Bertil Westerlund

In this study we created national vulnerability models for wind and snow damage based on a combination of remote sensing and Swedish National Forest Inventory (NFI) data and compared the performance of airborne laser scanning (ALS) -driven versus field-measured information in the modelling of damage vulnerability. The empirical training data consisted of circa 42 000 field plots, containing information about recent wind and snow damage, monitored between 2010-2022 in the Swedish NFI. Occurrence of damage was predicted using variables calculated from national ALS data (2009-2023), other mapped products, and weather data. In contrast to earlier large-scale damage models, we used direct ALS metrics to fully exploit ALS-derived forest structural information. Logistic regression was used for modelling, and separate models were created for southern and northern Sweden to consider the geographical differences in forest structure and climate conditions. Field data-based models performed slightly better than RS-based models, resulting in an AUC of 0.8 for northern and 0.73 for southern Sweden. Corresponding results for RS-based models were 0.77 and 0.69. Best models included both forest structural variables (ALS or field-based), tree species, terrain, and weather information. We successfully demonstrated the combination of ALS and NFI data to map forest vulnerability to wind and snow damage, which enables evaluation of the abundance and distributions of the damage vulnerability from stand to national level, and can be applied to a new region or damage type. By locating areas with high damage vulnerability, forest owners can more easily adapt forest management to increasing climate impacts.

Aim: To determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships.Location: Global.Time period: Recent.Major taxa studied: Trees.Methods: We integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community‐weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes).Results: Our models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within‐biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models.Main conclusions: Worldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within‐biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Thin, hair‐like lichens ( Alectoria , Bryoria , Usnea ) form conspicuous epiphyte communities across the boreal biome. These poikilohydric organisms provide important ecosystem functions and are useful indicators of global change. We analyse how environmental drivers influence changes in occurrence and length of these lichens on Norway spruce ( Picea abies ) over 10 years in managed forests in Sweden using data from >6000 trees. Alectoria and Usnea showed strong declines in southern‐central regions, whereas Bryoria declined in northern regions. Overall, relative loss rates across the country ranged from 1.7% per year in Alectoria to 0.5% in Bryoria . These losses contrasted with increased length of Bryoria and Usnea in some regions. Occurrence trajectories (extinction, colonization, presence, absence) on remeasured trees correlated best with temperature, rain, nitrogen deposition, and stand age in multinomial logistic regression models. Our analysis strongly suggests that industrial forestry, in combination with nitrogen, is the main driver of lichen declines. Logging of forests with long continuity of tree cover, short rotation cycles, substrate limitation and low light in dense forests are harmful for lichens. Nitrogen deposition has decreased but is apparently still sufficiently high to prevent recovery. Warming correlated with occurrence trajectories of Alectoria and Bryoria , likely by altering hydration regimes and increasing respiration during autumn/winter. The large‐scale lichen decline on an important host has cascading effects on biodiversity and function of boreal forest canopies. Forest management must apply a broad spectrum of methods, including uneven‐aged continuous cover forestry and retention of large patches, to secure the ecosystem functions of these important canopy components under future climates. Our findings highlight interactions among drivers of lichen decline (forestry, nitrogen, climate), functional traits (dispersal, lichen colour, sensitivity to nitrogen, water storage), and population processes (extinction/colonization).