Abstract
Ecosystems around the world are increasingly exposed to multiple, often interacting human activities, leading to pressures and possibly environmental state changes. Decision support tools (DSTs) can assist environmental managers and policy makers to evaluate the current status of ecosystems (i.e. assessment tools) and the consequences of alternative policies or management scenarios (i.e. planning tools) to make the best possible decision based on prevailing knowledge and uncertainties. However, to be confident in DST outcomes it is imperative that known sources of uncertainty such as sampling and measurement error, model structure, and parameter use are quantified, documented, and addressed throughout the DST set-up, calibration, and validation processes. Here we provide a brief overview of the main sources of uncertainty and methods currently available to quantify uncertainty in DST input and output. We then review 42 existing DSTs that were designed to manage anthropogenic pressures in the Baltic Sea to summarise how and what sources of uncertainties were addressed within planning and assessment tools. Based on our findings, we recommend future DST development to adhere to good modelling practise principles, and to better document and communicate uncertainty among stakeholders.
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References
Bennett, N.D., B.F.W. Croke, G. Guariso, J.H.A. Guillaume, S.H. Hamilton, A.J. Jakeman, S. Marsili-Libelli, L.T.H. Newham, et al. 2013. Characterising performance of environmental models. Environmental Modelling & Software 40: 1–20. https://doi.org/10.1016/J.ENVSOFT.2012.09.011.
Bonsdorff, E., A. Andersson, and R. Elmgren. 2015. Baltic Sea ecosystem-based management under climate change: Integrating social and ecological perspectives. Ambio 44: 333–334. https://doi.org/10.1007/s13280-015-0669-1.
Borgonovo, E. 2013. Sensitivity analysis in decision making. Wiley Encyclopedia of Operations Research and Management Science. https://doi.org/10.1002/9780470400531.eorms1076.
Carstensen, J., and M. Lindegarth. 2016. Confidence in ecological indicators: A framework for quantifying uncertainty components from monitoring data. Ecological Indicators 67: 306–317. https://doi.org/10.1016/J.ECOLIND.2016.03.002.
Chatfield, C. 1995. Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society Series A 158: 419. https://doi.org/10.2307/2983440.
Conley, D.J. 2012. Save the Baltic Sea. Nature 486: 463–464. https://doi.org/10.1038/486463a.
Davies, A.J., and M.J. Hope. 2015. Bayesian inference-based environmental decision support systems for oil spill response strategy selection. Marine Pollution Bulletin 96: 87–102. https://doi.org/10.1016/j.marpolbul.2015.05.041.
Eero, M., J. Hjelm, J. Behrens, K. Buchmann, M. Cardinale, M. Casini, P. Gasyukov, N. Holmgren, et al. 2015. Eastern Baltic cod in distress: Biological changes and challenges for stock assessment. ICES Journal of Marine Science 72: 2180–2186. https://doi.org/10.1093/icesjms/fsv109.
Grimm, V., U. Berger, D.L. DeAngelis, J.G. Polhill, J. Giske, and S.F. Railsback. 2010. The ODD protocol: A review and first update. Ecological Modelling 221: 2760–2768. https://doi.org/10.1016/j.ecolmodel.2010.08.019.
Halpern, B.S., M. Frazier, J. Potapenko, K.S. Casey, K. Koenig, C. Longo, J.S. Lowndes, R.C. Rockwood, et al. 2015. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nature Communications 6: 7615. https://doi.org/10.1038/ncomms8615.
Harwood, J., and K. Stokes. 2003. Coping with uncertainty in ecological advice: Lessons from fisheries. Trends in Ecology & Evolution 18: 617–622. https://doi.org/10.1016/J.TREE.2003.08.001.
HELCOM. 2007. HELCOM Baltic Sea action plan. Poland: Krakow.
Heuvelink, G.B.M., J.D. Brown, and E.E. van Loon. 2007. A probabilistic framework for representing and simulating uncertain environmental variables. International Journal of Geographical Information Science 21: 497–513. https://doi.org/10.1080/13658810601063951.
ICES. 2019. Cod (Gadus morhua) in subdivisions 24-32, eastern Baltic stock (eastern Baltic Sea). Report of the ICES Advisory Committee 27: 24–32. https://doi.org/10.17895/ices.advice.4747.
Jakeman, A.J., R.A. Letcher, and J.P. Norton. 2006. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21: 602–614. https://doi.org/10.1016/J.ENVSOFT.2006.01.004.
Khosravi, F., and U. Jha-Thakur. 2019. Managing uncertainties through scenario analysis in strategic environmental assessment. Journal of Environmental Planning and Management 62: 979–1000. https://doi.org/10.1080/09640568.2018.1456913.
Laurila-Pant, M., S. Mäntyniemi, R. Venesjärvi, and A. Lehikoinen. 2019. Incorporating stakeholders’ values into environmental decision support: A Bayesian Belief Network approach. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2019.134026.
