dataesr/scientific-paragraphs-categorization huggingface dataset:
Firstly, the field plots were not inventoried at the same time as the ALS acquisitions, but rather over a period of five years. A period of several years was necessary to carry out enough field inventories. Potential bias could be avoided by assigning a weight to each individual plot depending on the time interval between the field measurements and ALS data acquisition. Secondly, pulse density and plot positioning may affect ALS variable estimations to an extent that depends on stand type  [57, 58]. The ALS variables used, e. g. height variables (H median and H mean ), and variables derived from the raster canopy height model (Gap max or C f ), are likely to show low sensitivity to pulse density  [59]. Positioning errors lead to discrepancies between the trees considered in a plot and those that are actually measured for ALS data, thus potentially reducing the capacity of ALS variables to reliably describe the vegetation structure within a given field plot.
We expect a smaller impact of these discrepancies as the radius used to compute ALS variables increases. Plot size might partly explain that our tests were inconclusive for all but three models on the Lowland site when we considered only the variables computed with the same radius as the field plots, i. e. 9 m. Thirdly, the number of ALS variables tested in our study was limited since it was impossible to test several variables simultaneously in the Bayesian models we developed. Each test was also quite time-consuming. Wider ALS surveys with datasets more suited to Bayesian models would allow us to extend our approach. Furthermore, we recognize that our approach is limited in two respects. Firstly, for numerical reasons, we used a single value for abiotic variables, and did not include all the posterior distributions of the first model.
Conclusion Biodiversity monitoring and conservation are essential components of sustainable management in forest ecosystems, but they require progress in biodiversity modelling to facilitate the practical implementation of specific guidelines. Forest structure is known to play a major role in ecology by affecting e. g. micro-climate and habitat quality. In this study, we used a Bayesian approach, which is suitable for modelling the complex links between biodiversity and environmental conditions, in order to investigate the ability of ALS data to characterize forest structure and improve floristic biodiversity models. We assessed model improvements depending on: the study site, the biodiversity indicator predicted, the component of forest structure described by the ALS variables, and the radius used to estimate ALS variables.
Floristic biodiversity was assessed in the field using abundance and richness indices. This study highlighted the relevance of ALS data in quantifying forest structural characteristics in order to monitor floristic biodiversity. Shade-tolerant and heliophilous species richness were impacted by indicators linked to light penetration through the vegetation cover, with a corresponding reverse trend observed for the two groups. Our results also highlight the importance of being able to measure vegetation structure over an area beyond the plot on which the biodiversity is observed; neighbouring structure helped us explain local biodiversity. The use of ALS data resulted in several original findings; for example, shade-tolerant species might behave like forest interior species by avoiding the vicinity of large gaps.
Acknowledgments The authors thank the  Agence nationale pour la gestion des dechets radioactifs (Andra)  for providing the ALS dataset for the lowland site. We also thank the  Institut National de l'Information Geographique et Forestière (IGN) , the  Institut des sciences et industries du vivant et de l'environnement (AgroParisTech) , and the  Institut National de la Recherche Agronomique (INRA)  for providing field inventories. This study would not have been possible without the availability of ALS data on the Mountain site from the  FORESEE  project ( ANR-2010-BIOE-008 ), supported by the  French National Research Agency (ANR).
(2011a)  to be 0. 284 for ammonium, 0. 148 for organic matter, 0. 024 for sulfate, 0. 012 for nitrate and 0. 011 for chloride. It must be noted, however, that the uncertainties of mass concentrations with aerosol mass spectrometers are estimated at 20-35 % (2σ ) for the total mass  (Bahreini et al. , 2009). Furthermore,  Crenn et al. (2015)  reported reproducibility-related uncertainties of Q-ACSM concentration measurements of 9, 15, 19, 28 and 36 % for NR PM 1 , nitrate, organic matter, sulfate and ammonium, respectively, during an intercomparison that involved 13 Q-ACSMs in the Paris area during springtime. In addition to the relative ion transmission efficiency correction applied using a naphthalene internal standard, the collection efficiency (CE) due to particle losses induced by an incomplete vaporization and/or transmission through the aerodynamic lens was also determined for the whole dataset.
