dataesr/scientific-paragraphs-categorization huggingface dataset:
Effective benchmarking depends on the development of a regional database of agricultural soils, which this work begins. Such a database might follow the model of continental iSDA, which aggregates data from across government, nonprofit, and for-profit stakeholder sources  (Hengl et al. , 2021) , but in contrast with iSDA's extensive reliance on legacy data, focus specifically on contemporary samples collected recurrently from agricultural soils. Such a momentous data collection effort could be facilitated by advancements in soil testing and data aggregation through smartphone-enabled crowdsourcing and low cost, field-portable instrumentation  (Ewing et al. , 2021; Herrick et al. , 2013; Kelly et al. , 2022). Doing so could create a robust and broad set of ground measures from samples taken across the region  (Paustian, Collins, et al.
, 2019; Snapp, 2022). Beyond sub-Saharan Africa, this information could be incorporated into continental and global databases to support work in agriculture broadly, and carbon investment specifically. Understanding the potential for carbon storage is critical not only for smallholders in Malawi, but also for global adaptation and mitigation of climate change. Higher carbon soils would likely improve smallholder productivity which, when coupled with payments for carbon storage, could also increase household economic security, enhancing overall wellbeing in a country where over 80% of people live in rural areas  (Malawi National Statistical Office, 2016).
We compiled a new dataset representing 402 species sampled from 52 sites spanning four continents across the Neotropics, Paleotropics and subtropics (Supplementary Fig. 3  and Supplementary Table  1 ). These are regions where the role of soil P in regulating productivity is expected to be especially important  22, 38. The dataset includes species from over one-fifth of all known angiosperm families (Supplementary Table  2 ), encompassing a substantial part of global taxonomic richness and representative of the high plant diversity in the tropics and subtropics (Supplementary Fig. 4 ). Leaf P concentrations covered a wide range (50-fold) in contrast to previous analyses that spanned about 10fold in the upper part of the range (Supplementary Fig. 2 ). We compiled raw data for net photosynthesis responses to CO 2 concentration ("A net -C i curves"), calculating light-saturated photosynthetic rates and biochemical parameters V cmax and J max (electron-transport enabling RuBP regeneration capacity) by inverting the FvCB model  39.
The final dataset consisted of species-at-site mean values (n = 446 across sites) for light-saturated maximum net photosynthetic rate (A net ), V cmax , J max , and their mass-based quantities (A net_mass , V cmax_mass , J max_mass ) as well as leaf N and P concentrations (N mass and P mass , respectively), along with a key leaf structural trait, leaf dry mass per area (M a ). Here we demonstrate a dependence of photosynthetic biochemistry (V cmax and J max ) on leaf N  12, 17  but also leaf P on an unprecedented scale, across continents with different underlying soils and parent material geology and diverse plant taxa. We tested for effects of leaf P on J max in accordance with theory  24, 25  , and employed the new relationships in TBM scenarios for gross primary productivity across the tropics and subtropics to establish how these new relationships affect land-atmosphere CO 2 exchange relevant to atmospheric CO 2 drawdown across the tropics.
Results Low leaf P status clearly diminished V cmax -N and J max -N relationships on a mass basis (Fig. 1a, b  and Table  1 ). We determined this in a regression framework where we defined "low-P" status of plants based on a threshold for leaf P (P mass of 0. 92 mg g -1 ). Similar relationships held at a range of leaf P mass threshold values (see slopes analysed in Supplementary Fig. 5 ). The V cmax_mass -N mass slope was twice as steep for "moderate-P" species in this dataset as for low-P species (Fig. 1a ; Table  1 ): for a 5-fold increase in leaf N mass , V cmax_mass of moderate-P species increased 3. 3-fold whereas that of low-P species increased just 1. 8-fold. Similarly, J max_mass -N mass relationships were nearly 2-fold steeper for moderate-P species than for low-P species (Fig. 1b ; Table  1 ), as were A net_mass -N mass relationships (Table  1 ).
