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.

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.