AoB PLANTS

Growth kinematics and soil mechanics are key to explain how roots overcome the mechanical resistance of soil, yet few studies are linking these two factors. Formulas for cone penetration tests are typically used to infer the friction experienced by roots, but these fail to consider how growth affects the external forces applied on the root. This study formalised how expansive growth in the root apical meristem can reduce soil friction, and applied the framework to analyse the growth strategy of 6 plant species. The results of the analysis revealed trade-offs between reducing frictions, maintaining a desired growth trajectory and elongation rate. A shorter elongation zone can reduce the fraction of the mechanical energy lost to friction, but this is done at the expense of the elongation rate. A sharper tip or increased radius can help roots maintain the elongation rate at no energetic cost, but these strategies come with the cost of growth instability (tortuous roots) and decrease in specific root length respectively. During establishment, root strategies may therefore occupy a 2-dimensional trait space in which the mechanical efficiency of growth is balanced against the explorative-exploitative trade-off. HighlightsGrowth and form of root tips explain how plants overcome mechanical resistance from the soil Trade-offs link the energy lost by friction, growth stability and elongation rate of roots Larger roots allow faster growth independently of these trade-offs New framework formalises plants strategies to acquire soil resources

O_LIHeatwaves are increasing in frequency and intensity and may alter competitive interactions between native and non-indigenous plant species. C_LIO_LIWe compared the responses of the native Sporobolus maritimus and the non-indigenous Sporobolus anglicus to a simulated 5-day heatwave using an integrative approach combining morphological, physiological, biochemical, transcriptomic, and metabolomic analyses. C_LIO_LIS. maritimus showed reduced survival, greater physiological damage, and no recovery, indicating high sensitivity to heat stress. Conversely, S. anglicus exhibited a rapid and coordinated response, limited damage to the photosynthetic apparatus, and full recovery after stress. C_LIO_LIThese results highlight the greater resilience of S. anglicus and suggest a potential decline of the native species in the Venetian salt marshes under increasing heat stress. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/728445v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@14818aaorg.highwire.dtl.DTLVardef@4b5d40org.highwire.dtl.DTLVardef@1d7451dorg.highwire.dtl.DTLVardef@1fa8653_HPS_FORMAT_FIGEXP M_FIG C_FIG We investigated the effects of heatwaves on a native and NIS Sporobolus species from the Venice Lagoon using an integrative, multi-level approach during a simulated 5-day heatwave and recovery phase. The native species showed reduced survival, pronounced physiological damage, and no recovery, indicating high sensitivity to heat stress. Conversely, the NIS exhibited a rapid response, limited impairment of the photosynthetic apparatus, and full recovery. These findings indicate greater resilience of the NIS and suggest a potential decline of the native species in the Venice Lagoon under increasing heat stress. Image created with BioRender (www.biorender.com).

The transition to flowering in Arabidopsis thaliana is a complex process governed by many biological and environmental stimuli. Although many of the genes which regulate this process have been identified over the past 30 years, it remains unclear how these networks are integrated. In this study, we used the transcriptional responses of Col-0, Ler-1, and three mutant lines, to build a genome wide regulatory network of Arabidopsis thaliana during the flowering transition. The expression profiles of 22,810 genes across five genotypes were collected from the GEO database Series GSE57 from which we assigned flowering-time genes to different interacting modules by an adapted form of Hierarchical Complete Linkage Clustering (HCLC) after reconstruction of regulatory networks according to the Position Weight Matrix (PWM)-based method. Within these modules, we identified 77 core genes and 31 controller or driver genes. We identify two genes, LHY and, less expectedly, the transcription factor TCP16, to be topographically positioned at the regulatory hubs a nine-gene transcriptional control unit, implying they have the capacity to integrate information from across the flowering time pathways which interpret different environmental or endogenous cues during the vegetative-reproductive transition. Interrogating their behaviour across transcriptional datasets, we show that both LHY and TCP16 show transcriptional oscillations during the flowering transition, with a wavelength that varies depending on environmental conditions. We suggest that the transcriptional responses of LHY and TCP16 allow them to regulate the flow of information through the genetic networks which integrates different floral transition cues, and that genetic modelling approaches can provide new insights into the regulation of well-studied biological processes such as the flowering transition. Author summaryHow plants decide when to flower is a critical stage for completing their life cycles. It is also of key agricultural importance, as crops need to flower at the right time of year to allow efficient pollination and harvesting. Many genes are known to affect flowering time control in plants. Here, we use computational approaches to estimate how different genes interact in flowering time control in Arabidopsis, a small plant in the mustard family which is widely used for molecular studies. We use large-scale studies of how gene expression changes in different plant lines which have disrupted or adjusted flowering time to group the many genes involved in flowering into different interacting pathway, which we visualise as sets of coloured nodes controlling one another in a network. We show that two genes may have new rols in integrating information from different pathways, and discuss how their behaviour might help them to function as intregrators of biological information - including the daily oscaillations in their expression.

