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Abstract The relationship between stomatal traits and environmental drivers across plant communities has important implications for ecosystem carbon and water fluxes, but it has remained unclear. Here, we measure the stomatal morphology of 4492 species-site combinations in 340 vegetation plots across China and calculate their community-weighted values for mean, variance, skewness, and kurtosis. We demonstrate a trade-off between stomatal density and size at the community level. The community-weighted mean and variance of stomatal density are mainly associated with precipitation, while that of stomatal size is mainly associated with temperature, and the skewness and kurtosis of stomatal traits are less related to climatic and soil variables. Beyond mean climate variables, stomatal trait moments also vary with climatic seasonality and extreme conditions. Our findings extend the knowledge of stomatal trait–environment relationships to the ecosystem scale, with applications in predicting future water and carbon cycles. 

Summary A prevailing hypothesis posits that achieving higher maximum rates of leaf carbon gain and water loss is constrained by geometry and/or selection to limit the allocation of epidermal area to stomata (f_S). Under this ‘stomatal‐area minimization hypothesis’, higherg_s,maxis associated with greater numbers of smaller stomata because this trait combination increasesg_s,maxwith minimal increase inf_S, leading to relative conservation off_Ssemi‐independent ofg_s,maxdue to coordination in stomatal size, density, and pore depth. An alternative hypothesis is that the evolution of higherg_s,maxcan be enabled by a greater epidermal area allocated to stomata, leading to positive covariation betweenf_Sandg_s,max; we call this the ‘stomatal‐area adaptation hypothesis’. Under this hypothesis, the interspecific scaling betweeng_s,max, stomatal density, and stomatal size is a by‐product of selection on a moving optimalg_s,max.We integrated biophysical and evolutionary quantitative genetic modeling with phylogenetic comparative analyses of a global data set of stomatal density and size from 2408 vascular forest species. The models present specific assumptions of both hypotheses and deduce predictions that can be evaluated with our empirical analyses of forest plants.There are three main results. First, neither the stomatal‐area minimization nor adaptation hypothesis is sufficient to be supported. Second, estimates of interspecific scaling from common regression methods cannot reliably distinguish between hypotheses when stomatal size is bounded. Third, we reconcile both hypotheses with the data by including an additional assumption that stomatal size is bounded by a wide range and under selection; we refer to this synthetic hypothesis as the ‘stomatal adaptation + bounded size’ hypothesis.This study advances our understanding of scaling between stomatal size and density by mathematically describing specific assumptions of competing hypotheses, demonstrating that existing hypotheses are inconsistent with observations, and reconciling these hypotheses with phylogenetic comparative analyses by postulating a synthetic model of selection ong_s,max,f_S, and stomatal size. 

Abstract Shifts in stomatal trait distributions across contrasting environments and their linkage with ecosystem productivity at large spatial scales have been unclear. Here, we measured the maximum stomatal conductance (g), stomatal area fraction (f), and stomatal space-use efficiency (e, the ratio of g to f) of 800 plant species ranging from tropical to cold-temperate forests, and determined their values for community-weighted mean, variance, skewness, and kurtosis. We found that the community-weighted means of g and f were higher in drier sites, and thus, that drought ‘avoidance’ by water availability-driven growth pulses was the dominant mode of adaptation for communities at sites with low water availability. Additionally, the variance of g and f was also higher at arid sites, indicating greater functional niche differentiation, whereas that for e was lower, indicating the convergence in efficiency. When all other stomatal trait distributions were held constant, increasing kurtosis or decreasing skewness of g would improve ecosystem productivity, whereas f showed the opposite patterns, suggesting that the distributions of inter-related traits can play contrasting roles in regulating ecosystem productivity. These findings demonstrate the climatic trends of stomatal trait distributions and their significance in the prediction of ecosystem productivity. 

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures. 

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol⁻¹at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.