REFERENCES
Alper, B., N. H. Riche, F. Chevalier, J. Boy, and M. Sezgin. 2017. Visualization literacy at elementary school. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, Denver Colorado USA. Pages 5485–5497. http://dx.doi.org/10.1145/3025453.3025877
Armitage, D. R., R. Plummer, F. Berkes, R. I. Arthur, A. T. Charles, I. J. Davidson-Hunt, A. P. Diduck, N. C. Doubleday, D. S. Johnson, M. Marschke, P. McConney, E. W. Pinkerton, and E. K. Wollenberg. 2009. Adaptive co-management for social-ecological complexity. Frontiers in Ecology and the Environment 7: 95–102. http://www.jstor.org/stable/25595062
Arsenault, K. R., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar, S. V., … Verdin, J. P. 2020. The NASA hydrological forecast system for food and water security applications. Bulletin of the American Meteorological Society , 101(7): E1007–E1025. https://doi.org/10.1175/BAMS-D-18-0264.1
Bakermans, M. H., and Pfeifer, G. 2018. A model for translational science in undergraduate classrooms. Frontiers in Ecology and the Environment , 16(6): 319–321. https://doi.org/10.1002/fee.1920
Belia, S., F. Fidler, J. Williams, and G. Cumming. 2005. Researchers misunderstand confidence intervals and standard error bars.Psychological Methods 10: 389–396. https://doi.org/10.1037/1082-989X.10.4.389
Belovsky, G. E., D. B. Botkin, T. A. Crowl, K. W. Cummins, J. F. Franklin, M. L. Hunter, A. Joern, D. B. Lindenmayer, J. A. MacMahon, C. R. Margules, and J. M. Scott. 2004. Ten suggestions to strengthen the science of ecology. BioScience 54: 345–351. https://doi.org/10.1007/s10531-005-2631-1
Berthet, L., O. Piotte, É. Gaume, R. Marty, and C. Ardilouze. 2016. Operational forecast uncertainty assessment for better information to stakeholders and crisis managers. E3S Web of Conferences 7. https://doi.org/10.1051/e3sconf/20160718005
Bird, J. P., B. K. Woodworth, R. A. Fuller, and J. D. Shaw. 2021. Uncertainty in population estimates: A meta-analysis for petrels.Ecological Solutions and Evidence 2: 1–13.  https://doi.org/10.1002/2688-8319.12077
Bodner, K., C. Rauen Firkowski, J. R. Bennett, C. Brookson, M. Dietze, S. Green, J. Hughes, J. Kerr, M. Kunegel-Lion, S. J. Leroux, E. McIntire, P. K. Molnár, C. Simpkins, E. Tekwa, A. Watts, and M. J. Fortin. 2021. Bridging the divide between ecological forecasts and environmental decision making. Ecosphere 12: e03869. https://doi.org/10.1002/ecs2.3869
Bonneau, G., H. Hege, C. R. Johnson, M. M. Oliveira, K. C. Potter, P. Rheingans, and T. Schultz. 2015. Chapter 1: Overview and state-of-the-art of uncertainty visualization in Scientific Visualization , pages 3-27. Springer.
Börner, K., A. Bueckle, and M. Ginda. 2019. Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments.Proceedings of the National Academy of Sciences of the United States of America 116: 1857–1864. https://doi.org/10.1073/pnas.1807180116
Börner, K., A. Maltese, R. N. Balliet, and J. Heimlich. 2016. Investigating aspects of data visualization literacy using 20 information visualizations and 273 science museum visitors.Information Visualization 15: 198–213. https://doi.org/10.1177/1473871615594652
Boukhelifa, N., and D.J. Duke. Uncertainty visualization - why might it fail? 2009. In: Conference on Human Factors in Computing Systems - Proceedings (April), pp. 4051–4056. https://doi.org/10.1145/1520340.1520616
Briggs, D. J., C. E. Sabel, and K. Lee. 2009. Uncertainty in epidemiology and health risk and impact assessment. Environmental Geochemistry and Health 31: 189–203. https://doi.org/10.1007/s10653-008-9214-5
Bybee, R. W., J. A. Taylor, A. Gardner, P. V. Scotter, J. C. Powell, A. Westbrook, and N. Landes. 2006. The BSCS 5E Instructional Model: Origins, effectiveness, and applications. Colorado Springs, CO, USA.
