A few hosts have many parasites and many hosts have a few parasites - this axiom of macroparasite aggregation is one of the few general laws in disease ecology and has important implications for the dynamics of host-parasite systems. To understand the factors affecting the distribution of parasites across host individuals in a population, a bottom-up approach has traditionally been used in which parasite aggregation patterns are described by sequentially building biological, aggregating mechanisms into an unaggregated null model until the observed level of aggregation is achieved. On the other hand, macroecology has recently begun to implement a variety of top-down modeling techniques that do not try to elucidate every aggregating and disaggregating mechanism affecting a system. Rather, they attempt to predict observed aggregation patterns with a set of known constraints on the system (e.g. the mean number of parasites per host). Here we apply the top-down approach to empirical host-parasite distributions and compare the resulting inference to the more traditional bottom-up approach.
Results/Conclusions
Using an extensive parasitological dataset of 842 host-parasite distributions from five amphibian hosts and five trematode parasites, we show that this top-down approach provides a robust null hypothesis for host-parasite aggregation, meaning that top-down null models generally succeed in correctly predicting observed parasite aggregation where bottom-up null models fail, using the same number of parameters. Moreover, when these top-down models fail we show that they can be improved by accounting for known aggregating mechanisms such as host-heterogeneity and disaggregating mechanisms such as parasite-induced host mortality. This allows for a synthesis of the top-down approach with the more commonly used bottom-up approach. The top-down approach is rapidly gaining popularity in studies of free-living populations and communities and we show that it is also a powerful tool for understanding classic patterns in disease ecology such as parasite aggregation.