Mapping the ecological resilience of Atlantic postglacial heathlands.

Anthropogenic heathlands are semi-natural 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 resilience affordances 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 affordances of Atlantic postglacial 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 it in predicting heath resilience across postglacial 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 1,000 × 1,200 km of 5 × 5 km resolution.Historic heathland distributions far exceed areas that afford high heath resilience, suggesting that heath distribution and persistence depend on both abiotic and anthropogenic factors. : The map predicting the ecological resilience of Atlantic postglacial heaths can be used by managers working towards heath preservation and restoration to prioritize conservation efforts and to plan management practices across Atlantic Northern Europe. Together with the predictive model, it provides an important initial screening tool to assess heathland resilience in the absence of management as well as the impact of atmospheric nitrogen. The results are equally relevant for scholars who are interested in humans' role in increasing and decreasing ecosystem resilience. Our predictive method can be applied in other regions across the world by adding regionally specific variables.