dataesr/scientific-paragraphs-categorization huggingface dataset:
Plankton surface-trawl dataset Plankton nets can capture any debris larger than the net mesh and smaller than the net mouth, but net dimensions vary between studies and maximum particle size is often not reported. Since most particles collected in plankton nets are millimeters in size or smaller, from here forward we use the term 'microplastics' not in its strict definition (as particles <5 mm in size), but instead to conveniently refer to all plastic debris collected in surface-trawling plankton nets. There are two relevant measures for net-collected plastic debris: particle count and mass. Both have their merits. Samples are easier to count than to weigh, especially while underway at sea, and the number of particles may be more relevant for an exposure assessment. On the other hand, as a conservative variable, mass can more easily be related to source estimates, and will eventually be needed to close the mass balance of ocean plastics.
Because of these considerations, we report both measures. Plastic data collected using surface-trawling plankton nets were identified by literature search and data were assembled either directly from the publication or by contacting the corresponding author (table  S1 ). Additional unpublished data were provided by contributing authors. In total 27 floating debris studies were identified, with 11 854 surface trawls carried out between 1971 and 2013, spanning all major ocean basins except the Arctic. Given the long time span over which samples were collected, we addressed sampling year as a potential bias when we standardized the data (see section 2. 2). Net mesh ranged from 0. 15 to 3. 0 mm in size, although more than 90% of observations were collected using a manta net or neuston net with 0.
333 or 0. 335 mm mesh. Most studies did not report the maximum size of plastic debris collected. All data reported in units of #m -3 were converted to #m -2 by multiplying by the submerged height of the net, and then cast into units of #km -2. Nearly all studies reported plastic abundance in count units, and two-thirds reported data in mass units. However, the three largest datasets (comprising 82% of total observations) only reported counts. Conversions to mass for datasets in which only count was reported were made using factors derived from empirical data collected in similar geographic regions, during similar time periods and/or using similar sampling methods (table  S1 ). Microplastic abundance at the sea surface has been shown to vary with wind speed due to vertical mixing  (Kukulka et al 2012 , Reisser et al 2015) , yet most studies did not report wind data.
To evaluate the relationship between wind speed and plastic abundance as a source of variability in the data set, we used daily-averaged wind speed from the ECMWF ERA-Interim global atmospheric reanalysis (Dee et al 2011) interpolated to each surface trawl date and location. ERA-Interim output is available beginning 1 January 1979; thus, 222 surface trawls collected prior to 1979 were omitted from our analysis.
Data standardization using statistical modeling Microplastics sampled were collected in a wide range of conditions over a multi-decadal period. Before scaling ocean circulation model outputs with these data, we first removed variability associated with factors that could affect either the concentration of plastic in the ocean or the representativeness of the samples, such as sampling year, wind speed, distance of the tow, and others. We used a generalized additive model (GAM; Wood 2006), implemented in the R statistical language (R Core Team 2013), to estimate the relationships between these variables and the observed plastic concentration (in counts), and then used those relationships to adjust the observations to represent standardized conditions. We first created a base model using a spherical smooth term (two-dimensional spline) to represent location on the globe, assuming that repeated samples in the same location should share an underlying average value.
We defined the set of potential GAMs a priori and used the Akaike information criterion (AIC; Burnham and Anderson 2002) to determine the best model, balancing parsimony and fit across the full set of possible models. We chose a Tweedie distribution with a parameter of 1. 6 to allow for over-dispersion in the data. The best performing GAM was used to predict the plastic concentrations that would have been observed for each sample had it been taken under no-wind conditions in the year 2014 (hereafter the 'standardized dataset'). We also estimated the standard error in our predictions based on the variance-covariance structure among the fitted parameters, using the tools provided in the mgcv package  (Wood 2006). These standard errors were used to estimate the 95% confidence intervals on the standardized plastic concentrations.
