Publications

Publications are categorized as:
EFI Publications, Concept Papers, Methods Papers, Decision Support, and Case Studies

EFI publications

Dietze, M. C., et al. 2018. Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences 115(7): 1424-1432. https://doi.org/10.1073/pnas.1710231115

Dietze, M. C., et al. 2017. NEON workshop report: Iterative near-term ecological forecasting: Needs, opportunities, and challenges. pp 72. doi:10.6084/m9.figshare.4715317

Concepts papers

Clark, J. S., et al. 2001. Ecological Forecasts: An Emerging Imperative. Science, 293: 657–60.

Dietze M. C. 2017. Ecological Forecasting. Princeton University Press

Houlahan J., et al. 2017. The priority of prediction in ecological understanding. Oikos 126: 1–7.

Luo, Y., et al. 2011. Ecological forecasting and data assimilation in a data-rich era. Ecological Applications, 21(5): 1429-1442.

MacCracken, M. 2001. Prediction versus Projection—Forecast versus Possibility. WeatherZine, 26: 3–4.

Harris, D. J., S. D. Taylor, and E. P. White. 2018. Forecasting biodiversity in breeding birds using best practices. PeerJ 6:e4278; DOI 10.7717/peerj.4278

Dietze, M. C. 2017. Prediction in ecology: a first-principles framework. Ecological Applications, 27: 2048-2060. doi:10.1002/eap.1589

Luo, Y., T. F. Keenan, and M. Smith. 2014. Predictability of the Terrestrial Carbon Cycle. Global Change Biology, 21(5): 1737–51.

Petchey, O. L., et al. 2015. The ecological forecast horizon, and examples of its uses and determinants. Ecological Letters, 18: 597-611.

Shuman, F. 1989. History of numerical weather prediction at the National Meteorological Center. Weather Forecast, 4:286–296.

Weng, E., and Y. Luo. 2011. Relative Information Contributions of Model vs. Data to Short- and Long-Term Forecasts of Forest Carbon Dynamics. Ecological Applications, 21: 1490–1505.

Methods Papers

Clark, J. S. 2005. Why Environmental Scientists Are Becoming Bayesians. Ecol. Lett., 8: 2–14.

Dormann, C. F., et al. 2018. Model averaging in ecology: a review of Bayesian, information‐theoretic and tactical approaches for predictive inference. Ecological Monographs. doi:10.1002/ecm.1309

Hooten, M., and N. T. Hobbs. 2015. A guide to Bayesian model selection for ecologists. Ecol Monogr 85:3–28.

Hyndman, RJ and G. Athanasopoulos. 2018. Forecasting: principles and practice. OTexts 2nd edition, pp 382. ISBN 978-0987507112

Conn, P. B., et al. 2018. A Guide to Bayesian Model Checking for Ecologists. Ecological Monographs. http://doi.org/doi:10.1002/ecm.1314

Medlyn, B. E., et al. 2015. Using ecosystem experiments to improve vegetation models. Nature Climate Change, 5(6): 528–534. http://doi.org/10.1038/nclimate2621

van Oijen, M. 2017. Bayesian methods for quantifying and reducing uncertainty and error in forest models. Current Forestry Reports, 3(4): 269–280. http://doi.org/10.1007/s40725-017-0069-9

Wikle, C., and L. Berliner. 2007. A Bayesian tutorial for data assimilation. Physica D: Nonlinear Phenomena, 230(1–2): 1–16. http://doi.org/10.1016/j.physd.2006.09.017

Evensen, G. 2009b. The Ensemble Kalman Filter for Combined State and Parameter Estimation. IEEE Control Syst. Mag., 29 (3): 83–104.

Doucet, A., and A. M. Johansen. 2011. A tutorial on particle filtering and smoothing: fifteen years later. In D. Crisan & B. Rozovskii (Eds.), The Oxford Handbook of Nonlinear Filtering (Vol. 12, pp. 656–704). Oxford University Press. http://doi.org/10.1.1.157.772

Decision Support

Morgan, M. G. 2014. Use (and Abuse) of Expert Elicitation in Support of Decision Making for Public Policy. Proc. Natl. Acad. Sci. U.S.A., 111: 7176–84.

Milly, P.C.D., et al. 2008. Stationarity Is Dead: Whither Water Management? Science, 319: 573–74.

Bradford, J. B., et al. 2018. Anticipitory natural resource science and management for a dynamic future. Frontiers in Ecology and the Environment. http://doi.org/10.1002/fee.1806

Gregory, R., et al. 2012. Structured Decision Making: A Practical Guide to Environmental Management Choices. New York: Wiley.

Williams, P. J., and M. B. Hooten. 2016. Combining statistical inference and decisions in ecology. Ecol Appl 26:1930–1942.

Ketz, A. C., et al. 2016. Informing management with monitoring data: the value of Bayesian forecasting. Ecosphere, 7(11): e01587. http://doi.org/10.1002/ecs2.1587

Case Studies

Kuikka, S., J. Vanhatalo, and H. Pulkkinen. 2014. Experiences in Bayesian Inference in Baltic Salmon Management. Statistical Sci., 29: 42–49.

Scales, K. L.,  et al. 2017. Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. Ecological Applications, 27(8): 2313–2329. http://doi.org/10.1002/eap.1610

LaDeau, S. L., et al. 2011. Data-Model Fusion to Better Understand Emerging Pathogens and Improve Infectious Disease Forecasting. Ecological Applications, 21: 1443–60.

Hobbs N.T., et al. 2015. State-space modeling to support management of brucellosis in the Yellowstone bison population. Ecological Monographs, 85:525–556.

Ong, J.B.S., et al. 2010. Real-time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-like Illness from General Practice and Family Doctor Clinics in Singapore. PLoS One, 5: e10036.

Ibanez, I., et al. 2014. Integrated assessment of biological invasions. Ecological Applications, 24(1): 25–37.

Chen, Y., et al. 2011. Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies. Science, 334: 787–791. http://doi.org/10.1126/science.1209472

Thomas, R. Q., et al. 2017. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments. Biogeosciences 14: 3525–3547. 
*Received Ecological Forecasting Oustanding Publication Award

Tredennick, A. T., et al. 2016. Forecasting climate change impacts on plant populations over large spatial extents. Ecosphere, 7(10): e01525. http://doi.org/10.1002/ecs2.1525