dataesr/scientific-paragraphs-categorization huggingface dataset:
N 2 fixation rates obtained through the acetylene reduction method were excluded from this estimate due to the significant uncertainties described above. Applying these criteria, we selected 309 and 1642 depthintegrated N 2 fixation rates from version 1 and version 2, respectively. The greater number of data in version 2 potentially provided more constraints on estimating global marine N 2 fixation. We applied Chauvenet's criterion to identify outliers, using the log-transformed values of the selected data (see Sect. 2. 2). As a result, two high-value outliers were removed in version 1 (one in the North Pacific and one in the South Pacific) while no outliers were detected in version 2. This difference can be attributed to the larger number of data samples in version 2, which allowed for a more relaxed threshold in identifying outliers.
Diazotrophic abundance data Diazotroph cell abundances were determined by using standard light microscopy, and in some cases by using epifluorescence microscopy. A recent study used machine learning techniques to detect and enumerate diazotrophs in a large dataset of microscopic images  (Karlusich et al. , 2021). In the original database, only the cell abundances of Trichodesmium and heterocystous cyanobacteria were recorded. Version 2 also included datasets of enumerated abundance of all UCYN groups detecting them by tyramide signal amplification-FISH (TSA-FISH) using a specific DNA probe UCYN-238  (Biegala and Raimbault, 2008; Le Moal and Biegala, 2009; Le Moal et al. , 2011; Riou et al. , 2016). This method is also called CARD-FISH and was used specifically to enumerate UCYN-A  (Martínez-Pérez et al.
, 2016; Biegala and Raimbault, 2008; Le Moal et al. , 2011; Table 5). Cell abundance of Trichodesmium was recorded as the number of trichomes per volume of water in our database, although it was also reported in some studies as the number of cells or colonies per volume of water. In the latter cases, the data were converted to trichomes per volume of water by using a commonly used factor of 200 (132-241) trichomes colony -1  (Letelier and Karl, 1996) , similar to the conversion used in the original database  (Luo et al. , 2012). The abundance of heterocystous cyanobacterial cells was also recorded in this database. Based on the number of DDAs was reported in several studies, we assumed that two (reported range: 1-2) and five (reported range: 1-5) Richelia spp. filaments were associated with each Hemiaulus and Rhizosolenia cell, respectively  (Villareal et al.
Data distribution Version 2 of the database significantly expanded N 2 fixation rate measurements, filling spatial gaps, particularly in the In-dian Ocean and the Southern Hemisphere (Table  1 ; Figs. 2a  and b, 3a  and b ). The number of depth-integrated N 2 fixation rate measurements was tripled (Table  1 ; Figs. 2b  and 3b ). The largest fraction of new data derived from inclusion of nifH gene abundances, in particular data contributions from the Pacific and Atlantic oceans (Table  3 ; Figs. 2e  and f, 3e  and f ). Compared to other parameters, the new database contained only a modest increase in new cell abundances, mostly from subtropical oceans (Table  2 ; Figs. 2c  and d, 3c  and d ). Overall, there remained more limited data on N 2 fixation and diazotrophic abundance in the Arctic and Southern oceans, with a number of rate measurements reporting values below detection limits.
14c ). This could be partially attributed to enhanced detection limits in measurements. Consequently, while version 2 yielded a much higher arithmetic mean N 2 fixation rate compared to version 1 for the South Pacific Ocean (Table  8 ), their geometric means remained quite similar (Table  9 ). In the North Pacific Ocean, for the same reasons, the arithmetic mean N 2 fixation rates obtained from both versions were very close, while the geometric mean of version 1 was even higher than that of version 2 (Tables  8  and 9 ; Fig. 14a ). These analyses reveal that, despite the similarity in geometric means of N 2 fixation rates obtained from both versions of the database, the higher arith-metic means in version 2 were not coincidental. Instead, they were the direct outcome of the improved measurement methods and the expanded spatial and temporal coverage of marine N 2 fixation over the past decade.
