dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The study was funded by the  French National Research Agency (Agence Nationale de la Recherche ,  ANR ,  ANR-08-GENM038   PIG_FEED  Project). The authors thank  Yvon Billon  for line selection and animal care,  Alain Chauvin ,  Régis Janvier ,  Francis Le Gouevec  and  Vincent Piedvache  for animal care and  Yolande Jaguelin  and  Anne Pasquier  for laboratory analyses.
The three classical morpho-functional groups of epiedaphic, hemiedaphic and euedaphic were represented by a quite similar number of species, i. e. 35 epiedaphic, 31 hemiedaphic, and 32 euedaphic (Table  2 ). These groups differ in dispersal ability and other life traits such as reproduction, mobility, metabolic activity and feeding behaviour  (Hopkin 1997 ). In our dataset, they usually match the vertical gradient from surface to deep soil. So the petrophyte ecosystems covered by natural forest, with moss and decayed wood, provide diversified micro-microhabitats to a large diversity in each of these three functional groups. The most part of identified species in the calcareous canyons have a wide occurrence (Fig. 4 ). Between them 43. 3% have European, 20. 7% -cosmopolitan, 15. 3% -Palaearctic and 13.
, 2003). Biological data (individual size and weight) were obtained from monthly fish market samples analysed at the laboratory. Squid samples were obtained on a monthly basis from the bottom trawl fleet between January and December 2009. A total of 1361 and 643 individuals of L. vulgaris and L. forbesii were analysed respectively. A total of 806 cuttlefish individuals from the small-scale fishery taken between January and July were analysed monthly for two consecutive years  (2007, 2008).
Modelling and software The assessments were conducted using the Leslie-DeLury DMs with the Catch and Effort Data Analysis (CEDA) software  (Kirkwood et al. , 2003). The following indexed recruitment model was used: N t+1 = e -M (qCPUE t -C t + λR t ), where t is the time interval (fortnight), N is the population size in numbers at the start of the time interval t, M the natural mortality rate, q the catchability coefficient, C t the total catch during the time interval t, λ the recruitment constant of proportionality, R t the recruitment index and CPUE t the catch per unit effort during the time interval t. As CEDA is based on the numbers of animals, the monthly mean weights from the biological sampling were used to convert catches and abundance indices into numbers. As a time interval of two weeks was used for the modelling, weights were interpolated.
Although not optimal, this was the best compromise between using the high resolution of the catch data and the lower resolution of the biological data. Length-frequency data was used to calculate the recruitment indices (R t ). This index is the proportion of small individuals landed in time t to all small individuals landed during the depletion period, with "small" being below the modal landings modal size (≤10 cm for cuttlefish, ≤9 cm for squid)  (Pierce et al. , 1994). The model estimates the following parameters: the initial population (N1) and the current stock size, the expected catches for each time step during the depletion event (all in numbers), the catchability coefficient (q), the number of recruits (λ) and a goodness of fit measure (R 2 ). Bootstrapped 95% confidence intervals for q, N1 and λ were also calculated.
Input data and parameters In each fishing season, only the depletion period was analysed, which is the period corresponding to the time where the CPUE or the catches are declining constantly due to fishing. The time steps chosen for both assessments were periods of two weeks. The biweekly time scale was a trade-off between better resolution of weekly data and monthly availability of the biological data. In both squid and cuttlefish assessments, in addition to the years with available biological data, the two subsequent years were analysed using the biological information from that year. In cuttlefish, the biweekly landing data of the small scale trammel net fishery from 2007-2010 was analysed and as no biological sampling was performed in 2009-2010, the recruitment indices and mean weights from 2007 were used.
For squid, the catches of the bottom trawl fishery from 2009-2011 were analysed. DMs were fitted using mean weights and recruitment indices derived from the biological sampling of commercial landings in 2010 and 2011 (both loliginid species combined). Comparisons with data from on-board sampling showed that the observer programme was not a suitable source of information because of the irregular timing of observations and insufficient sample sizes. Hilborn & Walters (1992) suggested standardization of CPUE by using a Gaussian error GLM prior to employing them in stock assessment models in order to avoid biases related to spatial, temporal or fleet heterogeneity. Even if the method was successfully used to standardize various stock CPUEs including cephalopods  (Royer et al. , 2006) , it does not deal with zero inflated datasets.
