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1. Output model data from paradox of adaptive trait clines with non-clinal patterns in the underlying genes (Model Validation Program project)

   https://doi.org/10.26008/1912/bco-dmo.889769.1  

   Lotterhos, Katie; Lotterhos, Katie (January 2023, Biological and Chemical Oceanography Data Management Office (BCO-DMO))

   Background: Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype-environment associations (GEAs). This study used a novel set of In silico simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to multivariate environments. Zenodo archive of GitHub Repository of all code used to create the simulations. Every directory includes a README describing the code, and metadata files are included for the archived outputs. Modeling code details: Code was developed 2020-2022 Simulation code was developed in SLiM, recapitated in pyslim, filtered with vcftools, and analyzed with R. Code was developed by K. E. Lotterhos (PI) 

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2. Genotype–environment associations across spatial scales reveal the importance of putative adaptive genetic variation in divergence

   https://doi.org/10.1111/eva.13444  

   Alvarado, Allison H.; Bossu, Christen M.; Harrigan, Ryan J.; Bay, Rachael A.; Nelson, Allison R. P.; Smith, Thomas B.; Ruegg, Kristen C. (August 2022, Evolutionary Applications)

   Abstract Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide‐ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype–environment associations in the hermit thrush (Catharus guttatus), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype–environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature‐associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature‐related barriers to gene flow and/or temperature‐driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype–environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population‐level sensitivity to climate change and other stressors. 

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3. Putative adaptive loci show parallel clinal variation in a California‐endemic wildflower

   https://doi.org/10.1111/mec.17029  

   Hernández, Adriana I.; Specht, Chelsea D. (May 2023, Molecular Ecology)

   Abstract As the global climate crisis continues, predictions concerning how wild populations will respond to changing climate conditions are informed by an understanding of how populations have responded and/or adapted to climate variables in the past. Changes in the local biotic and abiotic environment can drive differences in phenology, physiology, morphology and demography between populations leading to local adaptation, yet the molecular basis of adaptive evolution in wild non‐model organisms is poorly understood. We leverage comparisons between two lineages ofCalochortus venustusoccurring along parallel transects that allow us to identify loci under selection and measure clinal variation in allele frequencies as evidence of population‐specific responses to selection along climatic gradients. We identify targets of selection by distinguishing loci that are outliers to population structure and by using genotype–environment associations across transects to detect loci under selection from each of nine climatic variables. Despite gene flow between individuals of different floral phenotypes and between populations, we find evidence of ecological specialization at the molecular level, including genes associated with key plant functions linked to plant adaptation to California's Mediterranean climate. Single‐nucleotide polymorphisms (SNPs) present in both transects show similar trends in allelic similarity across latitudes indicating parallel adaptation to northern climates. Comparisons between eastern and western populations across latitudes indicate divergent genetic evolution between transects, suggesting local adaptation to either coastal or inland habitats. Our study is among the first to show repeated allelic variation across climatic clines in a non‐model organism. 

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4. Herbivory and water availability interact to shape the adaptive landscape in the perennial forb, Boechera stricta

   https://doi.org/10.1093/evolut/qpae186  

   Jameel, M_Inam; Duncan, Lisa; Mooney, Kailen; Anderson, Jill_T; Friedman, ed., Jannice; Wolf, ed., Jason (December 2024, Evolution)

   Abstract Abiotic and biotic factors interact to influence phenotypic evolution; however, identifying the causal agents of selection that drive the evolution and expression of traits remains challenging. In a field common garden, we manipulated water availability and herbivore abundance across 3 years, and evaluated clinal variation in functional traits and phenology, phenotypic plasticity, local adaptation, and selection using diverse accessions of the perennial forb, Boechera stricta. Consistent with expectations, drought stress exacerbated damage from herbivores. We found significant plasticity and genetic clines in foliar and phenological traits. Water availability and herbivory interacted to exert selection, even on traits like flowering duration, which showed no clinal variation. Furthermore, the direction of selection on specific leaf area in response to water availability mirrored the genetic cline and plasticity, suggesting that variation in water levels across the landscape influences the evolution of this trait. Finally, both herbivory and water availability likely contribute to local adaptation. This work emphasizes the additive and synergistic roles of abiotic and biotic factors in shaping phenotypic variation across environmental gradients. 

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5. A Next Generation of Hierarchical Bayesian Analyses of Hybrid Zones Enables Model‐Based Quantification of Variation in Introgression in R

   https://doi.org/10.1002/ece3.70548  

   Gompert, Zachariah; DeRaad, Devon A; Buerkle, C Alex (November 2024, Ecology and Evolution)

   ABSTRACT Hybrid zones, where genetically distinct groups of organisms meet and interbreed, offer valuable insights into the nature of species and speciation. Here, we present a new R package,bgchm, for population genomic analyses of hybrid zones. This R package extends and updates the existingbgcsoftware and combines Bayesian analyses of hierarchical genomic clines with Bayesian methods for estimating hybrid indexes, interpopulation ancestry proportions, and geographic clines. Compared to existing software,bgchmoffers enhanced efficiency through Hamiltonian Monte Carlo sampling and the ability to work with genotype likelihoods combined with a hierarchical Bayesian approach, enabling inference for diverse types of genetic data sets. The package also facilitates the quantification of introgression patterns across genomes, which is crucial for understanding reproductive isolation and speciation genetics. We first describe the models underlyingbgchmand then provide an overview of the R package and illustrate its use through the analysis of simulated and empirical data sets. We show thatbgchmgenerates accurate estimates of model parameters under a variety of conditions, especially when the genetic loci analyzed are highly ancestry informative. This includes relatively robust estimates of genome‐wide variability in clines, which has not been the focus of previous models and methods. We also illustrate how both selection and genetic drift contribute to variability in introgression among loci and how additional information can be used to help distinguish these contributions. We conclude by describing the promises and limitations ofbgchm, comparingbgchmto other software for genomic cline analyses, and identifying areas for fruitful future development. 

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