Much of conservation planning has focused on how we should prioritize areas for protection based on biodiversity and cost, but less is known about how we should prioritize areas based upon the level of threat they face. We discuss two opposing threat prioritization strategies: frontier conservation (prioritizing high‐threat areas) and wilderness conservation (prioritizing low‐threat areas). Using a temporally explicit model, we demonstrate that the best strategy depends on a variety of factors, including protection costs, heterogeneity in biodiversity, biodiversity–area relationships, the rate of biodiversity recovery, the rate of change in threats through time, and the timeframe within which we measure conservation outcomes. By quantitatively comparing the impact of these strategies, we aim to shift the debate away from a simple dichotomy of frontier versus wilderness, toward an understanding of the context‐specific benefits of each option, and a discussion of how threat combines with other factors to determine spatial conservation priorities.

Despite exponential increases in the coverage of protected areas (PAs) over recent decades, global biodiversity continues to decline. One explanation for this lack of success is that the efficacy of conservation prioritization strategies is rarely measured in terms of conservation "impact," which requires comparing proposed PA networks to a counterfactual scenario in which no intervention is applied. This approach contrasts with measuring efficacy using surrogates for conservation impact, such as the extent, total biodiversity value, or representativeness of a proposed PA network. However, implementing an experimental counterfactual scenario is difficult because of time, funding, and ethical constraints. Here, we use an alternative and complementary approach: an ex-post analysis with counterfactual outcomes measured using historical empirical data on changes in biodiversity in unprotected landscapes. This approach allows for the comparison of different retrospectively implemented prioritization strategies to a real counterfactual outcome. In our analysis, we predict the impact of several alternative PA prioritization strategies in Queensland, Australia, using high-resolution datasets of vegetation clearing, habitat type, and land acquisition cost. Our results show that achieving conventional conservation targets does not equate to achieving impact, and that alternative, and relatively simple, prioritization strategies can achieve far greater impacts.