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Abstract Phenotypic plasticity can alter traits that are crucial to population establishment in a new environment before adaptation can occur. How often phenotypic plasticity enables subsequent adaptive evolution is unknown, and examples of the phenomenon are limited. We investigated the hypothesis of plasticity-mediated persistence as a means of colonization of agricultural fields in one of the world’s worst weeds, Raphanus raphanistrum ssp. raphanistrum. Using non-weedy native populations of the same species and subspecies as a comparison, we tested for plasticity-mediated persistence in a growth chamber reciprocal transplant experiment. We identified traits with genetic differentiation between the weedy and native ecotypes as well as phenotypic plasticity between growth chamber environments. We found that most traits were both plastic and differentiated between ecotypes, with the majority plastic and differentiated in the same direction. This suggests that phenotypic plasticity may have enabled radish populations to colonize and then adapt to novel agricultural environments. 

Abstract The study of adaptation helps explain biodiversity and predict future evolution. Yet the process of adaptation can be difficult to observe due to limited phenotypic variation in contemporary populations. Furthermore, the scarcity of male fitness estimates has made it difficult to both understand adaptation and evaluate sexual conflict hypotheses. We addressed both issues in our study of two anther position traits in wild radish (Raphanus raphanistrum): anther exsertion (long filament − corolla tube lengths) and anther separation (long − short filament lengths). These traits affect pollination efficiency and are particularly interesting due to the unusually high correlations among their component traits. We measured selection through male and female fitness on wild radish plants from populations artificially selected to recreate ancestral variation in each anther trait. We found little evidence for conflicts between male and female function. We found strong evidence for stabilizing selection on anther exsertion and disruptive selection on anther separation, indicating positive and negative correlational selection on the component traits. Intermediate levels of exsertion are likely an adaptation to best contact small bees. The function of anther separation is less clear, but future studies might investigate pollen placement on pollinators and compare species possessing multiple stamen types. 

Summary The mechanisms underlying trait conservation over long evolutionary time scales are poorly known. These mechanisms fall into the two broad and nonmutually exclusive categories of constraint and selection. A variety of factors have been hypothesized to constrain trait evolution. Alternatively, selection can maintain similar trait values across many species if the causes of selection are also relatively conserved, while many sources of constraint may be overcome over longer periods of evolutionary divergence. An example of deep trait conservation is tetradynamy in the large family Brassicaceae, where the four medial stamens are longer than the two lateral stamens. Previous work has found selection to maintain this difference in lengths, which we call anther separation, in wild radish,Raphanus raphanistrum.Here, we test the constraint hypothesis using five generations of artificial selection to reduce anther separation in wild radish.We found a rapid linear response to this selection, with no evidence for depletion of genetic variation and correlated responses to this selection in only four of 15 other traits, suggesting a lack of strong constraint.Taken together, available evidence suggests that tetradynamy is likely to be conserved due to selection, but the function of this trait remains unclear. 

Abstract Plants respond to wounding stress by changing gene expression patterns and inducing the production of hormones including jasmonic acid. This wounding transcriptional response activates specialized metabolism pathways such as the glucosinolate pathways in Arabidopsis thaliana. While the regulatory factors and sequences controlling a subset of wound-response genes are known, it remains unclear how wound response is regulated globally. Here, we how these responses are regulated by incorporating putative cis-regulatory elements, known transcription factor binding sites, in vitro DNA affinity purification sequencing, and DNase I hypersensitive sites to predict genes with different wound-response patterns using machine learning. We observed that regulatory sites and regions of open chromatin differed between genes upregulated at early and late wounding time-points as well as between genes induced by jasmonic acid and those not induced. Expanding on what we currently know, we identified cis-elements that improved model predictions of expression clusters over known binding sites. Using a combination of genome editing, in vitro DNA-binding assays, and transient expression assays using native and mutated cis-regulatory elements, we experimentally validated four of the predicted elements, three of which were not previously known to function in wound-response regulation. Our study provides a global model predictive of wound response and identifies new regulatory sequences important for wounding without requiring prior knowledge of the transcriptional regulators. 

