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1. Evaluating the roles of drift and selection in trait loss along an elevational gradient

   https://doi.org/10.5061/dryad.8sf7m0d0z  

   Buysse, Sophia; Pérez, Samuel G; Puzey, Joshua R; Garrison, Ava; Bradburd, Gideon; Oakley, Christopher G; Tonsor, Stephen J; Pico, F Xavier; Josephs, Emily B; Conner, Jeffrey K (January 2025, Dryad)

   {"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."]} 

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2. Hummingbird blood traits track oxygen availability across space and time

   https://doi.org/10.1111/ele.14235  

   Williamson, Jessie L.; Linck, Ethan B.; Bautista, Emil; Smiley, Ashley; McGuire, Jimmy A.; Dudley, Robert; Witt, Christopher C. (May 2023, Ecology Letters)

   Abstract Predictable trait variation across environments suggests shared adaptive responses via repeated genetic evolution, phenotypic plasticity or both. Matching of trait–environment associations at phylogenetic and individual scales implies consistency between these processes. Alternatively, mismatch implies that evolutionary divergence has changed the rules of trait–environment covariation. Here we tested whether species adaptation alters elevational variation in blood traits. We measured blood for 1217 Andean hummingbirds of 77 species across a 4600‐m elevational gradient. Unexpectedly, elevational variation in haemoglobin concentration ([Hb]) was scale independent, suggesting that physics of gas exchange, rather than species differences, determines responses to changing oxygen pressure. However, mechanisms of [Hb] adjustment did show signals of species adaptation: Species at either low or high elevations adjusted cell size, whereas species at mid‐elevations adjusted cell number. This elevational variation in red blood cell number versus size suggests that genetic adaptation to high altitude has changed how these traits respond to shifts in oxygen availability. 

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3. Broad concordance in the spatial distribution of adaptive and neutral genetic variation across an elevational gradient in deer mice

   https://doi.org/10.1093/molbev/msab161  

   Schweizer, Rena M; Jones, Matthew R; Bradburd, Gideon S; Storz, Jay F; Senner, Nathan R; Wolf, Cole; Cheviron, Zachary A (May 2021, Molecular Biology and Evolution)

   Sork, Victoria (Ed.)

   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. 

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4. The genomics and physiology of abiotic stressors associated with global elevational gradients in Arabidopsis thaliana

   https://doi.org/10.1111/nph.20138  

   Gamba, Diana; Lorts, Claire_M; Haile, Asnake; Sahay, Seema; Lopez, Lua; Xia, Tian; Takou, Margarita; Kulesza, Evelyn; Elango, Dinakaran; Kerby, Jeffrey; et al (September 2024, New Phytologist)

   Summary Phenotypic and genomic diversity inArabidopsis thalianamay be associated with adaptation along its wide elevational range, but it is unclear whether elevational clines are consistent among different mountain ranges.We took a multi‐regional view of selection associated with elevation. In a diverse panel of ecotypes, we measured plant traits under alpine stressors (low CO₂partial pressure, high light, and night freezing) and conducted genome‐wide association studies.We found evidence of contrasting locally adaptive regional clines. Western Mediterranean ecotypes showed low water use efficiency (WUE)/early flowering at low elevations to high WUE/late flowering at high elevations. Central Asian ecotypes showed the opposite pattern. We mapped different candidate genes for each region, and some quantitative trait loci (QTL) showed elevational and climatic clines likely maintained by selection. Consistent with regional heterogeneity, trait and QTL clines were evident at regional scales (c. 2000 km) but disappeared globally. Antioxidants and pigmentation rarely showed elevational clines. High elevation east African ecotypes might have higher antioxidant activity under night freezing.Physiological and genomic elevational clines in different regions can be unique, underlining the complexity of local adaptation in widely distributed species, while hindering global trait–environment or genome–environment associations. To tackle the mechanisms of range‐wide local adaptation, regional approaches are thus warranted. 

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5. Genome size variation and evolution during invasive range expansion in an introduced plant

   https://doi.org/10.1111/eva.13624  

   Cang, F. Alice; Welles, Shana R.; Wong, Jenny; Ziaee, Maia; Dlugosch, Katrina M. (December 2023, Evolutionary Applications)

   Abstract Plants demonstrate exceptional variation in genome size across species, and their genome sizes can also vary dramatically across individuals and populations within species. This aspect of genetic variation can have consequences for traits and fitness, but few studies attributed genome size differentiation to ecological and evolutionary processes. Biological invasions present particularly useful natural laboratories to infer selective agents that might drive genome size shifts across environments and population histories. Here, we test hypotheses for the evolutionary causes of genome size variation across 14 invading populations of yellow starthistle,Centaurea solstitialis, in California, United States. We use a survey of genome sizes and trait variation to ask: (1) Is variation in genome size associated with developmental trait variation? (2) Are genome sizes smaller toward the leading edge of the expansion, consistent with selection for “colonizer” traits? Or alternatively, does genome size increase toward the leading edge of the expansion, consistent with predicted consequences of founder effects and drift? (3) Finally, are genome sizes smaller at higher elevations, consistent with selection for shorter development times? We found that 2C DNA content varied 1.21‐fold among all samples, and was associated with flowering time variation, such that plants with larger genomes reproduced later, with lower lifetime capitula production. Genome sizes increased toward the leading edge of the invasion, but tended to decrease at higher elevations, consistent with genetic drift during range expansion but potentially strong selection for smaller genomes and faster development time at higher elevations. These results demonstrate how genome size variation can contribute to traits directly tied to reproductive success, and how selection and drift can shape that variation. We highlight the influence of genome size on dynamics underlying a rapid range expansion in a highly problematic invasive plant. 

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