dataesr/scientific-paragraphs-categorization huggingface dataset:
FireMIP experimental protocol and input datasets The nine DGVMs in FireMIP are driven with the same forcing data  (Rabin et al. , 2017). The atmospheric forcing is from CRU-NCEP v5. 3. 2 with a spatial resolution of 0. 5 • and a 6-hourly temporal resolution  (Wei et al. , 2014). The 1750-2012 annual global atmospheric CO 2 concentration is derived from ice-core and NOAA monitoring station data (Le  Quéré et al. , 2014). Annual land use and land cover change (LULCC) and population density at a 0. 5 • resolution for 1700-2012 are from  Hurtt et al. (2011) and Klein Goldewijk et al. (2010, HYDE v3. 1) , respectively. Monthly cloud-to-ground lightning frequency for 1901-2012, at 0. 5 • resolution, is derived from the observed relationship between present-day lightning and convective available potential energy (CAPE) anomalies  (Pfeiffer et al.
After the 1850s,     (1997 -2012 ), GFAS1. 2 and QFED2. 5 (2001 -2012 ), and FINN1. 5 and FEER1 (2003 -2012)   Earlier reconstructions based on fire proxies also show a big difference in long-term changes after the 1850s. The reconstruction based on the Global Charcoal Database version 3 (GCDv3,  Marlon et al. , 2016)  exhibits a decline from the late 19th century to the 1920s and then an upward trend until ∼ 1970, followed by a drop. The reconstructions based on the GCDv1  (Marlon et al. , 2008)  and ice-core CO records  (Wang et al. , 2010)  show a sharp drop since roughly the 1850s, while a steady rise is exhibited in the reconstruction based on icecore δ 13 CH 4 records  (Ferretti et al. , 2005). The simulated historical changes in FireMIP models (Fig. 6 ) fall into this fairly broad range of long-term trends in these reconstructions.
Summary and outlook Our study provides the first multi-model reconstructions of global historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. Two versions of the fire emission product are available, at the original spatial resolution for outputs of each FireMIP model and on a unified 1×1 •. The dataset is based on simulations of fire carbon emissions and vegetation distribution from nine DGVMs with state-of-the-art global fire models that participated in FireMIP and the most up-to-date emission factors over various land cover types. It will be available to the public at https://doi. org/10. 5281/zenodo. 3386620. Our study provides an important dataset with wideranging applications for the Earth science research community. First, it is the first multi-model-based reconstruction of fire emissions and can serve as a basis for further development of multi-source merged products of global and regional fire emissions and of the merging methodology itself.
van  Marle et al. (2017b)  presented an example of using part of the dataset to develop a multi-source merged fire emission product as a forcing dataset for CMIP6. In van  Marle et al. (2017b) , the median of fire carbon emissions from six FireMIP models was used to determine historical changes over most regions of the world. The merging method and merged product in van  Marle et al. (2017b)  are still preliminary and need to be improved in the future, e. g. , by weighting the different models depending on their global or regional simulation skills. Secondly, our dataset includes global gridded reconstructions for 300 years. It can thus be used for analyzing global and regional historical changes in fire emissions on interannual to multi-decadal timescales and their in-  This study also provides significant information on the recent state of fire model performance by evaluating the present-day estimates based on FireMIP fire models (also those used in the upcoming CMIP6).
(2019)  derived a dataset of fire duration from MODIS satellite observations, which provides a valuable dataset for developing parameterization of fire duration in global fire models. This study also identifies population density and LULCC as the primary uncertainty sources in fire emission estimates. Therefore, accurately modeling the responses to these remains a top priority for reducing uncertainty in historical reconstructions and future projections of fire emissions, especially given that modeling is the only way for future projections. For the response to changes in population density, many FireMIP models have not included the observed relationship between population density and fire spread (Table 2). Moreover,  Bistinas et al. (2014)  and  Parisien et al. (2016)  reported obvious spatial heterogeneity of the pop-ulation density-burned area relationship that is poorly represented in FireMIP models.
For the response to LULCC, improving the modeling of crop fires, pasture fires, deforestation and degradation fires, and human indirect effect on fires (e. g. , fragmentation of the landscape) and reducing the uncertainty in the interpretation of land use datasets in models are critical. Fire has been widely used in agricultural management during the harvesting, post-harvesting, or pre-planting periods  (Korontzi et al. , 2006; Magi et al. , 2012). Crop fire emissions are an important source of greenhouse gases and air pollutants  (Tian et al. , 2016; Wu et al. , 2017; Andreae, 2019). GFED4s reported that fires in croplands can contribute 5 % of burned area and 6 % of fire carbon emissions globally in the present day  (Randerson et al. , 2012; van der Werf et al. , 2017). In FireMIP, only CLM4.
