Hansen, L. J. & Hoffman, J. R. Climate Savvy: Adapting Conservation and Resource Management to a Changing World, Austral Ecology (Island Press, 2011).
Google Scholar
Huang, Q., Fleming, C., Robb, B., Lothspeich, A. & Songer, M. How different are species distribution model predictions? Application of a new measure of dissimilarity and level of significance to giant panda Ailuropoda melanoleuca. Ecol. Inform. 46, 114–124 (2018).
Google Scholar
Giorgi, F. & Mearns, L. O. Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method. Geophys. Res. Lett. 30, 1–4 (2003).
Google Scholar
Palmer, T. N., Doblas-Reyes, F. J., Hagedorn, R. & Weisheimer, A. Probabilistic prediction of climate using multi-model ensembles: From basics to applications. Philos. Trans. R. Soc. London B Biol. Sci. 360, 1991–1998 (2005).
Google Scholar
Dormann, C. F. et al. Prediction uncertainty of environmental change effects on temperate European biodiversity. Ecol. Lett. 11, 235–244 (2008).
Google Scholar
Spence, M. A. et al. A general framework for combining ecosystem models. Fish Fish. 19, 1031–1042 (2018).
Google Scholar
Janssen, P. H. M., Petersen, A. C., van der Sluijs, J. P., Risbey, J. S. & Ravetz, J. R. A guidance for assessing and communicating uncertainties. Water Sci. Technol. 52, 125–131 (2005).
Google Scholar
Frewer, L. The public and effective risk communication. Toxicol. Lett. 149, 391–397 (2004).
Google Scholar
Leiserowitz, A. A., Malbach, E. W., Roser-Renough, C., Smith, N. & Dawson, E. Climategate, public opinion, and the loss of trust. Am. Behav. Sci. 57, 818–837 (2012).
Google Scholar
Hyder, K. et al. Making modelling count: Increasing the contribution of shelf-seas community and ecosystem models to policy development and management. Mar. Policy 61, 291–302 (2015).
Google Scholar
Cartwright, S. J. et al. Communicating complex ecological models to non-scientist end users. Ecol. Model. 338, 51–59 (2016).
Google Scholar
Doyle, E. E. H., Johnston, D. M., Smith, R. & Paton, D. Communicating model uncertainty for natural hazards: A qualitative systematic thematic review. Int. J. Disaster Risk Reduct. 33, 449–476 (2019).
Google Scholar
Kamal, A. et al. Recent advances and challenges in uncertainty visualization: A survey. J. Vis. https://doi.org/10.1007/s12650-021-00755-1 (2021).
Google Scholar
Spiegelhalter, D. J. & Riesch, H. Don’t know, can’t know: Embracing deeper uncertainties when analysing risks. Philos. Trans. R. Soc. A 369, 4730–4750 (2011).
Google Scholar
Brodlie, K., Osorio, R. A. & Lopes, A. A review of uncertainty in data visualization. In Expanding the Frontiers of Visual Analytics and Visualization (eds Dill, J. et al.) 81–109 (Springer, 2012).
Google Scholar
MacEachren, A. M. et al. Visualizing geospatial information uncertainty: what we know and what we need to know. Cartogr. Geogr. Inf. Sci. 32, 139–160 (2005).
Google Scholar
Ibrekk, H. & Morgan, M. G. Graphical communication of uncertain quantities to nontechnical people. Risk Anal. 7, 519–529 (1987).
Google Scholar
Hawkins, E. The cascade of uncertainty in climate projections. https://www.climate-lab-book.ac.uk/2014/cascade-of-uncertainty/. 6 Feb 2014.
Wilby, R. L. & Dessai, S. Robust adaptation to climate change. Weather 65, 180–185 (2010).
Google Scholar
Daron, J., Lorenz, S., Taylor, A. & Dessai, S. Communicating future climate projections of precipitation change. Clim. Change. 166, 23 (2021).
Google Scholar
Kinkeldey, C., MacEachren, A. M. & Schiewe, J. How to assess visual communication of uncertainty? A systematic review of geospatial uncertainty visualisation user studies. Cartogr. J. 51, 372–386 (2014).
Google Scholar
van Vuuren, D. P. et al. The representative concentration pathways: An overview. Clim. Change 109, 5–31 (2011).
Google Scholar
Breslow, N. E. & Clayton, D. G. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88, 9–25 (1993).
Google Scholar
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar
Christensen, R.H.B. ordinal—Regression models for ordinal data. R package version 2018.4-19. https://CRAN.R-project.org/package=ordinal Deposited 19 Apr 2018.
Peterson, B. & Harrell, F. E. Partial proportional odds models for ordinal response variables. Appl. Stat. 39, 205–217 (1990).
Google Scholar
Momeni, A., Pincus, M. & Libien, J. Introduction to Statistical Methods in Pathology (Springer, 2018).
