How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents.

We compiled a global database for leaf, stem and root biomass representing c. 11 000 records for c. 1200 herbaceous and woody species grown under either controlled or field conditions. We used this data set to analyse allometric relationships and fractional biomass distribution to leaves, stems and roots. We tested whether allometric scaling exponents are generally constant across plant sizes as predicted by metabolic scaling theory, or whether instead they change dynamically with plant size. We also quantified interspecific variation in biomass distribution among plant families and functional groups. Across all species combined, leaf vs stem and leaf vs root scaling exponents decreased from c. 1.00 for small plants to c. 0.60 for the largest trees considered. Evergreens had substantially higher leaf mass fractions (LMFs) than deciduous species, whereas graminoids maintained higher root mass fractions (RMFs) than eudicotyledonous herbs. These patterns do not support the hypothesis of fixed allometric exponents. Rather, continuous shifts in allometric exponents with plant size during ontogeny and evolution are the norm. Across seed plants, variation in biomass distribution among species is related more to function than phylogeny. We propose that the higher LMF of evergreens at least partly compensates for their relatively low leaf area : leaf mass ratio.

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.

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.

Estimating forest floor carbon stocks in woodland formations in Spain.

The forest floor C stock needs to be accurately estimated in order to quantify its contribution to nutrient cycling and other ecological processes as well as for reporting purposes under international agreements. Hence, a modelling approach was used which involved testing three different types of models (GLM, GAM and random forest) to determine which one provided the best estimates of forest floor C stocks. The dataset employed contained over 1650 observations from different available sources embracing different climatic, topographic and biotic variables to be tested in the model. The approach that provided the best estimation of forest floor C stock was the random forest method, with forest type, latitude, altitude, canopy cover, mean summer temperature, annual accumulated temperature, summer precipitation, water deficit and the normalized difference vegetation index (NDVI) as covariates. To obtain a robust forecast, several iterations of the model were performed to estimate forest floor C stocks from the mean of the predictions. The model estimated a forest floor C stock of 0.148 ± 0.081 Pg, equivalent to a biomass of 0.381 ± 0.214 Pg, for a wooded area of almost 184,000 km2 in peninsular Spain and the Balearic Islands. The predictions were also presented in the form of a map showing the spatial distribution of the forest floor C stock. The results revealed a mean forest floor C stock of 8 Mg C ha-1 for Spanish forests and identified differences between coniferous (10.1 Mg C ha-1) and hardwood forests (6.3 Mg C ha-1).