dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The research presented in this paper is funded by  BELSPO (Belgian Science Policy Office)  in the frame of the  STEREO III programme - project  ECOPROPHET  ( SR/00/334 ), and co-funded by  EUMETSAT  ( LSA SAF program  for  CDOP-3 ) and the  Belspo/ESA   Prodex Program  ( PEA 4000110695 ). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux,  AsiaFlux ,  CarboAfrica ,  CarboEuropeIP ,  CarboItaly ,  CarboMont , ChinaFlux, Fluxnet-Canada, GreenGrass,  ICOS , KoFlux, LBA,  NECC ,  OzFlux-TERN ,  TCOS-Siberia , and  USCCC. The FLUXNET eddy covariance data processing and harmoniza-.
Reducing uncertainties about the future forest carbon sink thus requires an integrated understanding of NPP and HR across space or time. Recent studies have suggested that faster forest growth (NPP) leads to higher tree mortality at local and regional scales, particularly in tropical forests  10, 11. This has been recently demonstrated across spatial scales in boreal forests using tree-ring datasets  12  and is consistent with ESM projections across forest biomes  13. While the fraction of biomass subjected to tree mortality is often treated as a proportion (constant or varying) of standing stocks in ESM simulations  13  , this simple model representation of mortality and growth is commonly not observed in forests responding to global change. Ongoing climate change can result in disproportionate mortality relative to growth, from direct physiological mortality from more extreme drought and heat events, insect outbreaks, windthrow, lightning, and wildfire  14, 15.
However, to date it remains unclear how ground-based datasets, such as forest plot observations, could better constrain the projected forest carbon sink at broad spatial scales in a future climate. Because observational uncertainty has more influence than model ensemble uncertainty in EC  25  , a LOSS constraint based on forest plot data is expected to reduce the uncertainties in projections of future forest carbon sinks and associated feedbacks to climate. Here we generate spatially explicit patterns of LOSS from longterm (1951 to 2018) forest plot data (n = 2676; Supplementary Fig. 1 ) to constrain projected (2015-2099) NPP, HR and net ecosystem exchange (NEE = NPP -HR) in six DGVMs across continents -North America, South America, Africa and Asia & Australia. The data are from largely unmanaged forest plots, because management implies distinct LOSS patterns, and management/forestry is not incorporated in models.
Our approach is motivated by the positive heuristic relationships (Supplementary Figs. 2  and 3 ) between historical LOSS and projected NPP and HR carbon fluxes in DGVMs, in line with the known pattern of faster growth and higher mortality  [10] [11] [12] [13]  and the couplings of growth, mortality and respiration at long-term and broad spatial scales  20, 21. We used LOSS also because: (1) it can be directly measured in forest inventory datasets  13  with high accuracy relative to remote sensing; (2) LOSS remains less studied relative to NPP and LOSS is unrealistically represented (i. e. , as a proportion of NPP) in DGVMs  13. Thus, more work with LOSS data products is urgently needed in scientific communities; and (3) the accuracy of constraining the projected carbon cycle in DGVMs largely depends on the observational uncertainty  25.
We first used a random forest to upscale spatial variations of LOSS with 57 environmental variables (see Supplemental Data 1) to generate a spatially explicit map of LOSS at 0. 25-degree resolution across continents (see Methods). Second, we compared the observed spatial patterns of LOSS with the patterns of DGVM  35  in which tree mortality was explicitly reported (see Methods). Finally, two complementary approaches-a conventional emergent constraint and a machine learning constraint-were used to constrain the projected NPP and HR at continental scale in DGVMs (see Methods, Supplementary Fig. 4 ). The conventional emergent constraint (EC) approach was applied by identifying a statistical (linear) relationship between historical LOSS, aggregated or averaged at local forest-plot scale (using original forest plot data) or continental scale (using upscaled LOSS maps), and projected NPP and HR, summed at continental scale across the DGVM ensemble.
