Summary: 1. 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 (π_tlp), and the cell solute potential at full hydration, that is, osmotic potential (π_o). We present methods to determine these parameters 30 times more rapidly than the standard pressure–volume (p–v) curve approach, making feasible community‐scale studies of plant drought tolerance.2. We optimized existing methods for measurements of π_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 π_o and π_tlp from osmometer values_.3. The π_o determined with the osmometer (π_osm) was an excellent predictor of the π_o determined from the p–v curve (π_pv,r² = 0·80). Although the correlation of π_osm and π_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 π_pv could be made using π_osm, leaf density (ρ), and their interaction (r² = 0·85, all P < 2 × 10⁻¹⁰).4. The π_osm could also be used to predict π_tlp (r² = 0·86). Indeed, π_osm was a better predictor of π_tlp than leaf mass per unit area (LMA; r² = 0·54), leaf thickness (T; r² = 0·12), ρ (r² = 0·63), and leaf dry matter content (LDMC; r² = 0·60), which have been previously proposed as drought tolerance indicators. Models combining π_osm with LMA, T, ρ, or LDMC or other p–v curve parameters (i.e. elasticity and apoplastic fraction) did not significantly improve prediction of π_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.

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.

Summary: Processing raw reads of RNA-sequencing (RNA-seq) data, no matter public or newly sequenced data, involves a lot of specialized tools and technical configurations that are often unfamiliar and time-consuming to learn for non-bioinformatics researchers. Here, we develop the R package BP4RNAseq, which integrates the state-of-art tools from both alignment-based and alignment-free quantification workflows. The BP4RNAseq package is a highly automated tool using an optimized pipeline to improve the sensitivity and accuracy of RNA-seq analyses. It can take only two non-technical parameters and output six formatted gene expression quantification at gene and transcript levels. The package applies to both retrospective and newly generated bulk RNA-seq data analyses and is also applicable for single-cell RNA-seq analyses. It, therefore, greatly facilitates the application of RNA-seq. Availability and implementation: The BP4RNAseq package for R and its documentation are freely available at https://github.com/sunshanwen/BP4RNAseq. Supplementary information: Supplementary data are available at Bioinformatics online.

Spatial patterns in trait variation reflect underlying community assembly processes, allowing us to test hypotheses about their trait and environmental drivers by identifying the strongest correlates of characteristic spatial patterns. For 43 evergreen tree species (>1 cm dbh) in a 20‐ha seasonal tropical rainforest plot in Xishuangbanna, China, we compared the ability of drought‐tolerance traits, other physiological traits, and commonly measured functional traits to predict the spatial patterns expected from the assembly processes of habitat associations, niche‐overlap‐based competition, and hierarchical competition. We distinguished the neighborhood‐scale (0–20 m) patterns expected from competition from larger‐scale habitat associations with a wavelet method. Species’ drought tolerance and habitat variables related to soil water supply were strong drivers of habitat associations, and drought tolerance showed a significant spatial signal for influencing competition. Overall, the traits most strongly associated with habitat, as quantified using multivariate models, were leaf density, leaf turgor loss point (π_tlp; also known as the leaf wilting point), and stem hydraulic conductivity (r² range for the best fit models = 0.27–0.36). At neighborhood scales, species spatial associations were positively correlated with similarity in π_tlp, consistent with predictions for hierarchical competition. Although the correlation between π_tlp and interspecific spatial associations was weak (r² < 0.01), this showed a persistent influence of drought tolerance on neighborhood interactions and community assembly. Quantifying the full impact of traits on competitive interactions in forests may require incorporating plasticity among individuals within species, especially among specific life stages, and moving beyond individual traits to integrate the impact of multiple traits on whole‐plant performance and resource demand.

Turgor loss point (π_tlp) has been suggested to be a key trait for drought resistance in woody species. In herbaceous grassland species, the role of π_tlp for species drought survival has not yet been tested, although grasslands are projected to experience more frequent and intense droughts with climate change. To gain insights into the role of π_tlp for drought resistance of temperate perennial grassland species, we assessed π_tlp of 41 species common in Germany (20 forbs, 21 grasses). We directly related them to the species’ comparative whole‐plant drought survival and midday leaf water potentials under drought (Ψ_MD) assessed in a common garden drought experiment, and to species moisture association. Species drought survival increased with increasing π_tlp across all species as well as within forbs or grasses separately. Ψ_MD was positively related to π_tlp and drought survival. Our results imply that high π_tlp promotes drought survival of common perennial European temperate mesic grassland species by enabling them to maintain high leaf water potentials under drought, that is, a desiccation avoidance strategy. However, π_tlp was not related to species moisture association. The positive relationship between π_tlp and drought survival in herbaceous grassland species was opposite to the negative relationship previously established in woody plants, implying that mechanisms of drought resistance differ between woody and herbaceous species. Our results highlight the necessity of directly testing the relationship of functional traits to whole‐plant drought survival in different plant life forms, before using trait assessments for predicting plant responses to drought. A free plain language summary can be found within the Supporting Information of this article.