dataesr/scientific-paragraphs-categorization huggingface dataset:
Climate data For each site, the observed meteorological data are taken from the AMMA-CATCH observation system (African Monsoon Multidisciplinary Analysis-Coupling the Tropical Atmosphere and the Hydrological Cycle; www. amma-catch. org). The precipitation is interpolated by kriging over each site using more than 50 rain-gauge measurements for the 2003-2009 period (for a detailed description of the method used, see Kirstetter et al 2013) while the other variables (radiation, temperature, relative humidity and wind) are  used in a large number of studies, some of which focus on West Africa (e. g. Vigaud et al 2009 , Flaounas et al 2010 , Klein et al 2015) , and includes a large choice of physical schemes. Simulations are carried out for a domain extending from 10°S to 30°N and 45°W to 45°E, covering the large West African region, at a resolution of 80 km.
Three SW schemes and four CPSs are tested in this paper; their main characteristics are detailed in table 1 (and the supplementary material). For this set of experiments, the LSM is set to the Noah LSM and LU is set to the USGS (United States Geological Survey) LU map. Set #2. LSM and LU Atmospheric models are coupled to LSMs to resolve the fluxes at the interface between the continents and the atmosphere. They are forced by atmospheric fields and use global-scale soil and surface LU datasets. LU maps consist of typologies (e. g. 20 or 24 categories such as agriculture, deciduous forests, grasslands, etc. ), where each typology is characterized by specific albedo, soil moisture, surface roughness and emissivity values. In this set of simulations, four distinct LSMs and two LUs are tested. Due to the results inherited from Set #1, the SW scheme is set to Dudhia and the CPS is set to Kain-Fritsch with the modified trigger function (KF-t, table  1 ).
In this paper, we consider the climate inputs from the WRF or ERA-I models as the most suitable for crop yield simulations as they minimize the distance between the simulated yields and the reference yields obtained with in situ meteorological data. We were also able to validate the resulting simulated yields against the observed yield data (tier-3 evaluation); however, since the objective of this paper is to assess the sensitivity of the simulated crop yield to the RCMs biases rather than predict accurate yields in the two sites, the results reported in this paper are mainly dedicated to the tier-1 and tier-2 evaluations. We used the same metric for the tier-1 and tier-2 evaluations to measure the distance to the observed in situ data. We calculated the mean bias error (% MBE) of the climate inputs and simulated yields relative to the observed seasonal climate variables (tier-1 evaluation) and simulated yields using the observed in situ climate variables (tier-2 evaluation).
taken from synoptic stations. These data are considered as reference climate data and they are used to simulate the reference yields. The ERA-Interim dataset (ERA-I; Simmons et al 2007, Dee et al 2011) consists of a set of global gridded analyses describing the state of the atmosphere, land and ocean wave conditions from 1979 to date. Climate variables from this dataset are used at a resolution of 1. 5°to force the (i) WRF and (ii) crop models. 2. 3. Climate downscaling experimental setup All of the climate downscaling experiments are performed using the non-hydrostatic Advanced Research WRF ARW/WRF model, version 3. 3. 1 (WRF; Skamarock et al 2008). This model has been.
Acknowledgments Two anonymous reviewers and one editorial board member are thanked for their helpful comments that helped us improve the manuscript. WRF was provided by the  University Corporation for Atmospheric Research  website (www2. mmm. ucar. edu/wrf/users/download/). ERA-Interim data were provided by the  European Centre for Medium-Range Weather Forecast (ECMWF). WRF calculations were performed using HPC resources from  DSI-CCUB (Université de Bourgogne ).
Acknowledgments This work was funded by the  United States Agency for International Development (USAID)  and by the  Consultative Group of International Agricultural Research Program on Forests, Trees and Agroforestry (FTA). We thank  Gustavo Kattan  for helpful comments on an earlier version of this manuscript, as well as  Karen Holl ,  Marcela Bustamante-S ánchez , and one anonymous reviewer for their insightful reviews and recommendations.
Chlorophyll The available data on chlorophyll a (MOOSE database, only available for 2012 and 2013) show a content variability between 0. 9 (10 October and 6 November, 2012) and 14. 0 (3 April 2012) mg m -3 (Fig. 4a ). Chlorophyll a is frequently used as a proxy for photosynthetic organisms and the variation observed here is consistent with the hypothesis of a yearly phytoplanktonic spring bloom, with a larger magnitude for the 2012 event.
Acknowledgements. This work is a contribution to the  Labex  funded by the  French Government "Investissements d'Avenir" program  of the  French National Research Agency (ANR)  through the A* MIDEX  project (no. ANR-11-IDEX-0001-02 ). It was also supported by the  LEFE-CYBER (Les Enveloppes Fluides et l'Environnement) national program , as part of the  MORTIMER (Matière ORganique Terrestre rejetée par les fleuves et les rIvières en MER) research program. It was undertaken as part of the transversal research axis DEBAT of the  Mediterranean Institute of Oceanography, Marseille, France. Additional data were provided by " MOOSE" (Mediterranean Oceanic Observing System for the Environment)  with the support of the  "Agence de l'Eau Rhône-Méditerranée-Corse ". Special thanks to  Michel Fornier  for providing samples.
After the acquisition, the obtained mass spectrum was internally recalibrated according to some peaks which were unambiguously identified. The elemental composition of each detected signal with a signal-to-noise ratio (S/N) greater than 3 was determined by using Omega 8 Elemental Composition software (Varian-IonSpec Inc) with the following search criteria: C 6-100 13 C 0-2 H 6-100 N 0-20 O 0-20 S 0-5 K 0-5 general formula and a 2 ppm tolerance error. The addition of other atoms such as sodium did not modify the obtained assignments. When it was possible, the isotopic pattern was checked with Omega 8 Exact Mass Calculator software (Varian-IonSpec Inc).
Moreover, the high dynamic range of the analysis reveals very accurate details on molecular composition of each class of compounds. The Figure  2  clearly evidences the concomitant presence of oxygen and nitrogen atoms for a large amount of cigarette smoke components. To clearly represent on a same graph the elemental composition of these compounds, the three-dimension van Kreleven diagram is used. For each obtained molecular formula, the H/C, O/C and N/C atomic ratios are calculated and correspond to the x, y and z cartesian coordinates of the dot associated to this signal. In respect with the number of unsaturation, oxygen and nitrogen atoms, different lines and clouds are visible in the 3-D van Krevelen diagram (Figure  4 ). In spite of the usefulness of this data representation, it has to be noted that the information of the m/z ratio is lost and additional 2-D Kendrick maps are required to fully represent and account of the detected species.
The more or less efficient ionization of a given class of cigarette smoke components in respect with the used ionization technique significantly influences the nature and the distribution of the detected species. Consequently, the differences observed on the mass spectra obtained by ESI and LDI have not to be considered antagonism but relevant to the complementary ionization techniques to fully characterize the composition of a complex sample mixture. Indeed, the ESI favors the ionization of the most polar compounds of the mixture and LDI provides access to a wider range of high aromatic compounds, which are known to be non-polar compounds. An interesting feature is observed for LDI-FTICRMS data when the class distribution is detailed for the highest unsaturated species and compared to the other ones.
Western blotting For sample collection, killing buffer is added, followed by a centrifugation at 4 • C, 5000xg, 7 min. After two 1X PBS wash steps, the pellets are solubilized and sonicated (3×30 cycle, 1 s cycles, 80% Amplitude). Protein determination was performed using BCA protein assay kit from Pierce TM. Fifteen microgram of protein was loaded per lane and ran on 8-20% gradient polyacrylamide sodium dodecyl sulphate gels together with the ColorPlus prestained protein marker (NEB). The separated proteins were transferred on a 0. 2 m nitrocellulose membrane via semy-dry blotting. A list of antibodies and their dilutions is provided in Supplementary Table  S1. ECF substrate for AP was used for protein detection. Protein quantification and image processing were performed on AIDA software, with subsequent evaluations in Microsoft Excel.
