Longitudinal heightdiameter curves for Norway spruce, Scots pine and silver birch in Norway based on shape constraint additive regression models
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Abstract
Background
Generalized heightdiameter curves based on a reparameterized version of the Korf function for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and silver birch (Betula pendula Roth) in Norway are presented. The Norwegian National Forest Inventory (NFI) is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputation in forest inventories as well as future tree height predictions in growth and yield scenario simulations.
Methods
Generalized additive mixed models (gamm) are employed to detect and quantify potentially nonlinear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand age, as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured heightdiameter pairs are available. Based on the model selection of nonconstraint models, shape constraint additive models (scam) were fit to incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity.
Results
Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. A twodimensional spatially structured effect of UTMcoordinates accounts for the potential effects of large scale spatially correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the heightdiameter curve and both effects are positive.
Conclusions
In this investigation it is assumed that model effects in additive modelling of heightdiameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scam may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.
Keywords
Heightdiameter curve Norway spruce Scots pine Silver birch Norwegian national forest inventory Shape constrained additive modelsAbbreviations
 AIC
Akaike information criterion
 Alt
Altitude
 BAL
Basal area larger
 dbh
Diameter at breast height
 east
UTMeasting coordinate
 gam
Generalized additive model
 gamm
Generalized additive mixed model
 glmm
Generalized linear mixed model
 h
Tree height
 NFI
National forest inventory
 north
UTMnorthing coordinate
 qmD
Quadratic mean diameter
 scam
Shape constrained additive models
 scam_m
2step shape constrained additive mixed models
 scamm
Shape constrained additive mixed models
 SD
Soil depth category
Background
The prediction of tree height is of central importance, not only for the calculation of growing stock from sample inventories, but also in the prognosis of middle and longterm forest development for forest planning and in the analysis of timber supply. The estimation of single tree volume and assortment is made using speciesspecific taper functions which use tree height and the diameter at breast height (dbh) (and occasionally other stem diameters) as input parameters. Single tree volumes are then the basis for expanding total timber volume from sample forest inventories for any given evaluation and planning unit. With respect to dbh, information from fully documented experimental plots, or at least from concentric sample plots, can frequently be relied upon for tree height imputation in sample forest inventories and for generating realistic start values to initialize forest growth simulators. Measurements of tree height, however, are considerably more costly to obtain, so that often little or no data is available. If one to several height measurements are available in a stand or sample plot, heightdbh curves based on simple mixed models are applied which allow for local calibration of a mean population relationship and thus local prediction (e.g. CorralRivas et al. 2014). These models that employ exclusively diameter as predictor are purely data imputation tools and do not, for example, describe explicitly the effects of site or competition on the heightdbh relationship. Generalized height curves describe these effects (Larsen and Hann 1987; López et al. 2003; Temesgen and Gadow 2004). However, frequently the information on measured heightdbh pairs is not used for local calibration of the height predictions. A combination of both model approaches leads to generalized heightdiameter (hd) models, which can be locally and temporarily calibrated. Hence, these models are developed using either linear or nonlinear mixed models in which site, stand, competition variables but also regional units or geographic coordinates are used as covariates (Lappi 1997; Eerikäinen 2003; Calama and Montero 2004; Mehtätalo 2004; Nanos et al. 2004; Hökkä, 1997; Schmidt et al. 2011). From a more general point of view mixed models also provide a solution to the problem of correlated errors that results from grouped data structures and they quantify the variability between groups via random effects (Pinheiro and Bates 2000). This is highly relevant in hd modeling since in most forest growth and yield data bases several measurements origin from the same sample plot or trial and measurement occasion.
In forest growth simulations the actual projection of future tree heights is frequently based not on heightdbh curves but on height growth functions of dominant trees. These can be adapted for single trees using additional tree covariates like competition indices (Pretzsch 2009). However, if a longitudinal covariate, such as age or quadratic mean diameter, is used then valid future height projections can be obtained directly using the generalized hd model.
