Nevertheless, field data, particularly relating to microparasites, are most of the time serological (i.e. presence-absence data). Indeed, viral excretion is usually too short to make antigen detection an efficient tool to follow microparasites in natural populations, as host capture and sampling would have to be done exactly during the excretion period, especially during nonepidemic phases. Most field data are thus limited to observed frequencies of seronegative, seropositive and doubly seropositive individuals. In this context, the search for potential interactions between pairs of microparasites is traditionally done by calculating odds ratios in stratified data or by a Pearson's chi-square test of independence (e.g., [29, 51, 52] ). The latter compares the observed frequencies to the frequencies expected if parasites are independent, under the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals. However, such methods ignore confounding factors and/or the possible simultaneous action or interaction of several of them. Significant associations detected in this manner can therefore be either true biological interactions or statistical associations, with no means of distinguishing the two. Alternative methods have been therefore proposed to determine the expected frequencies in a modified chi-square analysis. Some are based on the estimation of ''pre-interactive'' species prevalences [53] , which requires previous knowledge of dominance relationships between parasites species. Some others are based on log-linear models [e.g., [54] [55] [56] . In addition, another way to take risk factors into account is to include them in a logistic regression analysis and to determine whether parasite B status is still a predictor of parasite A status [52, 57] . However, the main drawback of methods based on log-linear or logistic regression models is that they are based on an asymptotic approximation of the deviance, which might not be relevant for small sample size data.