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Disease-driven top predator decline affects mesopredator population genomic structure

Nature Ecology & Evolution volume 8, pages 293–303 (2024)Cite this article

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Abstract

Top predator declines are pervasive and often have dramatic effects on ecological communities via changes in food web dynamics, but their evolutionary consequences are virtually unknown. Tasmania’s top terrestrial predator, the Tasmanian devil, is declining due to a lethal transmissible cancer. Spotted-tailed quolls benefit via mesopredator release, and they alter their behaviour and resource use concomitant with devil declines and increased disease duration. Here, using a landscape community genomics framework to identify environmental drivers of population genomic structure and signatures of selection, we show that these biotic factors are consistently among the top variables explaining genomic structure of the quoll. Landscape resistance negatively correlates with devil density, suggesting that devil declines will increase quoll genetic subdivision over time, despite no change in quoll densities detected by camera trap studies. Devil density also contributes to signatures of selection in the quoll genome, including genes associated with muscle development and locomotion. Our results provide some of the first evidence of the evolutionary impacts of competition between a top predator and a mesopredator species in the context of a trophic cascade. As top predator declines are increasing globally, our framework can serve as a model for future studies of evolutionary impacts of altered ecological interactions.

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Fig. 1: Research context of the present study.
Fig. 2: Population genomic structure of the spotted-tailed quoll across Tasmania.

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Data availability

Raw sequence data and sample metadata necessary for reproducing the study have been deposited at NCBI under BioProject PRJNA922561 and BioSamples SAMN32664143–32664814. Any other relevant data can be found within the article and its Supplementary Information.

Code availability

Scripts for running analyses underlying this study’s results are publicly available in a GitHub repository (https://github.com/marcabeer/stquoll_landscape_genomics).

References

  1. Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).

    Article  PubMed  Google Scholar 

  3. Ripple, W. J. et al. What is a trophic cascade? Trends Ecol. Evol. 31, 842–849 (2016).

    Article  PubMed  Google Scholar 

  4. Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. Ecol. Lett. 12, 982–998 (2009).

    Article  PubMed  Google Scholar 

  5. Jachowski, D. S. et al. Identifying mesopredator release in multi‐predator systems: a review of evidence from North America. Mammal. Rev. 50, 367–381 (2020).

    Article  Google Scholar 

  6. Estes, J. A., Brashares, J. S. & Power, M. E. Predicting and detecting reciprocity between indirect ecological interactions and evolution. Am. Nat. 181, S76–S99 (2013).

    Article  PubMed  Google Scholar 

  7. Wood, Z. T., Palkovacs, E. P. & Kinnison, M. T. Eco-evolutionary feedbacks from non-target species influence harvest yield and sustainability. Sci. Rep. 8, 6389 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  8. Manel, S. & Holderegger, R. Ten years of landscape genetics. Trends Ecol. Evol. 28, 614–621 (2013).

    Article  PubMed  Google Scholar 

  9. Storfer, A., Patton, A. & Fraik, A. K. Navigating the interface between landscape genetics and landscape genomics. Front. Genet. 9, 68 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hand, B. K., Lowe, W. H., Kovach, R. P., Muhlfeld, C. C. & Luikart, G. Landscape community genomics: understanding eco-evolutionary processes in complex environments. Trends Ecol. Evol. 30, 161–168 (2015).

    Article  PubMed  Google Scholar 

  11. Cunningham, C. X. et al. Quantifying 25 years of disease‐caused declines in Tasmanian devil populations: host density drives spatial pathogen spread. Ecol. Lett. 24, 958–969 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hollings, T., Jones, M., Mooney, N. & Mccallum, H. Trophic cascades following the disease‐induced decline of an apex predator, the Tasmanian devil. Conserv. Biol. 28, 63–75 (2014).

    Article  PubMed  Google Scholar 

  13. Hollings, T., Jones, M., Mooney, N. & McCallum, H. Disease-induced decline of an apex predator drives invasive dominated states and threatens biodiversity. Ecology 97, 394–405 (2016).

