Empirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: A multimodel approach.

TL;DR: European beech growth is projected to decrease by 12-21% across most of its distribution range by 2050 due to climate change, with limited potential for growth increases at the northern edge in southern Scandinavia due to water limitation.

TL;DR: This study models aboveground biomass accumulation in beech forests at the southern range margin, identifying stand age and climatic variables as major drivers, with temperature range and seasonality being most influential, and site-specificity playing a significant role in biomass variation.

TL;DR: Decaying beech wood significantly affects soil properties and microbial diversity in mountain ecosystems. Soils affected by deadwood have higher pH, C and N concentrations and different composition of microorganisms compared to control soils.

TL;DR: This study applies sensitivity analysis to the FullCAM model, examining its sensitivity to parameters and inputs, and context dependency in relation to age, biomass, and disturbance severity, with implications for model development to predict Australia's forest carbon stocks accurately.

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.

TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.

TL;DR: In this article, the authors make a case for the importance of reporting variance explained (R2) as a relevant summarizing statistic of mixed-effects models, which is rare, even though R2 is routinely reported for linear models and also generalized linear models (GLM).