dataesr/scientific-paragraphs-categorization huggingface dataset:
The first 10% of trees were discarded, and a 50% consensus tree was constructed from the remaining trees. We checked similarity of results from the two runs, and samples were then combined. Convergence of the chains was checked using the program TRACER 1. 3  [60]. To check compatibility of calibration points, a cross-validation was used by repeating the analysis several times, leaving out in turn one of the calibrations and comparing node ages estimations. The maximum divergence in these estimations was observed between the topologies obtaining after leaving out the Cryptotis (9 Ma) and Otisorex (3. 5 Ma) calibration points, respectively. Values of divergence for nodes within the C. olivieri complex were comprised between 6. 6 and 9. 5%. The analysis was also performed using a standard mammalian mutation rate (2.
2% per Myr  [18] ). Estimates of genetic diversity, genetic structure and historical demography Genetic diversity, genetic structure and historical demography were estimated for the major clades revealed by our phylogenetic analyses using combined data from the three mitochondrial markers (267 specimens for which all three markers 16S, cytb and COI were available). Nuclear markers were not employed because of their much lower variability. Number of sequences, polymorphic sites, haplotypes and nucleotide differences, and the haplotype and nucleotide diversities were estimated using Arlequin 3. 11. Several procedures were undertaken to test the hypothesis of recent population growth. Mismatch distributions  [61] , Tajima's D  [62] , Fu and Li's F* and D*  [63]  and Fu's Fs tests  [64]  were calculated using Arlequin 3.
Acknowledgements Field campaigns were funded by  EU-DGVIII BIOFAC  and ECOFAC programs,  WHO-Ebola Research Project in Côte d'Ivoire, WWF Gabon ,  PGRR-GFA Terra Systems Project in Guinea, PAMF Bénin, INCO-DEV Treat Control in Guinea and Côte d'Ivoire, ANR Biodiversité IFORA Cameroon ,  Czech Science Foundation  project  n°P506-10-0983 ,  Barbara Brown Fund, Ellen Thorne Smith Fund, Marshall Field III Fund, John D. and Catherine T. MacArthur Foundation  and  World Wide Fund for Nature and Wildlife Conservation Society. We thank the  Ministère des Eaux et Forêts in Côte d'Ivoire ; the  Ministère de la Recherche Scientifique et de l'Innovation in Cameroon ; the  Direction de la Faune et de la Chasse in Gabon ; the  Ministère de l'Environnement, des Eaux et Forêts et du Développement Durable in Guinea , the  Wildlife Conservation Authority in Ethiopia , the  Forest Service  and the  Kenyan Wildlife Service in Kenya, the Wildlife Authority in Zambia , the  Institut National pour l'Environnement et la Conservation de la Nature in Burundi , the  Department of Game and Wildlife Authority in Uganda , the  Institut Congolais pour la Conservation de la Nature  and the  Centre de Recherche des Sciences Naturelles in DRC , the  Wildlife Conservation Society and Development Board in Rwanda , and the  Gorongosa Restoration Project and the  Museu de História Natural in Mozambique  for permission to work in the respective countries.
SOCAT makes surface water CO 2 data available through regular releases of quality controlled and documented, synthesis f CO2 (fugacity of CO 2 ) products for the global ocean and coastal seas  (Bakker et al. , 2012 (Bakker et al. , , 2014a;; Pfeil et al. , 2012; Sabine et al. , 2013). SOCAT version 1 was released in 2011, followed by version 2 in 2013, and version 3 in 2015. The SOCAT data products are available for download from www. socat. info, where they are archived and can also be used interactively. Version 3 contains 14. 5 million surface water fCO 2 values from 1968 to 2014  (Bakker et al. , 2014a, in preparation). They originate from seagoing fieldwork by scientists in 22 countries. The data were collected on more than one hundred ships, moorings and drifters. They are submitted to the database by individual scientists and quality control is then carried out by volunteer scientists prior to release.
The increase in data collection over the past four decades is striking (Fig. 3 ). Installation of autonomous, infrared CO 2 instruments on ships has provided repeated fCO 2 observations along major shipping lines from the early 1990s onwards. Nonetheless, the observations are sparse for much of the world's oceans. Numerous peer-reviewed, scientific publications and highprofile reports cite SOCAT (www. socat. info). Applications of SOCAT include: quantification of the ocean carbon sink and its variation (e. g. ,  Le Quéré et al. , 2014) ; provision of constraints for atmospheric inverse models used for estimating the land carbon sink; validation of ocean carbon models; studies of ocean acidification, ocean carbon cycling and genomics. Sustained, long-term surface ocean CO 2 observations and their synthesis are critical for early detection of any changes in atmospheric carbon fluxes.
