dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  Peter Uhe  and  Julie Arrighi  for a careful and critical reading of the manuscript. The Deutsche Wetterdienst (DWD), ECA&D and  NOAA/CPC  are acknowledged for providing the historical observational data and ECMWF for the (re)analyses. For the climate model ensemble data we thank the  Met Office  for the provision of the HadGEM3-A data; all of the volunteers of weather@home who have donated their computing time to generate the large ensemble simulations; all of the participants who computed simulations for climateprediction. net;our colleagues at the  Oxford eResearch Centre :  A. Bowery ,  M. Rashid ,  S. Sparrow  and  D. Wallom  for their technical expertise; the  Met Office Hadley Centre PRECIS team  for their technical and scientific support for the development and application of weather@home; our colleagues  Camiel Severijns  and  Erik van Meijgaard at KNMI  for their efforts in producing the 16-member EC-Earth and RACMO/EC-Earth ensembles; and the coordination and participating institutes of the  EURO-CORDEX initiative  for producing and.
This is ironic, given that four decades ago,  Marlier [1973]  stated that in comparison to the Congo Basin, "we know less about the physical characteristics of the Amazon. " Despite these low relative numbers of publications, there is a foundation of hydrologic research and measurements upon which to build opportunities for discovery (see sections 2 and 3). Historically, there have been more than 400 stream gauges operating throughout the Congo Basin at various times during the first half of the 1900s (Figure  1a ) [e. g. ,  Croneborg, 2013; M. A. Trigg, personal communication, 2013]. Some of the data from these gauges are available via the Global Runoff Data Center [Global Runoff Data Center (GRDC), 2014], whereas others are available from HYBAM  [HYBAM, 2015]. For example, we have downloaded from the Global Runoff Data Center (GRDC) and used discharge data from 96 gauges (21 with data after the year 2000).
Geology of the Congo Basin The Congo Basin sediments consist of about 8 km of Paleozoic marine formations and 1 km of overlying Mesozoic-Cenozoic lacustrine rocks  [Daly et al. , 1992; Giresse, 2005]. Prior to the rifting of the African and a Searches conducted December 2013 for all previous dates available in a given journal's online database. Search criteria were for the words "Congo," "Zaire," or "Amazon" in the titles or in the abstracts (Congo and Zaire numbers are combined in the Congo column). Resulting papers were further investigated for having significant discussion on hydrology, climate, or biogeochemistry of the particular basin. Devroey [1951] ; and thus, these gauges were operational before 1951. Countries are DRC, Democratic Republic of the Congo; RC, Republic of the Congo; CAR, Central African Republic; SS, South Sudan; U, Uganda; R, Rwanda; B, Burundi; T, Tanzania; Z, Zambia; and A, Angola.
Precipitation Both in situ and satellite-based precipitation data are available for the Congo Basin (Table  2a ). For example, monthly rainfall data for many locations throughout the African continent are available from SIEREM  [SIEREM, 2015; Boyer et al. , 2006] , which includes metadata on several thousand rain gauges throughout Africa and from 958 within the Congo Basin. The past century of raw data from these sources has been used to generate a gridded monthly rainfall database for the years 1940-1999  [Rouché et al. , 2010]. Raw data are not publicly available from the SIEREM website but can be requested from the National Services (contact information is given in the website). Metadata regarding river discharge, evaporation, temperature, atmospheric pressure, wind speed, relative humidity, water level, sunshine duration, and saturation vapor pressure are available from the SIEREM web pages.
e. , from 1951 to 1993. For example, the Oubangui basin experienced a 3. 2% decrease in rainfall when comparing  1951-1960 to 1961-1999 , whereas the entire Congo Basin experienced a decreased rainfall of 4. 5%. While the authors report that 160 rain gauging stations are available throughout the entire Congo Basin, their focus was on that data from the right bank tributaries which contain the only gauges with daily data, continuous over decades. Similarly,  Mahé et al. [2001]  analyzed rainfall data from 891 gauges across west and central Africa and suggest that for the Congo Basin any rainfall trend from 1951 to 1989 is minimal and that there are no discontinuities. Unfortunately, they were not allowed to present the raw precipitation values; fortunately, all of these data have been used to create the new monthly rainfall gridded data set over the period 1940-1999 that is available from the SIEREM website.
