1. Species distribution models (SDMs) are increasingly used in applied conservation biology, yet the predictive ability of these models is often tested only on detection/non-detection data. The probability of long-term population persistence, however, depends not only upon patch occupancy but upon more fundamental population parameters such as mean population density and stability over time.
2. Here, we test estimated probability of occurrence scores generated from SDMs built using species occupancy data against independent empirical data on population density and stability for 20 bird and butterfly species across 1941 sites over 15 years. We devised a measure of population stability over time which was independent of mean density and time-series duration, yet positively correlated with risk of local extinction. This may be a useful surrogate measure of population persistence for use in applied conservation.
3. We found that probability of occurrence scores were significantly positively correlated with mean population density for both butterflies and birds. In contrast, probability of occurrence scores were at best weakly positively correlated with population stability. Referring to established ecological theory, we discuss why SDMs may be appropriate for predicting population density but not stability.
4. Synthesis and applications. Species distribution models are often constructed using species occupancy data because, for the majority of species and regions, these are the best data available. The models are then often used for projecting species’ distributions in the future and identifying areas where management could be targeted to improve species’ prospects. However, our results suggest that an overreliance on these SDMs may result in an exclusive focus on landscape management approaches that promote patch occupancy and density, but may overlook features important for long-term population persistence such as population stability. Other landscape metrics that take into account habitat heterogeneity or configuration may be required to predict population stability. To understand species persistence under rapid environmental change, count data from standardised monitoring schemes are an invaluable resource. These data provide additional insights into the factors affecting species’ extinction risks, which cannot easily be inferred from species’ occupancy data.