Development of a Model of Natural Infection with Mycobacterium bovis in White-Tailed Deer

The objective of this study was to develop a suitable experimental model of natural Mycobacterium bovis infection in white-tailed deer (Odocoileus virginianus), describe the distribution and character of tuberculous lesions, and to examine possible routes of disease transmission. In October 1997, 10 mature female white-tailed deer were inoculated by intratonsilar instillation of 2 3 103 (low dose) or 2 3 105 (high dose) colony forming units (CFU) of M. bovis. In January 1998, deer were euthanatized, examined, and tissues were collected 84 to 87 days post inoculation. Possible routes of disease transmission were evaluated by culture of nasal, oral, tonsilar, and rectal swabs at various times during the study. Gross and microscopic lesions consistent with tuberculosis were most commonly seen in medial retropharyngeal lymph nodes and lung in both dosage groups. Other tissues containing tuberculous lesions included tonsil, trachea, liver, and kidney as well as lateral retropharyngeal, mandibular, parotid, tracheobronchial, mediastinal, hepatic, mesenteric, superficial cervical, and iliac lymph nodes. Mycobacterium bovis was isolated from tonsilar swabs from 8 of 9 deer from both dosage groups at least once 14 to 87 days after inoculation. Mycobacterium bovis was isolated from oral swabs 63 and 80 days after inoculation from one of three deer in the low dose group and none of four deer in the high dose group. Similarly, M. bovis was isolated from nasal swabs 80 and 85 days after inoculation in one of three deer from the low dose group and 63 and 80 days after inoculation from two of four deer in the high dose group. Intratonsilar inoculation with M. bovis results in lesions similar to those seen in naturally infected white-tailed deer; therefore, it represents a suitable model of natural infection. These results also indicate that M. bovis persists in tonsilar crypts for prolonged periods and can be shed in saliva and nasal secretions. These infected fluids represent a likely route of disease transmission to other animals or humans.

An animal movement model incorporating home range and habitat selection

Wildlife biologists are often interested in how an animal uses space and the habitat resources within that space. We propose a single model that estimates an animal’s home range and habitat selection parameters within that range while accounting for the inherent autocorrelation in frequently sampled telemetry data. The model is applied to brown bear telemetry data in southeast Alaska.

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.