You can also find everything on my Google Scholar profile.


Taylor, S.D., Browning, D.M., Baca, R.A., Gao, F. Accepted - In Press. Constraints and Opportunities for Detecting Land Surface Phenology in Drylands. Journal of Remote Sensing. [Preprint, Code, Data & Code Archive]

Browning, D. M., Russell, E. S., Ponce-Campos, G. E., Kaplan, N., Richardson, A. D., Seyednasrollah, B., … Taylor, S. D. 2021. Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework. Ecological Indicators, 131, 108147.

Taylor, Shawn D., and Dawn M. Browning. 2021. Multi-Scale Assessment of a Grassland Productivity Model. Biogeosciences 18 (6): 2213–20. [Code, Data & Code Archive]

Prince, K., Taylor, S., & Angelini, C. 2020. A global, cross-system meta-analysis of polychlorinated biphenyl biomagnification. Environmental Science & Technology.

Taylor, S.D., Marconi, S. 2020. Rethinking global carbon storage potential of trees. A comment on Bastin et al. (2019). Annals of Forest Science 77, 23. [Code, Data & Code Archive, Preprint]

Taylor, S.D., White, E.P., 2019. Automated data-intensive forecasting of plant phenology throughout the United States. Ecological Applications. [Code, Code Archive, Preprint]

Taylor S.D. 2019. Estimating flowering transition dates from status-based phenological observations: a test of methods. PeerJ 7:e7720 [Code, Code Archive, Preprint]

Taylor, S.D., J.M. Meiners, K. Riemer, M.C. Orr, E.P White. 2019. Comparison of large-scale citizen science data and long-term study data for phenology modeling. Ecology [Preprint,Code]

Harris, D.J., S.D. Taylor, E.P. White. 2018. Forecasting biodiversity in breeding birds using best practices. PeerJ, 6:e4278 [Code, Code Archive, Preprint]

White, E.P., G.M. Yenni, S.D. Taylor, E.M. Christensen, E.K. Bledsoe, J.L. Simonis, S.K.M. Ernest. 2018. Developing an automated iterative near-term forecasting system for an ecological study. Methods in Ecology and Evolution [Website, Data, Code, Preprint]


Taylor, SD and White, EP, 2020. Influence of climate forecasts, data assimilation, and uncertainty propagation on the performance of near-term phenology forecasts. bioRxiv,
[Code, Data & Code Archive]

Taylor, SD and Guralnick, RP, 2019. Opportunistically collected photographs can be used to estimate large-scale phenological trends. bioRxiv, 794396. [Data & Code Archive]

Taylor, SD. 2018. “NEON NIST Data Science Evaluation Challenge: Methods and Results of Team Shawn.” PeerJ Preprints 6: e26967v1.