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Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short‐term epidemic properties, such as controlling early outbreaks. Predicting long‐term endemic characteristics with limited retrospective data is more challenging. We used individual‐based modeling informed by individual variation in pathogen load and transmissibility to predict long‐term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed approximate Bayesian computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from 10 yr of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declines observed thus far are likely attributable to transient dynamics (initial dynamics after disease emergence). Only 21% of matching scenarios led to devil extinction within 100 yr following devil facial tumor disease (DFTD) introduction, whereasDFTDfaded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 yr, usually with long‐period oscillations. Our findings show that pathogen extirpation or host–pathogen coexistence are much more likely than theDFTD‐induced devil extinction, with crucial management ramifications. Accounting for individual‐level disease progression and the long‐term outcome of devil–DFTDinteractions at the population‐level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long‐term population declines at equilibria. Our modeling approach is widely applicable to other host–pathogen systems to predict disease impact beyond transient dynamics.

Habitat loss is the primary driver of biodiversity decline worldwide, but the effects of fragmentation (the spatial arrangement of remaining habitat) are debated. We tested the hypothesis that forest fragmentation sensitivity—affected by avoidance of habitat edges—should be driven by historical exposure to, and therefore species’ evolutionary responses to disturbance. Using a database containing 73 datasets collected worldwide (encompassing 4489 animal species), we found that the proportion of fragmentation-sensitive species was nearly three times as high in regions with low rates of historical disturbance compared with regions with high rates of disturbance (i.e., fires, glaciation, hurricanes, and deforestation). These disturbances coincide with a latitudinal gradient in which sensitivity increases sixfold at low versus high latitudes. We conclude that conservation efforts to limit edges created by fragmentation will be most important in the world’s tropical forests.