Habitat dynamics of flagship species for conservation prioritization in southern Europe

Biodiversity loss is accelerating due to human actions, and decision-making for conservation needs to be streamlined. Ex situ biodiversity modeling and monitoring based on satellite time-series data could be an affordable and cost-efficient tool for improving the prioritization of conservation areas. We developed a set of dynamic indicators for conservation prioritization based on a habitat suitability index (HSI) trend analysis of 6 flagship species (two vascular plants, bird, amphibian, reptile, and mammal) over 19 years (2001-2019) in Andalucía (southern Spain). The HSI models were derived from ecological niche models (MaxEnt) and satellite time-series data (MODIS) as predictors. Based on the annual HSI models of all species and using the spatial conservation prioritization tool Marxan, we derived interannual dynamic indicators of habitat quality for conservation prioritization. Overall, models showed a generalized habitat regression. The best predictors of habitat quality were related to vegetation composition and structure (land cover), climate (land surface temperature), and energy balance (evapotranspiration), matching with the ecology of climate (such as Abies pinsapo) or vegetation-dependent (such as Alytes dickhilleni) species. Marxan identified interannual dynamics for the priority areas outside and inside protected areas. Interannual variation in habitat quality led to shifting conservation priorities across Andalucia from 2001 to 2019. Only 10.5% of the region and 20% of protected areas showed high spatial stability. Stable zones appeared both inside and outside protected areas. The south and northeast consistently exhibited high-priority regions. The legacy indicator highlighted areas of historical importance that have since declined in importance. New high-value areas emerged in the south. Static and dynamic approaches to conservation planning differed significantly. Many areas prioritized in 2019 alone ranked lower when long-term trends were considered. Our multiscale method underscores the need to integrate temporal dynamics into effective conservation strategies to achieve long-term conservation objectives in an efficient way.