If laboratory experiments have clearly demonstrated that interspecific parasite interactions occur, often mediated by host immune responses [1, [33] [34] [35] , attempts to detect such effects in natural populations have generally been less successful. Indeed, detecting their existence on the field is not easy, due to complex networks of indirect effects making it difficult to infer underlying processes. Field studies are however essential as experimental systems are oversimplified and require an existing suspicion of interaction between the studied parasites. In addition, only studies in natural populations can give access to infection and co-infection probabilities. In other words, before studying their mechanisms in the lab, interactions of interest must be identified in the field. Main difficulties encountered in field studies are methodological. Many confounding factors can create statistical associations between parasites even if there is no true biological interaction between them, which may alter conclusions about the importance of interspecific interactions [36] [37] [38] [39] [40] . A similar transmission mode, for example, can alone increase the risk of co-infection. The excess of positive associations found in strongylid communities in domestic horses, ruminants and macropod marsupials is in particular likely to be due to the common habit of these hosts feeding on pastures contaminated with the larvae of a number of nematode species [41] [42] [43] . In addition, environmental, behavioural or host-specific factors can be associated with both types of infection and influence epidemiological and geographic patterns of infection and disease. Among such common risk factors, some have long been recognised, such as sexual behaviours for sexually transmitted diseases (e.g., [44] ), socio-economic status for infections particularly prevalent in poor regions such as helminth infection and malaria [45] , or age for many diseases (e.g., [46] ). As apparent associations between two infections may be due to common risk factors, they are crucial to identify and to take into account in the analysis. However, such confounding factors are difficult to control and few methods enable to take them into account.