Publications

My publications also appear on Google Scholar and ORCID.

Acosta, J. P., Park, S. W., Stewart, D., Lozano-Carvazos, E. A., Webb, S. L., & Schafer, T. L. J. (2026). Applying machine learning to interpolate movement trajectories of desert bighorn sheep. Environmental and Ecological Statistics. https://doi.org/10.1007/s10651-026-00713-w

Hoose, B. W., Frisbie, M., Schafer, T. L. J., Wu, X. B., Lopez, R. R., & Pierce, B. L. (2026). Landscape drivers of scaled quail occurrence across a broad spatiotemporal scale: Implications for habitat management. The Journal of Wildlife Management. https://doi.org/10.1002/jwmg.70285

Ren, R., Hooten, M. B., Schafer, T. L. J., Calzada, N. M., Hoose, B. W., Womble, J. N., & Gende, S. (2026). A multi-stage Bayesian approach to fit spatial point process models. Spatial Statistics. https://doi.org/10.1016/j.spasta.2026.100975

Schafer, T., Feng, M.-L., Che-Castaldo, J., Che-Castaldo, C., Matteson, D., & Getmansky Sherman, M. (2026). Quantifying temporal instability in ecological assemblages using modern portfolio theory. Preprint; Under Review at Ecography. https://doi.org/10.22541/authorea.15009261/v1

Schafer, T. L. J., & Matteson, D. S. (2024). Locally adaptive shrinkage priors for trends and breaks in count time series. Technometrics. https://doi.org/10.1080/00401706.2024.2407316

Wu, H., Schafer, T. L. J., & Matteson, D. S. (2024). Trend and variance adaptive Bayesian changepoint analysis and local outlier scoring. Journal of Business & Economic Statistics, 1–21. https://doi.org/10.1080/07350015.2024.2362269

Wu, H., Schafer, T. L. J., Ryan, S., & Matteson, D. S. (2024). Drift vs Shift: Decoupling trends and changepoint analysis. Technometrics, 1–16. https://doi.org/10.1080/00401706.2024.2365730

Davidow, M., Schafer, T. L. J., Merow, C., Che-Castaldo, J. P., Düker, M.-C., Feng, M.-L. E., & Matteson, D. S. (2023). Clustering future scenarios based on predicted range maps. Methods in Ecology and Evolution, 14, 1346–1360. https://doi.org/10.1111/2041-210X.14080

Owolabi, O. O., Schafer, T. L. J., Smits, G. E., Sengupta, S., Ryan, S., Wang, L., Matteson, D. S., Sherman, M. G., & Sunter, D. A. (2023). Role of variable renewable energy penetration on electricity price and its volatility across independent system operators in the United States. Data Science in Science. https://doi.org/10.1080/26941899.2022.2158145

VonBank, J. A., Cunningham, S. A., Schafer, T. L. J., Weegman, M. D., Link, P. T., Wikle, C. K., Kraai, K. J., Collins, D. P., & Ballard, B. M. (2023). Joint use of location and acceleration data to quantify habitat use transitions in arctic-nesting geese. Scientific Reports, 13, 2132. https://doi.org/10.1038/s41598-023-28937-x

Cunningham, S. A., Schafer, T. L. J., Wikle, C. K., Ballard, B. M., VonBank, J. A., Bearhop, S., Hilton, G. M., Walsh, A. J., Griffin, L. R., Fox, A. D., & Weegman, M. D. (2022). Time-varying effects of local weather on behavior and probability of breeding deferral in two Arctic-nesting goose populations. Oecologia, 201, 369–383. https://doi.org/10.1007/s00442-022-05300-x

Feng, M.-L. E., Owolabi, O. O., Schafer, T. L. J., Sengupta, S., Wang, L., Matteson, D. S., Che-Castaldo, J. P., & Sunter, D. A. (2022). Analysis of animal-related electric outages using species distribution models and community science data. Environmental Research: Ecology, 1(1), 011004. https://doi.org/10.1088/2752-664X/ac7eb5

Schafer, T. L. J., Wikle, C. K., & Hooten, M. B. (2022). Bayesian inverse reinforcement learning for collective movement. The Annals of Applied Statistics, 16(2), 999–1013. https://doi.org/10.1214/21-AOAS1529

Schindler, A. R., Cunningham, S. A., Schafer, T. L. J., Sinnott, E. A., Clements, S. J., DiDonato, F. M., Mosloff, A. R., Walters, C., Shipley, A. A., Weegman, M. D., & Zhao, Q. (2022). Joint analysis of structured survey and citizen science data improves precision of bird population trends and the extent of improvement depends on life history strategy. Scientific Reports, 12, 20289. https://doi.org/10.1038/s41598-022-23603-0

Che-Castaldo, J. P., Cousin, R., Daryanto, S., Deng, G., Feng, M.-L. E., Gupta, R. K., Hong, D., McGranaghan, R. M., Owolabi, O. O., Qu, T., Ren, W., Schafer, T. L. J., Sharma, A., Shen, C., Sherman, M. G., Sunter, D. A., Wang, L., & Matteson, D. S. (2021). Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects. Environment Systems and Decisions. https://doi.org/10.1007/s10669-021-09822-2

Schliep, E. M., Schafer, T. L. J., & Hawkey, M. (2021). Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data. Journal of Quantitative Analysis in Sports, 17(3), 241–254. https://doi.org/10.1515/jqas-2020-0051

Schafer, T. L. J., Wikle, C. K., Ballard, B. M., VonBank, J. A., & Weegman, M. D. (2020). Bayesian Markov model with Pólya-Gamma sampling for estimating individual behavior transition probabilities from accelerometer classifications. Journal of Agricultural, Biological and Environmental Statistics, 25(3), 365–382. https://doi.org/10.1007/s13253-020-00399-y

Schafer, T. L. J., & Wikle, C. K. (2019). Alternative learning strategies for collective animal movement. JSM Proceedings, Statistical Computing Section.

Schafer, T. L. J., Breck, S. W., Baruch-Mordo, S., Lewis, D. L., Wilson, K. R., Mao, J. S., & Day, T. L. (2018). American black bear den-site selection and characteristics in an urban environment. Ursus, 29(1), 25–31. https://doi.org/10.2192/URSUS-D-17-00004.2