Biomass allocation is crucial for predicting ecosystem responses to global change, and yet, whether patterns follow the plastic optimal partitioning theory (OPT) or the constrained allometric partitioning theory (APT) remains contentious across different biomes. A key uncertainty is whether vast, functionally distinct ecosystems, such as temperate and alpine drylands, show different allocation strategies. Here, we investigated root:shoot ratio (R/S) patterns across 120 sites spanning temperate and alpine drylands in northern China. Significant differences in allocation were observed, with temperate drylands showing lower R/S than alpine regions. In temperate drylands, R/S scaled allometrically with plant community size, consistent with APT, with key soil factors exerting only an indirect influence through their effects on plant community size. Conversely, in alpine drylands, R/S was insensitive to plant community size and instead responded directly to the mean annual temperature, a pattern indicative of OPT. We propose that this strategic divergence is linked to their underlying community functional structures. Communities with greater functional dissimilarity may achieve higher niche complementarity, providing the necessary capacity to optimize allocation in response to environmental constraints. Our findings demonstrate that climatic regimes drive alternative biomass allocation strategies, providing both a predictive framework for vegetation responses and a theoretical basis for dryland ecosystem restoration under climate change.

The data that support the findings of this study are openly available in the Figshare repository under the DOI:10.6084/m9.figshare.30478505.

Modern maize stems possess a well-developed vascular bundle system, which is critical for providing mechanical support and lodging resistance. However, characterization of the microanatomical features of vascular bundles and their functional implications in stem mechanics remains challenging, primarily due to technical limitations in high-throughput microanatomical analysis of stem tissues. We thus constructed data sets consisting of over 500,000 maize stem CT images from a maize diversity panel of 383 inbred lines. We evaluated 32 microanatomical phenotypes of maize basal internodes across two environments in different years. By incorporating engineering mechanics parameters, we calculated novel characteristics of the vascular bundles, including the moment of area (MOA) and the polar moment of inertia (PMOI). Through the high-density phenotypic data set, we identified multiple stem microanatomical phenotypes strongly associated with lodging resistance, particularly of vascular bundle mechanical traits. By integrating population genetic profiling, we discovered and confirmed that ZmLSM2 (U6 small nuclear ribonucleoprotein specific Sm-like 2) serves as a key regulator of stem mechanical strength, might function in RNA processing and maturation within vascular stem cells, identifying novel genetic targets for improving maize lodging resistance. This approach demonstrates the value of combining advanced phenotyping with multi-omics analyses for crop improvement. These discoveries will deepen the understanding of plant stem biomechanical principles and provide novel targets for enhancing lodging resistance in crop breeding programs.

CT cross-section images of the third internode from 383 maize inbred lines grown in Beijing and Sanya during two growing seasons can be downloaded via the link: https://pan.baidu.com/s/1CP2kkAmTvy1zi3QJGtKSWQ?pwd=JIPB. Extraction code: JIPB. The whole-genome methylation data and transcriptome data for three maize cultivars (B73, ZZ01, and CIMBL86) have been deposited in the Sequence Read Archive (SRA) database under the accession number PRJNA1254795. The data can be accessed via the following reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1254795?reviewer=j24bqrqs0742uisrogiee74c4g. All other reasonable requests for data and research materials are available by contacting the corresponding authors.

The evolution of spermatophytes (seed plants) is relatively well known in their evolutionary relationships over temporal changes, but their spatial evolution is another critical yet often neglected lens, especially using a taxon-based approach. Here, by integrating geographic distributions and origin locations across 429 spermatophyte families worldwide with unsupervised machine learning approaches, we constructed a Spermatophyte Spatial Evolutionary System that classifies global spermatophytes into 18 distribution types and six distribution supertypes within three primary floristic elements: cosmopolitan, tropical, and temperate. We found that the three elements all primarily originated from Gondwana, with the cosmopolitan element being the youngest and the temperate element being the oldest in terms of origin. They primarily formed during the Tertiary, particularly between the Eocene and Miocene, driven mainly by climate, long-distance dispersal, and tectonic movement, while each exhibited distinct migration routes and formation models. Our results provide novel insights into the spatial evolution of global spermatophytes and highlight that similar distribution patterns of spermatophytes were driven by their comparable formation processes and mechanisms at the levels of floristic element, distribution supertype, and type.

