Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber)

Abstract

The ability of Earth observation and remote sensing to identify the potential of species to restore ecosystems is a critical element in combating habitat degradation and biodiversity loss. Historical climate data and current species occurrence, coupled with geospatial modelling methods, can significantly aid in decision-making to support ecosystem restoration. We chose Eurasian beavers (spp. Castor fiber) as our case species, which are known ecosystem engineers that create wetland habitats and significantly alter/reinforce the morphology, dynamics, and hydraulics of riverine landscapes. To investigate their ecosystem restoration potential, we applied species distribution modelling (SDM) to determine their current and future distribution scenarios. Our SDMs used current and simulated future environmental data along with different classes of rasters such as elevation, inland swamps, water bodies, natural seasonally or permanently wet grasslands, open mires, riparian mixed forests, riparian swamp broadleaved forests, riparian swamp coniferous forests, watercourses, protected areas, and urban and built-up areas derived from Google Earth Engine to predict areas suitable for beaver habitats. Further, by using the zonation method, we were able to develop a stepping stone model to prioritize habitat conservation efforts. We provide habitat projections according to time periods of "2041–2060", "2061–2080" and "2081–2100", and to different climate change scenarios (SSP126, SSP245, SSP370, and SSP585). Thus, using a potential nature-based solution, we have employed remote sensing data and tools to develop a holistic framework for not only quantifying the ecosystem restoration potential of beavers but will also provide a new direction for understanding species-specific importance in climate change mitigation.