Methodologies

Abstract

Any boreal environment is a dynamic system that interacts with its natural and anthropogenic surroundings over different scales of time. A boreal forest system can be divided into several sub-systems, which affect each other via energy and matter flows. When studying a complex multicomponent system like a boreal forest, one can approach the interactions and the system as a whole from several directions. Physical quantities that describe the forest in its current state can be measured, as well as their variability in time. If the processes affecting the system and their interactions are known well enough, one can model the system and predict future behaviour of selected physical quantities. In a post-processing phase the modelled and measured parameters can be intercompared with the aid of statistical methods. Isaac Newton laid down the foundation of physics for several centuries, and in addition, he established a dynamic modelling tradition that is still very vital. The use of Newtonian methods was very limited in ecology until the beginning of the 20th century. The only application of dynamic models in ecology with a long history is that of the Lotka-Volterra equations which describe the predator-prey interaction. The limited measuring possibilities and great variation evidently hindered any larger scale use of dynamic models. Statistical methods were developed during the first half of the 20th century. Agricultural field experiments needed methods for proper conclusions under the practical irregularities arising from spatial and temporal variation as well as heterogeneity of material and management. Experimental designs and statistical tests were thus first developed for field crops. Forest inventories utilised very early the statistical methods that had been developed for sampling populations and drawing conclusions about their features. These ideas are still used in the determination of forest properties on various scales.