Hd models for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and silver birch (Betula pendula Roth) are presented in this paper, which allow an optimal height prediction for any given dbh in all of the example situations described below. This is true regardless of the number of available height measurements, as well as in those cases in which only measurements from an earlier inventory are available. Furthermore, an optimal combination of information from stand and site variables together with local height measurements is ensured. These requirements are fulfilled by using a generalized heightdiameter model which has been parameterized as a mixed model. Mixed models facilitate the local or temporal calibration of global models which have been determined using fixed covariate effects (Lappi 1997; Mehtätalo et al. 2015). As in our investigation only few causal site variables were available the covariates used are mainly proxies. In order to guarantee the highest possible accuracy of prediction in those cases for which no heightdbh pairs were available at all, complex linear predictors for the fixed effects are parameterized in generalized additive mixed models (gamm). Moreover for forecasts of future height development, it seems advantageous to describe the highest possible variance partition as a function of dynamic (i.e. timevarying) covariates through their fixed effects, because it can be assumed that the information from measured dbhheight pairs becomes less meaningful with increasing simulation period. The use of longitudinal variables, such as age or quadratic mean diameter (qmD) increases the possible applications of the models from purely data imputation to height projection in growth simulations. Finally implausible effects, resulting for certain data ranges of covariates in the gamm, are forced into plausible (as decided by experts) patterns by defining monotonicityrestrictions in shapeconstraint additive models (scam).

Height imputation, taking into account site and tree effects for single trees in the NFI and also for initializing growth simulators, when no representative height measurements are available. For the application, a measured or estimated dbh must be available.

Mediumterm future height predictions for the analysis of timber supply, forest development scenarios and silviculture scenario simulations, taking (fixed) site and tree effects into account.

Ensuring plausible height predictions for the whole data range of covariates by applying monotonicityconstraints where necessary for the fixed model effects.

Model calibration, i.e. local adaption of height predictions and projections using heightdiameter measurements.
Data
Summary statistics of the continuous variables used in the development of the longitudinal heightdiameter models. Statistics for quadratic mean diameter (species specific) and stand age were calculated for sample plot/measurement occasion means, statistics for altitude for sample plot means respectively
Minimum  25%quantile  Median  Mean  75%quantile  Maximum  

Norway spruce  
dbh (cm)  5.0  11.8  18.1  19.4  25.5  75.5 
Tree height h (m)  1.5  8.6  13.0  13.3  17.4  35.2 
Basal area larger BAL (m^{2} per 250 m^{2})  0.0  0.086  0.215  0.281  0.410  2.255 
Quadratic mean diameter qmD (cm)  5.0  11.7  15.4  16.4  20.0  64.3 
Stand age (Y)  2  41  75  79  115  359 
Altitude Alt (m)  2.0  205  355.0  394.1  555.0  1065.0 
Scots pine  
dbh (cm)  5.0  15.0  22.2  22.9  29.8  83.1 
Tree height h (m)  1.6  8.5  12.5  12.5  16.0  35.0 
Basal area larger BAL (m^{2} per 250 m^{2})  0.0  0.049  0.149  0.197  0.293  1.468 
Quadratic mean diameter qmD (cm)  5.0  16.6  22.2  23.0  28.2  78.4 
Stand age (Y)  1  55  98  92  125  359 
Altitude Alt (m)  0.0  175.0  300.0  350.8  500.0  985.0 
Silver birch  
dbh (cm)  5.0  7.6  10.8  12.3  15.3  80.0 
Tree height h (m)  1.9  5.8  7.7  8.4  10.3  30.8 
Basal area larger BAL (m^{2} per 250 m^{2})  0.1  0.051  0.130  0.180  0.257  1.704 
Quadratic mean diameter qmD (cm)  5.0  8.5  10.5  11.8  13.6  64.5 
Stand age (Y)  1  48  75  78  105  359 
Altitude Alt (m)  0.0  180.0  340.0  407.6  615.0  1130.0 
Number of sample plots by soil depth category and tree species used in the development of the longitudinal heightdiameter models
Soil depth category (only mineral soil) SD  

I (0–25 cm)  II (25–50 cm)  III (50–100 cm)  IV (>100 cm)  Sum  
Norway spruce  622  1552  1203  2256  5633 
Scots pine  1363  1342  653  1469  4827 
Silver birch  1220  1987  1429  2516  7152 
Methods
Model development was a multistep process. In a first step, gamm were parameterized in order to identify covariates with significant effects and to test model effects for nonlinearity. Based on this unrestricted model selection the model effects were tested for plausibility. If necessary, conditions such as monotonicity were specified and scam parameterized. The scam were then validated against the unconstrained models by comparing the fitting statistics standard error, explained deviance and Akaike information criterion (AIC). Because of the computational intensity, a direct parameterization of shape constraint additive mixed models (scamm) was only possible for small datasets given the available computing facilities. To develop models which can be locally calibrated, generalized linear mixed models (glmm) are therefore parameterized in which, on the basis of the scam predictions, conditional expectation values are entered as “a priori” information. The specification as a mixed model enables the partitioning of the total variance on different levels, and thereby, the calibration of a mean population model using additional hd measurements (Mehtätalo et al. 2015). Moreover the mixed model approach accounts for the grouped structure of the used NFIdata and the related correlated errors. The integration of the qmD as a covariate gives the models their longitudinal character and, consequently, the shifting hd relationship with time can be described as a function of the developmental stage of a stand. Even if the shift in the hd relationship should not be confused with incremental height growth, the approach opens up the possibility of sitesensitive height projections in growth simulations.
