Publications

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

EFI publications

Dietze, M. and H. Lynch. 2019. Forecasting a bright future for ecology. Frontiers in Ecology and the Environment. https://doi.org/10.1002/fee.1994

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.

Currie, D.J. 2018. Where Newton might have taken Ecology. Global Ecology and Biogeography 28: 18-27. https://doi.org/10.1111/geb.12842

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

Hobday, A.J., J. R. Hartog, J.P. Manderson, K.E. Mills, M.J. Oliver, A.J. Pershing, S. Siedlecki. 2019. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES Journal of Marine Science, fsy210. https://doi.org/10.1093/icesjms/fsy210

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