Automated Extraction of Movement Rationales for ABMs

I was left with several questions about the observation data. It was collected over a 1.5 year period, yet locations were only observed every 15 minutes. There was no explanation of the daily frequency of observation. If the observation is not continuous, can’t “movement” only be observed between the 15 minute intervals? Was the 75% calculation for movement based on data with many gaps? Interestingly, the group of monkeys remained in a 0.9 km2 box throughout 1.5 years of observation.

This type of ABM appears very simple. Three values (initiation, distance, and direction) are assigned according observed probability, and several additional constraining rules evaluate each new position, causing the algorithm to recalculate a point if certain condition are not met. This example of ABM for animal movement is additionally simplified by the lack of predation and migratory tendencies. Randomness is key to Red Colobus Monkey movement.

This seems to be a step forward for predicting animal movement. The article does not go into detail about the many other factors that may influence the movement of other species, and how these might be modeled by rules and constraints. AnotherĀ important aspect of ABM should be testing for accuracy against observed movement. Continued observation of Red Colobus Monkeys in KNP would be important for altering rules for the model as well as adding additional constraints or scenarios, such as land cover change (e.g. by human or wildfire) or predation.

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