Results and discussion Biogeographic pattern of LOSS. The original forest plot data aggregated at 0. 25 degree show large spatial variations (Fig. 1a ) across the continents, with the greatest LOSS in Asia & Australia (mean ± 1 SE; 6. 5 ± 0. 5 Mg ha -1 y -1 ) > South America (4. 9 ± 0. 2 Mg ha -1 y -1 ) and Africa (4. 6 ± 0. 2 Mg ha -1 y -1 ) > North America (2. 3 ± 0. 1 Mg ha -1 y -1 in boreal and 2 ± 0. 1 Mg ha -1 y -1 in temperate)  36  (Fig. 1b ; Supplementary Fig. 5a ). This pattern was robust to bootstrapping (1000 iterations) to randomly select 90% of plots for estimating the probability distribution of the mean continental values (Supplementary Fig. 5b ). The upscaled gridded LOSS maps generated by our random forest algorithm (see Methods) over the spatial domain of our full datasets shows hotspots of high LOSS in Southern Asia & Australia (> 6 Mg ha -1 y -1 ), Northwestern South America (Amazon) (> 5 Mg ha -1 y -1 ), and the western coast of North America (>3 Mg ha -1 y -1 )  10, [36] [37] [38]  (Supplementary Fig.
Ultimately, we used a bootstrap approach to generate 100 maps of mean value of LOSS with its distribution following the values of the average and 2 times of standard deviation of LOSS maps as a conservative constraint (see Methods). Then the 100 maps of mean value of LOSS were used to constrain the projected NPP or HR as ensemble means in our ML constraint and the uncertainty of the constraint was assessed. Our bootstrapping constraint approach by LOSS reduces this common bias of models and decreases projected NPP down to 7. 9, 2. 3, 2 Pg C y -1 in South America, Africa and Asia & Australia, compared to original NPP values of 9, 2. 4, 2. 3 Pg C y -1 (Fig. 4a ). The reason for this is that NPP or growth is strongly positively correlated with LOSS across space in both inventory data and DGVMs (Supplementary Figs.
Panels c and d used basal area and stand density index (SDI) as competition index, respectively. SDI was defined as the degree of crowding within stocked areas and quantified as a function of tree density and the quadratic mean diameter in centimeters. Basal area is strongly correlated with total biomass and higher LOSS in higher basal area may be merely because of its correlations. Thus, we used another competition metrics -SDI to further confirm the role of competition in LOSS. The error bars denote the 95% confidence interval. *P < 0. 05; **P < 0. 01; ***P < 0. 001. higher mortality and thus an emerging coupling of growth, mortality, and respiration averaged over long-term and broad spatial scales, which have been demonstrated in forest inventory datasets  11, 47  , tree rings  12  , eddy flux towers  20  and Earth System models  13  and DGVMs here.
Our approach assumes that this emergent coupling holds both in the short-term historical period and will hold in the future, thus allowing us to use observed historical LOSS to constrain the future projected NPP and HR carbon fluxes. Conclusions and implications. Models typically have a simple representation of tree mortality processes, as a fraction of standing stock  13  , but increasingly more detailed global datasets of tree biomass and demography will also make it possible to test more realistic simulations of competition for light, water and nutrients in the next generation of vegetation models  35, 48. The finite resources and vegetation carrying capacity govern the tradeoffs between growth and mortality and respiration across space or time. We note, however, that changes in availability and/or usage efficiency of resources (i.
e. , water and nutrients) could potentially change the tradeoffs or couplings of mortality and growth across space  18, 24  , thus highlighting the significance of accounting for resource usage efficiency in the model projections. Another non-modeled factor of decoupling is the role of climateinduced disturbances that could strongly increase LOSS and have a delayed positive or negative effect on NPP in the recovery phase. HR remains even more poorly understood and more simply represented than LOSS and NPP in DGVMs. Thus, the couplings of LOSS with HR could be weaker or more uncertain (subject to changes) relative to the couplings of LOSS and NPP at long term and broad spatial scales. This presumably explains why our ML constraint has a greater influence on NPP than HR found in this study. Our results show the potential of leveraging a machinelearning (ML) approach of constraint and forest demography data to constrain the projected NPP and HR at broad spatial scales.