Revised quality control criteria will enable inclusion of wellcalibrated CO 2 measurements by alternative sensors and on alternative platforms from version 3 onwards  (Wanninkhof et al. , 2013). For its version 4, SOCAT will accept, archive and make public additional parameters accompanying surface water CO 2 data, such as nutrients (SOCAT, 2014). SOCAT will not quality control the additional parameters, but would welcome other synthesis activities to do so. An automated data upload is available and allows preliminary quality control during data submission. As a consequence, it should enable a more rapid release of SOCAT data products.
Distribution and fluxes of nitrous oxide and halocarbons Both nitrous oxide (N 2 O) and halocarbons have received much attention within the SOLAS community; they are featured in two of the three foci of the SOLAS science plan and implementation strategy  (SOLAS, 2004). N 2 O is an atmospheric trace gas that plays an important role in both atmospheric chemistry and Earth's climate. The ocean is a major natural source of atmospheric N 2 O (IPCC, 2014), but global oceanic emission estimates are still associated with a high degree of uncertainty. This is partly caused by the fact that there was no database which could provide global oceanic N 2 O data sets. To this end, MEMENTO (the MarinE MethanE and NiTrous Oxide database; https://memento. geomar. de/de) was initiated by the European CoOperation in Science and Technology framework (COST) Action 735 and SOLAS  (Bange et al.
Interestingly, in the case of iodine, marine boundary layer concentrations were thought to be prohibitively low until a recent set of analytical advances have demonstrated that atmospheric iodine chemistry is widespread  (Liss and Johnson, 2014 and references therein). Model and laboratory studies now show that atmospheric iodine chemistry results in new particle formation and shifts in the hydrogen oxide radicals (HO x ) ratio and nitrogen oxide radicals (NO x ) ratios, which highly influence the oxidative capacity of the atmosphere  (Plane et al. , 2006; Saiz-Lopez et al. , 2012). During SOLAS, the oceanic source strengths and biogeochemical cycling of iodinated, brominated and chlorinated halocarbons have been investigated. The sparse database of halocarbons in ocean and atmosphere increased considerably during cruises into various oceanic regions at all latitudes during the last decade (e.
g. ,  Brinckmann et al. , 2012; Butler et al. , 2007; Liu et al. , 2011; O'Brien et al. , 2009; Pyle et al. , 2011; Yokouchi et al. , 2008). Much of these data has been compiled in the Halocarbons in the Ocean and Atmosphere (HalOcAt) database project (https://halocat. geomar. de/), which is still ongoing and currently consists of 200 data sets, comprising roughly 55,000 oceanic and 470,000 atmospheric data points of 19 different short-lived halogenated compounds. The first comprehensive global sea-to-air flux climatologies of the three important shortlived halogen carriers bromoform (CHBr 3 ), dibromomethane (CH 2 Br 2 ) and methyl iodide (CH 3 I) have been derived using the HalOcAt database  (Ziska et al. , 2013). The impact of these emissions on stratospheric ozone depletion was found to be highly dependent on the magnitude, location, and timing of their emission, being particularly significant in the tropics, but impacting the entire global atmosphere  (Hossaini et al.
(1987; Charlson, Lovelock, Andreae and Warren). The The CLAW hypothesis was one of the main driving hypotheses formulating the SOLAS mission. It outlines a clear biogeochemical interaction and feedback between the ocean and atmosphere that resides at the core of SOLAS research. During the SOLAS decade, more than 500 DMS-related studies accomplished within the broader SOLAS community have been published contributing more than half of the data in the Global Surface Water DMS Database (http://saga. pmel. noaa. gov/dms/). New insights into surface ocean biological production pathways and cycling, such as hypotheses explaining DMS(P) biogenic production  (Stefels et al. , 2007)  and the so-called summer paradox  (Vallina et al. , 2008)  were made. Technological advancements in direct measurement of open ocean DMS fluxes have resulted from SOLAS initiatives (e.
