dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, the Guineo-Congolian lowland rain forest region is characterized by widespread and old lineages. We found that the overlap between centres of phylogenetic endemism and PAs is high (> 85%). We show the vital role played by mountains acting simultaneously as cradles and museums of tropical African plant biodiversity. By contrast, lowland rainforests act mainly as museums for generic diversity. Our study shows that incorporating large-scale taxonomically verified distribution datasets and mega-phylogenies lead to an improved understanding of tropical plant biodiversity evolution.
present in a relatively low number of sites); unfilled squares, nonendemic taxon (i. e. present in a relatively high number of sites). L opez-Pujol et al. , 2011), suggesting that mountains are both cradles and museums of diversity  (Fjelds a & Lovett, 1997)  (Table  1 ). For plants, these hypotheses have not yet been tested using both large distribution datasets and dated molecular phylogenies. Botanical datasets recording African plant distribution information are becoming increasingly comprehensive  (Stropp et al. , 2016; Dauby et al. , 2016)  and are significantly improving our understanding of biodiversity and phytogeographical patterns across tropical Africa  (Sosef et al. , 2017; Droissart et al. , 2018). In parallel, the incorporation of evolutionary history information is now possible thanks to the generation of large phylogenies using existing data available on GenBank  (Hinchliff & Smith, 2014; Smith & Brown, 2018).
Together, this allows us to test the described hypotheses at new levels of precision. Here, we evaluate five main hypotheses (Table  1 ) related to the evolutionary and historical processes shaping tropical African biodiversity. We identify significant centres of neo-and paleo-endemism across the entire tropical African angiosperm flora at generic level based on a taxonomically verified distribution dataset and a newly generated mega-phylogeny of angiosperms. At the generic level, we identified areas of significant PD, PE and centres of neo-, paleo-or mixed-endemism across tropical Africa to answer the following questions: do previously identified areas of high taxon diversity display more PD and PE than expected by chance; are African mountains centres of diversification, centres of lineage persistence, or both; are African lowland rainforests museums or cradles of diversity? Finally, as centres of PE are of high conservation interest, we explored the overlap between the protected area network across tropical Africa and the different centres of endemism identified.
Datasets Plant occurrence data We used the RAINBIO dataset  (Dauby et al. , 2016) , a database of georeferenced occurrences of African vascular plants with comparatively high taxonomic quality. It represents a compilation of 614 022 botanical records collected from 1782 to 2015 providing distribution information for 25 356 species and 3158 genera within 273 families. This represents c. 89% of all known tropical African plant species. Species occurrence data were projected using the Africa Albers Equal Area Conic coordinate reference system (ESRI:102022, http://spatialreference. org/). For that, we transformed coordinates from decimal degrees into metres, the base unit of this coordinate reference system. The study area was divided into 100 9 100 km equal-area square sampling units (SUs). Using the RAINBIO dataset, we applied three filters, leading to the RAINBIO filtered dataset we used for all our analyses: Angiosperm filter.
, 2017). This can artificially increase the endemism of these SUs. To avoid potential bias caused by poorly sampled SUs, we included in our analyses only SUs in which at least 100 recorded occurrences are present. A threshold of at least 100 occurrences per SU was selected after testing different threshold values (results not presented here). Phylogenetic data We used a newly generated dated angiosperm phylogenetic tree (see  Janssens et al. , 2019 for details). Briefly, phylogenetic inference was conducted based on a sampling of two plastid markers (matK and rbcL) retrieved from GenBank for 36 234 plant species distributed across 8357 genera. The alignment was conducted with MAFFT  (Katoh et al. , 2002)  and refined with GENEIOUS 7. 0 (Auckland, New Zealand). The final topology results from maximum likelihood tree inference with RAXML 7.
4. 2 (Stamatakis, 2006) , constrained at the family level, under the general time-reversible substitution model with gamma rate heterogeneity. Divergence time was estimated using 52 fossil calibration points scattered among the angiosperms and dated using the penalized likelihood algorithm implemented in treePL  (Smith & O'Meara, 2012). The hard maximum and minimum age constraints of the angiosperms were set at 220 and 140 Myr, respectively  (Soltis et al. , 2002; Bell et al. , 2005; Moore et al. , 2007; Bell et al. , 2010; Magall on et al. , 2015). Finally, generic names in the RAINBIO dataset and in the phylogenetic tree were standardized using the Taxonomic Name Resolution Service tool  (Boyle et al. , 2013).
