dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We thank the  UAR 3278 CRIOBE in Mo 'orea for accommodation and technical supports. We are also grateful to  Louise Mazoyer  for technical assistance. The UHPLC-HRMS analyses were performed using the  Métabolites Secondaires Xénobiotiques Métabolomique Environnementale (MSXM) platform of Biodiversité et Biotechnologies Marines  ( Bio2Mar ) facilities at the  Université de Perpignan Via Domitia  (https://bio2mar-msxm. univ-perp. fr/)(accessed on  19 February 2023 ).
Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale. 2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness-productivity relationship. 3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species.
, 2012; Zhang, John, et al. , 2012)  and (ii) there is an interactive effect of evenness and richness on productivity across a range of environmental conditions. Disentangling these relationships between richness, evenness and productivity is critical for understanding the mechanisms governing the diversity-productivity relationship across broad spatial scales (Figure  1 ). Here, we used approximately 1 million forest inventory plots (sourced from the Global Forest Biodiversity Initiative database) to explore whether community evenness affects the relationship between richness and productivity across global forests. If evenness restricts the positive effect of richness on ecosystem productivity, then two things must hold: (i) richness trades off with evenness across forests globally, such that speciose communities tend to be dominated by a few species and (ii) there is an interactive effect of both richness and evenness on the productivity of forest ecosystems.
| MATERIAL S AND ME THODS To evaluate the relationship between evenness and richness, we incorporated all 1,011,027 forest inventory plots from the Global Forest Biodiversity Initiative (GFBI database, 2021, including data from  Condit et al. , 2019a Condit et al. , , 2019b)). Forest plot size ranged from 0. 0008 to 2. 0 ha. Because evenness and richness inherently vary with scale  (Gleason, 1922; Wilson et al. , 1999) , plot sizes smaller than 0. 02 and larger than 1. 5 ha were excluded to ensure comparable results. Indeed, the effect of plot size on richness is especially strong when plots are smaller than 0. 02 ha (r = 0. 39, r 2 = 0. 15, p < 0. 001) or larger than 1. 5 ha (r = -0. 84, r 2 = 0. 71, p < 0. 001). Elimination of these two plot size groups resulted in a filtered dataset with weak correlations between plot size and richness (r = 0.
13, r 2 = 0. 02, p < 0. 001), and plot size and evenness (r = -0. 09, r 2 = 0. 008, p < 0. 001). Although there is a range in plot sizes, 75% of the plots have a size between 0. 02 and 0. 06 ha. Quality controls of tree density estimations were conducted, and we removed plots with tree densities that fell outside the median ± 2. 5 times the median absolute deviation, a moderately conservative threshold, within each biome (0. 8% of total plots)  (Leys et al. , 2013). Additionally, we removed plots with unlikely biomass accumulation and productivity values (for details, see Section 2. 2). The final dataset (Figure  2   productivity, we fit linear models that simultaneously accounted for evenness and richness while controlling for a wide range of environmental variables, including temperature, precipitation and soil characteristics.
We estimated forest productivity using two complementary approaches. First, we used biomass estimates, calculated using regional allometric equations that included information about DBH and species information from each individual tree, and divided this information by forest age  (Poulter et al. , 2019)  to approximate standlevel biomass accumulation over time. Second, we estimated net primary productivity (NPP) for each plot using MODIS satellite data  Running et al. , 2011) , thus providing both ground-sourced and remotely sensed estimates of productivity. Satellite-derived NPP estimates largely agreed with ground measured temporal data for the plots where this information was available (r = 0. 65, p < 0. 001), see Section 2. 3 for more details. (.
