Habitat dynamics of flagship species for conservation prioritization in southern Europe

Abstract

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