Discussion and conclusions A major objective of this analysis is to inform the abundance and distribution of plastic debris in the ocean in order to ultimately assess marine animals' exposure to and impact from interaction with debris. Ours is the third study to estimate the amount and distribution of small floating plastic particles in the global ocean, using the largest dataset to date and three different ocean circulation models. While the previous two studies found coarse agreement in the global mass of plastics collected using surface-trawling plankton nets (7-35 thousand tons by Cózar et al 2014; 66 thousand metric tons by Eriksen et al 2014), our model solutions not only exceed these but also vary substantially from 93 to 236 thousand metric tons. Despite the wide discrepancy in these standing stock estimates, all analyses find the highest concentrations of net-collected plastics in the subtropical gyres, with the largest mass reservoir in the North Pacific Ocean, presumably because of its vast area and also the large inputs of plastic waste from coastlines of Asia and the United States  (Jambeck et al 2015).
g. , source functions and removal processes) also contribute to the discrepancies. The variation in model solutions in our study emphasizes that most of the ocean surface is undersampled for microplastics. Uncertainties in the Southern Hemisphere basins illustrate the lack of data even in high concentration subtropical gyres. The least sampled regions are areas of low plastic concentrations, where models predict between 30% and 70% of particles may reside (figure  6 ). Perhaps the starkest illustration is in the Mediterranean Sea, where models predict between 21% and 54% of global microplastic particles, equivalent to between 5% and 10% of global mass (because of small average particle size), are located. Our dataset has only 105 surface trawls concentrated in a very small region of the Western basin, whereas models predict the highest concentrations in ), yet more data, especially in the Eastern basin, is strongly needed.
Acknowledgments This work was conducted within the Marine Debris Working Group at the  National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara , with support from  Ocean Conservancy. We particularly thank  George Leonard  and  Nick Mallos  for their support, and  Steven D Gaines  for thoughtful insights. We thank  H Carson ,  A Cózar ,  M Doyle ,  C Fossi ,  G Lattin ,  C Panti ,  R Yamashita , and  A Zellers  for kind assistance in compiling plankton net data. EvS was supported by the  Australian Research Council  ( DE130101336 ). BDH and CW were supported by  CSIRO's Oceans and Atmosphere Flagship. JAF was supported by IMARES internal funds. NM was partly supported by  NASA  through grants  NNX08AR49G  and  NNX13AK35G. KLL was supported by the  National Science Foundation  ( OCE-1260403 ) and  Sea Education Association.
argo. ucsd. edu). The number of Argo profiles collected per year has increased from 50 000 in 2003 to more than 150 000 in 2013 with a steep increment from year 2003 to 2006 (from Histogram of profiles on Argo GDAC, http://www. argodatamgt. org/ Monitoring-at-GDAC/Active-floats-statistics). The number of available salinity observations has greatly increased because, before Argo, there were far fewer salinity observations compared with temperature, meaning that Argo data comprise a much higher proportion of available salinity observations. Moreover, datasets suited to the needs of operational forecasting systems  (Cabanes et al. 2013 , Legler et al. 2015)  have been developed and, owing to the technological evolution of the instruments, i. e. their transmission components and the communication system, these are able to provide an increasing number of observations in near real time.
Grid resolution Model System Table  1  shows the principal components in terms of models and data-assimilation schemes for all of the systems. Most of the European systems plus Canada use NEMO (Nucleus for European Modelling of the Ocean) as the OGCM. The other models are community models such as HYCOM (Hybrid Coordinate Ocean Model) for the US systems or MOM4 (Modular Ocean Model) for the Australian and Chinese systems. Japan and ECCO have their own OGCMsthe MRI. COM and MITgcm (MIT General Circulation Model) codes respectively. Six systems out of 12 also include an ice component. The inclusion and/or the increase in complexity of the ice component is yet another step in the evolution of the systems with respect to  Dombrowsky et al. (2009). The ice models differ from system to system. PSY3-PSY4 from Mercator use the LIM2 (Louvain-la-Neuve Sea Ice Model) code with the assumption that the ice dynamics are simulated by assuming that sea ice behaves as an elastic-viscous-plastic (EVP) continuum in dynamical interaction with atmosphere and ocean.
The standard deviation of the temperature data is well captured by all systems, but the REMO system yields a smaller standard deviation for salinity than the observations and the analyses of the other systems. The REMO RMSDs of temperature and salinity are larger than the other systems, and the correlation smaller. It is expected that the REMO system will improve its skills when Argo data are assimilated. Two systems, TOPAZ in the Arctic and the north Atlantic and the Mediterranean Forecasting System, also have a biogeochemical component (NORWECOM and OGS OPA-BTM, respectively;  Skogen 1998; Teruzzi et al. 2014 ) coupled with the physical system. The integration of the physical and biogeochemical models is very important, especially at regional and coastal levels. The importance of this link is clear, for example, in Figure  8 , where the gross primary production of carbon is shown from the Topaz4-NORWECOM system.
