Investigation of the Transmission of Mycobacterium bovis from Deer to Cattle Through Indirect Contact

Objective—To investigate the infection of calves with Mycobacterium bovis through oral exposure and transmission of M bovis from experimentally infected white-tailed deer to uninfected cattle through indirect contact. Animals—24 11-month-old, white-tailed deer and 28 6-month-old, crossbred calves. Procedure—In the oral exposure experiment, doses of 4.3 X 106 CFUs (high dose) or 5 X 103 CFUs (low dose) of M bovis were each administered orally to 4 calves; as positive controls, 2 calves received M bovis (1.7 X 105 CFUs) via tonsillar instillation. Calves were euthanatized and examined 133 days after exposure. Deer-to-cattle transmission was assessed in 2 phases (involving 9 uninfected calves and 12 deer each); deer were inoculated with 4 X 105 CFUs (phase I) or 7 X 105 CFUs (phase II) of M Bovis. Calves and deer exchanged pens (phase I; 90 days’ duration) or calves received uneaten feed from deer pens (phase II; 140 days’ duration) daily. At completion, animals were euthanatized and tissues were collected for bacteriologic culture and histologic examination. Results—In the low- and high-dose groups, 3 of 4 calves and 1 of 4 calves developed tuberculosis, respectively. In phases I and II, 9 of 9 calves and 4 of 9 calves developed tuberculosis, respectively. Conclusions and Clinical Relevance—Results indicated that experimentally infected deer can transmit M bovis to cattle through sharing of feed. In areas where tuberculosis is endemic in free-ranging white-tailed deer, management practices to prevent access of wildlife to feed intended for livestock should be implemented.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.