Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior

Capturing qualitative features of animal behavior requires recording
occurrences of behavior over time. Continuous sampling is best for
capturing brief behaviors, but can be very time consuming. Instantaneous
sampling can reduce the amount of labor required, but can miss
short-duration behaviors. We therefore synthesized these techniques by
continuously sampling during randomly scattered time intervals; a
technique we call piecewise continuous sampling. To optimize and test the
efficacy of this technique, we collected a continuous behavioral dataset
of harvester ant workers, and then we developed a protocol to estimate the
amount of sampling time necessary to reconstruct the proportion of time
animals spend in different behavioral states. This protocol finds the
sample size needed for the variance of the sample to converge on the
variation of the population. We then divided this estimated time into
equal-duration intervals that were randomly distributed across the entire
continuous dataset. Finally, we calculated both time-dependent and
time-independent error from this sample. We found that 4 to 16 sampling
intervals minimize both types of error simultaneously. This finding was
robust to differences in underlying behavior and was validated with
simulations, implying that this method could be used for many types of
organisms.