Epilogue, geoecological interrelationships
The interrelationships between the different factors affecting coastal dune fields are numerous and complicated, as is commonly found to be the case in geoecological studies. For this reason, the dynamic factors that cause changes in the Finnish coastal dune areas are classified in a matrix of landscape evolution (Fig. 59). All the factors in this classification are dynamic, i.e. they are constantly changing. Even geographical location is changing as a consequence of plate tectonics.
Fig. 59. Matrices depicting coastal dune landscape evolution and regional differences in the coastal dune fields of Finland. Each factor affects all the others in a geographical interaction network of this kind. The static characteristics of a site and its dynamic development are closely bound together.
Any piece of research work is bound to a certain time span, and for this reason regional differences and distributions are important factors, too. The dynamic classification does not include the primary static characteristics of individual sites, and thus the factors, that cause regional differences in the coastal dune fields of Finland are classified in a matrix of geographical location (Fig. 59). A certain type of soil, climate or land use pattern is usually connected with a certain plant community and fauna. Static factors form combinations, regional types, that occur repeatedly under comparable conditions. Areas of different sizes, from a global scale to a small ecosystem, can be chosen as an object of study. Climates, for example, may be global climatic types or the microclimates of dune slopes of different aspect. Certain climates are combined with certain vegetation and soils. Since the topic of this work is the coastal dune fields of Finland, the climatic differences are those prevailing between the latitudes 59o48' and 65o45' N.
The static characteristics and dynamic development of each site are very closely bound together. The aim in physical geography is to study different geographical locations and processes in order to reach conclusions and construct predictive models based on the regularities observable in the empirical findings.