The choice of the basic model, or rather, of the specific heightdiameter function, is crucial for the longitudinal hd model that is developed from it. Here, a special form of the Korffunction developed by Lappi (1997) is used, which is distinguished by the biological interpretability and comparatively low correlation of its parameters. These qualities are particularly advantageous when, as is the case here, the parameters, and thereby the realisation, of height curves are to be described as a function of site, stand, and single tree variables. Mehtätalo (2004, 2005) built on the work of Lappi (1997) and adapted and validated the model for spruce, pine and birch in Finland, a country with partly similar growth conditions. Additionally it is very important that the model is linear which enables the estimation of site, stand and tree effects and their validation for nonlinearity in a onestep procedure using gamm. Finally in our application the modified Korffunction showed an adequate flexibility which is illustrated in the results chapter. The basic version of the Korffunction used here (Eq. 1) is an alternative to the more frequently used variant, in which breast height (1.3 m) is subtracted from the tree height. In order to prevent the expected height values from taking on the value “zero” when the dbh is very small, Lappi added a small constant λ to the dbh, where dbh + λ can be interpreted as the diameter at ground height. Lappi (1997) then reparameterized the function (Eq. 2) because the expected values and the standard error of the “linear” parameters A and B are strongly correlated and the trend of B with age is difficult to interpret. This reparameterization, on the basis of expected values of the logarithmic tree height for trees with dbh of 30 and 10 cm (Eqs. 2 and 2.1), yields biologically meaningful parameters, as well as a clear reduction in correlation. Parameter A can then be interpreted as the expected value of the natural logarithm [ln(.)] of the height of a tree with dbh = 30 cm, while parameter B is the difference between the expected values of ln(tree height) between trees with dbh = 30 cm and dbh = 10 cm of the respective tree species. The parameters A, B, C and λ are referred to in this paper as first order parameters (of the Korffunction) in order to distinguish them from the second order parameters which describe the effects of site, stand, and single tree variables that are integrated into the model later.
h_{ kti }: height of tree i at time of inventory t at sample plot k;
dbh_{ kti }: dbh of tree i at time of inventory t at sample plot k;
x_{ kti }: reparameterized dbh of tree i at inventory date t at sample plot k;
A_{ kt }, B_{ kt }, C, λ: first order parameters of the heightdiameter model at time of inventory t at sample plot k;
ln(.): natural logarithm.
with additionally:
A, B: Fixed effects for the first order parameters of the heightdiameter model (reparameterized Korffunction);
α_{ k }, β_{ k }: Random effects for sample plot k with the vector of random parameters b_{ k } = (α_{ k }, β_{ k })′~N(0, D) and D denoting the corresponding variancecovariance matrix.
In this study all models are paramaterized as glmm or gamm with loglink function and Gamma as distribution assumption. By employing the Gamma distribution we assume a constant coefficient of variation σ with [Var(h_{ kti })]^{1/2} = σ E(h_{ kti }) and Var(h_{ kti }) = ϕ [E(h_{ kti })]^{2}. This corresponds to a loglinear model, but, using generalized models no transformation bias occurs when the prediction is backtransformed. We show in the results chapter that assuming a Gamma distribution leads to a sufficient variance stabilization in our case and in contrast to Lappi (1997) and Mehtätalo (2004, 2005) we did not model the residual variance explicitly. However, the ongoing development of approaches like gam for location and scale (Wood et al. 2016) will allow for a more flexible variance modelling in the future.