In addition to analysing the relationship between evenness and richness at a global scale and for the major forest biomes (boreal, temperate and tropical biomes), for every World Wildlife Fund (WWF) biome we selected a number of plots that is proportional to the forested area within that biome (Table  S2 ;  Olson et al. , 2001). By taking this subset of the data, we reduced the sampling bias by assuring that the results are more representative for either the forests globally or the boreal, temperate or tropical biomes. Regarding the global dataset, the majority of the plots fall within the temperate broadleaf and mixed forest biome and a smaller proportion of the data represents the tropical moist forest, but with subsetting the data we create a more representative global dataset. The same procedure is followed when subsetting biomes to represent the boreal, temperate and tropical biomes.
| Biomass estimation To estimate the above-ground biomass of each tree in extratropical biomes, we used 430 species-specific DBH-based allometric equations obtained from the GlobAllomeTree database to estimate the above-ground biomass of each tree  (Henry et al. , 2013). These allometric equations use a common logarithmic equation for estimating above-ground biomass from DBH measures  (Jenkins et al. , 2003) : where biomass is the total above-ground biomass (kg dry weight), DBH is the measured diameter at breast height (cm), ln is the logarithm to the base e (2. 718), and ß0 and ß1 are free parameters governing the effect of DBH on above-ground biomass. Following  Jenkins et al. (2003) , we applied back calculation to generate a pseudo dataset for biomass (1) for a DBH between 5 and 25 cm, each centimetre was assigned a corresponding pseudo biomass value;  (2)  for a DBH between 25 and 100 cm, every 5 cm were assigned a corresponding value, (3) for a DBH between 100 and 300 cm (maximum DBH), every 10 cm were assigned a corresponding value.
Consequently, we trained biomespecific allometric equations for each biome (varying in the 0 and 1 parameter estimates) based on the pseudo-DBH and biomass dataset  (Olson et al. , 2001)  (Table  S1 ; Figure  S3 ). (1) Evenness = exp(H) ∕ S, Biomass estimations for the tropical biomes followed the allometric equations for pantropical regions from  Chave et al. (2014) , which are available through the r package 'biomass'  (Réjou-Méchain et al. , 2017). These equations require information on wood density, and we compiled species-specific wood density estimates from the Global Wood Density Database  (Chave et al. , 2009)  and the BAAD database  (Falster et al. , 2015). To match the binomial species names between the GFBI and the wood density databases, we standardized species binomials using the Taxonomic Name Resolution Service platform  (Boyle et al.
| Biomass accumulation rates and productivity data We selected approximately 95% of the data by excluding plots that had biomass values greater than two times the standard deviation above and below the mean plot-level biomass per biome to filter potential outliers in biomass values due to errors in measurement or data management. Additionally, we excluded plots with biomass values higher than 1 million kg/ha, as these values were likely overestimating biomass due to the presence of a large tree in the plot  (Bastin et al. , 2018; Chave et al. , 2004; Slik et al. , 2013). This filtering procedure resulted in 367,565 plots that were used in the final analyses (see Table  S2  for an overview of the number of plots for the biomass and productivity analyses). All biomass values were divided by estimated forest age  (Poulter et al.
The density of trees was included as an independent variable in the analyses, as this can affect both evenness  (Wilson et al. , 1999)  and richness values  (Lomolino, 2000). Additionally, 'biome' was included as a factor in the global model, and we accounted for plot size in all models  (Poulter et al. , 2019). To explore the possible effect of plot size, we analysed the relationship between evenness and richness and the effect of both on the two measures of productivity for small, medium and large plot sizes for the dataset globally (Figure  S5 ). Additionally, we performed a sensitivity analysis on species richness, evenness and their interaction to plot size when predicting NPP or biomass accumulation rates in the global dataset (Figure  S6A ) and on biome level (Figure S6B-D). Collinearity between variables in our model was evaluated using variance inflation factors (VIF).
4, 0. 4-0. 6, 0. 6-0. 8, and 0. 8-1). The lowest evenness level <0. 4 was chosen as this represents the lower end of the evenness values in the dataset, while including sufficient observations in this category. The subsequent categories were defined as an increase of 20% in the evenness values. For each evenness level, productivity was predicted with every model covariate (e. g. climate, soil, human impact) set at its median value. Thus, each predicted productivity is marginal to the aggregate trend at the global scale and for each biome. To examine whether the relationship between richness, evenness and productivity varied with evenness index, we evaluated the global trend for six commonly used evenness indices (Figure  S7 ). We used a bootstrapping approach to incorporate uncertainty in the biomass calculations and satellite-derived NPP (Figure  S8 ).