    Article  PubMed  Google Scholar 

  14. Cunningham, C. X. et al. Top carnivore decline has cascading effects on scavengers and carrion persistence. Proc. R. Soc. B 285, 20181582 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cunningham, C. X., Scoleri, V., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Temporal partitioning of activity: rising and falling top‐predator abundance triggers community‐wide shifts in diel activity. Ecography 42, 2157–2168 (2019).

    Article  ADS  Google Scholar 

  16. Cunningham, C. X., Johnson, C. N. & Jones, M. E. A native apex predator limits an invasive mesopredator and protects native prey: Tasmanian devils protecting bandicoots from cats. Ecol. Lett. 23, 711–721 (2020).

    Article  PubMed  Google Scholar 

  17. Jones, M. E. & Barmuta, L. A. Diet overlap and relative abundance of sympatric dasyurid carnivores: a hypothesis of competition. J. Anim. Ecol. 67, 410–421 (1998).

    Article  Google Scholar 

  18. Belcher, C. A. Demographics of tiger quoll (Dasyurus maculatus maculatus) populations in south-eastern Australia. Aust. J. Zool. 51, 611–626 (2003).

    Article  Google Scholar 

  19. Belkhir, S., Hamede, R., Thomas, F., Ujvari, B. & Dujon, A. M. Season, weight, and age, but not transmissible cancer, affect tick loads in the endangered Tasmanian devil. Infect. Genet. Evol. 98, 105221 (2022).

    Article  PubMed  Google Scholar 

  20. Andersen, G. E., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Dietary partitioning of Australia’s two marsupial hypercarnivores, the Tasmanian devil and the spotted-tailed quoll, across their shared distributional range. PLoS ONE 12, e0188529 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jones, M. E. & Barmuta, L. A. Niche differentiation among sympatric australian dasyurid carnivores. J. Mammal. 81, 434–447 (2000).

    Article  Google Scholar 

  22. Andersen, G. E., Johnson, C. N. & Jones, M. E. Space use and temporal partitioning of sympatric Tasmanian devils and spotted‐tailed quolls. Austral Ecol. 45, 355–365 (2020).

    Article  Google Scholar 

  23. Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19, 4179–4191 (2010).

    Article  CAS  PubMed  Google Scholar 

  24. McRae, B. H. Isolation by resistance. Evolution 60, 1551–1561 (2006).

    PubMed  Google Scholar 

  25. Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).

    Article  PubMed  Google Scholar 

  26. Rellstab, C., Gugerli, F., Eckert, A. J., Hancock, A. M. & Holderegger, R. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24, 4348–4370 (2015).

    Article  PubMed  Google Scholar 

  27. Raj, A., Stephens, M. & Pritchard, J. K. fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197, 573–589 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Petkova, D., Novembre, J. & Stephens, M. Visualizing spatial population structure with estimated effective migration surfaces. Nat. Genet. 48, 94–100 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Peterman, W. E. ResistanceGA: an R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol. Evol. 9, 1638–1647 (2018).

    Article  Google Scholar 

  31. Capblancq, T. & Forester, B. R. Redundancy analysis: a Swiss army knife for landscape genomics. Methods Ecol. Evol. 12, 2298–2309 (2021).

    Article  Google Scholar 

  32. Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).

    Article  PubMed  Google Scholar 

  33. Caye, K., Jumentier, B., Lepeule, J. & François, O. LFMM 2: fast and accurate inference of gene–environment associations in genome-wide studies. Mol. Biol. Evol. 36, 852–860 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Heckerman, D. et al. Genetic variants associated with physical performance and anthropometry in old age: a genome-wide association study in the ilSIRENTE cohort. Sci. Rep. 7, 15879 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  35. Hayashi, S., Manabe, I., Suzuki, Y., Relaix, F. & Oishi, Y. Klf5 regulates muscle differentiation by directly targeting muscle-specific genes in cooperation with MyoD in mice. eLife 5, e17462 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pol, C. J. et al. Cardiac myocyte KLF5 regulates body weight via alteration of cardiac FGF21. Biochim. Biophys. Acta 1865, 2125–2137 (2019).

