Predictive analysis of tree-related microhabitats from inventory data: innovative approaches for monitoring and assessing forest biodiversity

Covering over 30% of the global land surface, forests are among the largest and most complex ecosystems on Earth, providing a multitude of goods and services to society. Increasing anthropogenic pressure and rapid climate change are leading to a depletion of forests, with a strong impact on the provision of ecosystem services, and thus on forest biodiversity. Given the important role played by forest biodiversity, its maintenance with the simultaneous provision of multiple ecosystem services is one of the objectives of the main European forest policies. Since the Third Ministerial Conference on the Protection of Forests in Europe, to the more current EU Forestry Strategy and the EU Biodiversity Strategy, the maintenance of forest biodiversity has been increasingly linked to sustainable forest management practices. However, ensuring the multifunctionality of forests, and in particular balancing forest biodiversity conservation and timber production, is not easy. While the conservation of biological diversity of singular species or taxa proved easier to implement, monitoring and conserving biodiversity at the ecosystem or landscape level is a more complex process. To solve this gap, several strategies and innovative management approaches were developed, such as integrative forest management. Integrative forest management (IFM) is an innovative management strategy that aims to fulfil several objectives in the same area but at different spatial and temporary scales: from single tree up to stand level and from limited to illimited protection. The IFM is a form of landscape-scale management that combines multiple services of forestry and a segregative approach. Through the release of protected islands, combined with managed areas, in turn, interspersed with old-growth-related areas and elements, the IFM allows for a landscape mosaic, maximising the three cornerstones of forestry: production, protection, and conservation. Acknowledged the complexity of forest biodiversity, the direct monitoring of species or taxa is often compromised by requirement of taxonomic experts, and for cost and time consuming, so that indirect monitoring methods have been developed. Among them, in addition to forest structural indicators (e.g., tree species composition, horizontal and vertical tree distribution, diameter or heigh variability), over the years, biodiversity-related elements such as deadwood, habitat trees and microhabitats, have been increasingly used as monitoring forest biodiversity. Defined as specific morphological singularities of above-ground trees that provide an essential life site for numerous species and taxa, tree-related microhabitats (TreMs) are a useful indicator of forest biodiversity and a characterising element of old-growth forests. Given their importance for forest biodiversity, awareness of these elements has increased in recent years, both from a practical and scientific point of view. This led to the emergence of new silvicultural approaches aiming to imitate natural processes and the most complex forests (e.g., Continuous Cover Forestry, Close-to-Nature Forest Management, Climate Smart Forestry). On the other hand, interest in the research of relationships between tree-related microhabitats and habitat trees and forest structure, management, or individual taxa or species has increased. Having ascertained that these structures, typical of primary and old-growth forests, also play a fundamental ecological role in managed forests, the main objective of this thesis was to identify and quantify the influence of key inventory parameters on the presence, abundance, and diversity of TreMs and habitat trees. Through the observation of the relationships between TreMs and the structural characteristics of individual trees, the influence of these structures on individual forest endangered species and the importance of the correct spatial distribution of habitat trees and TreMs in a forest stand, this work aims to predict the potential biodiversity of forest areas, based on TreMs and habitat trees, enabling, and facilitating large-scale biodiversity monitoring.