Satellite-based rainfall data are available from the well-known Tropical Rainfall Measuring Mission (TRMM)  [Kummerow et al. , 1998]  as well as from the Climate Prediction Center Morphing technique (CMORPH)  [Joyce et al. , 2004]  and from the Precipitation Estimation from Remote Sensing Information using Artificial Neural Network methodology (PERSIANN)  [Hsu et al. , 1997]. Using the Hillslope River Routing model,  Beighley et al. [2011]  compared the precipitation values from these three satellite-based methods and found that CMORPH and PERSIANN often provide significantly more rainfall compared to TRMM (Table  2a ). For example, they separated the basin into nine different Pfafstetter units (similar to sub-basins) and found that for the unit comparable to the Oubangui Basin mean-annual rainfall was 1227 mm in the TRMM data, whereas it was 2484 mm and 2876 mm in the CMORPH and PERSIANN data sets, respectively.
River Discharge Before 1960, there were more than 400 stream gauges throughout the Congo Basin, whereas today there are only about 10 operating stations [e. g. ,  Croneborg, 2013]  (note that not all of these stations are available via the GRDC). Two stations with amongst the longest, continuous records of discharge are at Brazzaville-Kinshasa and at Bangui. Congo River discharge data from the Kinshasa station in the DRC are available from the GRDC, with daily data extending from 1903 to 2010, and similarly available from the GRDC are daily discharge values on the Oubangui River at the Bangui station from 1935 to 2007 (Figure  6 ). Data from 1941 to 2015 for the Brazzaville station are available from the hydrological services of GRSEN Groupe de Recherche en Sciences Exactes et Naturelles and Service Commun d'Entretien des Voies Navigables.
The Kinshasa-Brazzaville gauges provide an estimate of discharge for just over 98% of the Congo Basin area (see areas in section 1. 1 and in Figure  2  and see historical description of the combined Kinshasa-Brazzaville gauges in section 1. 4). In all, the GRDC has discharge data from 96 stations within the Congo Basin and with the majority having more than 10 years of data (Table  3 ). [2009] used kayaks and pirogues equipped with differential GPS, ADCP, and echo sounders to measure channel depths and water velocities. Their discharge estimate of 35,800 m 3 /s at Luozi (DRC) compares well with 32,015 m 3 /s, which is the 10 day average from 4 to 13 July 2008 at the Kinshasa gauge (Table  2c ) and agrees with the values from the Brazzaville station. Their measurements are collected at nine locations between ~130 km and ~200 km downstream of Kinshasa.
[2010]  used the SRTM DEM, the GRFM mosaics, and Landsat data in a decision tree classification method to delineate wetlands throughout the center of the Congo Basin. They validated their classification using over 6000 measurements collected in five field sites and found that wetlands occupy 359,556 km 2 in their study region (5°N to 6°S and 13°E to 26°E). Betbeder et al. [2013]  used the Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index to identify forest types; they used ICESat to measure canopy heights and used PALSAR scenes from different flooding times to map inundation extents (PALSAR is the Phased Array type L band SAR, a follow on to JERS-1). They identified four different types of forest, three of them in which a canopy height could be discerned. Two of these were flooded and had canopy heights of 20 m and 30 m.
River discharge data collected before and after the installation of a hydroelectric facility might demonstrate that river flows have not been altered in terms of amplitude or timing; and thus, the hypothesis would be proven true (at least for that facility and perhaps for similar styles of installations). More generally, hydrologic models with reservoir and run-of-theriver hydraulics could be used to demonstrate the potential for impacts. 5. 7. The Annual-Average Amount of CO 2 and CH 4 Evasion From All Congo Basin Waters Is More Than 480 Tg C/yr, i. e. , More Than a Value Comparable to That of the Amazon per Unit Area If this hypothesis is true, then the Congo Basin plays a central role in global carbon dynamics, despite being less understood than the Amazon Basin. For example,  Raymond et al.
One small team of researchers cannot address all of these hypotheses. A few teams have provided important results, but the vast size of the basin, its varying geomorphology and plant cover, and the annual movement of the tropical rainbelt have limited the extrapolation of these findings. Many teams are needed, with international collaborations and the regular sharing of data and results. Such approaches will enable the resolution of discrepancies such as those of the multidecadal rainfall trends noted in section 2. 1 where some authors note long-term trends and others do not. Moreover, gaps in our understanding of hydrological processes and their controls can be overcome by taking the following actions: (1) further build the international collaborations of hydrologic researchers to include those from other countries in addition to France, Belgium, the DRC, and the RoC; (2) broaden funding to include not only federal agencies but also foundations, industry, and international groups; and (3) develop and maintain an open-access online database of hydrologic measurements and model results, Reviews of Geophysics 10.