High-resolution agarose gel-electrophoresis pUC18 plasmid extraction was performed from WTeng, TL and TR strains at an hourly rate of the strain's growth with the Roti Prep Plasmid Mini kit. Five hundred nanogram of DNA together with a lambda PstI marker is loaded onto a 1% agarose, 1X TBE (Tris, Boric Acid, EDTA) gel with 1. 5 g/ml chloroquine and run for 23 h at 45V. The gels were stained with ethidium bromide. 1D evaluation scans were done with AIDA software and the intensity of the bands was plotted over their position in pixels. The starting point was selected from the well for each lane.
Quantitative RT-PCR Five nanogram of total RNA from a 1 ng/l dilution was used per reaction in a total volume of 25 l. RNEasy kit was used for RNA extraction and the QuantiTect SYBR R Green RT-PCR kit from Qiagen R was used for RT-PCR reactions. cDNA synthesis was carried out at 50 • C for 30 min, followed by inactivation of reverse transcriptase and activation of DNA polymerase step at 95 • C for 15 min. Amplification reactions consisted of 40 cycles with denaturation at 95 • C for 15 s, primer annealing for 30 s (Supplementary Table  S2 ) and primer extension at 72 • C for 30 s. Fluorescence was recorded by the Stratagene MX3000P. Data analysis was done with LinRegPCR software (Version 2014. 1) and Microsoft Excel. In all RT-PCR data representations the y-axis is lnR, where R = Eff Ct Reference Eff Ct Target WTeng data was used as reference.
Biolog phenotypic microarrays and computation of the similarity tree Biolog experiment was performed in duplicate for each analysed strain (42) using PM11 to PM20 comprising 240 different compounds in increasing concentrations (four dilution steps) and providing 7. 680 data points in total used for computation of the similarity tree. In the first step of computation we investigated the distribution of growth values in all the analysed strains. The data showed no deviation in the distribution of growth values indicating no general growth deficiency of any strain and allowing an unbiased comparison of the samples. However, a pairwise t-test revealed significant differences between the strains in certain media. Based on the pairwise growth differences, we computed the Euclidean distance matrix of all pairs of samples.
Data collection Surveys of farmers were conducted in two different periods: 1999 and 2011 in Yaxcaba during the primary growing season (April to September). Sixty-one farmers in 1999 and 71 farmers in 2011 who had milpas in Yaxcaba were sampled, corresponding to approximately 10% of ejidatariosejido members with usufruct rights on common land in the community. The ejido is a legal form of common lands established by Mexico's Land Reform during the 20th Century  (1915 -1992) , in which members have usufruct rights on land that is owned by the community and managed for public benefits, primarily agriculture. Accordingly to what was reported by farmers in informal discussions during interviews, only about two hundred of the ejidatarios in Yaxcaba are actually making milpa. In addition, in 2011 more than 100 farmers not having formal ejidatario status made milpa on the ejido's lands, therefore not disposing of governmental agricultural subsidy payments.
Data analysis Processed data were analysed by using descriptive statistics to score scale responses, frequency distributions and mean comparisons. Statistical analyses were performed using the GraphPad Prism software Inc. , (La Jolla, California, USA). Total area planted to each maize variety, both local and modern, was calculated based on GPS measurements and farmers' diagrams and descriptions of their plots using the methods described in  Jarvis and Campilan (2006). Standard diversity indices for crop varietal diversity  (Jarvis et al. , 2008) , including richness (number) of maize varieties grown, and evenness estimated as a complement of D (1 2 D), where D is the Simpson measure of dominance, were calculated and transformed logarithmically 1/(1 2 LN)  (Magurran, 2003; Jarvis et al. , 2008).
Maize richness and evenness stability between 1999 and 2011 in Yaxcaba A comparison of average richness and evenness between 1999 and 2011 was performed in order to determine trends in maize varietal diversity conservation on farm. We failed to detect any significant difference in average household richness for maize between the data obtained in 1999 (  2    statistical significance (P ¼ 0. 0582) and a significant increase in evenness (P ¼ 0. 0451) were observed (Fig. 1(b) ). The analysis of the relationship between the two measures of diversity, richness and evenness, at household level was highly correlated in both 1999 and 2011 (Fig. 1(c )). At the community level, the number of varieties remained the same (community richness ¼ 13 varieties; Table  2 ). Community evenness increased slightly, indicating a more even distribution of the area planted to the 13 varieties in the community of Yaxcaba in 2011 than in 1999, which can be seen in Table  2.
