUSS ,  E ,  EA ,  EAC ,  EBH ,  EBUM ,  ECON ,  ECU ,  EGHB ,  EIF ,  EIU ,  EKY ,  ELN ,  EMMA ,  ENCB ,  EPM ,  ER ,  ERA ,  ESA ,  ETH ,  EXR ,  F ,  FAA ,  FAU ,  FAUC ,  FB ,  FCME ,  FCO ,  FCQ ,  FEN , FH,  FHO ,  FI ,  FLAS ,  FLOR , FM, FR,  FRS ,  FRU ,  FSC ,  FSU , FTG, FUEL, FULD,  FURB , G, GA, GAT, GB,  GDA ,  GENT ,  GEO ,  GES , GGO, GH, GI, GJO, GL, GLAM, GLM, GMDRC, GMNHJ, GOET, GOW, GRA, GRM,  GSW , GUA,  GUAM , GW,  GZU , H, HA,  HAC ,  HAL , HAM,  HAMAB ,  HAMU ,  HAO ,  HAS ,  HAST ,  HASU , HAW, HB, HBG, HBR, HCIB, HDD, HEID, HGM, HIB,  HIP ,  HITBC ,  HIWNT ,  HLU , HME, HNT, HNUB, HO, HPL, HRCB, HRP,  HSS ,  HSU , HTN, HU, HUA, HUAA, HUAL, HUAZ,.
Data availability The tree occurrence data are openly available from the TREECHANGE (https://github. com/wyeco/TC_conservation). The data related to analyses are available on Github (https://github. com/wyeco/tree_PE_ conservation) and mirrored on Zenodo (https://zenodo. org/record/ 8418763) 118. Current and future climate data were extracted from the CHELSA database (https://chelsa-climate. org/); paleoclimate data were obtained from the Paleoclim database (http://www. paleoclim. org) and ref. 45 ; the human modification index was from ref. 51. The world protected areas were downloaded from https://www. protectedplanet. net/en/thematic-areas/wdpa.
, 2009; Renssen and Osborn, 2003; Schmidt et al. , 2004; Otto-Bliesner et al. , 2006) , and the Last Millennium (e. g. Crowley, 2000; Jones et al. , 2001; Zorita et al. , 2005). These paleoclimate simulations have been routinely compared with proxy-based paleo data to evaluate their performance  (Masson et al. , 1999; Bonfils et al. , 2004; Brewer et al. , 2007; Bartlein et al. , 2011). However, the difference in spatial resolution between the simulated climate model results and proxy-based paleo reconstructions makes this comparison problematic and usually poses some uncertainties  (Renssen et al. , 2001; Ludwig et al. , 2019). Transient simulations of the Holocene with GCMs are still currently a challenge due to the numerical cost (it can take more than 4-Published by Copernicus Publications on behalf of the European Geosciences Union.
Lorenz et al. (2016)  performed a transient paleoclimate simulation with statistical downscaling for North America spanning the period between 21 ka BP and 2100 CE at 0. 5 • spatial resolution. Their study provides datasets that offer a standard collection of climate simulations that may be used to model the impact of past and future climate change on biodiversity. Dynamical downscaling is the technique used by global models to simulate the land-atmosphere interaction process by considering the sub-grid, orography, and other conditions over a local scale  (Feser et al. , 2011). Dynamical down-scaling is thus aimed at increasing the spatial resolution (horizontally and vertically) by simulating the regional subcomponent of the climate from global models  (Ludwig et al. , 2019). In contrast, statistical downscaling only assumes constant statistical relationships between large-and local-scale processes.
9-11. 7 ka BP). In addition, Gómez-Navarro et al. (  2013 ) performed a higher-resolution RCM simulation in Europe spanning from 1500-1990 CE, and their results compared to observed Climate Research Unit (CRU) datasets show an improvement in the climate model most particularly in areas of complex topography. In a different study, the same results were compared with empirical proxy-based reconstructions  (Gómez-Navarro et al. , 2015) , and the results show regional mean biases, particularly for summer temperature and winter precipitation. These biases were similar to biases found relative to CRU observational data in their earlier study (e. g. Gómez-Navarro et al. , 2013). Russo and Cubasch (2016)  present a dynamical downscaling for different time slices of middle-to-late Holocene in Europe using the RCM model COSMO-CLM.