Methods Forest plot datasets. Forest plot data used in this study met the following criteria  13  : (1) all plots had at least three consecutive censuses and long-term (>9 years) observations between the first and last census so that LOSS averaged over all censuses was representative of the historic forest status; (2) plots were natural, unmanaged forest stands that have not been disturbed by fires, harvesting, and other human activities; (3) the plots were largely mature or old-growth forests and were screened by criteria such as forest age or forest gymnosperm fraction (see Supplementary Information for details); (4) the plots were in a quasi-steady state and were screened by criteria that plots with growth more than 3 times of LOSS or LOSS more than 3 times of growth were excluded-such large differences (i.
e. , more than 3 times) between LOSS and NPP are likely due to disturbances such as fires that are not well-represented yet in DGVMs. Thus this criterion is thus used to further select natural mature and old-growth forest stands; and (5) the plots with low values of biomass (i. e. , <3 Kg m -2 ) were excluded because they are not fully stocked and thus not likely to be mature forests. Ultimately, we compiled a broadscale (n = 2676) and long-term (1951 to 2018) dataset of largely unmanaged forest plots in a quasi-steady state, distributed in Canada, USA, Amazon, Africa and Asia & Australia (Supplementary Fig. 1 ). When compared with DGVMs, the aboveground woody biomass loss was converted to total woody biomass loss including belowground roots using the root-shoot biomass ratio product  49. Otherwise, the original aboveground woody biomass loss (i.
e. , in the constraint or analysis of drivers of LOSS) was used and reported to avoid the additional uncertainty from the root-shoot biomass ratio product. More details for the criterion of plots selected, plot establishment, and measurements are described in Supplementary Methods. (2) a bootstrapping (100 times) approach was used for constraint of projected NPP or HR (see Methods) and ensemble means were reported; (3) the ANOVA test was used to explicitly evaluate whether the difference before and after constrain of projected NPP or HR was significant. * for P < 0. 05; ** for P < 0. 01; *** for P < 0. 001. DGVMs. Simulated NPP, mortality flux, and HR of six DGVMs (ORCHIDEE, CABLE-POP, JULES, LPJ-GUESS, LPJmL, and SEIB-DGVM) were publicly available from the carbon turnover inter comparison project https://zenodo.
All outputs were resampled and analyzed at 0. 5 degree. Geospatial modeling and environmental drivers. We used the Random Forest machine learning algorithm  50  (see Supplementary Methods) with the derived 57 environmental covariates including climate, vegetation and soil conditions (Supplementary Data 1) to extrapolate these relationships between LOSS and environmental conditions across continents and generate the first spatially-explicit and quantitative map of LOSS at the continental scale. The 57 environmental covariates were derived based on the georeferenced coordinates of forest plot data (n = 814) aggregated at 0. 25 degree. 10-fold cross-validation was used to evaluate the strength of prediction and the best model having high coefficient of determination and low standard deviation in the mean cross-validation were used to generate the map of LOSS.
2 is the scenario without the projected climate effects and Eq. 3 is the scenario with the projected climate effects. Climate effects were incorporated to account for their potential influence on the couplings or relationships between historical LOSS and projected NPP and HR across environmental (climate) gradients. CO 2 was not included in Eq. (  3 ) because of the minimal spatial heterogeneity on annual timescales. We clarify that all these variables were averages over the historical or future periods and our study thus focused on the spatial patterns of variables of interests in the quasi-steady state. We stress that the random forest model allowed for inclusion of all grid values of variables of interests and consideration of non-linearity. This was achieved with 1000 trees and 10 maximum tree depth and 80% of the data for training purpose and the rest 20% for validation.
With this approach, we surrogated the predictive relationship between the historical (1961-2014) LOSS and the projected (2015-2099) NPP and HR from a complex but physically driven DGVMs with an empirical machine learning model (random forest). Then, we feed the observed maps of LOSS derived from forest plots datasets into the trained random forest model to assess the impacts of historic LOSS on the projected NPP and HR. In this way, we were able to generate the spatially explicit  34  constraint of the projected NPP and HR, which was subsequently aggregated as sum within each continent to assess the forest carbon sink at the continental scale. Forest carbon sink was quantified by the difference of grid-level NPP and grid-level HR. The mechanistic underpinning justifying the linear (emergent) and non-linear (machine learning) constrain was the theory of faster growth and higher mortality  12, 13, 47  and thus couplings of growth, mortality and respiration averaged over long term and broad spatial scales  20, 21.
Acknowledgements A. P. B. acknowledges the funding from the l' Agence Nationale de la Recherche (Make Our Planet Great Again ;  ANR-18-MPGA-0007 ). HYHC acknowledges the funding from the  Natural Sciences and Engineering Research Council of Canada  [ RGPIN-2019-05109  and  STPGP506284 ] and the  Canadian Foundation for Innovation  ( 36014 ).
