Biological, Environmental, and Anthropogenic Drivers
Due to insufficient sample size, data for A. geoffroyi andS. oerstedii were not included for further statistical analyses.
Seven independent variables were explored for their association with
exposure to T. gondii in A. palliata and C.
imitator . Distance to villages, human population size, and human
population density in the nearest village were considered indirect
indicators of proximal domestic cats. The percentage of forest cover,
annual average temperature, and annual precipitation were used as
variables that affect oocyst survival. NP sex was used as an intrinsic
factor for oocyst exposure (Table 1).
Forest cover, annual average temperature, and annual precipitation were
measured within a 1.54 km2 circular area (700 m
radius) that was constructed around each sample“s geolocation point
(QGIS software, version 2.14). Data for these variables were obtained
from Costa Rica Digital Atlas (2014) and WorldClim (Hijmans et al.,
2005). The buffer size aimed to include both species“ home ranges and
adjacent areas (Wainwright 2007).
Generalized linear models (GLM) with binomial distribution were run in R
V3.4.2 (R Core Team, 2013) to explore associations between predictor
variables and T. gondii seropositivity. The original predictors
(forest cover and precipitation) were centered by subtracting their
means and scaled by dividing by their standard deviations. The most
parsimonious model was chosen by selecting the one with the lowest
Akaike information criterion (AIC). Akaike weights (wi) were calculated
to determine the evidence in favor of each model and estimate the
relative importance of variables (Table 2).
Each NP species was modeled separately. Multicollinearity among the
seven variables was evaluated by means of the variance inflation factor
(VIF). When analyzing all the proposed variables, a very high VIF value
(>10) was obtained suggesting multicollinearity, therefore
the model was reduced to four variables (sex, forest cover, annual
precipitation, and human population density). With these models, none of
the variables obtained a VIF value greater than 10 (Table 2).