# Stand Structure Impacts on Forest Modelling
## 5 Height
[70], discussing _Pinus sylvestris_, _Pica abies_, _Betula_pubescens_ uneven-aged stands in Finland, reported the good performance of the linear mixed models. The authors underlined the small bias and the flexibility of the model for different stand structures. Additionally, the independent variables used in the model were able to capture the tree and stand variability as random parameters, namely the stem form (north coordinate and elevation), tree allometry (stocking and thinning) and stand (absolute density measures). Ref. [49] discussed stands of _Picea abies_, _Fagus sylvatica_ and other conifer and broadleaved species in Slovakia, where the h-d models included tree, stand, competition indices and dummy variables for species as explanatory variables. The authors demonstrated that the accuracy improved when the dominant height, ratio of tree diameter at breast height and stand basal area, and stand basal area were used as covariates and when the height of one to four individuals were used to estimate the random effects. Ref. [47] also stressed the flexibility of the mixed models of even-aged pure and mixed stands of _Pinus pinaster_ and _Pinus sylvestris_ in Spain. The fitted model was able to accommodate composition, thinning (from below with moderate intensity), vertical structure and site quality, which were reflected in the tree allometry. The model developed outperformed those for the pure stands and the reason for the better performance was due to the inclusion of the random effects. Additionally, using mixed models, Ref. [39] compared the performance of the models using inventory data and stem analysis data for _Fagus sylvatica_ in pure and mixed stands, in France. The authors highlighted the advantages of modelling with data of growth measurements from stem analysis (dynamic relationships) in contrast with field inventory with a single measurement (static relationships), because in the former the information of the dynamics of tree growth was more detailed. The mixed models were able to detect the variability of growth in time. When silvicultural practices were performed, the trees from all social classes increased growth shortly after release (earlier in diameter than in height) and reduced growth gradually with the progressive canopy closure, the supressed trees were able to live for long periods with small or null diameter growth but continued to grow in height, and during the periods of release the individual trees showed wide variability in their diameter and height growth patterns. The flexibility of the mixed models made it possible to accommodate both social status and thinning. Likewise, Ref. [77] stressed that tree social status and competition had a high contribution to h-d generalised linear mixed models, regarding 44 species in France. The authors stated that: competition contributed more than temperature and precipitation; the mean temperature from March to September was an influential explanatory variable for 25% of the species, while precipitation was for 16% of the species; and the effect of temperature was stronger than that of precipitation. Similarly, [PERSON]. [78] reported that nonlinear generalised mixed effects models outperformed the nonlinear regression for mixed uneven-aged stands in Romania. Again, the mixed models were able to capture the individual tree allometry variability, which is characteristic of the uneven-aged stands. Additionally, [PERSON]. [79] compared two approaches with mixed models, additional random effects and co-modelling, with data of repeated measurements from forest inventories in Austria, with the goal of accommodating the temporal variability. The precision with additional random effects approach was the highest, while the co-modelling had the lower bias, and the approaches outperformed existing models. [PERSON]. [80] used field plot data to model h-d relationships in selection forests (uneven-aged) of _Fagus sylvatica_ in Germany with quantile regression.