The iterative search for the parameters λ and C for spruce, pine, and birch, with respectively 5613, 5219, and 7606 sample plots, proved to be too computationally intensive given the available computing facilities. Instead 20 samples, each containing 500 sample plots, where drawn from the dataset and models with different combinations of λ and C were parameterized (Eq. 2.2). Based on the optimal values determined by Mehtätalo (2004, 2005) for spruce (λ = 7, C = 1.564) and birch (λ = 6, C = 1.809), the value for λ was varied between 3 and 20 (in increments of 1), the value for C was varied between 0.3 and 2.5 (in increments of 0.1) and all of the resulting combinations tested. The AIC values of the resulting 20 models for each parameter combination were then averaged and the optimal parameter combination determined using the lowest average AIC value.
with additionally:
x_{ 1 }…x_{ n }: Covariates with 1dimensional effects on the hd relationship;
east_{ k }, north_{ k }: Easting and northing of sample plot k (UTMcoordinates);
f_{ 1a }(x_{ 1 })…f_{ na }(x_{ n }): 1dimensional penalised regression Psplines describing the level of the hd relationship (first order Parameter A);
f_{ 1b }(x_{ 1 })…f_{ nb }(x_{ n }): 1dimensional penalised regression Pspline describing the slope of the hd relationship (first order Parameter B);
f_{ sp a }: 2dimensional isotropic penalised thinplate regression spline capturing the structured spatial effect on the level of the hd relationship (first order Parameter A).
with differing to Eq. 3:
m_{ 1a }(x_{ 1 })…m_{ na }(x_{ n }): 1dimensional monotonic penalised regression Psplines describing the level of the hd relationship (first order Parameter A);
m_{ 1b }(x_{ 1 })…m_{ nb }(x_{ n }): 1dimensional monotonic penalised regression Psplines describing the slope of the hd relationship (first order Parameter B).
with additionally:
\( {\widehat{\mathrm{In}\left(\mathrm{E}\left[{h}_{kti}\right]\right)}}_{scam} \): Prediction of ln(tree height) using scam (Eq. 5) of tree i at inventory date t and sample plot k.
Results and discussion
with additionally:
qmD_{ kt }: Quadratic mean diameter of the tree species at inventory date t at sample plot k;
Alt_{ k }: Altitude of sample plot k;
BAL_{ kti }: Basal area larger (sum of the basal area of all trees larger than the reference tree) of tree i at inventory date t at sample plot k;
SD_{ k }: Soil depth category of sample plot k: I (0–25 cm), II (25–50 cm), III (50–100 cm), IV (> 100 cm);
east_{ k }, north_{ k }: Easting and northing of sample plot k (UTMcoordinates);
f_{ 1a }(.)…f_{ 3a }(.): 1dimensional penalised regression Psplines describing the level of the hd relationship (first order Parameter A);
f_{ 1b }(.): 1dimensional penalised regression Pspline describing the slope of the hd relationship (first order Parameter B);
ϕ_{ SD }: Vector of the regression coefficients for soil depth categories;
f_{ sp a }: 2dimensional isotropic penalised thinplate regression spline capturing the structured spatial effect on the level of the hd relationship (first order Parameter A);
α_{ k }, β_{ k }: Random effects for sample plot k with the vector of random parameters b_{ k } = (α_{ k }, β_{ k })′~N(0, D) with D denoting the corresponding variancecovariance matrix.
For all three tree species the effect of qmD on the first order parameter A decreases in the range of large values (Fig. 4). This was seen as unfeasible since for spruce from a qmD of ca. 25 cm and for pine and birch from a qmD of ca. 40 cm onwards, a decreasing level of the height curve with increasing qmD would be predicted (Fig. 4). It is assumed that the cause of this frequently occurring pattern is that the share of unfavourable sites is much higher in stands in advanced development stages than in younger stands, because such sites have on average poorer access, lower management intensity and a lower timber felling rate. It can also be assumed that, because of lower tree heights, unfavourable sites are less vulnerable to storm damage and that their share will therefore increase with advancing stand development stage.
