Data from: Does perspective matter? A case study comparing Eulerian and Lagrangian estimates of common murre (Uria aalge) distributions

Studies estimating species’ distributions require information about animal
locations in space and time. Location data can be collected using surveys
within a predetermined frame of reference (i.e., Eulerian sampling) or
from animal-borne tracking devices (i.e., Lagrangian sampling).
Integration of observations obtained from Eulerian and Lagrangian
perspectives can provide insights into animal movement and habitat use.
However, contemporaneous data from both perspectives are rarely available,
making examination of biases of each sampling approach difficult to
quantify. We compared distributions of a mobile seabird observed
concurrently from ship, aerial, and satellite tag surveys during May,
June, and July 2012 in the northern California Current. We calculated
utilization distributions to quantify and compare variability in common
murre (Uria aalge) space use and examine how sampling perspective and
platform influence observed patterns. Spatial distributions of murres were
similar in May, regardless of sampling perspective. Greatest density
distributions occurred in coastal waters off southern Washington and
northern Oregon, near large murre colonies and the mouth of the Columbia
River. Density distributions of murres estimated from ship and aerial
surveys in June and July were similar to those observed in May, whereas
distributions of satellite-tagged murres in June and July indicated
northward movement into British Columbia, Canada, resulting in different
patterns observed from Eulerian and Lagrangian perspectives. These results
suggest that the population of murres observed in the northern California
Current during spring and summer includes relatively stationary
individuals attending breeding colonies and non-stationary, vagile adults
and subadults. Given the expected growth of telemetry studies and advances
in survey technology (e.g., unmanned aerial systems), these results
highlight the importance of considering methodological approaches, spatial
extent, and synopticity of distribution data sets prior to integrating
data from different sampling perspectives.