Carey, C. C., K. J. Farrell, A. G. Hounshell, and K. O’Connell. 2020. Macrosystems EDDIE teaching modules significantly increase ecology students’ proficiency and confidence working with ecosystem models and use of systems thinking. Ecology and Evolution 10: 12515–12527. https://doi.org/10.1002/ece3.6757
Carey, C. C., W. M. Woelmer, M. E. Lofton, R. J. Figueiredo, B. J. Bookout, R. S. Corrigan, V. Daneshmand, A. G. Hounshell, D. W. Howard, A. S. L. Lewis, R. P. McClure, H. L. Wander, N. K. Ward, and R. Q. Thomas. 2022. Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting. Inland Waters 12: 107–120. https://doi.org/10.1080/20442041.2020.1816421
Carr, R. H., B. Montz, K. Semmens, K. Maxfield, S. Connolly, P. Ahnert, R. Shedd, and J. Elliott. 2018. Major risks, uncertain outcomes: Making ensemble forecasts work for multiple audiences. Weather and Forecasting 33: 1359–1373. https://doi.org/10.1175/WAF-D-18-0018.1
Centers for Disease Control and Prevention. 2022. CDC Launches New Center for Forecasting and Outbreak Analytics. Press Release. 19 April 2022. https://stacks.cdc.gov/view/cdc/116460
Chang, W., J. Cheng, J. J. Allaire, C. Sievert, B. Schloerke, Y. Xie, J. Allen, J. McPherson, A. Dipert, B. Borges. 2022. shiny: Web Application Framework for R. https://shiny.rstudio.com/
Cheong, L., S. Bleisch, A. Kealy, K. Tolhurst, T. Wilkening, and M. Duckham. 2016. Evaluating the impact of visualization of wildfire hazard upon decision-making under uncertainty. International Journal of Geographical Information Science 30: 1377–1404.
Cid, C. R., and R. V. Pouyat. 2013. Making ecology relevant to decision making: the human-centered, place-based approach. Frontiers in Ecology and the Environment 11: 447–448. https://doi.org/10.1890/1540-9295-11.8.447
Clemen, R. T., and T. Reilly. 2004. Making hard decisions with decision tools suite. 1st edition. Cengage Learning, Pacific Grove, Calif.
Correll, M., D. Moritz, and J. Heer. 2018. Value-suppressing uncertainty palettes. Conference on Human Factors in Computing Systems - Proceedings 2018-April. 1–11. https://doi.org/10.1145/3173574.3174216
Cvitanovic, C., S. K. Wilson, C. J. Fulton, G. R. Almany, P. Anderson, R. C. Babcock, N. C. Ban, R. J. Beeden, M. Beger, J. Cinner, K. Dobbs, L. S. Evans, A. Farnham, K. J. Friedman, K. Gale, W. Gladstone, Q. Grafton, N. A. J. Graham, S. Gudge, P. L. Harrison, T. H. Holmes, N. Johnstone, G. P. Jones, A. Jordan, A. J. Kendrick, C. J. Klein, L. R. Little, H. A. Malcolm, D. Morris, H. P. Possingham, J. Prescott, R. L. Pressey, G. A. Skilleter, C. Simpson, K. Waples, D. Wilson, and D. H. Williamson. 2013. Critical research needs for managing coral reef marine protected areas: Perspectives of academics and managers. Journal of Environmental Management 114: 84–91. https://doi.org/10.1016/j.jenvman.2012.10.051
Deitrick, S., and E. A. Wentz. 2015. Developing implicit uncertainty visualization methods motivated by theories in decision science.Annals of the Association of American Geographers 105(3): 531–551. https://doi.org/10.1080/00045608.2015.1012635
Dietze, M. C. 2017. Ecological Forecasting. Princeton: Princeton University Press.
