File(s) stored somewhere else
Please note: Linked content is NOT stored on Figshare and we can't guarantee its availability, quality, security or accept any liability.
Using a Continuous Time Correlated Random Walk and Bayesian Inference to Examine Spatial Use Patterns of Harbor Seals in Cook Inlet, Alaska, USA
Seventy-one harbor seals (Phoca vitulina) were
tagged with satellite-linked geo-locating depth
recorders in Cook Inlet, Alaska between 2004 and
2006. The recorders were deployed in the fall,
after molt, and in the spring before pupping and
breeding. With an average deployment length of
188 days, the data included over 62,000 ARGOS
locations across most months (lower in July and
August). We used a continuous-time correlated
random walk ('crawl' package in R) movement
model to estimate seal locations at hourly intervals
based on the locations and haul-out status.
Spatial use patterns were determined by drawing
(n=1000) from the posterior distribution of the
predicted tracks. We examined the effects of
season (fall, early winter, late winter, spring, and
summer) on the spatial use patterns of the
tagged seals. Use maps were created on a raster
grid with cell size of 100 square kilometers. Two
values were calculated for each cell for each
simulation: the number of hours all seals spent in
the cell, the number of seals that spent any hour
in the cell. Separate maps were made for each
season. A large portion of seal spatial use was
within 5 km of a haul-out and there was a seasonal
movement of seals out of Cook Inlet into
the western Gulf of Alaska in late fall and winter.
For pupping, breeding and molting, seals moved
back into Cook Inlet in the spring and early
summer. Although these results are similar to
those from other harbor seal studies, our Bayesian
approach for estimating spatial use patterns
accounts for, and provides an estimate of uncertainty
due to location error and irregular location
times. This method is broadly applicable to
other marine mammals tracked by ARGOS.