Food web reconstruction through phylogenetic transfer of low-rank network representation

Despite their importance in many ecological processes, collecting data and information on ecological interactions, and therefore species interaction networks, is an exceedingly challenging task. For this reason, large parts of the world have a deficit of data of which species interact, and what we can expect the network structure of these interactions to be. As data collection alone is unlikely to be sufficient at filling these global gaps, community ecologists must adopt predictive methods. In this contribution we develop such a method, relying on graph embedding (the extraction of explanatory latent variables from known graph structures) and transfer learning (the application of previous solution to novel problems with limited predictors overlap) in order to assemble a predicted list of trophic interactions between mammals of Canada. This interaction list is derived from extensive knowledge of the mammalian food web of Europe, despite the fact that there are fewer than 5% of common species between the two locations. We provide guidance on how this method can be adapted by substituting some approaches or predictors in order to make it more generally applicable to a broad family of ecological problems.