Acknowledgements The authors thank the  Swiss Agency for Development and Cooperation (SDC)  for providing financial support to this study, and the  International Development Research Centre (IDRC) ,  Canada  for funding the 1999 survey. Marianna Fenzi  was supported by a PhD scholarship from the  Centre Alexandre-Koyre, E ´cole des hautes etudes en sciences sociales (EHESS). The authors thank  Dr. Luis Dzib Aguilar  for helpful discussions. The 1999 Yaxcaba survey was carried out by  Jose Vidal Cob Uicab ,  Jaime Canul Ku ,  Luis Burgos May  and  Teresa Quinones. They also thank them for their data collection, and thank all  Yaxcaba  farmers for generously sharing their experience and knowledge. They thank  Efraim Moo Ake  for his crucial help in carrying out the field work.
General modeling details Throughout, we assessed parasite burden via the prevalences of the three most commonly observed helminth morphotypes (Subulura, Trichuris and Hymenolepis, see Table  1 ) as well as morphotype richness (count of distinct helminth egg morphotypes present in the sample). The morphotypes were analyzed separately due to their potentially differing clinical effects and seasonal fluctuations in their relative prevalence that might mask patterns of infection. Statistical analyses of the less common morphotypes (recovered in <10 infected samples) were not possible due to the extremely low number of infections, hence these data were used only in assessing parasite morphotype richness. Analyses of "overall parasite prevalence", i. e. infection by any of the discovered parasite morphotypes are shown in the Additional file 1: Tables  S4 to S7 , Figures  S2  and S3.
Parasite prevalence was modeled using generalized linear mixed models (GLMM) with a binomial error structure and a logit link function, and morphotype richness was modeled using a GLMM with a Poisson error distribution and a log-link function. All GLMMs were built using the R package lme4  [75]. Age, body mass and sample mass were each log-transformed prior to analyses to achieve normality and linearity. Age and body mass were further scaled and centered prior to analyses to improve the interpretation of interaction terms and relative effect sizes  [76]. Log-transformed sample mass was added as a covariate to all models to account for the influence of fecal mass on the likelihood of detecting parasite eggs. Individual identity was included as a random factor in all GLMMs to account for repeated sampling of the same individuals.
For each model, a random intercept model was selected based on lower AIC-values over a random slope and intercept structure. Satterthwaite estimation was used to compute P-values (lmerTest-package  [77] ). Marginal and conditional R 2scores  [78]  were computed to assess model fit. R describes the variance attributable to the fixed effects alone, whereas R 2 conditional reflects the combined proportion of variance caused by both fixed and random effects. Variation accountable to the random factor (individual identity) is reported throughout as σ 2. All analyses were performed in program R version 3. 1. 1 [79].
012 -2. 282 g). Throughout, when the interaction of age*sex and/or body mass exceeded a significance threshold of P > 0. 1, the terms were removed from the model to avoid overparameterization  [80]  and to improve sample size (not all samples could be matched to body mass measurements within a few days of the sample in the interest of minimizing occasions of animal handling). The sample sizes for each model are indicated in the result tables. The final, reduced models are reported below and the full models including all terms are included in the Additional file 1: Table  S2. The morphotype richness data were not overdispersed (goodness of fit tested in R package aods3  [81] ) or zero-inflated (tested with likelihood ratio tests using R package glmmADMB  [82, 83] ).
Within-individual change and selective mortality Finally, we assessed the relative contributions of selective mortality of parasitized individuals and age-related change in parasite load on the observed patterns. For this, we re-ran the models for parasite prevalence and morphotype richness using only longitudinal data (repeated measures from the same individuals sampled over multiple seasons). These data were split by sex rather than by season in order to maximize the number of samples per individual in each model. The models were run separately for the sexes (N = males: 55 samples/20 individuals, females: 99 samples/40 individuals) and including season of sampling as well as initially the interaction terms season*age and season*body mass(for full model see Additional file 1: Table  S3 ).
