Can mixing Quercus robur and Quercus petraea with Pinus sylvestris compensate for productivity losses due to climate change?

The climate change scenarios RCP 4.5 and RCP 8.5, with a representative concentration pathway for stabilization of radiative forcing of 4.5 W m-2 and 8.5 W m-2 by 2100, respectively, predict an increase in temperature of 1-4.5° Celsius for Europe and a simultaneous shift in precipitation patterns leading to increased drought frequency and severity. The negative consequences of such changes on tree growth on dry sites or at the dry end of a tree species distribution are well-known, but rarely quantified across large gradients. In this study, the growth of Quercus robur and Quercus petraea (Q. spp.) and Pinus sylvestris in pure and mixed stands was predicted for a historical scenario and the two climate change scenarios RCP 4.5 and RCP 8.5 using the individual tree growth model PrognAus. Predictions were made along an ecological gradient ranging from current mean annual temperatures of 5.5-11.4 °C and with mean annual precipitation sums of 586-929 mm. Initial data for the simulation consisted of 23 triplets established in pure and mixed stands of Q. spp. and P. sylvestris. After doing the simulations until 2100, we fitted a linear mixed model using the predicted volume in the year 2100 as response variable to describe the general trends in the simulation results. Productivity decreased for both Q. spp. and P. sylvestris with increasing temperature, and more so, for the warmer sites of the gradient. P. sylvestris is the more productive tree species in the current climate scenario, but the competitive advantage shifts to Q. spp., which is capable to endure very high negative water potentials, for the more severe climate change scenario. The Q. spp.-P. sylvestris mixture presents an intermediate resilience to increased scenario severity. Enrichment of P. sylvestris stands by creating mixtures with Q. spp., but not the opposite, might be a right silvicultural adaptive strategy, especially at lower latitudes. Tree species mixing can only partly compensate productivity losses due to climate change. This may, however, be possible in combination with other silvicultural adaptation strategies, such as thinning and uneven-aged management.

LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems.

The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation.

Climatic water availability modifies tree functional diversity effects on soil organic carbon storage in European forests.

Forest stand and environmental factors influence soil organic carbon (SOC) storage, but little is known about their relative impacts in different soil layers. Moreover, how environmental factors modulate the impact of stand factors, particularly species mixing, on SOC storage, is largely unexplored. In this study, conducted in 21 forest triplets (two monocultures of different species and their mixture on the same site) distributed in Europe, we tested the hypothesis that stand factors (functional identity and diversity) have stronger effects on topsoil (FF + 0-10 cm) C storage than environmental factors (climatic water availability, clay + silt content, oxalate-extractable Al-Alox) but that the opposite occurs in the subsoil (10-40 cm). We also tested the hypothesis that functional diversity improves SOC storage under high climatic water availability, clay + silt contents, and Alox. We characterized functional identity as the basal area proportion of broadleaved species (beech and/or oak), and functional diversity as the product of broadleaved and conifer (pine) proportions. The results show that functional identity was the main driver of topsoil C storage, while climatic water availability had the largest control on subsoil C storage. Functional diversity decreased topsoil C storage under increasing climatic water availability, but the opposite was observed in the subsoil. Functional diversity effects on topsoil C increased with increasing clay + silt content, while its effects on subsoil C were negative at increasing Alox content. This suggests that functional diversity effect on SOC storage changes along gradients in environmental factors and the direction of effects depends on soil depth.

The distribution of carbon stocks between tree woody biomass and soil differs between Scots pine and broadleaved species (beech, oak) in European forests.

While the impacts of forest management options on carbon (C) storage are well documented, the way they affect C distribution among ecosystem components remains poorly investigated. Yet, partitioning of total forest C stocks, particularly between aboveground woody biomass and the soil, greatly impacts the stability of C stocks against disturbances in forest ecosystems. This study assessed the impact of species composition and stand density on C storage in aboveground woody biomass (stem + branches), coarse roots, and soil, and their partitioning in pure and mixed forests in Europe. We used 21 triplets (5 beech-oak, 8 pine-beech, 8 pine-oak mixed stands, and their respective monocultures at the same sites) in seven European countries. We computed biomass C stocks from total stand inventories and species-specific allometric equations, and soil organic C data down to 40 cm depth. On average, the broadleaved species stored more C in aboveground woody biomass than soil, while C storage in pine was equally distributed between both components. Stand density had a strong effect on C storage in tree woody biomass but not in the soil. After controlling for stand basal area, the mixed stands had, on average, similar total C stocks (in aboveground woody biomass + coarse roots + soil) to the most performing monocultures. Although species composition and stand density affect total C stocks and its partitioning between aboveground woody biomass and soil, a large part of variability in soil C storage was unrelated to stand characteristics. Supplementary Information: The online version contains supplementary material available at 10.1007/s10342-022-01453-9.