The effects of BAL, Alt and the different categories of SD on the first order parameters seem plausible. All three species show monotone decreasing effects with increasing Alt (Fig. 4). Under Norwegian growth conditions it can be assumed that Alt is primarily a proxy variable for temperature, which decreases with increasing Alt. Precipitation, which increases with increasing Alt, is not able to fully compensate for the limiting factor of the temperaturesum. The weak gradient between 0 and 150 m Alt for all three tree species seems plausible, because it can be assumed that the growth conditions at these altitudes are relatively uniform, if all other influence factors are constant.
All three tree species show increasing effects with increasing soil depth SD (Fig. 4), although for pine and birch between categories III and IV and for spruce between categories II, III and IV the differences were not significant. The ranking is plausible, because with increasing soil depth better conditions with respect to water regime and nutrient supply can be assumed. Also the much lower level of the SD category I sites with very shallow soils can be judged to be plausible.
The effect of BAL is uniformly monotone increasing with a, depending on the tree species, more or less weak degressive tendency, which is most pronounced for pine (Fig. 4). BAL is a simple index describing the social rank and competition pressure of a tree within a sample plot. In assessing the effect it was assumed, that with increasing BAL (or increasing competition), light would become a growthlimiting factor. Thus, with increasing BAL the relation of heightgrowth to diametergrowth shifts in favour of height growth and greater tree heights are predicted, if all other factors remain constant (Fig. 4). With decreasing BAL the social rank of a tree increases and lower tree heights are predicted, if all other variables are equal because dominant, and as extreme cases solitary trees, invest more into diameter than height growth for stability reasons. Another way of interpreting the BAL effect is to compare trees that grow under equal site conditions but under different competition. These trees will have similar heights but different dbh as a function of growing space. Hence larger hd ratios can be assumed in denser stands and for higher competition (Zhang and Burkhart 1997; Zeide and Vanderschaaf 2002; Calama and Montero 2004). The model effect of competition is in accordance with several investigations about the effects of stand density on the h/d ratio (Calama and Montero 2004). Through the choice of BAL as the competition index it is implied that the hd relationship is not influenced by the harvest or death of trees which are smaller than the reference tree.
The effect of qmD on the first order parameter B is montone increasing with an asymptotic tendency from ca. qmD 45 cm for spruce (Fig. 4). For pine this effect is nearly linear increasing, while for birch it is monotone increasing with a degressive tendency.
The spatial trendfunction f_{ sp a }(east_{ k }, north_{ k }) captured largescale correlated differences of the hd relationship which were not described by the other covariates and lead to a clear improvement of the model accuracy for all tree species (Fig. 4). Apart from the northsouth gradients of temperaturesum and length of the vegetation period, it is perhaps above all the effect of distance to the coast and the resulting site differences that are modelled by this effect. It can also be supposed that the effects of further causal factors like, for instance, large scale geological differences, are accounted for by the regional location proxy variable. No further investigation concerning optimal variance partition between spatial trendfunction and random plot effects was made at this point.
with differing to Eq. 7:
Age_{ kt }: Non speciesspecific stand age at inventory date t at sample plot k.
The effects of stand age on the first order parameters A and B are not very sensitive and display implausible patterns (Fig. 5). In this context it must be mentioned that the specification of conditions in scam should only be applied in those cases in which the patterns of unconstrained effects seem basically plausible. The specification of conditions serves solely to suppress a too great and implausible flexibility, especially at the boundaries of the covariate data ranges. The problem of insensitive model effects and entirely implausible patterns, especially in those dataranges with many available datapoints, cannot be solved using the scam approach.
with differing to Eq. 7:
m_{ 1a }(qmD_{ kt }): 1dimensional penalised regression Pspline describing the effect of qmD on the level of the hd relationship;
mcc_{ 3a }(BAL_{ kti }): 1dimensional monotone increasing, and concave, penalised regression Pspline describing the effect of BAL on the level of the hd relationship;
m_{ 1b }(qmD_{ kt }): 1dimensional monotone increasing penalised regression Pspline describing the effect of qmD on the slope of the hd relationship.