Consequently, previous assessments of the global marine N 2 fixation rate were likely underestimated due to the absence of these new measurements. We must emphasize that this calculation simply used the average N 2 fixation rates in different ocean basins; therefore, our calculation can only be considered a first-order estimate. Furthermore, limited measurements have shown a large range of N 2 fixation rates in the Southern Ocean (Fig. 8 ). Considering its vast area, future measurements expanding coverage of N 2 fixation rates in the Southern Ocean (see  White et al. , 2022)  may help to better constrain the contribution of N 2 fixation to the N budget of the global ocean. The new database presented here also expands opportunities for improved statistical estimates of N 2 fixation patterns and global rates based on the modeling of environmental controls  (Luo et al.
Discussion 4. 1 Comparison of N 2 fixation measured using 15 N 2 bubble and dissolution methods To date, the origin of the discrepancy in the N 2 fixation rates estimated using different 15 N 2 tracer methods remains unclear. As shown above, the volumetric N 2 fixation rates obtained by the original 15 N 2 bubble method and the 15 N 2 dissolution method spanned a similar range (Fig. 1 ), while the average rates using the former method were significantly lower than that measured using the latter method (one-tailed Wilcoxon test, p < 0. 001, n = 2460 and 1128). With substantial data accumulated over the past decade, we further compared N 2 fixation rates measured using the two methods at close locations and sampling time, although the samples were not identical. We first binned data collected from the same months, horizontal locations (3 • latitude × 3 • longitude), and depth intervals (0-5, 5-25, 25-100, and 100-200 m) and calculated the average rates for each method in each bin.
(2018). While counting all Trichodesmium cells may be impractical, it would be valuable to report the number of cells in random samples of Trichodesmium trichomes. The same analyses for heterocystous cyanobacteria showed that the nifH gene copy abundances were approximately 2 orders of magnitude greater than the cell abundances in terms of both mean and distribution (Fig. 16b  and c ). It must be noted that this simple analysis used all the data in our database. The limited in situ measurements for identical samples resulted in a mean nifH : cell ratio of 76 for heterocystous cyanobacteria (Table  S2 ), consistent with our simple analysis. In contrast, much lower nifH : cell ratios (1. 51-2. 58) were derived from regression analysis for heterocystous cyanobacteria and UCYN-B collected in the subtropical North Pacific  (Gradoville et al.
g. , Trichodesmium, heterocystous cyanobacteria (e. g. , Riche- lia, Calothrix, Anabaena, Nodularia, Aphanizomenon), and some unicellulars (e. g. , Cyanothece, later Crocosphaera). Thus, qPCR enabled the study of diazotrophic targets (and their activity) without the need for microscopy to identify them, which came later as some diazotrophs did (and still do) require application of FISH techniques for identification  (Biegala and Raimbault, 2008). Additionally, qPCR allowed the study of in situ activity (gene expression) by diazotrophs without the need for cultivation. Although beyond the scope of the work presented here, important considerations should be taken into account when applying microscopy and qPCR datasets (Table  S3 ), for example, to biogeochemical models  (Meiler et al. , 2023).
, 2012; Hagino et al. , 2013). Because the host and UCYN-A should function together, the host biomass is allocated to UCYN-A. It has been reported that each haptophyte cell hosts one UCYN-A1 cell  (Cornejo-Castillo et al. , 2019)  or one UCYN-A2 cell  (Suzuki et al. , 2021). We used the empirically derived equation  (Verity et al. , 1992 ) C = 0. 433 × V 0. 863 (1) to estimate the biomass of UCYN-A and their hosts. The biomasses of a UCYN-A1 cell with a diameter of 1 µm and a UCYN-A2 cell with a diameter of 1. 6-3. 3 µm  (Cornejo-Castillo et al. , 2019; Martínez-Pérez et al. , 2016)  are 0. 2 and Because heterocystous cyanobacteria and their host diatoms form DDAs, similar to UCYN-A, we also suggest allocating the biomass of host diatoms to each associated diazotrophic cell (Table  S4 ). The biomasses of heterocystous cells and vegetative cells in Richelia filaments were updated according to the cell dimension data reported in  Caputo et al.