Data analyses Biodiversity analyses To explore the diversity, distribution and evolutionary history of the angiosperm in tropical Africa, we used the RAINBIO filtered dataset and the resulting dated phylogenetic tree to calculate a set of taxonomic and phylogenetic indices for each SU across our study area. All metrics were calculated using BIODIVERSE v. 2. 0  (Laffan et al. , 2010). We used the 'Biodiverse pipeline' to run BIODIVERSE directly from R (https://github. com/ NunzioKnerr/biodiverse_pipeline). Taxonomic indices Genus richness (GR) is the number of distinct genera (g) present in each SU: GR ¼ X g 1: Weighted endemism (WE) is the sum of the inverse of each genus's (g) geographical range (R g ). R g is measured as the number of SUs in which the genus occurs  (Crisp et al. , 2001) : WE ¼ X g 1 R g Phylogenetic indices Phylogenetic diversity (PD) is the sum of branch lengths connecting the root of the phylogenetic tree to all genera (tips of the phylogeny) within each SU  (Faith, 1992).
5-arcminute (c. 4. 6 9 4. 6 km at the equator) plots across the globe (GMBA mountain definition_V1. 0 database; K€ orner et al. , 2011) and shapefiles delineating 'montane areas' (GMBA mountain inventory_V1. 2; K€ orner et al. , 2017). For each SU, we calculated the mean ruggedness, the percentage of montane plots (i. e. percentage of 4. 6 9 4. 6 km plots whose ruggedness exceeds 200 m) and the percentage of overlap with 'montane areas' defined by GMBA. By contrast, we defined 'lowlands' on the elevation feature to avoid considering a highland plateau as a lowland area. Elevation data were retrieved from the digital elevation modelling data produced by the NASA Shuttle Radar Topographic Mission (SRTM; http://srtm. csi. cgiar. org/). We downloaded the 250 m resolution (SRTM v. 4. 1) data in GeoTiff raster format, and extracted a raster file of the tropical Africa region using QGIS 2.
CANAPE and protected areas network overlap CANAPE results were overlapped with the African protected areas network retrieved from the World Database on Protected Areas (WDPA, https://protectedplanet. net/, accessed June 2018). Protected areas exclusively related to marine or faunal protection (e. g. 'bird reserve', 'hunting reserve', 'faunal reserve') were excluded. The number of SUs that contained protected areas (PA) were counted for any PA and for the most restrictive PAs in terms of conservation (reported as International Union for Conservation of Nature (IUCN) categories Ia, Ib and II; PAs that have a main focus on biodiversity)  (IUCN, 2008).
Plant occurrence The RAINBIO filtered dataset contained 547 273 occurrences of 2345 genera distributed across 638 equal-area square SUs. GR is unevenly distributed across tropical Africa, ranging from 39 to 853 genera per SU (Fig. 3 ). The most diverse SUs are found around the Guinean Gulf, particularly in Gabon, western Cameroon, in the Dahomey Gap, the Eastern Phylogeny The unfiltered phylogenetic tree contained 36 234 species and 8357 genera. Based on the RAINBIO filtered dataset, this phylogenetic tree was pruned and included a final total of 1719 genera. Of these, 36% (618/1719) were not monophyletic in the unfiltered phylogenetic tree. In this case, we randomly sampled one of the branches of the nonmonophyletic genera. The tropical African flora at generic level represents 113 687 Myr of cumulative evolutionary history (i.
Biodiversity analyses Weighted endemism Raw values of WE (Fig. 4a ) followed a similar pattern to GR (Fig. 3 ) (R 2 = 0. 764; Supporting Information Fig. S1 ). Significantly high WE SUs (Fig. 4b ) are located in the eastern parts of tropical Africa, in Ethiopia, Kenya-Tanzania, Katanga (southeast DRC), Maputaland and scattered in southeast Africa. This indicated that these regions harbour more endemic genera than expected from random. By contrast, regions in Central and West Africa (i. e. the Guineo-Congolian region) are characterized by a significantly less WE than expected at random (Fig. 4b ). Randomization tests for PD Calculations of phylogenetic metrics were conducted on the 1719 genera found both in the phylogenetic tree and in the RAINBIO filtered dataset. PD is highly correlated with genus richness (R 2 = 0.
