dataesr/scientific-paragraphs-categorization huggingface dataset:
We defined traits for all arthropod species or families according to the availability of information on separate taxa and to dataset resolution (Appendix S1, Table  S2 ; 36 out of 58 datasets provided species-level identification). We used hierarchical cluster regression to identify parsimonious combinations of shared traits for organisms with shared responses to landscape filters (Appendix S1;  Kleyer et al. 2012). These combinations are defined as trait syndromes characterizing different responses of species groups to the environment (see Introduction). As trait syndromes may vary according to the functional group  (Lavorel & Garnier 2002) , we identified them separately for pollinators, natural enemies and pests (Figs. S3, S4 ). Trait syndromes are defined parsimoniously based on one or a few trait combinations.
Statistical analyses We calculated arthropod abundance in each site at three nested levels of community structure (all arthropods; pollinators, enemies and pests; trait syndromes within functional groups; Appendix S1). Pest control, pollination and yields were available from a subset of studies (Table  S3 ). For this subset, we calculated an ecosystem service index representing the amount of service provided (Appendix S1). We analyzed effects of landscape predictors on arthropod abundance and services using linear mixed effects models in R package lme4 v. 1. 1-15 (Bates et al. 2015). We focused on abundance because it has been found to drive ecosystem service provision  (Winfree et al. 2015). However, abundance and species richness were positively related across groups (estimates of linear mixed models relating richness to abundance using ln(x+1)-transformed data, with random intercept for study and year: 0.
ACKNOWLEDGEMENTS We thank all farmers, field and technical assistants, researchers and funders who contributed to the studies made available for this synthesis. F. Bötzl  and  L. Pfiffner  provided expertise and data on carabid traits. M. O'Rourke  provided expertise on pest traits. A. Kappes ,  S. König  and  D. Senapathi  provided technical support. We thank all members of the  Socio-Environmental Synthesis Center working group  on 'Decision-making tools for pest control' led by  D. Karp  and  B. Chaplin-Kramer  for fruitful discussions in the process of creating this paper. We are grateful to three anonymous reviewers and to the editor for constructive comments on a previous version of the manuscript. Funding was provided by the  European Union  to the  FP7  project  LIBERATION  (grant  311781 ) and by the 2013-2014.
Hydrographic data At each hydrographic station, seawater collection and vertical profiles of water column properties were made from an array of 24, 12-L bottles mounted on a rosette equipped with a Seabird SBE-911 plus CTD unit, which also included a Seapoint SCF Fluorometer for detection of chlorophyll a (Chl a). Close to the rapidly melting marginal ice zone, density profiles often did not exhibit a well-mixed layer nor a clear density step below; hence we calculated an equivalent mixed layer (hBD) as defined in  Randelhoff et al. (2017) , used for strongly meltwater-influenced layers in the marginal ice zone (MIZ). Also in accordance with  Randelhoff et al. (2019) , we defined the depth of the euphotic zone (Ze) as the depth where a daily irradiance of photosynthetically available radiation (PAR) of 0.
415 Einstein m -2 d -1 was measured. This value delimits the lower extent of the vertical phytoplankton distribution in the north Pacific subtropical gyre  (Letelier et al. , 2004). Based on Green Edge data,  Randelhoff et al. (2019)  showed that Ze estimated using the 0. 415 Einstein m -2 d -1 criteria was not statistically different from Ze estimated using the more widely-used 1% surface PAR criteria. Vertical profiles of instantaneous downwelling irradiance were measured to a depth of 100 m using a Compact Optical Profiling System (C-OPS, Biospherical Instruments, Inc. ). Daily PAR just above the sea surface was estimated using both in situ data recorded on the ship's meteorological tower and the atmospheric radiative transfer model SBDART  (Ricchiazzi et al. , 1998). More information about light transmittance in the water column and its measurement methodology (in particular for sea ice-covered stations) can be found in  Randelhoff et al.
