As a widespread phenomenon affecting terrestrial ecosystems worldwide, the extent and spatio-temporal scales at which the increasing number of reported events of climatechange-induced tree mortality could affect the ecology and carbon (C) sink capacity of terrestrial soils, remains unknown. We here study how regional-scale drought-induced tree mortality events registered after a very dry 2012 year in the Carpathians mountain range (Romania), which affected three of the most widely distributed conifer species: Scots pine, Black pine, and Silver fir, resulted in hot-spots of biogenic soil CO 2 emissions (soil respiration; R s ). Four to five years after the main mortality event, R s -related soil CO 2 emissions under dead trees were, on average, 21% higher than CO 2 emissions under living trees (ranging from 18 to 35%). Total (R s ) and heterotrophic (R H )-related soil CO 2 emissions were strongly determined by the soil environmental alterations following tree mortality (e.g. changes in quantity and quality of soil organic matter, microclimate, pH or fine root demography). Moreover, the massive mortality event of 2012 ultimately resulted in a stronger dominant role of successional vegetation (broadleaf seedlings, shrubland and grasses) in controlling those environmental factors that either directly or indirectly affected biotic soil fluxes (R s and R H ). We, therefore, show that apart from the well-known direct effects of climate change over soil CO 2 emissions, cascading effects triggered by climatechange-induced tree mortality could also exert a strong indirect impact over soil CO 2 emissions, altering the magnitude and the environmental controls of R s and hence determining ecosystem C budget and their response to climate.

Uncertainties arising from the so‐far poorly explained spatial variability of soil respiration (Rs) remain large. This is partly due to the limited understanding about how spatially variable Rs actually is, but also on how environmental controls determine Rs's spatial variability and how these controls vary in time (e.g., seasonally). Our study was designed to look more deeply into the complexity of Rs's spatial variability in a European beech even‐aged stand, covering both phenologically and climatically contrasting periods (spring, summer, autumn and winter). Although we studied a relatively homogeneous stand, we found a large spatial variability of Rs (coefficients of variation > 30%) characterized by strong seasonality. This large spatial variability of Rs suggests that even in relatively homogeneous stands there is a large potential source of error when estimating Rs. This was also reflected by the sampling effort needed to obtain seasonally robust estimates of Rs, which may actually require a number of samples above that used in Rs studies. We further postulate that the effect of seasonality on the spatial variability and environmental controls of Rs was determined by the seasonal shifts of its microclimatic controls: during winter, low temperatures constrain plant and soil metabolic activities and hence reduce Rs variability (temperature‐controlled processes), whereas during summer, water demand by vegetation and changes in water availability due to the microtopography of the terrain (i.e., slope) increase Rs variability (water‐controlled processes). This study provides novel information on the spatiotemporal variability of Rs and looks more deeply into the seasonality of its environmental controls and the architecture of their causal‐effect relationships controlling Rs's spatial variability. Our study further shows that improving current estimates of Rs at local and regional levels might be necessary in order to reduce uncertainties and improve CO2 estimates at larger spatial scales. Highlights The spatial variability of soil respiration (Rs) and its environmental controls vary seasonally. Seasonal shifts from temperature‐ to water‐controlled processes determine Rs's spatial variability. Besides microclimate, slope and grass cover explain the spatiotemporal variability of Rs. An intense sampling effort is needed to obtain robust Rs estimates even in homogeneous forests.