To standardize these datasets, the delta-GLM method (which combines a binomial error GLM dealing with presence/absence data and a Gaussian error GLM dealing with abundances) was developed and successfully used in recent years, including cephalopods  (Stefansson, 1996; Fletcher et al. , 2005; Acou et al. , 2011; Gras, 2013). In our study, four variables were considered to standardize CPUE: 1) fortnight period; 2) fishing area; 3) fishing season; and 4) vessel engine power (HP).
, 2011) , this observation prompted us to investigate the expression of these peptidase genes by time course RT-qPCR in larval midguts of GF or Lp WJL -associated individuals on a low-nutrition diet (see Figure  1A  for detailed experimental scheme). We found the expression profile of 7 peptidases (Jon66Cii, Jon66Ci, Jon44E, Jon65Ai, Jon99Ci, CG18179, and CG18180) presents a robust and detectable transcriptional signature during larval development (Figures  1B-1I ). Principal-component analysis (PCA) on the whole dataset (expression levels of the seven genes at all time points in both conditions) reveals that PC1, which explains 80. 5% of the observed variance, is enough to separate the GF and Lp WJL groups. Such separation indicates that peptidase expression levels in the midguts of Lp WJL -associated larvae remarkably differ from the GF condition (Figure  1B ).
This difference is also strongly significant when the whole dataset is analyzed by multivariate analysis of variance (MANOVA-Bacteria: p < 0,001; Figure 1B; Table  S1 ); furthermore, we compared each of the seven peptidases in GF and Lp WJL larvae during the same defined period of growth and found that the expression of any given peptidase is significantly elevated in Lp WJL animals along most of the time points (Figures 1C-1I and ANOVA-Bacteria; Table  S1 ). Notably, the peptidase genes expression tends to increase during larval development even in GF condition (MANOVA-Time and ANOVA-Time and GF Group:Time; Table  S1 ), but the trend of increase is only marginally affected by the bacterial association (MANOVA-Bacteria 3 Time and ANOVA-Bacteria 3 Time; Table  S1 ). Taken together, these results demonstrate that Lp WJL association triggers an overall increase in the expression levels of several intestinal peptidases but does not alter their expression dynamics during juvenile growth.
Lactobacillus plantarum Association Promotes Protein Digestion and Sustains AA Levels during Juvenile Growth We next questioned the physiological consequences of Lp WJLmediated enhanced intestinal proteolytic activity and wondered if that phenomenon increases protein digestion and free AAs levels in the host. To this end, we analyzed the relevant data from a global metabolomic study of GF and Lp WJL larvae raised for 2. 5 days AED on the low-nutrition diet (G. S. and F. L. , unpublished data). We focused on dipeptides and free AAs since these metabolites are the end-results of protein digestion process. 21 AAs and 50 dipeptides were detected and quantified in the lysates of GF and Lp WJL larvae (Table  S1 ). A PCA analysis of this dataset tends to group the biological replicates generated from the same condition and separates the samples according to the bacterial association for either the free AAs (Figure  3A ) or the dipeptides datasets (Figure  3B ).
, 2012). We first analyzed the entire expression dataset (for all time points, genotypes, and conditions) by projecting the expression results on two principal components (PCs) (Figure  4A ). This PCA clearly singled out the Lp WJL association effect on peptidase expression in wild-type animals (as seen in Figure  1C ). Of note, Lp WJL -associated peptidase expression data points grouped in two distant pools separated by genotype (WT versus Dredd), while the GF individuals data grouped closer. This signature indicates that Dredd governs peptidase expression in the larval midgut, with a marked effect upon Lp WJL association. The statistical analysis of the whole dataset confirms this observation and reveals a very strong statistical interaction between bacterial association and genotype (Figure  4A  and MANOVA-Bacteria 3 Genotype; Table  S1 ).