Dietze, M. C., A. Fox, L. M. Beck-Johnson, J. L. Betancourt, M. B. Hooten, C. S. Jarnevich, T. H. Keitt, M. A. Kenney, C. M. Laney, L. G. Larsen, H. W. Loescher, C. K. Lunch, B. C. Pijanowski, J. T. Randerson, E. K. Read, A. T. Tredennick, R. Vargas, K. C. Weathers, and E. P. White. 2018. Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences 115: 1424–1432. https://doi.org/10.1073/pnas.17102311
Eisenhauer, E., Williams, K. C., Margeson, K., Paczuski, S., Hano, M. C., and Mulvaney, K. 2021. Advancing translational research in environmental science: The role and impact of social sciences.Environmental Science and Policy , 120: 165–172. https://doi.org/10.1016/j.envsci.2021.03.010
Enquist, C. A. F., Jackson, S. T., Garfin, G. M., Davis, F. W., Gerber, L. R., Littell, J. A., … Shaw, M. R. 2017. Foundations of translational ecology. Frontiers in Ecology and the Environment , 15(10): 541–550. https://doi.org/10.1002/fee.1733
Fagerlin, A., C. Wang, and P. A. Ubel. 2005. Reducing the influence of anecdotal reasoning on people’s health care decisions: Is a picture worth a thousand statistics? Medical Decision Making 25: 398–405. https://doi.org/10.1177/0272989X05278931
Fawcett, L. 2018. Using interactive Shiny applications to facilitate research-informed learning and teaching. Journal of Statistics Education 26: 2–16. https://doi.org/10.1080/10691898.2018.1436999
Ferstl, F., M. Kanzler, M. Rautenhaus, and R. Westermann. 2017. Time-hierarchical clustering and visualization of weather forecast ensembles. IEEE Transactions on Visualization and Computer Graphics 23: 831–840. https://doi.prg/10.1109/TVCG.2016.2598868
Galesic, M., R. Garcia-Retamero, and G. Gigerenzer. 2009. Using icon arrays to communicate medical risks: Overcoming low numeracy.Health Psychology 28: 210–216. https://doi.org/10.1037/a0014474
Garcia-Retamero, R., M. Galesic, and G. Gigerenzer. 2010. Do icon arrays help reduce denominator neglect? Medical Decision Making 30: 672–684. https://doi.org/10.1177/0272989X10369000
Gerst, M. D., M. A. Kenney, A. E. Baer, A. Speciale, J. F. Wolfinger, J. Gottschalck, S. Handel, M. Rosencrans, and D. Dewitt. 2019. Using visualization science to improve expert and public understanding of probabilistic temperature and precipitation outlooks. Weather, Climate, and Society 12: 117-133. https://doi.org/10.1175/WCAS-D-18-0094.1
Gregory, R., L. Failing, M. Harstone, G. Long, T. McDaniels, and D. Ohlson. 2012. Structured decision making: A practical guide to environmental management choices. John Wiley and Sons.
Halpern, B. S., H. M. Regan, H. P. Possingham, and M. A. McCarthy. 2006. Accounting for uncertainty in marine reserve design. Ecology Letters 9: 2–11. https://doi.org/ 10.1111/j.1461-0248.2005.00827.x
Hammond, J. S., R. L. Keeney, and H. Raiffa. 2002. Smart choices: A practical guide to making better decisions. Crown Business, New York, NY.
