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Traits conserved across evolutionary time often provide compelling examples of key adaptations for a given taxonomic group. Tetradynamy is the presence of four long stamens plus two short stamens within a flower and is conserved across most of the roughly 4000 species in the mustard family, Brassicaceae. While this differentiation in stamens is hypothesized to play a role in pollination efficiency, very little is known about the potential function of the two stamen types. The present study sheds new light on this mystery using wild radish (Raphanus raphanistrum), a widespread and well-studied tetradynamous plant. We used data collected from slow-motion videos of pollinators visiting wild radish flowers to test three adaptive hypotheses (not mutually exclusive): (H1) short and long stamens are specialized for either feeding or pollinating; (H2) short and long stamens are specialized for different pollinator taxa; and (H3) the presence of short and long stamens increases pollinator movement and thus effectiveness. We find evidence consistent with hypothesis H3, but no evidence for hypotheses H1 or H2. Thus, tetradynamy may be an adaptation for generalized pollination, enabling effective visits by the variety of pollinators visiting most species of Brassicaceae. 

{"Abstract":["Traits conserved across evolutionary time often provide compelling\n examples of key adaptations for a given taxonomic group. Tetradynamy is\n the presence of four long stamens plus two short stamens within a flower\n and is conserved across most of the roughly 4000 species in the mustard\n family, Brassicaceae. While this differentiation in stamens is\n hypothesized to play a role in pollination efficiency, very little is\n known about the potential function of the two stamen types. The present\n study sheds new light on this mystery using wild radish (Raphanus\n raphanistrum), a widespread and well-studied tetradynamous plant. We used\n data collected from slow-motion videos of pollinators visiting wild radish\n flowers to test three non-mutually exclusive adaptive hypotheses: 1) short\n and long stamens are specialized for either feeding or pollinating, 2)\n short and long stamens are specialized for different pollinator taxa, and\n 3) the presence of short and long stamens increases pollinator movement\n and thus effectiveness. We find evidence consistent with hypothesis three,\n but no evidence for hypotheses one or two. Thus, tetradynamy may be an\n adaptation for generalized pollination, enabling effective visits by the\n variety of pollinators visiting most species of Brassicaceae."],"TechnicalInfo":["# Data from: Testing adaptive hypotheses for an evolutionarily conserved\n trait through slow-motion videos of pollinators The data contained in\n these files was generated from close observation of slow-motion video\n footage by the same experimenter for each variable. ## Description of\n Files ### MainData.csv Data related to slow-motion video analysis,\n including plant information, anther and stigma contact, and number of\n movements Missing data are indicated by "NA" #### Basic Video\n Info in Columns A:F * VideoID: unique individual video identifier *\n PlantID: unique individual plant identifier with the following format -\n "PopulationCode FamilyCode-Replicate" * PopulationCode: BINY =\n natural population, Sep = separation-selected, Exsertion =\n exsertion-selected * FamilyCode: unique 3-5 character code for a given\n maternal seed family * Replicate: individual plant number between 0 and 9,\n where replicate 0 is indicated by the lack of a hyphen and number * Date:\n date of observations * Year: year of observations * Pollinator: taxa of\n visiting pollinator * VideoLength: total length of visit in 1/8 real-time\n seconds #### Feeding Info in Columns G:N * G:K are binary columns in which\n 1 indicates the visit included foraging in the given category, 0 indicates\n lack of foraging, and ? indicates uncertainty ("Short" = short\n stamen anthers, "Long" = long stamen anthers) * L:N summarize\n the info from G:K in different ways * Foraging: whether the visit included\n foraging on nectar, pollen, or both * Feed_All: for visits including\n pollen-foraging, whether foraging was on short stamen anthers, long stamen\n anthers, or both * Feed_Bin: same as Feed_All but groups "Long"\n and "Short" into "One" #### Contact Info in Columns\n O:AM Columns have the following format:\n "ResponseVariable_BodySection_FlowerPart" * ResponseVariable is\n what kind of contact is being recorded and can take three values: * sec:\n duration of contact in 1/8 real-time seconds * bin: binary contact, 1 =\n contacted and 0 = not contacted * n: count of body sections contacted\n (sums binary contact with Legs, Ventral, Side) * BodySection is the part\n of the pollinator body contacted and can take four values: Ventral, Side,\n Legs, or Total (sum of prior 3) * FlowerPart is the part of the flower\n contacted by the pollinator and can take 4 values: S (short stamen\n anthers), L (long stamen anthers), Stigma, or Anthers (both short and long\n stamen anthers) #### Movement Info in Columns AN:AR * Between_Moves: # of\n movements from feeding on one stamen to another * Within_Moves: # of\n movements within stamen types, combining movements from long to long\n stamen ("Long.Long_Moves") and movements from short to short\n stamen ("Short.Short_Moves") * Total_Moves: total # of movements\n from one stamen to another ### DyeSwab.csv Data from small preliminary\n test in which 3 bees were swabbed with gelatin cubes after visiting\n flowers with short and long stamens marked with different colors of\n fluorescent dye. * ID: unique individual bee identifier * BodySection: the\n body section swabbed * NParticles: count of dye particles contained in\n gelatin swab * StamenType: type of stamen matching the color of counted\n particles ### Final_Analysis_Dryad.R R script of all analyses used in the\n paper. * Details provided as comments within script. * The script was run\n in RStudio running R v. 4.4.2."]} 