Those parameters are mainly influenced by particle shape (size, sphericity) and acidity, ammonium nitrate fraction and RH in the sampling line. Middlebrook et al. (2012)  have developed a correction algorithm based on AMS datasets which is applied on ACSM mass concentrations and which considers both RH and aerosol chemical composition to obtain a time-dependent correction of CE values ranging from 0. 45 to 0. 83 (Fig. S1f ). Nonetheless, due to the presence of the Nafion dryer at the entrance of the instrument, no RH corrections were necessary as RH values were below 30 %. A minor fraction (3. 2 %) of the data was excluded from the dataset due to unstable pa-rameters, which were generally observed after restarting the instrument following power outages.
(2008)  to deconvolve BC from fossil fuel and biomass burning, cannot be used in our conditions. The m/z 60 signal (C 2 H 4 O + 2 , fragment characteristic of levoglucosan from biomass burning) was mostly absent in the ACSM dataset during IOP-1, representing on average only 0. 003 (0. 007 and 0. 012 for the 95th and 99th percentiles, respectively; see Fig. S9 ) of the total OM, which is identical to the threshold value of 0. 3 % suggested by  Cubison et al. (2011)  to assess the presence of BB aerosols. This point is discussed further in the source apportionment results, in which we tend to attribute m/z 60 emissions to other sources. A few data points (less than 1 % of the dataset), under the influence of other combustion processes from specific local ac-tivities that may cause a bias in the deconvolution algorithm, have been excluded to derive Fe concentrations (see further discussion in Sect.
Geographical origins of air masses and chemical species Air masses reaching the site were characterized through 48 h back trajectories (every 3 h) retrieved from the computer version of the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT;  Draxler and Hess, 1998) , for an altitude set at one half of the mixed-layer depth and coupled with the GDAS (1 • ) meteorological database. Note that sea breeze phenomena, which occur on short timescales and spatial scales, cannot be satisfactorily reproduced by this type of model. However, given the dynamics of sea breezes, only back trajectories arriving on-site at 15:00 LT and eventually at 18:00 LT during sea breeze days (< 8. 2 %) could not be representative of the ground dynamic observations. Therefore all the back trajectories available for IOP-1 were kept and could be statistically grouped into clusters according to the variation of the total spatial variance, for the whole period and also by day type.
We also used pollution roses to identify local wind directions leading to high concentrations for each species or PMF factors, but also two additional tools provided by the ZeFir Igor-based package developed by  Petit et al. (2017) : (i) nonparametric regression (NWR,  Henry et al. , 2009)  plots, which combine smoothed surface concentrations and local wind speed and direction, to discriminate between local and more distant or regional sources; (ii) potential source contribution function (PSCF,  Polissar et al. , 2001 ) maps for regional sources, which couple time series of one variable with air mass back trajectories to redistribute the concentrations observed at the site into geographical emission parcels. As previously mentioned, Senegal is widely influenced by DD events transported from arid and semiarid regions of Sahara and Sahel.
Moreover, Mbour being a coastal site, the influence of SS particles on the measured aerosol mass concentrations may be significant. Thus, the contributions of these two aerosol types, in both the coarse and fine fractions of aerosol, have been investigated. PM 1 chemical mass closure was checked for all day types by subtracting from the TEOM-FDMS measurements the mass concentrations of species determined by ACSM (NR PM 1 ) and Aethalometer (BC + Fe); the fraction of unaccounted material therefore corresponded to DD and SS contributions. Figure  3a  shows the mass concentration time series of NR PM 1 measured by ACSM, total PM 1 by TEOM-FDMS, and PM 10 by TEOM during IOP-1. It must be noted that total PM 1 data acquired between 28 March and 10 April had to be invalidated due to instrument overheating during a dust storm event when the outside temperature reached 42 • C and PM 10 concentrations exceeded 600 µg m -3.