    Article  CAS  Google Scholar 

  37. Zhang, D.-H. et al. KLF5 regulates chicken skeletal muscle atrophy via the canonical Wnt/β-catenin signaling pathway. Exp. Anim. 69, 430–440 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Krivoruchko, A., Yatsyk, O. & Kanibolockaya, A. New candidate genes of high productivity in North-Caucasian sheep using genome-wide association study (GWAS). Anim. Gene 23, 200119 (2022).

    Article  CAS  Google Scholar 

  39. Pasandideh, M., Rahimi-Mianji, G. & Gholizadeh, M. A genome scan for quantitative trait loci affecting average daily gain and Kleiber ratio in Baluchi Sheep. J. Genet. 97, 493–503 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Xu, Z. et al. Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens. BMC Genomics 17, 594 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Fabbri, C. et al. Neuronal cell adhesion genes and antidepressant response in three independent samples. Pharmacogenomics J. 15, 538–548 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Stegemiller, M. R., Ellison, M. J., Hall, J. B., Sprinkle, J. E. & Murdoch, B. M. Identifying genetic variants affecting cattle grazing behavior experiencing mild heat load. Transl. Anim. Sci. 5, S61–S66 (2021).

    Article  CAS  Google Scholar 

  43. Hernández-Montiel, W. et al. Genome-wide association study reveals candidate genes for litter size traits in Pelibuey sheep. Animals 10, 434 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Winiarski, K. J., Peterman, W. E. & McGarigal, K. Evaluation of the R package ‘ResistanceGA’: a promising approach towards the accurate optimization of landscape resistance surfaces. Mol. Ecol. Resour. 20, 1583–1596 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. Kozakiewicz, C. P. et al. Comparative landscape genetics reveals differential effects of environment on host and pathogen genetic structure in Tasmanian devils (Sarcophilus harrisii) and their transmissible tumour. Mol. Ecol. 29, 3217–3233 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kierepka, E. M. & Latch, E. K. High gene flow in the American badger overrides habitat preferences and limits broadscale genetic structure. Mol. Ecol. 25, 6055–6076 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Cancellare, I. A. et al. Multiscale patterns of isolation by ecology and fine-scale population structure in Texas bobcats. PeerJ 9, e11498 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Jones, M. E. Guild Structure of the Large Marsupial Carnivores in Tasmania. PhD thesis, Univ. Tasmania (1995).

  49. Fraik, A. K. et al. Disease swamps molecular signatures of genetic–environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations. Evolution 74, 1392–1408 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Von Takach, B. et al. Population genomics of a predatory mammal reveals patterns of decline and impacts of exposure to toxic toads. Mol. Ecol. 31, 5468–5486 (2022).

    Article  Google Scholar 

  51. Wells, K. et al. Individual and temporal variation in pathogen load predicts long‐term impacts of an emerging infectious disease. Ecology 100, e02613 (2019).

    Article  PubMed  Google Scholar 

  52. Frankham, R. Relationship of genetic variation to population size in wildlife. Conserv. Biol. 10, 1500–1508 (1996).

    Article  Google Scholar 

  53. Lenormand, T. Gene flow and the limits to natural selection. Trends Ecol. Evol. 17, 183–189 (2002).

    Article  Google Scholar 

  54. Lowry, D. B. et al. Breaking RAD: an evaluation of the utility of restriction site‐associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17, 142–152 (2017).

    Article  CAS  PubMed  Google Scholar 

  55. Smith, C. C. et al. Landscape genomics of an obligate mutualism: concordant and discordant population structures between the leafcutter ant Atta texana and its two main fungal symbiont types. Mol. Ecol. 28, 2831–2845 (2019).

