ArticleEvolutionary applications2022
Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest.
Article in Evolutionary applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Harnessing macrogenomics to study changing marine ecosystems under global climate change.Proceedings. Biological sciences · 2026Review
- Assessing Future Maladaptation Risk in the Keystone SpeciesEvolutionary applications · 2026Article
- Advancing Marine Mammal Conservation Through Omics Tools and Integration of Indigenous Ecological Knowledge.Molecular ecology · 2026Review
- Climate-Driven Niche Tracking and Genomic Resilience Shape Future Distribution of a Widespread Agricultural Weed.Molecular ecology · 2026Article
- Haplotype Blocks Are Associated With Rapid Local Adaptation to Environmental Shifts in Wild Barley.Molecular ecology · 2026Article
- Differential Demographic Trajectories and Polygenic Adaptation to Monsoon Seasonality in Sky Island Hercules Beetle Populations of the American Southwest.Molecular ecology · 2026Article
- Landscape Genomics Reveals Divergent Adaptation Modes and Predicts Climate Vulnerability in Xinjiang Indigenous Sheep.Animals : an open access journal from MDPI · 2026Article
- Genomic Offsets Predict Survival With Low Accuracy in a Marine Common Garden.Molecular ecology · 2026Article
- How Robust Are Genomic Offset Predictions to Methodological Choices? Insights From Perennial Ryegrass.Molecular ecology · 2026Article
- Landscape Genomic Tools Can Inform Future Rootstock and Farmland Selection for an Agricultural Tree Nut From Its Wild Relatives.Molecular ecology · 2026Article
- Whole genome sequencing reveals interbay population structure of Saccharina latissima (sugar kelp) in western Iceland.BMC ecology and evolution · 2026Article
- Applying Both Landscape Genomic and Ecological Niche Model Predictions to Inform Conservation Strategies of a California Foundational Oak Species.Molecular ecology · 2026Article
- Landscape Genomic Analyses ofEvolutionary applications · 2026Article
- A Mosaic of Future Maladaptation Predicted for the Widespread TreeEvolutionary applications · 2026Article
- Defining Conservation Units in a Highly Diverse Species: A Case on Arctic Charr.Evolutionary applications · 2026Article
- Genomics-Driven Monitoring of Fraxinus latifolia (Oregon Ash) to Inform Conservation and EAB-Resistance Breeding.Molecular ecology · 2025Article
- Genomic Signatures of Climate-Driven (Mal)Adaptation in an Iconic Conifer, the English Yew (Evolutionary applications · 2025Article
- Genomic Vulnerability of a Sentinel Mammal Under Climate Change.Molecular ecology · 2025Article
- Applying Genomic Offsets to Breeding Programmes: Bridging Evolutionary Insights With Practical Applications.Evolutionary applications · 2025Article
- Application of Genomic Offsets to Inform Freshwater Fisheries Management Under Climate Change.Evolutionary applications · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.