An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)

Evaluation of shoot-growth variation in diverse carrot (Daucus carota L.) germplasm for genetic improvement of stand establishment

Carrot (Daucus carota L.) is a high value, nutritious, and colorful crop, but delivering carrots from seed to table can be a struggle for carrot growers. Weed competitive ability is a critical trait for crop success that carrot and its apiaceous relatives often lack owing to their characteristic slow shoot growth and erratic seedling emergence, even among genetically uniform lines. This study is the first field-based, multi-year experiment to evaluate shoot-growth trait variation over a 100-day growing season in a carrot diversity panel (N=695) that includes genetically diverse carrot accessions from the United States Department of Agriculture National Plant Germplasm System. We report phenotypic variability for shoot-growth characteristics, the first broad-sense heritability estimates for seedling emergence (0.68 < H2 < 0.80) and early-season canopy coverage ( 0.61 < H2 < 0.65), and consistent broad-sense heritability for late-season canopy height (0.76 < H2 < 0.82), indicating quantitative inheritance and potential for improvement through plant breeding. Strong correlation between emergence and canopy coverage (0.62 < r < 0.72) suggests that improvement of seedling emergence has great potential to increase yield and weed competitive ability. Accessions with high emergence and vigorous canopy growth are of immediate use to breeders targeting stand establishment, weed-tolerance, or weed-suppressant carrots, which is of particular advantage to the organic carrot production sector, reducing the costs and labor associated with herbicide application and weeding. We developed a standardized vocabulary and protocol to describe shoot-growth and facilitate collaboration and communication across carrot research groups. Our study facilitates identification and utilization of carrot genetic resources, conservation of agrobiodiversity, and development of breeding stocks for weed-competitive ability, with the long-term goal of delivering improved carrot cultivars to breeders, growers, and consumers. Accession selection can be further optimized for efficient breeding by combining shoot growth data with phenological data in this study's companion paper to identify ideotypes based on global market needs.

Linkage mapping of root shape traits in two carrot populations

This study investigated the genetic basis of carrot root shape traits using composite interval mapping in two biparental populations (n = 119 and n = 128). The roots of carrot F2:3 progenies were grown over 2 years and analyzed using a digital imaging pipeline to extract root phenotypes that compose market class. Broad-sense heritability on an entry-mean basis ranged from 0.46 to 0.80 for root traits. Reproducible quantitative trait loci (QTL) were identified on chromosomes 2 and 6 on both populations. Colocalization of QTLs for phenotypically correlated root traits was also observed and coincided with previously identified QTLs in published association and linkage mapping studies. Individual QTLs explained between 14 and 27% of total phenotypic variance across traits, while four QTLs for length-to-width ratio collectively accounted for up to 73% of variation. Predicted genes associated with the OFP-TRM (OVATE Family Proteins-TONNEAU1 Recruiting Motif) and IQD (IQ67 domain) pathway were identified within QTL support intervals. This observation raises the possibility of extending the current regulon model of fruit shape to include carrot storage roots. Nevertheless, the precise molecular mechanisms through which this pathway operates in roots characterized by secondary growth originating from cambium layers remain unknown.

Temporal phenotypic variation of spinach root traits and its relation to shoot performance

The root system is important for the growth and development of spinach. To reveal the temporal variability of the spinach root system, root traits of 40 spinach accessions were measured at three imaging times (20, 30, and 43 days after transplanting) in this study using a non-destructive and non-invasive root analysis system. Results showed that five root traits were reliably measured by this system (RootViz FS), and two of which were highly correlated with manually measured traits. Root traits had higher variations than shoot traits among spinach accessions, and the trait of mean growth rate of total root length had the largest coefficients of variation across the three imaging times. During the early stage, only tap root length was weakly correlated with shoot traits (plant height, leaf width, and object area (equivalent to plant surface area)), whereas in the third imaging, root fresh weight, total root length, and root area were strongly correlated with shoot biomass-related traits. Five root traits (total root length, tap root length, total root area, root tissue density, and maximal root width) showed high variations with coefficients of variation values (CV ≥ 0.3, except maximal root width) and high heritability (H2 > 0.6) among the three stages. The 40 spinach accessions were classified into five subgroups with different growth dynamics of the primary and lateral roots by cluster analysis. Our results demonstrated the potential of in-situ phenotyping to assess dynamic root growth in spinach and provide new perspectives for biomass breeding based on root system ideotypes.