To quantify the relative importance of evenness, richness, their interaction, environmental variables and human impact, we used the scaled calc. relimp function in R  (Grömping, 2006). This function evaluates the contribution of each independent variable to the variation explained, by averaging the contribution of each independent variable to the r 2 in terms of its sum of squares across all possible fitting sequences. The statistical analyses were performed in R version  3. 5. 1 (R Core Team, 2018). The code used to perform the statistical analyses can be found at Github, following this link: tinyu rl. com/3vvf52v9.
For the temperate region, satellite-derived NPP and productivity calculated from multiple times measured ground-sourced data were well correlated (r = 0. 65); however, we could not estimate the accuracy for the other biomes due to limited data. Despite the uncertainty in the biomass and productivity calculations, the main results were robust when considering the uncertainty within these two estimates of productivity individually (Figure  S8 ). We chose to consider these metrics in tandem specifically to minimize the data limitations of each, and ensure that our results are qualitatively robust to the choice of productivity metric. Additionally, in our dataset, we have considerable variability in plot sizes, which can affect both species richness and evenness values, and subsequently the importance of niche and neutral processes  (Gleason, 1922; Viana & Chase, 2019; Wilson et al.
, 1999). Yet, sensitivity analyses show that the differences in plot size do not change the main results (Figure  S5 ), with no consistent biases across plot sizes (Figure  S6 ). However, we do detect considerable noise across the range of plot sizes, partially due to unbalanced sample sizes in relation to plot size in between biomes. Although our work establishes baseline empirical trends, it is important to highlight that these results rely on correlative trends using observational data from a compilation of different databases and sources. Future experimental research will be needed to test the hypotheses we have presented and to explore direct causal relationships among evenness, richness and productivity. Long-term experimental studies with various plot sizes and successional stages will be critical for disentangling the relative importance of the different processes underpinning diversity and function.
This negative correlation between richness and evenness may partially contribute to the attenuating effect of species richness on ecosystem productivity observed in highly diverse communities  (Liang et al. , 2016). In addition, this apparent trade-off between richness and evenness may explain some of the idiosyncrasies observed in previous biodiversity-productivity analyses, as the slope of the richness-productivity relationship will vary considerably across time and space due to the confounding effect of evenness. These Members of the GFBI consortium (all authors not mentioned previously) provided data for the analysis. All authors assisted with revisions and gave final approval for publication.
Richness and evenness were calculated for each plot, enabling us to evaluate the nature of the relationship between these two components of diversity at biome and global scales. To examine the effect of evenness and richness on biomass accumulation and F I G U R E 2 (a) Location of the Global Forest Biodiversity Initiative (GFBI) plots used in this study, where the density of forest plots is indicated from low density (blue) to high density (red). (b) The distribution of evenness and richness in the boreal, temperate and tropical biomes. An evenness value of one resembles either a monospecific stand or an even abundance of species. The tail of richness values of the tropical biomes extends to 380 species (not shown in the graph). The majority of our dataset is composed of secondary forests (mean age is 52 years), and especially the monospecific and relatively species-poor stands were affected by human activity in some degree.
ages, using Google Earth Engine (Gorelick et al. , 2017;  Running   et al. , 2011)  to supplement the forest biomass analysis. NPP is calculated as absorbed fraction of photosynthetically active radiation, which is a combination of leaf area index and fraction of photosynthetically active radiation, while taking temperature and water stress into account as well(Running et al. , 2011). We calculated the mean productivity between 2000 (first year of NPP data availability) and 2009 (third quartile of forest age in our data), to obtain a robust NPP value and coincide the average NPP measurement year with the median forest age in our dataset. In the temperate biomes-the only region where sufficient DBH information was available across multiple years in the GFBI database-the ground-measured temporal changes in biomass were fairly well correlated with the satellite-derived productivity data (r = 0.