O_LIStored seeds are crucial repositories of plant genetic diversity. However, long-term storage inevitably causes seed deterioration and loss of viability, and chemical processes within the seeds during storage can influence germination and seedling establishment. Emerging evidence suggests that seed ageing can also affect traits of adult plants, yet the extent to which this phenomenon is relevant across species, particularly for wild plant species with high genetic variation, remains unclear. C_LIO_LITo address this, we focused on 14 grassland species and subjected their seeds to simulated long-term storage by exposing them to artificial ageing conditions (60% rH, 45{degrees}C). We then compared plants grown from the aged seeds with plants from fresh seeds in a common garden experiment. C_LIO_LIArtificially aged seeds germinated later, the developing seedlings had lower survival rates and reduced growth. Adult plants grown from aged seeds flowered later, produced fewer flowers, and had less biomass by the end of the first vegetation period than those from fresh seeds. The effect of the ageing treatment varied between species, but the trend was overall significant across species, with minor differences between perennials and annuals. Interestingly, in perennial plants, the effects vanished or were inverted in the second growing season, with plants growing from aged seeds flowering earlier and producing more biomass. C_LIO_LISynthesis. Our results show that seed storage affects seedling performance, plant growth, and flowering phenology. These direct storage effects should be considered when using stored seeds for species conservation, ecosystem restoration, or evolutionary research relying on stored seeds. C_LI

O_LIIn fluctuating environments, the kinetics of stomatal opening and closing influence the balance between carbon gain and water loss. Smaller guard cells may respond faster to fluctuating environmental conditions because of their greater surface area for osmolyte flux relative to cell volume. A related hypothesis is that operational stomatal conductance (gop) is often well below its theoretical maximum (gmax) because at this stomatal aperture, guard cell volume is poised to change rapidly with small changes in turgor pressure. C_LIO_LIWe analyzed 2,124 estimates of stomatal closure kinetics in response to an abrupt increase in vapor pressure deficit (VPD) among 29 diverse wild tomato populations in the genus Solanum. C_LIO_LILeaves with small guard cells and a lower gop to gmax ratio (fgmax) closed faster, but explained variation in kinetic parameters at different levels of biological organization. Guard cell size had high phylogenetic heritability and varied relatively little within populations, whereas fgmax varied mostly among individuals and between light intensity treatments. C_LIO_LISmaller stomata can be speedier, but only if stomata are held at an aperture where they are responsive to changing turgor pressure. Selection on stomatal speed may influence not only anatomical traits like guard cell size, but also physiological controls on gop. C_LI

BackgroundCell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana. However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. ResultsWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. ConclusionOur framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.

PremiseSeed size and morphology are critical traits in agriculture, ecology, and genetics, but high-throughput quantification of these traits is often limited by labor-intensive manual measurements or expensive, platform-specific imaging software. Methods and ResultsWe developed SeedMeasure, a lightweight, open-source, and cross-platform command-line tool written in Python that automates the measurement of seed area, length, and width from images. Using a simple imaging setup, the program processes images by correcting for perspective skew, filtering debris, and exports quantitative data alongside quality-check images. We validated SeedMeasure across nine diverse species, ranging from small Arabidopsis thaliana seeds to large Zea mays kernels. The tool quickly handles images using multithreading and demonstrates high reproducibility, yielding low coefficients of variation across repeated runs. ConclusionsCompared to existing software, SeedMeasure is free, offers faster processing through parallel computing, and provides standalone executables that require no programming dependencies. SeedMeasure offers an accessible, cost-effective, and high-throughput approach for rapid phenotypic profiling, making advanced seed morphological analysis available to researchers without specialized laboratory hardware.

