Scraping Behavior in Male White-tailed Deer as a Potential Means of Transmitting Chronic Wasting Disease

Chronic wasting disease (CWD) has become a concern for wildlife managers and hunters across the United States. High prevalence of chronic wasting disease (CWD) in older male white-tailed deer (Odocoileus virginianus) suggests that sex-specific social behavior may contribute to the spread of the disease among males. Scraping is a marking behavior performed by male white-tailed deer during the rut in which a pawed depression and associated over-hanging branch are marked with saliva, glandular secretions, urine, and feces. We placed 71 and 35 motion-activated cameras on scrapes in DeSoto National Wildlife Refuge in western Nebraska and eastern Iowa from Oct. – Nov. 2005 and Sept. – Nov. 2006, respectively. We recorded 5009 encounters and 1830 direct interactions. We developed an ethogram of behaviors of interest at scrapes. We found that males interacted with scrapes more frequently than females (P < 0.001). Male interactions were more complex, with 69% consisting of ≥2 observed behaviors versus 25% and 13% for females and fawns. We identified individual male deer ≥2.5 years old and determined the minimum number of different scrapes individuals visited and the number of individuals that visit a single scrape. Individuals that appeared on camera ≥5 times visited a mean of 3.9 scrapes (range = 1-15) and traveled a mean minimum distance of 978 m between consecutive scrapes. A mean of 5.1 individuals visited a single scrape, and up to 43% of individuals returned to a scrape previously visited at least once. We modeled Risk Values based on frequency of occurrence, duration, and Threat Values of each behavior, for contacting and transmitting CWD prions at scrapes. Adult males had the highest total Risk Values for contacting CWD prions (114.1) and shedding prions (59.4). The “grasp-lick branch” behavior had the highest Risk Value for adult males for both contacting and transmitting prions. Our study reveals a sex specific social behavior in male white-tailed deer that has the potential to spread chronic wasting disease between adult males in the population.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

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.