| Broadleafification, the process of increasing broadleaf tree species in conifer-dominated forests, is proposed to enhance biodiversity in boreal forests managed for timber production. The presence of broadleaved trees increases forest structural complexity, providing diverse habitats for species such as lichens, birds, bats, and beetles. However, broadleaf-rich habitats have been systematically reduced in Swedish forests, leading to a decline in species dependent on these habitats. Restoration efforts aim to reverse this trend, but significant knowledge gaps exist regarding the effectiveness of these actions. Despite efforts to restore forests towards broadleaf-rich stands, there is limited research on their ecological and biodiversity impacts. Restoration targets are often unclear, leading to inconsistent results. There is an urgent need to assess the effectiveness of broadleaf restoration in improving local biodiversity and to understand the landscape-scale effects on species communities.
This 4-year doctoral project aims to: 1) Determine if restored broadleaf-rich habitats sustain similar bird, bat, and insect communities as target ecosystems (natural broadleaf-rich forests); 2) Assess whether restoration success depends on landscape configuration and the presence of high conservation value forests, and 3) Identify efficient mitigation measures to guide future restoration efforts at various latitudinal scales. Building on an ongoing monitoring project (Effekt20) by Sveaskog, this study will be conducted in four Ecoparks Hornsö, Färna, Käringberget, and Rosfors, across hemiboreal and boreal Sweden. Each Ecopark is managed with multifunctional goals and combine timber production, recreation and biodiversity protection. We will sample 15 forest stands in each park, categorized as recently restored, target ecosystems, and stands to be restored. Using AudioMoths (passive-acoustic monitoring devices) to monitor bird and bat communities, and flight-intercept traps for insects, the project will analyze forest species assemblages in relation to local and landscape-scale habitat availability. Structural measurements of forest stands and surrounding landscape composition will be conducted using multi-dataset approaches, including newly published AI-based map tools. Data analysis will involve machine learning for species identification from acoustic recordings and generalized mixed-effect models to study species community relationships at various scales. Functional diversity will be assessed to understand how species communities relate to different forest landscapes.
This project addresses critical conservation needs within Swedish managed forests by providing guidelines to improve biodiversity in broadleaf-dominated stands through restoration measures. It supports landowners in enhancing forest biodiversity and contributes to national and international conservation objectives, such as Sustainable Development Goal 15, and the EU Restoration Law.
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