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The continuous decline of forest biodiversity highlights the importance of the development of cost-effective and ecologically sustainable land-use planning approaches. Spatial conservation prioritisation (SCP) can be regarded as a useful tool for this challenge. We produced high-resolution, national scale SCP analyses to identify unprotected forest areas that host valuable forest biodiversity. We used stand-based modelled dead wood potential (DWP) data as a primary surrogate for conservation value. In addition, data on forestry operations that have negative impacts on biodiversity, connectivity between forest areas, the observations of red-listed forest species, connectivity to forest habitats of special importance for biodiversity, and connectivity to permanent protected areas were included in the analyses. Analyses addressed the estimation of present value and that of future potential following increases in connectivity. The results show that there are high conservation priority forest areas all over Finland although their distribution is highly fragmented. Depending on the version of the analyses, the best 10% of the landscape contains from 49% to 88% of the conservation values, a significant portion of which lie outside the current protected area network. Consequently, as biodiversity continues to decline in Finland and as most of the Finnish forest area is under commercial management, the current protected area network cannot be expected to halt the ongoing decline of forest biodiversity. Therefore, these analyses provide much-needed information for decision-making. They are a pragmatic tool for the planning of forest conservation networks and commercial management of forests at regional and national scales. Peer reviewed

Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen ( tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests ( L., [L.] Karst., spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.PopulusPinus sylvestrisPicea abiesBetula

Maintaining enough green areas and ensuring fair access to them is a common planning challenge in growing and densifying cities. Evaluations of green area access typically use metrics like population around green areas (within a certain buffer), but these do not fully ensure equitable access. We propose that using systematic and complementarity-driven spatial prioritization, often used in nature conservation planning, could assist in the complex planning challenge. Here, we demonstrate the use of spatial prioritization to identify green areas with highest recreational potential based on their type and their accessibility for the residents of the Helsinki Metropolitan area, the capital district of Finland. We calculated travel times from each city district to each green area. Travel times were calculated separately to local green areas using active travel modes (walking and biking), and to large forests (attracting people from near and far) using public transport. We prioritized the green areas using these multimodal travel times from each district and weighting the prioritization with population data with Zonation, conservation prioritization software. Compared to a typical buffer analysis (population within a 500 m buffer from green areas), our approach identified areas of high recreational potential in different parts of the study area. This approach allows systematic integration of travel-time-based accessibility measures into equitable spatial prioritization of recreational green areas. It can help urban planners to identify sets of green areas that best support the recreational needs of the residents across the city. Peer reviewed

Climate solutions relying on forest bioenergy may be in conflict with carbon sequestration and storage by forests as well as conservation of biodiversity. We quantified effects of forest residue harvesting for bioenergy on both forest carbon balance and biodiversity in a boreal forest landscape. Through a modeling framework we simulated forest development in four real watersheds with three scenarios: i) with and ii) without forest residue harvesting, and iii) set aside to study the conservation potential of these landscapes in the future without management. We simulated changes in the forest carbon stocks, and in the quality and the quantity of deadwood resources for 100 years and combined this information with the information on species habitat associations based on expert judgements. In this study current practices of slash and stump harvesting reduced forest carbon stocks and deadwood volumes at the landscape scale, and consequently halved the emissions savings that can be obtained with bioenergy. In addition, logging residue harvesting reduced 15-21% the combined species conservation capacity of the landscape for red-listed, saproxylic species compared to forest management without bioenergy harvesting. Furthermore, the results indicated a potential conflict between areas of high bioenergy potential and high conservation potential. peerReviewed

Tree structure equations derived from pipe model theory (PMT) are well-suited to estimate biomass allocation in Scots pine (Pinus sylvestrisL.) and Norway spruce (Picea abies[L.] Karst.). However, age dependence of parameters should be accounted for when applying the equations. Peer reviewed

The Arctic is one of the least human-impacted parts of the world, but, in turn, tundra biome is facing the most rapid climate change on Earth. These perturbations may cause major reshuffling of Arctic species compositions and functional trait profiles and diversity, thereby affecting ecosystem processes of the whole tundra region. Earlier research has detected important drivers of the change in plant functional traits under warming climate, but studies on one key factor, snow cover, are almost totally lacking. Here we integrate plot-scale vegetation data with detailed climate and snow information using machine learning methods to model the responsiveness of tundra communities to different scenarios of warming and snow cover duration. Our results show that decreasing snow cover, together with warming temperatures, can substantially modify biotic communities and their trait compositions, with future plant communities projected to be occupied by taller plants with larger leaves and faster resource acquisition strategies. As another finding, we show that, while the local functional diversity may increase, simultaneous biotic homogenization across tundra communities is likely to occur. The manifestation of climate warming on tundra vegetation is highly dependent on the evolution of snow conditions. Given this, realistic assessments of future ecosystem functioning require acknowledging the role of snow in tundra vegetation models. Significance Plant functional traits are central instruments in developing understanding and predicting biodiversity patterns and ecosystems processes. Snow is an important ecological factor in cold climates, but its contribution to the evolution of functionality of tundra vegetation is poorly known and insufficiently addressed in the research. We show here that snow has a fundamental effect in mediating climate change impacts on functional composition and diversity of Arctic tundra vegetation. As a whole, Arctic landscapes may lose spatial heterogeneity because plant communities will be functionally more alike, although the local functional diversity may increase. Our results highlight that future snow conditions and their fine-scale variability should be acknowledged in the next generation of Arctic vegetation−ecosystem models.

Tree biomass models are increasingly needed for the estimation of carbon storage and for testing and parameterizing process-based growth models, but most biomass models have been estimated using da...