Sea ice satellite data Sea ice concentrations (SIC) have been extracted from AMSR2 radiometer daily data on a 3. 125 km grid  (Beitsch et al. , 2014). At each station, the SIC was extracted from the closest pixel using the Euclidian distance between the ship position and the centre of each pixel. The SIC is an index that ranges between 0 (complete open water) and 1 (complete ice cover). Here, we placed the ice-edge limit at stations with SIC = 0. 5. Based on SIC history, we also retrieved the number of days between the sampling day and the day at which SIC dropped below 0. 5 (DOW50) to estimate the timing of the melting of sea ice. DOW50 can take positive or negative values, depending if SIC at the time of sampling is >0. 5 (DOW50 negative) or <0. 5 (DOW50 positive).
Trait-based analysis A trait-based analysis was performed following  Fragoso et al. (2018)  to identify functional traits that determine species ecological niches (data used are listed in Table  S2 ). Briefly, eight diatom traits were selected according to their ecological relevance and availability of information in the literature: equivalent spherical diameter (ESD); surface area to volume ratio (S/V); optimum temperature for growth (T opt ); ability to produce ice-binding proteins (IBP); the presence or absence of long, sharp projections such as setae, spines, horns and cellular processes (spikes); degree of silicification, qualitatively based on examination of SEM images (silicified); the propensity to form resting spores (spores); and the ability to form colonies (colonies). More detailed description of the traits can be found in  Fragoso et al.
Acknowledgements The project is conducted under the scientific coordination of the  Canada Excellence Research Chair on Remote sensing of Canada's new Arctic  frontier and the  CNRS & Université Laval Takuvik Joint International Laboratory  ( UMI3376 ). We thank officers and crew of  CCGS Amundsen ,  Marie-Hélène Forget  and  Joannie Ferland  for organizing the fieldwork, and all other scientists and technicians involved in the  Green Edge campaign  for their contributions to fieldwork and data collection. They include in particular Pascal Guillot and the  Amundsen Science Data team  for the CTD data processing and quality control (UQAR-ISMER). Tonya Burgers  and  Brent Else  kindly provided the shipboard PAR measurements. Sandrine Hillion performed the 32 Si incubation experiments. We thank  I.
This in turn leads to a better understanding and reassessment of CloudSat uncertainties. We compared a total of four precipitation events, two per station, when CloudSat overpassed within 10 km of the station and we compared these two different datasets at each vertical level. The correlation between both datasets is near-perfect, even though climatic and ge-ographic conditions are different for the two stations. Using different CloudSat and MRR vertical levels, we obtain 10 km space-scale and short-timescale (a few seconds) CloudSat uncertainties from -13 % up to +22 %. This confirms the robustness of the CloudSat retrievals of snowfall over Antarctica above the blind zone and justifies further analyses of this dataset.
For this study, the MRR2 data used are processed with the Maahn and Kollias (2012) algorithm. Unlike Souverijns et al. (2017), we did not calibrate the ground radar dataset with CloudSat reflectivities (1) because we want an independent evaluation of the CloudSat CPR dataset and (2) because we do not consider surface precipitation rate comparisons. The mean precipitation profiles obtained over the MRR observation periods (2015-2016 for the DDU station and 2012 for the PE station) were also used to evaluate how typical the four precipitation events are  (Durán-Alarcón et al. , 2019). They are obtained using the same Z e /S r relationships as the ones introduced earlier (see Eqs. 1 and 2) and are separated into quantiles. According to  Maahn and Kollias (2012) , the minimum detection of both MRRs varies between -14 and -8 dBZ, corresponding to 0.
According to Fig. 3b , there is a poor concordance between the two datasets for low snowfall rate values. The MRR recorded low-level strong values until a null signal of precipitation from 1000 m upward, where CloudSat still recorded small but significant rates. An inversion of the precipitation   (Haran et al. , 2005). (b, e, h, k) The second column shows wind velocities (blue solid line) and wind directions (0 • indicating from the north) (red solid line) over the stations gathered with radiosoundings, except for panel (h), which is obtained with ERA-Interim. (c, f, i, l) The third column shows air temperatures (red solid line) and relative humidities with respect to ice (blue solid line) over the stations obtained with radiosoundings, except for panel (i), which is deduced from ERA-Interim.
Agreement between CloudSat and MRR datasets Figure  4  represents the correlation for (all data, all levels) CloudSat and MRR precipitation reports for the four events using the error bars shown in Fig. 3. Error bars for the MRRs are implemented by using the confidence intervals obtained with the Z e /S r relations. Large error bars correspond to the PE station's MRR and smaller ones represent the DDU stations's MRR confidence interval. CloudSat error bars represent the variance of measurements collected along the swath. A linear regression fit between CloudSat and MRRs is performed and shows a good correlation between both datasets.
