dataesr/scientific-paragraphs-categorization huggingface dataset:
Other Sectors Wetlands methane emission (Fwet) CH 4 emissions from wetland in this study were mainly derived from the Global Methane Budget (GMB)  (Saunois et al. , 2020). The GMB provides estimates for East Asia from 13 process-based models during 2000-2017. The dataset WetCHARTs  (Bloom et al. , 2017)   Oxidation of atmospheric CH 4 by methanotrophs in natural soils and N 2 O emissions from unmanaged soil were evaluated by the process-based terrestrial ecosystem model VISIT  (Ito et al. , 2018) , which contained four schemes for simulating the process. Results from simulating natural vegetation and croplands separately at each grid were used. The output data was at 0. 5° × 0. 5° resolution and a timeseries between 2000-2016 was extracted for our estimation. Termites CH 4 emission (Ftermite) Termites are known as a CH 4 source  (Ito et al.
2019 , Zhou et al. 2021). Overall, the agricultural sector emits 23. 7 ± 3. 4 Tg CH 4 yr -1 (Table  1 ). For natural ecosystems, the largest sources were wetlands and inland water bodies (lakes, ponds and reservoirs), which we estimated as 3. 5 ± 0. 5 Tg CH 4 yr -1 and 4. 1 ± 1. 8 Tg CH 4 yr -1 , respectively, according to several global and regional studies (see Methods). The ensemble of 13 wetland models estimated a large range of CH 4 emission from 0. 8 ± 0. 2 to 10. 4 ± 0. 5 Tg CH 4 yr - 1. The wetland CH 4 emission over East Asia only contributes less than 2% of global wetland CH 4 emission  (Saunois et al. 2020) , partly because the small fraction of global wetland area (~4%), according to global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M; Z. Zhang et al. (2021) ).
( Houghton & Nassikas, 2017) ; three data-based or model-based approaches, an average estimate derived from 18 dynamic global vegetation models (TRENDY), an estimate based on the latest Chinese forests land cover dataset by Yu et al. (2022)  and a process-based model estimate driven by high resolution satellite land cover maps(Leng et al. , under review)  were used to estimate the net flux of Fluc in East Asia. Considering the TRENDY models and Hansis et al. (2015)  were driven by a common land use forcing LUH2 (Chini et al. , 2021) , which contradicted groundbased and satellite evidence of land cover change in China (e. g. Yu et al. 2022 ) by showing increasing cropland area and decreasing forest area, we took the average from H&N2017, OSCAR, Yu et al. (2022)  andLeng et al. (under review).
For Japan, the Republic of Korea, Mongolia and the Democratic People's Republic of Korea, all six datasets estimates have been used. 2. 3. 2. 3 Lateral fluxesWood and food trade (Ftrade) Ftrade is the net lateral flux of crop and wood products related to trade across the boundaries of each region, calculated as the sum of the export and import fluxes of crop and wood products. For East Asia, we referred to the estimate from Ciais et al. (2021). They estimated the lateral flux of crop products based on the FAO database and Peters et al. (2012) for different forestry products for 2000s. For China, in a similar manner, Wang et al. (2022) updated the value during 2010-2016. Jiang et al. (2016) estimated the Ftrade based on the import and export data of crop and wood products from the FAO statistical databases.
and the ratio of wood products output between China and Europe based on FAO statistical databases. (2) Jiang et al. (2016) calculated the carbon pool changes of wood products due to local production in 2006-2009 using the production data of wood products from FAO statistical databases. For Japan, the annual carbon stock change in the harvested wood products pool has been evaluated in NIES since 1990 using IPCC Tier 2~3 methods. Land cover and land use change flux (Fluc) The net CO 2 flux from land cover, land use change, and forestry (Fluc) includes CO 2 fluxes from deforestation, afforestation, logging and forest degradation (including harvest activity), shifting cultivation (cycle of cutting forest for agriculture, then abandoning), and regrowth of forests (following wood harvest or agriculture abandonment) (Pierre Friedlingstein et al.
