Little comprehensive research on forest-ungulate interactions has been undertaken to gain
a holistic and deep understanding of the ecological, social and economic interdependencies …

Artificial neural networks (ANNs) are biologically inspired computational networks. Among
the various types of ANNs, in this chapter, we focus on multilayer perceptrons (MLPs) with …

We study and assess the potential habitats of a population of red deer in South-central
Slovenia. Using existing data on the deer population spatial distribution and size, as well as …

Complex spatial heterogeneity of ecological systems is difficult to capture and interpret using
global models alone. For this reason, recent attention has been paid to local spatial …

Predictive accuracy is the usual measure of success of Machine Learning (ML) applications.
However, experience from many ML applications in difficult, domains indicates the …

Symbolic machine learning methods induce explicitly represented symbolic models from
data. The models can thus be inspected, modified, used and verified by human experts and …

Management of German roe deer (Capreolus capreolus) populations is a challenge for
wildlife managers and foresters because population densities are difficult to estimate in …

Decision tree learning is among the most popular machine learning techniques used for
ecological modeling. Decision trees can be used to predict the value of one or several target …

Analysis of patterns of temporal variation in community dynamics was conducted by
combining two unsupervised artificial neural networks, the Adaptive Resonance Theory …

Background information about the scope and relevance of machine learning methods for
ecological analyses is presented. It is suggested that these techniques can extend our …