DATA AVA I L A B I L I T Y S TAT E M E N T The plot-level evenness data are stored in the Research Data Repository of the Research Collection of ETH Zürich http://doi. org/10. 3929/ethz-b-00059 7256. The GFBI database is 3rd party data, and is publicly available upon request at https://gfbin itiat ive. net/data/.
The nature and amounts of the N-groups on the surface of the carbon materials were determined by X-ray photoelectron spectroscopy (XPS) using a VG Scientific ESCALAB 200 A spectrometer. Transmission electron microscopy (TEM) was performed in a Jeol JEM 1400 (Zeiss, model EM 10 C) apparatus equipped with a SC1000 OriusTM CCD camera that operated at 80 kV. The sample was previously dispersed in ethanol. TEM images also allowed determining the average size of the iron particles of Fe/XGM before and after being used in the persulfate oxidation. For that, the ImageJ software was used.
Then, 0. 25 g/dm 3 of carbon-based material or Fe-impregnated catalyst was added and, immediately after, 3. 2 g/dm 3 of persulfate (which corresponds to 4. 5 g/ dm 3 of potassium persulfate) was also added. During the persulfate activated oxidation, the mixture was constantly stirred at 200 RPM using a stirrer bar and a magnetic stir plate (VWR). Control adsorption experiments were performed similarly, but without adding the persulfate. In previously predefined instants, samples were collected from the reactor. The concentration of PNP and total organic carbon (TOC) was immediately determined in the samples that were firstly filtered to remove the solids, and then diluted with cool water to slow down the reaction kinetics (between any remaining oxidant and the organic matter); such procedure revealed to be efficient to stop the reaction (by comparing the TOC value of a sample analyzed immediately and after being filtered, cooled and stored for 24 h, which were nearly identicaldata not shown).
Stability From the industrial perspective, it is crucial to have stable catalysts that can be used for long periods (in continuous processes) or in multiple batch cycles (for discontinuous processes). This was assessed for the most promising samples, i. e. the carbon material (XGM) and carbon material impregnated with iron (Fe/XGM). For this purpose, four consecutive experiments were carried out with the same sample; between cycles, the carbon-based material (or iron-containing carbon material) was recovered by filtration and dried overnight at 40 • C. Fig. 4  shows the results obtained, being possible to verify that the XGM sample lost activity during the four cycles, having the PNP and TOC removal, after 120 min, decayed from 74. 1 and 64. 4% for the 1 st cycle to 28. 5 and 23. 8% for the 4 th cycle, respectively (data along time for each cycle are shown in Fig.
Acknowledgments This work was financially supported by:  UIDB/00511/2020  and  UIDP/00511/2020  ( LEPABE ),  UIDB/50020/2020  and  UIDP/50020/ 2020  ( LSRE -LCM ) and  LA/P/0045/2020  ( ALiCE ) funded by national funds through  FCT/MCTES  ( PIDDAC ) and, project  NORTE-01-0145-FEDER-000069  ( Healthy Waters ) co-funded by  European Regional Development Fund (ERDF) , through  North Portugal Regional Operational Programme  ( NORTE 2020 ), under the  PORTUGAL 2020 Partnership Agreement. CSDR thanks the  Portuguese Foundation for Science and Technology (FCT)  for the financial support of her work contract through the  Scientific Employment Support Program  ( Norma Transitória   DL 57/2017 ). OSGPS  acknowledges  FCT  funding under the  Scientific Employment Stimulus -Institutional Call   CEECINST/00049/ 2018.
It is then partially transmitted through the beam splitter and collected by a Thorlabs F950FC-A collimator. The latter is fixed on a vertical rotating plate so that we can scan different observation directions around the exact back-scattering one in the incident plane. Although the incident light may be partially polarized after reflecting on the beam splitter, for small enough incident angles the diffuse interaction with the sample has the property of depolarizing the light. For large incidence angles, the reflection on the surface becomes more important and so are the polarization effects. To neglect these polarization effects, we will only consider incident angles smaller than 40 •. We use a fiber spectrometer OceanOptics USB650 along with the SpectraSuite software in order to detect and visualize the measured signal.
