Modeling the Future of Laurisilva Forests: Integrating Regional and Global Bioclimatic Datasets for Projections Beyond the Canary Islands

The laurel forest (laurisilva) represents a unique and biodiverse ecosystem currently confined to subtropical regions with specific climatic conditions. In the Canary Islands, these laurisilva forests, constrained to areas with high humidity and stable temperatures as the northern slopes of Tenerife, La Gomera, and La Palma, are of particular ecological importance hosting numerous endemic species. However, climate change poses a significant threat to these fragile habitats, with potential shifts in their distribution at both regional and global scales with new regions emerging as potential refuges for laurisilva forests. The main scope of this study is to explore the current distribution of laurisilva forests in the Canary Islands and projects to the future under different climate change scenarios for mid-century and end-century, its potential range in other archipelagos of Macaronesia and selected regions worldwide with similar climatic conditions.

Using Maxent as the primary modeling tool, we first trained the model by means of high-resolution bioclimatic indicators specifically designed for the Canary Islands, the so-called BICI-ULL dataset. This dataset was generated taking into account the intricate topography and diverse microclimatic patterns of the archipelago, providing a robust framework to delineate the current distribution of laurisilva. Once the model was trained, we used the global bioindicators from WorldClim and Chelsa to project the potential future distribution of laurisilva.

Thus, this methodology based on BICI-ULL allowed us to develop a detailed understanding of laurisilva distribution in the Canary Islands, while WorldClim and Chelsa facilitated the extrapolation of projections to broader geographic scales offering a framework for identifying potential refugia and new habitats for conservation planning of the laurisilva forests. These findings underline the importance of combining regional expertise with global datasets to inform conservation strategies for biodiverse but threatened ecosystems like laurisilva.