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Climatic similarity and genomic background shape the extent of parallel adaptation in Timema stick insects

Nature Ecology & Evolution volume 6, pages 1952–1964 (2022)Cite this article

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Abstract

Evolution can repeat itself, resulting in parallel adaptations in independent lineages occupying similar environments. Moreover, parallel evolution sometimes, but not always, uses the same genes. Two main hypotheses have been put forth to explain the probability and extent of parallel evolution. First, parallel evolution is more likely when shared ecologies result in similar patterns of natural selection in different taxa. Second, parallelism is more likely when genomes are similar because of shared standing variation and similar mutational effects in closely related genomes. Here we combine ecological, genomic, experimental and phenotypic data with Bayesian modelling and randomization tests to quantify the degree of parallelism and its relationship with ecology and genetics. Our results show that the extent to which genomic regions associated with climate are parallel among species of Timema stick insects is shaped collectively by shared ecology and genomic background. Specifically, the extent of genomic parallelism decays with divergence in climatic conditions (that is, habitat or ecological similarity) and genomic similarity. Moreover, we find that climate-associated loci are likely subject to selection in a field experiment, overlap with genetic regions associated with cuticular hydrocarbon traits and are not strongly shaped by introgression between species. Our findings shed light on when evolution is most expected to repeat itself.

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Fig. 1: Schematics to summarize the analyses conducted in this study.
Fig. 2: Map of species ranges and plots for within-species variation in climate PC scores.
Fig. 3: SNP window-climate association.
Fig. 4: Tests for parallel climate-associated SNP windows between species of Timema stick insects.
Fig. 5: Tests for introgression and ‘shared ecology’ and ‘shared genetics’ hypotheses.
Fig. 6: Evidence for excess overlap between 100 Kb windows associated with climate in nature and those that changed in an elevation-dependent manner during an experiment.

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Data availability

The genetic data used in this paper are associated with previous studies and are archived in the National Center for Biotechnology Information (NCBI) Short Read Archive under BioProject Accession Number PRJNA356405. The genome draft 0.3 is available on the Nosil Lab of Evolutionary Biology website (http://nosil-lab.group.shef.ac.uk/?page_id= 25). The climate data and PCA scores used for analyses, a list of accession numbers of sequences used in this study, genotype likelihood files, variant calling format files and associated scripts, input file and scripts for programmes such as Entropy, BayPass and Treemix are available on the DRYAD repository (https://doi.org/10.5061/dryad.51c59zwbr).

Code availability

Computer code is available at https://github.com/karwaan/Timema_climate_adaptation_genomics. All associated data files and scripts for specific analyses are archived on DRYAD (https://doi.org/10.5061/dryad.51c59zwbr). Correspondence for materials (data, scripts or samples) should be addressed to S.C. (samridhi.chaturvedi@gmail.com), Z.G. (zach.gompert@usu.edu) or P.N. (patrik.nosil@cefe.crns.fr).

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Acknowledgements

S.C. was supported by the Utah State University College of Science and School of Graduate Studies. M.M. was supported by Swiss National Science Foundation Postdoc Mobility grants PBBSP3_141367 and P300P3_147888. Z.G. was supported by the US NSF (DEB 1844941). This study is part of a project that has received funding from the European Research Council (ERC) to P.N., under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 770826 EE-Dynamics). J.L.F. was supported by NSF Dimensions in Biodiversity grant 1638997. The support and resources from the Center for High-Performance Computing at the University of Utah are gratefully acknowledged. We thank Landells-Hill Big Creek Reserve (https://doi.org/10.21973/N3NH24) where part of the samples included in this study were collected.

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Authors and Affiliations

  1. Department of Integrative Biology, University of California, Berkeley, CA, USA

    Samridhi Chaturvedi

  2. Department of Biology and Ecology Center, Utah State University, Logan, UT, USA

    Samridhi Chaturvedi, Zachariah Gompert & Patrik Nosil

  3. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

    Jeffrey L. Feder

  4. Molecular Ecology and Evolution Bangor, Environment Centre Wales, School of Natural Sciences, Bangor University, Bangor, UK

    Owen G. Osborne

  5. Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland

    Moritz Muschick

  6. Department of Fish Ecology and Evolution, Eawag, Swiss Federal Institute for Aquatic Science and Technology, Kastanienbaum, Switzerland

    Moritz Muschick

  7. Department of Biological Sciences, Royal Holloway University of London, Egham, UK

    Rüdiger Riesch

  8. The John Ines Center, Norwich Research Park, Norwich, UK

    Víctor Soria-Carrasco

  9. CEFE, Univ. Montpellier, CNRS, EPHE, IRD, Univ. Paul Valéry Montpellier 3, Montpellier, France

    Patrik Nosil

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Contributions

P.N., M.M., O.G.O., Z.G., J.L.F. and S.C. designed the study. P.N., M.M., R.R. and V.S.-C. collected data. Z.G., O.G.O. and S.C. analysed the data. S.C. wrote the manuscript with feedback from all co-authors.

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Correspondence to Samridhi Chaturvedi or Zachariah Gompert.

