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Inbreeding depression explains killer whale population dynamics

Nature Ecology & Evolution volume 7, pages 675–686 (2023)Cite this article

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Abstract

Understanding the factors that cause endangered populations to either grow or decline is crucial for preserving biodiversity. Conservation efforts often address extrinsic threats, such as environmental degradation and overexploitation, that can limit the recovery of endangered populations. Genetic factors such as inbreeding depression can also affect population dynamics but these effects are rarely measured in the wild and thus often neglected in conservation efforts. Here we show that inbreeding depression strongly influences the population dynamics of an endangered killer whale population, despite genomic signatures of purging of deleterious alleles via natural selection. We find that the ‘Southern Residents’, which are currently endangered despite nearly 50 years of conservation efforts, exhibit strong inbreeding depression for survival. Our population models suggest that this inbreeding depression limits population growth and predict further decline if the population remains genetically isolated and typical environmental conditions continue. The Southern Residents also had more inferred homozygous deleterious alleles than three other, growing, populations, further suggesting that inbreeding depression affects population fitness. These results demonstrate that inbreeding depression can substantially limit the recovery of endangered populations. Conservation actions focused only on extrinsic threats may therefore fail to account for key intrinsic genetic factors that also limit population growth.

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Fig. 1: Distribution, population structure, inbreeding and demographic history for five North Pacific killer whale populations.
Fig. 2: Effects of inbreeding on survival to age 40 yr and population growth.
Fig. 3: Genetic loads in North Pacific killer whales.

Data availability

Raw sequence data and our killer whale genome assembly are freely available at the China National GeneBank DataBase (CNGBdb) with accession number CNP0002439. Demographic data for the Southern Resident killer whales are freely available at https://doi.org/10.5281/zenodo.7011243. Other freely available reference genomes used here include Indo-Pacific dolphin (https://www.ncbi.nlm.nih.gov/assembly/GCA_003227395.1), North Atlantic right whale (https://www.dnazoo.org/assemblies/Eubalaena_glacialis) and Pacific white-sided dolphin (https://www.dnazoo.org/assemblies/Lagenorhynchus_obliquidens).

Code availability

Computer code used in this study is available at https://doi.org/10.5281/zenodo.7504838.

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Acknowledgements

We are grateful to P. Levin, X. Zeng, Y. He and Y. Zhang for facilitating this project. This project would not have been possible without the long-term monitoring of the SRKW population provided by K. Balcomb and the Center for Whale Research, WA, USA. The China National GeneBank provided technical support with sequence data. The North Gulf Oceanic Society provided DNA from Alaska resident killer whales. Funding was provided by the National Natural Science Foundation of China (grant nos. 42225604 and 41422604), ‘One Belt and One Road’ Science and Technology Cooperation Special Program of the International Partnership Program of the Chinese Academy of Sciences (183446KYSB20200016) and Blue granary scientific and technological innovation of China (2018YFD0900301-05).

Author information

Author notes

  1. These authors contributed equally: Marty Kardos, Yaolei Zhang, Kim M. Parsons, Yunga A.

Authors and Affiliations

  1. Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA

    Marty Kardos, Kim M. Parsons, Eric J. Ward, M. Bradley Hanson, Candice Emmons & Michael J. Ford

  2. BGI-Qingdao, BGI-Shenzhen, Qingdao, China

    Yaolei Zhang, Yunga A & Guangyi Fan

  3. BGI-Shenzhen, Shenzhen, China

    Yaolei Zhang, Xun Xu, Xin Liu & Guangyi Fan

  4. Marine Mammal and Marine Bioacoustics Laboratory, Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya, China

    Hui Kang, Peijun Zhang & Songhai Li

  5. North Gulf Oceanic Society, Homer, AK, USA

    Craig O. Matkin

  6. State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China

    Guangyi Fan

  7. Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China

    Songhai Li

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  1. Marty Kardos
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M.J.F., K.M.P., G.F. and S.L. initiated the study. C.O.M., M.B.H., C.E. and S.L. planned and conducted fieldwork and provided samples. Y.Z., P.Z., H.K., X.X., X.L. and Y.A. assembled the genome and conducted population structure analyses. Y.Z., P.Z., H.K., X.X., X.L., Y.A. and M.K. conducted bioinformatics. M.K. conducted analyses of demographic history and inbreeding. M.K. and E.J.W. conducted inbreeding depression and demographic projection analyses. M.K., M.J.F., Y.Z., K.M.P. and Y.A. wrote the first draft of the paper and all authors commented and contributed to the final draft.

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Correspondence to Marty Kardos, Michael J. Ford, Guangyi Fan or Songhai Li.