Multi-environment trials are central to cultivar evaluation because they reveal how candidate cultivars perform across locations, years, management conditions, and stress environments. The resulting yield matrix is a rich source of data on genotype-by-environment interaction, and a wide literature on estimation, decomposition, visualisation, and prediction of yield potential and stability has flourished. However the ultimate question of which cultivar to recommend on the basis of such a matrix is often left implicit. The question is far from trivial, and in this paper we formulate cultivar recommendation as an axiomatic ranking problem. This framework is rich enough to encompass the existing literature on stability indices, as well as any other deterministic ranking procedure. We show that many commonly used stability-based procedures can violate minimal criteria of efficiency or consistency. The result of such violations is that a cultivar with uniformly high yield could be ranked below a cultivar with uniformly low yield, or the relative ranks of two cultivars could depend on whether or not a third cultivar is present in the matrix. Our results prove that under a small number of such criteria the space of admissible rules collapses to the family of power means and their limiting cases. If we further wish to allow multiplication normalisation of yield, we are left with the geometric mean as the unique solution.

Climatic and biogeographic variables are often used as a proxy for tree genetic diversity, but local factors can also influence it. We propose that woodland age, presence of ancient trees, and population size could impact genetic diversity. Using the RBG Kew UK National Tree Seed Project as a study case, we examined how these factors are accounted for during seed collection. We found 42% of tree seed collections come from ancient woodlands and that 8.4% overlap with ancient trees. Sampled forest patches size ranges from few individuals to several thousand. We then carried out a pilot to examine the role of population size on functional traits variation, testing the relationship between population size and seed germination and seedling thermal stress sensitivity in three populations of the Betula pubescens Ehrh. complex. We found that seeds and seedlings from larger populations showed higher fitness and stress resistance. Our results highlight the importance of local factors to predict variation in functional traits, relevant for tree resilience. Existing seed collections of native species stored in conservation seed banks offer a valuable resource to explore these factors and improve our understanding of genetic diversity in tree populations, with implications for biodiversity conservation and forestry production.

While nitrogen fertilizers are widely used in agricultural production, their application incurs significant environmental and energetic costs. In contrast, some crops are less dependent on these fertilizers because they engage in symbioses with rhizobia, nitrogen-fixing bacteria provide ammonium to the plant in exchange for carbon. However, the carbon cost associated with nitrogen fixation can negatively impact crop yields. Improving the efficiency of this metabolic process could alleviate this impact on crop productivity. Mathematical models can help us quantitatively explore metabolic behavior and identify potential targets for metabolic engineering. In this work, we developed a kinetic model of determinate root nodule metabolism, where this symbiotic exchange of carbon from the plant and nitrogen from the bacteria occurs. We used this model to evaluate how the predicted metabolic behavior differs between inefficient and efficient nodules, and to identify potential engineering targets for improving nitrogen fixation efficiency and rate. We show that the enzymes phosphoenolpyruvate carboxylase and pyruvate kinase have significant influence on the predicted rate and efficiency of nitrogen fixation, especially when their expression is varied in combination with oxidative Pentose Phosphate Pathway enzymes like glucose-6-phosphate dehydrogenase and 6-phosphogluconolactonase. The model predicts that pairing a 3-fold decrease in glucose-6-phosphate dehydrogenase activity along with either a 3-fold increase in phosphoenolpyruvate carboxylase activity or decrease in pyruvate kinase activity could increase nitrogen fixation rate by 5.51% while improving nitrogen fixation efficiency by 7.74%.

Most extant plants show evidence of past polyploidization events in their genomes. Allopolyploids arise from hybridisation, resulting in the polyploid genome comprising subgenomes from different ancestors. Subsequent adaptation to their environment or selection pressure for specific traits has led to several allopolyploids exhibiting an unequal contribution from their subgenomes to their phenotype. Given the diversity of cultivars grown for different environments, it is possible that the associated regulatory changes may have given rise to different subgenome expression biases. Likewise, different tissues and developmental stages have distinct expression profiles that may correspond more strongly to one subgenome over the other(s). Here, we investigate different metrics for quantifying the contribution of each subgenome in space (tissue) and time (development) in cultivars of Brassica napus. Brassica napus has two subgenomes, A and C, from its ancestors Brassica rapa (A) and Brassica oleracea (C). We find that the C genome has higher overall expression than the A genome, whereas the average expression per gene is higher for the A genome. Direct comparison of homoeologous pairs reveals higher expression of genes on the C genome. We find that the degree of expression bias can change between cultivars, tissues and across time with bias quantification being strongly dependent on the metric. These findings help explain contradictory reports on expression bias and genome dominance. Significance statementWe demonstrate how different metrics of expression bias between subgenomes in polyploids can lead to conflicting inferences. We show that expression can be viewed as either A or C-biased, yet the differences are small, calling into question the relevance of subgenome dominance and expression bias in B. napus.