Summary Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co‐expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts.Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored.Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene‐to‐pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway‐best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality.Our study highlights the need to extensively explore expression‐based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons. 

Summary Revealing the contributions of genes to plant phenotype is frequently challenging because loss‐of‐function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such asArabidopsis thaliana.An image segmentation‐based method using the software ImageJ and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts.The segmentation‐based method was error‐prone (correlation between true and predicted seed counts,r² = 0.849) because seeds touching each other were undercounted. By contrast, the object detection‐based algorithm yielded near perfect seed counts (r² = 0.9996) and highly accurate fruit counts (r² = 0.980). Comparing seed counts for wild‐type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes.Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions. 

Abstract Phenotypic plasticity is the primary mechanism of organismal resilience to abiotic and biotic stress, and genetic differentiation in plasticity can evolve if stresses differ among populations. Inducible defence is a common form of adaptive phenotypic plasticity, and long‐standing theory predicts that its evolution is shaped by costs of the defensive traits, costs of plasticity and a trade‐off in allocation to constitutive versus induced traits. We used a common garden to study the evolution of defence in two native populations of wild arugulaEruca sativa(Brassicaceae) from contrasting desert and Mediterranean habitats that differ in attack by caterpillars and aphids. We report genetic differentiation and additive genetic variance for phenology, growth and three defensive traits (toxic glucosinolates, anti‐nutritive protease inhibitors and physical trichome barriers) as well their inducibility in response to the plant hormone jasmonic acid. The two populations were strongly differentiated for plasticity in nearly all traits. There was little evidence for costs of defence or plasticity, but constitutive and induced traits showed a consistent additive genetic trade‐off within each population for the three defensive traits. We conclude that these populations have evolutionarily diverged in inducible defence and retain ample potential for the future evolution of phenotypic plasticity in defence. 

Abstract Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established inOryza sativa(rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems. 