Data availability. Data of FireMIP fire emissions are freely available from https://doi. org/10. 5281/zenodo. 3386620 (Li et al. , 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-12545-2019-supplement. Author contributions. FL contributed to the processing and analyses of the fire emission dataset. SS and AA designed the FireMIP experiments and LF, SH, GL, CY, DB, SM, MF, JM, and TH performed FireMIP simulations. MA compiled the EF table. JK, AD, CI, GvdW, and CW provided satellite-based and CMIP estimates of fire emissions. FL prepared the first draft of manuscript and revised it with contributions from MVM and other co-authors. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. We are grateful to  Robert J. Yokelson ,  Zhong-Da Lin ,  Samuel Levis ,  Silvia Kloster ,  Margreet J. E. van Marle ,  Ben Bond-Lamberty ,  Jennifer R. Marlon , and  Xu Yue  for helpful discussions. We also thank two anonymous reviewers for their valuable comments and suggestions, and the editor  Qiang Zhang  for handling this paper. Financial support. This research has been supported by the  National Key R&D Program of China  (grant nos. 2017YFA0604302  and  2017YFA0604804 ), the  National Natural Science Foundation of China  (grant nos. 41475099  and  41875137 ), and the  CAS Key Research Program of Frontier Sciences  (grant no. QYZDY-SSW-DQC002 ). Maria Val Martin  is supported by the  US Joint Fire Science Program  ( 13-1-01-4 ) and the  UK Leverhulme Trust  through a  Leverhulme Research Centre Award  ( RC-2015-029 ).
Almut Arneth  acknowledges support from the  Helmholtz Association , its  ATMO program , and the  Impulse and Networking fund , which funded initial FireMIP activities. Almut Arneth  and  Stijn Hantson  also acknowledge  EU   FP7  project  BAC-CHUS (603445 ). Gitta Lasslop  is funded by the  German Research Foundation  ( 338130981 ). Brian I. Magi  is supported by the  NSF  ( BCS-1436496 ). Charles Ichoku is supported by  NASA  ( NNH12ZDA001N-IDS ). Review statement. This paper was edited by  Qiang Zhang  and reviewed by  Douglas Hamilton  and two anonymous referees.
(2017b) emis. , present-day veg. dist. , FireMIP model sims. , emis. factor GCDv3 charcoal records, WMO visibility obs. Abbreviations: GFED4: Global Fire Emissions Dataset version 4; GFED4s: GFED4 with small fires; GFAS1. 2: Global Fire Assimilation System version 1. 2; FINN1. 5: Fire Inventory from NCAR version 1. 5; FRP: fire radiative power; FEER1: fire emissions from the Fire Energetics and Emissions Research version1; QFED2. 5: Quick Fire Emissions Dataset version 2. 5; AOD: aerosol optical depth; GFED2: GFED version 2; RETRO: REanalysis of the TROpospheric chemical composition; GICC: Global Inventory for Chemistry-Climate studies; GCDv3: Global Charcoal Database version 3.
Consequently, we need robust methodological approaches to understand the sequential temporal patterns in the use of complementary habitats in order to take appropriate conservation and management actions. Habitats provide the resources (e. g. food, cover, thermal protection) that species need for survival and reproduction  (Manly et al. 2002). European-level mapping products (e. g. Corine Landcover, Copernicus; see eea. europa. eu) are often used by movement ecologists to quantify such resources (e. g. forest cover) by linking these maps with GPS locations obtained from animal tracking projects. Improved spatiotemporal resolution and range of both remote sensing products and animal tracking datasets are allowing ecologists to derive ever more detailed animal trajectories annotated with habitat information, and, hence, facilitate the study of the animal-habitat relationship over time  (Cagnacci et al.
