Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest

Áki Jarl Láruson, Matthew C. Fitzpatrick, Stephen R. Keller, Benjamin C. Haller, Katie E. Lotterhos*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF-predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic “population genetic” model with a single environmentally adapted locus; and (3) a polygenic “quantitative genetic” model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation.

Original languageEnglish
Pages (from-to)403-416
Number of pages14
JournalEvolutionary Applications
Volume15
Issue number3
DOIs
Publication statusPublished - Mar 2022

Bibliographical note

Funding Information:
The authors would like to thank the editor, two anonymous reviewers, and Christian Rellstab for comments that improved the final manuscript. The authors acknowledge funding from the National Science Foundation: 1655701 (to KEL and MCF), 2043905 (to KEL), 1656099 (to SRK), 1856450 (to SRK and MCF), and 1655344 (to MCF). The authors thank Molly Albecker, Thais Bittar, Alan Downey‐Wall, Sara Schall, & F. Dylan Titmuss on helpful comments on early drafts of the manuscript.

Publisher Copyright:
© 2022 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.

Other keywords

  • biodiversity
  • climate change
  • gradient forest
  • landscape genetics
  • local adaptation
  • population genetics
  • quantitative genetics
  • simulation
  • SLiM

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