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Climate change may shift species outside of their current climatic tolerances, which can be problematic for oceanic islands with limited options for species migration. Their alpine habitats, which represent islands within islands, are a special concern due to high endemism rates. The Canary Islands have two examples for such fragile ecosystems above 2000 m, on La Palma and Tenerife. This study contributes an assessment of the Canary Islands' climatic habitat for 47 alpine plant species under observed climate change (1959–1989 versus 1990–2019 periods) as well as for future projections (2041–2060 and 2061–2080 periods). We analyzed the contraction of climatic habitats and migration requirements (biotic velocity) for each species to compensate for observed and predicted climate change. Our results suggest that temporary climate refugia exist on Tenerife but are insufficient on La Palma, where habitat loss due to historical climate change has already created an area of significant concern: the Cumbre Vieja Natural Park. On Tenerife, current alpine species can find suitable climate habitats in the caldera area and on the Teide-Pico Viejo volcano. That said, migration paths toward these refugia are long and complex, and human-assisted migration may be required. Species-specific statistics to support such management interventions are provided in this study. [ABSTRACT FROM AUTHOR]

The monitoring of ecosystems and forests is an urgent requirement in the current framework of global change. It is particularly necessary on oceanic islands where their rich biodiversity is highly vulnerable, with many narrow-ranged endemic species. Quantifying and mapping forest health through key ecological variables are essential steps for management, but it will also be challenging and may require a lot of resources. Remote sensing has the potential to be a very useful tool to assess the development and conservation status of forests. We assessed the applicability of the light detection and ranging (LiDAR) on the laurel forests of La Gomera, making allometric equations for various measurements of the forest structure, linking field inventory from 2019 and 2017 LiDAR data through standard linear regressions. Decision trees and logistic regressions were also used to assess the performance of LiDAR in the recognition of young-growth and old-growth laurel forests. The obtained allometric models were a good fit in general and their predictions were in line with already known data. Likewise, decision tree and logistic regression to distinguish young-growth and old-growth forests had a similar performance in both cases, with a high to medium-high degree of accuracy. Therefore, LiDAR was revealed to be a useful tool for the monitoring of the laurel forest by the managers. [ABSTRACT FROM AUTHOR]