The V cmax -N and J max -N data for low P and moderate P species converged at low N mass but at higher N mass the fitted slopes diverged (P < 0. 01, Table  1 ). At a leaf N mass of 20 mg g -1 , near the median N mass of our data, both V cmax_mass and J max_mass increased by ~40% for species from low to moderate leaf P concentrations (Fig. 1a, b ). This clear inhibitory effect of low leaf P on photosynthetic-N relationships was observed when slopes were fit as either least-squares regressions or as standardised major axes (SMA) (Table  1  and Supplementary Table  3 ; see Methods and Supplementary text). Relationships between photosynthetic variables and leaf P mass are much less common in the literature than N-based relationships  11, 25  , but in this dataset P-based relationships were no less significant than the more common relationships with N (Fig.
1c, d ). In fact, in this predominately pan-tropical dataset (Table  1 ), leaf P mass on its own generally explained more variation in maximum photosynthesis rates and biochemistry per unit mass (V cmax_mass and especially J max_mass , Fig. 1c, d ) than did leaf N on its own. This demonstrates a strong modulation of photosynthetic biochemical capacity by leaf P for diverse broadleaved plants. For either mass-and area-based J max , the explanatory power (r 2 ) was about 9-13% higher for leaf P than for leaf N, with associated reductions in mean-square errors (Table  2 ) demonstrating leaf P effects on photosynthesis and the capacity for RuBP regeneration (J max_mass ; Fig. 1 ). 3. Analyses were done using ordinary least-square (OLS) regressions for different P status levels and all P levels together ('All P').
We further tested whether J max_mass -P mass slopes fitted to individual continents differed from slopes fitted to the remainder of the dataset. Differences among continents might occur, for example, as soil orders and the predominance of ancient eroded bedrock can differ substantially among regions  23, 38. However, continent-specific slope differences in J max_mass -P mass were not observed (Fig. 2 , P > 0. 1), nor was the J max_mass -P mass slope different for any continent compared to that of the remainder of the dataset (P > 0. 05, using continent as a covariate; Supplementary Table  4 ). There were similar results for V cmax_mass -P mass (Supplementary Table  4 ). Thus the observed relationships between leaf P and photosynthetic biochemistry are robust and convergent across continents.
Furthermore, climate parameters were not a significant covariate for these relationships (Supplementary Fig. 6 ). There were important taxonomic differences exhibited among the species sampled across continents (Supplementary Table  2 ), a characteristic feature of the diverse species richness in tropical forests. Hence the convergence in photosynthetic biochemistry-P mass relationships across continents was particularly surprising, lending support for the robustness of these relationships and their utility in TBMs. While soil P is generally believed to not routinely limit productivity in northern temperate ecosystems  35, 42  , a limited dataset from temperate zone Northern Hemisphere analysed in the same manner as our large and diverse cross-continent dataset was combined with the relevant temperate data from TRY  43.
Though still a very limited dataset relative to the tropical and subtropical species were analysed in Fig. 1 , results in Supplementary Fig. 7  were broadly consistent with the larger dataset of broadleaved evergreen species in Fig. 1. Considering N mass and P mass effects on photosynthetic traits in a multivariate regression framework (Table  2 ) led to conclusions consistent with the above analyses that grouped P mass into low and moderate categories (Fig. 1 ). That is, we observed statistically significant N mass × P mass interactions with V cmax_mass and J max_mass (interactions in all cases, P < 0. 001; Table  2 , Supplementary Fig. 8 ), indicating flatter trait relationships at lower P mass. Similar results of a reduced slope at low P mass were found when variation in M a was also accounted for in the model (Table  2 ).
Together, leaf N mass and P mass (and their interaction) explained 43% and 48% of variation in V cmax_mass and J max_mass , respectively (Table  2 ). Moderate multicollinearity was observed with a significant correlation between N mass and P mass (r 2 = 0. 39, P < 0. 0001) across the dataset. However, this does not affect predictability of V cmax_mass or J max_mass from N mass and P mass 44. On an area basis, V cmax -N area and J max -N area relationships showed significantly lower intercepts at lower leaf P status (all with P < 0. 02), rather than differences in slopes (Supplementary Table  3 ). That is, at any given leaf N area , lower P area leaves tended to have lower area-based V cmax , J max and A net. As further evidence of leaf P effects on the J max component of photosynthesis, as expected by theory  24, 27  , there was a reduction in the slope of the relationship between J max and V cmax that depended on leaf P status (leaf P mass classes; Fig.
