The population density of wildlife reservoirs contributes to disease transmission risk for
domestic animals. The objective of this study was to model the African buffalo distribution
of the Kruger National Park. A secondary objective was to collect field data to evaluate
models and determine environmental predictors of buffalo detection. Spatial distribution
models were created using buffalo census information and archived data from previous
research. Field data were collected during the dry (August 2012) and wet (January 2013)
seasons using a random walk design. The fit of the prediction models were assessed
descriptively and formally by calculating the root mean square error (rMSE) of deviations
from field observations. Logistic regression was used to estimate the effects of environmental
variables on the detection of buffalo herds and linear regression was used to identify
predictors of larger herd sizes. A zero-inflated Poisson model produced distributions
that were most consistent with expected buffalo behavior. Field data confirmed that environmental
factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation
density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds
were more likely to be detected in dense vegetation (P = 0.005) and during the wet
season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models
for African buffalo can produce biologically reasonable results but environmental factors
have significant effects and therefore could be used to improve model performance.
Accurate distribution models are critical for the evaluation of disease risk and to model
S1 Fig. a) Disaggregation model predictions (upper left); b) Poisson kriging model predictions
(upper right); c) Zero-inflated Poisson model predictions (lower left); d) Conditional autoregression
model predictions (lower right).
S1 Table. The comparison of quantitative variables between locations where buffalo were
observed in Kruger National Park during August 2012 and January 2013 compared to
hourly (non-buffalo) observations and the correlation of these variables with the observed
S2 Table. Univariate logistic regression to identify predictors of observing buffalo herds
based on field data collected for 105 detected herds of buffalo in Kruger National Park
during August 2012 and January 2013 compared to 234 hourly time points without buffalo
S3 Table. Univariate logistic regression to identify predictors of observing bachelor herds
in 104 herds of buffalo in Kruger National Park identified during August 2012 and January
S4 Table. Univariate linear regression for the estimation of effects of predictor variables
on observed buffalo herd size in 104 herds of buffalo in Kruger National Park identified
during August 2012 and January 2013.
S1 Code. WinBUGS code for performing the conditional autoregressive (CAR) model.
S1 File. Collected field data saved as comma separated file.