Sonja Vospernik

Understanding the drivers of tree mortality is essential for developing proactive management strategies to mitigate forest decline under climate change. However, empirical individual-tree mortality models parameterized with long-term inventory and climatic data remain scarce. We employed Generalized Additive Mixed Models (GAMMs) using Austrian Forest Inventory (AFI) data spanning a 40-year period (1981–2021) to analyse the mortality dynamics of Norway spruce (Picea abies) and Scots pine (Pinus sylvestris). Our findings reveal distinct, species-specific vulnerabilities to both stand structural factors and climatic stressors. Norway spruce mortality is strongly influenced by structural factors, such as diameter at breast height (DBH) and basal area of larger trees (BAL), yet remains acutely sensitive to climatic extremes—specifically unseasonably summer temperatures and exceptionally wet, stagnant summer conditions. Interestingly, spruce appears to benefit from warmer chronic temperatures across montane and lowland elevation zones, suggesting its primary threat lies in weather volatility rather than baseline warming. In contrast, Scots pine mortality is primarily governed by structural factors, exhibiting resilience to seasonal climate anomalies. However, this species begins to cross its physiological threshold in chronically warmer lowland regions and demonstrates increased vulnerability when mixed with broadleaves or Norway spruce—likely due to heightened competition for light and resources. This divergence underscores the superior resilience of Scots pine to episodic climatic shocks, whereas Norway spruce exists in a more precarious state, where even moderate climatic deviations can trigger significant mortality.

Global warming is expanding the length of the growing season. However, within this longer window, the number of effective growing days is decreasing due to drought, so that warming often leads to growth losses on dry sites. Tree species mixtures can enhance productivity and biodiversity and may mitigate climate-change impacts. To better understand the effects of climate and species mixing, high-resolution seasonal growth observations are valuable. Nevertheless, mixing effects have rarely been examined at the seasonal scale.In pure and mixed oak–pine stands at Maissau (475 m a.s.l.; mean annual temperature 10.1 °C; precipitation 475 mm year⁻¹), hourly radial growth of 48 trees has been monitored since 2017 using band dendrometers installed on dominant, intermediate, and suppressed trees. A hierarchical generalized additive model (GAM) with components for seasonal growth, shrinkage and swelling following rainfall, and diurnal water uptake was fitted to the data.The model explained 95% of cumulative diameter increment. Increment differed significantly among years, species, dominance classes, and mixtures. There was pronounced interannual variability, with lower growth rates and a shorter growing season in drier years. Growth of Quercus robur and Q. petraea started earlier and lasted longer than that of Pinus sylvestris. Dominant trees grew for approximately one month longer than suppressed trees. Mixture effects were small. Both species exhibited pronounced diurnal cycles of water uptake, which were stronger around the summer solstice than in spring and more pronounced in P. sylvestris than in Quercus spp.; differences due to mixture were again minor.Pinus sylvestris was more affected by drought than Quercus spp., which can be traced to their differing physiology. As a conifer, P. sylvestris pursues a conservative water-use strategy, closing stomata earlier than Quercus spp.; under drought this leads to strongly reduced photosynthesis and lower growth. Mixture had only a small beneficial effect for the two species studied, likely because niche complementarity is limited.

This study investigates the use of adaptive cluster sampling (ACS) for estimating bark stripping damage in forests, employing the Hansen–Hurwitz (HH) and Horvitz–Thompson (HT) estimators. Through simulations, we analysed the total, summer, and new bark stripping damage with varying grid sizes and sample sizes in eight full-censused stands in Northern Styria/Austria. The results showed that the HT estimator consistently had lower standard errors (SEs) (variability of the sample mean from the true population mean) than the HH estimator. SEs decreased with increasing grid space for new and summer damages, but increased for total damage up to 35 m, then remained stable. Inclusion probabilities (IP) were highest for total damage. ACS showed precision gains, particularly for rare and clustered damages like new damage, but did not achieve the target SE of 10%. Adaptive sampling is most beneficial for monitoring rare and clustered events, though precision remains limited, especially for new damage. The study suggests ACS is suitable for rare damage types (e.g., summer and new bark stripping wounds) but requires further refinement to meet operational precision targets. Future work should focus on integrating adaptive designs with practical field methods, such as fixed-radius plots and refined damage criteria.

