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Determinants of genetic variation across eco-evolutionary scales in pinnipeds

Nature Ecology & Evolution volume 4, pages 1095–1104 (2020)Cite this article

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An Author Correction to this article was published on 14 July 2021

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Abstract

The effective size of a population (Ne), which determines its level of neutral variability, is a key evolutionary parameter. Ne can substantially depart from census sizes of present-day breeding populations (NC) as a result of past demographic changes, variation in life-history traits and selection at linked sites. Using genome-wide data we estimated the long-term coalescent Ne for 17 pinniped species represented by 36 population samples (total n = 458 individuals). Ne estimates ranged from 8,936 to 91,178, were highly consistent within (sub)species and showed a strong positive correlation with NC (\({R}_{\mathrm{adj}}^2\) = 0.59; P = 0.0002). Ne/NC ratios were low (mean, 0.31; median, 0.13) and co-varied strongly with demographic history and, to a lesser degree, with species’ ecological and life-history variables such as breeding habitat. Residual variation in Ne/NC, after controlling for past demographic fluctuations, contained information about recent population size changes during the Anthropocene. Specifically, species of conservation concern typically had positive residuals indicative of a smaller contemporary NC than would be expected from their long-term Ne. This study highlights the value of comparative population genomic analyses for gauging the evolutionary processes governing genetic variation in natural populations, and provides a framework for identifying populations deserving closer conservation attention.

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Fig. 1: Sampling set-up and core population parameters of the 17 pinniped species investigated in this study.
Fig. 2: Historical changes in population size.
Fig. 3: Demographic parameter estimates.
Fig. 4: The relationship between Ne/NC ratio and Tajima’s D.

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Data availability

All data generated for this study are archived in the sequence read archive under bioproject no. PRJEB37019 at the National Centre of Biotechnology Information (www.ncbi.nlm.nih.gov/sra). For individual accession numbers see also Supplementary Table 6. All code used for the analyses and the alignments used to infer substitution rates are available at 10.5281/zenodo.3741488.

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Acknowledgements

Funding and research support were provided by The Royal Physiographic Society in Lund (to A.B.A.S. and J.B.W.W.), Swedish Research Council Formas (no. 231-2012-450 to J.B.W.W.), the Natural Science and Engineering Research Council of Canada (to A.B.A.S.), the Wenner-Gren Foundation (to A.B.A.S.), the Royal Swedish Academy of Sciences (to A.B.A.S.), the German Research Foundation (no. HO 5122/3-1 to J.I.H. and J.B.W.W.) and LMU Munich (to J.B.W.W.). SciLifeLab Uppsala provided sequencing support. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under project no. SNIC 2018-3-658. This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research Council, and is part of the Polar Science for Planet Earth Programme. We thank F. Gulland for sample collection.

Author information

Author notes

  1. These authors contributed equally: Claire R. Peart, Sergio Tusso, Saurabh D. Pophaly.

Authors and Affiliations

  1. Science of Life Laboratories and Department of Evolutionary Biology, Uppsala University, Uppsala, Sweden

    Claire R. Peart, Sergio Tusso, Chi-Chih Wu, Aaron B. A. Shafer & Jochen B. W. Wolf

  2. Division of Evolutionary Biology, Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany

    Claire R. Peart, Sergio Tusso, Saurabh D. Pophaly, Fidel Botero-Castro & Jochen B. W. Wolf

  3. Max Planck Institute for Plant Breeding Research, Cologne, Germany

    Saurabh D. Pophaly

  4. Laboratorio de Ecología de Pinnípedos ‘Burney J. Le Boeuf’, Centro Interdisciplinario de Ciencias Marinas, Instituto Politécnico Nacional, Baja California Sur, México

    David Aurioles-Gamboa

  5. Department of Natural Sciences, University of Houston-Downtown, Houston, TX, USA

    Amy B. Baird

  6. Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USA

    John W. Bickham

  7. British Antarctic Survey, Natural Environment Research Council, Cambridge, UK

    Jaume Forcada & Joseph I. Hoffman

  8. Elephant Seal Research Group, Sea Lion Island, Falkland Islands

    Filippo Galimberti & Simona Sanvito

  9. Department of Anatomy, University of Otago, Dunedin, New Zealand

    Neil J. Gemmell

  10. Department of Animal Behaviour, Bielefeld University, Bielefeld, Germany

    Joseph I. Hoffman

  11. Norwegian Polar Institute, Fram Centre, Tromsø, Norway

    Kit M. Kovacs & Christian Lydersen

  12. Department of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, Finland

    Mervi Kunnasranta & Tommi Nyman

  13. Natural Resources Institute Finland (Luke), Joensuu, Finland

    Mervi Kunnasranta

  14. Department of Ecosystems in the Barents Region, Norwegian Institute of Bioeconomy Research, Svanhovd Research Station, Svanvik, Norway

    Tommi Nyman

  15. Laboratory of Mammal Ecology, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil

    Larissa Rosa de Oliveira

  16. National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, Marine Mammal Laboratory, Seattle, WA, USA

    Anthony J. Orr

  17. The Saimaa Ringed Seal Genome Project, Institute of Biotechnology, University of Helsinki, Helsinki, Finland

    Mia Valtonen

  18. Forensic Science & Environmental Life Sciences, Trent University, Peterborough, Ontario, Canada

    Aaron B. A. Shafer

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  1. Claire R. Peart
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Contributions