g. ,  Huebert et al. , 2004; Marandino et al. , 2007). The importance of chemical compounds other than the hydroxyl radical, OH, in DMS oxidation reactions (e. g. , BrO) has also been identified  (Breider et al. , 2010; Lawler et al. , 2009). A major accomplishment by the combined efforts of SOLAS and COST Action 735 has been to update the first DMS climatology from  Kettle et al. (1999) ,  Kettle and Andreae (2000). The subsequent climatology by  Lana et al. (2011)  gives a more robust calculation of the seasonal, global DMS oceanic concentrations and air-sea fluxes based on the enhanced database. It has been and will continue to be used to better model the effects of DMS emissions on atmospheric chemistry and climate  (Levasseur, 2013). In the years since CLAW was first proposed, new SOLAS observations have become available that complicate the three steps in the simple feedback loop proposed.
(Baker and Croot, 2010; Landing and Paytan 2010; de Leeuw et al. , 2014) , which led to the recent development of a SOLAS aerosol and rainwater chemistry database (http://tinyurl. com/aerosol-rainwater) (Garçon et al. , 2014). These studies have also largely resolved the enigma of widely differing estimates of aerosol iron solubility  (Jickells and Spokes, 2001) , and quantified the relative contribution of aerosols to oceanic iron supply and productivity  (Boyd et al. , 2010) , the influence of Asian dust on the global radiation budget  (Uno et al. , 2009) , and the stimulation of upper ocean carbon export resulting from natural iron deposition following volcanic eruptions  (Achterberg et al. , 2013; Hamme et al. , 2010). These advances in our understanding of atmospheric links with the ocean iron cycle have improved modeling parameterizations and estimates of atmospheric dust supply to the ocean iron biogeochemical cycle on past, present and future timescales  (Mahowald et al.
The community has identified a number of examples of regional oceanic systems where integrated studies are particularly urgent  (Law et al. , 2013) , and need to be either initiated or expanded, including upwelling systems, sea ice areas, and coastal regions. More complete Earth system models are an obvious tool for future SOLAS research. For climate projections on timescales of several hundred years, coupled Earth system models have been developed that include the most up to date knowledge on chemical and biogeochemical processes, but assimilation of data into biogeochemical ocean models is still in its infancy. With respect to marine aerosols, modeling should particularly address the variable stoichiometry of atmospheric nutrients and surface ocean biota, with better representations of competitive interactions between plankton groups, aerosols, and organic matter aggregation and export processes.
Influence of spatial smoothing of the car-MAX-DOAS data In addition to the rotation of the CHIMERE data we also investigated if the nominal resolution of the CHIMERE data matches the spatial gradients observed by car-MAX-DOAS. For that purpose we applied a spatial smoothing (convolution with Gaussian kernels of different widths) to the car-MAX-DOAS results before they are compared to the CHIMERE data. Two of these comparisons are shown in Figs. 9 and 10. Both days were chosen because they represent cases of dif-ferent improvement after the application of smoothing and rotation. On 21 July 2009 (Fig. 9 ) best agreement between both data sets is found after the car-MAX-DOAS data are smoothed with a kernel of 4 km (in addition to a rotation of -20 • ). On 24 January 2010 (Fig. 10 ) best agreement is found if smoothing kernels between 8 and 12 km are applied (together with a rotation of 10 • ).
But on that day systematic differences remained even after applying both modifications. An overview on the effect of the spatial smoothing for all days is presented in Fig. 11. The results of the correlation analyses (top: correlation coefficients r 2 ; middle: y axis intercepts; bottom: slopes) are displayed as a function of the width of the applied smoothing kernel. The thin lines indicate the results for the individual days, and the thick lines indicate the averages of all days. For both original and rotated CHIMERE data best agreement (highest correlation coefficients and slopes closest to unity) is found after smoothing the car-MAX-DOAS data with kernels of 5 km or larger. As expected, much more consistent results are found for the comparison with the rotated CHIMERE data (right panel), because without rotation the possible mismatch between both data sets often prevents a meaningful comparison.