Instead, the striking convergence in our results from subtropical and tropical sites points to general mechanisms that may be in play for C 3 plants from other biomes where low-P soils occur. There is evidence that relationships like J max_mass -P mass are generalizable to Northern Hemisphere temperate woody plants (Refs. 19, 49, and Supplementary Fig. 7 ) but in this regard there is a clear need for further work involving temperate coniferous and deciduous trees. In fact, in the extensive TRY database, there is a paucity of Northern Hemisphere temperate records (Supplementary Fig. 7 ), particularly involving species-at-site values for J max , M a and P mass measured together. We identify this as an area for further research, involving both broadleaved and needle-leaved temperate and boreal species.
The cross-continent relationships for V cmax_mass and J max_mass with P mass that we have presented in Fig. 1c, d   Leaf P mass (mg g -1 ) J max_mass (nmol g -1 s -1 ) (d) Other Contin. Australia Fig. 2  | Comparison of J max_mass as a function of P mass for woody species on different continents. Each continent is shown compared to the full remainder of the dataset (denoted 'Other Continents'), for a South America, (b) Africa, (c) Asia, (d) Australia). Each point denotes a different species-site mean. There was no significant continent effect in the analysis (P > 0. 05; see Supplementary Table  5 ). The 95% CI for each relationship around each line is shown in grey for 'Other Continents', and the 95% CI for each continent is shaded in the corresponding colour for each continent being compared.
Either phenomenon would tend to shift the capacity toward an optimal balance between the two functional components of the photosynthetic apparatus in leaves at the top of the canopy. Such shifts explain why the J max /V cmax ratio is nearly constant in sunlit leaves across a wide range of situations  28, 49. However, despite the arguments and evidence of constancy for the J max /V cmax ratio  52  , the different roles of N and P in primary photosynthetic reactions (Supplementary Fig. 1 ) implies a small shift in J max /V cmax ratio with respect to lower leaf P mass that is consistent with our observations (Fig. 3 ). The J max /V cmax shift with increasing P mass in our dataset is not large (e. g. , a 10% reduction with low P mass ; Fig. 3 ), supporting a functional balance for the components of the photosynthetic apparatus  28.
9 ). The general and robust relationships of photosynthetic parameters with leaf P mass and N mass (Fig. 2 ) along with N-P interactions (Fig. 3 ) could readily be included by other terrestrial biosphere models  8, 57, 58  either with a P biogeochemistry submodel  40  or with existing global datasets of P spatial variability  59. This would ensure that GPP was appropriately constrained by both leaf N and P as two major limiting macronutrients around the globe  22, 23. The regulation of photosynthesis by leaf P and its effect on ratelimiting biochemical parameters has demonstrable consequences for large-scale forest C uptake (Fig. 4 ) with important implications for understanding the C cycle not only for the tropics but for low-P sites around the world. From our findings of consistent P constraints on photosynthetic biochemistry across continents, we argue that there is no longer any basis for ignoring the P effects on photosynthesis in TBMs even if P is not uniformly low throughout the tropics  23, 58.
Climate data not available from direct measurements at the sites were estimated based on gridded climate data  73. Only naturally occurring trees, shrubs and lianas at mature life stages were included in the data. Gaining access by construction-style cranes, leaves were sampled at considerable heights (20 m to 70 m above the ground) at six of the sites (Lambir Hills National Park, Sarawak, Malaysia; Bubeng, China; San Lorenzo National Park, Panama; Parque Natural Metropólitano, Panama; EucFACE, NSW, Australia; and Cape Tribulation, Queensland, Australia). For the remainder of the studies (n = 46 sites) leaves were sampled on branches that had been collected from mid-to-upper canopy positions, placed in water and recut to maintain a viable water supply. All leaves were identified by data contributors as "sunlit" to represent photosynthetic function in the sunlit portion of the tree canopy.
We required analyses of P concentrations as well as N concentrations for the dataset (Supplementary Fig. 10 ). Multiple individuals were measured for most of the species, and these data were averaged for a species-at-site average that was used in the analyses (n = 471 species-site values, n = 446 complete with both N mass and P mass ). Unlike previous such analyses  29, 32  , we focused our analysis on species-level variation, given that species described the largest source of variation in leaf P  74  (Supplementary Fig. 11 ). This approach also avoided excessive weight given to particular species that were represented by many multiple sample leaves in the analyses and minimised the possibility of finding statistically significant correlations due to a large number of data points but with low predictive power  61.
