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Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests
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  • Matthew Fitzpatrick,
  • Vikram Chhatre,
  • Raju Soolanayakanahally,
  • Stephen Keller
Matthew Fitzpatrick
University of Maryland Center for Environmental Science

Corresponding Author:mfitzpatrick@umces.edu

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Vikram Chhatre
University of Wyoming
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Raju Soolanayakanahally
Agri-Environment Services Branch -Agriculture and Agri-Food Canada
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Stephen Keller
University of Vermont, University of Vermont
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Gradient Forests is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we provide the first experimental evaluation of the ability of ‘genomic offsets’ - a metric of climate maladaptation derived from Gradient Forests - to predict organismal responses to environmental change. We used high-throughput sequencing, genome scans, and several methods (including Gradient Forests) to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar’s range also were planted in two common garden experiments. We used Gradient Forests to relate candidate loci to environmental gradients and to predict the expected magnitude of response (i.e., the genetic offset) of populations when transplanted from their “home” environment to the new environments in the common gardens. We then compared the predicted genetic offsets to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance in the common gardens: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance in the common gardens than did ‘naive’ climate distances. Our results provide preliminary evidence that genomic offsets may provide a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change.
15 Aug 2020Submitted to Molecular Ecology Resources
25 Aug 2020Submission Checks Completed
25 Aug 2020Assigned to Editor
25 Aug 2020Reviewer(s) Assigned
06 Oct 2020Review(s) Completed, Editorial Evaluation Pending
05 Nov 2020Editorial Decision: Revise Minor
14 Dec 2020Review(s) Completed, Editorial Evaluation Pending
14 Dec 20201st Revision Received
15 Dec 2020Reviewer(s) Assigned
19 Jan 2021Editorial Decision: Revise Minor
12 Feb 2021Review(s) Completed, Editorial Evaluation Pending
12 Feb 20212nd Revision Received
23 Feb 2021Editorial Decision: Accept
22 Mar 2021Published in Molecular Ecology Resources. 10.1111/1755-0998.13374