Abstract Understanding the movement of animals is fundamental to population and community ecology. Historically, it has
been difficult to quantify movement patterns of most fishes, but technological advances in acoustic telemetry have increased our
abilities to monitor their movement. In this study, we combined small-scale active acoustic tracking with large-scale passive
acoustic monitoring to develop an empirical movement model for sixgill sharks in Puget Sound, WA, USA. We began by testing
whether a correlated random walk model described the daily movement of sixgills; however, the model failed to capture
home-ranging behavior. We added this behavior and used the resultant model (a biased random walk model) to determine whether
daily movement patterns are able to explain large-scale seasonal movement. The daily model did not explain the larger-scale patterns
of movement observed in the passive monitoring data. In order to create the large-scale patterns, sixgills must have performed
behaviors (large, fast directed movements) that were unobserved during small-scale active tracking. In addition, seasonal
shifts in location were not captured by the daily model. We added these ‘unobserved’ behaviors to the model and were able to
capture large-scale seasonal movement of sixgill sharks over 150 days. The development of empirical models of movement allows
researchers to develop hypotheses and test mechanisms responsible for a species movement behavior and spatial distribution.
This knowledge will increase our ability to successfully manage species of concern [Current Zoology 58 (1): 103–115, 2012].
Keywords Hexanchus griseus, Movement, Correlated random walk, Biased random walk, Displacement, Movement model
Abstract Understanding the movement of animals is fundamental to population and community ecology. Historically, it hasbeen difficult to quantify movement patterns of most fishes, but technological advances in acoustic telemetry have increased ourabilities to monitor their movement. In this study, we combined small-scale active acoustic tracking with large-scale passiveacoustic monitoring to develop an empirical movement model for sixgill sharks in Puget Sound, WA, USA. We began by testingwhether a correlated random walk model described the daily movement of sixgills; however, the model failed to capturehome-ranging behavior. We added this behavior and used the resultant model (a biased random walk model) to determine whetherdaily movement patterns are able to explain large-scale seasonal movement. The daily model did not explain the larger-scale patternsof movement observed in the passive monitoring data. In order to create the large-scale patterns, sixgills must have performedbehaviors (large, fast directed movements) that were unobserved during small-scale active tracking. In addition, seasonalshifts in location were not captured by the daily model. We added these ‘unobserved’ behaviors to the model and were able tocapture large-scale seasonal movement of sixgill sharks over 150 days. The development of empirical models of movement allowsresearchers to develop hypotheses and test mechanisms responsible for a species movement behavior and spatial distribution.This knowledge will increase our ability to successfully manage species of concern [Current Zoology 58 (1): 103–115, 2012].
Keywords Hexanchus griseus, Movement, Correlated random walk, Biased random walk, Displacement, Movement model
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