Modelling above and below ground carbon dynamics in a mixed beech and spruce stand influenced by climate

Rötzer, Thomas; Seifert, Thomas; Pretzsch, Hans

doi:10.1007/s10342-008-0213-y

Cited by 55 publications

(33 citation statements)

References 31 publications

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“…From studies in other ecosystems, we know how severely spatial pattern (Pretzsch 1995;Pukkala 1989) and annual climatic conditions (Rötzer et al 2009;Wichmann 2001) can influence stand growth. Since previous mixed stand research has rarely considered these co-variables as far as we could ascertain, the focus of this study was to investigate the relevance of these co-variables.…”

Section: Introductionmentioning

confidence: 99%

Climate effects on productivity and resource-use efficiency of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) in stands with different spatial mixing patterns

Pretzsch

Dieler

Seifert

et al. 2012

Trees

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Species mixing is widely held to stabilize productivity, increase resilience and contribute to risk minimization in forest stands in need of special as a result of longevity. However, research on the effects of mixing on productivity and resource consumption so far yielded fairly incoherent results rather than general findings. We focused on the effects of the spatial mixing pattern and the annual climate conditions on the mixing effect, which to date have seldom been considered as modifiers of mixing effects. Nine years of intensive survey of four pure stands of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) and two mixed plots with different mixing pattern showed: (1) mixing hardly changed annual net primary productivity at stand level when Norway spruce and European beech are cultivated groupwise but increased by 37 % on account of a higher efficiency of water and light use in individual tree-wise mixture.(2) Favourable climatic conditions increased the superiority of mixed versus pure stands productivity, while, in particular, water stress cancelled the benefit of mixing considerably. (3) An interaction between the spatial pattern and variable climatic conditions was revealed. Both improved light and water use were found in favourable years in close inter-specific intermingling. However, in unfavourable years the spatial pattern played a less pronounced role in terms of productivity.

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Section: Introductionmentioning

confidence: 99%

Climate effects on productivity and resource-use efficiency of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) in stands with different spatial mixing patterns

Pretzsch

Dieler

Seifert

et al. 2012

Trees

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“…The belowground component of the forest is rarely addressed in our models (but see Rötzer et al (2009)) although it represents a large fraction of ecosystem respiration. There is a lack of reliable data on the structure and function of the belowground component.…”

Section: Gaps Of Knowledgementioning

confidence: 99%

Models for supporting forest management in a changing environment

Fontes¹,

Bontemps²,

Bugmann³

et al. 2010

For. syst.

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163

131

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Forests are experiencing an environment that changes much faster than during the past several hundred years. In addition, the abiotic factors determining forest dynamics vary depending on its location. Forest modeling thus faces the new challenge of supporting forest management in the context of environmental change. This review focuses on three types of models that are used in forest management: empirical (EM), process-based (PBM) and hybrid models. Recent approaches may lead to the applicability of empirical models under changing environmental conditions, such as (i) the dynamic state-space approach, or (ii) the development of productivity-environment relationships. Twenty-five process-based models in use in Europe were analyzed in terms of their structure, inputs and outputs having in mind a forest management perspective. Two paths for hybrid modeling were distinguished: (i) coupling of EMs and PBMs by developing signal-transfer environment-productivity functions; (ii) hybrid models with causal structure including both empirical and mechanistic components. Several gaps of knowledge were identified for the three types of models reviewed.The strengths and weaknesses of the three model types differ and all are likely to remain in use. There is a trade-off between how little data the models need for calibration and simulation purposes, and the variety of input-output relationships that they can quantify. PBMs are the most versatile, with a wide range of environmental conditions and output variables they can account for. However, PBMs require more data making them less applicable whenever data for calibration are scarce. EMs, on the other hand, are easier to run as they require much less prior information, but the aggregated representation of environmental effects makes them less reliable in the context of environmental changes. The different disadvantages of PBMs and EMs suggest that hybrid models may be a good compromise, but a more extensive testing of these models in practice is required.

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“…The latter approach is statistic by nature and has to be distinguished from models that are based on eco-physiological processes at the process level (e.g. Rötzer et al 2008;Gayler et al 2006;Zhang et al 2006). Growing space was computed by classical position dependent competition indices algorithms.…”

Section: Assessment Of Below Ground Resources and Indices Indicating mentioning

confidence: 99%

Evaluation of different approaches for modelling individual tree seedling height growth

Wagner¹,

Madsen

Ammer

2009

Trees

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We compared different approaches for modelling height growth of individual beech seedlings in a controlled factorial experiment as well as in field data from naturally regenerated beech seedlings under the canopy of overstorey mature beech trees. Several competition indices, a model of overstorey fine root density, relative photosynthetically active radiation (PAR) values, and soil water values were used in these approaches. In the factorial experiment relative PAR and soil water content were measured and used for the prediction of seedlings height growth. In the field experiment this was done by using relative PAR and estimated fine root biomass as a surrogate for below ground resource availability. The latter approach was compared with a model where we used various competition indices representing the impact of overstorey trees on beech seedlings. Our results suggested that (1) models which combine resource based growth functions are suitable for the prediction of individual height growth of beech seedlings. Resource based models offer the opportunity to investigate on the independent multiplicative effect of irradiance and water supply and their interactions on tree seedlings. It was (2) shown that a combined model could be used not only to predict individual height growth of beech seedlings in a controlled experiment but also in the field. The model parameters of a pure light response function for the controlled factorial experiment are comparable to those obtained in the field study. The results showed (3) that the precision of predicting beech seedlings height growth is comparable between the model types tested within this study. Approximately half of the observed variation in seedlings relative height growth rate could be explained. However, the simple competition index approach provides no information on the environmental factors constraining tree seedlings growth; whereas the multiplicative combined models can be used to get a better understanding of growth dynamics in the field.

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Modelling above and below ground carbon dynamics in a mixed beech and spruce stand influenced by climate

Cited by 55 publications

References 31 publications

Climate effects on productivity and resource-use efficiency of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) in stands with different spatial mixing patterns

Climate effects on productivity and resource-use efficiency of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) in stands with different spatial mixing patterns

Models for supporting forest management in a changing environment

Evaluation of different approaches for modelling individual tree seedling height growth

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