Acknowledgments R. G. M. S. is supported by the  U. S. NSF  via grants  ETBC-0851101  and  OCE-1333157. R. M. T. received a grant from the  Carnegie Foundation of New York , through the  Regional Initiative for Science Education program , to conduct his research study in the Congo Basin. D. A. , E. B. , and H. L. are funded by  NASA's Terrestrial Hydrology Program. F. O'L. funded by The  Leverhulme Trust. Data supporting the figures are freely available via the cited references.
, 2010; Wagner et al. , 2013; Wu et al. , 2016) ,  Chavana-Bryant et al. (2016)  have demonstrated in a tropical forest that EVI increased with leaf development (from youngest to the most mature cohorts), and then declined when leaves were at old and senescent stages. Here we assume that EVI represents the maturation of new leaves and that the highest value of EVI represents the highest greenness and canopy photosynthetic capacity, when leaves are fully mature. We use a unique satellite and groundbased combination of monthly data sets from 89 pan-tropical experimental sites (68 include aboveground wood productivity and 35 litter productivity measurements), their associated canopy photosynthetic capacity and climate to address the following questions. (i) Are seasonal aboveground wood productivity, litterfall productivity and photosynthetic capacity all dependent on climate? (ii) Does a coherent pan-tropical rhythm exist among these three key components of forest carbon fluxes? (iii) If so, is this rhythm primarily controlled by exogenous (climate) or endogenous (ecosystem) processes?.
Data sets We compiled publications reporting seasonal wood productivity of tropical forests. Seasonal tree growth measurements in 68 pantropical forest sites, representing 14 481 individuals, were obtained from published sources or directly from the authors (Table  1 , Fig. 1 ). The data set consists of repeated seasonal measurements of tree diameter, mostly with dendrometer bands (94. 1 %), electronic point surveys (4. 4 %) or graduated tapes (1. 5 %). The names of all recorded species were checked using the Taxonomic Name Resolution Service and corrected as necessary  (Boyle et al. , 2013; Chamberlain and Szocs, 2013). Botanical identifications were made at the species level for 11 967 trees, at the genus level for 1613 trees, family level for 171 trees and unidentified for 730 trees. Wood density values were taken from the Global Wood Density Database  (Chave et al.
, 2009; Zanne et al. , 2009)  or from the authors, when measured on the sample (Table  1 ). Direct determination was available for 455 trees and species mean was assumed for an additional 8671 trees. For the remaining 5355 trees, we assumed genus mean (4639), family mean (136) or site mean (580) of wood density values as computed from the global database  (Zanne et al. , 2009). Palms, lianas and species from mangrove environments were excluded from the analysis. Diameter changes were converted to biomass estimates using a tropical forest biomass allometric equation -which uses tree height (estimated in the allometric equation if not available), tree diameter and wood density  (Chave et al. , 2014)  -and then the mean monthly incre-ment of the sample was computed for each sample. Recently,  Cuny et al.
The maximum climatological water deficit (CWD) is computed with CRU data by summing the difference between monthly precipitation and monthly evapotranspiration only when this difference is negative (water deficit)  (Chave et al. , 2014). From the CGIAR-CSI, we used the Global High-Resolution Soil-Water Balance dataset, soil water content (swc, %)  (Zomer et al. , 2008). Additionally, we used monthly incoming radiation at the top of the atmosphere (rad, W m -2 ) covering the period from 2000 to 2015 at 1 • spatial resolution from the CERES instruments on the NASA Terra and Aqua satellites  (Loeb et al. , 2009)  and monthly incoming radiation at the surface (rad surf , W m -2 ) from CERES SYN1deg product computed for all-sky conditions, provided at 1 • spatial resolution from 2000 to 2015. Monthly incoming radiation at the surface (shortwave radiation) refers to radiant energy with wavelengths in the visible, near-ultraviolet, and near-infrared spectra and is produced using MODIS data and geostationary satellite cloud properties  (Kato et al.
