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High‐quality abundance data are expensive and time‐consuming to collect and often highly limited in availability. Nonetheless, accurate, high‐resolution abundance distributions are essential for many ecological applications ranging from species conservation to epidemiology. Producing models that can predict abundance well, with good resolution over large areas, has therefore been an important aim in ecology, but poses considerable challenges. We present a two‐stage approach to modeling abundance, combining two established techniques. First, we produce ensemble species distribution models (SDMs) of trees in Great Britain at a fine resolution, using much more common presence–absence data and key environmental variables. We then use random forest regression to predict abundance by linking the results of the SDMs to a much smaller amount of abundance data. We show that this method performs well in predicting the abundance of 20 of 25 tested British tree species, a group that is generally considered challenging for modeling distributions due to the strong influence of human activities. Maps of predicted tree abundance for the whole of Great Britain are provided at 1 km2 resolution. Abundance maps have a far wider variety of applications than presence‐only maps, and these maps should allow improvements to aspects of woodland management and conservation including analysis of habitats and ecosystem functioning, epidemiology, and disease management, providing a useful contribution to the protection of British trees. We also provide complete R scripts to facilitate application of the approach to other scenarios.

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1. Acute outbreaks of pests and disease are increasingly affecting tree populations around the world, causing widespread ecological effects. In Britain, ash dieback Hymenoscyphus fraxineus (Baral et al.) has severe impacts on common ash (Fraxinus excelsior L.) populations, and the emerald ash borer (Agrilus planipennis Fairmaire)is likely to add to the impact in future. This will cause significant changes to the character and functioning of many ecosystems. However, the nature of these changes and the best approach for conserving ecosystems after ash loss are not clear.2. We present a method to locate those areas most ecologically vulnerable to loss of a major tree species (common ash) and identify the resultant damage to distinctive ecosystem properties. This method uses the functional traits of species and their distributions to map the potential degree of change in traits across space and recommend management approaches to reduce the change. An analytic hierarchy process is used to score traits according to ecological importance.3. Our results indicate that in some areas of Britain, provision of ash-associated traits could be reduced by over 50% if all ash is lost. Certain woodland types, and trees outside woodlands, may be especially vulnerable to ash loss. However, compensatory growth by other species could halve this impact in the longer term. 4. We offer management guidance for reducing ecosystem vulnerability to ash loss, including recommending appropriate alternative tree species to encourage through planting or management in particular areas and woodland types. 5.Synthesis and applications. The method described in this paper allows spatially explicit assessment of species traits to be used in the restoration of ecosystems for the first time. We offer practical recommendations for the ash dieback outbreak in Britain to help conserve functional traits in ecosystems affected by the loss of ash. This technique is widely applicable to a range of restoration and conservation scenarios and represents a step forward in the use of functional traits in conservation.

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