Understanding tree growth in relation to environmental conditions is essential, particularly in the context of climate change,where rising temperatures, frequent droughts, and disturbances threaten forest health and productivity. This study useshigh-resolution data from four intensively monitored Picea abies stands in Austria (2010-2020), with dendrometers recordinghourly stem increments on 10 trees per site, allowing for detailed analysis of growth responses to environmental changes.For thispurpose we tested different generalized additive mixed models (GAMs) using environmental data collected on site. The best modelconsisted of combinations of soil moisture (SM) and soil temperature (ST) data. Furthermore we analysed how the relationshipsestablished differ for three different times during the growing season. We found that high SM consistently had a positive effect ontree growth, wheras the effect of ST varied depending on the timing.Our findings underscore the importance of monitoring soilconditions, particularly for species like Picea abies, which are known to react more sensitive to environmental changes due totheir shallow rooting systems and vulnerability to drought.

Norway spruce, which is sensitive to drought, and Scots pine, which is drought-resistant, are two of the most significant conifer species in Europe. In mixed stands, they can utilize resources more efficiently than in pure stands, leading to higher yields and reduced risk. Tree ring research is often used to study their growth in response to complex environmental factors. Machine learning, though rarely applied to tree ring analysis, might be well suited for modelling these complex relations. Data from 22 triplets (1 mixed and two pure plots of Norway spruce and Scots pine) covering a temperature and precipitation gradient of 3.2-9.2°C and 613 to 1075 mm respectively, were used in this study. On each plot, trees were mapped and measured for dbh, height and height to the crown base. 4490 increment cores were collected and synchronized in the lab. A random forest model with relative DBH, age, competition, mixture and climate variables explained 76.4% of the variation and proved effective in describing ecological relationships.

Forest mensuration is important to gain knowledge and information about forest stands. Because tree height often proves more difficult to measure than diameter, different statistical models are used for their estimation instead. In this paper, the data of 986 spruce trees (Picea abies KARST. (L.)), measured in the federal states of Salzburg and Tyrol (Austria), were used to train and compare random forest with more traditional approaches such as linear and non-linear mixed models and a classical uniform height curve. For model comparison, RMSE, percent RMSE, percent bias, and bias are used. For further visualization of the differences, residual plots, partial dependence plots, and conditional dependence plots are shown. The results show that random forest (RMSE 2.23 m) can compete with more traditional methods, such as non-linear (RMSE 2.14 m) and linear (RMSE 2.24 m) mixed models or uniform height curves (RMSE 2.92 m), but is not able to outperform those methods, especially when it comes to extrapolation or prediction in areas where training data are sparse or not available. Furthermore, the results show that the incorporation of additional covariates can improve the prediction of certain models.

Quercus robur/Quercus petraea and Pinus sylvestris are light demanding, drought resistant wide spread European tree species occupying a similar ecological niche on mesotrophic xeric and mesic sites. Increasing drought and heat-spells will decrease their growth, but growth reductions depend on the timing of drought within a year, site characteristics, and inter- and intraspecific competition.In this study we analysed tree ring series of 1589 trees growing on 36 triplets installed across Europe, spanning a gradient of mean annual temperatures from 5.5°C to 11.5°C. Tree ring data were analysed using a generalized additive model with random effects for triplet, year and tree level and a random slope for age, explaining 87 % of the variation of diameter increment.High values for potential evapotranspiration in spring and autumn, indicating warm temperatures, positively and non-linearly influenced diameter increment of both species, while the effect in June was linear and negative. Across Europe, June was the most influential month for tree growth. Further, tree growth declined with age and local density and increased with social position.Modelled random patterns showed no large scale trend, when plotted per site and year indicating highly site specific shifts in competitive advantages.