We provide a set of starting values for each of these. For our main factors, we also include information about how they might change in the future. For example, we include information about projected , rates of pesticide use and expected habitat loss or gain, and how each affects the monarch population. We run the model using a simulated population that grows and responds the way a real monarch population does. The results are predictions of monarch population size, year-by-year, extending as far into the future as we program the model to run (see figures 1 and 2). It allows us to answer the question “does the future monarch population ever reach the tipping point - a point at which extinction becomes inevitable?”
We run the model separately for the eastern and western populations. In both cases, we test the model on a million experimental populations (1 million simulations each for east and west populations). Remember, there is a degree of randomness built into the model that reflects changing environmental conditions. This means each time we run the model, the simulation is a little different, providing one possible future scenario. After a million experimental runs, we see a wide variety of possible futures. Monarchs may go extinct in some of these futures, but not in others.
How do you deal with the unknowns?
Every model has areas of uncertainty, especially when attempting to predict the future. We do not know how the main factors – climate change, habitat loss, pesticide use -- will change in the future. And we don’t know how the population will respond to changes in these factors. Our model was built by having species experts provide predictions about the way a population is likely to respond to a threat or benefit of a given magnitude, but these predictions themselves rely to some extent on assumptions. While we can get an idea of the where the “tipping point,” is, we can’t pinpoint it precisely.
It’s not possible to create a model that tells us exactly what is going to happen to the monarch population. But we can address this uncertainty by running our model many times with slightly different assumptions and values each time. This gives us a portfolio of conditions, ranging from the likely best case to the likely worst case, and we can see how changing our assumptions can affect the monarch population.
We don’t attempt to use models to predict the future with precision or rely on one single future scenario. Rather, we run many versions of our models many times each. This allows us to estimate the range of future conditions under various scenarios, how likely each is and even the potential impact of different human actions. Policy makers then have a wealth of information about possible and probable futures to work with. The results of modeling, together with other tools like expert elicitation or risk assessments give decision makers the scientific foundation they need to determine whether monarchs need protection, and if so, when and how to protect them.
Go to Monarch Initiative Page