scholarly journals Application of a Hybrid Forest Growth Model to Evaluate Climate Change Impacts on Productivity, Nutrient Cycling and Mortality in a Montane Forest Ecosystem

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0135034 ◽  
Author(s):  
Brad Seely ◽  
Clive Welham ◽  
Kim Scoullar
Keyword(s):  
Climate Change ◽  
Nutrient Cycling ◽  
Growth Model ◽  
Forest Ecosystem ◽  
Climate Change Impacts ◽  
Montane Forest ◽  
Forest Growth ◽  
Forest Growth Model

Dynamic coupling of allometric ratios to a process-based forest growth model for estimating the impacts of stand density changes

2020 ◽  
Vol 93 (5) ◽  
pp. 601-615
Author(s):  
Rüdiger Grote ◽  
David Kraus ◽  
Wendelin Weis ◽  
Rasmus Ettl ◽  
Axel Göttlein
Keyword(s):  
Climate Change ◽  
Early Development ◽  
Stand Density ◽  
Climate Change Impacts ◽  
Forest Growth ◽  
Canopy Layer ◽  
Mature Trees ◽  
Development Stages ◽  
Allometric Relations ◽  
Forest Growth Model

Abstract Process-based models are increasingly applied for simulating long-term forest developments in order to capture climate change impacts and to investigate suitable management responses. Regarding dimensional development, however, allometric relations such as the height/diameter ratio, branch and coarse root fractions or the dependency of crown dimension on stem diameter often do not account for environmental influences. While this may be appropriate for even-aged, monospecific forests, serious biases can be expected if stand density or forest structure changes rapidly. Such events occur in particular when forests experience disturbances such as intensive thinning or during early development stages of planted or naturally regenerated trees. We therefore suggest a calculation of allometric relationships that depends primarily on neighbourhood competition. Respective equations have been implemented into a physiology-based ecosystem model that considers asymmetric competition by explicit simulation of resource acquisition and depletion per canopy layer. The new implementation has been tested at two sites in Germany where beech (Fagus sylvatica) saplings have either been planted below a shelterwood of old spruces (Picea abies) or grown under clear-cut conditions. We show that the modified model is able to realistically describe tree development in response to stand density changes and is able to represent regeneration growth beneath a gradually decreasing overstorey of mature trees. In particular, the model could represent the faster crown size development in saplings until full ground coverage is established and a faster height growth afterwards. The effect enhances leaf area and thus assimilation per tree and increases carbon availability for stem growth at early development stages. Finally, the necessity to consider dynamic allometric relations with respect to climate change impacts is discussed, and further improvements are suggested.

scholarly journals A Forest Growth Model for the Natural Broadleaved Forests in Northeastern Korea

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 288 ◽  
Author(s):  
Jiseon Choi ◽  
Hyunjin An
Keyword(s):  
Growth Model ◽  
Forest Growth ◽  
Forest Growth Model

scholarly journals Identifying decision-relevant uncertainties for dynamic adaptive forest management under climate change

2020 ◽  
Vol 163 (2) ◽  
pp. 891-911
Author(s):  
Naomi Radke ◽  
Klaus Keller ◽  
Rasoul Yousefpour ◽  
Marc Hanewinkel
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Volume Growth ◽  
European Beech ◽  
Forest Growth ◽  
West Germany ◽  
Forest Growth Model ◽  
Study Results ◽  
Planning Cycle

AbstractThe decision on how to manage a forest under climate change is subject to deep and dynamic uncertainties. The classic approach to analyze this decision adopts a predefined strategy, tests its robustness to uncertainties, but neglects their dynamic nature (i.e., that decision-makers can learn and adjust the strategy). Accounting for learning through dynamic adaptive strategies (DAS) can drastically improve expected performance and robustness to deep uncertainties. The benefits of considering DAS hinge on identifying critical uncertainties and translating them to detectable signposts to signal when to change course. This study advances the DAS approach to forest management as a novel application domain by showcasing methods to identify potential signposts for adaptation on a case study of a classic European beech management strategy in South-West Germany. We analyze the strategy’s robustness to uncertainties about model forcings and parameters. We then identify uncertainties that critically impact its economic and ecological performance by confronting a forest growth model with a large sample of time-varying scenarios. The case study results illustrate the potential of designing DAS for forest management and provide insights on key uncertainties and potential signposts. Specifically, economic uncertainties are the main driver of the strategy’s robustness and impact the strategy’s performance more critically than climate uncertainty. Besides economic metrics, the forest stand’s past volume growth is a promising signpost metric. It mirrors the effect of both climatic and model parameter uncertainty. The regular forest inventory and planning cycle provides an ideal basis for adapting a strategy in response to these signposts.

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