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Extended data

Extended Data Fig. 1 PCA of climate variation.

Ordination of climate variation (22 variables, see Supplementary Table 2 for code descriptions) via principal component analysis (PCA). Points denote the study populations, colour-coded by species.

Extended Data Fig. 2 Manhattan plot for PC1.

Manhattan plots showing the strength of evidence for association (measured here using the Bayes factor from the software BayPass) between a SNP window and climate for PC1. Results are shown along the 13 linkage groups. In each panel title, the two values in parentheses are the number of SNP windows in the top 10% quantile (‘windows’), followed by the number of linkage groups with at least 1 SNP window in the top 10% quantile (‘LG’).

Extended Data Fig. 3 Manhattan plot for PC2.

Manhattan plots showing the strength of evidence for association (measured here using the Bayes factor from the software BayPass) between a SNP window and climate for PC2. Results are shown along the 13 linkage groups. In each panel title, the two values in parentheses are the number of SNP windows in the top 10% quantile (‘windows’), followed by the number of linkage groups with at least 1 SNP window in the top 10% quantile (‘LG’).

Extended Data Fig. 4 Parallelism tests for PC1.

Tests for parallel climate-associated SNP windows between species of Timema stick insects (all plots are for the top 10% empirical quantile) for PC1. Plot shows x-fold enrichments for the number of overlapping climate-associated SNP windows for PC1 for comparisons between multiple species, that is, beyond pairs of species (for example, 2 or more species, 3 or more species, 4 or more species). Gray dots denote x-fold values expected under 1000 randomizations for a null distribution. Black diamond denotes median of the x-fold values expected under 1000 randomizations for a null distribution. Red dot and N value above each group indicates the observed number of overlapping climate-associated SNP windows for each comparison. P-value above each group denotes whether the overlap is greater than expected by chance from a one-sided randomization test. * Indicates x-fold enrichments with P-value ≤ 0.05.

Extended Data Fig. 5 Parallelism tests for PC2.

Tests for parallel climate-associated SNP windows between species of Timema stick insects (all plots are for the top 10% empirical quantile) for PC2. Plot shows x-fold enrichments for the number of overlapping climate-associated SNP windows for PC2 for comparisons between multiple species, that is, beyond pairs of species (for example, 2 or more species, 3 or more species, 4 or more species). Gray dots denote x-fold values expected under 1000 randomizations for a null distribution. Black diamond denotes median of the x-fold values expected under 1000 randomizations for a null distribution. Red dot and N value above each group indicates the observed number of overlapping climate-associated SNP windows for each comparison. P-value above each group denotes whether the overlap is greater than expected by chance from a one-sided randomization test. * Indicates x-fold enrichments with P-value ≤ 0.05.

Extended Data Fig. 6 Tests of the ‘shared ecology’ versus ‘shared genetics’ hypothesis for PC1.

Test results of the ‘shared ecology’ versus ‘shared genetics’ hypotheses for PC1. (a) Scatterplot shows the relationship between X-fold enrichment (measure for parallelism) and climatic distance (measured as the distance in PC1 scores) based on a single-factor linear model. (b) Scatterplot shows the relationship between X-fold enrichment (measure for parallelism) and genetic distance (measured as pairwise phylogenetic distance) based on a single-factor linear model. (c) Scatterplot shows the relationship between climatic distance (measured as the distance in PC1 scores and is the distance in climate variables) and genetic distance (calculated as pairwise phylogenetic distance) based on a single-factor linear model. (d) Plot shows parameter estimates with standardized coefficients for the full model for PC1. This test was implemented for all eight species and 56 species pairs. Error bars indicate 95% equal-tail probability intervals (ETPIs). A negative or positive estimate that deviates from zero indicates the effect on parallelism.

Extended Data Fig. 7 Tests of the ‘shared ecology’ versus ‘shared genetics’ hypothesis for PC2.

Test results of the ‘shared ecology’ versus ‘shared genetics’ hypotheses for PC2. (a) Scatterplot shows the relationship between X-fold enrichment (measure for parallelism) and climatic distance (measured as the distance in PC2 scores) based on a single-factor linear model. (b) Scatterplot shows the relationship between X-fold enrichment (measure for parallelism) and genetic distance (measured as pairwise phylogenetic distance) based on a single-factor linear model. (c) Scatterplot shows the relationship between climatic distance (measured as the distance in PC2 scores and is the distance in climate variables) and genetic distance (calculated as pairwise phylogenetic distance) based on a single-factor linear model. (d) Plot shows parameter estimates with standardized coefficients for the full model only for PC2. This test was implemented for all eight species and 56 species pairs. Error bars indicate 95% equal-tail probability intervals (ETPIs). A negative or positive estimate that deviates from zero indicates the effect on parallelism.

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Chaturvedi, S., Gompert, Z., Feder, J.L. et al. Climatic similarity and genomic background shape the extent of parallel adaptation in Timema stick insects. Nat Ecol Evol 6, 1952–1964 (2022). https://doi.org/10.1038/s41559-022-01909-6

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  • DOI: https://doi.org/10.1038/s41559-022-01909-6

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