The photosynthesis-transpiration-stomatal conductance (An-E-gs) model framework is widely used for estimating photosynthesis, transpiration, and stomatal conductance in plants. The model equations are solved by numerical iteration, and the converged model values are deemed the solution. However, there has been no general guarantee that the iterative procedure converges to a solution or that the procedure leads to convergence. Building on the recent proof of the existence of a unique set of solutions, we herewith propose a numerical algorithm that is guaranteed to converge to the solution for the An-E-gs model framework. We first analytically prove that the proposed algorithm necessarily converges to a solution. We then demonstrate the convergence across contrasting combinations of leaf temperature, relative humidity, light, atmospheric CO2, and wind speed. We further demonstrate rapid convergence with the algorithm: no more than ca. 10 iterations for approximately 10-3 mol CO2 m-2 s-1 precision in net photosynthesis and no more than ca. 20 iterations for 10-7 mol CO2 m-2 s-1 precision. By guaranteeing convergence to the solution, this algorithm eliminates concerns about nonconvergence in leaf gas-exchange calculations and is expected to serve as a robust foundation for a range of studies from leaf-level gas exchange to global-scale carbon and water cycle dynamics.

Phenomic prediction (PP) is a breeding value prediction method using near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, there is still little understanding in the genetics community of what pre-processing does and why it increases performances. Consequently, the choice of pre-processing is done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose a PCA-based pre-processing method where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative and orthogonal features of spectra instead of many correlated, uninformative wavelengths. We tested this new pre-processing method on five data sets representing four plant species (maize, rice, sorghum and grapevine). Results show that it performs as good, or better than the best classical chemometric pre-processing methods in almost all cases. Combining PCA-based and classical chemometric pre-processing methods maximizes predictive ability. Moreover, this pre-processing method opens up possibilities of better understanding and selecting parts of the spectral information that are relevant for the prediction of breeding values. Indeed, components representing together about 1% of spectral variability were found to be responsible for most of PP predictive ability. Plain language summaryCultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimation of breeding values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput genetic value estimation method that is increasingly being used. It often uses near infrared spectroscopy measurements as predictors of genetic values that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a new pre-processing approach that performs as good as or better than the best chemometric pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core IdeasO_LIWorking on principal components of spectra instead of wavelengths increases predictive ability of phenomic prediction and performs as good as or better than classical chemometrics pre-processing C_LIO_LIWorking on principal components of spectra requires less optimization of parameters than chemometrics pre-processing C_LIO_LIAbout 1% of spectral variance is responsible for most of the predictive power of phenomic prediction C_LIO_LIWorking on principal components of spectra pre-processed with classical chemometrics pre-processing can increase predictive ability even more C_LIO_LIPCA-based methods are valuable to optimize predictive ability of phenomic prediction and could be used more widely in the quantitative genetics field C_LI

Leaf shape varies widely across plant taxa and has repeatedly been shown to affect ecophysiology, interspecific interactions, and fitness. We used population genomics, genome wide association studies (GWAS), and comparative genomics to determine the genetic basis and evolutionary history of an uncharacterized Mendelian leaf shape polymorphism in Ipomoea hederacea. To do so, we assembled a reference genome and generated whole genome sequencing for 123 individuals from 55 populations. We identified a 117 kb indel that perfectly co-segregates with leaf shape by conducting a GWAS and assessing differences in coverage. Syntenic orthologs for genes on the indel were present in five other Ipomoea species with various leaf shapes, indicating the indel is newly arisen deletion in I. hederacea despite similar leaf shape phenotypes in the other species. More broadly, these results illustrate how a range of leaf shape phenotypes can be produced by distinct genetic mechanisms even in closely related species. Although none of the genes on the indel itself are known leaf shape candidates, there are multiple leaf shape candidate genes in close proximity that are involved in the auxin biosynthesis pathway. Additionally, the genes within the indel have gene functions that could affect other potentially ecologically relevant traits that have previously been shown to be associated with leaf shape in I. hederacea. Therefore, the pleiotropic effect of the indel polymorphism can have important implications for understanding the ecological mechanisms influencing a well-documented leaf shape latitudinal cline in I. hederacea. Significance StatementIpomoea hederacea has been used to investigate the ecological and evolutionary effects of leaf shape because of a well-documented latitudinal leaf shape cline governed by an uncharacterized Mendelian polymorphism in this species. We identified a 117 kb indel that perfectly co-segregates with leaf shape in I. hederacea. The indel appears to be a distinct genetic mechanism than those governing similar leaf shapes in other closely related Ipomoea species. Additionally, possible pleiotropic effects of the indel on other traits has important implications for understanding the ecology and evolution of leaf shape variation in I. hederacea. Therefore, both the results and the new genomic resources that we developed will help facilitate future work understanding the genetic, developmental, and ecological mechanisms governing leaf shape variation.

