dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses and metrics We obtained data of 30 simulations (table  1 ) and structured the comparisons in terms of forcing data and biome models. Basic metrics such as mean, standard deviation, and coefficient of variation (standard deviation divided by the mean) were examined first. Metrics specific to biospheric studies such as long-term mean, seasonal change, interannual change, and linear trend of GPP were also examined. To separate the variabilities caused by forcing data and biome models, two-way analysis of variance (ANOVA) was conducted (R 3. 4. 0; R Core Team 2017) for the 30 global annual mean GPP estimates in 1981-2000. For grid-based benchmarking, the mean annual GPP in 2001-2010 for each grid was correlated with those estimated by using MODIS and flux up-scaling data. We also performed correlation analysis on the 5°-averaged GPP values and GOME-2 SIF data to assess their relationship.
Several metrics related to GPP properties were examined. Seasonal-cycle amplitude (SCA) of GPP, which reflects vegetation activity and affects atmospheric CO 2 (Graven et al 2013, Wenzel et al 2016), was defined as the difference between the maximum and minimum monthly values for each year. Resource-use efficiencies of GPP are expected to provide insights into underlying mechanisms and limiting factors of photosynthetic production. Based on the incoming solar radiation data and simulated GPP and evapotranspiration, we calculated radiationuse efficiency (RUE) and water-use efficiency (WUE) as follows: RUE ¼ GPP=DSR ð1Þ and where DSR is downward short wave radiation at the land surface and TR is vegetation transpiration. In general, photosynthetically active radiation (PAR) accounts for about 48% of DSR  (McCree 1972) , and RUE as defined by equation (  1 ) may be easily converted into a PAR-based value.
For the DLEM, JULES, and VEGAS models, WUE was calculated using total evapotranspiration rates, because TR data files were not supplied for these models. Therefore, in this study, inter-model comparisons were made only for relative change from the 1996-2000 annual average. Note also that RUE could be defined using solar radiation absorbed by the canopy (e. g. Ruimy et al 1999), which is inferred from leaf area index and the canopy attenuation coefficient (typically 0. 5) of Lambert-Beer's law. However, because a few models do not supply leaf area index data, we used equation (1), which is applicable to all model runs. WUE ¼ GPP=TR ð2Þ.
Global terrestrial GPP by biome model Global terrestrial GPP simulated by the biome models using the four forcing datasets during the common overlapping period of 1981-2000 was 115. 7 ± 11. 0 Pg C yr -1 (mean ± standard deviation of all simulations), ranging from 98. 4 to 141. 2 Pg C yr -1 (table  2 ). Although this may seem to be a broad range, most values fell within the range of previously reported values (table  3 ). Most of the simulated GPP values were not very different from those of MODIS (112 Pg C yr -1 , 2000-2012) and flux upscaled (132 Pg C yr -1 , 1982-2011) estimates. As shown by the ANOVA result (table  4 ), the variability in the estimated global GPP was primarily attributable to inter-model variability (84% of total); inter-data variability and residual (interaction term) accounted for 4.
8% and 11%, respectively. Apparent inter-model differences were found, ranging from 100. 1 ± 1. 8 Pg C yr -1 for JULES to 133. 1 ± 6. 2 Pg C yr -1 for CARAIB. In contrast, model-ensemble GPP was not significantly different among the four forcing datasets, ranging from 112. 5 ± 9. 2 Pg C yr -1 for WATCH to 116. 3 ± 9. 9 Pg C yr -1 for WFDEI (table  2 ). In terms of differences among the forcing datasets, DLEM showed the smallest variability (2. 9 Pg C yr -1 ) and VISIT the largest variability (20. 0 Pg C yr -1 ) between the maximum and minimum values. Few common patterns were found in the dependence on forcing data among the biome models. Four biome models (DLEM, LPJ-GUESS, LPJmL, and ORCHIDEE) simulated the highest GPP when using the Princeton forcing data, and four models (CARAIB, JULES, ORCHIDEE, and VEGAS) simulated the lowest GPP when using the GSWP3 forcing data.
Trends in GPP The simulated global terrestrial GPP showed similar interannual variability, irrespective of biome model and forcing data. The average slope of the linear trend was estimated as 0. 30 ± 0. 07% yr -1 or 0. 35 ± 0. 09 Pg C yr -2 , and differed among simulations: from the shallowest slope in VEGAS driven by GSWP3 data (0. 19% yr -1 ) to the steepest slope in LPJ-GUESS driven by WATCH data (0. 41% yr -1 ). These GPP slopes were steeper than those of the flux up-scaling (0. 14% yr. In contrast, a recent study  (Campbell et al 2017)  of long-term atmospheric observation of carbonyl sulfide showed that terrestrial GPP has increased at a higher rate than previously thought before: 31 ± 5% in the 20th century (assuming linearity, 0. 31 ± 0. 05% yr -1 ). While their results show higher incremental trends in the late 20th century, the mean incremental trend by ISIMIP2a models is comparable with the atmospheric observation-based estimate.
