Increasing deer populations threaten the conservation of many different habitats, but predicting the outcomes of management to control their impacts is difficult because vegetation changes slowly and can follow different trajectories. One approach to overcome this challenge is to develop models that predict the responses of vegetation to future management interventions. Tanentzap et al. develop one of the first spatially-explicit models to predict the spread of forest regeneration in response to deer management. They specifically focus on how upland birchwoods, a habitat of high conservation value, spread across the Scottish Highlands in response to browsing by red deer and land management.
Tanentzap et al. show that mangers must reduce animal impacts rather than densities to increase woodland regeneration. But, other factors, such as ground cover, must also be favourable for tree establishment once deer numbers are reduced. Predictions from the model were verified with field data, demonstrating that they can aid conservation practitioners in the real world. An extended benefit of the model developed by the authors is that it can be used to predict above-ground carbon storage, and so can inform national strategies for maximizing carbon capture.