Conservation often focuses on ‘ecologically intact’ habitats with little human influence. But where all such habitats have been lost or modified, identifying promising restoration targets is a key goal. We describe a direct approach to identify high conservation value targets using predictive distribution maps of taxa that, based on habitat affinity, ease of detection and abundance can be used to infer native species richness and prioritize conservation investment. We used 1169 avian point counts in a 1560 km2 study area, remote-sensed data and models incorporating imperfect detectability to predict habitat occupancy in 18 widely-distributed native birds; 12 of which were determined by experts to be positive indicators of old-forest conditions. Forest-association scores for these 12 species where then used as weights in a composite distribution map of the probability of community occurrence, which corresponded well with the occurrence of old forest stands mapped by aerial photography. Our results indicate that composite maps of widespread indicators improve site prioritization by incorporating the behavioural and demographic responses of a diverse range of indicators to variation in patch size, configuration and adjacent human land use.