Hemming, V., A. E. Camaclang, M. S. Adams, M. Burgman, K. Carbeck, J. Carwardine, I. Chadès, L. Chalifour, S. J. Converse, L. N. K. Davidson, G. E. Garrard, R. Finn, J. R. Fleri, J. Huard, H. J. Mayfield, E. M. Madden, I. Naujokaitis‐Lewis, H. P. Possingham, L. Rumpff, M. C. Runge, D. Stewart, V. J. D. Tulloch, T. Walshe, and T. G. Martin. 2022. An introduction to decision science for conservation. Conservation Biology 1–16. https://doi.org/10.1111/cobi.13868
Henri, D. A., L. M. Martinez-Levasseur, J. F. Provencher, C. D. Debets, M. Appaqaq, and M. Houde. 2022. Engaging Inuit youth in environmental research: Braiding Western science and Indigenous knowledge through school workshops. The Journal of Environmental Education 53: 261–279. https://doi.org/10.1080/00958964.2022.2125926
Hounshell, A. G., K. J. Farrell, and C. C. Carey. 2021. Macrosystems EDDIE teaching modules increase students’ ability to define, interpret, and apply concepts in macrosystems ecology. Education Sciences  11(8): 382. https://doi.org/10.3390/educsci11080382
Howes, E., and B. Cruz. 2009. Role-playing in science education: an effective strategy for developing multiple perspectives. Journal of Elementary Science Education 21: 33–46.
Hullman, J. 2020. Why authors don’t visualize uncertainty. IEEE Transactions on Visualization and Computer Graphics 26: 130–139. https://doi.org/10.1109/TVCG.2019.2934287
Huron, S., S. Carpendale, A. Thudt, A. Tang, and M. Mauerer. 2014. Constructive visualization. Pages 433–442 Proceedings of the 2014 Conference on Designing Interactive Systems. Association for Computing Machinery, New York, NY, USA.
Jackson-Blake, L. A., F. Clayer, E. De Eyto, A. S. French, M. D. Frías, D. Mercado-Bettín, T. Moore, L. Puértolas, R. Poole, K. Rinke, M. Shikhani, L. Van Der Linden, and R. Marcé. 2022. Opportunities for seasonal forecasting to support water management outside the tropics.Hydrology and Earth System Sciences 26: 1389–1406. https://doi.org/10.5194/hess-26-1389-2022
Joslyn, S., and S. Savelli. 2010. Communicating forecast uncertainty: Public perception of weather forecast uncertainty. Meteorological Applications 17: 180–195.  https://doi.org/10.1002/met.190
Kamal, A., P. Dhakal, A. Y. Javaid, V. K. Devabhaktuni, D. Kaur, J. Zaientz, and R. Marinier. 2021. Recent advances and challenges in uncertainty visualization: a survey. Journal of Visualization 24: 861–890. https://doi.org/10.1007/s12650-021-00755-1
Kasprzak, P., L. Mitchell, O. Kravchuk, and A. Timmins. 2020. Six years of Shiny in research – Collaborative development of web tools in R.The R Journal 12(2): 20-42. https://doi.org/10.32614/RJ-2021-004
Kinkeldey, C., A. M. MacEachren, M. Riveiro, and J. Schiewe. 2017. Evaluating the effect of visually represented geodata uncertainty on decision-making: systematic review, lessons learned, and recommendations. Cartography and Geographic Information Science44: 1–21. https://doi.org/10.1080/15230406.2015.1089792
Kox, T., H. Kempf, C. Lüder, R. Hagedorn, and L. Gerhold. 2018. Towards user-orientated weather warnings. International Journal of Disaster Risk Reduction 30: 74–80. https://doi.org/10.1016/j.ijdrr.2018.02.033