ACKNOWLEDGMENTS We thank  S. Guillemin  for technical help;  M. Strigini  and  D. Ma  for critical reading of the manuscript;  J. Royet  and  T. Kawecki  for support during this work;  I. Miguel-Aliaga ,  N. Silverman ,  B. Charroux ,  S. Merabet ,  FlyORF , and the  Bloomington stock center  for fly lines;  B. Lemaitre  and  W. J. Lee  for bacterial strains; and the  Arthro-Tools  platform of the  SFR Biosciences Gerland/ Lyon- sud for Fly work. This work was funded by the  ANR  ( ANR 2010 JCJC 1304 01 ) and an  ERC  starting grant ( FP7/2007(FP7/ -2013-N 309704)-N 309704 ). G. S. is funded by the  ''Fondation pour la Recherche Medicale. ''The lab of F. L. is sponsored by  EMBO YIP, ATIP/AVENIR, FINOVI , and  FSER.
whether particular groupings of species, e. g. according to plant functional types (PFTs), with similar O 3 sensitivities can be identified. We also compared regression functions based on the same updated dataset using the updated flux methodology with those using the formerly accepted concentration-based approach (AOT40 1  ). To investigate the parameterisation of the flux model, this study applied two different methods to estimate stomatal O 3 fluxes. The first method was that used previously by  Karlsson et al. (2007) , but with updates to include new parameterisations defined in the 2010 revision of the UNECE Mapping manual  (LRTAP Convention, 2010). These 'real species' parameterisations incorporate new data to define parameter values and also identify climate specific parameterisations to account for different species ecotypes.
A second method tested the suitability of a simplified parameterisation, i. e. standard functions that describe the effect of light, vapour pressure deficit and temperature on stomatal conductance (g s ) irrespective of species, called simplified parameterisation or simple model from hereon. Application of this method was used to test the hypothesis that a simplified parameterisation of the multiplicative g s model leads to a similarly robust DRR as compared to the 'real species' parameterisation. As such it examined whether there is a need for a rigorous species-specific parameterisation of the model, which often complicates large-scale application of the flux based method both for flux-effect as well as total O 3 deposition estimation. This study also presented the opportunity to compile new doseeresponse functions and datasets for two species (i.
Some of the new datasets are derived from experimental O 3 fumigations or filtrations conducted under varying levels of water supply. This allows the effect of reduced water availability on PODy to be investigated using a new soil water balance method described in  Büker et al. (2012). This is particularly important as the exclusion of data representing non-optimal water supply, which is known to reduce g s  (Büker et al. , 2012)  and therefore stomatal O 3 flux, has been cited as reason why the stomatal flux approach sometimes does not provide a substantial improvement of DRR developed using the concentration based AOT40 index (e. g. Karlsson et al. , 2007). Finally, this study also investigated whether species groupings of O 3 sensitivity can be defined. Past studies have been rather inconclusive in defining DRR that are able to represent different tree PFTs.
This analysis provided an opportunity to investigate these issues in further detail using more advanced methods for estimating stomatal O 3 flux. The overall aim of this study was to evaluate the performance of PODy and AOT40 metrics in predicting biomass reductions for forest trees. This was achieved through analysis of the statistical performance of linear regressions of DRR constructed using data collected from fumigation and filtration studies. This work was conducted with a view to developing new DRR that could be used in the derivation of critical levels within the LRTAP Convention.
Estimating gs for 'real' species modelling The parameters and functions used for the estimation of 'real' species g s are those defined in the mapping manual of the Convention on Long-Range Transboundary Air Pollution (LRTAP)  (LRTAP Convention, 2010)  with the exception of i. g max values which have been revised according to newly available datasets since the publication of  Karlsson et al. (2007 ) (i. e. LRTAP Convention, 2010; Marzuoli et al. , 2009)  and a recently modified diffusivity ratio of O 3 to water vapour of 0. 663  (Grünhage et al. , 2012) ; ii. a phenology function which is defined either by latitude according to LRTAP  Convention (2010)  or by information provided by holders of the experimental data included in this study (see Section 2. 5 and Table  1  and Table  S1  for further details) and; iii.
f PPFD ¼ f min þ 0:9*ð1 À EXPðlight a*PPFDÞÞ (2) where f PPFD follows a rectangular hyperbola and light_a is 0. 006; the structure of the formula is such that f min also operates during the night-time period. This parameterisation has proved reasonable for the two most common species in the present dataset, birch  (Uddling et al. , 2004 (Uddling et al. , , 2005) )  and Norway spruce  (Tarvainen et al. , 2013) , and the assumption is made that other species will follow a similar f PPFD. This f D (except for its "min" formulation) is supported by the theory on tree hydraulics and has been shown to hold for a broad range of tree species  (Oren et al. , 1999). The commonly used bellshaped f T (LRTAP Convention, 2010) is given by: f D ¼ minð1; 1 À 0:6*lnðDÞÞ (3) 0. 0 0. 0 0. 0 0. 0 1. 0 1. 0 0.
