Non-linear response of Norway spruce to climate variation along elevational and age gradients in the Carpathians.

Climate change, namely increased warming coupled with a rise in extreme events (e.g., droughts, storms, heatwaves), is negatively affecting forest ecosystems worldwide. In these ecosystems, growth dynamics and biomass accumulation are driven mainly by environmental constraints, inter-tree competition, and disturbance regimes. Usually, climate-growth relationships are assessed by linear correlation due to the simplicity and straightforwardness of modeling. However, applying this method may bias results, since the ecological and physiological responses of trees to environmental factors are non-linear, and usually bell-shaped. In the Eastern Carpathian, Norway spruce is at the southeasternmost edge of its natural occurrence; this region is thus potentially vulnerable to climate change. A non-linear assessment of climate-growth relationships using machine-learning techniques for Norway spruce in this area had not been conducted prior to this study. To address this knowledge gap, we analyzed a large tree-ring network from 158 stands, with over 3000 trees of varying age distributed along an elevational gradient. Our results showed that non-linearity in the growth-climate response of spruce was season-specific: temperatures from the previous autumn and current growing season, along with water availability during winter, induced a bell-shaped response. Moreover, we found that at low elevations, spruce growth was mainly limited by water availability in the growing season, while winter temperatures are likely to have had a slight influence along the entire elevational gradient. Furthermore, at elevations lower than 1400 m, spruce trees were also found to be sensitive to previous autumn water availability. Overall, our results shed new light on the response of Norway spruce to climate in the Carpathians, which may aid in management decisions.

Identifying drivers of non-stationary climate-growth relationships of European beech.

The future performance of the widely abundant European beech (Fagus sylvatica L.) across its ecological amplitude is uncertain. Although beech is considered drought-sensitive and thus negatively affected by drought events, scientific evidence indicating increasing drought vulnerability under climate change on a cross-regional scale remains elusive. While evaluating changes in climate sensitivity of secondary growth offers a promising avenue, studies from productive, closed-canopy forests suffer from knowledge gaps, especially regarding the natural variability of climate sensitivity and how it relates to radial growth as an indicator of tree vitality. Since beech is sensitive to drought, we in this study use a drought index as a climate variable to account for the combined effects of temperature and water availability and explore how the drought sensitivity of secondary growth varies temporally in dependence on growth variability, growth trends, and climatic water availability across the species' ecological amplitude. Our results show that drought sensitivity is highly variable and non-stationary, though consistently higher at dry sites compared to moist sites. Increasing drought sensitivity can largely be explained by increasing climatic aridity, especially as it is exacerbated by climate change and trees' rank progression within forest communities, as (co-)dominant trees are more sensitive to extra-canopy climatic conditions than trees embedded in understories. However, during the driest periods of the 20th century, growth showed clear signs of being decoupled from climate. This may indicate fundamental changes in system behavior and be early-warning signals of decreasing drought tolerance. The multiple significant interaction terms in our model elucidate the complexity of European beech's drought sensitivity, which needs to be taken into consideration when assessing this species' response to climate change.

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe.

To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.