Global Mammal Parasite Database
About the database
The Global Mammal Parasite Database is a compilation of records of parasites and their hosts that have been documented in the published scientific literature. As the quote above suggests, mammals are an extremely well-studied group of animals, and there are thousands of published reports and scientific studies describing their parasites and the abundance of parasites in wild populations.
We have systematically searched the literature on mammalian parasites to produce three primary databases covering primates, carnivores and terrestrial hooved mammals (which includes all perissodactyls and artiodactyls). All of the entries in this database come from wild populations. In the future we will be expanding out taxonomic coverage to other groups of mammals.
How we found the information
To create the GMPD, the parasite data was compiled as individual records of micro- or macroparasites reported in free-living primate species by using primate Latin binomials as search keywords in the major online reference databases. Details are provided for each of the databases, but in general, we searched by primate genus name and common taxonomic variants. In addition to using electronic databases, we also examined edited volumes, reviews, and studies that were cited by publications that we located in our first round of searches. For some of the databases (e.g., primates), we are continuing to update our files and welcome information you may have on parasites in wild species.
Generating the Data
Once we locate a reference, members of our team review it, looking for specific pieces of information. After confirming that the parasite was sampled from the wild, we record host and parasite taxonomy, the type of parasite (virus, protozoan, fungi, arthropod, helminth, bacteria), the number of hosts sampled, and location and year of sampling. When possible, we also record the primary mode of parasite transmission, symptoms and effects on host mortality or morbidity, the prevalence and intensity of infection, and any age or sex differences that may be reported.
Analysis of Data
After double checking the data, we extract specific datasets for testing hypotheses involving prevalence of infection, parasite species richness, or the geographical distribution of disease risk. The datasets provided online here allow other users to extract similar files.
A variety of issues arise in the comparative study of parasitism. Consider, for example, a study aimed at understanding patterns of parasite species richness. We can count the number of parasites for each host, but a complication arises because a parasite may be missing from a host species because the host has been sampled insufficiently. It is possible to control for “sampling effort” in analyses of parasite richness by using data on the number of citations for each species (as a measure of overall research interest in a host), and by counting the number of animals that were sampled in the publications that make up data for a particular host.
Another issue involves phylogeny. A trait may be shared among species because it has evolved independently in those species, or because it is shared among species with a common ancestor that also had the trait (i.e., shared through common descent). Many of the analyses with the GMPD therefore require information on phylogenetic relationships among the hosts or parasites. It is also possible to test whether the traits in question are correlated with phylogeny – an important step when we are interested in traits that are distributed through ecological processes.
All data from the literature are not of equal quality, and we can assign greater confidence to some estimates than others. Thus, in some of our analyses we have assigned confidence scores to characteristics such as transmission mode and host specificity. These scores allow us to weight the value of different data points.
Finally, many interesting questions concerning the conservation implications of parasites require geographic information. By providing information on sampling locality in the form of geographic coordinates, it is possible to import data from the databases directly into GIS.