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Abstract Organisms can improve their fitness by modifying their environments—a process known as (positive) niche construction. Since niche construction is inherently costly, requiring time and energy to perform, niche constructors are vulnerable to displacement by non-niche-constructing invaders that exploit the constructed habitats. One way constructors could avoid such displacement is by adapting to withstand the invaders and thus undergoing evolutionary rescue. Here, we first analytically approximate the probability that a niche-constructing population—one building reproductive habitats—undergoes evolutionary rescue from habitat exploitation by an invading species. Then, we evaluate the approximation under two different fitness costs of construction: a fecundity cost and a mortality cost. We find that fecundity costs are not only less harmful than mortality costs but can even promote rescue compared with no costs by reducing the rate at which constructors attempt reproduction and thus construction. The resulting lower habitat density slows invasion, which then buys constructors more time to mutate. This invasion-slowing benefit can be stronger if the fecundity cost, instead of deriving from construction, stems from niche destruction, where organisms destroy their own habitats. Our results suggest that the same fitness costs rendering constructors vulnerable to habitat exploitation can help rescue constructors from such exploitation. 

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.