Undergraduate Thesis at the Math Department of the University of California, Davis. 2021.
Note: The original Blue Whale IBM and the files in the Utils folder were originally written by Dr. Stephanie Dodson. Modifications and other files, including New Utils, were made by Sameerah Helal.
Individual Based Models (IBMs) are commonly used to study animal migrations and foraging behaviours. These flexible models are powerful in identifying the mechanisms driving animal movement; however, when fed spatially or temporally coarse environmental data, IBMs can often produce inaccurate model outcomes. Here, we investigate how model adaptations can mitigate negative consequences of poor data using an IBM of blue whales. Specifically, we find that coarse data leads whales to clump together in their foraging behaviours and migrations paths. Algorithm adaptations like altering the rate at which the whales update their locations can reduce the locational clustering effect incited by spatially coarse data, and introducing available fine data to coarse data can mitigate the behavioural inaccuracies caused by temporally coarse data. These improvements are verified utilization distributions of whale positions, behavioural state plots, and associated metrics.