    Article  PubMed  Google Scholar 

  56. Parsley, M. B. et al. Multiple lines of genetic inquiry reveal effects of local and landscape factors on an amphibian metapopulation. Landsc. Ecol. 35, 319–335 (2020).

    Article  Google Scholar 

  57. Wenzel, M. A., Douglas, A., James, M. C., Redpath, S. M. & Piertney, S. B. The role of parasite-driven selection in shaping landscape genomic structure in red grouse (Lagopus lagopus scotica). Mol. Ecol. 25, 324–341 (2016).

    Article  CAS  PubMed  Google Scholar 

  58. Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife—threats to biodiversity and human health. Science 287, 443–449 (2000).

    Article  ADS  CAS  PubMed  Google Scholar 

  59. Buck, J. C. & Ripple, W. J. Infectious agents trigger trophic cascades. Trends Ecol. Evol. 32, 681–694 (2017).

    Article  PubMed  Google Scholar 

  60. Hamede, R. K. et al. Transmissible cancer in Tasmanian devils: localized lineage replacement and host population response. Proc. R. Soc. B 282, 20151468 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Ali, O. A. et al. RAD capture (Rapture): flexible and efficient sequence-based genotyping. Genetics 202, 389–400 (2016).

    Article  CAS  PubMed  Google Scholar 

  62. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  63. Rochette, N. C., Rivera‐Colón, A. G. & Catchen, J. M. Stacks 2: analytical methods for paired‐end sequencing improve RADseq‐based population genomics. Mol. Ecol. 28, 4737–4754 (2019).

    Article  CAS  PubMed  Google Scholar 

  64. Stammnitz, M. R. et al. The evolution of two transmissible cancers in Tasmanian devils. Science 380, 283–293 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  65. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Westerman, M. et al. Phylogenetic relationships of dasyuromorphian marsupials revisited. Zool. J. Linn. Soc. 176, 686–701 (2016).

    Article  Google Scholar 

  67. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Beer, M. A., Kane, R. A., Micheletti, S. J., Kozakiewicz, C. P. & Storfer, A. Landscape genomics of the streamside salamander: implications for species management in the face of environmental change. Evol. Appl. 15, 220–236 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  69. O’Leary, S. J., Puritz, J. B., Willis, S. C., Hollenbeck, C. M. & Portnoy, D. S. These aren’t the loci you’re looking for: principles of effective SNP filtering for molecular ecologists. Mol. Ecol. 27, 3193–3206 (2018).

    Article  Google Scholar 

  70. Waples, R. S. & Anderson, E. C. Purging putative siblings from population genetic data sets: a cautionary view. Mol. Ecol. 26, 1211–1224 (2017).

    Article  PubMed  Google Scholar 

  71. Peterman, W., Brocato, E. R., Semlitsch, R. D. & Eggert, L. S. Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages. PeerJ 4, e1813 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Selmoni, O., Vajana, E., Guillaume, A., Rochat, E. & Joost, S. Sampling strategy optimization to increase statistical power in landscape genomics: a simulation‐based approach. Mol. Ecol. Resour. 20, 154–169 (2020).

    Article  PubMed  Google Scholar 

  73. Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).

    Article  CAS  PubMed  Google Scholar 

  74. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  75. Andersen, G. E., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Use of anthropogenic linear features by two medium-sized carnivores in reserved and agricultural landscapes. Sci. Rep. 7, 11624 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  76. TASVEG 4.0 (DPIPWE, 2020); https://nre.tas.gov.au/conservation/development-planning-conservation-assessment/planning-tools/monitoring-and-mapping-tasmanias-vegetation-(tasveg)/tasveg-the-digital-vegetation-map-of-tasmania

  77. McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89, 2712–2724 (2008).

    Article  PubMed  Google Scholar 

  78. Clarke, R. T., Rothery, P. & Raybould, A. F. Confidence limits for regression relationships between distance matrices: estimating gene flow with distance. J. Agric. Biol. Environ. Stat. 7, 361–372 (2002).