When the gas to liquid flow rate ratio was fixed at 1, initial CH4 and CO2 concentrations of 10. 1 and 26. 4 mg L -1 , respectively, were recorded in the stripping gas stream. Figure  10  presents the transmembrane flux of the biogas at different Reynold numbers. At a Reynold number of 5. 19, flux values of 6. 06 and 14. 85 mg m -2 min -1 were recorded for CH4 and CO2, respectively. In similar conditions, the flux values recorded for the synthetic mixed effluents were 5. 27 and 85. 71 mg m -2 min -1 for CH4 and CO2, respectively. It is obvious from the data mentioned here that similar to the degassing efficiency, the CH4 flux was higher for the AnMBR permeate than for the synthetic mixed effluent (as discussed in Section 3. 3). For CO2, the opposite effect was observed as the flux of the synthetic mixed effluent was very high compared to the flux from the AnMBR permeate.
1 and 26. 4 mg L -1 , respectively, were recorded in the stripping gas stream. Figure  10  presents the transmembrane flux of the biogas at different Reynold numbers. At a Reynold number of 5. 19, flux values of 6. 06 and 14. 85 mg m -2 min -1 were recorded for CH 4 and CO 2 , respectively. In similar conditions, the flux values recorded for the synthetic mixed effluents were 5. 27 and 85. 71 mg m -2 min -1 for CH 4 and CO 2 , respectively. It is obvious from the data mentioned here that similar to the degassing efficiency, the CH 4 flux was higher for the AnMBR permeate than for the synthetic mixed effluent (as discussed in Section 3. 3). For CO 2 , the opposite effect was observed as the flux of the synthetic mixed effluent was very high compared to the flux from the AnMBR permeate. This seems to be due to the low dissolved CO 2 contents of the AnMBR permeate in comparison to the synthetic effluent, which was prepared with a high CO 2 content and in unfavorable conditions for CH  Figure  11  represents the experimental mass transfer coefficients of the biogas against the liquid flow rate.
Morphometric analysis Measurements were based on  Euzet and Le Brun (1990) , and taken using LAS version 4. 12. 0 (Figs  2 3 4 5 6 7 ), and given in µm by the range in parentheses, and number of individuals. Drawings of the sclerotized parts of the Monogeneans were made with Corel Draw ® 2019 software using pictures taken from the camera. All analyses were performed using R 4. 1. 2 software (R Core Team 2022). Data from 45 individuals of one new species, 10 individuals of the other, and 10 individuals of B. parauchenoglanii, were subjected to a Principal Component Analysis (PCA). This analysis was done using the FactoMineR  (Husson et al. 2020)  and factoextra  (Kassambara and Mundt 2020)  packages in R. Bagrobdella auchenoglanii, B. fraudulenta and B. anthopenis, each one represented by the mean values of the analyzed parameters obtained in  Euzet and Le Brun (1990)  were added to the analysis as supplementary individuals.
RESULTS The identification of host species was confirmed by the molecular analysis, and the sequences were deposited in Genbank (Identification number ON682514). Gills of eleven A. occidentalis specimens were examined for parasite infection. Seven out of eleven hosts were found be parasitized. The most parasitized fish host is the sole specimen caught at the Mouth of Mutambala River. This individual hosted a total of 286 Monogeneans, 269 Bagrobdella vanhovei sp. nov. and 17 B. vansteenbergei. sp. nov. The other ten individuals that were captured off the Orthodox Church of Uvira possessed only Bagrobdella vanhovei sp. nov. individuals. Among them, four had no monogeneans, three had one individual, and the other three hosts had, five, six and 35 individuals respectively. The two new species of Bagrobdella which were found are described herein.
However, this positive impact does not reach its full potential due to the low temporal availability of optical-based LAI observations, which is, at best, every 10 d, and can suffer from months of missing data over regions and seasons with heavy cloud cover such as winter or in monsoon conditions. In that context, this study investigates the assimilation of low-frequency passive microwave vegetation optical depth (VOD), available in almost all weather conditions, as a proxy for LAI. The Vegetation Optical Depth Climate Archive (VODCA) dataset provides near-daily observations of vegetation conditions, which is far more frequent than optical-based products such as LAI. This study's goal is to convert the more frequent X-band VOD observations into proxy-LAI observations through linear seasonal re-scaling and to assimilate them in place of direct LAI observations.