Significant concentrations of evolutionary history and taxonomic endemism In this study, we combined for the first time at the near continental level a large floristic dataset within a phylogenetic framework to examine more than 113 000 Myr of evolutionary history of tropical African angiosperms. Our GR map (Fig. 3 ) corroborates previous studies highlighting Cameroon, Gabon, the Albertine Rift, the Eastern Arc Mountains and coastal Tanzania as containing high taxon richness  (Linder, 2001; K€ uper et al. , 2004; Lovett et al. , 2005; Plumptre et al. , 2007; Sosef et al. , 2017). S2 ). For each box, the bold horizontal line corresponds to the median; the lower and upper bounds of the box correspond to first and third quartiles, respectively; the upper vertical line extends from the upper bound of the box to the highest value of the distribution, no further than 1.
Several studies also found areas of mixed-endemism in other montane regions, such as in South America  (Fjelds a, 1994; Fjelds a & Lovett, 1997; Bitencourt & Rapini, 2013), and China (L opez-Pujol et al. , 2011). In tropical Africa, previous identification of centres of neo-and paleo-endemism in plants focused on only two plant clades a priori assumed to be neo-or paleo-endemics, because of a lack of dated plant phylogenetic trees  (Fjelds a & Lovett, 1997). Fjelds a & Lovett (1997)  reported co-occurrences of neo-and paleo-endemics in some mountains, such as the Cameroon Volcanic Line or the Albertine Rift, but they did not detect the same East African regions to act as both museums and cradles, as reported here (Fig. 6 ). This underlines the importance of integrating randomization and categorization procedures together with a comprehensive phylogeny and angiosperm wide datasets to detect patterns of significant neo-, paleo-and mixedendemism  (Mishler et al.
, 2014; Thornhill et al. , 2016). By contrast, most parts of West and Central Africa present nonsignificant CANAPE SUs, indicating that these regions harbour no more endemic genera than expected. Randomization of the RPD revealed concentrations of old genera in lowland rainforests of Guineo-Congolia (Figs  5b, 7 ). As mentioned earlier, these genera also appear to be widespread (low WE; Fig. 4b ). Several local museums of biodiversity have been proposed to occur within this region  (Fjelds a & Lovett, 1997; Murienne et al. , 2013) , which, according to  Linder (2014) , contain the oldest flora of tropical Africa, and here appears to be a museum of diversity as a whole. Our study highlights the importance of incorporating largescale taxonomically verified distribution datasets with mega-phylogenies, which lead to an improved understanding of tropical plant biodiversity evolution.
, 2016). This makes the 11 SUs detected as centres of neo-endemism (Fig. 6 ) strong cradles of diversity. The completeness of the plant distribution dataset as well as the resolution of the phylogeny may also affect our estimate of PE. Here, even if the RAINBIO dataset represents c. 89% of the known diversity in tropical Africa, it is the best-quality data that are available to date  (Dauby et al. , 2016). The relatively high proportion of nonmonophyletic genera (36%) that are present in the unfiltered angiosperm tree is inherent and unavoidable when making use of large-scale phylogenetic approaches. On one hand, this is caused by the compromise between the amount of markers used and the number of species included in the dataset (see, e. g. ,  Qian & Jin, 2016; Smith & Brown, 2018). On the other hand, there are still some taxonomical uncertainties regarding the generic status of certain angiosperm African genera (e.
Acknowledgements This work was co-funded by the  French Foundation for Research on Biodiversity (FRB)  and the  Provence-Alpes-Côte d'Azur r egion (PACA) region through the Centre for Synthesis and Analysis of Biodiversity data (CESAB) programme , as part of the RAINBIO research project (http://rainbio. cesab. org/)and the  Agence Nationale de la Recherche  (grant number  ANR-15-CE02-0002-01 ), both to TLPC. JCS considers this work a contribution to his VILLUM Investigator project ' Biodiversity Dynamics in a Changing World ' funded by  VILLUM FONDEN  (grant  16549 ) and his  TREECHANGE  project funded by  Independent Research Fund Denmark|Natural Sciences  (grant  6108-00078B ). GD was partly funded by the  Belgian Fund for Scientific Research (F. R. S. -FNRS ).
Nadir observations look straight down from the satellite and glint observations look slightly offset from the solar reflected glint spot (resulting in seasonal shifts in longitude for glint observations). OCO-2 additionally takes dense observations targeting TCCON stations or other sites. This targeted data has previously been validated for OCO-2 v7  (Wunch et al. , 2017). This work focuses on validation of OCO-2 v8 with standard mode observations. We validate both XCO2 and the lowermost tropospheric column (LMT) introduced in  Kulawik et al. 75 (2016). OCO-2 continues the CO2 near infrared column record from  GOSAT (launched in 2009) , and from  SCIAMACHY (launched in 2002). OCO-2, however, has better precision, a smaller footprint, and collects about a hundred times more observations than GOSAT.
