Shapes Can Be Puzzles Too

This week has been all about the shapes of data on my two projects. I know that sounds odd, so let me explain. Our lab is preparing to attend and present at two upcoming conferences: one in November and the other in December. For at least one of these conferences, Kim is presenting on some of the modeling work we've done with the Mississippi Sound and Bight, and the enormity of this model. Remember that the model we are working on is much larger than average, as I've been able to find a few models with 40-50 compartments, and many with less, but our model is 75 compartments right now. Defining so many compartments in the model is quite taxing both on the computers while they run the calculations and on the researchers who have to find information to drive the compartments' responses to model conditions. However, if the data and computing power are available, then creating such a large model allows for more likely model outcomes. For the first conference, Kim is going to be presenting some of this work and wants to include some of the visuals from the model. One of these visuals, the food web diagram, shows the connections between all the compartments in the model, which the software generates automatically. However, this diagram is quite messy and illegible, so one of the shape-specific tasks I've been working on this week is creating mockups for the final diagram to improve readability. This process involves lots of dragging names of model compartments around, resizing text, and finding the right fit for all 75 items in the diagram.

The other shape-specific task this week was generating minimum convex hulls for some of the oyster data. A minimum convex hull is the smallest shape that contains all the data points on a spatial scale. So if I have three points, the convex hull will likely be a triangle. If I have four points, the shape could be a trapezoid, a square, or a triangle, depending on where the points are located in relation to each other. I am analyzing the spatial component of the oyster data because oysters are sessile organisms, so they are limited by the suitable habitat space around them. Therefore, if an area has more suitable habitat space, the growth of oysters might be greater than an area with less suitable habitat space available. Luckily, one of our programs can run these spatial calculations for me, but part of the work--as I mentioned last week--requires quality control checks. Today these QA/QC checks were super valuable, since a few of the data points had latitude and longitude coordinates that were unlikely based on the rest of the sampling locations. By reevaluating the data, I was able to generate these minimum convex hulls--which you can see in this week's photo--and use the information about their sizes to help inform some of the oyster data.

Next week we have another all hands on deck meeting, and I'm excited to see the progress we've made this past month. Until next week!