(2019)  using the same empirical equation above. The carbon biomass of host diatom cells was calculated using an empirical equation  (Menden-Deuer and Lessard, 2000) : C = 0. 117 × V 0. 881 , ( 2 ) where C is the diatom cell carbon biomass (pg C cell -1 ) and V is the average cell biovolume (µm 3 ) of each diatom genus, for which values from a database  (Harrison et al. , 2015)  were used in this study (Table  S4 ). Each host diatom associates with multiple heterocysts. The numbers of Richelia heterocysts associated with Hemiaulus, Rhizosolenia, and Chaetoceros were observed to be within the range of 1-2, 1-5, and 3-10 respectively  (Villareal et al. , 2011; Yeung et al. , 2012; Caputo et al. , 2019) ; we selected both the maximum and min-imum to do the estimation. The number of vegetative cells in each heterocyst was also updated according to  Caputo et al.
Conclusions In this study, we updated the global oceanic diazotrophic database by  Luo et al. (2012)  by adding new measurements reported in the past decade. Although the spatial coverage of the data was greatly expanded by this effort, the data distribution is still uneven, with most measurements reported from the Pacific and Atlantic oceans. Using the updated database, the estimation of global oceanic N 2 fixation based on arithmetic rates in ocean basins was increased from 74 ± 7 to 223 ± 30 Tg N yr -1. This change is largely attributable to a new estimate for the Indian Ocean and a much elevated estimate for the South Pacific Ocean, the latter of which would account for ∼ 40 % of global N 2 fixation. This high estimation for the South Pacific Ocean is in line with its qualification as a hotspot for diazotrophy  (Messer et al.
, 2016; Bonnet et al. , 2017) , partly due to iron fertilization processes in this region  (Bonnet et al. , 2023). Due to data sparsity, our updated estimation did not include N 2 fixation in the Southern and Arctic oceans. Furthermore, data were more concentrated in surface seawater, and a significant amount of data were measured with incubation periods shorter than a daily cycle (24 h), limiting reliable evaluations of depth-integrated N 2 fixation rates. Although this result suggests more balanced N inputs and losses in the global ocean than the previous estimate suggested, large uncertainties still exist. We also compared the N 2 fixation rates measured using the addition of a bubble of labeled gas or the addition of dissolving https://doi. org/10. 5194/essd-15-3673-2023 Earth Syst. Sci.
Data, 15, 3673-3709, 2023 Table  10. Recommended carbon biomass conversion factors and their likely ranges for diazotrophic groups. 15 N 2 gases reported at the same location and month (not necessarily in identical samples). The results indicated that the original 15 N 2 bubble method produces lower rates than the 15 N 2 dissolution method in 69 % of the cases. These results reveal that, despite decades of effort, the ocean is still undersampled in terms of the distribution of diazotrophs and N 2 fixation rate measurements. Our analyses suggest that prioritizing N 2 fixation measurements in the South Pacific Ocean, Indian Ocean, and high northern latitudes can significantly reduce the current uncertainty of N 2 fixation rates in the global ocean. Nevertheless, we believe that this updated diazotrophic database, supplemented with enhanced data from the past decade, is timely and can be helpful to scientists studying the marine biogeochemical cycle of N.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-3673-2023-supplement. Financial support. This research has been supported by the National Natural Science Foundation of China (grant nos. 41890802 and 42076153). Individual authors were also supported by other awards. Review statement. This paper was edited by Xingchen Wang and reviewed by Christopher Somes and one anonymous referee.
Sampling date (Julian day) was included as a predictor variable in all StDMs to incorporate any natural changes in element concentration over time. All continuous variables were included as a second order polynomial to allow for non-linearity in the data. To assess the predictive performance of the StDMs, we ran each element specific GLM 100 times and for each iteration split the data into a new training (75%) and testing (25%) set for which we calculated the MSE (using the testing data) and R 2 (using the training data). We then compared the distribution of MSE and R 2 of the testing and training data to those generated by 100 models where the response variable of the original data was randomly resampled. We considered the performance of the StDMs to be satisfactory as the prediction error was always lower and the amount of variation explained was always higher than for the models based on randomized datasets (see Figs.
Funding This study was financially supported by the  Danish Environmental Protection Agency  (J. nr. 2019-7208 ), by  ANR   JCJC  ( ANR-21-CE34-0001 ATCAF- project), the  Carlsberg foundation  ( CF17-0097  and  CF18-0279 ) and  Independent Research Fund Denmark/ DFF Nature and Universe , grant No. 0135-00140B.