The TCCON dataset The GGG2014 TCCON dataset is used in this paper  (Wunch et al. , 2015). The locations of the TCCON stations used for this work are shown in Figure  1b. The TCCON data are averaged over 90 minutes to reduce error. It would be more ideal to average TCCON data ± 60 or 90 minutes within each satellite observation, rather than pre-averaging the TCCON data, but due to processing time, the pre-averaging approach was used. As previously discussed, OCO-2 has different observing modes, 135 and different TCCON sites can be used for the validation of one or more mode types. The TCCON XCO2 estimates are also derived from remote sensing observation, though with extensive aircraft validation to tie them to WMO standards  (Wunch et al. , 2011). They typically have a systematic error of about 0.
These were downloaded on 9/2018 from https://www. esrl. noaa. gov/gmd/ccgg/obspack/data. php?id=obspack_co2_1_GLOBALVIEWplus_v4. 0_2018-08-02 (Dlugokencky et al. , 2018)  and include local times between 11:30 am and 3:30 pm. The "remote oceanic" locations used in 175 this paper are selected to have at least 97% ocean along a circle with a 5 degree radius around the location, which are summarized here and shown in Fig. 1 : We validate the NASA OCO-2 v8 dataset using the bias-corrected Lite files, available from https://disc. gsfc. nasa. gov/datasets/OCO2_L2_Lite_FP_V8r/summary?keywords=OCO-2 (O'  Dell et al. , 2018). The documentation for this dataset is available from https://co2. jpl. nasa. gov/#mission=OCO-2. The algorithmic improvements in v8 include (1) using lamp, solar, and lunar data to produce a radiometric correction that accounts for the effects of icing, solar 190 diffuser and lamp degradation, (2) spectroscopy updates, and (3) addition of a retrieved stratospheric aerosol, which improved biases seen near the southern and northern edges of OCO-2 data seasonally (described in  Wunch et al.
OCO-2 Averaged products 195 Averaging is useful for combining similar information over scales smaller than model resolution, to manage errors, and reduce dataset size. Averaging allows combining geographically similar observations and assigning an error to these assuming correlated and random error values. Assimilation methods generally assume errors from different observations are not correlated, and therefore assimilation of 1000 separate observations, each with error of 1 ppm, into a single grid box would result in errors of < 0. 1 ppm, which is much smaller than the actual errors after averaging. Different averaging schemes are 200 possible, e. g. weighting by predicted errors, checking agreement with neighboring observations (buddy check), and weighting by local variability. Three of these schemes are described below and validated against TCCON and aircraft data.
Averaged products can have a significantly reduced data volume. The number of good quality land glint observations colocated with Lamont TCCON (geometric co-location, ± 3 degrees latitude, ±5 degrees longitude, ± 1 hour) is:  175,000 for the OCO-2 lite product 205  1,300 for OCO-2 Baker-averaged product (described in Section 2. 2. 2)  1,200 for OCO-2 Liu-averaged product (described in Section 2. 2. 3)  8,700 for OCO-2 20-average product (described in Section 2. 2. 4) For comparison, there are 7,000 quality observations for GOSAT nadir land (over 7 years). https://doi. org/10. 5194/amt-2019-257 Preprint. Discussion started: 28 October 2019 c Author(s) 2019. CC BY 4. 0 License.
v8 Baker "10-second" average 210 A 10-second average product (~0. 6 degree) was created by David Baker (described in  Crowell et al. , 2019)  to reduce the computational burden of assimilation and to assign errors to averaged quantities. For most assimilation systems, 0. 6 degree is below the assimilation system resolution. This product averages 10 seconds of OCO-2 observations, spanning 0. 6 degrees of latitude. If all soundings are cloud free and have converged, their quality flags are good, and a 10-second collection includes 240 observations. Because soundings that are cloudy or have bad quality flags are not included, the mean number of 215 observations included in this averaged product is 74, and the number of observations per bin ranges from 1 to 240. This dataset emphasizes evenly weighting data at one-second intervals  (Crowell et al.