1b ), and individual-level simulation trees (Fig. 1c ). Simulation trees were therefore based on the true landscape context where each individual actually ranged. Finally, we combined real and simulated trajectories to produce trees where real and simulated habitat use sequences with a high degree of similarity were grouped together. After computing the proportions of simulated patterns in each cluster, we could define sequential habitat use cluster types and assign these identified tags to the real sequences included in that cluster (Fig. 1c/d ). Finally, we pruned the output tree and visualised only the classified real trajectories, to facilitate interpretation (Fig. 1d ). We now describe each step in detail. [Figure  1  near here] Real trajectories -exploratory trees (Fig. 1a ) We extracted roe deer trajectories from the EuroDEER database  (Cagnacci et al.
In IM-SAM, we propose a standardised procedure to identify the most robust and informative tree. For the trees based on real trajectories only (Fig. 1a ) we did not include any cutoff, since we used them for exploratory purposes only. For the clustering of simulated trajectories only, and both simulated and real trajectories in the final classification (Fig. 1c ), instead, we allowed all cut-off values (that correspond to distances from the last common node) that generated from a minimum of 2 to a maximum of 20 clusters per tree. For each cut-off value, we computed the Jaccard bootstrapping index for each cluster using bootstrap resampling of n number of sequences (BJ, or bootmean; see Hennig 2007, R-package fpc, clusterboot) using 1000 iterations  (De Groeve et al. 2016) , and then we computed the median BJ of all clusters (BJMD € [0, 1]), and the BJ interquartile range (BJIQ € [0, 1]).
For example, in the case of four habitat classes with different forest cover density (<25%, 25-50%, 50-75%, >75%), a forest cover density of <25% is more similar to the 25-50% category than to the >75% one. In the R package TraMineR such substitution weights can be based on automatic computation of transition rates, probability or user-defined  (Gabadinho et al. 2011)  (2018), offers interesting future avenues for more complex combinations of habitat (or contextual) variables. In IM-SAM, simulations of sequential habitat use patterns are performed in the individual reference area of use that were obtained with a simple geometric method (MCP 100 %). However, there is no limitation on using other methods to assess the area of use, for example, to overcome the sample size dependence of MCP  (Spencer et al.
Acknowledgement We thank  Fondazione Edmund Mach and Special Research Fund (BOF) of Ghent University  for their financial support to JDG. FC was supported by the  Sarah  and  Daniel Hrdy Fellowship  during part of the development of this ms. We wish to thank the  EuroDEER collaborative  project to provide geospatial and environmental data (paper no. 009  of the EuroDEER series; www. euroungulates. org). The EuroDEER spatial database is hosted by  Fondazione Edmund Mach. We are grateful to the  Copernicus  project for the use of free data (European Union, EEA - European Environment Agency). We are particularly grateful to  Ferdinando Urbano  for his support with data management, and to  Lino Ometto  for precious advice in data exploration. The GPS data collection of the  Fondazione Edmund Mach  was supported by the  Autonomous Province of Trento  under grant number  3479  to FC ( BECOCERWI-Behavioral Ecology of Cervids in Relation to Wildlife Infections ), and by the help of the Wildlife and  Forest Service of the Autonomous Province of Trento  and the  Hunting Association of Trento Province (ACT).
82%) and 503,985 true GPS locations (97. 18%), of which 273,230 (52. 69%) were classified as open and 230,755 (44. 50%) as closed habitat. See Appendix S2 for the complete data preparation procedure. Cover Density 2012 (TCD, EEA 2012, 20m spatial resolution), thus obtaining the biweekly sequences of habitat use. We reclassified TCD into two distinct classes, using a cut-off point for pixel-level tree cover density of 50%, distinguishing closed (C, ≥50%) and open (O, <50%) habitats. The final dataset consisted of 5402 biweekly habitat use sequences of 404 animals (236 females and 168 males) from nine European roe deer populations characterised by varying forest composition (see map in Appendix S1: Southcentral Norway, NK5; Southwest France, FR8; Switzerland, CH25; Southern Germany, DE15, DE31; Southeast Germany, DE1; Northern Italy, IT1, IT24; Eastern Austria, AU17).
Gonad development Twelve individuals per replicate were randomly sampled in July 2012 (n = 36 individuals per treatment). This corresponds to the beginning of the spawning period in H. tuberculata  (Girard, 1972). For each individual, the muscle was separated from the gonad-digestive gland (GDG comprising crop, stomach, spiral cecum and gonad) and from the rest of the soft tissues (head, gills, heart, mantle, hypobranchial glands, anus, and intestine) by the same experimenter to reduce dissection variability. Total soft tissue (weighed before muscle separation), muscle, GDG, the rest of the soft tissues and shell were weighed to the nearest 0. 01 g, just after dissection. Shell length was measured to the nearest 0. 5 mm. Reproduction data were averaged per replicate (compartment) for analysis.
