Data from: Humans outperform Merlin SoundID in field-based point-count surveys

Point counts are integral to bird population monitoring, and ​multiple
methods have been developed to improve their reliability. However, these
improved protocols are time-consuming, expensive, and often require hiring
surveyors with inconsistent abilities. Merlin Sound ID, an automated
bird-sound classifier that can run in the field on mobile devices, could
be a part of the solution to the problems associated with point counts by
functioning as a no-cost observer with consistent performance. Here, we
compare the accuracy of Merlin and human observers in 144 paired
field-based point-counts. Our goals were to evaluate how the species
identified, number of individual detections, and precision for humans and
Merlin compared. We also examined the consistency of Merlin across
different devices. Human observers reported 382 individual detections over
the course of the study, 72% more than the 222 detections reported by
Merlin. Precision was similar with 92% and 86% for humans and Merlin,
respectively. Merlin detected four species missed by humans, but also
detected 12 false positive species. Merlin disagreed with itself on 57% of
point counts when running on two different devices at the same time. We
propose Merlin not as a replacement for human point counters, but as an
aid to increase detection probability of point-count surveys as secondary
observers, with humans acting as reviewers and arbitrating disagreements
between devices when needed.  This study required no ethical
permits.