Model Runs Don't Look Pretty

This week has been all about working through small projects and some trainings, and included in those small projects were some model runs. While I've spent many blogs explaining the ecology of our model simulations and runs, I haven't taken much time to discuss what the model runs look like on the computer. If it wasn't obvious by this week's blog title, the model outcomes don't look pretty, although I feel like most science - outside of science as presented in movies - doesn't have visually appealing results. Instead, we get lots of numbers, sometimes figures, and then we work to interpret those numbers and beautify the results before our presentations. We have one beautiful visual component of our ecosystem models, so far, which is the graph generated for every model run. This figure shows the relative biomass of each group in our model, which is overwhelming to look at since we have more than 70 groups in our model.

For this week, though, I have created a figure using randomly generated data to explain the results our model generates, and how we interpret those results. For our first project, we are working to understand how openings of the Bonnet Carré Spillway affect the mortality of oysters in the Mississippi Sound. For each opening scenario, we input the environmental data provided by our physical modeling team, and we get data on the biomass of each group of organisms for every year of our model runs. We also get a readout of the end/start biomass, which is the biomass of each group at the end of a model run or at the end of a specified time interval divided by the biomass at the start of a model run or at the beginning of a specified time interval. In simpler terms, we are looking at how the biomass of a group has changed between two time points, and these are the data I've created and plotted in this figure. On the x-axis you can see the age class (stanza) of the oysters in order of increasing age, and on the y-axis you can see the end/start biomass values.  An end/start biomass of 1.0 means that the end biomass is exactly equal to the starting biomass, suggesting that any growth was balanced by an equal amount of biomass lost due to mortality. Values above this 1.0 mark are scenarios where growth > mortality and values below the 1.0 mark are scenarios where growth < mortality.

Additionally, I've added in three red lines--one for each stanza. These red lines indicate the ecological tipping point, or the threshold where a small change can lead to a fundamental shift in the ecosystem. Perhaps a better way to think about the ecological tipping point here is that if a scenario causes the end/start biomass to fall below the tipping point, the scenario led to mortality that is beyond the natural mortality the stanza faces and the stanza may not recover without external support. For these fake data, I've set all the tipping points to 0.5 for simplicity. From this figure, we can see that scenario 1 caused spat and seed to fall below the tipping point, with sack oysters slightly above the tipping point. Importantly, while the tipping point represents an extreme case, any scenario that causes the end biomass to fall below the starting biomass--causes less growth than mortality--creates an unstable population. When we run the model, we will end up with multiple real scenarios and plot all the scenarios against the field-derived tipping points to determine how each opening scenario affected the oyster growth and mortality. I will also work on the beautification step after all the model runs because while this style of figure is really helpful for quick and initial model result interpretation, I recognize that I can improve this type of figure with some color blocking and descriptive text.

That's it for this week. Stay tuned to hear what's happening in the lab during next week's blog.

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