, 2022). It is extremely challenging to accurately estimate the carbon balance change associated with land-use change because of current lack of information on the spatial pattern of deforestation and associated changes in biomass and soil carbon stocks 2. 3. 2. 2 Ecosystem modelling estimates Net biome productivity (NBP) NBP considers the carbon balance from the point of view of ecosystems and usually been quantified in process-based ecosystem models (Ciais et al. 2020). Spatially gridded NBP were obtained with simulation 2 (S2) from DGVMs up to 2019 from the TRENDY v9 dataset. 18 estimates have been provided in S2 from different DGVMs (CABLE-POP, CLASSIC, CLMS, DLEM, IBIS, ISAM, ISBA_CTRIP, JSBACH, JULES, LPJ-GUESS, LPJwsl, LPX-Bern, OCN, ORCHIDEE, ORCHIDEEv3, SDGVM, VISIT, YIBS). All the models are forced with observed climatology, atmospheric CO 2 , time-invariant pre-industrial land-cover distribution and preindustrial wood harvest rates, to model the contemporary global carbon cycle.
been synthesized in RECCAP-2, all fluxes are rescaled to consistent estimates of surface area of lakes and reservoirs (after HydroLAKES, Messager et al. (2016) ) and rivers (Allen & Pavelsky, 2018). They were further corrected for effects of seasonal ice-cover and ice out(Lauerwald et al. submitted). Two datasets of carbon emissions from fire were collected in our study, the fourth version of the Global Fire Emissions Database (GFED4. 1s, van der Werf et al. (2017)) and the Live Vegetation Biomass Carbon for the 21st Century (LVBC, Xu et al. , 2021). GFED4. 1s combined satellite information on fire activity and vegetation productivity to estimate gridded Fire CO 2 emissions (Ffire) 2. 4 CH 4 and N 2 O 2. 4. 1 Top-down inversions monthly burned area and fire emissions of different fire types: boreal forest fires, temperature forest fires, tropical forest fires, peat fires and agricultural waste burning.
For China and Japan, the national GHG reports NCCC and NIES were also collected, respectively. 2 O emission from agricultural soils associated with fertilizer, crop residues, and other N additions to soils are captured. Both direct and indirect agricultural soil emissions need to be considered. It is primarily (more than half) attributable to the increase of fertilizer input to uplands Manure management (Fmanure) In the case of nonruminant CH 4 emissions, there are about 970 million domestic swine in the world, and nearly half of them are in China (FAO, 2021). The large swine population produces considerable amounts of CH 4 emissions through manure production and management processes (P. Xu et al. , 2019; L. Zhang et al. , 2021). We synthesized the estimates from the latest CEDS, EDGAR and FAOSTAT datasets and Zhang et al.
CH 4 emissions from enteric fermentation are estimated in nine livestock species: cattle2 O. In addition to the datasets mentioned above, here we also used a combination of datasets, the Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths (PRIMAP-HIST) emission series (Gütschow et al. , 2016). The PRIMAP-HIST dataset combined several published datasets to create 2 comprehensive sets (HISTCR and HISTTP) of GHG emission pathways from the years 1850 to 2018. Different priorities are given depending on the data types. In HISTCR scenario, country-reported data (CRF, BUR, UNFCCC) is prioritized over third party data (CDIAC, FAO, Andrew, EDGAR, BP). In HISTTP scenario, third-party data (CDIAC, FAO, Andrew, EDGAR, BP) is prioritized over country-reported data (CRF, BUR, UNFCCC).
This is consistent among the ground-and satellite-based measurements and chemistry-climate model simulations in the middle and upper stratosphere, despite the larger variability of the ground-based measurements. This is apparent in the evolution of observed and modelled annual mean deseasonalized ozone anomalies, relative to the 1998-2008 climatology of each individual dataset in Figure 2, in the upper stratosphere (42 km or 2 hPa) and in the lower stratosphere (19 km or 70 hPa). Ozone anomalies over 2017-2020 in the upper stratosphere from most datasets are positive relative to the 1998-2008 average, consistent with expectations from the CCM simulations. In contrast, lower-stratospheric ozone anomalies over 2017-2020.
Acknowledgements MPC and SB thank numerous colleagues for past discussions on the topics described here. In particular, MPC thanks the colead authors of the recent WMO assessment ozone chapters:  Michelle Santee ,  Birgit Hassler  and  Paul Young. MPC also thanks  Ryan Hossaini  and  Sandip Dhomse  for specific comments. SB thanks  Sergey Khaykin ,  Gwenael Berthet , and  Jean-Paul Vernier. MPC thanks  UK NERC  for support through the  LSO3  project ( NE/V011863/1 ). SB thanks the support of the  French Agence Nationale de la Recherche (ANR)   PyroStrat  project (Grant No:  21-CE01-0028-01 ) and  Centre National d'Etudes Spatiales (CNES)   BeSAFE  project. https://doi. org/10. 5194/egusphere-2023-1409Preprint. Discussion started: 30 June 2023 c Author(s)  2023. CC BY 4. 0 License.
