 So, the next step is consequences. This is really where ecological forecasts come into play. You're focused on understanding the likelihood of what would happen given different management options or alternatives. In some cases, your forecasts are predictions. You're developing information that's going to be consumed by different decision makers and we see this all the time with weather forecasts or seasonal temperature and precipitation forecasts. That information is used and translated in a way by decision makers to help them make particular decisions. In a number of cases, when we're working on really complex environmental management decisions, we will have to produce forecasts that are projections. That's where we need to take boundary conditions and scenarios and combine that with our alternatives to really understand what would happen in the future. So, the assumptions end up being really important and you want to develop assumptions that are realistic for the particular problem that you're working on. Again, one of the things that is most important when thinking about combining ecological forecasts with decisions is the time horizon. Since ecological forecasts and decisions are both thinking about the future, one of the big mismatches between forecasts and decision making is where the time scale for the forecast or the time scale for the decision are off and thus the information that's produced is not useful even though the forecast could be developed in a way that might be useful for decision making. Additionally, as I mentioned earlier, this is where performance measures come into play. When you think about your model output, you want to be thinking about how you design your model output in a way that links to specific performance measures that link to objectives that we care about for that particular decision. And then finally, you have to remember that with developing these ecological forecasts, one of the reasons why we say ecologists and decision makers need to work hand in hand to think about how we define the consequences of these different objectives is because if we're not working together, we may produce forecasts that we intend to be useful that ultimately are not or decision makers may request and become frustrated when they need information that is simply infeasible to produce given what we know now.