There is high-level political support for the use of green infrastructure (GI) across Europe, to maintain viable populations and to provide ecosystem services (ES). Even though GI is inherently a spatial concept, the modern tools for spatial planning have not been recognized, such as in the recent European Environment Agency (EEA) report. We outline a toolbox of methods useful for GI design that explicitly accounts for biodiversity and ES. Data on species occurrence, habitats, and environmental variables are increasingly available via open-access internet platforms. Such data can be synthesized by statistical species distribution modeling, producing maps of biodiversity features. These, together with maps of ES, can form the basis for GI design. We argue that spatial conservation prioritization (SCP) methods are effective tools for GI design, as the overall SCP goal is cost-effective allocation of conservation efforts. Corridors are currently promoted by the EEA as the means for implementing GI design, but they typically target the needs of only a subset of the regional species pool. SCP methods would help to ensure that GI provides a balanced solution for the requirements of many biodiversity features (e.g., species, habitat types) and ES simultaneously in a cost-effective manner. Such tools are necessary to make GI into an operational concept for combating biodiversity loss and promoting ES.

The European Union (EU) set clear climate change mitigation targets to reach climate neutrality, accounting for forests and their woody biomass resources. We investigated the consequences of increased harvest demands resulting from EU climate targets. We analysed the impacts on national policy objectives for forest ecosystem services and biodiversity through empirical forest simulation and multi-objective optimization methods. We show that key European timber-producing countries – Finland, Sweden, Germany (Bavaria) – cannot fulfil the increased harvest demands linked to the ambitious 1.5°C target. Potentials for harvest increase only exists in the studied region Norway. However, focusing on EU climate targets conflicts with several national policies and causes adverse effects on multiple ecosystem services and biodiversity. We argue that the role of forests and their timber resources in achieving climate targets and societal decarbonization should not be overstated. Our study provides insight for other European countries challenged by conflicting policies and supports policymakers.

Summary: Accurate estimates of gamete and offspring dispersal range are required for the understanding and prediction of spatial population dynamics and species persistence. Little is known about gamete dispersal in fungi, especially in lichen‐forming ascomycetes. Here, we estimate the dispersal functions of clonal propagules, gametes and ascospores of the epiphytic lichenLobaria pulmonaria. We use hierarchical Bayesian parentage analysis, which integrates genetic and ecological information from multiannual colonization and dispersal source data collected in a large, old‐growth forest landscape. The effective dispersal range of gametes is several hundred metres to kilometres from potential paternal individuals. By contrast, clonal propagules disperse only tens of metres, and ascospores disperse over several thousand metres. Our study reveals the dispersal distances of individual reproductive units; clonal propagules, gametes and ascospores, which is of great importance for a thorough understanding of the spatial dynamics of ascomycetes. Sexual reproduction occurs between distant individuals. However, whereas gametes and ascospores disperse over long distances, the overall rate of colonization of trees is low. Hence, establishment is the limiting factor for the colonization of new host trees by the lichen in old‐growth landscapes.

The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km²) by comparison against forecasts from a model based on systematically collected colonization–extinction data. We fitted species distribution models using citizen science observations of an old‐forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020‐2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization–extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set‐aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization–extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization–extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization–extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.