Meteorological variability and GPP To clarify the characteristics of environmental responsiveness, which should account for inter-model variability, the estimated GPP values were correlated with temperature, precipitation, solar radiation, and CO 2 conditions for 1981-2000. For temperature, the inter annual variability was comparable among the four forcing datasets (figure  2(a) ). The estimated GPP responded similarly to temperature variability irrespective of forcing data and biome models, with values ranging from 3. 1 Pg C yr -1 K -1 (ORCHIDEE driven by WFDEI) to 6. 1 Pg C yr -1 K -1 (DLEM driven by WATCH). For precipitation, a moderate difference was found among the forcing data, from 772 kg H 2 O m -2 yr -1 (Princeton) to 821 kg H 2 O m -2 yr -1 (WATCH; figure 2(b)). The biome models showed comparable positive responsiveness to precipitation, spanning from 0.
064 Pg C yr -1 (kg H 2 O m -2 yr -1 ) -1 (JULES driven by Princeton) to 0. 117 Pg C yr -1 (kg H 2 O m -2 yr -1 ) -1 (VISIT driven by GSWP3). For solar Environ. Res. Lett. 12 (2017) 085001 radiation, a conspicuous difference was found among the forcing data, spanning from 172 W m -2 (WATCH) to 190 W m -2 (Princeton). These forcing data also differed in decadal trends and interannual variability. For example, only the Princeton data showeda clear incremental trend (+0. 14 W m -2 yr -1 ) with large interannual variability, whereas the others showed small negative trends. Remarkably, the biome models responded differently to solar radiation among the forcing data (figure  2(c )); the estimated GPP only responded positively to solar radiation in Princetondriven simulations. The high solar radiation in the Princeton dataset may be associated with the high GPP of five models (CARAIB, DLEM, LPJ-GUESS, LPJmL, and ORCHIDEE; table 2).
92) with the reference data (figure  3 ); others also showed R 2 values of 0. 8 or higher. The benchmarking of annual GPP using independent datasets confirmed that the biome models could capture the major global patterns of photosynthetic productivity as obtained by upscaling and remote sensing approaches. Along with advancements in the flux measurement dataset and machine-learning algorithms, further useful fieldbased datasets (e. g. Jung et al 2017) will become available for model benchmarking. The relationship between simulated GPP and satellite-observed SIF data (figure  4 ) seems reasonable, considering the noisiness of the data. In most biome models, simulated GPP was linearly related Because SIF is more intimately coupled with photosynthetic biophysical processes, benchmarking with SIF is expected to provide additional information on vegetation activity.
Indeed,  Zhang et al (2016c)  used SIF data to obtain an ensemble GPP by weighting estimates by multiple models. We confirmed that monthly GPP simulated by the biome models shows comparable seasonal change with that in SIF (figure  S3 ). In tropical areas, however, the seasonal signal was very weak, and such correspondence was unclear (figure  S3(c )), implying that several models have difficulty with the environmental responsiveness of tropical ecosystems. In accordance with its effectiveness as a proxy of photosynthetic activity, more and higher-quality SIF data will soon be provided by satellites, including the Greenhouse gas Observation Satellite (GOSAT; Frankenberg et al 2011) of Japan and the Fluorescence Explorer (FLEX) of the European Space Agency. We expect a considerable increase in usage of SIF data by terrestrial vegetation studies in the coming years.
The benchmarking of global terrestrial GPP revealed different characteristics of the participant models. For example, LPJmL and ORCHIDEE captured long-term mean GPP with higher agreement with observational data than the others. DLEM showed better agreement with SIF data and smaller inter-data variability. VEGAS showed slightly lower GPP and smaller incremental trends, whereas Environ. Res. Lett. 12 (2017) 085001 CARAIB showed relatively higher GPP and larger trends. LPJ-GUESS captured intermediate mean annual GPP and larger incremental trends. JULES and VISIT showed lower or higher total GPP but captured intermediate incremental trends. Therefore, we should be aware that the current biome models have their own pros and cons, and we need caution in specifying which model works best for a given purpose (e.
