Data
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.5061/dryad.qq20s&rft.title=Genome-wide scans detect adaptation to aridity in a widespread forest tree species&rft.identifier=10.5061/dryad.qq20s&rft.publisher=Edith Cowan University&rft.description=Patterns of adaptive variation within plant species are best studied through common garden experiments, but these are costly and time-consuming, especially for trees that have long generation times. We explored whether genome-wide scanning technology combined with outlier marker detection could be used to detect adaptation to climate and provide an alternative to common garden experiments. As a case study, we sampled nine provenances of the widespread forest tree species, Eucalyptus tricarpa, across an aridity gradient in southeastern Australia. Using a Bayesian analysis we identified a suite of 94 putatively adaptive (outlying) sequence-tagged markers across the genome. Population-level allele frequencies of these outlier markers were strongly correlated with temperature and moisture availability at the site of origin, and with population differences in functional traits measured in two common gardens. Using the output from a canonical analysis of principal coordinates we devised a metric that provides a holistic measure of genomic adaptation to aridity that could be used to guide assisted migration or genetic augmentation.&rft.creator=Brad Potts&rft.creator=Dorothy Steane&rft.creator=Elizabeth McClean&rft.creator=Margaret Byrne&rft.creator=René Vaillancourt&rft.creator=Suzanne Prober&rft.creator=William Stock&rft.date=2021&rft.relation=https://doi.org/10.1111/mec.12751&rft.relation=https://ro.ecu.edu.au/ecuworkspost2013/259/&rft_rights= http://creativecommons.org/publicdomain/zero/1.0/&rft_subject=assisted migration&rft_subject=canonical analysis of principal coordinates&rft_subject=climate resilience&rft_subject=Eucalyptus&rft_subject=genome-wide scan&rft_subject=outlier analysis&rft_subject=plant DNA&rft_subject=adaptation&rft_subject=article&rft_subject=Australia&rft_subject=Bayes theorem&rft_subject=biological model&rft_subject=climate&rft_subject=gene frequency&rft_subject=genetic marker&rft_subject=genetics&rft_subject=plant genome&rft_subject=principal component analysis&rft_subject=tree&rft_subject=Adaptation&rft_subject=Physiological&rft_subject=Bayes Theorem&rft_subject=Climate&rft_subject=DNA&rft_subject=Plant&rft_subject=Gene Frequency&rft_subject=Genetic Markers&rft_subject=Genome&rft_subject=Models&rft_subject=Genetic&rft_subject=Principal Component Analysis&rft_subject=Trees&rft_subject=Medicine and Health Sciences&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Other view details

Access:

Open

Full description

Patterns of adaptive variation within plant species are best studied through common garden experiments, but these are costly and time-consuming, especially for trees that have long generation times. We explored whether genome-wide scanning technology combined with outlier marker detection could be used to detect adaptation to climate and provide an alternative to common garden experiments. As a case study, we sampled nine provenances of the widespread forest tree species, Eucalyptus tricarpa, across an aridity gradient in southeastern Australia. Using a Bayesian analysis we identified a suite of 94 putatively adaptive (outlying) sequence-tagged markers across the genome. Population-level allele frequencies of these outlier markers were strongly correlated with temperature and moisture availability at the site of origin, and with population differences in functional traits measured in two common gardens. Using the output from a canonical analysis of principal coordinates we devised a metric that provides a holistic measure of genomic adaptation to aridity that could be used to guide assisted migration or genetic augmentation.

Notes

This dataset was originally published at:

https://doi.org/10.5061/dryad.qq20s

This dataset is part of a larger collection

Click to explore relationships graph

-106.34677,56.13037

-106.346771,56.130366

Identifiers