Taraxacum kok-saghyz Rodin (TKS) is a promising alternative crop source for producing high-quality natural rubber (NR) and has become an ideal model plant for studying NR biosynthesis, regulation mechanisms, and production. So far, only a very limited number of functional genes related to NR biosynthesis have been identified in TKS. To achieve a systematic identification of its novel functional genes, we developed a mutant system denoted sense/antisense RNA expression (SARE) and have generated more than 8,000 transgenic TKS plants. A series of mutants with altered phenotypes, particularly changes in NR contents, were identified. To evaluate the efficiency of this library, we chose one mutant, c112, which exhibits a significant increase in NR content, for in-depth characterization. The c112 mutant arose from the sense insertion of a dormancy-associated gene1 (DRM1)/auxin repressed protein (ARP) gene, which we named high natural rubber content1 (HRC1). In the c112 mutant, the concentrations of NR precursors isopentenyl pyrophosphate and dimethylallyl diphosphate decreased, while geranylgeranyl diphosphate increased, suggesting that HRC1 regulates metabolic flux in NR biosynthesis. In summary, the developed TKS SARE mutant library provides valuable genetic resources for identifying key functional genes to accelerate the domestication of TKS from wild species to economic crops through molecular breeding.

Accurately predicting gross primary productivity (GPP) is crucial for understanding carbon cycling; however, most studies have predominantly investigated GPP using only environmental metrics, overlooking the pivotal role of functional traits as intermediaries between the environment and GPP and the predictive potential of GPP. Therefore, this study developed a three-dimensional “engine” framework to predict GPP and tested it by leveraging functional traits from 2,040 plant communities on the Tibetan Plateau, incorporating environmental factors and the length of the plant-growing season. Our results highlight that while the environment exerts a dominant direct influence on GPP dynamics, the contribution of leaf density traits to GPP prediction should not be overlooked. The proposed framework achieved a prediction accuracy close to 0.92, underscoring its feasibility in GPP prediction. However, incorporating the nitrogen-to-phosphorus ratio into the framework diminished the model's predictive accuracy. Within the stoichiometric dimension alone, the prediction accuracy significantly increased with the number of input traits, indicating a substantial potential for enhancing predictive capability. In the dimension of environmental factors, incorporating more environmental factors does not significantly enhance the model's predictive ability. Our research facilitates the dynamic, continuous, and relatively accurate monitoring of GPP, contributing to a better understanding of carbon cycle dynamics and supporting informed ecosystem planning and management.

The data of N and P are available from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Terre.tpdc.272420 and https://doi.org/10.11888/Terre.tpdc.272695.

Plant microbiome plays a vital role in plant fitness and ecosystem functioning, yet its response to global environmental change remains poorly understood. Using an 18-year field experiment, we investigated the effects of climate warming and nitrogen deposition on the diversity of leaf epiphytic and endophytic bacterial communities in two dominant plant species (Stipa breviflora and Cleistogenes songorica) of a temperate desert steppe. We found that warming and nitrogen addition increased both epiphytic and endophytic bacterial diversity, but via different mechanisms. Specifically, epiphytic diversity increased with leaf temperature and transpiration rate, whereas greater endophyte diversity was linked to higher leaf carbon and nitrogen concentrations. Structural equation modeling revealed that both epiphytic and endophytic diversity were negatively associated with plant diversity. Our results demonstrate different mechanisms driving similar responses of leaf epiphytic and endophytic bacterial diversity to global change, and point to a negative feedback loop between phyllosphere bacterial and plant diversity.

The data that support the findings of this study are openly available in Figshare at 10.6084/m9.figshare.27313161.

