Interactions among the red deer (Cervus elaphus, L.) population, meteorological parameters and new growth of the natural regenerated forest in Snežnik, Slovenia

Following a preliminary study (Stankovski et al., Ecol. Modelling, 108, 1998), we use machine learning techniques to conduct a more detailed analysis of the interactions among the red deer population, meteorological parameters and new forest growth. We use the machine learning program M5 (Quinlan, Proc. 10th Int. Conf. Machine Learning, Morgan Kaufmann, San Mateo CA, 1993) that learns regression trees to automate the modelling of dynamic interactions. An area of 40 000 hectares of naturally regenerated forest on the high Dinaric Karst of Notranjska, Slovenia, is studied. The analysis uses data collected during the period 1976–1993, which include several meteorological parameters, the degrees of browsing intensity of new growth of woody plants (beech and maple), and parameters about the population of red deer. Models of the degree of beech browsing and calf weight were studied earlier; here, we automatically induce models of the red deer population size, the degree of beech and maple browsing, calf weight for 1- and 2-year-olds, and hind weight. The induced models are evaluated in terms of predictive accuracy and interpreted for their explanatory power. The models show that the meteorological parameters, the parameters of the red deer population and the rates of the browsing intensity of the new growth form a complex system with closely related parameters. While these interactions can be mainly explained by our current knowledge, we still gain some new knowledge from the automatically induced models. The results emphasise the importance of a pluralistic approach and a holistic perception of the system formed by meteorological conditions, the red deer population and the new growth in a forest ecosystem.