Description of sites and datasets This study used O 3 experimental fumigation or filtration datasets collected for eight different species: birch (Betula pendula), beech (Fagus sylvatica), temperate oak (Quercus robur or petraea), Norway spruce (Picea abies), Scots pine (Pinus sylvestris), Aleppo pine (Pinus halepensis), Holm oak (Quercus ilex) and poplar hybrids (Populus maximowicz Â Populus berolinensis or Populus deltoides Â Populus nigra). These datasets were from ten different sites in seven countries (Sweden, Finland, U. K. , France, Switzerland, Spain and Italy), thereby representing all major European climatic zones defined in the UNECE Mapping Manual  (LRTAP Convention, 2010). Six experiments from France, Italy, Spain and Sweden contained reduced water supply treatments created either by i.
reduced irrigation for up to 10 weeks, ii. reduced precipitation (with the help of an Open Top Chamber (OTC) roof) for up to 20 weeks or iii. no irrigation but full precipitation for the entire growing season. Further details of these treatments by dataset are provided in Table  3. One of the datasets that had been included in the earlier study by  Karlsson et al. (2007)  is excluded from this study due to too few replicates having been conducted in the experiment. This study also includes three new datasets for Holm oak and poplar collected from sites in France, Italy and Spain (Table  3 ). Apart from two open-field systems in Grignon (Le Thiec, pers. comm. ;  Ahmad et al. , 2012)  and Kuopio  (Oksanen, 2003) , all other sites were equipped with Open Top Chambers (OTC) for the exposure of young trees to O 3.
This equation assumes a constant ozone-induced percentage reduction in biomass accumulation across years in multi-year experiments. For experiments that used non-filtered ambient air (NF) rather than CF as control treatment or that had a PODy or AOT40 of greater than 0 for the CF treatment, the biomass of the control was recalculated as the Y-intercept value resulting from the linear regression of biomass (Y-axis) and O 3 exposure (X-axis) data  (Fuhrer, 1994). The reduction in biomass for each treatment was calculated relative to the hypothetical biomass at zero AOT40 or zero PODy, which enabled the comparison of experiments that had either CF or NF control treatments. Species were investigated either as single or groups of species. Broader species groupings allowed analysis of responses according to plant functional types, with needleleaf trees (Picea abies, Pinus halepensis and Pinus sylvestris) and broadleaf deciduous trees (Betula pendula, Fagus sylvatica, Populus spec.
Effect of reduced water supply Datasets of six experiments using five different species (Picea abies, P. halepensis, Quercus robur, Q. ilex and Populus spec. ) comprised data points representing both well-watered and reduced-water conditions. The data points of the reduced water supply treatments are positioned within the general data cloud of Figs. 2  and 4e6  (not indicated in these figures), which suggests that they do not represent clearly distinguishable O 3 -uptake or  -response conditions as compared to the well-watered treatments. To more clearly observe the size of the effect of reduced water supply on g s and consequently on PODy, the experimental datasets that included a water stress treatment (15 data points in total) were directly compared with the well-water treatments of the same experiments using the "real species" model.
Discussion We analysed data from 24 European field experiments to derive O 3 flux based DRR for Norway spruce, beech and birch, Holm oak and Aleppo pine, as well as the two PFTs of broadleaf deciduous trees and needleleaf trees. The inclusion of Holm oak and Aleppo pine datasets for the first time enabled the derivation of a combined DRR for Mediterranean trees only (a separate DRR for Holm oak was recently published by  Alonso et al. (2014) ). Despite these two species representing different PFTs, their grouping is justified due to both of them being evergreen, physiologically active throughout the year and able to cope well with Mediterranean climatic conditions, including drought. However, it should be noted that broadleaf deciduous species are also common in the Mediterranean and that these species have been shown to be more sensitive to O 3 than the broadleaf evergreen species Holm oak used in this study  (Calatayud et al.