CAMS Reanalysis (2009 through 2017) 265 The CAMS reanalysis model output was downloaded from http://apps. ecmwf. int/datasets/data/cams-ghg-inversions/ and goes through December 2017 (see description https://confluence. ecmwf. int/pages/viewpage. action?pageId=58131166). This model is used to extend aircraft profiles to the top of the atmosphere (up to 0. 1 hPa, or 65 km) for evaluation of the error introduced The Goddard Modeling and Assimilation Office (GMAO) produced a high resolution, global nature run for CO2  (Putman et al. , 2014). This data is run/sampled at 7 km and 50 km gridding every 30 minutes. The run extends through half of  2005, 2006, and half of 2007. The difference between this model co-located to the validation data versus co-located to the satellite observations is used to quantify co-location error.
Station bias and bias variability: grouping comparisons versus validation data to determine regional biases The TCCON dataset provides a timeseries of validation data at particular locations; whereas the ATom dataset provides a range of latitudes at particular times. The validation data are grouped by "station", which is a TCCON location, an ESRL 325 location, or an ATom campaign. The overall bias for each station is called the "station bias" and gives an idea of the sizes of regional biases. Table  2  shows the station bias for each coincidence type. The only category in Table  2  that had an overall bias was ATom versus OCO-2 ocean glint, which had a -0. 7 ppm bias (OCO-2 low). Appendix B shows biases for each https://doi. org/10. 5194/amt-2019-257 Preprint. Discussion started: 28 October 2019 c Author(s) 2019.
, 2012; Lejeune et al. , 2019) , belongs to the CRYOBS-CLIM observatory (the CRYosphere, an OBServatory of the CLI-Mate), which is a part of the national research infrastructure OZCAR (Critical Zone Observatories -Application and Research)  (Gaillardet et al. , 2018). It has also been a Global Cryosphere Watch Cryonet site since 2017 and belongs to the International Network for Alpine Research Catchment Hydrology (INARCH). In this paper, we present a unique meteorological and blowing snow data set for each winter of the period 2000-2016. Meteorological data are available from four automatic weather stations (AWSs) surrounding the experimental site. Blowing snow data stem from two sources: (i) a database of blowing snow occurrence and (ii) blowing snow fluxes derived from snow particles counters from winter 2010-2011 to 2015-2016.
Meteorological and snow depth data Table  1  provides an overview of the meteorological and snow parameters measured around CLB, with the corresponding instrument type, models and height. Each AWS located around CLB measures wind speed and direction at a time step of 15 or 10 min depending on the station and/or the period. Additionally, measurements of air temperature are available at three AWSs. Finally, three AWSs situated around the pass measure snow depth using ultrasonic sensors (at AWSs Lac Blanc and Muzelle) and a laser sensor (at AWS Col). They are available between 1 December and 31 March of each winter. This period has been selected since it corresponds to the main period during which most of blowing snow events occur at CLB  (Vionnet et al. , 2013). Data from AWSs Lac Blanc and Dome are available from winter 2000-2001 to 2015-2016, whereas data from AWSs Muzelle and Col are available from winter  2002-2003 and 2010-2011, respectively.
Snow depth Snow depth is measured using ultra-sound depth sensors at AWSs Lac Blanc and Muzelle. These sensors determine the distance to the snow surface by sending out ultrasonic pulses and listening for the returning echoes that are reflected from the surface. The time from transmissions to the return of an echo and the speed of sound in the air are then used for obtaining the distance measurement. Since the speed of sound in the air varies with air temperature, a correction of the distance calculation is carried out using the air tem-perature measurements previously described. Time series of snow depth measurements were visually inspected and outliers removed from the data set, most often occurring during snowfall. Ultra-sound depth sensors provide measurements accurate within 1 cm. The beam angle is 30 • , which gives an observed surface area of 8 m 2 for a sensor mounted at 6 m above a snow-free ground (the average sensor height at AWSs Muzelle and Lac Blanc, Table  1 ).
Atmospheric parameters from a meteorological reanalysis Atmospheric data from the SAFRAN ( Analysis System Providing Atmospheric Information to Snow) meteorological reanalysis  (Durand et al. , 1993 (Durand et al. , , 2009) )  at the elevation of Col du Lac Blanc in the Grandes Rousses range are provided from 1 August 2000 to 31 July 2016. SAFRAN combines meteorological fields from a global numerical weather prediction system with neighbouring in situ and remotely sensed observations to estimate meteorological variables in the French mountains. They are available per 300 m elevation bands for areas known as massifs (23 in the French Alps), which were defined for their climatological homogeneity  (Durand et al. , 1993). To obtain SAFRAN data at the elevation of CLB (2720 m a. s. l. ), we used a weighted average of the data from the Grandes Rousses massif for the elevation bands 2700 and 3000 m.