Adaptational lag to temperature in valley oak (
            <i>Quercus lobata</i>
            ) can be mitigated by genome-informed assisted gene flow

Browne, Luke; Wright, Jessica W.; Fitz-Gibbon, Sorel; Gugger, Paul F.; Sork, Victoria L.

doi:10.1073/pnas.1908771116

Cited by 110 publications

(139 citation statements)

References 47 publications

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“…Here, the concept of “genomic vulnerability” is defined as the mismatch between current and predicted future genomic variation, where the vulnerability of individual populations is a measure of the amount of genomic change required to track climate change over time (Dawson, Jackson, House, Prentice, & Mace, 2011). This information can be used to profile population vulnerability over species distributions, helping to identify populations most at risk from climate change, and reveal “climate‐ready” genotypes that can be used to enhance climate resilience in restoration plantings (Breed et al., 2019; Broadhurst et al., 2008; Browne, Wright, Fitz‐Gibbon, Gugger, & Sork, 2019; Prober et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatial, climate and ploidy factors drive genomic diversity and resilience in the widespread grass Themeda triandra

et al. 2020

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Global climate change poses a significant threat to natural communities around the world, with many plant species showing signs of climate stress. Grassland ecosystems are not an exception, with climate change compounding contemporary pressures such as habitat loss and fragmentation. In this study, we assess the climate resilience of Themeda triandra, a foundational species and the most widespread plant in Australia, by assessing the relative contributions of spatial, environmental and ploidy factors to contemporary genomic variation. Reduced-representation genome sequencing on 472 samples from 52 locations was used to test how the distribution of genomic variation, including ploidy polymorphism, supports adaptation to hotter and drier climates. We explicitly quantified isolation by distance (IBD) and isolation by environment (IBE) and predicted genomic vulnerability of populations to future climates based on expected deviation from current genomic composition. We found that a majority (54%) of genomic variation could be attributed to IBD, while an additional 22% (27% when including ploidy information) could be explained by two temperature and two precipitation climate variables demonstrating IBE. Ploidy polymorphisms were common within populations (31/52 populations), indicating that ploidy mixing is characteristic of T. triandra populations. Genomic vulnerabilities were found to be heterogeneously distributed throughout the landscape, and our analysis suggested that ploidy polymorphism, along with other factors linked to polyploidy, reduced vulnerability to future climates by 60% (0.25-0.10). Our data suggests that polyploidy may facilitate adaptation to hotter climates and highlight the importance of incorporating ploidy in adaptive management strategies to promote the resilience of this and other foundation species.

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Section: Introductionmentioning

confidence: 99%

Spatial, climate and ploidy factors drive genomic diversity and resilience in the widespread grass Themeda triandra

et al. 2020

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“…Identifying genomic patterns associated with adaptation in wild populations can provide information to support management strategies as well as facilitate fundamental discoveries (Garner et al, 2016;Sgrò, Lowe, & Hoffmann, 2011). We can improve our understanding of the response of species to changing climates and their evolutionary potential by leveraging knowledge about adaptive genetic variation in natural populations (Browne, Wright, Fitz-Gibbon, Gugger, & Sork, 2019;Razgour et al, 2019;Sork, 2017). Genotype-environment association (GEA) methods are used to identify potentially adaptive loci in non-model systems based on correlations between allele frequencies and environmental data.…”

Section: Introductionmentioning

confidence: 99%

Regarding theF-word: the effects of dataFilteringon inferred genotype-environment associations

Ahrens

Jordan

Bragg

et al. 2020

Preprint

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Genotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results. These effects could be amplified in downstream predictions, including management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for adaptation to environment. Using empirical and simulated datasets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the dataset, lessening the power to detect adaptive variants (i.e. simulated true positives) with strong and weak strength of selections. Regardless, strength of selection was a good predictor for GEA detection, but even SNPs under strong selection went undetected. We further show that filtering can significantly impact the predictions of adaptive capacity of species in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending largely on the study system, availability of genomic resources, and desired objectives of the study.

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“…Furthermore, there are now substantial genomic resources available, including an annotated reference genome (Sork, Fitz-Gibbon, et al, 2016) and transcriptome (Cokus et al, 2015). In addition, surveys of trait variation in the field (e.g., Albarrán-Lara et al, 2015) and ongoing research at a large-scale common garden that includes samples at multiple spatial scales (Delfino Mix et al, 2015) suggests variation in fitness as measured by leaf and growth-related traits consistent with local adaptation to current or past environmental gradients (Browne et al, 2019). Furthermore, Browne et al (2019) demonstrated that SNPs can be used to identify genotypes associated with higher growth rates under warmer temperatures in this species.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, surveys of trait variation in the field (e.g., Albarrán-Lara et al, 2015) and ongoing research at a large-scale common garden that includes samples at multiple spatial scales (Delfino Mix et al, 2015) suggests variation in fitness as measured by leaf and growth-related traits consistent with local adaptation to current or past environmental gradients (Browne et al, 2019). Furthermore, Browne et al (2019) demonstrated that SNPs can be used to identify genotypes associated with higher growth rates under warmer temperatures in this species. Previous landscape genomic studies in Q. lobata were limited in sample size or the number of genetic loci (Sork, Squire, et al, 2016), necessitating further study, but provide some candidates for comparison along with those from a water stress gene expression experiment (Gugger et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Landscape genomics of Quercus lobata reveals genes involved in local climate adaptation at multiple spatial scales

et al. 2020

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Understanding how the environment shapes genetic variation provides critical insight about the evolution of local adaptation in natural populations. At multiple spatial scales and multiple geographic contexts within a single species, such information could address a number of fundamental questions about the scale of local adaptation and whether or not the same loci are involved at different spatial scales or geographic contexts. We used landscape genomic approaches from three local elevational transects and rangewide sampling to (a) identify genetic variation underlying local adaptation to environmental gradients in the California endemic oak, Quercus lobata; (b) examine whether putatively adaptive SNPs show signatures of selection at multiple spatial scales; and (c) map putatively adaptive variation to assess the scale and pattern of local adaptation. Of over 10 k single-nucleotide polymorphisms (SNPs) generated with genotyping-by-sequencing, we found signatures of natural selection by climate or local environment at over 600 SNPs (536 loci), some at multiple spatial scales across multiple analyses. Candidate SNPs identified with gene-environment tests (LFMM) at the rangewide scale also showed elevated associations with climate variables compared to the background at both rangewide and elevational transect scales with gradient forest analysis. Some loci overlap with those detected in other oak species, raising the question of whether the same loci might be involved in local climate adaptation in different congeneric species that inhabit different geographic contexts. Mapping landscape patterns of adaptive versus background genetic variation identified regions of marked local adaptation and suggests nonlinear association of candidate SNPs and environmental variables. Taken together, our results offer robust evidence for novel candidate genes for local climate adaptation at multiple spatial scales.

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Adaptational lag to temperature in valley oak ( Quercus lobata ) can be mitigated by genome-informed assisted gene flow

Cited by 110 publications

References 47 publications

Spatial, climate and ploidy factors drive genomic diversity and resilience in the widespread grass Themeda triandra

Spatial, climate and ploidy factors drive genomic diversity and resilience in the widespread grass Themeda triandra

Regarding theF-word: the effects of dataFilteringon inferred genotype-environment associations

Landscape genomics of Quercus lobata reveals genes involved in local climate adaptation at multiple spatial scales

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