The products from all the systems (except the Japanese products) are distributed in the same format, NetCDF, a standard for encoding oceanographic data. The data policy is different from centre to centre: in some cases, the access to the data is free, and in others some restrictions are applied. Most of the centres developed dedicated catalogues in order to aid the users to discover the dataset they need. The structure, flexibility and performances of these informatics tools have increased significantly in recent years and have helped to serve products not only to the scientific research community but also to a wider community of users. Depending on the system characteristics, all the GODAE systems deliver forecast products for the next 7-18 days, or for the next 7 months, as in the case of the MOVE/MRI.
MFS (INGV, Italy) The current-wave (NEMO-WWW-III) interaction will be further developed, and the tidal signal will be introduced. The system resolution will be increased, and more data will be assimilated with the variational assimilation scheme  (OceanVAR, Dobricic and Pinardi 2008). In particular, the satellite SST and the floats trajectories  (Nilsson et al. 2011 ) will be introduced in the pool of the data assimilated. The real-time validation suite of the system will be further developed in order to provide a more accurate validation at a sub-basin scale. 5. 8. BlueLink/oceanMAPS (Bureau of Meteorology, Australia) OceanMAPS system will be upgraded to OceanMAPS3. The model horizontal resolution will be increased to 1/10 in all the model domains (the resolution is now 1/10 around Australia and 1 everywhere else) from 76°S to 76°N longitude.
GOFS (NRL/NAVOCEANO, USA) The ice model (CICE) will be added to the system and the number of the vertical level will be increased. The Improved Synthetic Ocean Profiles (ISPO) will be inserted into the new version of GOFS. ISPO is a technique developed by the US Navy to construct synthetic vertical profiles projecting remotely observed SSH and SST downward from the surface using a global database of statistical relationship  (Helber et al. 2013). The horizontal resolution of the system will be increased to 1/25 with the addition of the tides being planned for year 2016. A coupled GOFS3. 5-WWW-III system is planned to be operational in 2018.
ACKNOWLEDGEMENTS We are thankful to many people for discussions of this project, especially  Richard Butler ,  Christian Foth ,  Adriana L ópez-Arbarello ,  Mario Bronzati ,  Femke Holwerda ,  Randy Irmis ,  Roland Sookias ,  Markus Moser ,  David Legg ,  Christophe Hendrickx , and  Nizar Ibrahim. Thanks  to  Rudyard Sadleir ,  Roger Benson ,  Patrick O'Connor , and  Juan Canale  for sharing photographs and giving helpful comments. Andrea Cau ,  Fernando Novas ,  Christophe Hendrickx , and an anonymous reviewer are thanked for critical comments on the manuscript. Sandra Chapman  and  Paul Barrett  gave us access to Sigilmassasaurus and Baryonyx material at the NHM in London, and  Paul Sereno  generously allowed access to material of Suchomimus and a cast of FSAC-KK 11888 in Chicago, for which we are grateful.
Database Database contains one-hour time step data: (i) rainfalls at 5 rain gauges inside or near the basin in order to take into account the heterogeneity of rainfalls, and discharges at Lavallette for 15 intense floods before 2010, and 3 intense floods after 2010. Moreover chemical data are available only for the two last events of 2014 (Table  1 ). Table  1  focuses on the last events which were investigated in the present work.
Acknowledgements. The authors would like to thank  Bruno Janet  and  Arthur Marchandise  from  SCHAPI (Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations ), the  French  flood warning service which funded this study. They are also grateful to  Meteo France ,  SPC  Med-Ouest and SPC GD which provided data. Our thanks are also extended to  Dominique Bertin  for his extremely fruitful collaboration within the design and implementation of the Neural Network simulation tool: RnfPro.