The Brazilian tuna longline fleet, operating in the equatorial and southwestern Atlantic, is used as a case study. The overall goal is to provide information needed to aid in the design of management measures and improve conservation efforts for this species. Both fisheries dependent (fisheries logbooks, observers) and independent (electronic tags) datasets were used as complementary sources of information. This integrating approach provided a better understanding of the interactions between oceanic sharks and this fishery. Vulnerability was assessed with regard to both spatial dynamics (horizontal) and depth distribution (vertical) of both sharks and longline gear, taking into consideration seasonality and strategies in the fishery.
Tagging A total of 11 oceanic whitetip sharks were tagged with pop-up satellite archival tags (PAT), manufactured by Wildlife Computers (Redmond, USA), in the South Atlantic Ocean. From these, 7 reported data to the satellites as scheduled, 1 reported prematurely and 3 never reported (Table  1 ). Two models of PAT were used: 7 MK10s and 4 miniPATs, with all of the non-reporting tags being Mk10s. Both models were set to collect data on depth, water temperature and ambient light level (for estimation of geolocation) every 10 seconds. The Mk10, however, only transmitted a summary of that data in the form of depth and temperature histograms, while the MiniPATs transmitted time series data at a resolution of 5 minutes. The first two Mk10 tags (AOCS1 and AOCS2) were programmed to summarize the collected data into one-hour histograms while all others were set to generate six-hour histograms.
One MK10 was recovered (AOCS3) allowing the complete time series, with 10-second resolution, to be accessed. The miniPATs were programmed to transmit only depth time series data and not temperature. Temperature data were received as a daily histogram (24 hours). Both tag models transmitted light data in the form of two daily light curves (representing dawn and dusk) that were used to reconstruct geographic positions. The MiniPATs and the recovered MK10 also provided a daily analysis of the surface mixed layer depth and the amount of time the tagged shark spent therein. All OCS were caught off the Northeast coast of Brazil by a commercial tuna longliner and tagging was conducted by an on-board observer with the help of the crew. Sharks were brought onboard to be measured and tagged and were out of the water for no longer than five minutes prior to release.
Vertical distribution Depth data were analyzed in relation to the different periods of the day to account for diel movement patterns. Day, night, dawn and dusk data were grouped according to local times, which were estimated using the calculation procedure available from NOAA (http://www. srrb. noaa. gov/highlights/sunrise/sunrise. html). Data from the Mk10 tag that were summarized into six-hour histograms could not be grouped. Day was defined as the period between sunrise and sunset, and night as the period between astronomical dusk and astronomical dawn. Dusk comprised the hours between sunset and astronomical dusk, while dawn comprised the hours between astronomical dawn and sunrise. To compare daytime and nighttime depth distributions, Pearson's chi-squared test was performed at 95% confidence level.
Horizontal Movements The horizontal movements of tagged sharks were estimated by processing the data received from the Argos satellite system using the manufacturer light-based geolocation software (WC-GPE: Global Position Estimator Program suite, available at: www. wildlifecomputers. com). Longitude is estimated from the time of local noon and latitude from the length of the day through the dawn and dusk symmetry method. In order to minimize the errors usually associated to this geolocation estimation  [12, 13] , the tracks were post-processed using the IKNOS Walk model  [14]. In this approach, tracks are corrected and interpolated at fixed intervals by bootstrapping random walk particles. The locations are estimated from a cloud of weighted particles and corrections can be applied based on known constraints or available data.
Fisheries data Fishing effort data from the Brazilian tuna longline fleet between 1999 and 2011, totaling 65,277 sets, were used to assess the vulnerability of oceanic whitetip sharks to this fishery. The Brazilian fleet operates in a wide area of the equatorial and southwestern Atlantic Ocean and uses two distinct fishing strategies commonly referred to as "Japanese" (JAP) and "Spanish" (SPA). In general, vessels fishing with the Spanish strategy target swordfish (Xiphias gladius) and set surface longlines (down to 100 m) at night, using lightsticks and squid as bait. Alternatively, vessels fishing with the Japanese strategy target various tuna species and set deep longlines (down to more than 200 m) early in the morning, using small pelagic fish as bait (mainly mackerel)  [17]. Fishing effort was represented by the number of hooks deployed, grouped into 5°squares for spatial analyses.
