Citizen scientists reliably count endangered Galápagos marine iguanas from drone images
Abstract 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 monito...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08381-9 |
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| author | Andrea Varela-Jaramillo Christian Winkelmann Andrés Mármol-Guijarro Juan M. Guayasamin Gonzalo Rivas-Torres Sebastian Steinfartz Amy MacLeod |
| author_facet | Andrea Varela-Jaramillo Christian Winkelmann Andrés Mármol-Guijarro Juan M. Guayasamin Gonzalo Rivas-Torres Sebastian Steinfartz Amy MacLeod |
| author_sort | Andrea Varela-Jaramillo |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-d038759a1dd04a2f83d6b7c7f820e70d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d038759a1dd04a2f83d6b7c7f820e70d2025-08-20T03:46:06ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-08381-9Citizen scientists reliably count endangered Galápagos marine iguanas from drone imagesAndrea Varela-Jaramillo0Christian Winkelmann1Andrés Mármol-Guijarro2Juan M. Guayasamin3Gonzalo Rivas-Torres4Sebastian Steinfartz5Amy MacLeod6Institute of Biology, Molecular Evolution and Systematics of Animals, University of LeipzigEberswalde University for Sustainable Development3DiversityLaboratorio de Biología Evolutiva, Colegio de Ciencias Biológicas y Ambientales COCIBA, Instituto Biósfera, Universidad San Francisco de Quito USFQLaboratorio de Biología Evolutiva, Colegio de Ciencias Biológicas y Ambientales COCIBA, Instituto Biósfera, Universidad San Francisco de Quito USFQInstitute of Biology, Molecular Evolution and Systematics of Animals, University of LeipzigInstitute of Biology, Molecular Evolution and Systematics of Animals, University of LeipzigAbstract 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.https://doi.org/10.1038/s41598-025-08381-9Aerial imageryCitizen scienceUAVsWildlife monitoringZooniverse |
| spellingShingle | Andrea Varela-Jaramillo Christian Winkelmann Andrés Mármol-Guijarro Juan M. Guayasamin Gonzalo Rivas-Torres Sebastian Steinfartz Amy MacLeod Citizen scientists reliably count endangered Galápagos marine iguanas from drone images Scientific Reports Aerial imagery Citizen science UAVs Wildlife monitoring Zooniverse |
| title | Citizen scientists reliably count endangered Galápagos marine iguanas from drone images |
| title_full | Citizen scientists reliably count endangered Galápagos marine iguanas from drone images |
| title_fullStr | Citizen scientists reliably count endangered Galápagos marine iguanas from drone images |
| title_full_unstemmed | Citizen scientists reliably count endangered Galápagos marine iguanas from drone images |
| title_short | Citizen scientists reliably count endangered Galápagos marine iguanas from drone images |
| title_sort | citizen scientists reliably count endangered galapagos marine iguanas from drone images |
| topic | Aerial imagery Citizen science UAVs Wildlife monitoring Zooniverse |
| url | https://doi.org/10.1038/s41598-025-08381-9 |
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