McNeish, D. 2016. On using Bayesian methods to address small sample problems. Structural Equation Modeling: A Multidisciplinary Journal 23: 750–773. https://doi.org/10.1080/10705511.2016.1186549.
Milner-Gulland, E.J., and K. Shea. 2017. Embracing uncertainty in applied ecology. Edited by Andre Punt. Journal of Applied Ecology 54: 2063–2068. https://doi.org/10.1111/1365-2664.12887.
Morgan, M.G. 2014. Use (and abuse) of expert elicitation in support of decision making for public policy. Proceedings of the National academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1319946111.
Nygård, H., F.M. van Beest, L. Bergqvist, J. Carstensen, B. G. Gustafsson, B. Hasler, J. Schumacher, G. Schernewski, et al. 2020. Decision support tools used in the Baltic Sea area: Performance and end-user preferences. Environmental Management. https://doi.org/10.1007/s00267-020-01356-8.
Refsgaard, J.C., J.P. van der Sluijs, A.L. Højberg, and P.A. Vanrolleghem. 2007. Uncertainty in the environmental modelling process: A framework and guidance. Environmental Modelling & Software 22: 1543–1556. https://doi.org/10.1016/J.ENVSOFT.2007.02.004.
Regan, H. M., M. Colyvan, and M. A. Burgman. 2002. A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological Applications 12: 618–628. https://doi.org/10.1890/1051-0761(2002)012%5b0618:atatou%5d2.0.co;2.
Reusch, T.B.H., J. Dierking, H.C. Andersson, E. Bonsdorff, J. Carstensen, M. Casini, M. Czajkowski, B. Hasler, et al. 2018. The Baltic Sea as a time machine for the future coastal ocean. Science Advances 4: 8195.
Schmolke, A., P. Thorbek, D.L. DeAngelis, and V. Grimm. 2010. Ecological models supporting environmental decision making: A strategy for the future. Trends in Ecology & Evolution 25: 479–486. https://doi.org/10.1016/j.tree.2010.05.001.
Steffen, W., K. Richardson, J. Rockstrom, S.E. Cornell, I. Fetzer, E.M. Bennett, R. Biggs, S.R. Carpenter, et al. 2015. Planetary boundaries: Guiding human development on a changing planet. Science 347: 1259855. https://doi.org/10.1126/science.1259855.
Sullivan, T. 2002. Evaluating environmental decision support tools. New York: Upton.
Tapinos, E. 2012. Perceived environmental uncertainty in scenario planning. Futures 44: 338–345. https://doi.org/10.1016/j.futures.2011.11.002.
Uusitalo, L., A. Lehikoinen, I. Helle, and K. Myrberg. 2015. An overview of methods to evaluate uncertainty of deterministic models in decision support. Environmental Modelling & Software 63: 24–31. https://doi.org/10.1016/J.ENVSOFT.2014.09.017.
van der Bles, A.M., S. van der Linden, A.L.J. Freeman, J. Mitchell, A.B. Galvao, L. Zaval, and D.J. Spiegelhalter. 2019. Communicating uncertainty about facts, numbers and science. Royal Society Open Science 6: 181870. https://doi.org/10.1098/rsos.181870.
van der Vaart, E., D. Prangle, and R.M. Sibly. 2018. Taking error into account when fitting models using Approximate Bayesian Computation. Ecological Applications 28: 267–274. https://doi.org/10.1002/eap.1656.
Walker, W.E., P. Harremoës, J. Rotmans, J.P. van der Sluijs, M.B.A. van Asselt, P. Janssen, and M.P. Krayer von Krauss. 2003. Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment 4: 5–17. https://doi.org/10.1076/iaij.4.1.5.16466.
Zurell, D., J. Franklin, C. König, P.J. Bouchet, C.F. Dormann, J. Elith, G. Fandos, X. Feng, et al. 2020. A standard protocol for reporting species distribution models. Ecography. https://doi.org/10.1111/ecog.04960.
Acknowledgements
This review is a contribution from the BONUS DESTONY project. BONUS DESTONY has received funding from BONUS (Art. 185), funded jointly by the EU and the Swedish Research Council FORMAS. We wish to thank all participants of DESTONY, all DST developers and end users for all the hard work and knowledge produced when compiling and reviewing the DST list. We also wish to thank three anonymous reviewers for their constructive feedback on a previous manuscript draft.
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van Beest, F.M., Nygård, H., Fleming, V. et al. On the uncertainty and confidence in decision support tools (DSTs) with insights from the Baltic Sea ecosystem. Ambio 50, 393–399 (2021). https://doi.org/10.1007/s13280-020-01385-x
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DOI: https://doi.org/10.1007/s13280-020-01385-x