Abstract Traits that have lost function sometimes persist through evolutionary time. Persistence may occur if there is not enough standing genetic variation for the trait to allow a response to selection, if selection against the trait is weak relative to drift, or if the trait has a residual function. To determine the evolutionary processes shaping whether nonfunctional traits are retained or lost, we investigated short stamens in 16 populations of Arabidopsis thaliana along an elevational cline in northeast Spain. A. thaliana is highly self-pollinating and prior work suggests short stamens do not contribute to self-pollination. We found a cline in short stamen number from retention of short stamens in high-elevation populations to incomplete loss in low-elevation populations. We did not find evidence that limited genetic variation constrains short stamen loss at high elevations, nor evidence for divergent selection on short stamens between high and low elevations. Finally, we identified loci associated with short stamens in northeast Spain that are different from loci associated with variation in short stamens across latitudes from a previous study. Overall, we did not identify the evolutionary mechanisms contributing to an elevational cline in short stamen number so further research is clearly warranted. 

{"Abstract":["Traits that have lost function sometimes persist through evolutionary\n time. Persistence may occur if there is not enough standing genetic\n variation for the trait to allow a response to selection, if selection\n against the trait is weak relative to drift, or if the trait has a\n residual function. To determine the evolutionary processes shaping whether\n nonfunctional traits are retained or lost, we investigated short stamens\n in 16 populations of Arabidopsis thaliana along an elevational cline in\n northeast Spain. A. thaliana is highly self-pollinating and prior work\n suggests short stamens do not contribute to self-pollination. We found a\n cline in short stamen number from retention of short stamens in high\n elevation populations to incomplete loss in low elevation populations. We\n did not find evidence that limited genetic variation constrains short\n stamen loss at high elevations, nor evidence for divergent selection on\n short stamens between high and low elevations. Finally, we identified loci\n associated with short stamens in northeast Spain that are different from\n loci associated with variation in short stamens across latitudes from a\n previous study. Overall, we did not identify the evolutionary mechanisms\n contributing to an elevational cline in short stamen number so further\n research is clearly warranted. This dryad dataset includes the GWAS output\n results. See the github for phenotypic data and SRA for genotypic data."],"TechnicalInfo":["# Evaluating the roles of drift and selection in trait loss along an\n elevational gradient Dataset DOI:\n [10.5061/dryad.8sf7m0d0z](10.5061/dryad.8sf7m0d0z) ## Description of the\n data and file structure These files are the relatedness matrices and GWAS\n output files for a GWAS on short stamen number in *A.\n thaliana* from an elevation gradient across the Pyrenees. The\n associated paper is "Evaluating the Roles of Drift and Selection in\n Trait Loss along an Elevational Gradient" by Buysse et al. The code\n used to generate the files can be found on\n github: [https://github.com/sfbuysse/A_thaliana_StamenLoss_2025](https://github.com/sfbuysse/A_thaliana_StamenLoss_2025).  The input data is SNP information for 61 genotypes from 16 native populations of *A. thaliana*. ### Files and variables #### File: RelatednessMatrices.zip **Description:** **RelatednessMatrices.zip** contains centered Relatedness Matrices made with GEMMA v0.98.4. Relatedness matrices are *.cXX.txt and *.log.txt show the code and run log information. allSNPs.PlinkFiltering_Asin, allSNPs.PlinkFiltering_Binary, allSNPs.PlinkFiltering_raw : identical relatedness matrices made using all SNPs in the dataset after filtering with Plink. Names were changed to match the phenotype files to run the GWAS.  allSNPs.PlinkFiltering*_*raw_subset : centered relatedness matrix made with all SNPs after plink filtering but only the individuals with some short stamen loss (mean short stamen number < 2). NoCent.PlinkFiltering_Asin, NoCent.PlinkFiltering_Binary, NoCent.PlinkFiltering_raw  : identical relatedness matrices made after excluding the centromere region and filtering with Plink. Names were changed to match the phenotype files to run the GWAS.  