Larkin, J. H., and H. A. Simon. 1987. Why a diagram is (sometimes) worth ten thousand words. Cognitive Science 11: 65–100. https://doi.org/10.1111/j.1551-6708.1987.tb00863.x
Lechner, A. M., W. T. Langford, S. A. Bekessy, and S. D. Jones. 2012. Are landscape ecologists addressing uncertainty in their remote sensing data? Landscape Ecology 27: 1249–1261. https://doi.org/10.1007/s10980-012-9791-7
Lélé, S., and Norgaard, R. B. (2005). Practicing Interdisciplinarity.BioScience 55(11): 967–975. https://doi.org/10.1641/0006-3568(2005)055[0967:PI]2.0.CO;2
Lewis, A. S. L., C. R. Rollinson, A. J. Allyn, J. Ashander, S. Brodie, C. B. Brookson, E. Collins, M. C. Dietze, A. S. Gallinat, N. Juvigny-Khenafou, G. Koren, D. J. McGlinn, H. Moustahfid, J. A. Peters, N. R. Record, C. J. Robbins, J. Tonkin, and G. M. Wardle. 2022a. The power of forecasts to advance ecological theory. Methods in Ecology and Evolution 14: 746-756. https://doi.org/10.1111/2041-210X.13955
Lewis, A. S. L., W. M. Woelmer, H. L. Wander, D. W. Howard, J. W. Smith, R. P. McClure, M. E. Lofton, N. W. Hammond, R. S. Corrigan, R. Q. Thomas, and C. C. Carey. 2022b. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability.Ecological Applications 32: e2500. https://doi.org/10.1002/eap.2500
Link, J. S., T. F. Ihde, C. J. Harvey, S. K. Gaichas, J. C. Field, J. K. T. Brodziak, H. M. Townsend, and R. M. Peterman. 2012. Dealing with uncertainty in ecosystem models: The paradox of use for living marine resource management. Progress in Oceanography 102: 102–114. https://doi.org/10.1016/j.pocean.2012.03.008
Lofton, M.E., T.N. Moore, Thomas, R.Q., and C.C. Carey. 20 September 2022. Macrosystems EDDIE: Using Data to Improve Ecological Forecasts. Macrosystems EDDIE Module 7, Version 1. https://macrosystemseddie.shinyapps.io/module7.
Maltese, A., J. Harsh, and D. Svetina. 2015. Data visualization literacy: investigating data interpretation along the novice-expert continuum. Journal of College Science Teaching 45: 84.
McClintock, B. T., J. D. Nichols, L. L. Bailey, D. I. MacKenzie, W. L. Kendall, and A. B. Franklin. 2010. Seeking a second opinion: Uncertainty in disease ecology. Ecology Letters 13: 659–674. https://doi.org/10.1111/j.1461-0248.2010.01472.x
McKenzie, G., M. Hegarty, T. Barrett, and M. Goodchild. 2016. Assessing the effectiveness of different visualizations for judgments of positional uncertainty. International Journal of Geographical Information Science 30: 221–239. https://doi.org/10.1080/13658816.2015.1082566
Melbourne-Thomas, J., S. Wotherspoon, B. Raymond, and A. Constable. 2012. Comprehensive evaluation of model uncertainty in qualitative network analyses. Ecological Monographs 82: 505–519. https://doi.org/10.1890/12-0207.1
Miles, M. B., A. M. Huberman, J. Saldana. 2020. Qualitative data analysis: A methods sourcebook , 4th ed.; SAGE Publications Inc.: Thousand Oaks, CA, USA.
Milner-Gulland, E. J., and K. Shea. 2017. Embracing uncertainty in applied ecology. Journal of Applied Ecology . 54:2063–2068. https://doi.org/10.1111/1365-2664.12887
Moore, T. N., Carey, C.C. and Thomas, R. Q. 13 October 2021. Macrosystems EDDIE: Understanding Uncertainty in Ecological Forecasts. Macrosystems EDDIE Module 6, Version 1. http://module6.macrosystemseddie.org.
