Mid Sweden University

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Therefore, the location of the remaining forests with natural structures and processes over landscapes and large regions is a key objective. Here we integrated machine learning (Random Forest) and open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests. Using independent spatial stand- and plot-level validation data, we confirmed that our predictions correctly represent different levels of forest naturalness, from degraded to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fill an urgent gap for assessing the achievement of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.