Estimation of absorbing compound concentrations in PM 1 Fe and BC concentrations obtained after correction led to averaged values of 0. 55 ± 0. 85 and 0. 36 ± 0. 37 µg m -3 , respectively, over the whole IOP-1 dataset. The deconvolution led to an average decrease of 45 % of BC concentrations (factor of 2. 2), which is much higher than the 11 % decrease observed by  Doumbia et al. (2012)  in Dakar, where local BC sources are predominant. In our study the decrease even reached 83 % under the influence of Saharan dust events and therefore the influence of DD on absorption measurements could never be neglected. As depicted in Fig. 4 , BC concentrations hardly exceeded 3 µg m -3 , except throughout punctual and short-term episodes during which they reached higher values, with a half-hourly average maximum of 3.
01-3. 6 µg m -3 ) obtained during IOP-1 in Mbour is within the same order of magnitude as the one reported by Liousse Fe concentrations estimated from PM 1 absorption measurements are considered as an indicator of DD in the fine fraction. The following comparisons were carried out only on coincident measurements, due to missing data in the PM 1 and PM 10 datasets, corresponding to lower average concentrations of Fe (0. 39 and 0. 53 µg m -3 for the PM 1 and PM 10 data comparison, respectively). Although weak correlations were found between Fe and total PM 1 concentrations (r = 0. 55) and unaccounted PM 1 (r = 0. 47), Fe concentrations showed higher correlations with PM 10 (r = 0. 70; see Fig. 4 ). This could be explained by the lack of PM 1 mass concentration measurements during intense dust events, as well as DD domination in the coarse fraction, while the fine fraction is mainly driven by NR and BC species during most of the IOP-1 (Fig.
They also evidenced a seasonal variability of non-SS SO 4 for marine air masses, increasing during summer, which was attributed to increased photochemistry and changes in the emission of dimethyl sulfide due to higher biological activities in the ocean. This activity can be traced back using satellite data from Aqua MODIS, in particular the algae concentrations along the Senegalese coast (Ocean biology processing group, 2003). Figure  5  represents the average contributions of NR PM 1 species, BC and Fe in PM 1 over the entire IOP-1 and more specifically for continental, sea breeze and marine days. Total PM 1 pie charts, including unaccounted-for aerosol species obtained by chemical mass closure, have been reported in the supplementary material section (Fig. S3 ). Figure  5  shows that although an almost equal proportion of organic and sulfate species (31 and 32 % respectively) was measured on average over the whole IOP-1, strong differences regarding day types can be highlighted: continental and sea breeze days present similar averaged compositions, with a major contribution of OM (36-40 %) followed by SO 4 with 21-24 %.
Source apportionment of OM PMF was first applied without constraints on the whole IOP-1 organic database and then separately on continental, sea breeze and marine extracted datasets (see also Supplement S4). For each run, 3 to 10 factors were tested and only solutions with a normalized Q/Q exp ratio close or less than 1 were taken into account. Mass spectra were compared to reference profiles for identification, and the con-sistency with daily profiles was checked. Without any constraints applied to the model, one or two factors identified as oxygenated organic aerosols (OOA, dominated by the CO + 2 fragment at m/z 44) were easily retrieved for all datasets. An unknown factor with peaks at m/z 58, 60, 83 and 91 appears for the whole IOP-1 when the number of factors increases (and is mixed with one of the OOA in the reduced datasets).
This factor appeared constantly above 4 factors in the unconstrained runs from 3 to 10 factors (Fig. S6 ) and was associated with one of the OOA for the continental, sea breeze and marine 4-factor unconstrained solutions (Fig. S7 ). Besides, the model encountered difficulties in separating hydrocarbon-like organic aerosol (HOA) from cooking-like organic aerosol (COA) for the entire IOP-1 dataset and the continental and marine days. Both COA and HOA share similar m/z peaks at 27, 41, 55 (C n H + 2n-1 ) and 29, 43, 57 (C n H + 2n+1 ) that correspond to hydrocarbon fragmentation. Nonetheless, they can be differentiated based on 41/43 and 55/57 ratios, more important for COA  (Fröhlich et al. , 2015; Mohr et al. , 2012) , while HOA presents more specific and intense peaks at 69-71. Moreover, under continental influence, they tend to be emitted from the same wind directions corresponding to the urbanized area from northwest to northeast.