In fact,  Temmerman et al. (2002) ,  Musselman et al. (2006)  and  Dizengremel et al. (2009)  discuss the concept of varying detoxification potentials of trees in detail. They agree that given the biochemical processes (i. e. antioxidant capacity, turnover and transport) that are known to be involved in ozone scavenging, it is certain that plants have a capacity for detoxification and that this varies between species. However, the complexity of these biochemical processes currently makes it difficult to quantify this capacity either absolutely or in relative terms for different species or species groups. The new flux-based DRR for Norway spruce is associated with a lower R 2 (0. 34) than reported by LRTAP Convention (2010) (R 2 ¼ 0. 55), mainly because one dataset that had been used in these two previous studies and that was characterised by high PODy values had to be excluded due to a limited replication of experimental treatments.
The inclusion of datasets representing experiments performed under restricted water availability neither substantially improved nor worsened the DRR; the data points from restricted water experiments fell within the range of data points from well-watered experiments. This provides further support for use of flux based methods as compared to the concentration-based AOT40 approach, which cannot account for environmental impacts on O 3 uptake by plants. The difference in the performance between the flux based and the AOT40-based DRR found in this study was comparable to earlier studies by  Karlsson et al. (2007)  and the LRTAP Convention (2010), despite the fact that the DO 3 SE model now has a much more sophisticated soil moisture deficit module that has been successfully validated  (Büker et al.
Conclusions The analysis presented here derived new O 3 flux based DRR for nine European forest tree species using varying statistically derived flux thresholds according to the species sensitivity to O 3. Flux based DRR were mostly more robust than concentration based DRR. The study showed that it is possible to define DRR both by species as well as by plant functional types and that the use of a simplified parameterisation of the g s model can provide reasonable accuracy in the calculation of POD y for non-Mediterranean tree species. The findings suggest that critical levels as published by the UNECE (LRTAP Convention, 2010) could be re-defined using the updated PODy estimates and biomass response datasets presented here. They also support the 'y' thresholds currently used in the PODy metrics but do suggest that there may be justification in considering differential 'y' thresholds for broadleaf and needleleaf species (the former having a higher 'y' threshold than the latter) based on an objective characterisation of key statistical parameters and how these compare between species and PFT groupings.
Karlsson et al. (2007)ents. Those shaded in grey are new datasets as compared toKarlsson et al. (2007). AA ¼ ambient air; CF ¼ Charcoal-filtered; NF ¼ Non-filtered (ambient); CF/NFþ/þþ ¼ Charcoal-filtered or Non-filtered plus addition of O 3. OTC ¼ Open top chamber.
Acknowledgements We would like to thank the  UK Department for Environment, Food and Rural Affairs (Defra)  ( AQ0601 ) for their continuous financial support in developing and applying the DO 3 SE model as part of contract  AQ0601. This research was part-funded by the European  FP7-ENV-2011/282910  ECLAIRE (effects of climate change on air pollution impacts and response strategies for European ecosystems) project.
The biological diversity of the Earth is being rapidly depleted due to the direct and indirect consequences of human activities. Specialist or rare species are generally thought to be more extinction prone than generalist or common species. Testing this assumption however requires that the rarity and ecological specialization of the species are quantified. Many indices have been developed to classify species as generalists vs. specialists or as rare vs. common, but large data sets are needed to calculate these indices. Here, we present a list of specialization and rarity values for more than 2800 plant species of continental France, which were computed from the large botanical and ecological dataset SOPHY. Three specialization indices were calculated using species co-occurrence data. All three indices are based on (dis)similarity among plant communities containing a focal species, quantified either as beta diversity in an additive (Fridley et al.
Ecological specialization and rarity indices Type of data Table How data was acquired Specialization and rarity indices were computed from the large botanical and ecological database SOPHY (GIVD ID EU-FR-003) using R 3. 0. 1  [13]  and the code provided by Fridley et al. [6]  in Appendix S1, Zeleny  [15]  in Appendix S2 and Baselga et al. [1].