Study design was performed by J.B.W.W., A.B.A.S. and C.R.P. Laboratory work was done by A.B.A.S. and C.-C.W. Data analysis was carried out by C.R.P., S.T., S.D.P., F.B.-C., J.B.W.W. and A.B.A.S. Samples were provided by A.B.B., A.J.O., C.L., D.A.-G., F.G., J.W.B., J.F., J.I.H., J.B.W.W., K.M.K., L.R.O., M.K., M.V., N.J.G., S.S. and T.N. The initial manuscript draft was written by C.R.P., A.B.A.S. and J.B.W.W. All authors edited the manuscript, contributed to interpretation of results and approved the final version of the manuscript.

Corresponding authors

Correspondence to Aaron B. A. Shafer or Jochen B. W. Wolf.

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The authors declare no competing interests. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the US Government, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. Additionally, the findings and conclusions in the paper are those of the author(s) and do not necessarily represent the views of the National Marine Fisheries Service, NOAA.

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Extended data

Extended Data Fig. 1 Principal component analysis (part1).

Principal component analysis (first two axes) for Arctocephalus australis, Arctocephalus forsteri, Arctocephalus galapagoensis, Arctocephalus gazella and Callorhinus ursinus.

Extended Data Fig. 2 Principal component analysis (part2).

Principal component analysis (first two axes) for Cystophora cristata, Erignathus barbatus, Eumetopias jubatus, Halichoerus grypus, Mirounga angustirostris and Mirounga leonina.

Extended Data Fig. 3 Principal component analysis (part3).

Principal component analysis (first two axes) for Odobenus rosmarus, Pagophilus groenlandicus, Phoca vitulina, Pusa hispida, Zalophus californianus and Zalophus wollebaeki.

Extended Data Fig. 4 Relationship between effective population size and census population size.

Relationship between effective population size (Ne) and census population size (NC) per species as in Fig. 1c (main text) including species labels. Blue line: lineal regression line; shade: 95% confidence interval of regression. The y-axis follows a natural logarithmic scale.

Extended Data Fig. 5 The ratio of effective population size and census population size in relation to Tajima’s D.

The ratio of effective population size and census population size in relation to Tajima’s D representing the species’ demographic history; as in Fig. 4a (main text) including species and population labels. Note that the lineal regression (blue line) only refers to the population chosen to represent the species (black points) and the depicted relationship without considering the influence of other predictor variables. 95% confidence interval of the lineal regression is shown in shade. The y-axis follows a natural logarithmic scale.

Extended Data Fig. 6 Census population size (Nc) and population size (Ne) in relation to Tajima’s D per species.

Census population size (Nc) and population size (Ne) in relation to Tajima’s D per species (top and bottom panels respectively). The natural logarithm has been used for transformation of Ne and Nc values. Blue line: lineal regression line; shade: 95% confidence interval of regression. Note that the Galápagos seals (ArcGal, ZalWol) lie close to the expected relationship between Tajima’s D and Ne, whereas they show strong negative residuals for the relationship with Nc. This is consistent with a very recent reduction in census population size contributing to the high Ne/Nc ratio above one.

Extended Data Fig. 7 Examples of problems identified from visual inspection of the mapped ddRAD reads.

Screenshot examples of mapped reads displayed in the UCSC browser resulting from a ddRAD library constructed using MseI and SbfI. Black bars joining the SbfI to MSeI cut sites are potential clusters. Blue bar show aligned reads. The extension of reads beyond the MSeI cut site are the ‘ghost’ PCR extensions with higher visible mismatches and indels.

Extended Data Fig. 8 Distribution of ddRAD loci across the reference genomes.

Distribution of ddRAD loci (black points) across the largest 20 scaffolds in species with both reference genome and ddRAD data. Red points correspond to the end of scaffold. For Zalophus californianus data correspond to population SMargarita.

Extended Data Fig. 9 Phylogeny of species used to estimate µ.

Phylogenetic relationships of five pinniped species (Arctocephalus gazella, Leptonychotes weddellii, Neomonachus schauinslandii, Odobenus rosmarus, Zalophus californianus) and the outgroups dog (Canis lupus familiaris) and panda (Ailuropoda melanoleuca). The phylogeny was obtained by pruning the tree of Higdon et al.48 to include only the five species above. Branch lengths are proportional to divergence times, numbers next to nodes show estimated node ages.

Extended Data Fig. 10 The collinearity of life-history traits extracted from ref. 28.

Correlogram of the explanatory variables illustrating the degree of collinearity.

Supplementary information

Supplementary Information

Supplementary methods, Tables 4 and 5 and legends for Supplementary Tables 1–3 and 6.

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Supplementary Tables 2 (with legend), 3 and 6.

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Peart, C.R., Tusso, S., Pophaly, S.D. et al. Determinants of genetic variation across eco-evolutionary scales in pinnipeds. Nat Ecol Evol 4, 1095–1104 (2020). https://doi.org/10.1038/s41559-020-1215-5

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