A C C E P T E D M A N U S C R I P T A visual gonad index (VGI) ranked from 0 to 10 based on the progression of the early stages of gonad development was directly scored after dissection. Photographs were taken in order to verify direct observation afterward. The scores were adapted from the criteria defined by  McAveney et al. (2004). This fine scale VGI allows good estimates of the error variance in the data and is adapted to distinguish between samples at the early stages of gonad development. Because of the extensive reticulation with the gut and small size of the GDG, the gonads could not be separated from the gut during dissection. In order to determine the relative proportion of gonads from the conical appendage, the GDG was sectioned across the midpoint of the conical appendage. The samples were fixed in Davidson solution for 24 h and then kept in 70 % alcohol.
Data analysis Data are represented as means and standard error (SE). Statistical analysis was performed with R version 3. 5. 1 software. To study the effect of period and algae species on chemical algal composition, an analysis of variance was used, with algae (B, D, E, G, H, L, P and U) and period (t0-2, t2-4, t4-6, t6-8, t8-11 for dry matter content, degradation and carbohydrate analysis; t0, t4, t8, for protein analysis; period was not included in the model for lipid because only one period analysis), and the interaction between algae and period as fixed factors. When normal distribution of the residuals and The seasonal effect of algal diet on the two-month daily weight gain and daily length gain was analysed by a linear mixed effects analysis using the lmerTest package  (Bates et al. , 2012 ) and using the method described by Winter (2013).
Square root transformation was used to homogenise variances between the treatments for DGW. The model included the period (t0-2, t2-4, t4-6, t6-8, t8-11), the 6 monospecific algal diets (D, G, H, L, P and U), and an interaction between period and diet as fixed effects. The cages were indicated as a random factor (18 cages). B and E diets could not be included in the model due to missing data for the last period. In addition, because M diet was composed of algae in different proportion at each season, it was not included in the model. For the post-hoc analysis, the difflsmeans package was used. It calculated differences of Least Squares Means for the factors of lmer mixed effects model and used the Satterthwaite's approximation to degrees of freedom.
A C C E P T E D M A N U S C R I P T Multiple linear regression analysis was not performed because normality of the residues, and linearity between dependent variables and algal composition variables could not be fulfilled as well as the low number of observations per predictor  (Quinn and Keough, 2002). Instead, a descriptive Principal Component Analysis (PCA) was performed with the package FactoMineR  (Lé et al. , 2008). All the variables were reduced and scaled. Because the proportion of algae eaten was not measured in the mixed diet, the PCA was performed only on the 8 monospecific diets, using the two-month daily weight gain and daily length gain (DGW and DGL for t0-2, t2-4, t4-6, t6-8, t8-11), and the biochemical algal analysis corresponding to the same period. Because it would have been too difficult to interpret with the large numbers of variables, a second PCA was performed with the same variables to study relationship between growth and amino-acid free and total composition.
Zero air measurements generated with the zero air generator were statistically not significantly different from those achieved by a bottle. To correctly account for the photomultiplier background and chemical interferences due to reactions of ozone with ambient alkenes additional pre-chamber measurements were performed every 5 minutes as well as at the beginning of zero air measurements and calibrations for 25 s each. This correction is removing a large fraction of the interference signal from alkenes. However, in regions where alkene concentrations are notably high, the CLD is prone to enhanced backgrounds. A schematic setup of the two-channel CLD instrument is given in Figure  2. The total measurement uncertainty (TMU) in the NO data is 5. 5 % at a 5 min integration time and a confidence level of 1.
The limit of detection in the NO channel was estimated as the full width at half maximum of the frequency distribution of all zero air measurements obtained during the campaign to be 9 ppt v at a 5 min integration time and a confidence level of 1. The TMU in the NO 2 data is 7 % ± 112 ppt v at a confidence level of 1 and an integration time of 5 min. As the zero air measurements in the NO 2 channel produced an increased background affected by memory effects after exposure to high NO x levels e. g. during measurements of stack emissions, the NO 2 raw data were initially processed without converter background subtraction. As we therefore expect the CLD NO 2 data to be offset due to not being initially background corrected, the converter background was estimated at 112 ppt v from the centre of a Gaussian fit representing the difference of 1-minute averaged CLD NO 2 and concurrent cavity ring-down spectroscopy (CRDS) NO 2 measurements for data points below 10 ppb v as we expect the NO 2 CLD to be offset due to not being background corrected.
