10.7291/D1J66X
Wood, Sarah
0000-0003-4618-3888
University of California, Santa Cruz
Año Nuevo Island Animal Count: analyzing citizen science pinniped counts
from drone imagery
Dryad
dataset
2020
drone imagery
Marine mammals
crowd-sourced data
2020-05-22T00:00:00Z
2020-05-22T00:00:00Z
en
1146126 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Fluctuations in marine mammal abundance can reveal changes in local
ecosystem health and inform conservation strategies. Unmanned aircraft
systems (UAS) such as drones are increasingly being used to photograph and
count marine mammals in remote locations; however, counting animals in
images is a laborious task. Crowd-sourced science has the potential to
considerably reduce the time required to conduct these censuses but must
first be validated against expert counts to confirm accuracy. Our
objectives were to examine the citizen science counts for accuracy,
identify costs and benefits of drone imagery and citizen science for
pinniped censuses, and make recommendations for future uses of the data.
We obtained and uploaded drone imagery of Año Nuevo Island in California
to a custom citizen science website (sealcount.com) that instructed
volunteers to count seals and sea lions. Across 212 days, over 1,500
volunteers counted northern elephant seals, harbor seals, California sea
lions, and Steller sea lions in 90,000 photographs. We created five simple
algorithms to extract one count per photograph from the crowd-sourced data
and then analyzed each algorithm for accuracy by comparing to expert
counts. We found that the median was the most accurate metric for
extracting counts of seals but not sea lions. Volunteers consistently
underestimated sea lions, so removing minimum values was the best strategy
for extracting accurate counts of sea lions. We also found that while
citizen scientists were able to accurately count adult seals, their
accuracy was lower during pupping season, when small pups were present but
difficult to detect. With proper precautions, citizen science saves money,
labor, and time, while producing large amounts of accurate data that can
be used to analyze a suite of biological patterns. Future applications
include analyses of geo-spatial patterns within and between species,
quantifying interspecific niche partitioning, and life history phenology.