BC and K DD are empirical constants dependent on instrument characteristics, and α and β are BC and DD absorption exponents, respectively. The α and β values of -1 and -4, respectively, have been determined in the visible range (from 470 to 660 nm) by Fialho et al. (2014) with a dataset acquired in the Cape Verde islands, located approximately 500 km from our sampling site. Fialho et al. (2006)  have replaced dust with iron in order to calculate an iron concentration from dust absorption:.
Acknowledgements. Laura-Hélèna Rivellini 's PhD grant and the SHADOW campaign are financially supported by the  CaPPA project (Chemical and Physical Properties of the Atmosphere) , which is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 ", by the  Regional Council Hauts-de-France  and the  European Regional Development Fund (ERDF). Edited by: James Allan Reviewed by: three anonymous referees.
K BC and K DD are empirical constants dependent on instrument characteristics, and α and β are BC and DD absorption exponents, respectively. The α and β values of -1 and -4, respectively, have been determined in the visible range (from 470 to 660 nm) by Fialho et al. (2014) with a dataset acquired in the Cape Verde islands, located approximately 500 km from our sampling site. Fialho et al. (2006)  have replaced dust with iron in order to calculate an iron concentration from dust absorption:.
2 μm filtered (Thermo Fisher Scientific, Villebon-sur-Yvette, France) rumen at 37°C. After 96 hours of anaerobic incubation on 5% sheep's blood-enriched agar (bioMérieux) colonies were punctiform, approximately round and translucid, with a mean diameter of 0. 1 to 0. 3 mm. Bacterial cells were small Gramnegative cocci ranging in length from 0. 4 to 0. 6 μm. Strain Marseille-P3083 T tested catalase and oxidase negative. Sporulation test (20 minutes at 80°C) was negative, and no growth was achieved under aerobic or microaerophilic (campyGEN, Oxoid) conditions. The 16S rRNA gene was sequenced using fD1-rP2 primers as previously described  [3]  using a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France). Strain Marseille-P3083 T exhibited a 93. 57% sequence identity with Pseudoflavonifractor capillosus strain ATCC 29799 T (GenBank accession no.
This latter is a macro-molecule with a structure very similar to that of the side-chain of Nafion, and has been demonstrated to be an efficient alternative for clarifying the local structure of the ionomer. [23, 36, 51]  We have performed an extensive multi-scale experimental characterization of Aquivion, and Molecular Dynamics simulations of Nafion and Aquivion. These extended new data sets integrate and complete the range of experimental data we have put together recently,  [23]  and simulation results on ionic surfactants. [36, 37]  We establish that sub-diffusion of adsorbed water is the key universal property lying at the bottom of the multi-scale transport behavior within the PFSA materials. Surprisingly, this feature has been until now somehow overlooked in the present context (with only a few exceptions, see below), while it is a lively subject of interest in biophysics, among other fields.
II. RESULTS AND DISCUSSION In the following, we first underline the variations of morphology of the PFSA membranes and the model case of PFOS as obtained both by SAXS and MD, in different hydration conditions. Next, we provide a complete description of the dynamics of the hydration protons and charge carriers, by simultaneously discussing QENS, NMR and MD data for all materials. We finally focus on the hydronium ions dynamics, based on an integrated discussion of QENS and MD data. Details about the techniques used are given in the Methods section. Raw data and additional details on the analysis performed are reported in the Electronic Supporting Information (ESI †).
We show together data sets obtained by SAXS/SANS and MD simulation for Aquivion, Nafion, and PFOS. [23]  The dashed line dw ∝ λ, is a guide for the eyes. Analogous procedures are used to calculate dw for both data sets. A complete discussion of these data is included in the main text. Second, in order to establish a precise measure of the transport/morphology correlation, the latter must be concisely encoded in a single descriptor determinable in experiments. Surprisingly, the mere average size of the ionic domains, d w , turns out to be sufficient for this aim, in particular in hydration conditions where confinement at the nanoscale, more than the total water content, primarily determines the dynamics. [23]  Measurements of d w can be determined by SAXS experiments, that we have performed on Aquivion membranes to complement those on Nafion and PFOS.