The iLOVECLIM model The iLOVECLIM model (version 1. 0)  (Roche et al. , 2014)  is a three-dimensional model with a simplified representation of the atmosphere relative to GCMs. This simplification and its lower spatial resolution make iLOVECLIM much faster than coupled GCMs  (Goosse et al. , 2010; Kitover et al. , 2015). It is a fork of the LOVECLIM 1. 2 model code  (Goosse et al. , 2010) , and these share their main climate system components. Here we apply a version that includes the following components: the atmospheric model ECBilt  (Opsteegh et al. , 1998) , the sea ice ocean component CLIO (Coupled Largescale Ice-Ocean model,  Goosse and Fichefet, 1999) , and the terrestrial vegetation model VECODE  (Brovkin et al. , 1997). Our model version is a direct follow-up of the ECBilt-CLIO-VECODE coupled climate model and has been successfully used to simulate some key past and future climates, for example the LGM (e.
Acknowledgements. We thank Marie José Gaillard, who provided input in setting up this work. We also thank Heikki Seppä and Basil Davis, who assisted us with proxy-based reconstructions. Finally, we want to give special thanks to the editor (Laurie Menviel) and the two anonymous reviewers for their comments and suggestions, which helped to improve the paper. Financial support. The research is financed through the European Union's Horizon 2020 research and innovation programme within the TERRANOVA project, no. 813904. The paper only reflects the views of the authors, and the European Union cannot be held responsible for any use which may be made of the information contained therein. Review statement. This paper was edited by Laurie Menviel and reviewed by two anonymous referees.
Code availability. The iLOVECLIM source code is accessible at http://www. elic. ucl. ac. be/modx/elic/index. php?id=289 (UCL, 2021). The developments on the iLOVECLIM source code are hosted at http://forge. ipsl. jussieu. fr/ludus (IPSL, 2021); due to copyright restrictions they cannot be publicly accessed. Request for access can be made by contacting Didier M. Roche (didier. roche@lsce. ipsl. fr). For this study, we used the model at revision 1147.
Data availability statement The datasets presented in this study can be found in online repositories. The code used for the study is available at: https:// github. com/OceanParcels/atlanteco_tara_connectivity_plankton and the data generated at: https://doi. org/10. 24416/UU01-HVXLB0. Ocean model data used in this study is accessible at https://doi. org/ 10. 25424/cmcc/glob16-atlantic-2021.
(Ahrens et al. , 2005). The velocity field is simulated based on a 3-D full Stokes ice-flow model with the firn rheological law  (Gagliardini and Meyssonnier, 1997). The model is implemented using the finite element software Elmer/Ice  (Gagliardini et al. , 2013). We performed a steady-state simulation with fixed glacier geometry. The mathematical formulation of the ice-flow problem follows  (Zwinger et al. , 2007)  with some simplifications and includes the Stokes and the volume balance evolution equations, the stress-free surface boundary condition and the no-slip bedrock boundary condition. Unlike  (Zwinger et al. , 2007) , we used only dynamical equations and did not consider thermo-mechanical coupling. The model used an approximation of the density profile measured in the borehole drilled in 2009  (Mikhalenko et al.
Elbrus is situated (Fig. 3a ). In case (ii), the outflow velocity vectors were directed outward from the computational domain along the normal to its lateral surface. The outflow velocity value was considered constant in space and time. Our simulation showed that the geometry and the length of the backward trajectories were influenced by the presence or absence of the outflow rather than the particular value of nonzero outflow velocity. Therefore, this velocity value was not an essential parameter in our modeling. Additionally, no field data on velocities were available. In our model experiments, we obtained the backward trajectories of maximal length under conditions (ii). Since the maximum extent of the ice core catchment area was of interest in our study, we present the results of this model case.
Climate data Data from 16 weather stations located in the foothill or mountainous regions of the Caucasus (Russia and Georgia) were used 215 for the statistical estimates of the precipitation field in the North Caucasus, as well as for comparing the time series of precipitation with snow accumulation on the WP drilling site (Table  1 ). Most of the stations are located in lowland and foothill areas (less than 1000 m asl), 5 -in the middle mountain zone, and 4 -in high mountains (more than 2000 m asl). The CAPE (convective available potential energy) was calculated for separating summer and winter seasons using the ERA5 reanalysis data. We also used Global Precipitation Climatology Centre (GPCC) v2020, 0. 25° monthly precipitation dataset from 1891-220 present calculated from global station data for field correlation analysis.