, 2011). In addition to the temporal series of climate variables, we extracted the global ecological zones (GEZs) of the sites. These GEZs are defined by the Food and Agriculture Organization of the United Nations (FAO) and relies on a combination of climate and (potential) vegetation  (FAO, 2012). Because at some sites wood productivity or litterfall measurements are older than the EVI measurements (before 2002), and, for recent site measurements, climate data are not yet available (after 2012), all the data sets were averaged monthly by site. Then, in order to remove the site effect on the mean and the variance of the variables and to analyse only seasonality, all the variables were centred on zero and scaled to a variance of 1 by site. That is, for a given variable of a site, monthly values were subtracted by their annual mean and divided by their annual standard deviation.
Discussion We have found a remarkably strong climate signal in the seasonal carbon cycle components studied across tropical forests. While wood and litterfall production appear to be dependent on a single major climate driver across the tropics (water availability), the control of photosynthetic capacity varies according to the increase in annual water availability, shifting from water-only to light-only drivers. Minimum aboveground wood production tends to occur in the dry season. While this result is not new  (Wagner et al. , 2014) , here we confirm this pattern with a large database of wood production measurements (68 sites). Months with the lowest water availability are less favourable for cell expansion, as water stress is known to inhibit this process, as observed in dry tropical sites  (Borchert, 1999; Krepkowski et al.
Acknowledgements. This project and F. H. W. have been funded by the  Fapesp (Fundação de Amparo à Pesquisa do Estado de São Paulo , processo  13/14520-6 ). L. E. O. C. A. thank the support of  FAPESP  (grant  50533-5 ) and  CNPQ  (grant  304425/2013-3 ). J. P. L. and M. M. T. were funded by the  CNPq  and the  FAPEMIG. B. P. M. was funded by the  Australian Research Council  for the project " Understanding the impact of global environmental change on Australian  forests and woodlands using rainforest boundaries and Callitris growth as bio-indicators", grant number:  DP0878177. A. B. was funded by the  German Research Foundation (DFG)  for the project  BR1895/15  and the projects  BR1895/14  and  BR1895/23 (PAK 823 ). F. A. C. and J. M. F. were funded by the  CNPq  (grant  476477/2006-9 ) and the  Fundação O Boticário de Proteção a Natureza  (grant.
F. R. C. C. was funded by the  CNPq/PELD  "Impactos antrópicos no ecossistema de floresta tropical -site Manaus", Processo  403764/2012-2. J. G. was supported from the  US Forest Service-International Institute of Tropical Forestry. A. D. G. funding was provided through  ARC Linkage  (Timber harvest management for the Aboriginal arts industry: socio-economic, cultural and ecological determinants of sustainability in a remote community context,  LP0219425 ). S. F. O. was funded by the  National Science Foundation BE/CBC : Complex interactions among water, nutrients and carbon stocks and fluxes across a natural fertility gradient in tropical rain forest ( EAR 421178 ) and  National Science Foundation Causes  and implications of dry season control of tropical wet forest tree growth at very high water levels: direct vs.
indirect limitations ( DEB 842235 ). E. E. M. was funded by the  Academy of Finland  (project:  266393 ). L. M. was funded by a grant provided by the  European Union  ( FP6 ,  INCO/SSA)  for a 2 year (2006)(2007)(2008) project on management of indigenous tree species for restoration and wood production in semi-arid miombo woodlands in East Africa (MITMIOMBO). F. V. was supported by the  German Research Foundation (DFG)  by funding the projects  BR 1895/14-1/2 (FOR 816 ) and  BR 1895/23-1/2 (PAK 823 ). L. K. K. was supported by the  Malaysian Palm Oil Board. D. M. D. was funded by the  Hermon Slade Foundation  (Grant  HSF 09/5 ). Data recorded at Paracou, French Guiana, were partly funded by an " Investissement d'Avenir " grant from the  ANR  ( CEBA :  ANR-10-LABX-0025 ). H. A. M. and J. J.
Acknowledgements. The authors would like to acknowledge  Météo-France  for making the SAFRAN meteorological archive available for this study, and  SCHAPI-Banque Hydro  for the hydrometrical series. They would also like to acknowledge the reviews of  Alberto Viglione ,  Francis Chiew ,  Tim McVicar  and an anonymous reviewer, which contributed to improving this paper. Edited by:  R. Merz Reviewed  by:  F. H. S. Chiew ,  L. Gudmundsson , and  A. Viglione.