Mechanistic findings from benchmarking The incremental GPP trends (figure 2(a)) could be attributable to several mechanisms: (1) enhancement of photosynthetic capacity, (2) elongation of growing period, (3) expansion of canopy leaf area, and (4) land-use and land-cover change. As attempted by  Xia et al (2015) , factors related to leaf area (phenology and vegetation structure) and photosynthetic capacity (physiology) could be combined using an appropriate metric. The simulated GPP trend occurred heterogeneously over the land area. At a global scale, it is well correlated with the trend of mean leaf area index (LAI) trend in the different biome models and forcing datasets (figure  6 ). Note that several models assumed a stationary land-use and plant functional type distribution but showed a similarly positive relationship.
Therefore, the incremental GPP trend was at least partly attributable to LAI expansion, which in turn, has several causes. Apparently, the CO 2 fertilization effect played an important role, but observational evidences provide inconsistent implications for the model sensitivity as noted previously (Kolby Smith et al 2015, Campbell et al 2017). We need to seek additional data and metrics to reduce the uncertainty; for example,  Wenzel et al (2016)  implied that SCA is a useful metric to constrain the simulated GPP trend. Spatial variability in the simulated GPP trends (figure  S4 ) has additional mechanistic implications. For each forcing dataset, positive trends occurred from tropical to boreal regions, especially in North America, Eurasia, and eastern Australia. In contrast, consistently negative trends occurred in Central America, a part of South America, and eastern Central Africa.
Concluding remarks We conducted a benchmarking of eight biome models and confirmed that these current biome models can approximately capture basic features such as the spatial distribution of productivity (i. e. capability of CO 2 fixation). Such benchmarking of model-estimated GPP has been conducted by previous model inter comparison studies (table 3), and this ISI-MIP study provided several new findings. By using multiple meteorological forcing datasets, it revealed contributions of inter-model (primary) and inter-data (secondary) variabilities. Focusing on recent decades, it was possible to assess model responses to several wellknown extreme events. Importantly, the results of the biome sector could be combined with other sectors such as water and agriculture, allowing us to conduct more human-relevant analyses.
Recent advancements in instruments and data-analysis studies are providing an increasing amount of data for benchmarking, allowing us to examine the validity and limitations of models in more detail. Although several benchmarking studies of biome models have been conducted, this study provided some new insights. First, this study provided results of WUE and RUE and the seasonalcycle amplitude of GPP, allowing us to address the underlying mechanisms of the historical changes. Second, our use of multiple forcing datasets permitted us to account for a wider range of estimation uncertainty than in previous studies. The comparison emphasized the large discrepancy in solar radiation among the forcing datasets. Third, the use of flux upscaled GPP and satellite-derived SIF data provided more insights than a simple comparison between models.
the context of model benchmarking and global change study, extreme events are gathering increasing attentions. Here we focused on two extreme events: the huge eruption ofMt. Pinatubo in 1991  (McCormick et al 1995)  and the strong ENSO in 1997-1998. Simulated global GPP markedly decreased after the Mt. Pinatubo eruption irrespective of biome model and forcing dataset (figure5(a)). Model-mean GPP anomalies in 1992 differed among the forcing datasets, ranging from -1. 51 Pg C yr -1 for WFDEI cases to -2. 25 Pg C yr -1 for GSWP3 cases. In contrast, observational evidence suggests that the terrestrial biosphere could absorb more carbon, which would account for the decline in the atmospheric CO 2 growth rate observed after the eruption (Keeling et al 1995). The time-series of solar radiation in the forcing data showed few volcanic signals after the eruption event (data not shown): only the WATCH dataset shows a clear decline in solar radiation by (about 2 Wm -2 ) in 1992.
Moreover, the forcing datasets did not contain a variable for the diffuse component of solar radiation. Several studies revealed the importance of diffuse radiation for the increased canopy radiation absorp-tion and carbon assimilation (Roderick et al 2001, Gu et al 2002, Kanniah et al 2013), which would explain the low CO 2 growth rate after the huge volcanic eruption. By appropriately including solar radiation factors, several model studies have successfully simulated the change in terrestrial carbon budget after the volcanic eruption (Lucht et al 2002, Frölicher et al 2013). In the present study and other multimodel studies (e. g. Le Quéré et al 2015), however, most biome models were unsuccessful in simulating the anomalous GPP increase after the eruption. Many studies have assessed the impacts of ENSO events on the terrestrial carbon budget, which affects the atmospheric CO 2 growth rate (e.