Form and contour: breeding and genetics of organ shape from wild relatives to modern vegetable crops

Shape is a primary determinant of consumer preference for many horticultural crops and it is also associated with many aspects of marketing, harvest mechanics, and postharvest handling. Perceptions of quality and preference often map to specific shapes of fruits, tubers, leaves, flowers, roots, and other plant organs. As a result, humans have greatly expanded the palette of shapes available for horticultural crops, in many cases creating a series of market classes where particular shapes predominate. Crop wild relatives possess organs shaped by natural selection, while domesticated species possess organs shaped by human desires. Selection for visually-pleasing shapes in vegetable crops resulted from a number of opportunistic factors, including modification of supernumerary cambia, allelic variation at loci that control fundamental processes such as cell division, cell elongation, transposon-mediated variation, and partitioning of photosynthate. Genes that control cell division patterning may be universal shape regulators in horticultural crops, influencing the form of fruits, tubers, and grains in disparate species. Crop wild relatives are often considered less relevant for modern breeding efforts when it comes to characteristics such as shape, however this view may be unnecessarily limiting. Useful allelic variation in wild species may not have been examined or exploited with respect to shape modifications, and newly emergent information on key genes and proteins may provide additional opportunities to regulate the form and contour of vegetable crops.

Morphological characterizations of parsnip (Pastinaca sativa L.) to select superior genotypes

Parsnip (Pastinaca sativa L.) is an edible root that has long been used in cooking and preparing baby food and livestock. The present study was performed to evaluate the phenotypic diversity of 69 accessions of this species to select superiors in terms of root quality in Paykan village, Isfahan province, Iran, in the year 2022. There were significant differences among the accessions investigated (ANOVA, p < .01). Coefficient of variation (CV) was more than 20.00% in the majority of measured characters (64 out of 66 characters), indicating high diversity among the accessions. Foliage width (crown) ranged from 10 to 55 cm with an average of 32.32 cm. Root shape was tapering (33), obtriangular (10), narrow oblong (5), wide oblong (5), obovate (13), and fusiform (3). Root length ranged from 81.2 to 294 mm with an average of 166.44 mm. Root diameter at its middle point ranged from 15.58 to 125.12 mm with an average of 51.83 mm. Root weight ranged from 15 to 1200 g with an average of 315.36 g. Inner core (xylem) pigmentation/color was cream yellow (11 accessions), light yellow (12), yellow (42), dark yellow (2), and yellow-light orange (2). In the cluster analysis based on Ward's method, the accessions were divided into two main clusters according to morphological traits. This is despite the fact that parsnip is part of the medicinal plant native and valuable in most farms in tropical cities. Compared with carrots, parsnip plants are more adaptable to different environmental conditions. The accessions studied here showed high phenotypic diversity. Based on ideal values of the important and commercial characters of parsnip, such as root length, root weight, inner core (xylem) pigmentation/color, root shape, flesh color intensity, flesh palatability, and total soluble solids, 14 genotypes, including Parsnip-3, Parsnip-9, Parsnip-24, Parsnip-32, Parsnip-32, Parsnip-48, Parsnip-51, Parsnip-52, Parsnip-58, Parsnip-60, Parsnip-62, Parsnip-65, Parsnip-67, and Parsnip-69, were promising and are recommended for cultivation.

A trait-environment relationship approach to participatory plant breeding for organic agriculture

The extent of intraspecific variation in trait-environment relationships is an open question with limited empirical support in crops. In organic agriculture, with high environmental heterogeneity, this knowledge could guide breeding programs to optimize crop attributes. We propose a three-dimensional framework involving crop performance, crop traits, and environmental axes to uncover the multidimensionality of trait-environment relationships within a crop. We modeled instantaneous photosynthesis (A_sat ) and water-use efficiency (WUE) as functions of four phenotypic traits, three soil variables, five carrot (Daucus carota) varieties, and their interactions in a national participatory plant breeding program involving a suite of farms across Canada. We used these interactions to describe the resulting 12 trait-environment relationships across varieties. We found one significant trait-environment relationship for A_sat (taproot tissue density-soil phosphorus), which was consistent across varieties. For WUE, we found that three relationships (petiole diameter-soil nitrogen, petiole diameter-soil phosphorus, and leaf area-soil phosphorus) varied significantly across varieties. As a result, WUE was maximized by different combinations of trait values and soil conditions depending on the variety. Our three-dimensional framework supports the identification of functional traits behind the differential responses of crop varieties to environmental variation and thus guides breeding programs to optimize crop attributes from an eco-evolutionary perspective.

Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values

The principal phenotypic determinants of market class in carrot-the size and shape of the root-are under primarily additive, but also highly polygenic, genetic control. The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided.

Detection of Chromosomal Segments Introgressed from Wild Species of Carrot into Cultivars: Quantitative Trait Loci Mapping for Morphological Features in Backcross Inbred Lines

Cultivated carrot is thought to have been domesticated from a wild species, and various phenotypes developed through human domestication and selection over the past several centuries. Little is known about the genomic contribution of wild species to the phenotypes of present-day cultivars, although several studies have focused on identifying genetic loci that contribute to the morphology of storage roots. A backcross inbred line (BIL) population derived from a cross between the wild species Daucus carota ssp. carota "Songzi" and the orange cultivar "Amsterdam forcing" was developed. The morphological features in the BIL population became more diverse after several generations of selfing BC₂F₁ plants. Only few lines retained features of wild parent. Genomic resequencing of the two parental lines and the BILs resulted in 3,223,651 single nucleotide polymorphisms (SNPs), and 13,445 bin markers were generated using a sliding window approach. We constructed a genetic map with 2027 bins containing 154,776 SNPs; the total genetic distance was 1436.43 cM and the average interval between the bins was 0.71 cm. Five stable QTLs related to root length, root shoulder width, dry material content of root, and ratio of root shoulder width to root middle width were consistently detected on chromosome 2 in both years and explained 23.4-66.9% of the phenotypic variance. The effects of introgressed genomic segments from the wild species on the storage root are reported and will enable the identification of functional genes that control root morphological traits in carrot.

The influence of the Or and Carotene Hydroxylase genes on carotenoid accumulation in orange carrots [Daucus carota (L.)]

The Or and CH genes are necessary for the accumulation of high amounts of β-carotene and other carotenoid pigments in carrot roots, in addition to the Y and Y₂ genes. Carrot taproot color results from the accumulation of various carotenoid and anthocyanin pigments. Recently, the Or gene was identified as a candidate gene associated with the accumulation of β-carotene and other provitamin A carotenoids in roots. The specific molecular mechanisms involved with this process, as well as the interactions between Or and the other genes involved in this process are not well understood. In order to better characterize the effect that Or alleles have on conditioning the accumulation of carotenoids in roots, we analyzed an F₃ family fixed homozygous recessive for y and y₂, derived from a cross between an orange carrot and a white wild carrot, segregating for the two known Or alleles, which we name Or_c and Or_w. QTL mapping across three different environments revealed that the accumulation of several carotenoids was associated with the Or_c allele, with consistent patterns across environments. A second QTL on chromosome 7, harboring a carotene hydroxylase gene homologous to Lut5 in Arabidopsis, was also associated with the accumulation of several carotenoids. Two alleles for this gene, which we name CH_c and CH_w, were discovered to be segregating in this population. Our study provides further evidence that Or and CH are likely involved with controlling the accumulation of β-carotene and may be involved with modulating carotenoid flux in carrot, demonstrating that both were important domestication genes in carrot.

Improvement of Phosphorus Use Efficiency in Rice by Adopting Image-Based Phenotyping and Tolerant Indices

Phosphorus is one of the second most important nutrients for plant growth and development, and its importance has been realised from its role in various chains of reactions leading to better crop dynamics accompanied by optimum yield. However, the injudicious use of phosphorus (P) and non-renewability across the globe severely limit the agricultural production of crops, such as rice. The development of P-efficient cultivar can be achieved by screening genotypes either by destructive or non-destructive approaches. Exploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study. Eighteen genotypes were selected for hydroponic study from the soil-based screening of 68 genotypes to identify the traits through non-destructive (geometric traits by imaging) and destructive (morphology and physiology) techniques. Geometric traits such as minimum enclosing circle, convex hull, and calliper length show promising responses, in addition to morphological and physiological traits. In 28-day-old seedlings, leaves positioned from third to fifth played a crucial role in P mobilisation to different plant parts and maintained plant architecture under P deficient conditions. Besides, a reduction in leaf angle adjustment due to a decline in leaf biomass was observed. Concomitantly, these geometric traits facilitate the evaluation of low P-tolerant rice cultivars at an earlier stage, accompanying several stress indices. Out of which, Mean Productivity Index, Mean Relative Performance, and Relative Efficiency index utilising image-based traits displayed better responses in identifying tolerant genotypes under low P conditions. This study signifies the importance of image-based phenotyping techniques to identify potential donors and improve P use efficiency in modern rice breeding programs.