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Nature Ecology & Evolution thanks Andrew Foote and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Neighbour joining (NJ) tree and admixture analysis of killer whale population structure.

One false killer whale sample was used as the outgroup to construct the NJ tree.

Extended Data Fig. 2 Historical Ne estimates (thick lines) and 95% confidence intervals (shaded regions) over the last 50 generations were estimated from patterns of linkage disequilibrium (LD) among linked SNPs with the program GONE (46).

The results for all 150 generations are shown in Fig. 1d in the main text. Here, we zoom in on the estimated effective sizes over the last 50 generations for Transient (green), Alaska Resident (dark blue), and Southern Resident (light blue) killer whales.

Extended Data Fig. 3 Annual survival estimates for an average 20-year-old, by sex, as a function of FROH values.

The thin blue lines represent 5,000 random MCMC draws of the estimated relationship between survival and FROH in our Bayesian model. The thick solid lines represent the posterior mean. The shaded areas represent the central 50% (dark) and 95% (light) of survival probability across the 5,000 MCMC draws. These results can be compared directly to Extended Datas Fig. 5 which shows an increase in estimated annual survival as HSNP increases.

Extended Data Fig. 4 Cumulative survival estimates to age 40, by sex, as a function of FROH.

The thin blue lines represent 5,000 random MCMC draws of the estimated relationship between cumulative survival and FROH in our Bayesian model. The thick solid lines represent the posterior mean. The shaded areas represent the central 50% (dark) and 95% (light) of survival probability across the 5,000 MCMC draws. These results can be compared directly to Extended Data Fig. 6 which shows an increase in estimated cumulative survival as HSNP increases.

Extended Data Fig. 5 Annual survival estimates by sex, as a function of HSNP.

The thin blue lines represent 5,000 random MCMC draws of the estimated relationship between cumulative survival and HSNP in our Bayesian model. The thick solid lines represent the posterior mean. The shaded areas represent the central 50% (dark) and 95% (light) of survival probability across the 5,000 MCMC draws. These results can be compared directly to Extended Data Fig. 3 which shows a decrease in estimated annual survival as FROH increases.

Extended Data Fig. 6 Cumulative survival estimates to age 40, by sex, as a function of HSNP.

The thin blue lines represent 5,000 random MCMC draws of the estimated relationship between cumulative survival and HSNP in our Bayesian model. The thick solid lines represent the posterior mean. The shaded areas represent the central 50% (dark) and 95% (light) of survival probability across the 5,000 MCMC draws. These results can be compared directly to Extended Data Fig. 4 which shows a decrease in estimated cumulative survival as FROH increases.

Extended Data Fig. 7 Relationship of age at death with FROH and sex. Relationship between FROH and age at death is shown for analyses using minimum ROH lengths of 1 Mb (A) and 10 Mb (B) for females (left) and males (right).

The relationship between FROH and age at death is shown for analyses using minimum ROH lengths of 1 Mb (top row) and 10 Mb (bottom row) for females (left) and males (right). Solid lines are fitted values from the statistical results shown in Supplementary Table 14. The model is of the form \({{{\mathrm{Age}}}} = B_0 + B_{F_{{{{\mathrm{ROH}}}}}} + {{{\mathrm{sex}}}}\), and the fitted lines shown in the plots have sex-specific intercepts according to the output (Supplementary Table 14). This analysis is based on the 28 SRKW individuals with sequence data that have died over the course of the study.

Extended Data Fig. 8 Lifetime reproductive success for SRKW females, calculated via the age-based models in our analysis plotted against FROH.killer whale population under models 1–15 (Supplementary Table 15).

FROH was measured using ROH with minimum lengths of 1 Mb and 10 Mb (different coloured lines as indicated in the legend).

Extended Data Fig. 9 Simulated future population trends with (red) and without (grey) inbreeding depression in the SRKW population under models 1–15 (Supplementary Table 15).

Each thin line represents one of 200 simulation replicates. Thick lines represent the median projected population size through time. The shaded areas represent the central 50% (dark) and 95% (light) of population size.

Extended Data Fig. 10 Site-frequency spectra based on analysis of 12 individual genomes from the Southern Resident (left), Alaska Resident (middle), and Transient killer whales (right).

The SFS for putatively deleterious and neutral derived alleles are shown as blue and orange lines, respectively. Derived allele frequencies of 0 and 1 are excluded.

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Supplementary Information

Supplementary Methods, Figs. 1–18 and Tables 1–16.

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Kardos, M., Zhang, Y., Parsons, K.M. et al. Inbreeding depression explains killer whale population dynamics. Nat Ecol Evol 7, 675–686 (2023). https://doi.org/10.1038/s41559-023-01995-0

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