Calculation of the CloudSat uncertainties The CloudSat 2C-SNOW-PROFILE product already contains its own uncertainties estimates, calculated from hypothetical parameters such as the mass-diameter distribution of the hydrometeors, their microphysical and scattering properties. Our analysis suggests that under Antarctic (and probably polar) conditions, this uncertainty can be significantly reduced. By assuming that CloudSat and MRR snowfall rates datasets follow a normally distributed deviation from the mean, a correlation coefficient is calculated in order to establish the degree of similarity between both observations. By using the covariance of both data records, we found a correlation coefficient of 0. 99, which confirms a very good agreement between both radar data (see Appendix). For each CloudSat vertical bin, we calculated the distance of satellite measurement to the corresponding interpolated MRR observation.
Conclusion CloudSat remote-sensing observations were compared with two in situ micro-rain radars at the coastal French Dumont d'Urville and mountainous Belgian Princess Elisabeth stations in East Antarctica. The comparison of four cases of precipitation that coincide with CloudSat observations shows a near-perfect correlation. This comparison also reveals a difference in the CloudSat dataset with respect to the MRR for very light precipitation. This might be precipitable cloud water recorded by CloudSat or an MRR limitation due to a strong attenuation of the signal through important precipitation. From our correlation and statistical studies based on the quantification of the CloudSat deviation to the MRR values, we assessed new CloudSat precipitation uncertainties ranging from -13 % to +22 % based on this short-time and small space-scale study.
This new assessment of the Cloud-Sat uncertainties, in spite of the limited number of events, provides confidence in the retrieval given the different climatic and geographical conditions of the two stations. It also justifies further analysis of this dataset in this region of the globe, where snowfall is critical and poorly known. Subsequent studies using weak precipitation rates profiles over other Antarctic regions, particularly in the interior of the continent, will strengthen the robustness of this new range of uncertainties and corroborate the difference recorded by both CPR and MRRs. Moreover, the Earth Cloud Aerosol and Radiation Explorer (EarthCare) spaceborne radar, with a much better vertical resolution, should be even more instructive and improve our understanding of clouds and snowfall in the polar regions, where field observations are so hard to perform.
Acknowledgements. This work was supported by the  French National Research Agency  (grant number:  ANR-15-CE01-0003 ). This work was supported by the  Belgian Science Policy Office (BELSPO ; grant number  BR/143/A2/AEROCLOUD ) and the  Research Foundation Flanders (FWO ; grant number  G0C2215N ). Irina V. Gorodetskaya thanks the following for financial support:  CESAM  ( UID/AMB/50017/2019 ), the  FCT/MEC  (through national funds) and co-funding by  FEDER  (within the  PT2020 Partnership Agreement and Compete 2020 ). The authors thank  Jacopo Grazioli  for his help and advice during the review period and  Anna-Lea Albright  as well as  Max Popp  for their proofreading. Edited by: Florent Dominé Reviewed by: two anonymous referees.
Data availability. Data from the micro-rain radar at Dumont d'Urville station were obtained with the logistical support of the French Polar institute (IPEV; program CALVA) and are available at https://doi. org/10. 1594/PANGAEA. 882565 (Berne et al. , 2017). CloudSat data are freely available via the CloudSat Data Processing Center (http://www. cloudsat. cira. colostate. edu/, last access: 11 January 2018). Data from the micro-rain radar at the Princess Elisabeth station can be obtained at http://www. aerocloud. be (last access: 11 January 2018).
