Training ecologists to think with uncertainty in mind

  • Brewer C
  • Gross L
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Predictive capacity is needed to anticipate the consequences of global change. Along with the computational challenges inherent in accounting for uncertainly in models of ecological and physical processes related to global change, we face educational challenges related to developing the intellectual capital for thinking with uncertainty in mind. Hand in hand with promoting the kinds of research needed to advance the evolving science of ecological forecasting, we need to set an education agenda for developing and enhancing computational literacy of current and future ecologists, managers, and policymakers. Key elements of an educational agenda are (1) defining the audiences and what each needs to know and be able to do; (2) developing curricula and pedagogical strategies in which thinking skills and conceptual understanding are, by design, linked; (3) addressing training needs of faculty to teach effectively about uncertainty to these different audiences; and (4) creating and implementing assessment tools to explore the impact of programs designed to train ecologists to think with uncertainty in mind. Assessing the extent to which new training models have impacted forecasting research and the conceptual understanding of ecologists is an important topic for future scholarship in education, and a natural area for collaboration between forecasting and education researchers. TRAINING ECOLOGISTS TO "THINK WITH UNCERTAINTY IN MIND" Anticipating the causes and consequences of global change requires that we develop an understanding of the roles that uncertainty and variability play in en-vironmental processes (Clark et al. 2001, Michener et al. 2002). Accounting for uncertainty in ecological forecasting has significant implications, given that model results may be used to inform decision-making and, if the results are perceived to be credible, society's responses (e.g., Gross 1994a, Peterson et al. 2003, Piel-ke and Conant 2003). Dealing with uncertainty effec-tively in ecological models requires much more than experts skilled in the application of the technical tools used to develop projections of the future (e.g., Brewer 2001). It also requires a pool of scientists with knowl-edge and skills that allow them to collaborate effec-tively with managers and policymakers. Promoting the research that is needed to advance the evolving science of ecological forecasting (see other articles in this Special Feature) requires an agenda for developing and enhancing computational literacy of current and future ecologists, managers and policy-makers, and the general public (Gross 2000). This re-quires integrating and synthesizing new conceptual knowledge while creating opportunities in our

Author-supplied keywords

  • Assessment
  • Computational literacy
  • Ecological forecasting
  • Education
  • Professional development
  • Uncertainty

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  • Carol A. Brewer

  • Louis J. Gross

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