NoCent.PlinkFiltering_raw_subset. : centered relatedness matrix made after excluding the centromere and plink filtering but only the individuals with some short stamen loss (mean short stamen number < 2). #### File: GWAS.zip **Description:** **GWAS.zip** contains GWAS output files. The GWAS output files are  *.assoc.txt and the code information is  *.log.txt. GWAS were run in GEMMA v0.98.4. Within each .assoc.txt file the columns are as follows: * chr = chromosome * rs = snp id (chromosome:base pair position) * ps = base pair position * n_miss = number of genotypes missing genetic information at that SNP * allele1 = minor allele * allele2 = major allele * af = minor allele frequency * beta = affect size * se = standard error for beta * log_lH1 = log liklihood of alternative hypothesis that beta does not equal 0 (H0 is that beta =0) * l_remle = restricted maximum liklihood estimates for lambda * l_mle = maximum liklihood estimates for lambda * p_wald = p value from the Wald test * p_lrt = p value from liiklihood ratio test * p_score = p value from score test allSNPs.PlinkFiltering_Asin.c : include allSNPs after filtering with plink. phenotypes were arcsine transformed before GWAS. Centered relatedness matrix used. allSNPs.PlinkFiltering_Binary.c : include allSNPs after filtering with plink. phenotypes were transformed to a binary trait before GWAS - no short stamen loss = 0, any short stamen loss = 1. Centered relatedness matrix used. allSNPs.PlinkFiltering_raw.c : include allSNPs after filtering with plink. phenotypes were not transformed before GWAS. Centered relatedness matrix used. allSNPs.PlinkFiltering*_*raw_subset.c : include allSNPs after filtering with plink. phenotypes were not transformed before GWAS but the individuals used were subset down to only those that had some short stamen loss (mean short stamen number < 2). Centered relatedness matrix used. NoCent.PlinkFiltering_Asin.c : Centromere excluded. Plink Filtering as before. Arcsine transformed phenotypes. Centered relatedness matrix. NoCent.PlinkFiltering_Binary.c : Centromere excluded. Plink Filtering as before. Phenotypes converted to a binary trait. Centered relatedness matrix. NoCent.PlinkFiltering_raw.c : Centromere excluded. Plink Filtering as before. Phenotypes not transformed. Centered relatedness matrix. NoCent.PlinkFiltering_raw_subset.c : Centromere excluded. Plink Filtering as before. Individuals subset to only those that had some short stamen loss. Centered relatedness matrix. ## Code/software We used GEMMA v0.98.4 to create the files. ## Access information Other publicly accessible locations of the data: * [https://github.com/sfbuysse/A_thaliana_StamenLoss_2025](https://github.com/sfbuysse/A_thaliana_StamenLoss_2025) : scripts and information for creation of input files and use of output files after generation. * Genotypic data used is submitted to NCBI SRA as accession PRJNA1246133."]} 

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. 

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including post-translational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on hold-out, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies. 

Fall armyworm is one of the main pests of conventional and Bacillus thuringiensis (Bt) corn in many countries in the Americas, Africa, Asia and in Australia. We conducted diet-overlay bioassays to determine the status of susceptibility to four Bt proteins (Cry1A.105, Cry2Ab2, Cry1F and Cry1Ac) in three different populations of fall armyworm from Mexico, and one population from Puerto Rico. Bioassays showed that fall armyworms from Puerto Rico were resistant to Cry1F with a resistance ratio 50 (RR50) higher than 10,000 ng/cm2 and to Cry1Ac with a RR50 = 12.2 ng/cm2, displaying the highest median lethal concentration (LC50) values to all Bt proteins tested. The effective concentration 50 (EC50) values further confirmed the loss of susceptibility to Cry1F and Cry1Ac in this population. However, LC50 and EC50 results with Cry1A.105 and Cry2Ab2 revealed that fall armyworm from Puerto Rico remained largely susceptible to these two proteins. The Mexican populations were highly susceptible to all the Bt proteins tested and displayed the lowest LC50 and EC50 values to all Bt proteins. Our results suggest that Cry1F and Cry1Ac resistance is stable in fall armyworm from Puerto Rico. However, this population remains susceptible to Cry1A.105 and Cry2Ab2. Results with Mexican fall armyworms suggest that possible deployment of Bt corn in Mexico will not be immediately challenged by Bt-resistant genes in those regions. 

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.