These interaction terms were dropped from the model where they had non-significant effects. To estimate whether parasite load predicts intermediate term survival, we assessed the 6 month survival probability of individuals as a function of their overall infection rate and morphotype richness. Apparent 6 month survival was extracted from the long term capture data for all individuals sampled at least once in either dry season (2010/2012) or in the 2012 rainy season (no data were available for the season following the 2011 rainy season). The individual was assigned survival status 1 if it was recaptured in the subsequent 3 month field season (i. e. 2011/2013 rainy season respectively for dry seasons 2010/2012 or 2012 dry season for 2012 rainy season). Individuals that were not recaptured within the next season were assumed to have died.
Acknowledgements The authors thank the technicians at the  Institut Pasteur of Madagascar  for analyzing the samples,  Elise Huchard  for helpful discussions,  Josue Rakotoniaina ,  Eva Pechouskova  and l'equipe Kirindy, especially  Bruno Tsiverimana  for help in capturing animals and collecting samples,  Peter Kappeler  for logistic support,  Département de Biologie Animale of the University of Antananarivo  for their continued collaboration and  Ministère de l'Environment et des Eaux et Fôrets, MINEEF, Direction des Eaux et Forêts of Madagascar  and  CNFEREF Morondava  for permitting research in Kirindy. Helpful comments from  Simon Verhulst  and two anonymous reviewers improved an earlier version of the manuscript. Funding for the data collection and parasite analyses was provided by the  DFG  ( KR3834/1-1 ).
Conclusion Our study provides baseline data on the gastrointestinal parasites in populations of free-ranging, forest-dwelling West African monkeys. More specifically, we provide the first parasite information on the cercopithecid monkey populations within Côte d'Ivoire's Taï National Park. During our study, we recorded 23 gastrointestinal parasites with relatively high prevalence. No cestode species were recovered. Only coprological analyses based on morphological and morphometric examination were performed. Some identifications were made at the family or genus level; however, several parasites represent zoonoses. Further assessment of these via molecular analysis is underway which will allow us to not only examine incidences of cross-infection among these primates, but also evaluate the potential presence of zoonoses in this region.
Acknowledgements. We thank the following agencies for permission to conduct fieldwork in the Taï Forest:  Ministère de l'Enseignement Supérieur et de la Recherche Scientifique , and the  Ministère de l'Environnement et des Eaux et Forêts, Office Ivoirien des Parcs et Réserves (OIPR). We thank the assistants of the  Taï Monkey Project  and its field coordinator  Dr. Anderson Bitty. We are grateful to the  Centre Suisse de Recherches Scientifiques en Côte d'Ivoire and the Plateau Technique de Microbiologie des Hôpitaux Universitaires de Strasbourg (HUS)  for technical assistance. Thanks are also due to  Drs. Nathan Tian Bi  and  Jean Coulibaly  for statistical assistance. This work was financially supported by the  Programme d'Appui Stratégique à la Recherche Scientifique  (PASRES, Project No.
& Key message Three options are proposed to improve the accuracy of national forest biomass estimates and decrease the uncertainty related to tree model selection depending on available data and national contexts. & Introduction Different tree volume and biomass equations result in different estimates. At national scale, differences of estimates can be important while they constitute the basis to guide policies and measures, particularly in the context of climate change mitigation. & Method Few countries have developed national tree volume and biomass equation databases and have explored its potential to decrease uncertainty of volume and biomasttags estimates. With the launch of the GlobAllomeTree webplatform, most countries in the world could have access to country-specific databases. The aim of this article is to recommend approaches for assessing tree and forest volume and biomass at national level with the lowest uncertainty.
Various authors report significant differences in forest biomass and carbon stock estimates depending on the used model  (Kenzo et al. 2009; Melson et al. 2011; Alvarez et al. 2012; Kuyah et al. 2012; Ngomanda et al. 2014). The adequate development and use of tree models are a key to improve the robustness of forest carbon and stock change estimates. Tree models and volume tables are crucial for quantifying many forest services such as the production of commercial volume, bioenergy, and biomass. Most countries use volume models and biomass expansion factors to report national forest biomass (FAO 2010), although these models were developed for different purposes and used in different ways to meet specific objectives at local and national levels. Some countries, such as Mexico, Indonesia, and Vietnam, are developing databases and guidelines for the use of tree models  (Inoguchi et al.