
TRANSFERINT
Transferring new adaptive forest management practices to improve the resilience of Mediterranean forests
Rural abandonment in recent decades, together with climate change, has made forest areas highly vulnerable to the impacts of global change, and this situation threatens the provision of ecosystem services. This is the case for many coastal holm oak forests, where the abandonment of management has led to structures highly vulnerable to drought, pests, and wildfires.
Over the last decade, several demonstrative projects have monitored different parameters indicative of forest growth, tree vitality, and fire risk prevention in various conventional management practices. This proposal aims to take a step further by incorporating new nature-based forestry management strategies and including additional parameters related to multifunctional management, biodiversity conservation, and climate resilience.
The main objective is to transfer the knowledge generated, contributing to a new adaptive management of holm oak forests to improve the resistance and resilience of Mediterranean forests to climate change.
Objectives
Specifically, the project aims to:
- Implement innovative forest management practices based on a nature-oriented perspective, enabling forests to improve their ecological value and increase their climate resilience. These new practices will be compared with the previous conventional practices implemented in the same study areas.
- Monitor the effects of the different management strategies, including conventional and innovative practices, through a network of long-term monitoring plots and the evaluation of innovative parameters from the perspective of the ecosystem services provided by forests and their capacity for climate resilience.
- Transfer the results obtained, taking into account the main stakeholders (forest owners, technical staff, and public administration) at local and regional levels, and raise awareness in society about the challenge of sustainable forest management and the need for long-term data series to evaluate it.