Understanding plant adaptive strategies that determine species distributions and ecological optima is crucial for predicting responses to global change drivers. While functional traits provide mechanistic insights into distribution patterns, the specific trait syndromes that best predict elevational optima, particularly in less-studied regions such as the Himalayas, remain unclear. This study employs a novel hierarchical framework integrating morphological, anatomical, and physiological traits to explain elevational distributions among 310 plant species across a 3,500-m gradient (2,650–6,150 m). We analyzed 95,000 floristic records collected from 4,062 localities spanning 80,000 km² in Ladakh, NW Himalayas, India, to define elevational optima and link them with 17 functional traits from over 7,800 individuals. We assessed the roles of moisture and cold limitations on trait–optima relationships by comparing two contrasting habitats (dry steppe and wetter, colder alpine). The predictive power of functional traits was more pronounced in the alpine species facing more extreme abiotic stress than the steppe species. Our results indicate that conservative life history strategies strongly predict elevational optima in alpine areas, while drought avoidance and competitive dominance are key in steppe habitats. Trait syndromes combining short stature, compact growth forms, enhanced storage tissues, and features promoting water-use efficiency (δ¹³C), freezing resistance (fructan levels), and nutrient retention (high root nitrogen and leaf phosphorus) explained 61% of the variation in alpine species' optima. Conversely, lifespan and clonal propagation determined the optima of steppe species at lower elevations. The study emphasizes the importance of functional trait combinations in determining elevational optima, highlighting that alpine species prioritize resource conservation and stress tolerance, while steppe species focus on competitive growth strategies. This multi-trait approach contrasts with previous research focusing on single trait–elevation relationships, providing novel insights into the diverse mechanisms shaping elevational distributions and offering valuable predictive power for assessing vegetation responses to future climate change.

Data are available in Supporting Information Appendix.

Yield improvement has long been an important task for soybean breeding in the world in order to meet the increasing demand for food and animal feed. miR396 genes have been shown to negatively regulate grain size in rice, but whether miR396 family members may function in a similar manner in soybean is unknown. Here, we generated eight soybean mutants harboring different combinations of homozygous mutations in the six soybean miR396 genes through genome editing with clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated nuclease (Cas) 12SF01 in the elite soybean cultivar Zhonghuang 302 (ZH302). Four triple mutants (mir396aci, mir396acd, mir396adf, and mir396cdf), two quadruple mutants (mir396-abcd and mir396acfi), and two quintuple mutants (mir396abcdf and mir396bcdfi) were characterized. We found that plants of all the mir396 mutants produced larger seeds compared to ZH302 plants. Field tests showed that mir396adf and mir396cdf plants have significantly increased yield in growth zones with relatively high latitude which are suited for ZH302 and moderately increased yield in lower latitude. In contrast, mir396abcdf and mir396bcdfi plants have increased plant height and decreased yield in growth zones with relatively high latitude due to lodging issues, but they are suited for low latitude growth zones with increased yield without lodging problems. Taken together, our study demonstrated that loss-of-function of miR396 genes leads to significantly enlarged seed size and increased yield in soybean, providing valuable germplasms for breeding high-yield soybean.

The RNA-seq data were deposited in the National Center for Biotechnology Information Sequence Read Archive (BioProject: PRJNA1084194).

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.

The resequencing data for the soybean accessions in this project were deposited in the Sequence Read Archive database of NCBI (www.ncbi.nlm.nih.gov) under accession number PRJNA681974 (Li et al., 2022) and in the Genome Sequence Archive (GSA) database of BIG Data Center (https://ngdc.cncb.ac.cn) under bioProject number PRJCA002030 (Liu et al., 2020). Genotype data resulting from those two projects were directly used.

Biomass in forests sequesters substantial amounts of carbon; although the contribution of aboveground biomass has been extensively studied, the contribution of belowground biomass remains understudied. Investigating the forest biomass allocation is crucial for understanding the impacts of global change on carbon allocation and cycling. Moreover, the question of how climate factors affect biomass allocation in natural and planted forests remains unresolved. Here, we addressed this question by collecting data from 384 planted forests and 541 natural forests in China. We evaluated the direct and indirect effects of climate factors on the belowground biomass proportion (BGBP). The average BGBP was 31.09% in natural forests and was significantly higher (38.75%) in planted forests. Furthermore, we observed a significant decrease in BGBP with increasing temperature and precipitation. Climate factors, particularly those affecting soil factors, such as pH, strongly affected the BGBP in natural and planted forests. Based on our results, we propose that future studies should consider the effects of forest type (natural or planted) and soil factors on BGBP.