A Digital Image-Based Phenotyping Platform for Analyzing Root Shape Attributes in Carrot

Root shape in carrot (Daucus carota subsp. sativus), which ranges from long and tapered to short and blunt, has been used for at least several centuries to classify carrot cultivars. The subjectivity involved in determining market class hinders the establishment of metric-based standards and is ill-suited to dissecting the genetic basis of such quantitative phenotypes. Advances in digital image acquisition and analysis has enabled new methods for quantifying sizes of plant structures and shapes, but in order to dissect the genetic control of the shape features that define market class in carrot, a tool is required that quantifies the specific shape features used by humans in distinguishing between classes. This study reports the construction and demonstration of the first such platform, which facilitates rapid phenotyping of traits that are measurable by hand, such as length and width, as well as principal component analysis (PCA) of the root contour and its curvature. This latter approach is of particular interest, as it enabled the detection of a novel and significant quantitative trait, defined here as root fill, which accounts for 85% of the variation in root shape. Curvature analysis was demonstrated to be an effective method for precise measurement of the broadness of the carrot shoulder, and degree of tip fill; the first principal component of the respective curvature profiles captured 87% and 84% of the total variance. This platform's performance was validated in two experimental panels. First, a diverse, global collection of germplasm was used to assess its capacity to identify market classes through clustering analysis. Second, a diallel mating design between inbred breeding lines of differing market classes was used to estimate the heritability of the key phenotypes that define market class, which revealed significant variation in the narrow-sense heritability of size and shape traits, ranging from 0.14 for total root size, to 0.84 for aspect ratio. These results demonstrate the value of high-throughput digital phenotyping in characterizing the genetic control of complex quantitative phenotypes.

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.

Exploring genetic architecture for pod-related traits in soybean using image-based phenotyping

Mature pod color (PC) and pod size (PS) served as important characteristics are used in the soybean breeding programs. However, manual phenotyping of such complex traits is time-consuming, laborious, and expensive for breeders. Here, we collected pod images from two different populations, namely, a soybean association panel (SAP) consisting of 187 accessions and an inter-specific recombinant inbred line (RIL) population containing 284 RILs. An image-based phenotyping method was developed and used to extract the pod color- and size-related parameters from images. Genome-wide association study (GWAS) and linkage mapping were performed to decipher the genetic control of pod color- and size-related traits across 2 successive years. Both populations exhibited wide phenotypic variations and continuous distribution in pod color- and size-related traits, indicating quantitative polygenic inheritance of these traits. GWAS and linkage mapping approaches identified the two major quantitative trait loci (QTL) underlying the pod color parameters, i.e., qPC3 and qPC19, located to chromosomes 3 and 19, respectively, and 12 stable QTLs for pod size-related traits across nine chromosomes. Several genes residing within the genomic region of stable QTL were identified as potential candidates underlying these pod-related traits based on the gene annotation and expression profiling data. Our results provide the useful information for fine-mapping/map-based cloning of QTL and marker-assisted selection of elite varieties with desirable pod traits.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01223-2.

Mining for Candidate Genes Controlling Secondary Growth of the Carrot Storage Root

Background: Diverse groups of carrot cultivars have been developed to meet consumer demands and industry needs. Varietal groups of the cultivated carrot are defined based on the shape of roots. However, little is known about the genetic basis of root shape determination. Methods: Here, we used 307 carrot plants from 103 open-pollinated cultivars for a genome wide association study to identify genomic regions associated with the storage root morphology. Results: A 180 kb-long region on carrot chromosome 1 explained 10% of the total observed phenotypic variance in the shoulder diameter. Within that region, DcDCAF1 and DcBTAF1 genes were proposed as candidates controlling secondary growth of the carrot storage root. Their expression profiles differed between the cultivated and the wild carrots, likely indicating that their elevated expression was required for the development of edible roots. They also showed higher expression at the secondary root growth stage in cultivars producing thick roots, as compared to those developing thin roots. Conclusions: We provided evidence for a likely involvement of DcDCAF1 and/or DcBTAF1 in the development of the carrot storage root and developed a genotyping assay facilitating the identification of variants in the region on carrot chromosome 1 associated with secondary growth of the carrot root.

Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry

BACKGROUND

Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit shape is an inherently multi-dimensional, continuously variable trait and not adequately described by a single categorical or quantitative feature. Morphometric approaches enable the study of complex, multi-dimensional forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC). We use these human-recognizable shape categories to select quantitative features extracted from multiple morphometric analyses that are best fit for genetic dissection and analysis.

RESULTS

We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered 4 principal shape categories, and inferred progression using PPKC. We extracted 68 quantitative features from digital images of strawberries using a suite of morphometric analyses and multivariate statistical approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68% to 99% for the newly created phenotypic variables for describing a shape.

CONCLUSIONS

Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches that we applied in strawberry should apply to other fruits, vegetables, and specialty crops.

Review: Climate change impacts on food security- focus on perennial cropping systems and nutritional value

Anthropogenic increases in fossil fuel emissions have been a primary driver of increased concentrations of atmospheric carbon dioxide ([CO₂]) and other greenhouse gases resulting in warmer temperatures, alterations in precipitation patterns, and increased occurrence of extreme weather events in terrestrial areas across the globe. In agricultural growing regions, alterations in climate can challenge plant productivity in ways that impact the ability of the world to sustain adequate food production for a growing and increasingly affluent population with shifting access to affordable and nutritious food. While the knowledge gap that exists regarding potential climate change impacts is large across agriculture, it is especially large in specialty cropping systems. This includes fruit and vegetable crops, and perennial cropping systems which also contribute (along with row crops) to our global diet. In order to obtain a comprehensive view of the true impact of climate change on our global food supply, we must expand our narrow focus from improving yield and plant productivity to include the impact of climate change on the nutritional value of these crops. In order to address these questions, we need a multi-faceted approach that integrates physiology and genomics tools and conducts comprehensive experiments under realistic depictions of future projected climate. This review describes gaps in our knowledge in relation to these responses, and future questions and actions that are needed to develop a sustainable future food supply in light of global climate change.

Non-destructive phenotyping for early seedling vigor in direct-seeded rice

BACKGROUND

Early seedling vigor is an essential trait of direct-seeded rice. It helps the seedlings to compete with weeds for water and nutrient availability, and contributes to better seedling establishment during the initial phase of crop growth. Seedling vigor is a complex trait, and phenotyping by a destructive method limits the improvement of this trait through traditional breeding. Hence, a non-invasive, rapid, and precise image-based phenotyping technique is developed to increase the possibility to improve early seedling vigor through breeding in rice and other field crops.

RESULTS

To establish and assess the methodology using free-source software, early seedling vigor was estimated from images captured with a digital SLR camera in a non-destructive way. Here, the legitimacy and strength of the method have been proved through screening seven diverse rice cultivars varying for early seedling vigor. In the regression analysis, whole-plant area (WPA) estimated by destructive-flatbed scanner (WPAs) and non-destructive imaging (WPA_i) approaches was strongly related (R² > 83%) and suggested that WPA_i can be adapted in place of destructive methods to estimate seedling vigor. In addition, this study has identified a set of new geometric traits (convex hull and top view area) for screening breeding lines for early seedling vigor in rice, which decreased the time by 80% and halved the cost of labor in data observation.

CONCLUSIONS

The method demonstrated here is affordable and easy to establish as a phenotypic platform. It is suitable for most glasshouses/net houses for characterizing genotypes to understand the plasticity of shoots under a given environment at the seedling stage. The methodology explained in this experiment has been proven to be practical and suggested as a technique for researchers involved in direct-seeded rice. Consequently, it will help in the simultaneous screening of genotypes in large numbers, the identification of donors, and in gaining information on the genetic basis of the trait to design a breeding program for direct-seeded rice.

Accumulating crop functional trait data with citizen science

Trait-based ecology is greatly informed by large datasets for the analyses of inter- and intraspecific trait variation (ITV) in plants. This is especially true in trait-based agricultural research where crop ITV is high, yet crop trait data remains limited. Based on farmer-led collections, we developed and evaluated the first citizen science plant trait initiative. Here we generated a dataset of eight leaf traits for a commercially important crop species (Daucus carota), sampled from two distinct regions in Canada, which is 25-fold larger than datasets available in existing trait databases. Citizen-collected trait data supported analyses addressing theoretical and applied questions related to (i) intraspecific trait dimensionality, (ii) the extent and drivers of ITV, and (iii) the sampling intensity needed to derive accurate trait values. Citizen science is a viable means to enhance functional trait data coverage across terrestrial ecosystems, and in doing so, can directly support theoretical and applied trait-based analyses of plants.