Biodiversity loss is accelerating due to human actions and decision-making for conservation needs to be streamlined. The ex-situ biodiversity modelling and monitoring based on satellite time series data could be an affordable and cost-efficient tool for improving the prioritization of conservation areas. Here, we provide a set of dynamic indicators for conservation prioritization by applying a habitat suitability trend analysis of six flagship species over 19 years (2001 – 2019) derived from ecological niche models (MaxEnt) and time series of the Moderate-Resolution Imaging Spectroradiometer (MODIS) in Andalucía (Spain, Southern Europe). Based on the annual HSI models of all species and using the spatial conservation prioritization tool Marxan, we derived inter-annual dynamic indicators for conservation prioritization as cost-effectiveness, adequacy, stability and conservation legacy. Overall, models showed a high performance (AUCmean = 0.92±0.07, Boyce index = 0.84±0.15 and Null models AUCmean= 0.63 ± 0.08), predicting 31% of negative trends and only 19% of positive trends for the habitat availability across all species, showing a generalized habitat regression. The most contributing predictors were related to vegetation composition and structure (land cover), climate (land surface temperature), and energy balance (evapotranspiration), matching with the species ecology. Marxan identified interannual dynamics for the priority areas, both outside and inside protected areas. Our results suggested that (1) additional areas than those currently protected need to be prioritized, and (2) areas recently degraded could be restored considering the past importance for the conservation of the target species. In summary, this model-based system supported by a well-established conservation planning software provides a new set of dynamic priority-area indicators informing about the adequacy and effectiveness of conservation areas to meet long-term conservation goals at a regional scale, and being applicable to other species, ecosystems and socio-economic contexts across scales.