Frequency distributions from 49 published wildlife host–macroparasite
systems were analysed by maximum likelihood for
goodness of fit to the negative binomial distribution. In 45 of the 49
(90%) data-sets, the negative binomial distribution
provided a statistically satisfactory fit. In the other 4 data-sets the
negative binomial distribution still provided a better
fit than the Poisson distribution, and only 1 of the data-sets fitted the
Poisson distribution. The degree of aggregation was
large, with 43 of the 49 data-sets having an estimated k of less
than 1. From these 49 data-sets, 22 subsets of host data
were available (i.e. host data could be divided by either host sex, age,
where or when hosts were sampled). In 11 of these
22 subsets there was significant variation in the degree of aggregation
between host subsets of the same host–parasite
system. A common k estimate was always larger than that obtained
with all the host data considered together. These
results indicate that lumping host data can hide important variations in
aggregation between hosts and can exaggerate the
true degree of aggregation. Wherever possible common k estimates
should be used to estimate the degree of aggregation.
In addition, significant differences in the degree of aggregation between
subgroups of host data, were generally associated
with significant differences in both mean parasite burdens and the prevalence
of infection.