Tracking post-processing The 2D positions of each post-larval fish in groups of 5 were detected at a sampling rate of 5Hz. This automated process is not error-free: in some frames, fewer than five blobs were detected (low signal for smaller juveniles swimming at greater depth or due to overlaps) or more than five blobs (fish reflecting on the Plexiglas when swimming close to the aquarium sides). The tracking postprocessing pipeline using Python scripts involved seven steps (Figure  1D ). First, frames for which only one or two fish were detected were removed to reduce group mean value noise. Second, reflection biases mentioned above were limited by removing the outermost blob when a pair of fish and wallreflected fish was potentially detected (i. e. , when two vertically-or horizontally-aligned blobs were very close to each other and to the edge of the aquarium).
Lastly, the time-series for each of the four behavioural measures (group centre Xand Y-position, dispersion and individual velocities) were binned into 5-second intervals for the statistical tests, and into 1-second intervals to find the best behavioural model fit. Detected 2D positions were used to characterize post-larvae behavioural responses. For each condition, the overall behaviour obtained combining data from all tested groups is visible in the animation of the 2D density heat maps generated every second. The X and Y positions of the centre of the groups, the individual velocities and the dispersions were averaged across time-bins of 5s to perform statistical comparisons (Student's t-tests), and across time-bins of 1s to fit the behavioural models (regressions). Individual velocities are extracted from the reconstruction of the trajectories, which was based on the identification of each fish from one frame to another (see text).
The average behavioural measures obtained for the tested groups (n=12 or n=16), binned in 1-second intervals, were fitted using each of the three models. The fit quality was estimated with the root mean square error (RMSE) between the average data points and the model predictions. In order to avoid data over-fitting, we used a limited number of models and selected the best model based on the trade-off between fit quality (RMSE) and the number of parameters. Linear models (for static conditions) and linear periodic models (for dynamic conditions) have one parameter fewer than the quadratic and exponential models. They were favoured when the RMSE difference with the other models was below 5% (e. g. , if the linear and quadratic fits had RMSEs of 10 and 9. 6 resp. , the linear model was selected).
Acknowledgements We would like to thank the technical staff of the  CRIOBE  for their support, and in particular  Matthieu Reynaud  for capturing the larvae used in the experiments. This work received support from the  French government  under the  France 2030 investment plan , as part of the  Initiative d'Excellence d'Aix-Marseille Université -A*MIDEX , from the  Fondation de France  ( 2019-08602 ), from the  Office Français de la Biodiversité  ( AFB/2019/385 -OFB. 20. 0888 ), and from the  Agence Nationale de la Recherche  ( ANR-19-CE34-0006-Manini  and  ANR-19-CE14-0010-SENSO ).
Comparisons between relevant trials (paired Student t-tests) and the deviation from zero of the group's X and Y positions (Student t-tests against a single value of 0) were conducted for each time bin. The alpha value for significance was adjusted using Bonferroni's correction for multiple comparisons on a single dataset and visualized in the plots using different grey levels (white for P>0. 05 and from light grey for P<0. 05/1 to black for P<0. 05/n Tests with n Tests = 3 for either the 3 paired comparisons or the 3 single-value comparisons). Plots displaying the timeseries of the four behavioural measures (binned in 5-second intervals) and the results from the statistical comparisons are provided in the Appendix Fig A5, Fig A9 and Fig A13 for Experiment 1, 2 and 3, respectively.
Instead of individual tree or the sample-plots observations most frequently used to study mortality patterns, we used wall-to-wall data on forest mortality covering 750,000 ha, including 47,450 managed Scots pine stands from southern Poland, of which 2547 were heavily affected by mortality during the drought in 2015-2019. By drought-induced mortality we also mean mortality by other causes facilitated by drought such as insect and fungal pathogen gradations  (Boczoń et al. , 2021; Mezei et al. , 2017; Nordkvist et al. , 2023). To eliminate the impact of background mortality and therefore avoid interpreting self-thinning and random events involving small gaps in stands as drought-induced mortality, we defined stand-level mortality as those in which the area occupied by dead trees removed within sanitation felling exceeded assumed threshold of 1000 m 2.