Creeping bentgrass (Agrostis stolonifera) is a turfgrass species established on golf course surfaces but is criticized for high irrigation requirements. While genetic variation for water deficit stress tolerance exists between cultivars, the lack of defined critical soil water potential thresholds (Soil {Psi}crit) for this species complicates precise irrigation strategies and benchmarks for plant breeding. This study utilized a polyethylene glycol (PEG) infused agar-based system to simulate water potential reductions and determine the water potential threshold ({Psi}crit) for seedling root elongation. Creeping bentgrass cv Pure distinction seedlings were subjected to six water potentials ({Psi}) ranging from -0.36 MPa (no PEG applied) to -1.72 MPa. Daily digital imaging was used to measure root elongation over 5 days. Results across two experiments demonstrated that creeping bentgrass seedlings are highly sensitive to mild reductions in {Psi}. A reduction to -0.61 MPa significantly decreased root length and growth rates by over 50% compared to the control. Regression models predicted that a {Psi}crit of approximately -0.45 MPa reduced daily root growth by 25%, while upwards {Psi} of -1.0 MPa resulted in a 75% reduction of seedlings root growth. Furthermore, seedlings exposed to the lowest water potentials were predicted to require an additional 30 to 46 days to achieve the same root length as control plants. These findings establish specific {Psi}crit benchmarks for water deficit stress tolerance using a PEG-based system to induce dehydration. These methods can be used in breeding programs, and will help develop more accurate experiments examining the mechanisms of water deficit stress tolerance.

Modern agriculture faces major sustainability challenges, including stagnating yields, dependence on fossil resources, and severe environmental impacts. Increasing intra- and interspecific diversity within plots through agroecological design is a promising method for enhancing crop productivity and stability. However, mixed-crop performance remains highly variable, and the genetic architecture of interactions within heterogeneous canopies is poorly understood. Two quantitative genetic frameworks have been proposed: trait-based models, which describe how interacting traits shape phenotypes, and variance-based models, which treat neighbor genotype effects as "black-box" social effects. However, existing variance-based models have been developed almost exclusively for intraspecific interactions and simple neighborhoods. We propose a general multispecies framework describing how a focal plants phenotype and total breeding value arise from its own direct effects and from the indirect effects of conspecific and heterospecific neighbors. We derived analytical expressions for phenotypic variance, inter-individual covariance, total breeding value variance, and relative heritable variance, which explicitly account for spatial structure, relatedness, and environmental similarities. Using a two-species alternating-row field layout and extensive simulations based on flexible variance-covariance structures, we evaluated the statistical power and bias of joint mixed-model estimators of direct and indirect genetic and environmental effects under a wide range of parameter combinations. Our results show that accurate separation of direct and indirect effects depends on trait heritability and replication, and that modeling genetic covariances across effects and species substantially improves estimation accuracy. This framework provides a unified, individual-centered basis for analyzing complex multispecies neighborhoods and quantifying the breeding potential of plant communities. Article SummaryGrowing several crop species or varieties together in the same field can boost yield and stability, but the outcome is unpredictable and the genetic causes remain unclear. We developed a theoritical & statistical framework that links each plants performance to its own genes and to those of its neighbors, both from the same and from a different species. Computer simulations of a two-species field showed that these direct and neighbor-driven genetic effects can be reliably separated when enough plants are measured per variety. The framework opens the way to breeding crop mixtures that perform well specifically when grown alongside another species.

The sequence of leaf physiological thresholds underlying plant responses to water deficit is thought to be functionally coordinated; yet, to what extent this coordination is maintained across genotypes and environments remains poorly documented at the intraspecific level. We characterized the sequence of stomatal closure, turgor loss and xylem embolism in the leaves of two genotypes of the riparian species Populus nigra (DRA-038 vs. PG-31) subjected to control, additional nitrogen or additional potassium treatments. Under control conditions, embolism measurements using the optical vulnerability method showed that DRA-038 was more vulnerable than PG-31, in agreement with measurements performed on stems with the reference Cavitron method. Stomatal closure consistently preceded xylem embolism, while bulk leaf turgor loss was typically observed once xylem embolism had already reached 50%. Hydraulic thresholds responded to treatments in a genotype-dependent manner, the intrinsically more vulnerable genotype DRA-038 being typically more plastic. However, despite variations across genotypes and treatments, the trait sequence remained tightly coordinated such that stomatal safety margins (SSMs) remained virtually null. These findings support a strong mechanistic integration of leaf hydraulic thresholds in poplar across genetic units and varying environments, questioning whether to favour intrinsic tolerance or plastic capacities in breeding future drought-tolerant genotypes.

