Identifier

etd-06052012-163626

Degree

Doctor of Philosophy (PhD)

Department

Oceanography and Coastal Sciences

Document Type

Dissertation

Abstract

Simulating animal movement in spatially-explicit individual-based models (IBMs) is both challenging and critically important to accurately estimating population dynamics. I compared four distinct movement approaches or sub-models (restricted-area search, kinesis, event-based, and run and tumble) in a series of simulation experiments. I used an IBM loosely based on a small pelagic fish that simulated growth, mortality, and movement of a cohort on a 2-dimensional grid. First, I tested the sub-models calibrated (i.e., trained) with a genetic algorithm in one set of environmental conditions in three other novel environments. The sub-models performed well, except restricted-area search and event-based that needed to be trained in environments with gradients similar to the test environment. Also, run and tumble only trained in steep habitat quality gradients. The sub-models were then trained and tested across a range of spatio-temporal resolutions (cell size and time step). The sub-models generally performed well across resolutions, but the sub-models did not perform equally well at all resolutions. Kinesis and run and tumble performed better at coarser resolutions, and restricted-area and event-based performed better at finer resolutions. I attributed the trends across resolution to differences in how the habitat quality individuals experienced changed at each time step. Finally, I trained and tested the sub-models in an IBM with dynamic prey and predator fields. I trained and tested the sub-models in dynamic and static versions of the environment. Sub-models trained in the dynamic environment performed well in both dynamic and static test environments; however, sub-models trained in static environment did not perform consistently well in dynamic test environment. Overall, restricted-area search, kinesis, and event-based were robust across the range of conditions in which I tested them, but run and tumble only performed well in environments with very steep habitat quality gradients. In selecting a movement sub-model, researchers should consider the assumptions of potential sub-models, the observed movement patterns of the species of interest, the shape and steepness of the underlying habitat quality gradient, and the spatio-temporal resolution of the model. Sub-models that will be applied in dynamic conditions should be calibrated in comparable dynamic conditions.

Date

2012

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Rose, Kenneth

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