Anthropogenic heathlands are semi-cultural ecosystems with a unique cultural and biodiversity value, considered worthy of preservation across most of the world. Their rate of loss, however, is alarming. Currently, we know little about the heathlands’ actual span of instability and their association with abiotic and anthropogenic factors, including how much additional intervention they need to persist. Consequently, we are missing out on vital knowledge for conservation, management, and the historical persistence of heathlands. This paper develops a method to assess the ecological resilience of Atlantic inland heaths in the absence of human management. We use 12 existing cases of heathland succession to establish a four-step resilience grade for each site, which we regress onto a series of explaining factors and use in predicting postglacial heath resilience across Atlantic Northern Europe. We find that temperature, humidity, elevation and sandiness have a positive correlation with high heathland resilience. Our predictive mapping shows an uneven distribution of ecological heath resilience across Atlantic Northern Europe within an area of 1000x1200 km of 5x5 km resolution. Compared with archaeological and palaeoecological evidence, prehistoric heathland distributions far exceed areas that afford high heath resilience, suggesting that heath distribution and persistence depends on both abiotic and anthropogenic factors. Policy implications: The predictive map can be used as an initial screening tool for managers working towards heath preservation and restoration to predict resilience, prioritize conservation, and to plan management practices across Atlantic Northern Europe. The developed model and predictive map provide an important initial screening tool to assess heathland resilience for ecologists and will help managers to prioritize conservation efforts of heathland ecosystems. The results are equally relevant for scholars who are interested in humans’ role in increasing and decreasing ecosystem resilience. Moreover, we speculate that our predictive method can be applied in other regions across the world by adding regionally-specific variables.