Moore, T. N., R. Q. Thomas, W. M. Woelmer, and C. C. Carey. 2022a. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4: 604–633. https://doi.org/10.3390/forecast4030033
Moore, T.N., C.C. Carey, and R.Q. Thomas. 2022b. Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting (Instructor Materials) ver 3. Environmental Data Initiative. https://doi.org/10.6073/pasta/1da866a2eb79be84195e785a4370010c
Nadav-Greenberg, L., S. L. Joslyn, and M. U. Taing. 2008. The effect of weather forecast uncertainty visualization on decision making.Journal of Cognitive Engineering and Decision Making 2: 24-47
Nativi, S., Mazzetti, P., and Craglia, M. 2021. Digital ecosystems for developing digital twins of the earth: The destination earth case.Remote Sensing 13(11): 1–25. https://doi.org/10.3390/rs13112119
National Oceanic and Atmospheric Administration. 2022. Strategic Plan or Fiscal Year 2022-2026. https://www.noaa.gov/sites/default/files/2022-06/NOAA_FY2226_Strategic_Plan.pdf
Olston, C., and J. D. Mackinlay. 2002. Visualizing data with bounded uncertainty. Pages 37–40 IEEE Symposium on Information Visualization, 2002. INFOVIS IEEE Comput. Soc, Boston, MA, USA. https://doi.org/10.1109/INFVIS.2002.1173145
Padilla, L. M., I. T. Ruginski, and S. H. Creem-Regehr. 2017a. Effects of ensemble and summary displays on interpretations of geospatial uncertainty data. Cognitive Research: Principles and Implications2: 1–16. https://doi.org/10.1186/s41235-017-0076-1
Padilla, L., P. S. Quinan, M. Meyer, and S. H. Creem-Regehr. 2017b. Evaluating the impact of binning 2D scalar fields. IEEE Transactions on Visualization and Computer Graphics 23: 431–440. https://doi.org/10.1109/TVCG.2016.2599106
Potter, K., P. Rosen, and C. R. Johnson. 2012. From quantification to visualization: A taxonomy of uncertainty visualization approaches.IFIP Advances in Information and Communication Technology 377: 226–247.
R Core Team. 2022. R: A Language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Raftery, A. E. 2016. Use and communication of probabilistic forecasts.Statistical Analysis and Data Mining 9: 397–410. https://doi.org/10.1002/sam.11302
Ramos, M. H., S. J. Van Andel, and F. Pappenberger. 2013. Do probabilistic forecasts lead to better decisions? Hydrology and Earth System Sciences 17: 2219–2232. https://doi.org/10.5194/hess-17-2219-2013
Rieley, M. 2018. Big data adds up to opportunities in math careers : Beyond the numbers: U.S. Bureau of Labor Statistics. https://www.bls.gov/opub/btn/volume-7/big-data-adds-up.htm.
Robinson, P., Genskow, K., and Shaw, B. 2012. Barriers and opportunities for integrating social science into natural resource management: lessons from National Estuarine Research Reserves. Environmental Management 998–1011. https://doi.org/10.1007/s00267-012-9930-6
Ruginski, I. T., A. P. Boone, L. M. Padilla, L. Liu, N. Heydari, H. S. Kramer, M. Hegarty, W. B. Thompson, D. H. House, and S. H. Creem-Regehr. 2016. Non-expert interpretations of hurricane forecast uncertainty visualizations. Spatial Cognition and Computation 16: 154–172. https://doi.org/10.1080/13875868.2015.1137577
Ruhl, N., P. Crumrine, J. Oberle, C. Richmond, S. Thomas, and S. Wright. 2022. Harnessing the Four-Dimensional Ecology Education Framework to redesign an introductory ecology course in a changing higher education landscape. Ecosphere 13:e03857. https://doi.org/10.1002/ecs2.3857
Selutin, V. D., and E. V. Lebedeva. 2017. Teaching probability theory and forecasting-based mathematical statistics to Bachelors of economics.Advances in Social Science, Education and Humanities Research 97: 264–268. https://doi.org/10.2991/cildiah-17.2017.46