Understanding soil moisture and its relationship with different climatic and soil characteristics is essential for better analysing the interactions between forest and soil water dynamics, allowing us to more precisely predict climatic changes. The present paper investigates the temporal variability of soil moisture in three different forest ecosystems (LTER -long term ecological research site) with the same soil type (Eutric Cambisol). Soil moisture was measured daily from 2011 to 2016 by using three sensors at three different depths (20, 40, 70 cm). We identified the interactions between soil properties, vegetation type, local climatic conditions and soil moisture. In order to establish the temporal variability of the soil moisture content, we have applied two procedures, namely the Fourier series and the neural network fitting. A high variability in time and depth for soil volumetric water content was identified. The highest soil moisture levels were recorded at higher depths (70 cm) for almost all surfaces, with the exception of the Fundata surface because of the occurrence of limestone. In the mountainous areas, with higher precipitation (Fundata and Predeal sites), volumetric soil water content was mainly influenced by soil physical characteristics. Soil moisture levels below the drought level were only recorded for the Stalpeni site from September to October 2012. There was a delay between the precipitation event and soil humidification of 0.4-0.8 time units (days). We also found a significant correlation between soil moisture and soil texture and a weak correlation with vegetation type. Temperature influenced soil moisture levels at almost all depths, while precipitation only had an impact when there was a delay of 1 or 2 days. Our results can serve as a scientific base in the monitoring and analysing of soil moisture against the background of a changing climate.

In this paper, site-specific allometric biomass models were developed for European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) to estimate the aboveground biomass in Șinca virgin forest, Romania. Several approaches to minimize the demand for site-specific observations in allometric biomass model development were also investigated. Developing site-specific allometric biomass models requires new measurements of biomass for a sample of trees from that specific site. Yet, measuring biomass is laborious, time consuming, and requires extensive logistics, especially for very large trees. The allometric biomass models were developed for a wide range of diameters at breast height, D (6–86 cm for European beech and 6–93 cm for silver fir) using a logarithmic transformation approach. Two alternative approaches were applied, i.e., random intercept model (RIM) and a Bayesian model with strong informative priors, to enhance the information of the site-specific sample (of biomass observations) by supplementing with a generic biomass sample. The appropriateness of each model was evaluated based on the aboveground biomass prediction of a 1 ha sample plot in Șinca forest. The results showed that models based on both D and tree height (H) to predict tree aboveground biomass (AGB) were more accurate predictors of AGB and produced plot-level estimates with better precision, than models based on D only. Furthermore, both RIM and Bayesian approach performed similarly well when a small local sample (of seven smallest trees) was used to calibrate the allometric model. Therefore, the generic biomass observations may effectively be combined with a small local sample (of just a few small trees) to calibrate an allometric model to a certain site and to minimize the demand for site-specific biomass measurements. However, special attention should be given to the H-D ratio, since it can affect the allometry and the performance of the reduced local sample approach.

Because of their role in carbon and nutrient exchange, litterfall and leaf area have been increasingly studied in the last few decades. However, most existing information comes from managed forests, while comparable data for virgin forests is scarce. To address this scarcity, we investigated a mixed beechsilver fir virgin forest located in the Southern Carpathian Mountains, using 78 litter traps to measure the annual litterfall production, litter composition and leaf area index (LAI). The LAI was calculated in two ways: directly, by using litter traps, and indirectly, based on hemispherical photographs. Furthermore, we investigated the influence of different stand and environmental characteristics on litter production, total foliar mass and LAIs. Annual litter productivity ranged from 1.8 to 8.3 t ha −1 with a mean of 3.5 t ha −1 . Litter was composed mainly of beech leaves (66%) along with a lower percentage of silver fir needles (16%). The total foliar dry mass (sum of beech leaves and silver fir needles) increased significantly with the proportion of beeches and decreased with the median stand age. The LAI determined by using litter traps had a mean value of 5.06 m 2 m −2 , ranging from 3.52 to 8.22, and was characterised by a higher variability than the LAI estimated indirectly using the hemispherical approach (which had a mean value of 3.65 and a range of 2.30-5.28). The two indices did not correlate with each other. We found no significant relation between the LAIs and any stand or environmental variables. We conclude that in the more complex forests, such as the virgin beech -silver fir mixed forest we studied, annual foliar dry mass is more closely related to stand characteristics than is LAI. We also note significant limitations of both LAI estimation methods, which indicate that a more elaborate approach to estimating LAI is needed.