Nominal catch per unit of effort (CPUE) data were analyzed to account for possible seasonal variations. A subsample of the dataset previously analyzed by Tolotti et al. [17]  was considered for this purpose. This dataset was collected by the Brazilian Observer Program from foreignchartered tuna longline vessels between 2004 and 2010. Spatial bounds for this data were selected based on the shark high utilization areas (from tag data), as well as on the area where the fishing effort was concentrated. The CPUE represented the number of OCS/1,000 hooks per quarter. Mean values were calculated using the sum of all catch and all effort in each quarter. Data were also grouped according to the fishing strategy. Two-sample Kolmogorov-Smirnov tests were used for inter-quarter comparisons at a 95% confidence level.
To evaluate the spatial overlap between fisheries and tagging data precisely, it would be necessary to use a CPUE index that accounts for the depth distribution of the hooks. In the absence of such data, an occurrence index was adopted. For this analysis, data were also grouped into 5°squares and by quarter. For each square, the number of sets with the presence of OCS were summed and then divided by the total number of observed sets. The occurrence values were then paired with the Kernel densities (also grouped by quarters and 5°× 5°s quares). For each square, density values were summed. A Spearman's Rank test was conducted to verify possible correlation between fisheries and tagging data (occurrence and Kernel density). The non-parametric Spearman's Rank was chosen after verifying that the two datasets did not conform to a bivariate normal distribution (Royston's Multivariate Normality Test, p = 9.
Tag performance In total, 719 geolocation days, 1,643,249 depth records and 1,537,920 temperature readings from the 8 reporting tags were analyzed. Time at depth histograms totaled 951 and time at temperature 1,340. Overall, the miniPATs had higher reception rates than the MK10s for both general and geolocation data (Table  2 ). Except for one miniPAT (OCS5), reception rates were approximately 80% for general data and over 97% for geolocation. The OCS5 miniPAT stopped collecting data after 104 days of deployment, but popped to the surface and transmitted the data after 140 days as scheduled. Nonetheless, these data sets were almost entirely received (86. 2% and 99. 0% of general and geolocation data, respectively). The MK10 tags varied considerably in their reporting rates, and ranged from as low as 7% to up to 53% of the general data and from 10% to 63% for geolocation data (Table  2 ).
Temperature preferences and vertical distribution Oceanic whitetip sharks exhibited a strong preference for warm and shallow waters. Tagged sharks remained at temperatures between 24 and 30°C for approximately 96% of the monitoring period (Fig  1 ). The time spent inside the mixed layer was very similar for all sharks, regardless of their size, and it varied between 70 and 83% (Table  3 ). AOCS4 and 7 entered the coldest waters of all tagged sharks. The minimum recorded temperatures were 8. 2°C, with a  corresponding depth of 368 m, for AOCS4, and 8. 8°C at 448 m, for AOCS7. The latter also represented the deepest recorded dive of this study. For more than 95% of the monitoring period tagged sharks remained above 120 m, during both day and night periods (Fig  1 ). The aggregated data showed similar frequency distributions between day and night, although during the day shallower depth intervals were slightly more frequent, whilst the night distribution showed higher frequencies in deeper intervals.
Horizontal vulnerability Oceanic whitetip sharks tagged in the southwestern Atlantic Ocean appear to have a certain degree of site fidelity to the area off northeast Brazil around the equator, where all of their  tracks started. Five out of eight sharks have ended their tracks relatively close to their starting points, even after traveling several thousand kilometers. Three of these sharks also remained within 500 km of the tagging locations during most of the tracking period. Although time limitations prevent definitive conclusions from being drawn, a recent study conducted in the Bahamas (northwestern Atlantic) also reported that OCS returned to the tagging area after long migrations  [20]. The Bahamian study gathered data from 10 OCS with deployment periods up to 245 days. In the year following their study, scuba divers also spotted one of the tagged sharks near the tagging location  [20].
This study has been carried out with financial support from the  Commission of the European Communities , specific  RTD programme of Framework Programme 7, "Theme 2-Food, Agriculture, Fisheries and Biotechnology ", through the research project  MADE (Mitigating adverse ecological impacts of open ocean fisheries ). It does not necessarily reflect its views and in no way anticipates the Commission's future policy in this area. The fishery data was made available by the  Ministry of Fisheries and Aquaculture of Brazil. The authors acknowledge  Conselho Nacional de Desenvolvimento Científico e Tecnológico  (www. cnpq. br),Fundação  CAPES  (www. capes. gov. br) and  France Filière Pêche  (www. francefilierepeche. fr) for providing a fellowship to  M. T. Tolotti.