Counts of birds attending leks traditionally have been used as an index to the population size of greater sage‐grouse (Centrocercus urophasianus) and, more recently, as a means to estimate population size. The relationship between this index and the actual population has not been studied. We used intensive counts of individually marked and unmarked greater sage‐grouse on leks to evaluate how sex and age of birds, time of day, and time of season impact lek‐attendance patterns and lek counts. These within‐season sources of variation need to be considered when estimating detection probability of birds on leks and ultimately adjusting the lek‐count index to estimate true population parameters. On average, 42% of marked adult males, 4% of marked hens, and 19% of yearling males were observed on leks per sighting occasion with all 15 known leks being intensively counted. We discovered that lek counts as currently conducted may be useful as an index to greater sage‐grouse populations, but standardization of protocols is needed to allow for better spatial and temporal comparisons of lek‐count data. Also the probability of detecting birds on leks must be estimated in order to relate lek counts to population parameters. Lastly, we evaluated use of the bounded‐count methodology for correcting lek‐count data. We showed large biases associated with this technique and below‐nominal coverage of confidence intervals even at large numbers of counts, demonstrating the unreliability of the bounded‐count method to correct lek‐count data.

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

A guidance committee, composed of representatives from several State and Federal wildlife agencies, was instrumental in helping us frame the decision context for this risk assessment.

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation—especially along the veterinary/ecological research interface—remains.