2017 ) as reference dust model. In this paper, we obtain a database of metallicity measurements and combine these with the rich DustPedia dataset. We studied the global dust and metal scaling relations and improve the observational constraints (466 galaxies) on the evolution of the dust-to-metal ratio. These measurements will be key for constraining chemical and dust evolution models, and the role of dust grain growth in particular. Section 2 is dedicated to our literature compilation of emission line fluxes and extraction of spectrophotometry of archival data from the Multi Unit Spectroscopic Explorer (MUSE;  Bacon et al. 2010 ) instrument at the ESO VLT telescope. In Section 3, we describe how we used these line fluxes to derive metallicities and combine measurements for individual regions into a global metallicity.
bined into a global metallicity in Section 3. 4. IFUs are closely packed bundles of fibres that allow to perform a resolved study of the gas-phase metallicities in galaxies and is the preferred method when available. To extend our sample of sources with resolved metallicities, we supplemented the literature IFU data with MUSE data from the ESO archive, as described in Section 2. 2. The emission lines of galaxies are attenuated both by internal and external dust. To account for this, the emission line intensities are corrected, first for Galactic extinction 1 and then using the Balmer decrement given by C(Hβ) = log Hα Hβ theor -log Hα Hβ obs 0. 4 × [(k(λ Hα ) -k(λ Hβ )] (1) where k(λ) = A λ /E(B -V) is the reddening curve for the diffuse ISM version of the THEMIS  (Köhler et al. 2014; Jones et al.
MUSE spectrophotometry With its 0. 2 arcsec pixel scale, spectral range of 4750 -9350 Å, spectral sampling of 1. 25 Å (R = 1770 -3590), and field of view of 1 arcmin × 1 arcmin, MUSE provides the most highresolution IFU observations to date. This enables resolved studies of the dust-to-gas and dust-to-metal ratio (at a resolution set by the dust maps; e. g. this work), as well as resolved attenuation studies through Balmer decrements and in the continuum (e. g. Viaene et al. 2017) , and studies of the gas and stellar kinematics (e. g. Guérou et al. 2017). This wealth of data will undoubtedly be of use in future DustPedia papers and we have thus supplemented our literature fluxes by MUSE spectroscopy. By querying the ESO archive 2  , we found 79 of the DustPedia galaxies have public MUSE data available, often with multiple data cubes for the same galaxy.
After downloading the data cubes we inspected the astrometry, which revealed in some cases it was offset by up to 12 arcsec. We fixed the astrometry using GAIA 3 and the NOMAD  (Zacharias et al. 2004 ) and 2MASS  (Skrutskie et al. 2006 ) point source catalogues. Once the astrometry was corrected, we masked the stars in the images using an adapted version of PTS 4 (Verstocken et al. in  prep. ). The NOMAD and 2MASS point source catalogues were again used to identify the stars and the radii of the masked regions were determined using a curve-of-growth technique. This method also erroneously masks bright Hii regions, which are often included in the point source catalogues, and we thus use an additional criterion to only mask the stars. If the Hα emission is clearly detected (5σ) and its peak flux is more than twice as bright as the stellar continuum, we identify this region as an Hii region and the pixels are not masked.
These masking limits are somewhat arbitrary, but perform well upon visual inspection. Additionally, a small amount of stellar contamination will barely affect the final fluxes, and our method is thus robust to moderate changes to the masking limits. Given our focus on the dust-metal interplay for which our sub-sample of DustPedia galaxies with MUSE (and Herschel) data is uniquely suited, it is of little use to have metallicities to much better resolution than the dust maps. Consequently, we bin all the MUSE pixels to have the same pixel size and positions as the Herschel SPIRE 250 µm maps. The SPIRE 250 µm pixel size is 6 arcsec. We will thus bin 900 MUSE pixels for each Herschel pixel and determine the mean flux and standard deviation in each binned pixel. Binning 900 pixels increases the signal-tonoise ratio of the MUSE spectra by a factor 30.
GANDALF line fitting Emission lines were measured by running each spectrum through a modified (as detailed below) version of the Gas AND Absorption Line Fitting algorithm (GANDALF 6  ;  Sarzi et al. 2006). GANDALF simultaneously fits the emission and absorption lines and is designed to separate the relative contribution of the stellar continuum and of the nebular emission in the spectra of nearby galaxies, while measuring the gas emission and kinematics. GANDALF uses a combination of stellar templates based on the MILES stellar library  (Sánchez-Blázquez et al. 2006)  to describe the stellar continuum and the pPXF code of  Cappellari & Emsellem (2004)  to derive the stellar kinematics. For each MUSE data-cube, GANDALF is first run on the full spectrum (i. e. averaging all the binned pixels in the cube).