Chlorophyll fluorescence ( F/F M ) was measured using a photosynthesis yield analyzer (MINI-PAM; Heinz Walz GmbH, Effeltrich, Germany) under ambient radiation and steady-state conditions. All gas exchange and chlorophyll fluorescence traits were measured along 4 days between 8 am and 12 am under cloudless conditions. Plant size was calculated as S = H * V where H is plant height and V is the number of vegetative stems. We tested the relationship between S and biomass on a subset of 31 individuals collected within and outside shrubs in the same field site; both variables were positively correlated (r = 0. 95, p < 0. 001, n = 31), similar to data obtained in other species elsewhere  (Prieto et al. , 2011; de Bello et al. , 2012). We then used plant size as a surrogate for plant biomass to avoid destructive measurements.
Community-Level Data We recorded the number of understory species and individuals by sampling 30 quadrats under each shrub species and 120 in open areas (Supplementary Table  S1 ). For the largest shrubs, Bupleurum spinosum (canopy area of 4992 ± 515 cm 2 ; mean ± SE), Cytisus galianoi (8898 ± 947 cm 2 ) and Genista versicolor (7290 ± 1298 cm 2 ) we sampled one 25 cm × 25 cm quadrat randomly placed within the canopy, and for the smallest shrub, Hormathophylla spinosa (2452 ± 263 cm 2 ), we sampled the whole area beneath the canopy. A quadrat of the same size (25 cm × 25 cm, or size of the whole canopy in case of H. spinosa) was randomly sampled in a nearby open area (at least 1 m away from the shrub). Measurements were done at the peak growing season (mid July 2011).
Statistical Analyses We used redundancy analysis (RDA; Canoco 4. 5 package; ter  Braak and Šmilauer, 2002)  to assess correlation between Euphorbia traits in different microhabitats. RDA provides a good estimate of main trends in the data and can be considered an extension of multivariate regression for a multivariate response variable  (Lepš and Šmilauer, 2003). The parametric test is replaced by Monte Carlo permutations to overcome problems with distributional characteristics. We used the forward selection procedure to identify the main environmental characteristics explaining a significant part of the variation in Euphorbia traits. First, we assessed the global significance of the model. Second, we used partial analyses (i. e. , we included the tested factor as the only explanatory variable and the other factors as covariates) to obtain the variability explained by each individual factor and their corresponding significance (P-value).
RESULTS Redundancy analysis showed significant conditional effects of three environmental characteristics on Euphorbia growth, one continuous variable (PAR), and two categorical variables related to species identity, open areas and N-fixing ability (Figure  1 ). These environmental traits altogether explained 18. 4% of variance. Partial effects of all selected explanatory variables were highly significant (Open areas, P = 0. 002; Functional type, P = 0. 004; PAR, P = 0. 024). These variables explained 5. 5, 5. 0, and 3. 2% of variance in our dataset, respectively. The first ordination axis was positively correlated with PAR and tell apart Euphorbia individuals according to their position under shrubs (negative values) versus open areas (positive values). All reproductive traits (number of reproductive stems, infrutescence volume, total seeds and seed mass) were positively correlated to this first axis; similarly, F/Fm' , LDMC and C/N were also positively correlated to PAR and open areas (Axis 1).
ACKNOWLEDGMENTS We thank  Cristina Armas ,  Christian Schöb ,  Francisco Padilla , and  Manuela Guler  for help in the field and laboratory, and the  Organismo Autónomo Parques Nacionales  (grant  0002/9 ) and  MINECO  ( CGL2014-51090-R ) for financial support. Cristina Armas  provided valuable comments on earlier versions of this manuscript and two anonymous reviewers provided valuable comments to improve the manuscript. PM was supported by  JAE-Doc Program (CSIC)  co-financed by  ESF , and later by MSMT  LM2015078. IP was supported by a  CSIC  contract (project  RNM 4821 ) and then by an  Agence Nationale de la Recherche  project (Ecosfix  ANR-10-STRA-003-001 ). JM was supported by  Postdok_BIOGLOBE  ( CZ. 1. 07/2. 3. 00/30. 0032 )co-financed by  ESF  and  CR  budget.