Smith Mason, J., D. Retchless, and A. Klippel. 2017. Domains of uncertainty visualization research: a visual summary approach.Cartography and Geographic Information Science 44: 296–309. https://doi.org/10.1080/15230406.2016.1154804
Spiegelhalter, D., M. Pearson, and I. Short. 2011. Visualizing uncertainty about the future. Science 333: 1393–1400. https://doi.org/10.1126/science.1191181
Schwartz, M. W., Hiers, J. K., Davis, F. W., Garfin, G. M., Jackson, S. T., Terando, A. J., … Brunson, M. W. 2017. Developing a translational ecology workforce. Frontiers in Ecology and the Environment 15(10): 587–596. https://doi.org/10.1002/fee.1732
Tait, A. R., T. Voepel-Lewis, B. J. Zikmund-Fisher, and A. Fagerlin. 2010. The effect of format on parents’ understanding of the risks and benefits of clinical research: A comparison between text, tables, and graphics. Journal of Health Communication 15: 487–501. https://doi.org/10.1080/10810730.2010.492560
Tulloch, A. I. T., V. Hagger, and A. C. Greenville. 2020. Ecological forecasts to inform near-term management of threats to biodiversity.Global Change Biology 00: 1-13. https://doi.org/10.1111/gcb.15272
Turner, S. W. D., W. Xu, and N. Voisin. 2020. Inferred inflow forecast horizons guiding reservoir release decisions across the United States.Hydrology and Earth System Sciences 24: 1275–1291. https://doi.org/10.5194/hess-24-1275-2020
Vance-Chalcraft, H. D., and N. O. Jelks. 2022. Community-engaged learning to broaden the impact of applied ecology: A case study.Ecological Applications e2768. https://doi.org/10.1002/eap.2768
Vought, R.T., and K.K. Droegemeier. 2020. “M-20-29: Fiscal Year (FY) 2022 Administration Research and Development Budget Priorities and Cross-Cutting Actions.” https://www.whitehouse.gov/wp-content/uploads/2020/08/M-20-29.pdf.
Wesslen, R., A. Karduni, D. Markant, and W. Dou. 2022. Effect of uncertainty visualizations on myopic loss aversion and the equity premium puzzle in retirement investment decisions. IEEE Transactions on Visualization and Computer Graphics 28: 454–464. https://doi.org/10.1109/TVCG.2021.3114692
Wiggins, A., A. Young, and M. A. Kenney. 2018. Exploring visual representations to support data re-use for interdisciplinary science.Proceedings of the Association for Information Science and Technology 55: 554–563. https://doi.org/10.1002/pra2.2018.14505501060
Willson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2022. Assessing opportunities and inequities in undergraduate ecological forecasting education. https://doi.org/10.5281/zenodo/7702393
Woelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., … Willson, A. M. 2021. Ten simple rules for training yourself in an emerging field. PLoS Computational Biology 17(10): 1–12. https://doi.org/10.1371/journal.pcbi.1009440
Woelmer, W.M., R.Q. Thomas, T.N. Moore, and C.C. Carey. 2022a. Macrosystems EDDIE Module 8: Using Ecological Forecasts to Guide Decision-Making (Instructor Materials) ver 3. Environmental Data Initiative. https://doi.org/10.6073/pasta/ad8adb1329f2a75bdd522fd22f2cb201
Woelmer, W.M., T.N. Moore, R.Q. Thomas, and C.C. Carey. 2022b. Macrosystems EDDIE Module 8: Using Ecological Forecasts to Guide Decision-Making (R Shiny application) (v1.1). Zenodo. https://doi.org/10.5281/zenodo.7074674
Wu, J., K. B. Jones, H. Li, and O. L. Loucks. 2006. Scaling and uncertainty analysis in ecology. Methods and applications. Springer, New York.
Zikmund-Fisher, B. J., H. O. Witteman, M. Dickson, A. Fuhrel-Forbis, V. C. Kahn, N. L. Exe, M. Valerio, L. G. Holtzman, L. D. Scherer, and A. Fagerlin. 2014. Blocks, ovals, or people? Icon type affects risk perceptions and recall of pictographs. Medical Decision Making 34(4): 443–453. https://doi.org/10.1177/0272989X1351170