A small, limited dataset from Europe and North America that was compiled from direct measurements and from the TRY database  43  analysed in the same manner as our large and diverse cross-continent dataset (Supplementary Fig. 8 ) to compare the results with deciduous and gymnosperm species (five species of Quercus and Pinus, as major Northern Hemisphere genera). To ensure that drought did not confound our results, measurements were collected as much as possible during the wet season or the early part of the dry season. Also, leaves that showed very low stomatal conductances (<30 mmol H 2 O m -2 s -1 ) were removed from the analysis as photosynthetic metabolism in such cases could either be limited by low nutrients or low conductance to CO 2 diffusion and hence would not be diagnostic for low N and P concentrations.
We also ensured that A mass > 20 nmol g -1 s -1 and the curve-fit CV < 30% for the initial slope as criteria for inclusion to the overall dataset, consistent with previous analyses of photosynthetic capacity  19, 60, 75. As leaf P mass and other variables were approximately log-normally distributed, we transformed these variables appropriately in the analyses. In some analyses we treated leaf P as a covariate and grouped species into two leaf P classes: "moderate P" and "low P", based on a threshold corresponding to the median leaf P concentration in the dataset (leaf P mass of 0. 92 mg g -1 ) similar to what was used previously  19. In so doing, we recognize that low leaf P mass may not always reflect soil availability due to species-level mechanisms that can affect P uptake  50  , though leaf P mass is most relevant to leaf internal physiology.
Data fitting and statistical analyses The curve fitting used the plantecophys package  39  for least-squares minimisation in R. The fits were obtained using an inversion of the FvCB biochemical model of leaf photosynthesis  30  which is employed in the land surface portion of the World Climate Research Programme's CMIP6 models  77  and many other land surface models  21. Enzyme kinetic constants are used to compute V cmax and J max normalised to 25 °C according to functions representing acclimation and adaptation of photosynthetic temperature response kinetics  78  using site temperatures summarised in Supplementary Table  1  and Ref. 73. They are reported in all figures as temperature-normalised to 25 °C. We made common assumptions about the kinetic coefficients for the Rubisco enzyme and biophysical constants in the model for all species apart from these photosynthetic Arrhenius temperature response parameters in Ref.
7-22. 6 in Ref. 68) to an N:P range of 5-60, corresponding to the 25th and 75th percentiles of the N:P in the leaf photosynthesis dataset. As a result of the wider range of leaf N:P ratios, we replaced the scaling function for plant P acquisition processes (biochemical mineralisation and root uptake, Ref. 40) by a sigmoidal function [f(PN plant )] and chose the coefficient such that processes sharply increases between plant labile N:P ratio of 15 to 25: f PN plant = 1 1 + exp À N:P 2 À Á + 10 À Á À Á ð1Þ The N:P ratio of 15 roughly corresponds to the ratio where plant communities have shown a switch from N to P limitation  23, 81  and we chose this ratio as the middle of the co-limitation range. With the modified model, we performed pan-tropical and subtropical simulation on a 2°× 2°spatial resolution using the simulation protocol in Ref.
82. The protocol takes into account historic changes in land cover, CO 2 concentration, climate, and N and P deposition since 1860 (called 'experiment S1' in Ref. 82). Climate forcing was derived from the CRUNCEP v. 7 meteorological dataset (National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) and Climatic Research Unit-University of East Anglia). For the simulations, first the cycles of C, N and P were brought into equilibrium (<1% in global stocks) with the boundary conditions of 1860. Second, the simulation was continued to 2012 using time series of land cover (SYNMAP), climate (CRUNCEP7), atmospheric deposition, fertilizer and CO 2 concentration (NOAA GLOBALVIEW-CO 2 dataset). For the analysis, we used the average GPP over a 21-year period (1992-2012) to represent the present-day productivity of grid cells and evaluate how the mathematical formulation for photosynthesis involving P would affect vegetation GPP.
Subsequent to the modelling, data was added from two other sites, but the relationships with N and P remained similar to those used in the modelling (Fig. 3 , Table  2 ). The relationships used by the model did not use the N × P interaction term (Table  2 ). Nonetheless, we found a less-pronounced effect of leaf P on GPP by about 15% with these relationships compared to the relationships based on the complete dataset shown in Table  2 , so the model results we report in Fig. 4b-d  are slightly more conservative than if we had implemented the relationships from the full dataset.