We calculated the overall volume of living trees and deadwood. For deadwood, we calculated the volume for each status (standing or downed deadwood), decay stage (slightly vs. highly decayed) and diameter class (deadwood DBH < 40 cm vs. ≥ 40 cm). We compiled all this information (i. e. , status, species, decay stage and DBH) to calculate deadwood diversity. Finally, we used the dbFD function from the FD R-package to calculate the mean status value of the woody resources and the dispersion value for each plot, based on an ordinal scale (i. e. , living trees = 3, snags = 2, logs = 1). For more information, see https://doi. org/10. 5281/zenodo. 7816954.
To adjust the decline variable to our dataset, we used linear and equations. We added forest and stand as nested random effects on the intercept in the mixed models to account for repeated measurements and the spatial configuration of the sampling design. Since some traps were not continuously functional, for example, because they fell from the tree, we also added an offset of the log-number of effective traps across the entrapment season, weighted by the efficacy of each type of trap. To assess model quality, we used marginal R² (function R2, performance R-package;  Lüdecke et al. , 2021). We used the differences in AICc scores (function AICc, AICCmodavg R-package;  Mazerolle, 2023)  to compare the fit among models. When the linear and quadratic models were undifferentiated, we show only the results for the simplest one, i.
Our results also support their value as forest bioindicators, as previously proposed by  Maleque et al. (2009). Nonetheless, our study also underlines the relative lack of knowledge of this group in temperate forests  (Hilszczański, 2018; Jonsell et al. , 2023). Except for a few well-known species, it was difficult to obtain information on the biology and ecology of the taxa we collected. We therefore had to limit the number of ecological traits in our analyses, and our current list of traits is still partially incomplete. This is unfortunate since databases of ecological traits have proven to be useful tools for ecological studies and conservation purposes (e. g. ,  Bouget et al. , 2013; 2019). Consequently, considering the growing threats to biodiversity in general, and to insect communities in particular  (Wagner, 2020) , we call for thorough investigations on the diversity and ecology of Hymenoptera to make better use of their potential as bioindicators  (Maleque et al.
ACKNOWLEDGEMENTS We thank  C. Moliard  ( INRAE ),  G. Parmain  ( INRAE ),  X. Pineau  ( LBLGC ) and  O. Denux  ( INRAE ) for their technical assistance. We also thank  T. Wood ,  S. Flaminio  and  P. Rosa  for their help in determining the Andrenidae, Halictidae and Chrysididae. We are grateful to the  National Forest Office (Office National des Forêts)  for their field assistance. This work was supported by  Région Centre-Val de Loire  Project no. 2018-00124136  ( CANOPEE ) coordinated by  A. Sallé.
Finally, each otolith section was glued to a glass slide, sonicated for 5 min in ultrapure water to decontaminate its surface, and dried under the laminar flow hood before being stored in a separate plastic bag. All sections were photographed before and after analysis using a camera (Olympus ProgRes C5) linked to a microscope (Olympus BX41; magnification 100× and 200×; software ProgRes). Otolith elemental composition was determined by laser-ablation inductively coupled plasma mass spectrometry (LA-ICPMS) at the University of Montpellier (UMR 5243 Geosciences) using a 193 nm Excimer Laser System (CompEx 102; Lambda-Physiks) coupled to an Element XR sector field ICPMS (ThermoFisher). To reduce potential bias linked to variations in machine accuracy between analysis sessions, otoliths from different sampling sites and years were mixed on each of the analysis plates run on the LA-ICPMS system.
Moreover, analysis of a certified otolith reference material (MACS 3) at the beginning and end of each analysis session was used for intersession measurement quality control. Calculations of final otolith concentrations and the machine limits of de tection (LOD) were made using the 'ElementR' package  (Sirot et al. 2017 ) in R v. 4. 1. 2 (R Core Team 2021). Apart from 43 Ca, which was used as a proxy of the ablated otolith material, a total of 22 chemical elements were originally analyzed. However, only 10 ( 11 B, 138 Ba, 59 Co, 52 Cr, 65 Cu, 31 P, 85 Rb, 86 Sr, 66 Zn, and 89 Y) had at least 25% of their measures above the LOD in at least one of the habitats sampled and were therefore retained for subsequent analysis. All element concentrations were normalized to the respective concentrations of 43 Ca and are expressed in mg g -1 for Sr; μg g -1 for B, Ba, Co, Cr, P, and Zn; and pg g -1 for Cu, Rb, and Y.