The stellar templates that had a weight of 2% or higher are then stored and only these templates are used when fitting the spectra of each binned pixel. This significantly speeds up the fitting, as well as ensuring that nearby regions in the galaxy have somewhat similar stellar populations. Using only these templates still results in excellent fits to the data for both pPXF and GANDALF (Figure  1 ; bottom). The present version of GANDALF (v1. 5) includes an uncertainty determination on the fluxes as well as reddening by interstellar dust using a  Calzetti et al. (2000)  dust model (using Hα/Hβ = 2. 86 as in Section 2. 1). However, in order to allow consistent comparison with the dust emission and other dust properties within the DustPedia framework, we instead use the reddening curve from the THEMIS dust model.
DustPedia Dust masses, stellar masses and star formation rates (SFR) for the DustPedia galaxies will be presented in  Nersesian et al. (in prep. ). These results are derived from the aperture-matched DustPedia Photometry presented in  Clark et al. (2018)  using the SED fitting package CIGALE  (Noll et al. 2009). CIGALE creates a library of SED templates for which the energy balance between the energy absorbed by dust in the UV-optical and the energy re-emitted in the infrared is maintained. This library is build 9 When the 'universal prior' is used, the comparison with other samples is less good (Section 6. 1). assuming a delayed and truncated SF history  (Ciesla et al. 2016)  along with stellar emission from the stellar population models of  Bruzual & Charlot (2003)   To obtain Hi masses for the DustPedia sample we have performed a literature compilation (see also  Casasola et al.
The inflows and outflows of gas are simple parametrisations proportional to the SFR. The inflows in this work are pristine gas and the inflow term in Equation 10 thus becomes zero. The outflows drive out dust at a rate which is the product of the current dust-to-gas ratio and the gas outflow rate. In this work we compare the DustPedia observations to three models from DV17b. The parameters used for these three models are given in Table  2. The aim here is not to find the best model or to constrain the dust evolution parameters, but rather to put our DustPedia observations into context and to provide a basic interpretation for the observed trends. The DustPedia data will be used in future work to constrain a next generation of chemical evolution models. Model I is the simplest model, with only stellar source of dust and no inflows or outflows.
Radial gradients Before studying the global DustPedia metallicities, we investigated in this section whether the gradients within our well-Fig. 5. Radial metallicity gradients plotted against stellar mass (top left), the Hi-to-stellar mass ratio (top right), the effective radius from a single Sérsic model fit  (Mosenkov et al. 2018 ) in kpc (bottom left) and the dust-to-stellar mass ratio (bottom right). The DustPedia sample is divided in LTG (green) and ETG (magenta). We find weak correlations. sampled galaxies correlate with any other galaxy properties. The average gradients and standard deviations for each calibration are given in Table  3. There are significant differences between the different calibrations (see Section 6. 1). In Figure  5  (left), we have plotted how the gradients depend on stellar mass for all the sources in the well constrained sub-sample (at least five data points covering a range of radii at least 0.
We find this relation results in slightly higher dust-to-gas ratios than the average for our sample. Some of this offset might be due to the use of a different metallicity calibration 13. The Rémy-Ruyer et al. (  2014 ) trend traces the DGS data at low metallicity, yet the observed DustPedia dust-to-gas ratios are lower in general. Our more complete sample of Dust-Pedia galaxies is thus key to properly constrain models of the dust and chemical evolution of galaxies. We have fitted a power law and broken power law to the DustPedia 14  LTGs for each of the metallicity calibrations using orthogonal distance regression (taking into account uncertainties on both the dust-to-gas ratios and metallicities). ETGs have not been included as dust destruction by hot gas sputtering might lower their dust-to-gas ratio (see also Figure  14  in Section 6.
Dust-to-metal ratio Our sample is the largest sample of galaxies for which the dust and metal content are measured consistently over all galaxies. When we combine DustPedia with the 73 galaxies in our comparison samples, we arrive at a sample of 539 sources (compared to 382 for the combined sample in DV17b). In addition to the better statistics and better consistency across samples, our sample has made use of the resolved information in these galaxies to arrive at a better estimate of the global estimate. Our data has revealed a more gradually and continuously evolving dust-tometal ratio, rather than two separate regimes for high and low metallicity galaxies (as in  Rémy-Ruyer et al. 2014, and DV17). The more gradual increase is attributed to different regions in the galaxy reaching the critical metallicity at different times.