Acknowledgments: The authors would like to thank  Alain Ibanez  and  Simon Giraud  ( Institut Néel ) for the hematite samples syntheses. We also thank  Corinne Félix  ( Institut Néel ) for her help in developing the back-scattering measurement device, as well as  Bernard Schmitt  and  Olivier Brissaud  ( IPAG ) for their help in the reflectance measurements using SHADOWS gonio-spectrophotometer. At last, we thank  Romain Pacanowski  ( INRIA ) for the proofreading of this paper. This work was financially supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ,  Cross Disciplinary Program   Patrimalp).
L'acquisition photographique in situ étant impossible, en raison d'évidents problèmes logistiques, nous avons procédé en utilisant les données mises à disposition par la ressource en ligne proposée par Google Street View. Les panoramas sphériques navigables sur le net ont été utilisés pour réaliser une campagne virtuelle simulée, reprenant la même logique et les mêmes principes, dans la mesure du possible, utilisés dans une campagne réelle. A partir des images traitées, grâce à l'utilisation d'un logiciel d'assemblage de photos capable de reconnaître les points homologues entre deux ou plusieurs cadres afin de les joindre de manière transparente 4 , les élévations de toutes les façades du village ont été reproduites, juste avant le tremblement de terre 5 , ce qui, comparé aux documents cadastraux modernes, a permis de créer une copie numérique de la ville (fig.
e. , longline, pole-and-line, etc. ). However, new studies to test and valid this hypothesis are needed. Another potential benefit of dFADs is their use as observatory platforms for scientific purposes. A single dFAD can remain at sea for months, sometimes even years, and can cover thousands of kilometres collecting acoustic data on fish and plankton biomass and oceanographic data. The spatial and temporal data that could be collected by dFADs therefore represent a fisheries-independent dataset in a pelagic ecosystem that no scientific program alone could achieve and with no extra cost. This new data source could complement traditional fisheries-dependent data in order to improve stock assessments for tunas, sharks, and billfishes.
Hall and Roman (2013)  reported that for the period 1995-2007 in the WCPO, turtle catch rates were two times higher for dFAD sets than for FSC sets although 75%-90% are released alive  (Bourjea et al. , 2014; Restrepo et al. , 2017). Other bony fishes such as mahimahi (Coryphaena hippurus, Coryphaenidae), oceanic triggerfish (Canthidermis maculate, Balistidae), rainbow runner (Elagatis bipinnulata, Carangidae)  (Forget et al. , 2020) , and wahoo (Achanthocybium solandri, Scombridae) (Torres-Irineo,  Amandè, et al. , 2014)  have also been reported as common bycatch in dFAD sets although not all of these bony fishes are discarded. Amandè, Dewals, et al. (2017)  F I G U R E 3 Frequency in weight of the catch at size by tropical tuna species (from ICCAT T2CS database) in the PS fishery in the Atlantic Ocean for the period 2010-2020.
Early dFAD design included surplus netting from PS gear of large mesh size of 10-20 cm (4-8 inches)  (Itano, 2007). These large mesh netting panels used in traditional dFAD designs are known to cause incidental entanglements of marine megafauna, such as sharks and turtles, that exhibit an associative behaviour towards dFADs  (Bourjea et al. , 2014; Filmalter et al. , 2013; Hall & Roman, 2013). Shark and turtle entanglements are considered to be hard to detect. Unless the dFAD is lifted out of the water or the entanglement occurs close enough to the surface to be seen, the incident may go undetected. Most captains do not lift dFADs out of the water when checking for fish or making a set  (Murua et al. , 2017 ). Observer's data show that the number of turtles entangled in traditional dFADs has been consistently low across oceans, but quantification of entanglements is difficult as these processes are only periodically observed over short time scales (fishers and thus observers, visit dFADs only a few times, whereas the dFAD itself may remain drifting in the open ocean from weeks to years).