During recent decades, there have been numerous attempts to identify the key determinants of parasite communities and several influential variables have been clarified at either infra-, component or compound community scales. However, in view of the possible complexity of interactions among determinants, the commonly-used exploratory and statistical modelling techniques have often failed to find meaningful ecological patterns from such data. Moreover, quantitative assessments of factors structuring species richness, abundance, community structure and species associations in parasite communities remain elusive. Recently, because they are ideally suited for the analysis of complex and highly interactive data, there has been increasing interest in the use of classification and regression tree analyses in several ecological fields. To date, such approaches have never been used by parasitologists for field data. This study aims to both introduce and illustrate the use of multivariate regression trees in order to investigate the determinants of parasite abundance in a multi-scale quantitative context. To do this, we used new field epidemiological data from 1489 coral reef fishes collected around two islands in French Polynesia. We evaluated the relative effect and interactions of several host traits and environmental factors on the abundance of metazoan parasite assemblage at several scales and assessed the impact of major factors on each parasite taxon. Our results suggest that the islands sampled, the host species and host size are equal predictors of parasite abundance at a global scale, whereas other factors proved to be significant predictors of a local pattern, depending on host family. We also discuss the potential use of regression trees for parasitologists as both an explorative and a promising predictive tool.