    Article  Google Scholar 

  79. Shirk, A. J., Landguth, E. L. & Cushman, S. A. A comparison of regression methods for model selection in individual‐based landscape genetic analysis. Mol. Ecol. Resour. 18, 55–67 (2018).

    Article  PubMed  Google Scholar 

  80. Bowcock, A. M. et al. High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368, 455–457 (1994).

    Article  ADS  CAS  PubMed  Google Scholar 

  81. Oyler-McCance, S. J., Fedy, B. C. & Landguth, E. L. Sample design effects in landscape genetics. Conserv. Genet. 14, 275–285 (2013).

    Article  Google Scholar 

  82. Valentin, S. Geobuffer: R package for constructing geodesic buffers using metric radii. GitHub https://github.com/valentinitnelav/geobuffer (2019).

  83. Neteler, M., Bowman, M. H., Landa, M. & Metz, M. GRASS GIS: a multi-purpose open source GIS. Environ. Model. Softw. 31, 124–130 (2012).

    Article  Google Scholar 

  84. Epps, C. W. & Keyghobadi, N. Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change. Mol. Ecol. 24, 6021–6040 (2015).

    Article  PubMed  Google Scholar 

  85. Oksanen, J. et al. vegan: Community Ecology Package (2020); https://cran.r-project.org/web/packages/vegan/index.html

  86. Fitzpatrick, M. C., Chhatre, V. E., Soolanayakanahally, R. Y. & Keller, S. R. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol. Ecol. Resour. 21, 2749–2765 (2021).

    Article  CAS  PubMed  Google Scholar 

  87. Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol. Ecol. 27, 2215–2233 (2018).

    Article  CAS  PubMed  Google Scholar 

  88. Frichot, E. & François, O. LEA: an R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).

    Article  Google Scholar 

  89. McFadden, D. in Frontiers in Econometrics (ed. Zarembka, P.) 105–142 (Academic Press, 1974).

  90. Warton, D. I. & Hui, F. K. C. The arcsine is asinine: the analysis of proportions in ecology. Ecology 92, 3–10 (2011).

    Article  PubMed  Google Scholar 

  91. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank M. F. Lawrance, R. A. Kane, B. McCulloch, S. L. Bartel and R. M. Rautsaw for constructive comments during project development. Funding was provided by the National Science Foundation Division of Environmental Biology through grant NSF DEB 2027446 (A.S., M.J.M., H.M., M.E.J.), the National Institute of General Medical Sciences under the National Institutes of Health under the US Department of Health and Human Services through grant R01-GM126563-01 (A.S., P.A.H., H.M. and M.E.J.), and the National Science Foundation Graduate Research Fellowship Program under Award 1842493 (M.A.B.). Sample collection was additionally funded by the Australian Research Council through Discovery and Linkage Program grants LP130100949 (M.E.J.), DP110103069 (M.E.J., H.M.), DP110102656 (M.E.J., H.M.), LP0989613 (M.E.J.) and LP0561120 (M.E.J., H.M.), and a Future Fellowship (FT100100031) to M.E.J.

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Authors and Affiliations

  1. School of Biological Sciences, Washington State University, Pullman, WA, USA

    Marc A. Beer & Andrew Storfer

  2. School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia

    Kirstin M. Proft, David G. Hamilton, Rodrigo Hamede, Christopher P. Burridge & Menna E. Jones

  3. Hilo Core Genomics Facility, University of Hawaii at Hilo, Hilo, HI, USA

    Anne Veillet

  4. Department of Integrative Biology, Michigan State University, W.K. Kellogg Biological Station, Hickory Corners, MI, USA

