Visualizing Spatial Model Results

Click the figure to get a closer look at the species distribution across the blue boxes.

This week, I worked on initial results from one of our spatiotemporal models, which we are using to understand how Mississippi River management scenarios affect the health of fish, shellfish, and fisheries in the northern Gulf of Mexico. The results from these model runs come in multiple forms, including yearly biomass data, yearly fisheries landings data, some economic data regarding fisheries landings, and spatiotemporal distributions data. This last option, like a species distribution model, provides insight to how mobile species respond to changes in the environment and how sessile, non-mobile organisms respond in terms of biomass loss. While I cannot show results from this work since it is ongoing, I created a figure here using artificial values that helps explain how these models work and how we interpret the model results.










​From the model, we get a month by month distribution of organisms across the entire model domain, and the model gives us these distributions in a column and row format. From these column and row data, we can create a map of our model domain and plot the biomass of each individual across the entire geographic space. We can then follow a single species across the entire model run by plotting its biomass for every month and we can interpret month-to-month differences in the biomasses but also the locations of the biomass within the model. In the first panel here, model group A is only present in the upper left portion of the figure, which is Lake Borgne. In the second panel here, model group A has moved eastward and the individuals are no longer living within the lake. These distributional changes for mobile species indicate that something about the environment caused a fleeing response in the model group; perhaps the temperature in that area got too high, or the salinity dropped. A predator may have been successful in that original area or the model group may be following their own prey somewhere else. When we see distributional changes, we note which months these changes occurred between and then we can investigate what environmental factors changed during this time step. 

We can also create figures called difference plots, which frequently help us visualize the differences between the time steps. We subtract one plot from another to show shifts in distributions or loss of biomass, which can also identify model areas that became unsuitable to the model group during a time step. When researchers are using spatial models to evaluate differences between management scenarios, the researchers frequently use a baseline model or a model that evaluates a future without action and then compares scenarios to that baseline to show how much a management action improves the distribution or biomass of a key model group or how much a management action harms the model group. 

For this work, our next steps will be to investigate the key model groups and identify which time steps showed the largest differences between the different scenarios. Then, we will investigate which factors likely influenced the distributions and how ongoing Mississippi River management scenarios can shape the health of the fish, shellfish, and fisheries in the northern Gulf.

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