Table 2 A list of variables considered for inclusion in generalized linear mixed models predicting pathogen and parasite exposure. Variable descriptions and rationales or predictions are provided; a * indicates the variable was included in the final complete model, a ⁺ indicates the variable was included in the geographic model.

Latitude⁺

Latitude at study area centroid

Latitude may capture geographic variation in pathogen infections; we predicted that seroprevalence decreases as latitude increases.

Longitude⁺

Longitude at study area centroid

Longitude may capture geographic variation in pathogen infections.

Age class*⁺

Estimate of wolf age class: pup (< 1), subadult (1–2), and adult (≥ 3)

As individuals age, they have more time to be exposed to pathogens, thus older wolves will have higher seroprevalence. Age category is less error-prone than numerical age estimates.

Year*

Biological year, birth month = first month

Pathogen exposure may be predictable by year (i.e., endemics), or unpredictable (i.e., epidemics).

Study area*

Study area abbreviation

Study area may describe variation in pathogen exposure, not accounted for by other variables.

Habitat quality*

Index for habitat quality based on land cover type and topography

A continuous estimate of the habitat quality of the study area, this covariate considers habitat characteristics that carnivores, especially wolves, positively select. This is a proxy for the presence of sympatric carnivore hosts. Prediction: seroprevalence increases with habitat quality.

Human density*

Number of people/1000-km²

Provides information about how urban the area is, and thus the potential for contact between unvaccinated dogs or synanthropic species (e.g., rodents, coyotes, raccoons, skunks, cats) and wolves. Prediction: seroprevalence increases with human density.

Wolf density*

Number of wolves/1000-km²; mean annual density results in one estimate per study area

Population density is related to direct transmission rates and environmental contamination. Prediction: seroprevalence increases with wolf density.

Pack size*

Mean annual pack size; one estimate per study area

This tells us about the daily contacts of a wolf, which differs from contact rate at the population-level. Prediction: seroprevalence increases with pack size.

Sex*

Male or Female

There is evidence that males have higher pathogen prevalence than females across many taxa and pathogens—we predict males have higher seroprevalence.

Coat color*

Gray or Black

The locus that confers black coat color in wolves is linked to beta-defensin genes, which increases the responsiveness of the innate immune system. We assume gray = missing k-locus, black = presence of k-locus. Prediction: black wolves have higher seroprevalence.

Age

Estimate of wolf age; integer to two decimal places

As individuals age, they have more time to be exposed to pathogens, thus we predicted older wolves have higher seroprevalence.

Social status

Breeder or non-breeder

Breeders typically have higher stress levels and energetic demands than non-breeders, which we predict increases seroprevalence.

Prey species

Top two primary prey species

N. caninum or T. gondii may be more prevalent in different intermediate hosts. Prediction: seroprevalence is higher where white-tailed deer are a primary prey species.

Pack membership

Name of the pack the wolf was a member of when sampled

There may be heterogeneities in pathogen exposure based on pack membership.

Pack density

Number of packs/1000-km²; mean annual density results in one estimate per study area

Contact among wolves from different packs is likely influenced by the number of packs in the population. Prediction: seroprevalence increases with pack density.