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Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

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

Áki Jarl Láruson¹, Matthew C Fitzpatrick², Stephen R Keller³

¹Department of Natural Resources Cornell University Ithaca New York USA.

Evolutionary Applications

|April 7, 2022

Summary

Gradient Forest Offset (GF Offset) shows potential for predicting allele loss due to environmental change. However, its accuracy can be affected by population genetics, genome structure, and environmental factors.

Keywords:

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

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Area of Science:

Ecology
Evolutionary Biology
Computational Biology

Background:

Gradient Forest (GF) is a machine learning method for analyzing biodiversity patterns along environmental gradients.
GF Offset, a measure derived from GF, is proposed to predict the loss of adapted alleles under environmental change but requires further validation.

Purpose of the Study:

To assess the robustness of GF Offset to assumption violations.
To examine the relationship between GF Offset and fitness measures.
To explore the influence of genomic architecture and demographic factors on GF Offset predictions.

Main Methods:

Utilized SLiM simulations with explicit genome architecture and spatial metapopulations.
Evaluated GF Offset in neutral, monogenic, and polygenic models.
Assessed GF Offset's correlation with fitness offsets across different genetic architectures.

Main Results:

GF Offset demonstrated a broad correlation with fitness offsets in both single-locus and polygenic models.
Neutral demography, genomic architecture, and adaptive environment characteristics were found to confound the GF Offset-fitness relationship.
The predictive power of GF Offset is influenced by underlying genetic and environmental complexities.

Conclusions:

GF Offset is a promising tool for predicting maladaptation.
Understanding the limitations and assumptions of GF Offset is crucial for accurate predictions, especially concerning rapid environmental change.
Further research is needed to refine GF Offset's application in diverse evolutionary and ecological contexts.