Citizen scientists reliably count endangered Galápagos marine iguanas from drone images.

Population surveys are essential for conservation, but are often resource-intensive. Modern technologies, like drones, facilitate data collection but increase the analysis burden. Citizen Science (CS) offers a solution by engaging non-specialists in data analysis. We evaluated CS for monitoring marine iguanas, focusing on volunteers' accuracy in detecting and counting individuals in aerial images. During three phases of our Zooniverse project, over 13,000 volunteers contributed 1,375,201 classifications from 57,838 images; each classified up to 30 times. Using a Gold Standard dataset of expert counts from 4,345 images, we evaluated optimal aggregation methods for CS-inputs. Volunteers achieved 68-94% accuracy in detection, with more false negatives than false positives. The standard 'majority vote' aggregation approach (where the answer given by the majority of individual inputs is selected) produced less accuracy than when a minimum threshold of five volunteers (from the total independent classifications) was used. Image quality significantly influenced accuracy; by excluding suboptimal pilot-phase data, volunteer counts were 91-92% accurate. HDBSCAN clustering yielded the best results. We conclude that volunteers can accurately identify and count marine iguanas from drone images, though there is a tendency for undercounting. However, even CS-based data analysis remains relatively resource-intensive, underscoring the need to develop an automated approach.