Phenotypic plasticity can alter traits that are crucial to population\n establishment in a new environment, before adaptation can occur. How often\n phenotypic plasticity enables subsequent adaptive evolution is unknown,\n and examples of the phenomenon are limited. We investigated the hypothesis\n of plasticity-mediated persistence as a means of colonization of\n agricultural fields in one of the world’s worst weeds, Raphanus\n raphanistrum ssp. raphanistrum. Using non-weedy native populations of the\n same species and subspecies as a comparison, we tested for\n plasticity-mediated persistence in a growth chamber reciprocal transplant\n experiment. We identified traits with genetic differentiation between the\n weedy and native ecotypes as well as phenotypic plasticity between growth\n chamber environments. We found that most traits were both plastic and\n differentiated between ecotypes, with the majority plastic and\n differentiated in the same direction. This suggests that phenotypic\n plasticity may have enabled radish populations to colonize and then adapt\n to novel agricultural environments."],"TechnicalInfo":["# Growth Chamber Reciprocal Transplant Dataset\n [https://doi.org/10.5061/dryad.4mw6m90kb](https://doi.org/10.5061/dryad.4mw6m90kb) This dataset contains the phenotypic data collected from plants grown in the growth chamber reciprocal transplant experiment, as well as the conditions in the growth chambers. ## Description of the data and file structure The dataset contains three sheets: "Chamber Conditions", "Main Data", and "Leaf Data" (although much of the information in "Leaf Data" has been incorporated into "Main data") ### Chamber Conditions This sheet contains the temperature and day length set points for each chamber each week. All temperature and day length information from the two weather stations used (LGER and KBTL) were collected from [www.wunderground.com](http://www.wunderground.com). Variables: * Granada, Spain (LGER) - the dates from which we collected temperature and day length information from the Grenada, Spain weather station (LGER) to simulate in the Winter Annual chamber * HighTempGrenada - the Winter Annual chamber's daytime set points, based on the average maximum temperature in Grenada on a given day * LowTempGrenada - the Winter Annual chamber's nighttime set points, based on the average minimum temperature in Grenada on a given day * DayLengthGrenada - the length of time the Winter Annual chamber was in its day cycle (lights on and typically higher temps), based on length of visible light in Grenada * DayStartGrenada - programed start of day time in the Winter Annual growth chamber * DayEndGrenada - programmed end of day time in the Winter Annual growth chamber * Date Set - the real-life date on which we changed the chamber conditions. * Augusta, MI (KBTL) - the dates from which we collected temperature and day length information from the Augusta, MI, USA weather station (KBTL) to simulate in the Spring Annual chamber * HighTempAugusta - the Spring Annual chamber's daytime set points, based on the average maximum temperature in Augusta on a given day * LowTempAugusta - the Spring Annual chamber's nighttime set points, based on the average minimum temperature in Augusta on a given day * DayLengthAugusta - the length of time the Spring Annual chamber was in its day cycle (lights on and typically higher temps), based on length of visible light in Augusta * DayStartAugusta - programed start of day time in the Spring Annual growth chamber * DayEndAugusta - programmed end of day time in the Spring Annual growth chamber ### Main Data This sheet contains all of the data used in our analyses, as well as descriptors for plants and growth chambers. Variables: * Chamber # - the number designation of the four growth chambers used in this study * Environment - the growing conditions in a given growth chamber, with "Winter Annual" corresponding to the "Grenada, Spain (LGER)" columns in Chamber Conditions, and "Spring Annual" corresponding to "Augusta, MI (KBTL)" * Ecotype - variety of* R. raphanistrum*, either weedy or native * Population - the six source populations used in this study identified by their location codes, with the final two letters denoting country or state (FR=France, ES=Spain, NY=New York, NC=North Carolina) and the first two letters denoting a specific location in those areas (available in Table 1 of the manuscript) * Matriline -  a line number is listed when discrete matrilines are known from field collections, but not for seeds collected in bulk (in which case the cell will be blank) * Flat - plants were arranged into four flats in each chamber, and the flats within a chamber were each assigned a number (1-4) * Position - the position of each plant within a flat was also tracked and pots were assigned a position number (1-35) * Pot# - Number assigned to each plant to give it a unique identifier -- for plants with individual matrilines tracked, pot # only went up to 2, while plants with unknown matrilines had pot numbers up to 40 to ensure individuals could be tracked * Plant Date - the date seeds were sown into each pot * Germ[1-5] - the date that each one of 5 seeds planted emerged as a germinant -- blank cells indicate that a germinant did not emerge * Plant Kept - the emergence date of the single plant that remained in the pot after excess germinants were thinned; missing values mean no germinants emerged or did not survive past the seedling stage * Days to Emergence - calculated as the day of emergence minus the planting date; missing values mean no germinants emerged or did not survive past the seedling stage * Rosette Photo Date - the date on which overhead and side photos of plants were taken, also the day the plants first showed signs of bolting (buds visible); missing values mean the plant did not survive to bolting * \\# Rosette Leaves - the number of leaves in the basal rosette, counted on the day of bolting; missing values mean the plant did not survive to bolting * Rosette Height - the vertical height of the tallest free-standing basal rosette leaf, measured from the height of the soil (cm); missing values mean the plant did not survive to bolting * 1st flower date - the date on which the first flower on a plant opened; missing values mean the plant did not survive to flowering * Days to First Flower - calculated as 1st flower date minus emergence date; missing values mean the plant did not survive to flowering * 1st Flower Height - measured on the first flower date, it is the vertical distance from the soil to the point at which the first open flower's pedicel connects to the main stalk (cm); missing values mean the plant did not survive to flowering * Ovule # - collected from typically the third flower to open, it is the number of ovules in one flower of a given plant; missing values mean the plant did not survive to flowering or ovules were not clearly visible * Notes - any additional information on a plant that we tracked * Blossom Photo Date - the date on which we took top and side photographs of at least the third flower to open, taken at the same time that ovule number was counted; missing values mean the plant did not survive to flowering  * PetalLength - measured using a top-view photo in Image J, the distance from the tip of the petal to where it meets the floral tube in the center of the floral display (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * PetalWidth - measured using a top-view photo in Image J, the distance from the widest part of the petal, perpendicular to the line measured for petal length (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * Tube - measured using a side-view photo in Image J, the length of the most clearly visible petal from where it meets the pedicel to the apex of its curve outward (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * LAnther - measured using a side-view photo in Image J, the length of the anther of the long stamen from where it meets its filament to its tip (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * LFilament - measured using a side-view photo in Image J, the length of the frontmost (closest to the camera) long filament from where it meets the pedicel to where it meets its anther (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * SAnther - measured using a side-view photo in Image J, the length of the anther of the short stamen from where it meets its filament to its tip (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * SFilament - measured using a side-view photo in Image J, the length of the frontmost (closest to the camera) short filament from where it meets the pedicel to where it meets its anther (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * Pistil - measured using a side-view photo in Image J, the length of the pistil made by drawing a line down the center of the pistil from the top of the stigma to where it meets the pedicel (mm); missing values mean the plant did not survive to flowering or the view in the photo was obscured so the measurement could not be taken * AntherExsertion - calculated as long filament length minus the tube length (mm); missing values mean the plant did not survive to flowering or that either one of the values needed for the measurement was missing * AntherSeparation - calculated as long filament length minus the short filament length (mm); missing values mean the plant did not survive to flowering or one or that either one of the values needed for the measurement was missing * FlowerSize - the geometric mean of all floral traits (excluding anther exsertion and anther separation; mm); missing values mean the plant did not survive to flowering or one or more flower trait was missing * LeafWidth - measured using either a top-view or side-view photo in Image J, the distance between each edge of the leaf measured at its widest point, with the line being perpendicular to the central leaf vein on the largest fully visible leaf (more information in the Leaf Data sheet; cm); missing values mean the plant did not survive to bolting or a picture was not taken * LeafLength -  measured using either a top-view or side-view photo in Image J using the segmented line tool, follow the central vein of the largest visible leaf from the center of the rosette to the tip of the leaf (more information in the Leaf Data sheet; cm); missing values mean the plant did not survive to bolting or a picture was not taken ### Leaf Data This sheet includes some additional information about Leaf Length and Leaf Width measurements. Side image was only used when leaf was not flat or clearly visible in the top image. Variables: * Top Photo Image - image ID of the top view photo of the plant being measured * Ecotype - the ecotype of the plant (more information in Main Data) * Population - the population that the plant belongs to (more information in Main Data) * Plant Label - the label visible in the image -- includes population, matriline (when available), and pot # * Leaf Width (cm) - measured using the top-view photo in Image J, the distance between each edge of the leaf measured at its widest point, with the line being perpendicular to the central leaf vein on the largest fully visible leaf; missing values mean that a picture was not taken or the leaf was obscured in the top view photo * Leaf Length 1 (cm) - measured using the top-view photo in Image J using the segmented line tool, follow the central vein of the largest visible leaf from the center of the rosette to the tip of the leaf; missing values mean that a picture was not taken or the leaf was obscured in the top view photo * Side Photo Image - Image ID of the side view photo of the plant being measured; side image was only used when leaf was not flat or clearly visible in the top image, so missing values indicate that the length and width of the leaf could be reliably measured using the top view photo * Leaf Length 2 (cm) - measured using the side-view photo in Image J using the segmented line tool, follow the central vein of the largest visible leaf from the center of the rosette to the tip of the leaf; missing values mean that a picture was not taken or that the length of the leaf could be reliably measured using the top view photo * Leaf Width 2 (cm) - measured using the side-view photo in Image J, the distance between each edge of the leaf measured at its widest point, with the line being perpendicular to the central leaf vein on the largest fully visible leaf; missing values mean that a picture was not taken or that the width of the leaf could be reliably measured using the top view photo * Notes - any additional information about the the measurement of a particular plants' leaf length or width"]}