    Christopher P. Kozakiewicz

  5. CANECEV, Centre de Recherches Ecologiques et Evolutives sur le Cancer, Montpellier, France

    Rodrigo Hamede

  6. Environmental Futures Research Institute, Griffith University, Nathan, Queensland, Australia

    Hamish McCallum

  7. Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, USA

    Paul A. Hohenlohe

  8. Department of Integrative Biology, University of South Florida, Tampa, FL, USA

    Mark J. Margres

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Contributions

A.S., M.J.M., H.M. and M.E.J. devised the project. K.M.P., R.H., D.G.H. and C.P.B. contributed samples, and M.A.B., K.M.P. and C.P.K. carried out DNA extractions. A.V. completed RADseq DNA library preparations and, along with P.A.H., assisted genotyping. A.S. supervised the project and M.A.B. carried out all analyses. M.A.B. and A.S. drafted the paper, and all authors contributed to the final version.

Corresponding author

Correspondence to Andrew Storfer.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks the anonymous reviewers 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 Relative support for different values of K.

Optimal K was determined by a) maximum marginal likelihood in FastStructure and b) minimum BIC in DAPC. Error bars in A indicate standard error across replicates.

Extended Data Fig. 2 Population genomic structure across Tasmania.

a) FastStructure results for K = 3. b) DAPC results for K = 3. c, d) FastStructure results for K = 4-5. Each individual is represented by a pie chart reflecting the proportion of ancestry assigned to each genetic cluster.

Extended Data Fig. 3 EEMS plots of effective migration rates.

Effective migration rate surfaces were determined for a) 250-, b) 500-, and c) 1000-deme lattices. Color indicates regions where effective migration is higher (blue) or lower (orange) than expected under a model of isolation by distance.

Extended Data Fig. 4 All 100 linear mixed effects models with maximum likelihood population effects evaluating the contribution of abiotic and biotic variables to isolation-by-resistance.

a–c) Models ordered left-to-right by increasing (worsening) AICc. d–f) Models ordered left-to-right by decreasing (worsening) marginal R-squared left to right. A, D) Average AICc of models based on 10,000 bootstrap replicates. B, E) Average marginal R-squared of models. Marginal R-squared is the proportion of variance in pairwise individual genetic distances explained by a resistance surface representing the composite of the indicated variables. C, F) Matrix indicating inclusion of environmental variables in each model. Isothermality (IT), TS (temperature seasonality), mean annual temperature (MAT), annual precipitation (AP), devil density lagged by 20, 15, 10, and 5 quoll generations (for example, Gen20_devil), landcover classes (TASVEG) and temperature diurnal range (TDR) are abbreviated in the matrix.

Extended Data Fig. 5 Partitioning of model deviance in GDM.

a) Percentages of total model deviance attributable to different factors, with individual environmental factors collapsed into climate, landcover, and generations diseased (DFTD). b) Percentages of deviance explained attributable to individual environmental factors and geography. Percentages in parentheses indicate contributions to total model deviance. Some explained deviance cannot be attributed uniquely to geography versus the environment (Geog-Env confound) or can be attributed uniquely to the environment but not individual environmental factors (Env. Confound).

Extended Data Fig. 6 GDM splines relating pairwise genetic distance to pairwise environmental differences.

Environmental factors were centered and scaled by standard deviation to enable plotting on the same axes.

Extended Data Fig. 7 Pearson’s correlation coefficients for pairs of environmental factors.

Numbers indicate the value of Pearson’s correlation coefficient for a pair of environmental factors. Blue colours indicate positive values and red colours indicate negative values of Pearson’s correlation coefficient.

Extended Data Table 1 Iterative filtering of spotted-tailed quoll genomic data
Full size table
Extended Data Table 2 Environmental factors considered for analyses
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Extended Data Table 3 Numbers of SNPs detected as significantly associated with environmental factors using pRDA and LFMM
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Supplementary information

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Supplementary Table 1

SNPs and nearby genes identified as associated with indicated environmental factors using GEA analyses.

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Beer, M.A., Proft, K.M., Veillet, A. et al. Disease-driven top predator decline affects mesopredator population genomic structure. Nat Ecol Evol 8, 293–303 (2024). https://doi.org/10.1038/s41559-023-02265-9

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