A new statistical approach to disease surveillance may improve scientists’ and managers’ ability to detect chronic wasting disease earlier in white-tailed deer by targeting higher-risk animals.
A new statistical approach to disease surveillance may improve scientists’ and managers’ ability to detect chronic wasting disease earlier in white-tailed deer by targeting higher-risk animals. This approach can also provide financial and personnel savings for agencies that are required to monitor for wildlife diseases, including the National Park Service, or NPS.
Chronic wasting disease, or CWD, is a major health concern for wild deer populations, and it is present in more than 20 states. Early detection of CWD gives wildlife managers more options to minimize establishment of the disease and to limit its geographic spread.
Many land managers have relied on random sampling to detect CWD, which makes it difficult to achieve sufficient sample sizes needed to detect the disease if it is present. That’s why a group of researchers developed and tested a new statistical model to increase sampling efficiency, making CWD early detection more feasible and less expensive. Their paper, “Application of a Bayesian Weighted Surveillance Approach for Detecting Chronic Wasting Disease in White-tailed Deer,” was published in the Journal of Applied Ecology on June 18.
“This has been a fantastic opportunity for collaboration,” said Dr. Jenny Powers, Acting Chief, Wildlife Conservation Branch of the National Park Service and co-author of the paper. “Parks provide the perfect natural laboratory, and our partners bring expertise that makes this modeling approach a benefit to all land managers.”
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Image via USGS.
