Summary1. Across plant species, drought tolerance and distributions with respect to water availability are strongly correlated with two physiological traits, the leaf water potential at wilting, that is, turgor loss point (p tlp ), and the cell solute potential at full hydration, that is, osmotic potential (p o ). We present methods to determine these parameters 30 times more rapidly than the standard pressurevolume (p-v) curve approach, making feasible community-scale studies of plant drought tolerance. 2. We optimized existing methods for measurements of p o using vapour-pressure osmometry of freeze-thawed leaf discs from 30 species growing in two precipitation regimes, and developed the first regression relationships to accurately estimate pressure-volume curve values of both p o and p tlp from osmometer values . 3. The p o determined with the osmometer (p osm ) was an excellent predictor of the p o determined from the p-v curve (p pv, r 2 = 0AE80). Although the correlation of p osm and p pv enabled prediction, the relationship departed from the 1 : 1 line. The discrepancy between the methods could be quantitatively accounted for by known sources of error in osmometer measurements, that is, dilution by the apoplastic water, and solute dissolution from destroyed cell walls. An even stronger prediction of p pv could be made using p osm, leaf density (q), and their interaction (r 2 = 0AE85, all P < 2 · 10 )10). 4. The p osm could also be used to predict p tlp (r 2 = 0AE86). Indeed, p osm was a better predictor of p tlp than leaf mass per unit area (LMA; r 2 = 0AE54), leaf thickness (T; r 2 = 0AE12), q (r 2 = 0AE63), and leaf dry matter content (LDMC; r 2 = 0AE60), which have been previously proposed as drought tolerance indicators. Models combining p osm with LMA, T, q, or LDMC or other p-v curve parameters (i.e. elasticity and apoplastic fraction) did not significantly improve prediction of p tlp . 5. This osmometer method enables accurate measurements of drought tolerance traits across a wide range of leaf types and for plants with diverse habitat preferences, with a fraction of effort of previous methods. We expect it to have wide application for predicting species responses to climate variability and for assessing ecological and evolutionary variation in drought tolerance in natural populations and agricultural cultivars.

Many species face increasing drought under climate change. Plasticity has been predicted to strongly influence species' drought responses, but broad patterns in plasticity have not been examined for key drought tolerance traits, including turgor loss or 'wilting' point (πtlp ). As soil dries, plants shift πtlp by accumulating solutes (i.e. 'osmotic adjustment'). We conducted the first global analysis of plasticity in Δπtlp and related traits for 283 wild and crop species in ecosystems worldwide. Δπtlp was widely prevalent but moderate (-0.44 MPa), accounting for 16% of post-drought πtlp. Thus, pre-drought πtlp was a considerably stronger predictor of post-drought πtlp across species of wild plants. For cultivars of certain crops Δπtlp accounted for major differences in post-drought πtlp. Climate was correlated with pre- and post-drought πtlp, but not Δπtlp. Thus, despite the wide prevalence of plasticity, πtlp measured in one season can reliably characterise most species' constitutive drought tolerances and distributions relative to water supply.

13Spatial patterns in trait variation reflect underlying community assembly processes, allowing us 14 to test hypotheses about their trait and environmental drivers by identifying the strongest 15 correlates of characteristic spatial patterns. For 43 evergreen tree species (> 1cm dbh) in a 20 ha 16 seasonal tropical rainforest plot in Xishuangbanna, China, we compared the ability of drought 17 tolerance traits, other physiological traits and commonly measured functional traits to predict the 18 spatial patterns expected from the assembly processes of habitat associations, niche overlap-19 based competition, and hierarchical competition. We distinguished the neighborhood-scale (0-20 20m) patterns expected from competition from larger-scale habitat associations with a wavelet 21 method. Species' drought tolerance and habitat variables related to soil water supply were strong 22 drivers of habitat associations, and drought tolerance showed a significant spatial signal for 23 influencing competition. Overall, the traits most strongly associated with habitat, as quantified 24 using multivariate models, were leaf density, leaf turgor loss point (π tlp ; also known as the leaf 25 wilting point), and stem hydraulic conductivity (r 2 range for the best fit models = 0.27-0.36). At 26 neighborhood scales, species spatial associations were positively correlated with similarity in 27 π tlp , consistent with predictions for hierarchical competition. Although the correlation between 28 π tlp and interspecific spatial associations was weak (r 2 < 0.01), this showed a persistent influence 29 of drought tolerance on neighborhood interactions and community assembly. Quantifying the full 30 impact of traits on competitive interactions in forests may require incorporating plasticity among 31 individuals within species, especially among specific life stages, and moving beyond individual 32 traits to integrate the impact of multiple traits on whole-plant performance and resource demand. 33

The chitosan-based coating with antimicrobial agent has been developed recently to control the decay of fruits. However, its fresh keeping and antimicrobial mechanism is still not very clear. The preservation mechanism of chitosan coating with cinnamon oil for fruits storage is investigated in this paper. Results in the atomic force microscopy sensor images show that many micropores exist in the chitosan coating film. The roughness of coating film is affected by the concentration of chitosan. The antifungal activity of cinnamon oil should be mainly due to its main consistent trans-cinnamaldehyde, which is proportional to the trans-cinnamaldehyde concentration and improves with increasing the attachment time of oil. The exosmosis ratios of Penicillium citrinum and Aspergillus flavus could be enhanced by increasing the concentration of cinnamon oil. Morphological observation indicates that, compared to the normal cell, the wizened mycelium of A. flavus is observed around the inhibition zone, and the growth of spores is also inhibited. Moreover, the analysis of gas sensors indicate that the chitosan-oil coating could decrease the level of O2 and increase the level of CO2 in the package of cherry fruits, which also control the fruit decay. These results indicate that its preservation mechanism might be partly due to the micropores structure of coating film as a barrier for gas and a carrier for oil, and partly due to the activity of cinnamon oil on the cell disruption.

The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies.