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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. 

Abstract Emerging infectious diseases (EIDs) not only cause catastrophic declines in wildlife populations but also generate selective pressures that may result in rapid evolutionary responses. One such EID is devil facial tumour disease (DFTD) in the Tasmanian devil. DFTD is almost always fatal and has reduced the average lifespan of individuals by around 2 years, likely causing strong selection for traits that reduce susceptibility to the disease, but population decline has also left Tasmanian devils vulnerable to inbreeding depression. We analysed 22 years of data from an ongoing study of a population of Tasmanian devils on Freycinet Peninsula, Tasmania, to (1) identify whether DFTD may be causing selection on body size, by estimating phenotypic and genetic correlations between DFTD and size traits, (2) estimate the additive genetic variance of susceptibility to DFTD, and (3) investigate whether size traits or susceptibility to DFTD were under inbreeding depression. We found a positive phenotypic relationship between head width and susceptibility to DFTD, but this was not underpinned by a genetic correlation. Conversely, we found a negative phenotypic relationship between body weight and susceptibility to DFTD, and there was evidence for a negative genetic correlation between susceptibility to DFTD and body weight. There was additive genetic variance in susceptibility to DFTD, head width and body weight, but there was no evidence for inbreeding depression in any of these traits. These results suggest that Tasmanian devils have the potential to respond adaptively to DFTD, although the realised evolutionary response will critically further depend on the evolution of DFTD itself. 

Abstract Understanding age structure in populations is foundational for ecological study, yet ascertaining the age of individuals in the wild can be problematic. Methods typically rely on the prior determination of a relationship between age and some morphological measurement specific to the species or animal group under investigation.We present a novel method to incorporate multiple measures of animal size and development to estimate age, using Bayesian parallel regression to integrate multiple regression relationships into predictions.We apply the method to Tasmanian devils, a carnivorous marsupial that is threatened by a transmissible cancer. We estimate devil age based on measures of mass, head width, canine over‐eruption and molar characteristics.Practical implication. The method provides a flexible framework with potential application to a range of species; however, the method is best suited to capture–mark–recapture style studies where repeated measures data have been collected for individuals across multiple points in time.