Standard errors, explained deviance and AIC of shape constrained and unconstrained generalized hd models for spruce, pine and birch in Norway
Spruce  Pine  Birch  

Standard error (m)  
gam  2.06  2.33  1.64 
scam (Eq. 8)  2.07  2.34  1.64 
gamm (Eq. 7) prediction based on fixedeffects  2.09  2.36  1.64 
gamm (Eq. 7)  1.38  1.30  1.04 
scam_m (Eq. 6)  1.40  1.33  1.01 
Explained deviance (%)  
gam  87.8  80.3  78.0 
scam (Eq. 8)  87.7  80.2  77.8 
gamm (Eq. 7)  94.1  93.3  90.4 
scam_m (Eq. 6)  94.0  93.1  90.4 
AIC  
gam  284,162.3  225,179.1  215,256.8 
scam  284,307.9  225,267.6  215,701.0 
AIC scam in proportion to gam (%)  100.05  100.04  100.21 
A comparison of the prediction accuracy of the gamm and scam_m, taking both fixed and random effects into account, also shows only minimal differences. The standard errors of the gamm for spruce and pine are slightly lower, and for birch slightly higher, than those of the scam_m. Comparing the gam and scam (or gamm and scam_m) based on their explained deviance confirms again, that shape constraints only result in marginal differences. The AICvalues of the gam are also only slightly lower than those of the scam (Table 3). Because of the stepwise parameterization of the scam_m, a comparison of scam_m and gamm by means of AIC is not possible.
The standard errors of height prediction applying only fixed effects are the highest in pine followed by spruce and birch (Table 3). In comparison the reduction in standard error using the full mixed models are about 1 m for pine, 0.7 m for spruce and 0.6 m for birch. The standard errors of the mixed models are rather similar for spruce and pine and the lowest for birch. The explained deviance using only fixed effects is considerably higher for spruce compared to pine and birch whereas the values of the mixed models are similar for spruce and pine and the lowest for birch.
Estimated parameters for the variance components (standard deviation SD) and dispersion parameter for shape constrained and unconstrained generalized hd models for spruce, pine and birch in Norway. Lower and upper limits for the 0.95% confidence intervals are given in brackets
SD(α_{ k })  SD(β_{ k })  Dispersion parameter ϕ  

Spruce  
gamm (Eq. 7)  0.1159 (0.1129, 0.1189)  0.1330 (0.1287, 0.1375)  0.0132 (0.0130, 0.0133) 
scam_m (Eq. 6)  0.1102 (0.1074, 0.1131)  0.1339 (0.1296, 0.1383)  0.0134 (0.0133, 0.0136) 
Pine  
gamm (Eq. 7)  0.1611 (0.1572, 0.1650)  0.1733 (0.1663, 0.1805)  0.0141 (0.0139, 0.0142) 
scam_m (Eq. 6)  0.1578 (0.1540, 0.1617)  0.1849 (0.1776, 0.1925)  0.0144 (0.0142, 0.0146) 
Birch  
gamm (Eq. 7)  0.1349 (0.1317, 0.1383)  0.0833 (0.0798, 0.0870)  0.0174 (0.0172, 0.0176) 
scam_m (Eq. 6)  0.1345 (0.1313, 0.1379)  0.0864 (0.0829, 0.0901)  0.0175 (0.0173, 0.0177) 
Conclusions
Based on hd models for spruce, pine and birch in Norway a model comparison of unconstrained gamm and scam_m was made. For the hd models it was shown that scam_m combines the flexibility of gamm with the assurance that all model effects will be plausible. Plausible model effects can be forced by setting conditions such as monotonicity, convexity, concavity or combinations thereof. The full flexibility of additive regression models remains within the constraint conditions. As was shown in the cases studied, constrained and unconstrained effects can be combined within the same model. There were only marginal differences in the predictive accuracy of hd models which had been parameterized as gamm or scam_m. At the same time, the scam_m models are made more generally applicable, especially for predictions based on external data, by the ability to take expert knowledge into account. Because forest growth data is normally more or less unbalanced, the number of potential uses for scam_m models is large.
Notes
Acknowledgements
We would like to thank two anonymous reviewers for constructive comments.
Funding
This study was supported by the Norwegian Institute of Bioeconomy Research (NIBIO).
Availability of data and materials
Data are available upon request under certain constraints.
Authors’ contributions
The first author (MS) conducted the data analysis, model development and wrote the first draft. JB substantially contributed to the data preparation. All authors jointly discussed the results, drew conclusions and finalized the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
The authors give the consent for publication.
Competing interests
The authors declare that they have no competing interests.
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