(2019)  find that L-band VOD conveys large amounts of information relative to AGB, primarily related to wood biomass in forested areas. Teubner et al. (2021)  find that while X-band VOD correlates well with in situ FLUXNET observations of GPP, L-band VOD is poorly correlated to GPP over either low or high vegetation types. The VOD dataset used in this study is the Vegetation Optical Depth Climate Archive (VODCA)  (Moesinger et al. , 2020) , which combines the VOD retrievals from numerous sensors into spatially homogeneous series, extending the periods of data between sensors. With this combination, VOD retrievals are, on average over the contiguous United States (CONUS) domain, at temporal frequencies of between 1 and 2 d. This study seeks to use the link between LAI and X-band VOD to transform VOD into a proxy for LAI and assimilate it as LAI in LDAS-Monde.
We examine the impact of the assimilation of the more frequent VOD observations, and we investigate future uses of VOD data in the LDAS-Monde system. Section 2 of this article details LDAS-Monde, the satellite-derived data used in our data assimilation and the independent observations used for evaluation, as well as outlining the experiment setup and assimilation scenarios. Section 3 provides comparisons of LAI and VOD data as well as the results of these experiments and the evaluation against our measuring datasets. Section 4 goes into discussion about the meaning of the results and how they can be interpreted. And Sect. 5 provides conclusions on the work, as well as highlighting future work in the same direction.
ISBA land surface model For nature (i. e. , non-urban) tiles as determined by land use databases, the ISBA LSM simulates heat, carbon, water, and other surface fluxes. Included within ISBA are several individual components simulating snow, hydrology, soil, and vegetation in the land surface system. The version of ISBA in this work uses the 12-layer snow parameterization scheme  (Boone and Etchevers, 2001; Decharme et al. , 2016) , which better represents snow compaction, soil temperature, and surface albedo than previous snow schemes. This study focuses on the evolution of vegetation, specifically concerning vegetation responses in drought events and requiring accurate simulation of vegetation and soil moisture dynamics. To that end, the ISBA-A-gs  (Calvet et al. , 1998; Calvet, 2000; Calvet et al.
Land cover The configuration of ISBA for this study uses the ECO-CLIMAP Second Generation (ECOCLIMAP-SG)  (Calvet and Champeaux, 2020)  land use database, the evolution of ECOCLIMAP-II  (Faroux et al. , 2013). ECOCLIMAP-SG uses 12 land surface patch classes comprised of nine plant types (evergreen broadleaf trees, needleleaf trees, deciduous broadleaf trees, herbaceous, tropical herbaceous, wetlands, C 3 crops, C 4 crops, and C 4 irrigated crops). The remaining three patch classes are the non-vegetation surfaces of rocks, bare soil, and permanent snow and ice. LDAS-Monde converts urban surfaces to bare rock for use in ISBA. A map of dominant land cover from ECOCLIMAP is shown in Fig. S1  in the Supplement.
, 2018 (Hersbach et al. , , 2020) )  to drive ISBA. This dataset provides hourly data, globally over a 0. 25 • × 0. 25 • grid. The ERA5 reanalysis is itself a product of data assimilation, combining model data and observations around the world to create this consistent dataset from 1950present. ERA5 assimilates atmospheric observations every 12 h, which updates to a new, more accurate forecast. Its uncertainty is measured by sampling a 10-member ensemble every 3 h, and the mean and spread of the ensemble are precomputed and provided to users. While not a real-time product, preliminary ERA5 data are available with an approximate 5 d delay, with a higher-quality controlled release after 2-3 months.
Surface soil moisture SSM observations are taken from the European Space Agency (ESA) Climate Change Initiative (CCI), merging SSM observations from microwave radiometers and scatterometers, with temporal coverage from 1978. The SSM data are provided in volumetric form (m 3 m -3 ) and are at a 0. 25 • ×0. 25 • spatial resolution. Snow cover, freezing ground temperature, and dense vegetation all greatly effect SSM retrievals, and this dataset provides quality flags for those conditions. Importantly, high elevation is known to negatively affect retrieval quality, and thus pixels with an average altitude above 1500 m above sea level are filtered out. This elevation filter does eliminate a large portion of the western and central western United States, mostly from the Rocky Mountains. As with previous studies using ESA CCI SSM  (Albergel et al.