Wim Aertsen

Radial growth change of temperate tree species in response to altered regional climate and air quality in the period 1901–2008

Both increasing and decreasing 20th century growth trends have been reported in forests throughout Europe, but only for few species and areas suitable modelling techniques have been used to distinguish individual tree growth (operating on a local scale) from growth change due to exogenous factors (operating on a broad geographical scale). This study relates for the first time observed growth changes, in terms of basal area increment (BAI) of dominant trees of pedunculate oak, common beech and Scots pine, in north-west European temperate lowland forests (Flanders) to climate, atmospheric CO2 and tropospheric O3 concentrations, N deposition, site quality and forest structure for more than a century (the period 1901–2008), applying mixed models. Growth change during the 20th century is observed for oak (increasing growth) and beech (increasing growth until the 1960s, growth decline afterwards), but not for pine. It was possible to relate growth change of oak and beech to climate time series and N deposition trends. Adding time series for CO2 and O3 concentration did not significantly improve model results. For oak and beech a switch from positive to negative growth response with increasing nitrogen deposition throughout time is observed. Growth increase for oak is mainly determined by the interaction between growing season temperature and soil water recharge. It is reasonable to assume that the observed growth trend for oak will continue for as long as early season water availability is not compromised. The decreasing trend in summer relative air humidity observed since the 1960s in the study area can be a main cause of recent beech BAI decrease. A further growth decline of beech can be expected, independent of site quality.

Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA)

Accurate estimation of site productivity is crucial for sustainable forest resource management. In recent years, a variety of modelling approaches have been developed and applied to predict site index from a wide range of environmental variables, with varying success. The selection, application and comparison of suitable modelling techniques remains therefore a meticulous task, subject to ongoing research and debate. In this study, the performance of five modelling techniques was compared for the prediction of forest site index in two contrasting ecoregions: the temperate lowland of Flanders, Belgium, and the Mediterranean mountains in SW Turkey. The modelling techniques include statistical (multiple linear regression - MLR, classification and regression trees - CART, generalized additive models - GAM), as well as machine-learning (artificial neural networks - ANN) and hybrid techniques (boosted regression trees - BRT). Although the selected predictor variables differed largely, with mainly topographic predictor variables in the mountain area versus soil and humus variables in the lowland area, the techniques performed comparatively similar in both ecoregions. Stochastic multicriteria acceptability analysis (SMAA) was found a well-suited multicriteria evaluation method to evaluate the performance of the modelling techniques. It has been applied on the individual species models of Flanders, as well as a species-independent evaluation, combining all developed models from the two contrasting ecoregions. We came to the conclusion that non-parametric models are better suited for predicting site index than traditional MLR. GAM and BRT are the preferred alternatives for a wide range of weight preferences. CART is preferred when very high weight is given to user-friendliness, whereas ANN is recommended when most weight is given to pure predictive performance.

Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees

AimsThe aim of this study is on the one hand to identify the most determining variables predicting the site productivity of pedunculate oak, common beech and Scots pine in temperate lowland forests of Flanders; and on the other hand to test whether the accuracy of site productivity models based exclusively on soil or forest floor predictor variables is similar to the accuracy achieved by full ecosystem models, combining all soil, vegetation, humus and litterfall composition related variables.MethodsBoosted Regression Trees (BRT) were used to model in a climatically homogeneous region the relationship between environmental variables and site productivity. A distinction was made between soil (soil physical and chemical), forest floor (vegetation and humus) and ecosystem (soil, forest floor and litterfall composition jointly) predictors.ResultsOur results have illustrated the strength of BRT to model the non-linear behaviour of ecological processes. The ecosystem models, based on all collected variables, explained most of the variability and were more accurate than those limited to either soil or forest floor variables. Nevertheless, both the soil and forest floor models can serve as good predictive models for many forest management practices.ConclusionsSoil granulometric fractions and litterfall nitrogen concentrations were the most effective predictors of forest site productivity in Flanders. Although many studies revealed a fertilising effect of increased nitrogen deposition, nitrogen saturation seemed to reduce species’ productivity in this region.

Forest structure and soil fertility determine internal stem morphology of Pedunculate oak: a modelling approach using boosted regression trees

This study aims at the explanation of internal stem morphology of vital (co)dominant Pedunculate oak (Quercus robur L.) trees in homogeneous even-aged high-forests by the factors tree age, forest structure and site quality, using boosted regression trees as a powerful modelling technique. The study area covers the region of Flanders (Northern Belgium), which is characterised by the absence of strong topographic and climatic gradients. For 76 adult sample trees covering the entire productivity range of Pedunculate oak, morphological characteristics were derived from measurements of ring width or heartwood area on wood cores. Forest structure, soil physicochemical properties, humus quality, vegetation indices and litter nutrient contents were quantified at each sample location. Model predictive performance and generality are good. Tree age effects correspond to expected trends in age-related radial growth and heartwood portion. Even if management of oak trees in even-aged high-forests is rather similar over Flanders, forest structure is the most important factor determining ring width, followed by soil fertility. Heartwood portion is determined by soil fertility and crown structure. Effects of topsoil and humus physicochemical characteristics, litter nutrient contents and water supply mainly confirm autecological knowledge on oak. However, variables related to soil water availability are only occasionally relevant, and always of lower importance than soil fertility. The low importance of water availability in the models contradicts results from other studies, and the potential effect of confounding is discussed. The observed growth reduction at low litter N/P ratios might be indirectly linked to early litterfall.