Publications

Peer-reviewed Publications

Ketz, A. C., Storm, D. J., & Samuel, M. D., 2019. Chronic wasting disease and implications for cervid populations. CAB Reviews, 14(38):1-15(DOI: 10.1079/PAVSNNR201914038 )

Ketz, A. C., Johnson, T. L., Hooten, M. B. & Hobbs, N. T., 2019. A hierarchical Bayesian nested multinomial approach for handling missing classification data. Ecology and Evolution, 9(6), 3130-3140(DOI: 10.1002/ecs2.1587 )

Ketz, A. C., Johnson, T. L., Monello, R. J., Mack, J., George, J. L., Kraft, B. R., Wild, M. A., Hooten, M. B. & Hobbs, N. T., 2018. Estimating abundance of an open population with an N -mixture model using auxiliary data on animal movements. Ecological Applications, 28(3): 816-825(DOI: 10.1002/eap.1692 )

Ketz, A. C., Johnson, T. L., Monello, R. J. & Hobbs, N. T., 2016. Informing management with monitoring data: the value of Bayesian forecasting Ecosphere, 7(11)(DOI: 10.1002/ecs2.1587 )

In Prep

Ketz, A. C., Storm, D. J., Russell, R. E., Samuel, M. D. & Walsh, D. P., An integrated population model incorporating dynamic spatial-temporal disease transmission.

Ketz, A. C., Storm, D. J. & Walsh, D. P., Bayesian integrated age-period survival modeling for complete lifespan analysis

Ketz, A. C., Storm, D. J. & Walsh, D. P., Bayesian integrated age-period survival modeling for complete lifespan analysis

Ketz, A. C., Storm, D. J. & Samuel, M. D., Implications of pathogen-mediated selection on heterogeneous transmission and survival for cervids exposed to chronic wasting disease

Ketz, A. C., Storm, D. J. & Walsh, D. P., Using semi-supervised machine learning anomaly detection for prediction of parturition of ungulates.