Host-parasite checklists are essential resources in ecological parasitology, and are regularly used as sources of data in comparative studies of parasite species richness across host species, or of host specificity among parasite species. However, checklists are only useful datasets if they are relatively complete, that is, close to capturing all host–parasite associations occurring in a particular region. Here, we use three approaches to assess the completeness of 25 checklists of metazoan parasites in vertebrate hosts from various geographic regions. First, treating checklists as interaction networks between a set of parasite species and a set of host species, we identify networks with a greater connectance (proportion of realized host–parasite associations) than expected for their size. Second, assuming that the cumulative rise over time in the number of known host–parasite associations in a region tends toward an asymptote as their discovery progresses, we attempt to extrapolate the estimated total number of existing associations. Third, we test for a positive correlation between the number of published reports mentioning an association and the time since its first record, which is expected because observing and reporting host–parasite associations are frequency-dependent processes. Overall, no checklist fared well in all three tests, and only three of 25 passed two of the tests. These results suggest that most checklists, despite being useful syntheses of regional host–parasite associations, cannot be used as reliable sources of data for comparative analyses.

The fine-scale genetic variation and population structure of lichen-forming fungi is little known and sampling strategies are rarely recommended or discussed. I tested if the sample sizes of molecular data sets used in recent population studies of Xanthoria parietina revealed all haplotypes potentially present and, accordingly, quantified how many haplotypes were potentially missing in the samples. Data sets were concatenated from two geographical regions in Scandinavia and investigated if the sampling reached saturation at two levels: 1) individual-based using rarefaction curves and 2) population-based using species accumulation curves. At both levels, the matrices of two molecular markers (IGS and ITS) were analysed separately. The molecular markers show similar and parallel patterns in all analyses. Rarefaction analyses did not reveal different patterns for populations in different habitats, i.e., bark and rock. Species accumulation curves estimated with the Chao 1 richness estimator indicated that 23% of the IGS and 8% of the ITS haplotypes were not detected. Corresponding figures from an abundance-based coverage estimator (ACE) were 37% and 18%. Pilot studies are recommended to determine appropriate sample sizes for genetic-based population studies of lichen-forming fungi.

In most real-world contexts the sampling effort needed to attain an accurate estimate of total species richness is excessive. Therefore, methods to estimate total species richness from incomplete collections need to be developed and tested. Using real and computer-simulated parasite data sets, the performances of 9 species richness estimation methods were compared. For all data sets, each estimation method was used to calculate the projected species richness at increasing levels of sampling effort. The performance of each method was evaluated by calculating the bias and precision of its estimates against the known total species richness. Performance was evaluated with increasing sampling effort and across different model communities. For the real data sets, the Chao2 and first-order jackknife estimators performed best. For the simulated data sets, the first-order jackknife estimator performed best at low sampling effort but, with increasing sampling effort, the bootstrap estimator outperformed all other estimators. Estimator performance increased with increasing species richness, aggregation level of individuals among samples and overall population size. Overall, the Chao2 and the first-order jackknife estimation methods performed best and should be used to control for the confounding effects of sampling effort in studies of parasite species richness. Potential uses of and practical problems with species richness estimation methods are discussed.