Acknowledgments We thank  Matthew Fisher ,  Philippe J. R. Kok  and one anonymous reviewer for comments on an earlier version of this manuscript. This work was supported by a grant from the  BiodivERsA  project  RACE (Risk Assessment of Chytridiomycosis  to European amphibian biodiversity; www. bd-maps. eu). We thank  Anne Corval  for providing technical supports during the study and  Damien Davy  and the  OHM Oyapoque (Observatoire Homme-Milieux)  for providing facilities in Saint-Georges and  Cedric Husson  and the ADNG association ( Association pour la découverte de la Nature Guyanaise ) for providing facilities in Saint-Laurent. We thank  Antoine Baglan ,  Sebastien Barrioz ,  Daniel Baudain ,  Aurélien Bertran ,  Marguerite Delaval ,  Jennifer Devillechabrolle ,  Antoine Fouquet ,  Mickael Guerin ,  Hélène Guillen ,  Vincent Prié ,  Max  and  Eva Ringler ,  Marine Robillard ,  Bibiana Rojas ,  Vincent Rufray ,  Jean-François Szpigel ,  Johan Tascon ,  Audrey Thonnel ,  Jean-Pierre Vacher , and  Ombeline Vrignaud , for help in the field.
This work has benefited from two " Investissement d'Avenir " grants managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01  and  TULIP, ref. ANR-10-LABX-0041 ) and benefitted through the use of the Nouragues station of material and technical support from  USR 3456  and  AnaEE-S infrastructure  ( ANR-11-INBS-0001 ). This work was financially supported by a grant from the  AMAZONIE research program  and from the  BioDiversa- project  RACE (Risk Assessment of Chytridiomycosis  to European amphibian biodiversity) to DSS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Specifically we sought examples of monitoring in the tropics where design, implementation, and use of the monitoring are achieved autonomously by local people as part of a longestablished process of land and/or resource management. This is close to the category of "autonomous local monitoring" described in  Danielsen et al. 's (2009)  typology of participatory monitoring (see also  Danielsen et al. 2014b ). We used the terms "indigenous monitoring," "traditional monitoring," "community monitoring," "autonomous monitoring," "indigenous guarding," and "traditional guarding" in the ISI Web of Science database (http://thomsonreuters. com/thomson-reuters-web-of-science/. ) The results and additional literature are discussed in Appendix 1.
Acknowledgments: We thank the communities of Kay, Metaweja, and  Yoke in Mamberamo Raya  for their participation, assistance, patience, and hospitality. We thank  Teresia Yeuw ,  Michael Padmanaba  (Center for International Forestry Research; CIFOR), and others team members who collected data, and  Serge Rafanoharana  (CIFOR),  Agus Mohammad Salim (CIFOR) , and  Hendi Sumantri  ( Conservation International ) for their assistance in generating maps. We acknowledge the l' Agence Française pour le Développement (AFD)  for their financial support to the project  Collaborative Land Use Planning in Papua (COLUP)  and to the  U. S. Agency for International Development (USAID)  and the  Norwegian Agency for Development (NORAD)  for their financial support of the research project Participatory Measuring, Reporting and Verifying (PMRV).
We conducted a one-way univariate PERMANOVA test based on Euclidean distance  (Anderson 2001; McArdle and Anderson 2001). Although the variables were univariate, we conducted PERMANOVA tests because the null distribution of the test statistic in PERMANOVA is produced through permutation (n = 9999) avoiding the normality assumptions required for parametric tests. Analyses were carried out using PRI-MER v. 6  (Clarke and Gorley 2006)  in the PERMANO-VA+ module  (Anderson et al. 2008).
We evaluated the robustness of each comparison by estimating the c=h 2 crit (i. e. , the c/h 2 value beyond which P ST is significantly higher than F ST ; Brommer 2011) considering the upper limit of the 95% CI of h and F STall. Considering the intrinsic characteristics of the P ST and the F ST previously exposed, caution is necessary when interpreting P ST -F ST comparisons. In this context,  Brommer (2011)  showed that the lower the c=h 2 crit value, the more robust the conclusion regarding the impact of selection on the considered trait and suggested that c=h 2 crit should be lower than 0. 2 to draw realistic inferences. The computations were performed in R (http://cran. r-project. org/), and an example of the scripts used for the P ST -F ST comparisons and the sensitivity analyses is presented in Appendix S4.
