The complexity of ecosystems is staggering, with hundreds or thousands of species interacting in a number of ways from competition and predation to facilitation and mutualism. Understanding the networks that form the systems is of growing importance, e.g. to understand how species will respond to climate change, or to predict potential knock-on effects of a biological control agent. To discover information about complex ecological systems efficiently, new tools for inferring the structure of networks from field data are needed. In the present study, we investigate the viability of various machine learning methods recently applied in molecular systems biology. We assess the performance of these methods on both simulated data as well as presence/absence data for 39 European warblers. We quantify the network reconstruction accuracy with various correlation and receiver operator characteristics curve (ROC) measures. We propose a novel model extension that includes bio-climate covariates and successfully deals with spatial autocorrelation.