Data is available at https://www.scidb.cn/s/z6neA3.

Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change. Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems, although it remains to be tested in areas with dwarf and sparse vegetation, such as the Tibetan Plateau. We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect. We estimated community structural and functional traits, as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices (VIs) derived from hyperspectral images. We quantified the contributions of environmental drivers, VIs, and community traits to community function by structural equation modelling (SEM). Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index, enhanced vegetation index, and simple ratio. Structural equation modelling showed that VIs and community traits positively affected community function, whereas environmental drivers and specific leaf area had the opposite effect. Additionally, VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits. This study demonstrates that community-level spectral reflectance will help scale plant trait information measured at the leaf level to larger-scale ecological processes. Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.

The data that support the findings of this study are available from the corresponding author on reasonable request.

The flora of China is well known for its high diversity and endemism. Identifying centers of endemism and designating conservation priorities are essential goals for biodiversity studies. However, there is no comprehensive study from a rigorous phylogenetic perspective to understand patterns of diversity and endemism and to guide biodiversity conservation in China. We conducted a spatial phylogenetic analysis of the Chinese angiosperm flora at the generic level to identify centers of neo- and paleo-endemism. Our results indicate that: (i) the majority of grid cells in China with significantly high phylogenetic endemism (PE) were located in the mountainous regions; (ii) four of the nine centers of endemism recognized, located in northern and western China, were recognized for the first time; (iii) arid and semiarid regions in Northwest China were commonly linked to significant PE, consistent with other spatial phylogenetic studies worldwide; and (iv) six high-priority conservation gaps were detected by overlaying the boundaries of China's nature reserves on all significant PE cells. Overall, we conclude that the mountains of southern and northern China contain both paleo-endemics (ancient relictual lineages) and neo-endemics (recently diverged lineages). The areas we highlight as conservation priorities are important for broad-scale planning, especially in the context of evolutionary history preservation.

The dominant species of a biome can be regarded as its genuine indicator. Evergreen broadleaved forests (EBLFs) in subtropical East Asia harbor high levels of species biodiversity and endemism and are vital to regional carbon storage and cycling. However, the historical assembly of this unique biome is still controversial. Fagaceae is the most essential family in East Asian subtropical EBLFs and its dominant species are vital for the existence of this biome. Here, we used the dominant Fagaceae species to shed light on the dynamic process of East Asian subtropical EBLFs over time. Our results indicate high precipitation in summer and low temperature in winter are the most influential climatic factors for the distribution of East Asian subtropical EBLFs. Modern East Asian subtropical EBLFs did not begin to appear until 23 Ma, subsequently experienced a long-lasting development in the Miocene and markedly deteriorated at about 4 Ma, driven jointly by orogenesis and paleoclimate. We also document that there is a lag time between when one clade invaded the region and when its members become dominant species within the region. This study may improve our ability to predict and mitigate the threats to biodiversity of East Asian subtropical EBLFs and points to a new path for future studies involving multidisciplinary methods to explore the assembly of regional biomes.

Many genes encoding CCT domain‐containing proteins regulate flowering time. In rice (Oryza sativa), 41 such genes have been identified, but only a few have been shown to regulate heading date. Here, to test whether and how additional CCT family genes regulate heading date in rice, we classified these genes into five groups based on their diurnal expression patterns. The expression patterns of genes in the same subfamily or in close phylogenetic clades tended to be similar. We generated knockout mutants of the entire gene family via CRISPR/Cas9. The heading dates of knockout mutants of only 4 of 14 genes previously shown to regulate heading date were altered, pointing to functional redundancy of CCT family genes in regulating this trait. Analysis of mutants of four other genes showed that OsCCT22, OsCCT38, and OsCCT41 suppress heading under long‐day conditions and promote heading under short‐day conditions. OsCCT03 promotes heading under both conditions and upregulates the expression of Hd1 and Ehd1, a phenomenon not previously reported for other such genes. To date, at least 18 CCT domain‐containing genes involved in regulating heading have been identified, providing diverse, flexible gene combinations for generating rice varieties with a given heading date.