Acknowledgments We are grateful to  Bernard de Ligondes ,  Roland Graille , and  Fr ed eric Zuberer  ( Centre d'Oc eanologie de Marseille ) for support during sampling and design of the experimental plates and to  Jean-Marie Dominici  ( R eserve Naturelle de Scandola, Parc R egional de Corse ) for his help during the experimentation conducted in Palazzu. We thank  Caroline Rocher  for technical assistance during genotyping analyses and  Guillem Salazar  for his support during R programming. We show gratitude to  Drs. Oph elie Ronce  and Fr ed erique Viard,  Jose M. Montoya  and  J erôme Goudet  for useful comments during the preparation of the manuscript and to  Dr. Cl ement Gilbert  for precious advices during the redaction process. We are also grateful to  Jean-Georges Harmelin  for sharing data regarding genetic differentiation based on allozyme loci.
This study was funded by the  "Provence-Alpes-Côte d'Azur" region (J-B. Ledoux Doctoral Fellowship ) and by the  IFB Foundation (Institut Franc ßais de la Biodiversit e) , the environmental department of  TOTAL-FINA-ELF group , an " Action Concert ee Initiative" (ACI) program  " Jeunes chercheurs 2003 " (number  JC5360 ) from the  French Research Ministry  and the  MEDCHANGE  project funded by the  Agence Nationale pour la Recherche (ANR)  J-B. L. is supported by a post-doctoral grant  SFRH/BPD/74400/2010  from  Fundacão para a Ciencia e Tecnologia  (FCT; Portugal).
Funding  Information J-B. Ledoux  is supported by a post-doctoral grant  SFRH/BPD/74400/2010  from  Fundacão para a Ciência e Tecnologia (FCT). This study was funded by the  "Provence-Alpes-Côte d'Azur" region (J-B. Ledoux Doctoral Fellowship ) and by the  IFB Foundation (Institut Franc ßais de la Biodiversit e) , the environmental department of  TOTAL-FINA-ELF group , an " Action Concert ee Initiative" (ACI) program  " Jeunes chercheurs 2003 " (number  JC5360 ) from the  French Research Ministry  and the  MEDCHANGE  project funded by the  Agence Nationale pour la Recherche (ANR).
In our study, we used Level 2 data of size distributions and refractive indices at four retrieval wavelengths 0. 44, 0. 67, 0. 87 and 1. 02. Moreover, we also included some Level 1. 5 refractive indices, when 0. 2 < AOD(440) < 0. 4, but only when a quality-checked Level 2 size distribution exists. In other words, we applied otherwise the same rigorous quality control that is required for Level 2 data, but we only relaxed the AOD requirement at 440 nm from 0. 4 to 0. 2. We selected on purpose also these cases of possibly somewhat lower AOD, in order to not bias our sample, and thus the estimate of the DRE, towards higher aerosol loading. However, the AOD at 440 nm is typically above 0. 4 in the IGP region, so the set of almucantar refractive indices that we included turned out to be insignificantly different to that of the "full" Level 2 (not shown).
We included four AERONET sites for our data analysis, covering wide conditions in the Indo-Gangetic Plain (IGP): Karachi and Lahore in Pakistan, and Kanpur and Gandhi College in India. The measurements covered the following time periods: Gandhi  College: 4/2006 College: 4/ -3/2010;; Kanpur: 1/2001 Kanpur: 1/ -4/2012;; Karachi: 9/2006 Karachi: 9/ -8/2011;; Lahore: 4/2007 Lahore: 4/ -10/2011. Figure  1  shows the locations of these sites overlaid in the annual mean AOD map from MODIS Terra. In the IGP there are large local emissions of aerosols from various sources: heavy particulate pollution from industrial sources, strong vehicular emissions, use of fossil fuels, and widespread biomass and agricultural crop residue burning. In addition, the IGP is strongly affected by seasonal (pre-monsoon) mineral dust transported mainly from the Thar Desert (e.
Calculation of the radiative effect The radiative transfer calculations were performed by using the libRadtran package  (Mayer and Kylling, 2005). We used a two-stream solver and correlated-k approximation of Kato et al. (1999)  with bands from 1 to 31 (from 240. 1 to 3991 nm) to cover the entire shortwave (SW) range. The direct radiative effect of BrC, the DRE of BrC, at the top of the atmosphere (TOA) was calculated on a monthly basis, as the difference between two cases: including all aerosols and excluding BrC. The former was based on monthly mean size distribution and refractive index, while the latter set was formed by excluding the volume fraction of BrC. By excluding the BrC fraction, both refractive index and size distribution were then modified. The refractive index for the "non-BrC" case was created by the volume averaging mixing rule and including all the other components except for BrC (the scattering host, black carbon, goethite and hematite).