Estimating Tree Survival: A Study Based on the Estonian Forest Research Plots Network

Sims, Allan; Kiviste, Andres; Hordo, Maris; Laarmann, Diana; Gadow, Klaus von

doi:10.5735/085.046.0409

Cited by 30 publications

(22 citation statements)

References 18 publications

(1 reference statement)

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“…The next superior competition selection approach, CZR 0.4h , was simple in practice and in accordance with studies showing that, the optimal influence zone radius strongly depended on the tree's initial dimensions (D'Amato & Puettmann, 2004;Sims et al, 2009). Considering the third suitable competitor selection method, similar to several other studies, the opening angle of 50°-60° performed well (Biging & Dobbertin, 1995;Pretzsch, 2009;Oheimb et al, 2011) where bigger angles (80° and 100° in this study) mainly de-11 Analysis of Competition Between Individual Trees young stands, where the stands are usually dense, because these types of indices give more weight to trees that are closer to the subject tree.…”

Section: Discussionmentioning

confidence: 91%

“…In the early stage, pioneer birches grow quite fast and vigorously (Hynynen et al, 2011), and spatial CIs explain competition effects better in dense young stands, since they account for the short distances between the neighbouring trees, that are competing for resources. In older stands, due to mortality induced by different factors, including competition (Sims et al, 2009), the number of trees decline and nonspatial CIs are adequate for competition studies.…”

Section: Discussionmentioning

confidence: 99%

“…The radius of influence zone was defined as a fraction of the stand's average height for each plot; CZR 0.4h was set equal to 0.4 average height of plot (Sims et al, 2009). 2.…”

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confidence: 99%

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Analysis of Individual Tree Competition on Diameter Growth of Silver Birch in Estonia

2015

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Aim of study: The present study evaluates a set of competition indices including spatially explicit indices combined with different competitor selection approaches and non-spatially explicit competition indices. The aim was to quantify and describe the neighbouring effects on the tree diameter growth of silver birch trees.Area of study: Region throughout Estonia. Material and methods:Data from the Estonian Network of Forest Research Plots was used. After quantifying the selected indices, the best non-spatial indices and spatial indices (combined with neighbour selection methods) were separately devised into a growth model as a predictor variable to assess the ability of the diameter growth model before and after adding competition measures. To test the species-specific effect on the competition level, the superior indices were recalculated using Ellenberg's light indicators and incorporated into the diameter growth model.Main results: Statistical analyses showed that the diameter growth is a function of neighbourhood interactions and spatial indices were better growth predictors than non-spatial indices. In addition, the best selections of competitive neighbours were acquired based on the influence zone and the competition elimination angle concepts, and using Ellenberg's light values had no significant improvement in quantifying the competition effects.Research highlights: Although the best ranking spatial competition measures were superior to the best non-spatial indices, the differences were negligible.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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Analysis of Individual Tree Competition on Diameter Growth of Silver Birch in Estonia

2015

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“…Previous studies on this topic in Estonia have been done by Sims et al (2009) and Laarmann et al (2009). Sims et al (2009) showed that relative height has the greatest effect on single tree survival.…”

Section: Introductionmentioning

confidence: 99%

“…Sims et al (2009) showed that relative height has the greatest effect on single tree survival. Laarmann et al (2009) analysed the share of mortality causes (game, insects, etc.)…”

Section: Introductionmentioning

confidence: 99%

Assessment of tree mortality on the Estonian Network of Forest Research Plots/ Puude väljalangevuse hindamine metsa kasvukäigu püsiproovitükkidel

et al. 2014

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Abstract. Assessment of tree mortality provides deeper understanding of forest structure and functioning. This enables evaluation of stand sustainability and provides information on stand productivity, diversity and health condition. Tree mortality can be assessed by spatiotemporal patterns as well as by studying the processes and causes of mortality. Tree mortality is caused by specifi c disturbance agents or by the complex effect of various disturbances. The purpose of this study is to examine tree mortality in Estonian forests, determine the causes of tree death, and estimate how different management regimes affect tree mortality and its causes. This study is based on 360 sample plots measured in 2003-2005 and re-measured in 2008-2010. The sample plots were divided into recently managed (RM, 146 plots) and low intensity/unmanaged (LU, 214 plots) plots based on forest management regime. In total, 53,990 trees were measured, of which 20,020 were located on RM and 33,970 on LU plots. The tree mortality for 5-year period was 3.4% on RM plots and 8.0% on LU plots. The main cause of tree mortality in RM stands was insect damage, which attributes to 29.8% of tree mortality, whereas in LU stands the main cause was tree competition, which attributes to 45.4% of tree mortality. The analysis of tree mortality indicated that an increase in relative tree diameter in both stand types contributes to an increase in mortality due to insect, wind or fungal damage and diseases. Opposite results were received with respect to competition -the smaller the tree relative diameter, the more probable it is that a tree will die because of competition with neighbouring trees. The analysis of game damage and other causes of tree mortality showed that these were not dependent on the relative diameter of trees. The analysis of the overall probability of tree mortality revealed that relatively smaller trees have a higher probability of mortality than larger trees.

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How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model

Hülsmann

Bugmann

Cailleret

et al. 2018

Ecological Applications

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Dynamic Vegetation Models (DVMs) are designed to be suitable for simulating forest succession and species range dynamics under current and future conditions based on mathematical representations of the three key processes regeneration, growth, and mortality. However, mortality formulations in DVMs are typically coarse and often lack an empirical basis, which increases the uncertainty of projections of future forest dynamics and hinders their use for developing adaptation strategies to climate change. Thus, sound tree mortality models are highly needed. We developed parsimonious, species-specific mortality models for 18 European tree species using >90,000 records from inventories in Swiss and German strict forest reserves along a considerable environmental gradient. We comprehensively evaluated model performance and incorporated the new mortality functions in the dynamic forest model ForClim. Tree mortality was successfully predicted by tree size and growth. Only a few species required additional covariates in their final model to consider aspects of stand structure or climate. The relationships between mortality and its predictors reflect the indirect influences of resource availability and tree vitality, which are further shaped by species-specific attributes such as maximum longevity and shade tolerance. Considering that the behavior of the models was biologically meaningful, and that their performance was reasonably high and not impacted by changes in the sampling design, we suggest that the mortality algorithms developed here are suitable for implementation and evaluation in DVMs. In the DVM ForClim, the new mortality functions resulted in simulations of stand basal area and species composition that were generally close to historical observations. However, ForClim performance was poorer than when using the original, coarse mortality formulation. The difficulties of simulating stand structure and species composition, which were most evident for Fagus sylvatica L. and in long-term simulations, resulted from feedbacks between simulated growth and mortality as well as from extrapolation to very small and very large trees. Growth and mortality processes and their species-specific differences should thus be revisited jointly, with a particular focus on small and very large trees in relation to their shade tolerance.

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Estimating Tree Survival: A Study Based on the Estonian Forest Research Plots Network

Cited by 30 publications

References 18 publications

Analysis of Individual Tree Competition on Diameter Growth of Silver Birch in Estonia

Analysis of Individual Tree Competition on Diameter Growth of Silver Birch in Estonia

Assessment of tree mortality on the Estonian Network of Forest Research Plots/ Puude väljalangevuse hindamine metsa kasvukäigu püsiproovitükkidel

How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model

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