2.4 Simulations
A total of 90 scenarios were simulated with each combination of drone
flight pattern (n = 6), animal movement pattern (n = 3), and animal
speed (n = 5) iterated 10,000 times, resulting in a total of 900,000
simulations. For each simulation, the number of times the animal was
captured within the image taken by the drone was recorded and the mean
and standard deviation (SD) of the raw counts were reported for model
replicates to compare various combinations of our variables; these also
act as measures of effect size. Accuracy of the survey counts was based
on the deviation from the true value (i.e., one animal; Hone, 2008). We
also report the percentage of simulations that returned the correct
number of animals (n = 1), omitted the animal, or had multiple counts
among scenarios. We compare subsampled landscape (transect and
systematic point) counts to control scenario counts using a randomly
placed, stationary animal on the landscape and report differences in
mean and SD of the raw counts. A full description of the simulations,
following the ODD protocol (Overview, Design concepts, and Details) for
agent-based models (Grimm et al. , 2020), is provided in the
Appendix.
Results
Flight pattern, animal movement pattern, and animal speed all affected
the count bias with flight pattern appearing to have the most influence
(Fig. 2). With one animal on the landscape, the mean and standard
deviation of animal counts ranged from 0.2 ± 0.7 to 3.2 ± 2.7 animals
among flight patterns, from 1.1 ± 1.1 to 1.6 ± 2.1 animals among
movement patterns, and 1.2 ± 1.2 to 1.5 ± 2.0 animals among animal
speeds. Although flight pattern was the most influential variable
determining accurate animal counts in drone surveys, combinations of
various animal movement patterns and speeds also resulted in more
accurate counts of the simulated animal within various flight patterns
(Fig. 2 and 3).
For flight patterns, the lawnmower pattern with 0% overlap was the
least biased of all animal movement types and speeds (1.1 ± 0.6 animals,
Fig. 2) with comparatively high accuracy (73.2% of simulations with
correct counts; Fig. 3). The next most accurate flight pattern was the
lawnmower pattern with 20% overlap (63% of simulations with correct
counts) followed by the lawnmower with 40% and 60% overlap (45.7% and
33.6% of simulations with correct counts, respectively; Fig. 3). Counts
increased overall with lawnmower overlap percentage, averaging 1.4 ±
0.9, 1.9 ± 1.4, and 3.2 ± 2.7 animals for 20%, 40%, and 60% overlap,
respectively (Fig. 2). The transect and systematic point flight patterns
were the most likely to omit the animal in the drone survey (0.2 ± 0.7
and 0.4 ± 0.5 animals, respectively; Fig. 2). The transect flight
pattern very rarely returned an accurate animal count across movements
and speeds (Fig. 2) and mostly omitted (87.1%) the animal, as did the
systematic points (63.1%; Fig. 3). As the transect flight pattern
captured 10% of the landscape, it should have captured the animal in
10% of our simulations; however, the average count for a moving animal
was 0.2 ± 0.7, indicating that animal movement influenced survey counts,
especially when compared to the average of 0.1 ± 0.3 for the stationary
animal transect count. Similarly, the systematic points flight pattern,
with images covering 25% of the landscape, had an average count of 0.4
± 0.5 mobile animals, compared to 0.25 ± 0.4 stationary animals.
Animal counts were most accurate for the correlated random walk (1.1 ±
1.1 animals) among drone flight patterns for almost all animal speeds
(Fig. 2). Generally, the random and biased animal walks resulted in
overestimated animal counts (1.6 ± 2.1 and 1.6 ± 1.9 animals,
respectively), particularly when overlap increased for lawnmower
patterns from 20% to 60% (Fig. 2). The correlated random walking
animal resulted in the least number of multiple counts (12.0%), with
36.0% and 37.7% of simulations having multiple counts for the random
and biased random walking animal, respectively (Fig. 3). Animal movement
resulted in the omission of the animal in 20.3% (correlated random
walk), 32.1% (biased random walk), and 33.7% (random) of simulations
(Fig. 3).
Varying the speed of the animal exhibited one clear trend among
variables; increasing animal speed increased the variation around counts
(i.e., lowered precision) for most flight patterns and animal walks
(Fig. 2). The lawnmower pattern with 60% overlap and, to a lesser
extent the 40% overlap, tended to overestimate animal counts, with
average counts and variability nearly doubling, as animal speed
increased from 2 to 10 m/s during random and biased random walking (Fig
2). In contrast, increasing animal speed tended to decrease multiple
counts for the correlated random walk (Fig. 3). Animal speed also
influenced the number of correct counts in some cases, with the
percentage of correct survey counts decreasing for the 0% (66.0% and
69%) and 20% (51.8% and 53.7%) overlap for the random and biased
walks, respectively, but increasing for the correlated random walk for
those flight patterns (84.4% and 83.5%, respectively; Fig. 3). The
number of correct survey counts also increased with animal speed for
systematic points when the animal had a correlated random walk (38.3%
correct at 2 m/s to 84.6% correct at 10 m/s; Fig. 3).