Changing climatic conditions can have various consequences for forest ecosystems, from increasing frequencies of forest fires, ice and windstorm events to pathogen outbreaks and mass mortalities. The Standardized Precipitation Index (SPI) was chosen for the evaluation of drought impact on the radial growth of trees after extensive preliminary testing of various calculated monthly climate parameters from the CARPATCLIM database. SPI was calculated for periods between 3 and 36 months for different sites (lowland and mountainous parts of Serbia, Southeast Europe), from which Quercus robur, Q. cerris, Fagus sylvatica and Pinus sylvestris samples were acquired. Bootstrapped Pearson%s correlations between SPI monthly indices and radial growth of tree species were calculated. We found that 12-month SPI for summer months may be a good predictor of positive and negative growth of different species at different sites. The strongest positive correlations for five of six tree-ring width chronologies were between 12-month June and 14-month September SPI, which implies that high growth rates can be expected when the autumn of the previous year, and winter, spring and summer of the current year, are well supplied with precipitation, and vice versa (low precipitation in given period/low growth rates).
COBISS.SI-ID: 4998822
We analysed sapwood characteristics in 344 pedunculate oak (Quercus robur L.) samples from the south-western part of the Pannonian Basin. The samples came from 13 sites, located in Slovenia, Croatia and Serbia. The trees had an average of 13.3 sapwood rings, with a minimum of 5 and maximum of 32. Fifteen log-log linear regression models were employed to assess the statistical relationship between sapwood and heartwood variables. The number of sapwood rings (NSW), which is usually needed in dendroarchaeological dating, is significantly related to the number of heartwood rings (NHW), heartwood width (WHW) and heartwood growth rate (GHW). Older and more slowly growing trees had a higher average number of sapwood rings. Using NHW and WHW, we employed an additional multiple regression model and calculated coefficients for NSW predictions for real-world dendroarchaeological dating from the south-western part of the Pannonian Basin.
COBISS.SI-ID: 5319334
It is generally observed that precipitation is gradually depleted in 18O and 2H isotopes as elevation increases (‘altitude’ effect) or when moving inland from seacoasts (‘continental’ effect); the regionally accurate estimation of these large-scale effects is important in isotope hydrological or paleoclimatological applications. Nevertheless, seasonal and spatial differences should be considered. Stable isotope composition of monthly precipitation fallen between January 2016 and December 2018 was studied for selected stations situated along an elevation transect and a continental transect in order to assess the isotopic ‘altitude’ and ‘continental’ effects in modern precipitation across the Adriatic–Pannonian region. Isotopic characteristics argue that the main driver of the apparent vertical depletion of precipitation in heavy stable isotopes is different in summer (raindrop evaporation) and winter (condensation), although, there is no significant difference in the resulting ‘altitude’ effect. Specifically, an ‘altitude’ effect of -1.2‰/km for ?18O and -7.9‰/km for ?2H can be used in modern precipitation across the Adriatic–Pannonian region. Isotopic characteristics of monthly precipitation showed seasonally different patterns and suggest different isotope hydrometeorological regimes along the continental transect. While no significant decrease was found in ?18O data moving inland from the Adriatic from May to August of the year, a clear decreasing trend was found in precipitation fallen during the colder season of the year (October to March) up to a break at ~400 km inland from the Adriatic coast. The estimated mean isotopic ‘continental’ effect for the colder season precipitation is -2.4‰/100 km in ?18O and -20‰/100 km in ?2H. A prevailing influence of the Mediterranean moisture in the colder season is detected up to this breakpoint, while the break in the ?18O data probably reflects the mixture of moisture sources with different isotopic characteristics. A sharp drop in the d-excess ()3‰) at the break in precipitation ?18O trend likely indicates a sudden switch from the Mediterranean moisture domain to additional (mainly Atlantic) influence, while a gradual change in the d-excess values might suggest a gradual increase of the non-Mediterranean moisture contribution along the transect.
COBISS.SI-ID: 20979715
Machine learning (ML) is a widely unexplored field in dendroclimatology, but it is a powerful tool that might improve the accuracy of climate reconstructions. In this paper, different ML algorithms are compared to climate reconstruction from tree-ring proxies. The algorithms considered are multiple linear regression (MLR), artificial neural networks (ANN), model trees (MT), bagging of model trees (BMT), and random forests of regression trees (RF). April-May mean temperature at a Quercus robur stand in Slovenia is predicted with mean vessel area (MVA, correlation coefficient with April-May mean temperature, r = 0.70, p ( 0.001) and earlywood width (EW, r = %0.28, p ( 0.05). Similarly, June-August mean temperature is predicted with stable carbon isotope (%13C, r = 0.72, p ( 0.001), stable oxygen isotope (%18O, r = 0.32, p ( 0.05) and tree-ring width (TRW, r = 0.11, p ) 0.05 (ns)) chronologies. The predictive performance of ML algorithms was estimated by 3-fold cross-validation repeated 100 times. In both spring and summer temperature models, BMT performed best respectively in 62% and 52% of the 100 repetitions. The second-best method was ANN. Although BMT gave the best validation results, the differences in the models% performances were minor. We therefore recommend always comparing different ML regression techniques and selecting the optimal one for applications in dendroclimatology.
COBISS.SI-ID: 5155750
An extended European tree-ring network was compiled from various sources of tree-ring data from Europe, northern Africa and western Asia. A total of 1860 tree-ring chronologies were used to compare correlation coefficients calculated with aggregated day-wise and month-wise mean temperature, sums of precipitation and standardised precipitation-evapotranspiration index (SPEI). For the daily approach, climate data were aggregated over periods ranging from 21 to 365 days. Absolute correlations calculated with day-wise aggregated climate data were on average higher by 0.060 (temperature data), 0.076 (precipitation data) and 0.075 (SPEI data). Bootstrapped correlations are computationally expensive and were therefore calculated on a 69.4% subset of the data. Bootstrapped correlations indicated statistically significant differences between the daily and monthly approach in approximately 1% of examples. A comparison of time windows used for calculations of correlations revealed slightly later onset and earlier ending day of the year for the daily approach, while the largest differences between the two approaches arise from window lengths: Correlations calculated with day-wise aggregated climate data were calculated using fewer days than the monthly approach. Differences in the onset and ending dates of periods for the daily and monthly approaches were greater for precipitation and SPEI data than for temperature data.
COBISS.SI-ID: 5453734