Acknowledgements This work was supported in part by grants from the  Australian Research Council Discovery scheme  (grants  DP160102452  and  DP210100115 ) and the  NSW Research Attraction and Acceleration Program  (independent grants to D. S. E. and M. D. K. ). D. S. E. also acknowledges research fellowships through the  Chinese Academy of Sciences President's International Fellowship Initiative , Grant No. 2018VBA0015 , and the  German Academic Exchange program  (DAAD). M. D. K. acknowledge support from the  Australian Research Council Centre of Excellence for Climate Extremes  ( CE170100023 ), the  ARC Discovery Grant  ( DP190101823 ). I. J. W. acknowledges support by  Australian Research Council  ( DP170103410 ). Y. S. , P. C. , D. S. G. , L. T. V. and I. A. J. are funded by the " IMBALANCE-P " project of the  European Research Council  ( ERC-2013-SyG-610028 ).
A. P. W. was also supported by the  US DOE, Office of Science, Office of Biological and Environmental Research  at  Oak Ridge National Laboratory , which is managed by  UT-Battelle, LLC , for the  US DOE  under contract  DE-AC05-00OR22725. J. L. Z. received funding from the  National Natural Science Foundation of China  ( 31870385 ) and the  CAS 135 program  ( 2017XTBG-F01 ). K. J. B. , T. F. D. , F. Y. I. and P. M. were supported by the  UK National Environment Research Council 'Tropical Biomes in Transition (TROBIT) ' consortium via research grant  NE/ D01185x/1  to the  University of Edinburgh. T. F. D. and S. G. received funds from  USAID  for funding via the  PEER program  (grant agreement  AID-OAA-A-11-00012 ). The contribution of P. R. was supported by the  U. S. NSF Biological Integration Institutes  grant  DBI-2021898.
Such variants have been associated with various neurological and psychiatric diseases including Charcot-Marie-Tooth neuropathy  9  , schizophrenia  10  and autism  11, 12. Attempts to understand the relationship of structural variation with ALS have been limited by sequencing technology, computational burden, and the small number of samples  13, 14. Measuring the intensity of signals derived from a genotyping array is the most used method in detecting copy number variants  15, 16  , but advances in sequencing technology and increased computing power have now made it feasible to study structural variation by more direct means  17. Here, we report the analysis of structural variation in known ALS genes using 6,580 whole genome sequences and genotypephenotype correlations, using the Project MinE whole genome sequencing and deep phenotype dataset  18.
Age of onset and age of death analyses The mean age of onset for all people with apparently sporadic ALS was 60. 7 years (SD 11. 84) and the mean age at death was 65. 3 years (SD 10. 61). The Kolmogorov-Smirnov test showed nonnormal distributions for both datasets (p < 0. 001). The test for skewness showed -0. 33 for age of onset and -0. 48 for age of death, indicating an approximately symmetric distribution. The mean age of onset in people with C9orf72 gene expansion was 2. 7 years younger than those with no C9orf72 gene expansion (p = 8. 8 × 10 -8 , 95% CI for the difference 1. 2 to 4. 2 years). The mean age of onset in people with VCP gene inversion was 3 years younger than for people with no VCP gene inversion (p = 4. 2 × 10 -13 , 95% CI for the difference 2. 2 to 3. 7 years). Additionally, the mean age of onset in those with ERBB4 gene insertion was one year younger than for those with no ERBB4 insertion (p = 0.
The final number of samples passing quality control was 6195 whole genome sequences each representing one individual, and making this one of the largest such datasets in the world. In keeping with previous findings, we have found genotypephenotype correlations in risk genes for ALS, the most striking of which is the finding of insertion in the ERBB4 gene in 71. 4% of people with respiratory onset ALS compared with 46. 4% of those with non-respiratory onset, and just 25. 3% of the general population. This is the largest genetic study of respiratory onset ALS, but because the frequency of respiratory onset in ALS is only about 1 to 2%  2, 3, 20  , the absolute numbers are still small. Nevertheless, the finding is possible because such a large proportion of affected people have the same genetic variation.