Dale T Clement, Dylan G Gallinson, Rodrigo K Hamede, Menna E Jones, Mark J Margres, Hamish McCallum, Andrew Storfer
{"title":"Coevolution promotes the coexistence of Tasmanian devils and a fatal, transmissible cancer.","authors":"Dale T Clement, Dylan G Gallinson, Rodrigo K Hamede, Menna E Jones, Mark J Margres, Hamish McCallum, Andrew Storfer","doi":"10.1093/evolut/qpae143","DOIUrl":null,"url":null,"abstract":"<p><p>Emerging infectious diseases threaten natural populations, and data-driven modeling is critical for predicting population dynamics. Despite the importance of integrating ecology and evolution in models of host-pathogen dynamics, there are few wild populations for which long-term ecological datasets have been coupled with genome-scale data. Tasmanian devil (Sarcophilus harrisii) populations have declined range wide due to devil facial tumor disease (DFTD), a fatal transmissible cancer. Although early ecological models predicted imminent devil extinction, diseased devil populations persist at low densities, and recent ecological models predict long-term devil persistence. Substantial evidence supports the evolution of both devils and DFTD, suggesting coevolution may also influence continued devil persistence. Thus, we developed an individual-based, eco-evolutionary model of devil-DFTD coevolution parameterized with nearly 2 decades of devil demography, DFTD epidemiology, and genome-wide association studies. We characterized potential devil-DFTD coevolutionary outcomes and predicted the effects of coevolution on devil persistence and devil-DFTD coexistence. We found a high probability of devil persistence over 50 devil generations (100 years) and a higher likelihood of devil-DFTD coexistence, with greater devil recovery than predicted by previous ecological models. These novel results add to growing evidence for long-term devil persistence and highlight the importance of eco-evolutionary modeling for emerging infectious diseases.</p>","PeriodicalId":12082,"journal":{"name":"Evolution","volume":" ","pages":"100-118"},"PeriodicalIF":3.1000,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolution","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1093/evolut/qpae143","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Emerging infectious diseases threaten natural populations, and data-driven modeling is critical for predicting population dynamics. Despite the importance of integrating ecology and evolution in models of host-pathogen dynamics, there are few wild populations for which long-term ecological datasets have been coupled with genome-scale data. Tasmanian devil (Sarcophilus harrisii) populations have declined range wide due to devil facial tumor disease (DFTD), a fatal transmissible cancer. Although early ecological models predicted imminent devil extinction, diseased devil populations persist at low densities, and recent ecological models predict long-term devil persistence. Substantial evidence supports the evolution of both devils and DFTD, suggesting coevolution may also influence continued devil persistence. Thus, we developed an individual-based, eco-evolutionary model of devil-DFTD coevolution parameterized with nearly 2 decades of devil demography, DFTD epidemiology, and genome-wide association studies. We characterized potential devil-DFTD coevolutionary outcomes and predicted the effects of coevolution on devil persistence and devil-DFTD coexistence. We found a high probability of devil persistence over 50 devil generations (100 years) and a higher likelihood of devil-DFTD coexistence, with greater devil recovery than predicted by previous ecological models. These novel results add to growing evidence for long-term devil persistence and highlight the importance of eco-evolutionary modeling for emerging infectious diseases.
Billie T. Lazenby, Mathias W. Tobler, William E. Brown, Clare E. Hawkins, Greg J. Hocking, Fiona Hume, Stewart Huxtable, Philip Iles, Menna E. Jones, Clare Lawrence, Sam Thalmann, Phil Wise, Howel Williams, Samantha Fox, David Pemberton
IF 9.8 1区 生物学PLoS BiologyPub Date : 2020-11-24DOI: 10.1371/journal.pbio.3000926
Young Mi Kwon, Kevin Gori, Naomi Park, Nicole Potts, Kate Swift, Jinhong Wang, Maximilian R Stammnitz, Naomi Cannell, Adrian Baez-Ortega, Sebastien Comte, Samantha Fox, Colette Harmsen, Stewart Huxtable, Menna Jones, Alexandre Kreiss, Clare Lawrence, Billie Lazenby, Sarah Peck, Ruth Pye, Gregory Woods, Mona Zimmermann, David C Wedge, David Pemberton, Michael R Stratton, Rodrigo Hamede, Elizabeth P Murchison
IF 4.5 1区 生物学Molecular EcologyPub Date : 2024-09-28DOI: 10.1111/mec.17531
Kasha Strickland, Menna E. Jones, Andrew Storfer, Rodrigo K. Hamede, Paul A. Hohenlohe, Mark J. Margres, Hamish I. McCallum, Sebastien Comte, Shelly Lachish, Loeske E. B. Kruuk
期刊介绍：
Evolution, published for the Society for the Study of Evolution, is the premier publication devoted to the study of organic evolution and the integration of the various fields of science concerned with evolution. The journal presents significant and original results that extend our understanding of evolutionary phenomena and processes.
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