[36]  Fully consistent experimental and MD d w data for the ionomers and surfactants are all shown as a function of λ in Figure  2. For all materials d w monotonously increases with λ. More precisely, we clearly observe that the ionomers data stay very close at all hydration, exhibiting a linear variation d w (λ) ∝ λ of slope 1 for d w < 10 Å and λ-values pertaining to the affinely swelling lamellar phase (dashed line). This evidence indicates that differences in the polymer backbone structure, including EW or side-chains length, do not primarily control the features of the nano-confinement. In addition, in the same affine deformation region the swelling behavior in PFOS is completely analogous to that of the ionomers. The elongated polymer aggregates therefore seem to be diluted very similarly to the surfactant lamellar phases, in the same hydration region where the latter are stable.
[15, 59, 63, 64]  In this case, sophisticated multiscale models that integrate the various processes must be employed to consistently reproduce the experimental spectra. These procedures are annoyed by two problematic aspects. First, Neutron scattering (similarly to NMR) cannot a-priori discriminate among bare charge carriers (H + ) and the hydration protons (H 2 O). As a consequence, the total signal is the sum of various contributions, making modeling extremely difficult. Second, even if the probed (Q, ω)-domain is quite extended, data analysis ultimately condenses the information in a restricted set of spectroscopic parameters, corresponding to only a few separated relevant length and time scales. How to overcome this difficulty is one of the main messages of this work (see below). We have recorded QENS spectra of hydrated Aquivion membranes at five energy resolutions, and have analyzed the data following a method used recently for Nafion.
The diffusion of fast protons is significantly slowed-down at the nanoscale with respect to local scale, D nano being reduced by a factor 4 (< 2) at low (high) hydrations with respect to D loc. Moreover, nanoscale and mesoscale diffusion coefficients are comparable in the highly hydrated membranes, but D nano deviates from D S at d w ≤ 10. Alltogether these data indicate that transport in Aquivion is certainly scale-dependent, with a reduction of mobility occurring at all hydrations but with different strengths at molecular, nanoscopic and mesoscopic length scales.
(  1 ) and (  2 ), plotted parametrically against lMD(t) = r 2 (t) 1/2. Extended similar ranges of hydration λ were probed in experiments and simulation. with the above experimental measurements, which involve intricated modeling of very complex objects like the S(Q, ω). We have therefore performed an analysis similar as the above on the synthetic structures of Nafion, Aquivion and PFOS, generated on very extended time scales. Now, however, the starting point of the analysis is the mean-squared displacement of water molecules that can be written at the atomic level in the very simple form, r 2 (t) = 1 N w Nw i=1 |r i (t) -r i (0)| 2. ( 1 ) Here N w is the number of water molecules, and r i (t) is the position vector of the oxygen atom of molecule i at time t. Our data are shown in Figure  S12  † for Aquivion and Nafion , and in Ref.
[23]  Indeed, if we represent D S as a function of Φ (Figure  S11 † ), data of all PFSA materials fall on the same curve at large water uptake, typically for Φ ≥ 0. 2. We therefore conclude that a substantial invariance of the dynamical behavior of the absorbed water under changes of the details of the macro-molecular structure survives at all length-scales, in every case where confinement at the nano-scale plays a non-negligible role. Despite the relative crudeness of the molecular models used in our simulations, we surprisingly find a remarkable agreement between the experimental and numerical data sets, both showing a continuous reduction of mobility while increasing the length scale at a given hydration, this effect being much more pronounced at low hydration. The MD data independently confirm that Nafion and Aquivion membranes are characterized by very similar modifications of the transport properties upon hydration.
In Figure S13 †, we plot r 2 (t) calculated via the analogous of Eq. (  1 ) for the hydronium ions, in Aquivion and Nafion at the indicated values of λ. Similar results are obtained for the PFOS. A variable degree of sub-diffusion is clear in these data, with power-law exponents at low λ even lower (α 0. 3) than those pertaining to water molecules in the same hydration conditions. The anomalous character of the dynamics therefore seems to be stronger for the charged species, a feature one can correlate to the enhanced ionic interactions with the acid functions decorating the ionic domains boundaries. (We note, for completeness, that this is at odds with Refs. [73, 74] ) From these data we can extract time and length scalesdependent effective diffusion coefficients based on Eq. (  2 ), which we now sample at values of l * MD l slow exp = 2σ s , in complete analogy with the case of water molecules.