Sanitation felling data Sanitation felling data were used to analyse the effect of drought on mortality intensity of Scots pine in the research area and for modelling stand-level mortality probability. Sanitation felling is a harvest of trees for the purpose of removing insects or diseases from a stand  (Pietzsch et al. , 2021). In forests managed by State Forests in Poland sanitation felling are mandatory for forest protection reasons. Depending on the occurrence of tree mortality, sanitation felling is carried out on an ongoing basis. The analyses used data on the total volume of timber harvested within sanitation felling in each stand in each year over the last decade. Thanks to the annual resolution, the sanitation felling data allows to follow the pattern of forest mortality at stand-level scale in different years.
For stands with mortality in which sanitation felling is carried out, evidence cards are established in State Forest information system. The cause of mortality and volume of the dead trees are documented. The volume of each dead tree removed as sanitation felling is individually measured, however the sanitation felling database contains the standlevel records with aggregated volume of dead trees removed each year attributed to one of two groups of causes of mortality: 1) drought/pests and 2) windbreaks. In this study only the data from group 1) -drought/ pests, were used. In the majority of the stands subjected to sanitation felling, the total volume of trees harvested in sanitation felling did not exceed 2 m 3 (Supplementary Fig. 1 ). In all age classes, the size of total volume of trees removed from single stand within sanitation felling in 2015-2019 most often did not exceed 2% of the stand volume (Supplementary Fig.
Relationship between intensity of mortality and climatic water balance To determine the relationship between tree mortality intensity and drought, we analysed the relationship between the yearly aggregated volume of dead trees harvested on the research area as sanitation felling and climatic water balance (CWB) in particular years. Short-term (bimonthly) data on climatic water balance (CWB), which is calculated as a difference between precipitation and potential evapotranspiration are publicly available as part of the Agricultural Drought Monitoring System program, developed by the Institute of Soil Science and Plant Cultivation -National Research Institute in Puławy  (MINROL, 2023). Bimonthly CWB is published with a 10-day step. To assess the intensity of drought in individual years, averaged CWB values were determined for the period April-September and for the period May-August.
Measurement of the detailed characteristics of individual stands using airborne laser scanning point clouds To measure characteristics of individual forest stands, we used airborne laser scanning (ALS) data. The ALS point clouds were collected between 2011 and 2015. The density of the point cloud was at least four pulses per square metre. The data were collected in leaf-off conditions (October to April) with the maximum scan angle at a level of 25 •. The ALS data were processed using algorithms available in lidR package for R  (Roussel et al. , 2020) : the point clouds were normalised to the height above ground using the "normalize_height" algorithm; for creating the Canopy Height Model (CHM) we used the "p2r" algorithm which uses the maximum point height as value for CHM (the subcircle parameter was set to 0.
Determination of remaining stand and site characteristics ALS-derived top height and stand age retrieved from the Forest Data Bank (FDB, 2018) were used to calculate the SI, which was used to describe site productivity. The study concerns managed Scots pine stands that are even-aged and mainly originate from artificial regeneration. As management plans have been in place for nearly 150 years in the study area, the age of the managed Scots pine stands is well documented. SI was calculated using the base age 100 years according to the model developed for Scots pine in Poland  (Socha et al. , 2020c). The remaining stand and site properties were estimated using the data from FDB: age, dominant species, percentage share of dominant species, soil type, site type, the humidity of site (wet, fresh, dry).
Verification of mortality events in individual stands The Scots pine stands indicated in the database as affected by mortality in 2015-2019 attributed to drought/pests have been verified using the land cover change masque obtained from the Landsat imagery. The first step of this process consisted of two land cover classifications using imagery from July 29, 2013 (Landsat 5) and August 31, 2019 (Landsat 8). Land cover classes included deciduous forest, coniferous forest, low vegetation, and other areas. Reference samples for training and validation were manually delineated based on the Landsat images; 275 samples were prepared for 2013 and 293 for 2019. The classification was performed using the Random Forest (RF) algorithm. RF is a nonparametric, ensemble classifier consisting of a combination of decision trees  (Breiman L.