Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America

The presence of many pathogens varies in a predictable manner with
latitude, with infections decreasing from the equator towards the poles.
We investigated the geographic trends of pathogens infecting a widely
distributed carnivore: the gray wolf (Canis lupus). We compiled a large
serological dataset of nearly 2000 wolves from 17 study areas, spanning
80º longitude and 50º latitude. Generalized linear mixed models were
constructed to predict the probability of seropositivity of four important
viruses: canine adenovirus, herpesvirus, parvovirus, and distemper virus –
and two parasites: Neospora caninum and Toxoplasma gondii. Canine
adenovirus and herpesvirus were the most widely distributed pathogens,
whereas N. caninum was relatively uncommon. Canine parvovirus and
distemper had high annual variation, with western populations experiencing
more frequent outbreaks than eastern populations. Seroprevalence of all
infections increased as wolves aged, and denser wolf populations had a
greater risk of exposure. Probability of exposure was positively
correlated with human density, suggesting that dogs and synanthropic
animals may be important pathogen reservoirs. Pathogen exposure did not
appear to follow a latitudinal gradient, with the exception of N. caninum.
Instead, clustered study areas were more similar: wolves from the Great
Lakes region had lower odds of exposure to the viruses, but higher odds of
exposure to N. caninum and T. gondii; the opposite was true for wolves
from the central Rocky Mountains. Overall, mechanistic predictors were
more informative of seroprevalence trends than latitude and longitude.
Individual host characteristics as well as inherent features of ecosystems
determined pathogen exposure risk on a large scale. Here we provide the
serological dataset and the R code used in Brandell et al. 2021. See the
README file for a description of the dataset and generalized linear mixed
models (GLMM); see Brandell et al. 2021 main text and
Supplementary Information for additional information about data
collection and cleaning, research permits, and variable descriptions and
rationales.