Acknowledgments This study was conducted as a part of the second phase of ISIMIP organized by the  Potsdam Institute of Climate Impact Research (ISIMIP2a)  and financed from the  German Federal Ministry of Education and Research (BMBF , grant no. 01L1201A1 ). It used data obtained from the  Deutsche Klimarechenzentrum (German Climate Computing Center ) data server. P. C. acknowledges support from  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. J. C. received support from the  European Commission 's  7th Framework Program  ( EU/FP7 ) project. C. M. received support from the  EU/FP7  under Grant Agreement 603864 (HELIX). A. I. was supported by the  Environmental Research and Technology Development Fund  ( S-10 ) of the  Ministry of the Environment, Japan , and a  grant-in-aid  (no.
This helped us to better define syndynamics (i. e. , succession, transgression, regression, fluctuation, regeneration, degeneration) and to relatively adjust the human pressure related values during the assessment (according to  Faliński, 1999; Falińska, 2003; Pedrotti, 2013). Surfaces, distributions and phytolittoral were determined by ArcGIS 10. 2 software. In order to define plant communities, plant specimens were collected, examined, and identified following  Mirek et al. (2002) , whereas the syntaxonomic nomenclature follows  Matuszkiewicz (2008).
(2006). At this point, we considered only 14 among 22 lakes, because only they could be successfully compared using others indices. In order to verify the analysis undertaken for selected lakes: the Polish index (ESMI) and the English Trophic Ranking Scores (TRS) were specified  (Palmer et al. , 1992; Ciecierska, 2008). An analysis of lake similarity was made regarding three ecological zones A, B, C (in Statistica Software, ver. 10. 0). For this purpose, a hierarchical method based on the technique of agglomeration was used. About statistical analysis, concerning the point distance, we adopted the Euclidean distance, whereas the Ward's method was used to estimate the distance between the clusters  (Stanisz, 2007).
, 2006; Escoube et al. , 2012)  and our single measurement of procedural blank yielding  74/70 Ge ~ 1. 6 ‰. Seawater samples from the Pacific Ocean located SW of Hawaii (FeMo Deep station) and nearby Loihi Seamount show remarkable homogeneity in  74/70 Ge and Ge concentrations (Table  1 ). A comparison between unfiltered and filtered samples show a difference of less than 2 % in Ge concentrations and less than 0. 3 ‰ in  74/70 Ge values -both being close to the analytical uncertainties. Considering that both filtered and unfiltered samples were stored acidified to pH 1. 8 for several months, these results suggest that Ge occurs essentially in the dissolved form at these water depths (i. e. between 959 to 3497 m). Considering the entire dataset obtained for the composite samples recovered during cruise Loihi 2014, we obtained average values of  74/70 Ge = 3.
6 ± 3. 3 pM, which is higher but also within error of the intercept of 1. 27 ± 0. 24 pM reported previously using a much larger dataset  (Sutton et al. , 2010). The Ge isotopic profiles at Sars and WAP show distinct features. Firstly, surface waters are characterized by the heaviest δ 74/70 Ge values at ~ 3. 5 to 3. 7 ‰. Secondly, a minimum of δ 74/70 Ge is observed at mid-water depths, between 500 and 1000 m. Below 1000 m, δ 74/70 Ge values remain relatively constant at both stations, yielding an average of 3. 17 ± 0. 19 ‰ (2SD, n = 7) and 3. 21 ± 0. 21 ‰ (2SD, n = 5) for Sars and WAP respectively. Ge isotope compositions at the Burdwood Bank station also fall within a range of ~ 2. 9 to 3. 3 ‰, except for a single sample at 3250 m that show lighter δ 74/70 Ge at ~ 2. 4 ‰.
Germanium/Silicon and Ge isotope composition of deep sea sponges Modern sponges from the Southern Ocean present δ 74/70 Ge values ranging from 1. 79 to 2. 45 ‰ in agreement with previously reported δ 74/70 Ge values of Pacific Ocean sponges  (Rouxel et al. , 2006). The dataset include different species of demosponges (Demospongiae) and glass-sponges (Hexactinellida) sampled at water depths of 600 to 2266 m. As presented in Fig. 8 , no systematic relationship between δ 74/70 Ge values and other parameters could be identified. In particular, we did not find an inverse linear relationship between sponge δ 74/70 Ge and [Si], which has been previously reported for δ 30 Si and [Si]  (Hendry et al. , 2010). Compared to overlying seawater, sponges are fractionated toward light Ge isotopes, with a Ge isotope fractionation factor  74/70 Geopal-SW ranging from -0.
