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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 Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (N_e) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward‐time, individual‐based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data. 

Abstract Rates of phenotypic evolution vary markedly across the tree of life, from the accelerated evolution apparent in adaptive radiations to the remarkable evolutionary stasis exhibited by so-called “living fossils.” Such rate variation has important consequences for large-scale evolutionary dynamics, generating vast disparities in phenotypic diversity across space, time, and taxa. Despite this, most methods for estimating trait evolution rates assume rates vary deterministically with respect to some variable of interest or change infrequently during a clade’s history. These assumptions may cause underfitting of trait evolution models and mislead hypothesis testing. Here, we develop a new trait evolution model that allows rates to vary gradually and stochastically across a clade. Further, we extend this model to accommodate generally decreasing or increasing rates over time, allowing for flexible modeling of “early/late bursts” of trait evolution. We implement a Bayesian method, termed “evolving rates” (evorates for short), to efficiently fit this model to comparative data. Through simulation, we demonstrate that evorates can reliably infer both how and in which lineages trait evolution rates varied during a clade’s history. We apply this method to body size evolution in cetaceans, recovering substantial support for an overall slowdown in body size evolution over time with recent bursts among some oceanic dolphins and relative stasis among beaked whales of the genus Mesoplodon. These results unify and expand on previous research, demonstrating the empirical utility of evorates. [cetacea; macroevolution; comparative methods; phenotypic diversity; disparity; early burst; late burst] 

Abstract When species are continuously distributed across environmental gradients, the relative strength of selection and gene flow shape spatial patterns of genetic variation, potentially leading to variable levels of differentiation across loci. Determining whether adaptive genetic variation tends to be structured differently than neutral variation along environmental gradients is an open and important question in evolutionary genetics. We performed exome-wide population genomic analysis on deer mice sampled along an elevational gradient of nearly 4000 m of vertical relief. Using a combination of selection scans, genotype-environment associations, and geographic cline analyses, we found that a large proportion of the exome has experienced a history of altitude-related selection. Elevational clines for nearly 30% of these putatively adaptive loci were shifted significantly up- or down-slope of clines for loci that did not bear similar signatures of selection. Many of these selection targets can be plausibly linked to known phenotypic differences between highland and lowland deer mice, although the vast majority of these candidates have not been reported in other studies of highland taxa. Together, these results suggest new hypotheses about the genetic basis of physiological adaptation to high-altitude, and the spatial distribution of adaptive genetic variation along environmental gradients. 

Genomic data are being produced and archived at a prodigious rate, and current studies could become historical baselines for future global genetic diversity analyses and monitoring programs. However, when we evaluated the potential utility of genomic data from wild and domesticated eukaryote species in the world’s largest genomic data repository, we found that most archived genomic datasets (87%) lacked the spatiotemporal metadata necessary for genetic biodiversity surveillance. Labor-intensive scouring of a subset of published papers yielded geospatial coordinates and collection years for only 39% (51% if place names were considered) of these genomic datasets. Streamlined data input processes, updated metadata deposition policies, and enhanced scientific community awareness are urgently needed to preserve these irreplaceable records of today’s genetic biodiversity and to plug the growing metadata gap.