Publications

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

EFI publications

Pennisi, E. 2019. An ecologicst with an eye toward forecasting the future. ScienceInsider Interview.

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. DOI: 10.1126/science.293.5530.657

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. 288 pp. ISBN 9781400885459

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

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

Houlahan J.E., et al. 2017. The priority of prediction in ecological understanding. Oikos 126: 1–7. https://doi.org/10.1111/oik.03726

Luo, Y., T. F. Keenan, and M. Smith. 2014. Predictability of the Terrestrial Carbon Cycle. Global Change Biology, 21(5): 1737–51. https://doi.org/10.1111/gcb.12766

Luo, Y., et al. 2011. Ecological forecasting and data assimilation in a data-rich era. Ecological Applications, 21(5): 1429-1442. https://doi.org/10.1890/09-1275.1

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

Petchey, O. L., et al. 2015. The ecological forecast horizon, and examples of its uses and determinants. Ecology Letters, 18: 597-611. https://doi.org/10.1111/ele.12443

Shuman, F.G. 1989. History of numerical weather prediction at the National Meteorological Center. Weather Forecast, 4:286–296. https://doi.org/10.1175/1520-0434(1989)004%3C0286:HONWPA%3E2.0.CO;2

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. https://doi.org/10.1890/09-1394.1

Methods Papers

Clark, J. S. 2005. Why Environmental Scientists Are Becoming Bayesians. Ecology Letters, 8: 2–14. https://doi.org/10.1111/j.1461-0248.2004.00702.x

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

Dormann, C. F., et al. 2018. Model averaging in ecology: a review of Bayesian, information‐theoretic and tactical approaches for predictive inference. Ecological Monographs, 88(4); 485-504. https://doi.org/10.1002/ecm.1309

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

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

Hooten, M., and N. T. Hobbs. 2015. A guide to Bayesian model selection for ecologists. Ecological Monographs, 85:3–28. https://doi.org/10.1890/14-0661.1

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

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

Decision Support

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. Wiley-Blackwell. 312 pp. ISBN: 978-1-444-33341-1

Hobday, A.J., et al. 2019. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsy210

Ketz, A. C., et al. 2016. Informing management with monitoring data: the value of Bayesian forecasting. Ecosphere, 7(11): 01587. http://doi.org/10.1002/ecs2.1587

Milly, P.C.D., et al. 2008. Stationarity Is Dead: Whither Water Management? Science, 319: 573–74. DOI: 10.1126/science.1151915

Morgan, M. G. 2014. Use (and Abuse) of Expert Elicitation in Support of Decision Making for Public Policy. Proceedings of the National Academy of  Science, U.S.A., 111: 7176–84. https://doi.org/10.1073/pnas.1319946111

Williams, P. J., and M. B. Hooten. 2016. Combining statistical inference and decisions in ecology. Ecological Applications, 26:1930–1942. https://doi.org/10.1890/15-1593.1

Case Studies

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

Hardegree, S.P., J.T. Abatzoglou, M.W. Brunson, M.J. Germino, K.C. Hegewisch, C.A. Moffte, D.S. Pilliod, B.A.Roundy, A.R. Boehm, G.R. Meredith. 2018. Weather-centric rangeland revegetation planning. Randeland Ecology & Management, 71(1): 1-11. https://doi.org/10.1016/j.rama.2017.07.003

Hobbs N.T., et al. 2015. State-space modeling to support management of brucellosis in the Yellowstone bison population. Ecological Monographs, 85:525–556. https://doi.org/10.1890/14-1413.1

Ibanez, I., et al. 2014. Integrated assessment of biological invasions. Ecological Applications, 24(1): 25–37. https://doi.org/10.1890/13-0776.1

Kuikka, S., J. Vanhatalo, and H. Pulkkinen. 2014. Experiences in Bayesian Inference in Baltic Salmon Management. Statistical Science, 29(1): 42–49. http://dx.doi.org/10.1214/13-STS431

LaDeau, S. L., et al. 2011. Data-Model Fusion to Better Understand Emerging Pathogens and Improve Infectious Disease Forecasting. Ecological Applications, 21: 1443–60. https://doi-org.proxy.library.nd.edu/10.1890/09-1409.1

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. https://doi.org/10.1371/journal.pone.0010036

Paniw, M. et al. 2019. Life history responses of meerkats to seasonal changes in extreme environments. Science, 363(6427): 631-635. DOI: 10.1126/science.aau5905 *Received 2019 Ecological Forecasting Oustanding Publication Award

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

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 2018 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