4. 4. A preliminary estimate of Ge isotope composition of seawater and implications for the global Ge budgetResults for the deep Pacific Ocean off Hawaii and deep Atlantic off Bermuda (BATS station) > 1000 m water depths show nearly identical δ 74/70 Ge of 3. 19 ± 0. 31 ‰ (2SD, n = 9) and 2. 93 ± 0. 10 ‰ (2SD, n = 2). Vertical distributions of Ge concentrations and isotope compositions in the Southern Ocean for water depths > 1300 m yielded an average δ 74/70 Ge value of 3. 13 ± 0. 2 5‰ (2SD, n = 14). These observations suggest that Ge isotope composition is relatively homogeneous in deep seawater and is not modified significantly through global thermohaline circulation and the biogenic uptake of dissolved silica. Hence, even with this relatively limited dataset, it is possible to determine a global deep seawater composition of approximately δ 74/70 Ge = 3.
Acknowledgements: Support for Rouxel was provided by LabexMer  ANR-10-LABX-19-01 ,  Europole Mer  and  FP7  (# 247837 ) grants. International mobility was provided to Guillermic by the LabexMer  ANR-10-LABX-19-01  and the  Royal Society  ( Hendry grant  # UF120084 ). Jill Sutton  is thanked for her advice. Emmanuel Ponzevera  (IFREMER) and  Yoan Germain  (IFREMER) are thanked for their technical support, and  Rosalind Rickaby  ( Univ. Oxford)  is thanked for the generous use of laboratory space. Brian Glazer ,  Gabrielle Weiss ,  Angelos Hannides ,  Kristen Fogaren  ( Univ. Hawaii ), and  Isabelle Baconnais  ( Univ. Brest ) are thanked for their support during cruise FK140626 aboard  R/V Falkor. Southern Ocean  water and sponge sample collection was funded by the  National Science Foundation (OPP ANT  grants # 0944474  and # 0902957 ;  PIs Robinson  and Waller).
Root and shoot system architectures were respectively recorded on four randomly selected plants per plot with a scanner equipped with WinRhizo and WinFolia softwares (Regent Instruments Inc. , Quebec, Canada) according to Rozier et al. 24. Stem diameter was measured with a hand-held digital caliper at the base of the plants (c. a. 5 mm above the seed). Roots and shoots of four randomly selected plants per plot were oven-dried (24 h, 60 °C) and weighted to estimate their dry weight. Roots and the most recent mature leaf (forth leaf from the seed) of four randomly-selected plants per plot were sampled and dipped in liquid nitrogen at harvest to inactivate enzymatic reactions. They were lyophilized and the soil attached to the roots carefully removed with a soft brush. Cleaned roots and leaves were cut into 1 mm pieces and ball-milled for 8 min at 30 Hz (TissueLyser II, Qiagen, Hilden, Germany).
Metabolites were extracted from 7 mg dry powder with a biphasic water/chloroform/methanol solvent system using ribitol (Sigma-Aldrich, St Louis, USA) as a standard according to Rozier et al. 24. Metabolites were silylated, analyzed by GC-MS (Agilent 7890 A and 7000 A, Santa Clara, USA), annotated using MetAlign and TagFinder softwares and their content estimated with MassHunter software (Agilent, Santa Clara, USA) according to Rozier et al. 24.
2B ). Plants from field FC differed from the others by having a larger stem diameter and a higher shoot biomass. In all fields, A. lipoferum CRT1-coated seeds yielded plants with different morphologies than those originating from mock-coated seeds. Their morphological and photosynthetic characteristics remained, nevertheless, field-specific (Fig. 2A, B ). Higher yields paralleled higher root-to-shoot biomass ratios, a condition that was most marked in non-inoculated plants of field Corg. Similar conclusions were also drawn with the 2014 dataset (data not shown). A PCA of the plant morphological and physiological characteristics also grouped samples according to their field and inoculation status. Higher yields were also associated with a lower photosynthetic potential and a higher root-to-shoot biomass ratio despite the fact that field Corg was not part of the analysis and that field FC had the highest yield in 2014 unlike in 2015.
To focus on the impact of inoculation on plant morphology, physiology and yield, and to diminish the impact of inter-bloc differences within fields, a similar PCA analysis was conducted on a dataset containing the percentage values of the modification of the plant parameters by A. lipoferum CRT1 seed coating on each bloc (five blocks per field). It separated the plants of field L from the others on the first axis (Fig. 2C ) in parallel with the yield enhancements due to A. lipoferum CRT1 in this field in 2015 (Table  2 ). With this representation, modifications of the morphological parameters correlated with each other according to the organ type and negatively correlated with the modifications of the photosynthetic potential (Fig. 2D ). Surprisingly, yield increase with A. lipoferum CRT1 did not associate with a bacterium-induced modification of the root-to-shoot biomass ratio.