High heterozygosity and tetrasomic inheritance complicate studies of asexually propagated polyploids, such as potato. Reverse genetics approaches, especially mutant library construction, can be an ideal choice if a proper mutagenesis genotype is available. Here, we aimed to generate a model system for potato research using anther cultures of Solanum verrucosum, a self‐compatible diploid potato with strong late blight resistance. Six of the 23 regenerants obtained (SVA4, SVA7, SVA22, SVA23, SVA32, and SVA33) were diploids, and their homozygosity was estimated to be >99.99% with 22 polymorphic InDel makers. Two lines—SVA4 and SVA32—had reduced stature (plant height ≤80 cm), high seed yield (>1,000 seeds/plant), and good tuber set (>30 tubers/plant). We further confirmed the full homozygosity of SVA4 and SVA32 using whole‐genome resequencing. These two regenerants possess all the characteristics of a model plant: diploidy, 100% homozygosity, self‐compatibility, and amenability to transgenesis. Thus, we have successfully generated two lines, SVA4 and SVA32, which can potentially be used for mutagenesis and as model plants to rejuvenate current methods of conducting potato research.

Sequence data have been deposited in the National Center for Biotechnology Information Sequence Read Archive (SRA) under BioProject accession number PRJNA647231.

Wheat (Triticum aestivum L.) is a staple food crop consumed by more than 30% of world population. Nitrogen (N) fertilizer has been applied broadly in agriculture practice to improve wheat yield to meet the growing demands for food production. However, undue N fertilizer application and the low N use efficiency (NUE) of modern wheat varieties are aggravating environmental pollution and ecological deterioration. Under nitrogen-limiting conditions, the rice (Oryza sativa) abnormal cytokinin response1 repressor1 (are1) mutant exhibits increased NUE, delayed senescence and consequently, increased grain yield. However, the function of ARE1 ortholog in wheat remains unknown. Here, we isolated and characterized three TaARE1 homoeologs from the elite Chinese winter wheat cultivar ZhengMai 7698. We then used CRISPR/Cas9-mediated targeted mutagenesis to generate a series of transgene-free mutant lines either with partial or triple-null taare1 alleles. All transgene-free mutant lines showed enhanced tolerance to N starvation, and showed delayed senescence and increased grain yield in field conditions. In particular, the AABBdd and aabbDD mutant lines exhibited delayed senescence and significantly increased grain yield without growth defects compared to the wild-type control. Together, our results underscore the potential to manipulate ARE1 orthologs through gene editing for breeding of high-yield wheat as well as other cereal crops with improved NUE.

The rhizosheath, a layer of soil grains that adheres firmly to roots, is beneficial for plant growth and adaptation to drought environments. Switchgrass is a perennial C4 grass which can form contact rhizosheath under drought conditions. In this study, we characterized the microbiomes of four different rhizocompartments of two switchgrass ecotypes (Alamo and Kanlow) grown under drought or well-watered conditions via 16S ribosomal RNA amplicon sequencing. These four rhizocompartments, the bulk soil, rhizosheath soil, rhizoplane, and root endosphere, harbored both distinct and overlapping microbial communities. The root compartments (rhizoplane and root endosphere) displayed low-complexity communities dominated by Proteobacteria and Firmicutes. Compared to bulk soil, Cyanobacteria and Bacteroidetes were selectively enriched, while Proteobacteria and Firmicutes were selectively depleted, in rhizosheath soil. Taxa from Proteobacteria or Firmicutes were specifically selected in Alamo or Kanlow rhizosheath soil. Following drought stress, Citrobacter and Acinetobacter were further enriched in rhizosheath soil, suggesting that rhizosheath microbiome assembly is driven by drought stress. Additionally, the ecotype-specific recruitment of rhizosheath microbiome reveals their differences in drought stress responses. Collectively, these results shed light on rhizosheath microbiome recruitment in switchgrass and lay the foundation for the improvement of drought tolerance in switchgrass by regulating the rhizosheath microbiome.

