Sampling Considerations for Disease Surveillance in Wildlife Populations

Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estima...

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Bibliographic Details
Published in:The Journal of wildlife management 2008-01, Vol.72 (1), p.52-60
Main Authors: Nusser, Sarah M, Clark, William R, Otis, David L, Huang, Ling
Format: Article
Language:English
Subjects:
Animal populations
case studies
Chronic wasting disease
Creutzfeldt-Jakob disease
Deer
Design
disease detection
Disease models
disease prevalence
Disease prevalence rates
disease surveillance
Disease transmission
Ecologists
Epidemiology
estimation
Estimators
MANAGEMENT AND CONSERVATION
Odocoileus virginianus
Population
Probability
probability analysis
sample design
sampling
Sampling studies
simulation models
Special Section: Observational Studies
Studies
Surveillance
waiting time distribution
Wasting syndrome
wildlife diseases
wildlife management
Wildlife population estimation
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Summary:Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife scientists often assume probability sampling and random disease distributions to calculate sample sizes, convenience samples (i.e., samples of readily available animals) are typically used, and disease distributions are rarely random. We demonstrate how landscape-based simulation can be used to explore properties of estimators from convenience samples in relation to probability samples. We used simulation methods to model what is known about the habitat preferences of the wildlife population, the disease distribution, and the potential biases of the convenience-sample approach. Using chronic wasting disease in free-ranging deer (Odocoileus virginianus) as a simple illustration, we show that using probability sample designs with appropriate estimators provides unbiased surveillance parameter estimates but that the selection bias and coverage errors associated with convenience samples can lead to biased and misleading results. We also suggest practical alternatives to convenience samples that mix probability and convenience sampling. For example, a sample of land areas can be selected using a probability design that oversamples areas with larger animal populations, followed by harvesting of individual animals within sampled areas using a convenience sampling method.
ISSN:0022-541X
1937-2817
DOI:10.2193/2007-317