Modelling the population dynamics of red deer (Cervus elaphus L.) with regard to forest development

Recent advances in artificial intelligence in general, and in machine learning in particular, enable scientists to apply new machine learning technics to their specific areas. In our work we apply such a machine learning technique to the modelling of population dynamics of red deer for the 40 000 hectares co-natural manage forest area on high Karst of Notranjska in Slovenia. We used the RETIS program, a machine learning tool developed by A. Karaliè at the Institute Jožef Stefan in Ljubljana. This program induces regression trees from data, and has already been applied to several ecological problems. RETIS was applied on data, collected in the period 1976–1994, which included several meteorological parameters, parameters about the state of the forest, and parameters about the population of the red deer. Given these data about the observed system, the system RETIS automatically induces a model which has the form of a regression tree. We evaluate our induced models qualitatively and quantitatively. For the qualitative evaluation, we present an expert interpretation of the models. We show that quantitatively, using the models (we use a relative prediction error) and given the meteorological parameters during winter and summer and an estimate of the number of red deer in the area, it is possible to predict the state of the forest in the near future. This is very important for maintaining the balance between red deer population and other parameters of the forest, which will allow sustainable development of the complex forest ecosystem.