Interactions between plant hosts and their microbiotas are becoming increasingly evident, while the effects of plant communities on microbial communities in different geographic environments are poorly understood. Here, the differentiation of licorice plant ecotypes and the distribution of root-associated microbiotas were investigated across five sampling sites in northwest China. The interactions between the environment, plant and microbial communities, and their effects on licorice root secondary metabolites, were elucidated. The plant community was clearly differentiated into distinct ecotypes based on genotyping-by-sequencing and was primarily driven by geographic distance and available soil nitrogen. The bulk soil and root-associated microbiotas (rhizosphere soil and root endosphere) partially correlated with plant community, but all were significantly discriminated by plant clade. Moreover, these microbiotas were explained to different extents by distinct combinations of environment, geography, and plant community. Similarly, three structural equation models showed that licorice root secondary metabolites were complicatedly modulated by multiple abiotic and biotic factors, and were mostly explained by these factors in the rhizosphere model. Collectively, the results provide novel insights into the role of environment–plant–microbiota interactions in regulating root secondary metabolites. That should be accounted for when selecting appropriate licorice planting sites and management measures.

Sequencing datasets are available on the NCBI website under the accession numbers SAMN22209877 through SAMN22209911. The climatic data are available from the database Worldclim (http://www.worldclim.org/).

Targeting‐induced local lesions in genomes (TILLING) is a powerful reverse‐genetics tool that enables high‐throughput screening of genomic variations in plants. Although TILLING has been developed for many diploid plants, the technology has been used in very few polyploid species due to their genomic complexity. Here, we established an efficient capillary electrophoresis‐based TILLING platform for allotetraploid cultivated tobacco (Nicotiana tabacum L.) using an ethyl methanesulfonate (EMS)‐mutagenized population of 1,536 individuals. We optimized the procedures for endonuclease preparation, leaf tissue sampling, DNA extraction, normalization, pooling, PCR amplification, heteroduplex formation, and capillary electrophoresis. In a test screen using seven target genes with eight PCR fragments, we obtained 118 mutants. The mutation density was estimated to be approximately one mutation per 106 kb on average. Phenotypic analyses showed that mutations in two heavy metal transporter genes, HMA2S and HMA4T, led to reduced accumulation of cadmium and zinc, which was confirmed independently using CRISPR/Cas9 to generate knockout mutants. Our results demonstrate that this powerful TILLING platform (available at http://www.croptilling.org) can be used in tobacco to facilitate functional genomics applications.

Cotton (Gossypium hirsutum L.) is a major crop and the main source of natural fiber worldwide. Because various abiotic and biotic stresses strongly influence cotton fiber yield and quality, improved stress resistance of this crop plant is urgently needed. In this study, we used Gateway technology to construct a normalized full‐length cDNA overexpressing (FOX) library from upland cotton cultivar ZM12 under various stress conditions. The library was transformed into Arabidopsis to produce a cotton‐FOX‐Arabidopsis library. Screening of this library yielded 6,830 transgenic Arabidopsis lines, of which 757 were selected for sequencing to ultimately obtain 659 cotton ESTs. GO and KEGG analyses mapped most of the cotton ESTs to plant biological process, cellular component, and molecular function categories. Next, 156 potential stress‐responsive cotton genes were identified from the cotton‐FOX‐Arabidopsis library under drought, salt, ABA, and other stress conditions. Four stress‐related genes identified from the library, designated as GhCAS , GhAPX , GhSDH , and GhPOD , were cloned from cotton complementary DNA, and their expression patterns under stress were analyzed. Phenotypic experiments indicated that overexpression of these cotton genes in Arabidopsis affected the response to abiotic stress. The method developed in this study lays a foundation for high‐throughput cloning and rapid identification of cotton functional genes.