# Stand Structure Impacts on Forest Modelling
## 3 Reasons for Modelling  
Due to the longevity and variability of forest stands, modelling is needed to assess and predict their development and yields in space and time. Several reasons can be pointed out for modelling, namely the labour and costs of forest inventory and its periodicity (between 5 to 10 years), as well as the fact that in some areas it is difficult to implement forest inventory due to stand structure and/or topography, and some tree and stand variables cannot be measured (e.g., volume). Models enable the prediction of tree and/or stand dynamics in space and time, and the development and testing of different models of silviculture which are not feasible in practice due to time and costs.  
Traditionally, for modelling, the inputs correspond to a timeframe between crown closure and harvest [2]. However, the evaluation of the stand structure and silvicultural practices are of relevance before crown closure, which is the reason why sets of models have been developed (e.g., [36; 37]). Additionally, models for pure even-aged stands originate in biased predictions for uneven-aged pure and mixed stands, which originated a new set of models (e.g., [38; 39]). Again, regime influences stand an individual tree development which triggered the development of models for high forest and coppice regime (e.g., [40; 41]).  
Models are abstractions or simplified representations of some aspects of reality [42; 43]. They represent an attempt to describe and simplify certain assumptions, determining afterwards their logical relationships [42] and are composed of one or several equations that predict dendrometric variables [2], competition, growth and yield [2; 3; 44]. Forest ecosystem modelling can be performed at different spatial and temporal scales, from cells to continents and seconds to millennia. The models can be less or more complex. Its complexity is determined by its goals and by the existing knowledge of the system [45].  
Trees and stands have large variability due to a suite of factors, such as species, density, stand structure and site (cf. [2]). This led to the development of a wide range of models derived from diverse datasets (forest inventory, remote sensing and ancillary data, such as topography, soils and climate), with various mathematical methods and techniques. Models have increased in complexity, and frequently accuracy and precision, in time with the better knowledge of the systems; with the survey of more variables in the forest inventories; and with improved computer hardware and software capacities to compute large datasets.  
Modelling goes far beyond applying statistical techniques to datasets and developing equations [2]. Forest models can comprise one or a set of equations that model growth, regeneration, mortality and changes in stand structure. The models provide an efficient way to predict forest resources, yet one of their primary roles is the ability to explore management alternatives [43]. Forest management is implemented utilizing a set of silvicultural practices from stand installation to final harvest. The initial stand structure, whether from natural or artificial regeneration, and the silvicultural practices prescribed (methods, intensity and periodicity) and their timing have a strong effect on the stand structure dynamics and individual tree allometry. The diversity of models of silviculture derives in a wide range of stand structures. It is not feasible to have research trials to characterise and describe all possible management options. Models, especially if implemented in simulators, particularly when they have visualisation software, enable the testing of many models of silviculture and serve as a tool to help the practice of silviculture [2].