An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios

Abstract

Dynamic landscape simulation of the forest requires an initial regeneration stock specific to the characteristics of each simulated stand. Forest inventories, however, are sparse with regard to regeneration. Moreover, statistical regeneration models are rare. We introduce an inventory-based statistical model type that (1) quantifies regeneration biomass as a fundamental regeneration attribute and (2) uses the overstory's quadratic mean diameter (Dq) together with several other structure attributes and the Site Index as predictors. We form two such models from plots dominated by European beech (Fagus sylvatica L.), one from national forest inventory data and the other from spatially denser federal state forest inventory data. We evaluate the first one for capturing the predictors specific to the larger scale level and the latter one to infer the degree of landscape discretization above which the model bias becomes critical due to yet unquantified determinants of regeneration. The most relevant predictors were Dq, stand density, and maximum height (significance level p < 0.0001). If plot data sets for evaluation differed by the forest management unit in addition to the average diameter, the bias range among them increased from 0.1-fold of predicted biomass to 0.3-fold.

Exploring Nonlinear Intra-Annual Growth Dynamics in Fagus sylvatica L. Trees at the Italian ICP-Forests Level II Network

“…Generalized Additive Mixed Models (GAMM) have been implemented (i) to study the influence of the main climatic variables on beech growth trends, (ii) to evaluate whether ecological conditions affected local growth, and (iii) to assess if some plots could be acknowledged as potential sources of adaptation to climatic drivers (i.e., growth not significantly affected by climatic variables). This modelling technique has been recently used to investigate a bimodal growth pattern in Spain [17] and regeneration processes in national forest inventory plots in Germany [46]. GAMMs are an extension of generalized additive models (GAMs)allowing a flexible modelling of nonlinear patterns in the response variable that uses a sum of smoothing functions of the involved covariates and the random variable [47,48].Withthe smooth terms being extremely adaptable, the model does not need to be specified in terms of detailed parametric relationships.…”

Analyzing the effect of silvicultural management on the trade-off between stand structural heterogeneity and productivity over time

“…SILVA was developed, and evaluated, and has been successfully applied in forest practice since 1989 by the Chair of Forest Growth (Pretzsch 1992;Pretzsch et al 2006). SILVA enables the testing of the effect of different types of silvicultural practices on forest growth and other ecosystem functions and services (Poschenrieder et al 2018;Schwaiger et al 2018aSchwaiger et al , 2018b.…”

“…The final felling phase was aimed at trees with a height > 31 m, applying a light selective thinning and a target diameter felling with a removed standing volume of up to 500 m 3 ha −1 and a diameter between 5 and 200 cm for deciduous trees and a diameter between 20 and 200 cm for conifers. In this scenario, in addition to natural regeneration (Poschenrieder et al 2018), Norway spruce (4000 trees ha −1 ), and Douglas-fir (Pseudotsuga menziesii) (100 trees ha −1 ) were planted during the regeneration phase.…”

Douglas-fir climate sensitivity at two contrasting sites along the southern limit of the European planting range

“…The various empirical and experimental approaches to assess plant response to climate variability and climate change over large geographic areas (Bréda et al 2006;Chen et al 2010) include provenance trials or common gardens (Lipow et al 2003;Rehfeldt et al 2014), empirical analyses of growth and mortality in permanent forest inventory plots (Stephenson et al 2014;Poschenrieder et al 2018;Marchi 2019;Pecchi et al 2019), remote sensing and detection of net primary productivity (Smith et al 2008;Fassnacht et al 2014) and dendrochronological analysis of growth-climate correlations from tree-ring time series (Eilmann and Rigling 2012;Mazza et al 2018;Avanzi et al 2019). One of the most relevant and added values of the last type of approach is that long-term climate-growth responses can be analysed and should elucidate species requirements so that predictions by growth simulators can be adjusted, even for Douglasfir trees outside the native predicted range (Castaldi et al 2017), and that tree responses to long-term climate variations can be predicted.…”