Data from: Drones count wildlife more accurately and precisely than humans

Knowing how many individuals are in a wildlife population allows informed management decisions to be made. Ecologists are increasingly using technologies, such as remotely piloted aircraft (RPA; commonly known as "drones," unmanned aerial systems or unmanned aerial vehicles), for wildlife monitoring applications. Although RPA are widely touted as a cost-effective way to collect high-quality wildlife population data, the validity of these claims is unclear. Using life-sized, replica seabird colonies containing a known number of fake birds, we assessed the accuracy of RPA-facilitated wildlife population monitoring compared to the traditional ground-based counting method. The task for both approaches was to count the number of fake birds in each of 10 replica seabird colonies. We show that RPA-derived data are, on average, between 43% and 96% more accurate than the traditional ground-based data collection method. We also demonstrate that counts from this remotely sensed imagery can be semi-automated with a high degree of accuracy. The increased accuracy and increased precision of RPA-derived wildlife monitoring data provides greater statistical power to detect fine-scale population fluctuations allowing for more informed and proactive ecological management.Main datasetThis dataset contains the true number of birds for each colony, as well as the ground counts, RPA-derived manual counts and RPA-derived semi-automated counts for each colony. This dataset was used for all analyses.MASTER_AllCountData.csvRPA-derived imagery of bird coloniesCompressed folder containing photographs of each colony (n=10) at each sample height (n=4).Colony_imagery.zipSemi-automated aerial image counting approach - archived source code and datasetThis is a DOI for the source code and dataset used for the semi-automated aerial image counting approach which has been archived on figshare and released under a Creative Commons Attribution 4.0 International License. The current version of the code is available via Bitbucket (https://bitbucket.org/trungtpham/bird-detection).R script for analysesScript used to analyse ground counts, RPA-derived manual counts and RPA-derived semi-automated counts.MASTER_analysisScript_v1.4.