In order to create a database of juvenile otolith fingerprints capturing most of the spatiotemporal variability in otolith chemical record for each estuary, all spots between the transition zone and the otolith edge (3-8, depending on the fish) were kept for each YOY analyzed. The number of spots obtained for each fish varied due to a few factors, including the size and age of the fish captured from different estuaries, their date of entry into the estuary, the respective location of the transition zone structure in each individual, and differences in the otolith radius. Each spot is expected to comprise approximately a few weeks to a month, considering an average width of 2. 2 μm for each daily increment in otoliths  (Gonzalez et al. 2022). However, there is a trend of otolith increment width decreasing with age and size, ranging from ~4.
A cross-validation procedure was used for each potential RF training data set, which consisted of randomly selecting 75% of the multielemental signatures to train the RF and then using the remaining signatures (25%) to assess the accuracy in re-assigning each sample to its known origin. After identifying the optimal RF training data set, which consisted of the number and list of elements providing the best discrimination between locations, the contribution of each individual element to the overall spatial discrimination was assessed by calculating the mean decrease in Gini Index following its removal from the training data set (Breiman 2001). A higher decrease for an element indicates it is more essential for discrimination. To specify the re-assignment errors between juvenile habitats using the optimal training data set, specific accuracies and true skill statistics (TSS;  Allouche et al.
Juvenile habitat assignment for sub-adult and adult fish The optimal RF training data set was used to identify the origin of the 40 sub-adult and adult fish captured offshore based on the 307 multi-elemental signatures obtained from the juvenile portion of their otoliths (RF test data set). To minimize errors in habitat assignment due to spatiotemporal variation in environmental conditions during the juvenile life of individual fish and evaluate the reliability of juvenile habitat prediction for each fish, the most probable habitat of origin was predicted for each of the successive signatures acquired from the juvenile part of the otoliths (Fig. 2d ). A threshold of 70% consistent assignment was set as an a priori limit for allocating each fish to a specific habitat. Therefore, fish were assigned to a past juvenile origin (site or habitat type) only if more than 70% of the signatures from their juvenile life were specifically attributed to it (Fig.
The maximum accuracy obtained for the optimal RF training data set in each case is indicated (dashed lines). For each size combination and RF training data set, the minimum and maximum accuracy obtained is indicated by the grey area, while the mean (± SD) is presented by squares. The list of selected elements for each optimal RF training data set is presented in the tables and according to their mean decrease in Gini Index Author copy estuaries (Fig. 6 ). In total, 20. 2% of the 307 multi-elemental fingerprints recorded in the juvenile part of the otoliths of sub-adult and adult fish were assigned to the sea. However, very few fish (5%) were assigned purely to the marine habitat during their first year of life (Fig. 6 ). Most of the marine assignments were observed at the end of the juvenile transect in fish attributed to an estuary, for the last or the last 2 spot(s) measured before the annulus, depending on the fish.
Variation in otolith elemental fingerprints The accuracy of re-assignment was highest (94-95%) for the marine coastal zone, regardless of the RF   2 ). Elemental signatures for the sea include those recorded during the larval life and the otolith edge signature of subadult specimens captured at sea. Element concentrations were normalized to 43 Ca and are expressed as ratios to Ca. In each case, the horizontal line and the box correspond to the median and the interquartile range. Letters indicate significant differences in elements based on post hoc Dunn's tests (p < 0. 05). Colors represent the 3 groups of estuaries ('type-1', 'type-2', and the Mundaú-Manguaba lagoon system) and the sea. See Table  2  for location definitions training data set used and despite the strong marine influence in studied estuaries (Table  1 ).
Therefore, the juvenile portions of the otoliths analyzed also included some elemental signatures that were deposited during periods of high freshwater inputs. This might explain the high variability in otolith elemental signatures observed for some estuaries. The use of sequential otolith signatures increases the inter-annual stability of the elemental fingerprints database by covering most of the spatiotemporal variability of elements recorded within the estuarine life of the juvenile specimens  (Tournois et al. 2017). As a result of this intra-annual variability, otolith signatures in estuaries were relatively stable across years. Only 2 of the sites investigated (SUA and RIO) presented significant inter-annual changes in their otolith signatures. Yet spatial differences exceeded temporal ones for all investigated sites.