Restoration of coastal salt marshes is often limited by their capability to revegetate, either through natural recruitment or active planting methods. Despite the critical need for efficient revegetation methods, direct seeding remains an underrepresented approach in coastal wetland restoration. Additionally, tidal inundation poses special challenges for coastal Seed-Based Restoration (SBR) relative to terrestrial habitats, with tides displacing seeds from the marsh platform. To determine factors potentially influencing successful establishment in coastal wetlands, we conducted a series of greenhouse and lab experiments with Salicornia pacifica (pickleweed), the dominant plant in California marshes. We determined pickleweed seed viability using standardized germination tests. Additionally, we tested factors that influence pickleweed seed viability and germination rates, such as soil moisture, soil type, and sowing depth. We found that pickleweed seeds had an average viability of 22.5%, which increased with larger-sized seeds. We also determined that the most effective dormancy-breaking pretreatments varied by soil type: a one-day cold stratification in freshwater maximized germination in benign soils, whereas a seven-day cold stratification in saltwater maximized germination in stressful soils. Finally, we determined that the optimal conditions for sowing seeds are surface sowing under moderate moisture. Creating conditions to maximize viability and germination is crucial to ensure the greatest chance of successful revegetation post restoration. Our study, which sequentially tested factors affecting different phases of the early life history of a dominant foundation species, can inform SBR for other coastal plants. This approach can help coastal land managers successfully implement SBR for habitat restoration.

Incomplete information on distributions for a high proportion of the world's plant species together with biases in global biodiversity data mean that current estimates of plant diversity patterns are skewed. A key issue is that current predictions rely on a subset of species that is not representative of all plant species. Here we tested the feasibility of a representative sampling approach for mapping global vascular plant diversity at the finest scale where comprehensive data is available. Using the World Checklist of Vascular Plants as a reference, we generate random samples of species with increasing sample sizes from the global species pool. We compare the diversity patterns retrieved from the samples against the patterns of the reference dataset using spatially weighted correlation coefficients and four different diversity metrics. We find that at the botanical country scale, representative global maps of species and phylogenetic diversity can be created with small numbers of species (~1% [0.2% and 0.4%, respectively]) at the botanical country scale. For effective growth form and family diversity sample sizes encompassing ~20% [19.2% and 19.5%, respectively] of all species are needed. Random samples require markedly fewer species to reach high correlations than when restricting the pool of species to single plant families or genera. We show that when representative samples are used robust inferences of plant diversity patterns can be made from only a small proportion of species.

O_LIFloral nectar plays a critical role in shaping the ecology and evolution of plant-pollinator interactions. Effective and efficient methods that allow for broad-scale sampling of nectar volume and sugar concentration across a diversity of taxa are needed to improve our understanding of many dimensions of mutualistic plant-pollinator interactions--including their basic ecology and evolution, their responses to environmental change, and their conservation and restoration. C_LIO_LIDespite the key importance of nectar for mediating plant-pollinator interactions, quantifying floral nectar in the field from many different plant species is challenging because there is often no one-size-fits-all sampling method that is effective across a diversity of floral structures and nectar traits. Different methods require different preparation, and sampling from many species involves a variety of logistical challenges. C_LIO_LIHere we provide a methodological roadmap for sampling floral nectar in the field from many different plant species. We describe our nectar collection methods in detail, including necessary equipment, calculations, and approaches appropriate for different floral morphologies. We also provide a troubleshooting guide for common problems encountered while collecting nectar in the field. To demonstrate the utility and effectiveness of our methods for collecting nectar from many different species, we present results on nectar trait variation from 53 species in an ecosystem. C_LIO_LIOur method illustrates that nectar traits vary considerably within and among plant species, indicating that large-scale nectar sampling projects are an important consideration for many basic and applied questions in pollination ecology and evolution. We hope that across many plant communities and ecosystems, our paper provides a practical roadmap for how to navigate the complexities of quantifying floral nectar traits. C_LI