Empirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: A multimodel approach.

Process-based models and empirical modelling techniques are frequently used to (i) explore the sensitivity of tree growth to environmental variables, and (ii) predict the future growth of trees and forest stands under climate change scenarios. However, modelling approaches substantially influence predictions of the sensitivity of trees to environmental factors. Here, we used tree-ring width (TRW) data from 1630 beech trees from a network of 70 plots established across European mountains to build empirical predictive growth models using various modelling approaches. In addition, we used 3-PG and Biome-BGCMuSo process-based models to compare growth predictions with derived empirical models. Results revealed similar prediction errors (RMSE) across models ranging between 3.71 and 7.54 cm2 of basal area increment (BAI). The models explained most of the variability in BAI ranging from 54 % to 87 %. Selected explanatory variables (despite being statistically highly significant) and the pattern of the growth sensitivity differed between models substantially. We identified only five factors with the same effect and the same sensitivity pattern in all empirical models: tree DBH, competition index, elevation, Gini index of DBH, and soil silt content. However, the sensitivity to most of the climate variables was low and inconsistent among the empirical models. Both empirical and process-based models suggest that beech in European mountains will, on average, likely experience better growth conditions under both 4.5 and 8.5 RCP scenarios. The process-based models indicated that beech may grow better across European mountains by 1.05 to 1.4 times in warmer conditions. The empirical models identified several drivers of tree growth that are not included in the current process-based models (e.g., different nutrients) but may have a substantial effect on final results, particularly if they are limiting factors. Hence, future development of process-based models may build upon our findings to increase their ability to correctly capture ecosystem dynamics.

Impact of an Extremely Dry Period on Tree Defoliation and Tree Mortality in Serbia.

This paper presents research results on forest decline in Serbia. The results were obtained through monitoring defoliation of 34 tree species at 130 sample plots during the period from 2004 to 2018. This research aimed to determine whether the occurrence of defoliation and tree mortality were caused by drought. Defoliation was assessed in 5% steps according to the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) methodology. All the trees recorded as dead were singled out, and annual mortality rates were calculated. To determine changes in air temperature and precipitation regimes during the study period, we processed and analysed climatic data related to air temperature and precipitation throughout the year and in the growing season at 28 main weather stations in Serbia. Tree mortality patterns were established by classifying trees into three groups. The first group of trees exhibited a gradual increase in defoliation during the last few years of monitoring, with dying as the final outcome. The second group was characterised by sudden death of trees. The third group of trees reached a higher degree of defoliation immediately after the first monitoring year, and the trees died after several years. Tree mortality rates were compared between years using the Standardised Precipitation Evaporation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI), the most common methods used to monitor drought. The most intensive forest decline was recorded during the period from 2013 to 2016, when the largest percentage of the total number of all trees died. According to the annual mortality rates calculated for the three observation periods (2004-2008, 2009-2013, and 2014-2018) the highest forest decline rate was recorded in the period from 2014 to 2018, with no statistically significant difference between broadleaved and coniferous tree species. As the sample of coniferous species was small, the number of sample plots should be increased in order to achieve better systematic forest condition monitoring in Serbia. The analysis of the relationship between defoliation and climatic parameters proved the correlation between them. It was noted that the forest decline in Serbia was preceded by an extremely dry period with high temperatures from 2011 to 2013, supporting the hypothesis that it was caused by drought. We therefore conclude that these unfavourable climatic conditions had serious and long-term consequences on forest ecosystems in Serbia.

Differential effects of alcohol intoxication on S100B levels following traumatic brain injury.

BACKGROUND: In an acute care setting, evaluation of traumatic brain injury (TBI) is often complicated by alcohol intoxication. The purpose of this study is to evaluate the clinical utility of the protein S100B as a biochemical marker for identifying brain injury in patients who are intoxicated at the time of injury. METHODS: The study participants were 160 patients who presented to a large urban Level I Trauma Centre in Vancouver, Canada. Patients were classified into four clinical groups (medical controls, trauma controls, mild TBI, and definite TBI) and two day-of-injury alcohol intoxication groups (i.e., sober and intoxicated). Blood samples were collected via venipuncture in heparinized tubes within 8 hours of injury. Measures of S100B concentration were obtained using a commercially available assay kit (Sangtec 100 Elisa). RESULTS: For those patients who were sober at the time of injury, higher S100B levels were associated with TBI when compared with other physical injuries and general medical complaints. However, for patients who were intoxicated at the time of injury, there were uniformly low S100B levels across all clinical groups. CONCLUSIONS: Although there seems to be a strong association between S100B levels and TBI, further research is required to establish the clinical role of S100B in patients with suspected TBI, particularly in patients whose clinical presentation is complicated by alcohol intoxication.