Google Scholar
Turner, H. & Firth, D. Bradley-Terry models in R: The BradleyTerry2 package. J. Stat. Softw. 48, 1–21 (2012).
Google Scholar
Akaike, H. Information theory and an extension of maximum likelihood principle. In Proceedings of the 2nd International Symposium on Information Theory. Tsahkadsor, Armenia, USSR, 2–8 September (1973).
Rubinstein, R. The cross-entropy method for combinatorial and continuous optimization. Methodol. Comput. Appl. Probab. 1, 127–190 (1999).
Google Scholar
Kendall, M. G. A new measure of rank correlation. Biometrika 30, 81–93 (1938).
Google Scholar
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).
Google Scholar
Firth, D., qvcalc: Quasi variances for factor effects in statistical statistical models. R package version 0.9-1. https://CRAN.R-project.org/package=qvcalc. Deposited 19 Sept 2017.
Pihur, V., Datta, S. & Datta, S. RankAggreg: Weighted rank aggregation (2018). R package version 0.6-4. https://CRAN.R-project.org/package=RankAggreg. Deposited 18 Mar 2018.
Belia, S., Fidler, F., Williams, J. & Cumming, G. Researchers misunderstand confidence intervals and standard error bars. Psychol. Methods 10, 389 (2005).
Google Scholar
Cumming, G., Fidler, F. & Vaux, D. L. Error bars in experimental biology. J. Cell Biol. 177, 7–11 (2007).
Google Scholar
Hullman, J., Resnick, P. & Adar, E. Hypothetical outcome plots outperform error bars and violin plots for inferences about reliability of variable ordering. PLoS ONE 10, e0142444 (2015).
Google Scholar
Correll, M. & Gleicher, M. Error bars considered harmful: Exploring alternate encodings for mean and error. IEEE Trans. Vis. Comput. Graph. 20, 2142–2151 (2014).
Google Scholar
Lipkus, I. M. & Hollands, J. G. The visual communication of risk. JNCI Monogr. 25, 149–163 (1999).
Google Scholar
Spiegelhalter, D., Pearson, M. & Short, I. Visualizing uncertainty about the future. Science 333, 1393–1400 (2011).
Google Scholar
Few, S. Solutions to the problem of over-plotting in graphs. Visual Business Intelligence Newsletter (2008).
Daron, J. D., Lorenz, S., Wolski, P., Blamey, R. C. & Jack, C. Interpreting climate data visualisations to inform adaptation decisions. Clim. Risk Manag. 10, 17–26 (2015).
Google Scholar
Heer, J. & Agrawala, M. Multi-scale banking to 450. IEEE Trans. Vis. Comput. Graph. 12, 701–708 (2006).
Google Scholar
Lorenz, S., Dessai, S., Paavola, J. & Forster, P. M. The communication of physical science uncertainty in European National Adaptation Strategies. Clim. Change 132, 143–155 (2015).
Google Scholar
Quispel, A., Maes, A. & Schilperoord, J. Graph and chart aesthetics for experts and laymen in design: The role of familiarity and perceived ease of use. Inf. Vis. 15, 238–252 (2016).
Google Scholar
Saary, M. J. Radar plots: A useful way for presenting multivariate health care data. J. Clin. Epidemiol. 61, 311–317 (2008).
Google Scholar
Vaughan, N. E. & Gough, C. Expert assessment concludes negative emissions scenarios may not deliver. Environ. Res. Lett. 11, 95003 (2016).
Google Scholar
Peltier, J. Excel Charting Dos and Don’ts (Peltier Technical Services, 2013).
Lohse, G. L. The role of working memory on graphical information processing. Behav. Inf. Technol. 16, 297–308 (1997).
Google Scholar
Grainger, S., Mao, F. & Buytaert, W. Environmental data visualisation for non-scientific contexts: Literature review and design framework. Environ. Model. Softw. 85, 299–318 (2016).
Google Scholar
Few, S. Heatmaps: to bin or not to bin? Visual Business Intelligence Newsletter (2017).
Moreland, K. Diverging color maps for scientific visualization. In Advances in Visual Computing (eds Bebis, G. et al.) 92–103 (Springer, 2009).
Google Scholar
Alrehiely, M., Eslambolchilar, P. & Borgo, R. Evaluating different visualization designs for personal health data. In Proceedings of the 32nd International BCS Human Computer Interaction Conference, Belfast, UK, 4–6 July (2018).
Saket, B., Endert, A. & Demiralp, C. Task-based effectiveness of basic visualizations. IEEE Trans. Visual Comput. Graph. 25, 2505–2512 (2019).
Google Scholar
Dahshan, M., Polys, N. F., Jayne, R. S. & Pollyea, R. M. Making sense of scientific simulation ensembles with semantic interaction. Comput. Graph. Forum. 39, 325–343 (2020).
Google Scholar
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