Setting the threshold for calculating the difference of the two concurrent data sets to 10 ppb v is somewhat arbitrary, however, changing this limit to 5 ppb v or 20 ppb v does not significantly vary the estimated offset of the CLD NO 2 data. The offset correction of 112 ppt v was taken as the ultimate absolute measurement uncertainty of the CLD NO 2 measurement. Further corrections of to the final CLD data include residence time corrections as well as corrections for NO and O 3 losses and the subsequent formation of NO 2 in the sampling line  (Ryerson et al. , 2000). Both NO and NO 2 CLD data have also been corrected for nonlinearities for concentrations higher than 55 ppb v , as experienced during probing of stack emissions.
Further measurements used in this study An extensive set of concurrent measurements providing mixing ratios of O 3 , NO 2 , HCHO, OH, HO 2 , absolute humidity and actinic flux, temperature and pressure data obtained during AQABA was used in this study. Ozone was measured with an absorption photometer (Model 202 Ozone Monitor, 2B Technologies, Boulder, Colorado) based on the well-established absorption of the mercury line in the Hartley band at 254 nm  (Viallon et al. , 2015). Eliminating water and particle interferences during sampling was achieved via sampling through a nafion tube and a Teflon filter. The ozone monitor was zeroed ten times during the campaign. NO 2 was further measured by cavity ring-down spectroscopy  (Sobanski et al. , 2016)  and used for correcting the instrumental background of the CLD NO 2 data, as described above (the correction was taken as the ultimate absolute measurement uncertainty in the CLD NO 2 data).
Note that in this study we will use the NO 2 CLD data rather than the NO 2 CRDS data as the temporal coverage of the CLD NO 2 data over the course of the campaign is about 60 % compared to about 35 % for the cavity ring-down measurement. Formaldehyde (HCHO) was measured with an Aerolaser 4021 (AERO-LASER GmbH, Garmisch-Partenkirchen, Germany), which is a fully automatized monitor based on the Hantzsch technique  (Kormann et al. , 2003). H 2 O measurements were obtained using a cavity ring-down spectroscopy monitor (PICARRO G2401, Santa Clara, California) supervised by Laboratoire des Sciences du Climat et de l'Environnement (LSCE)  (Kwok et al. , 2015). Measurements of OH and HO 2 were performed with the custom-built HydrOxyl Radical measurement Unit based on fluorescence Spectroscopy (HORUS) instrument based on laser-induced fluorescence (LIF) spectroscopy of the OH molecule and NO titration of HO 2 to OH followed by LIF spectroscopy detection of the OH molecule  (Martinez et al.
, 2010; Regelin et al. , 2013). HO 2 data used in this study is still preliminary due to not yet quantified interference of organic peroxy radicals RO 2. This correction is expected to reduce the final HO 2 concentrations by less than 20 % around noontime, which will not significantly change the results presented here. Wavelength resolved down-welling actinic flux was measured with a spectral radiometer (model CCD Spectroradiometer 85237). The j-values for NO 2 and O 3 were not corrected for upwelling UV radiation and were estimated to have a ~ 10 % measurement uncertainty  (Meusel et al. , 2016). The radiometer was installed 10 m above sea level, respectively 5 m above the front deck surface. Decreases in sensitivity due to sensor contamination with e. g. sea-spray were corrected with a linear interpolation between two (daily) cleaning events.
, 2002). After sampling a fresh emission e. g. a ship plume, for which NO x went up typically to values of several tens of ppb v with simultaneous titration in O 3 , we assume that PSS is re-established on a time scale of 2 minutes  (Thornton et al. , 2002; Mannschreck et al. , 2004). To best approximate PSS in our analysis we have restricted the estimation of RO 2 on time frames ± 2 h around noontime for which we expect the smallest relative changes in the actinic flux. Noontime for each day was determined as the centre of a Gaussian fit that was applied to the actinic flux data. We applied a Gaussian Fit to the actinic flux data as this fitting method is sufficient to estimate the centre of the diurnal actinic flux. To further limit the effect of periods for which PSS is not fulfilled, we use the median instead of the average that is often disproportionately biased by strong NO x sources nearby.