The RF algorithm has been shown to be valuable and versatile in identifying fish past habitats using otolith elemental fingerprints from different life stages  (Tournois et al. 2013 , Delerue-Ricard et al. 2019 ) in both temperate  (Mercier et al. 2011 , Tournois et al. 2017 , Prichard et al. 2018 ) and tropical regions  (Paillon et al. 2014 , Teichert et al. 2018). In this study, the overall accuracy of the RF algorithm with the optimal training data set (80%) confirms its value for discriminating juvenile habitats for tropical coastal fishes. However, the RF algorithm had a limited capacity to discriminate each site separately due to the high overlap in multi-elemental fingerprints between some estuaries. The use of sequential elemental signatures of a given fish considered the temporal and spatial variability in elemental composition during the estuarine life, but it may also have artificially increased the precision of our local fingerprints of each estuary.
Acknowledgements. This work was supported by the  ' Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco'  ( FACEPE/CAPES APQ 0146-2. 05/17 ), the  Universidade Federal Rural de Pernambuco  ( UFRPE 015/2018 ), the  CNPq (National Council for Scientific and Technological Development)  for providing research grants to T. F. (no. 307422/2020-8 ) and P. J. D. -N. (no. 310320/2020-8 ), and by  CAPES (Coordination for the Improvement of Higher Education Personnel)  for providing a scholarship to J. G. G. We thank the members of  BIOIMPACT and MARBEC laboratories  for their efforts on field campaigns and laboratory analysis. We thank  N. N. Fabré  and  I. Assis  for providing some samples used in this study, and  O. Bruguier  for his support with laboratory analysis. The bathymetry information for the study area was provided by the  SMC  project ( Ministério do Meio Ambiente  [MMA],  Brazilian Cooperation Agency  [ABC] and the  Spanish Agency of International Cooperation for the Development [AECID]).
This article is a contribution to the PADDLE project, which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 734271 , to the  LMI TAPIOCA  and to the  SMAC  project ( CAPES/ COFECUB  no. 88881. 142689/2017-01 ). LITERATURE CITED Allouche O , Tsoar A, Kadmon R (2006) Assessing the accuracy of  species distribution models: prevalence , kappa and the true skill statistic (TSS). J Appl Ecol 43: 1223-1232 Andersen KH,  Jacobsen NS ,  Jansen T ,  Beyer JE  (2017) When in life does density dependence occur in fish populations? Fish Fish 18: 656-667 Aschenbrenner A,  Hackradt CW ,  Ferreira BP  (2016a) Spatial variation in density and size structure indicate habitat selection throughout life stages of two southwestern Atlantic snappers.
Acknowledgements. This review article has been carried out within the framework of the Global Tipping Points Report 2023. N. W. and J. F. D. acknowledge support from the  European Research Council Advanced Grant  project  ERA (Earth Resilience in the Anthropocene ,  ERC-2016-ADG-743080 ). J. F. D. is grateful for financial support by the project  CHANGES  funded by the  German Federal Ministry for Education and Research (BMBF)  within the framework ' PIK_Change ' under grant  01LS2001A. R. W. acknowledges financial support via the  Earth Commission , hosted by  FutureEarth. The Earth Commission  is the science component of the  Global Commons Alliance , a sponsored.
Illumina MiSeq sequencing and data processing MetaFast library preparation and amplicon sequencing were performed on an Illumina 2 × 250 bp MiSeq platform by Fasteris SA, Switzerland (www. fasteris. com). We used a pipeline based on VSEARCH  (Rognes et al. , 2016)  and available in GitHub (https://github. com/BPerezLamarque/Scripts/) for data processing  (Perez-Lamarque et al. , 2022; Petrolli et al. , 2021). Briefly, paired-end reads were merged using the fastq_mergepairs function with default parameters. We applied a quality check and removed merged reads with more than two alignment errors. Then filtered merged reads were assigned to the respective samples based on tagged primers (demultiplexing) with 0 accepted error regarding the primers and tag sequences using the Cutadapt tool  (Martin, 2011).
We removed chimeras de novo by using the VSEARCH uchime3_denovo option and assigned the taxonomy to each amplicon sequence variant (ASV) using the usearch_global option with default parameters. We used SILVA 138. 1 and UNITE (v. 8. 0) databases for bacteria and fungi, respectively  (Kõljalg et al. , 2020; Quast et al. , 2012). The ASV tables were then filtered from contaminants based on comparisons with amplified negative controls using the DECONTAM (prevalence method) R package  (Davis et al. , 2018). Only ASVs with long sequences (>200 bp), assigned to the bacterial and fungal kingdom, presenting an acceptable abundance (≥ 10 reads) and amplified in at least one sample, were kept for subsequent analyses.