Species-specific, pan-European diameter increment models based on data of 2.3 million trees

Schelhaas, Mart-Jan; Hengeveld, G.M.; Heidema, Nanny; Thürig, Esther; Rohner, Brigitte; Vacchiano, Giorgio; Vayreda, Jordi; Redmond, John; Socha, Jarosław; Fridman, Jonas; Tomter, Stein Michael; Polley, H. Wayne; Barreiro, Susana; Nabuurs, Gert‐Jan

doi:10.1186/s40663-018-0133-3

Cited by 33 publications

(62 citation statements)

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“…The former model was developed with the expression of diameter increment as a function of DBH as a main predictor and other variables (Table 2) as covariate predictors, while the latter model includes stand age as an additional covariate predictor. Of all variables measured in the sample plots, diameter is probably the most harmonized one, available for the largest number of trees, available as repeated measurements on the same tree, and directly measured without further interpretation [44]. Tree diameters are commonly correlated with other tree-and stand-level variables that are relatively more difficult to measure, e.g., stem volume or tree biomass.…”

Section: Tree Diameter Growth: a Theoretical Context And Modelling Apmentioning

confidence: 99%

“…Diameter growth rate of individual trees can be expressed as a diameter increment or basal area increment per year. The basal area increment models are often preferred over the diameter increment models, but Vanclay [3] and Schelhaas et al [44] argue that both model types are essentially the same, since one can be derived from the other.…”

Section: Tree Diameter Growth: a Theoretical Context And Modelling Apmentioning

confidence: 99%

“…The growth model possessing these two properties can be derived from the integral form of any mathematical functions by applying the derivative rules [46][47][48]. Generally, diameter of a tree develops according to asymmetric sigmoidal function through time, with relatively slow in the early age, but rapidly increasing growth at establishment, almost constant growth during the matured-phase followed by a slow decline of the growth during the senescence [44,49]. Since creating new tree rings is essential for water transport, and diameter increment will theoretically never reach zero, even though rings can be very small at old age [44].…”

Section: Tree Diameter Growth: a Theoretical Context And Modelling Apmentioning

confidence: 99%

See 2 more Smart Citations

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Sharma

Štefančík

Vacek

et al. 2019

Forests

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Individual tree growth and yield models precisely describe tree growth irrespective of stand complexity and are capable of simulating various silvicultural alternatives in the stands with diverse structure, species composition, and management history. We developed both age dependent and age independent diameter increment models using long-term research sample plot data collected from both monospecific and mixed stands of European beech (Fagus sylvatica L.) in the Slovak Republic. We used diameter at breast height (DBH) as a main predictor and other characteristics describing site quality (site index), stand development stage (dominant height and stand age), stand density or competition (ratio of individual tree DBH to quadratic mean diameter), species mixture (basal area proportion of a species of interest), and dummy variable describing stand management regimes as covariate predictors to develop the models. We evaluated eight versatile growth functions in the first stage using DBH as a single predictor and selected the most suitable one, i.e., Chapman-Richards function for further analysis through the inclusion of covariate predictors. We introduced the random components describing sample plot-level random effects and stochastic variations on the diameter increment, into the models through the mixed-effects modelling. The autocorrelation caused by hierarchical data-structure, which is assumed to be partially reduced by mixed-effects modelling, was removed through the inclusion of the parameter accounting for the autoregressive error-structures. The models described about two-third parts of a total variation in the diameter increment without significant trends in the residuals. Compared to the age independent mixed-effects model (conditional coefficient of determination, R c 2 = 0.6566; root mean square error, RMSE = 0.1196), the age dependent model described a significantly larger proportion of the variations in diameter increment ( R c 2 = 0.6796, RMSE = 0.1141). Diameter increment was significantly influenced differently by covariate predictors included into the models. Diameter increment decreased with the advancement of stand development stage (increased dominant height and stand age), increasing intraspecific competition (increased basal area proportion of European beech per sample plot), and diameter increment increased with increasing site quality (increased site index) and decreased competition (increased ratio of DBH to quadratic mean diameter). Our mixed-effects models, which can be easily localized with the random effects estimated from prior measurement of diameter increments of four randomly selected trees per sample plot, will provide high prediction accuracies. Our models may be used for simulating growth of European beech irrespective of its stand structural complexity, as these models have included various covariate variables describing both tree-and stand-level characteristics, thinning regimes, except the climate characteristics. Together with other forest models, our models will be used as inputs to the growth simulator to be developed in the future, which is important for decision-making in forestry.

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Section: Tree Diameter Growth: a Theoretical Context And Modelling Apmentioning

confidence: 99%

Section: Tree Diameter Growth: a Theoretical Context And Modelling Apmentioning

confidence: 99%

Section: Tree Diameter Growth: a Theoretical Context And Modelling Apmentioning

confidence: 99%

See 1 more Smart Citation

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Sharma

Štefančík

Vacek

et al. 2019

Forests

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“…While the original formulations of DVMs were sometimes developed completely independent of field data—simply due to the lack of relevant data (Botkin et al 1972 a )—increasingly available large datasets (Lichstein et al 2014, Cailleret et al 2017, Schelhaas et al 2018), new techniques for the acquisition (Hyyppa et al 2008, Choat et al 2018) and analysis of ecological data (Hartig et al 2012) provide a large potential for better ecosystem understanding.…”

Section: Introductionmentioning

confidence: 99%

Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity

2020

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“…Site is then usually modeled as being constant over time, explained by location specific variables like height above sea level, latitude or longitude. Just recently the importance of climate change on tree growth helped to intensify the modeling of retrospective climates, enabling more flexibility in modeling tree growth under changed climate [16,17].…”

Section: Introductionmentioning

confidence: 99%

Climate Sensitive Tree Growth Functions and the Role of Transformations

Zell

2018

Forests

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The aim of this study is to develop climate-sensitive single-tree growth models, to be used in stand based prediction systems of managed forest in Switzerland. Long-term observations from experimental forest management trials were used, together with retrospective climate information from 1904 up to 2012. A special focus is given to the role of transformation of modelling basal area increment, helping to normalize the random error distribution. A nonlinear model formulation was used to describe the basic relation between basal area increment and diameter at breast height. This formulation was widely expanded by groups of explanatory variables, describing competition, stand development, site, stand density, thinning, mixture, and climate. The models are species-specific and contain different explanatory variables per group, being able to explain a high amount of variance (on the original scale, up to 80% in the case of Quercus spec.). Different transformations of the nonlinear relation where tested and based on the mean squared error, the square root transformation performed best. Although the residuals were homoscedastic, they were still long-tailed and not normal distributed, making robust statistics the preferred method for statistical inference. Climate is included as a nonlinear and interacting effect of temperature, precipitation and moisture, with a biological meaningful interpretation per tree species, e.g., showing better growth for Abies alba in warm and wet climates and good growing conditions for Picea abies in colder and dryer climates, being less sensitive on temperature. Furthermore, a linear increase in growth was found to be present since the 1940s. Potentially this is an effect of the increased atmospheric CO 2 concentration or changed management in terms of reduced nutrient subtractions from forest ground, since industrialization lowered the demand of residue and slash uptake.

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Species-specific, pan-European diameter increment models based on data of 2.3 million trees

Cited by 33 publications

References 50 publications

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity

Climate Sensitive Tree Growth Functions and the Role of Transformations

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