Dataset: Output model data from paradox of adaptive trait clines with non-clinal patterns in the underlying genes (Model Validation Program project)

ValidatedRelease Date:2024-01-01Final no updates expectedDOI: 10.26008/1912/bco-dmo.889769.1Version 1 (2023-02-13)Dataset Type:model results

Principal Investigator, Contact: Katie Lotterhos (Northeastern University)

BCO-DMO Data Manager: Sawyer Newman (Woods Hole Oceanographic Institution)

Project: CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP) (Model Validation Program)

Abstract

Background: Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype-environment associations (GEAs). This study used a novel set of In silico simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple qu...

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Cite Dataset

See metadata files associated with simulation outputs in the repository.

The associated modeling code is archived at 10.5281/zenodo.7622893.

The tutorial associated with the publication is published at https://marineomics.github.io/RDAtraitPredictionTutorial.html.

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Blanquart, F., Kaltz, O., Nuismer, S. L., & Gandon, S. (2013). A practical guide to measuring local adaptation. Ecology Letters, 16(9), 1195–1205. https://doi.org/10.1111/ele.12150
Methods
Coop, G., Witonsky, D., Di Rienzo, A., & Pritchard, J. K. (2010). Using Environmental Correlations to Identify Loci Underlying Local Adaptation. Genetics, 185(4), 1411–1423. https://doi.org/10.1534/genetics.110.114819
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Dixon, P. (2003). VEGAN, a package of R functions for community ecology. Journal of Vegetation Science, 14(6), 927–930. doi:10.1111/j.1654-1103.2003.tb02228.x
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Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L., & Lasky, J. R. (2015). Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Molecular Ecology, 25(1), 104–120. Portico. https://doi.org/10.1111/mec.13476
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Forester, B. R., Lasky, J. R., Wagner, H. H., & Urban, D. L. (2018). Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Molecular Ecology, 27(9), 2215–2233. Portico. https://doi.org/10.1111/mec.14584

Lotterhos, K. (2023) Output model data from paradox of adaptive trait clines with non-clinal patterns in the underlying genes (Model Validation Program project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2023-02-13 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.889769.1 [access date]

Terms of Use

This dataset is licensed under Creative Commons Attribution 4.0.


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Funded by the U.S. National Science Foundation