Mixed forests generally outperform monospecific forests in terms of productivity, stability, and resilience and are becoming increasingly important for sustainable forest management. However, accurate estimates of tree biomass allocation, as well as aboveground and component biomass in mixed forests, remain scarce. Our study addressed three different objectives to identify differences in biomass between mixed and monocultures and develop biomass models: (1) identification of biomass growth patterns in mixed and monoculture stands using analysis of covariance (ANCOVA), (2) investigation of the best fitting approach to modeling aboveground biomass using logarithmic regression and nonlinear mixed-effects models, and (3) fitting compartment biomass proportion models by Dirichlet regression, considering the additivity property.
View Article and Find Full Text PDFForest 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-Al) but that the opposite occurs in the subsoil (10-40 cm).
View Article and Find Full Text PDFProcess-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.
View Article and Find Full Text PDFUnlabelled: 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.
View Article and Find Full Text PDFSci Total Environ
September 2021
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.
View Article and Find Full Text PDFWe 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.
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